Datasets:

rockmiin

/

ml-codeparrot-train

like 0

dimkal/mne-python

mne/simulation/tests/test_evoked.py

3

3170

# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # # License: BSD (3-clause) import os.path as op import numpy as np from numpy.testing import assert_array_almost_equal from nose.tools import assert_true, assert_raises import warnings from mne.datasets import testing from mne import (read_label, read_forward_solution, pick_types_forward, read_evokeds, read_cov) from mne.time_frequency import morlet from mne.simulation import generate_sparse_stc, generate_evoked from mne.io import Raw from mne.utils import run_tests_if_main warnings.simplefilter('always') data_path = testing.data_path(download=False) fwd_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif') raw_fname = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data', 'test_raw.fif') ave_fname = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data', 'test-ave.fif') cov_fname = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data', 'test-cov.fif') @testing.requires_testing_data def test_simulate_evoked(): """ Test simulation of evoked data """ raw = Raw(raw_fname) fwd = read_forward_solution(fwd_fname, force_fixed=True) fwd = pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads']) cov = read_cov(cov_fname) label_names = ['Aud-lh', 'Aud-rh'] labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label)) for label in label_names] evoked_template = read_evokeds(ave_fname, condition=0, baseline=None) evoked_template.pick_types(meg=True, eeg=True, exclude=raw.info['bads']) snr = 6 # dB tmin = -0.1 sfreq = 1000. # Hz tstep = 1. / sfreq n_samples = 600 times = np.linspace(tmin, tmin + n_samples * tstep, n_samples) # Generate times series from 2 Morlet wavelets stc_data = np.zeros((len(labels), len(times))) Ws = morlet(sfreq, [3, 10], n_cycles=[1, 1.5]) stc_data[0][:len(Ws[0])] = np.real(Ws[0]) stc_data[1][:len(Ws[1])] = np.real(Ws[1]) stc_data *= 100 * 1e-9 # use nAm as unit # time translation stc_data[1] = np.roll(stc_data[1], 80) stc = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep, random_state=0) # Generate noisy evoked data iir_filter = [1, -0.9] with warnings.catch_warnings(record=True): warnings.simplefilter('always') # positive semidefinite warning evoked = generate_evoked(fwd, stc, evoked_template, cov, snr, tmin=0.0, tmax=0.2, iir_filter=iir_filter) assert_array_almost_equal(evoked.times, stc.times) assert_true(len(evoked.data) == len(fwd['sol']['data'])) # make a vertex that doesn't exist in fwd, should throw error stc_bad = stc.copy() mv = np.max(fwd['src'][0]['vertno'][fwd['src'][0]['inuse']]) stc_bad.vertices[0][0] = mv + 1 assert_raises(RuntimeError, generate_evoked, fwd, stc_bad, evoked_template, cov, snr, tmin=0.0, tmax=0.2) run_tests_if_main()

bsd-3-clause

pkruskal/scikit-learn

sklearn/cluster/__init__.py

359

1228

""" The :mod:`sklearn.cluster` module gathers popular unsupervised clustering algorithms. """ from .spectral import spectral_clustering, SpectralClustering from .mean_shift_ import (mean_shift, MeanShift, estimate_bandwidth, get_bin_seeds) from .affinity_propagation_ import affinity_propagation, AffinityPropagation from .hierarchical import (ward_tree, AgglomerativeClustering, linkage_tree, FeatureAgglomeration) from .k_means_ import k_means, KMeans, MiniBatchKMeans from .dbscan_ import dbscan, DBSCAN from .bicluster import SpectralBiclustering, SpectralCoclustering from .birch import Birch __all__ = ['AffinityPropagation', 'AgglomerativeClustering', 'Birch', 'DBSCAN', 'KMeans', 'FeatureAgglomeration', 'MeanShift', 'MiniBatchKMeans', 'SpectralClustering', 'affinity_propagation', 'dbscan', 'estimate_bandwidth', 'get_bin_seeds', 'k_means', 'linkage_tree', 'mean_shift', 'spectral_clustering', 'ward_tree', 'SpectralBiclustering', 'SpectralCoclustering']

bsd-3-clause

pkruskal/scikit-learn

sklearn/linear_model/tests/test_bayes.py

296

1770

# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Fabian Pedregosa <fabian.pedregosa@inria.fr> # # License: BSD 3 clause import numpy as np from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import SkipTest from sklearn.linear_model.bayes import BayesianRidge, ARDRegression from sklearn import datasets from sklearn.utils.testing import assert_array_almost_equal def test_bayesian_on_diabetes(): # Test BayesianRidge on diabetes raise SkipTest("XFailed Test") diabetes = datasets.load_diabetes() X, y = diabetes.data, diabetes.target clf = BayesianRidge(compute_score=True) # Test with more samples than features clf.fit(X, y) # Test that scores are increasing at each iteration assert_array_equal(np.diff(clf.scores_) > 0, True) # Test with more features than samples X = X[:5, :] y = y[:5] clf.fit(X, y) # Test that scores are increasing at each iteration assert_array_equal(np.diff(clf.scores_) > 0, True) def test_toy_bayesian_ridge_object(): # Test BayesianRidge on toy X = np.array([[1], [2], [6], [8], [10]]) Y = np.array([1, 2, 6, 8, 10]) clf = BayesianRidge(compute_score=True) clf.fit(X, Y) # Check that the model could approximately learn the identity function test = [[1], [3], [4]] assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2) def test_toy_ard_object(): # Test BayesianRegression ARD classifier X = np.array([[1], [2], [3]]) Y = np.array([1, 2, 3]) clf = ARDRegression(compute_score=True) clf.fit(X, Y) # Check that the model could approximately learn the identity function test = [[1], [3], [4]] assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)

bsd-3-clause

nightjean/Deep-Learning

tensorflow/examples/learn/iris_custom_decay_dnn.py

29

2039

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Example of DNNClassifier for Iris plant dataset, with exponential decay.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from sklearn import datasets from sklearn import metrics from sklearn.cross_validation import train_test_split import tensorflow as tf def optimizer_exp_decay(): global_step = tf.contrib.framework.get_or_create_global_step() learning_rate = tf.train.exponential_decay( learning_rate=0.1, global_step=global_step, decay_steps=100, decay_rate=0.001) return tf.train.AdagradOptimizer(learning_rate=learning_rate) def main(unused_argv): iris = datasets.load_iris() x_train, x_test, y_train, y_test = train_test_split( iris.data, iris.target, test_size=0.2, random_state=42) feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input( x_train) classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3, optimizer=optimizer_exp_decay) classifier.fit(x_train, y_train, steps=800) predictions = list(classifier.predict(x_test, as_iterable=True)) score = metrics.accuracy_score(y_test, predictions) print('Accuracy: {0:f}'.format(score)) if __name__ == '__main__': tf.app.run()

apache-2.0

andrewcmyers/tensorflow

tensorflow/contrib/imperative/examples/mnist.py

68

4576

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """MNIST training in imperative mode TensorFlow.""" # pylint: disable=redefined-outer-name from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow.contrib.imperative as tf from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets IMAGE_SIZE = 28 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE NUM_CLASSES = 10 BATCH_SIZE = 100 NUM_EPOCHS = 2 LEARNING_RATE = 0.1 class Model(object): """Fully connected model for MNIST.""" def __init__(self, hidden1_units, hidden2_units): """Create the model parameters.""" self.params = [] # Hidden 1 with tf.name_scope('hidden1'): self.weights1 = tf.Variable( np.random.normal(scale=1.0 / np.sqrt(float(IMAGE_PIXELS)), size=[IMAGE_PIXELS, hidden1_units]), dtype=tf.float32, name='weights') self.biases1 = tf.Variable( np.zeros([hidden1_units]), dtype=tf.float32, name='biases') # Hidden 2 with tf.name_scope('hidden2'): self.weights2 = tf.Variable( np.random.normal(scale=1.0 / np.sqrt(float(hidden1_units)), size=[hidden1_units, hidden2_units]), dtype=tf.float32, name='weights') self.biases2 = tf.Variable( np.zeros([hidden2_units]), dtype=tf.float32, name='biases') # Linear with tf.name_scope('softmax_linear'): self.sm_w = tf.Variable( np.random.normal(scale=1.0 / np.sqrt(float(hidden2_units)), size=[hidden2_units, NUM_CLASSES]), dtype=tf.float32, name='weights') self.sm_b = tf.Variable( np.zeros([NUM_CLASSES]), dtype=tf.float32, name='biases') self.params = [self.weights1, self.biases1, self.weights2, self.biases2, self.sm_w, self.sm_b] def __call__(self, images): """Run the model's forward prop on `images`.""" hidden1 = tf.nn.relu(tf.matmul(images, self.weights1) + self.biases1) hidden2 = tf.nn.relu(tf.matmul(hidden1, self.weights2) + self.biases2) logits = tf.matmul(hidden2, self.sm_w) + self.sm_b return logits model = Model(128, 32) data = read_data_sets('/tmp/mnist_train') def get_test_accuracy(): """Gets the model's classification accuracy on test data.""" num_examples = data.test.num_examples test_images = np.split(data.test.images, num_examples/BATCH_SIZE) test_labels = np.split(data.test.labels.astype(np.int32), num_examples/BATCH_SIZE) num_correct = 0 for _, (images, labels) in enumerate(zip(test_images, test_labels)): with tf.new_step(): logits = model(images) predictions = tf.argmax(tf.nn.softmax(logits), axis=1) num_correct += np.sum(predictions.value == labels) return float(num_correct) / float(num_examples) num_examples = data.train.num_examples train_images = np.split(data.train.images, num_examples/BATCH_SIZE) train_labels = np.split(data.train.labels.astype(np.int32), num_examples/BATCH_SIZE) for epoch in range(NUM_EPOCHS): for i, (images, labels) in enumerate(zip(train_images, train_labels)): with tf.new_step() as step: logits = model(images) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=logits, name='xentropy') loss = tf.reduce_mean(cross_entropy, name='xentropy_mean') gradients = tf.gradients(loss, model.params) step.run([v.assign_sub(LEARNING_RATE * g) for g, v in zip(gradients, model.params)]) if i % 10 == 0: print('Loss after {} steps = {}'.format(i, loss)) if i % 100 == 0: print('Test accuracy after {} steps = {}' .format(i, get_test_accuracy()))

apache-2.0

ammarkhann/FinalSeniorCode

lib/python2.7/site-packages/scipy/spatial/_procrustes.py

41

4466

""" This module provides functions to perform full Procrustes analysis. This code was originally written by Justin Kucynski and ported over from scikit-bio by Yoshiki Vazquez-Baeza. """ from __future__ import absolute_import, division, print_function import numpy as np from scipy.linalg import orthogonal_procrustes __all__ = ['procrustes'] def procrustes(data1, data2): r"""Procrustes analysis, a similarity test for two data sets. Each input matrix is a set of points or vectors (the rows of the matrix). The dimension of the space is the number of columns of each matrix. Given two identically sized matrices, procrustes standardizes both such that: - :math:`tr(AA^{T}) = 1`. - Both sets of points are centered around the origin. Procrustes ([1]_, [2]_) then applies the optimal transform to the second matrix (including scaling/dilation, rotations, and reflections) to minimize :math:`M^{2}=\sum(data1-data2)^{2}`, or the sum of the squares of the pointwise differences between the two input datasets. This function was not designed to handle datasets with different numbers of datapoints (rows). If two data sets have different dimensionality (different number of columns), simply add columns of zeros to the smaller of the two. Parameters ---------- data1 : array_like Matrix, n rows represent points in k (columns) space `data1` is the reference data, after it is standardised, the data from `data2` will be transformed to fit the pattern in `data1` (must have >1 unique points). data2 : array_like n rows of data in k space to be fit to `data1`. Must be the same shape ``(numrows, numcols)`` as data1 (must have >1 unique points). Returns ------- mtx1 : array_like A standardized version of `data1`. mtx2 : array_like The orientation of `data2` that best fits `data1`. Centered, but not necessarily :math:`tr(AA^{T}) = 1`. disparity : float :math:`M^{2}` as defined above. Raises ------ ValueError If the input arrays are not two-dimensional. If the shape of the input arrays is different. If the input arrays have zero columns or zero rows. See Also -------- scipy.linalg.orthogonal_procrustes scipy.spatial.distance.directed_hausdorff : Another similarity test for two data sets Notes ----- - The disparity should not depend on the order of the input matrices, but the output matrices will, as only the first output matrix is guaranteed to be scaled such that :math:`tr(AA^{T}) = 1`. - Duplicate data points are generally ok, duplicating a data point will increase its effect on the procrustes fit. - The disparity scales as the number of points per input matrix. References ---------- .. [1] Krzanowski, W. J. (2000). "Principles of Multivariate analysis". .. [2] Gower, J. C. (1975). "Generalized procrustes analysis". Examples -------- >>> from scipy.spatial import procrustes The matrix ``b`` is a rotated, shifted, scaled and mirrored version of ``a`` here: >>> a = np.array([[1, 3], [1, 2], [1, 1], [2, 1]], 'd') >>> b = np.array([[4, -2], [4, -4], [4, -6], [2, -6]], 'd') >>> mtx1, mtx2, disparity = procrustes(a, b) >>> round(disparity) 0.0 """ mtx1 = np.array(data1, dtype=np.double, copy=True) mtx2 = np.array(data2, dtype=np.double, copy=True) if mtx1.ndim != 2 or mtx2.ndim != 2: raise ValueError("Input matrices must be two-dimensional") if mtx1.shape != mtx2.shape: raise ValueError("Input matrices must be of same shape") if mtx1.size == 0: raise ValueError("Input matrices must be >0 rows and >0 cols") # translate all the data to the origin mtx1 -= np.mean(mtx1, 0) mtx2 -= np.mean(mtx2, 0) norm1 = np.linalg.norm(mtx1) norm2 = np.linalg.norm(mtx2) if norm1 == 0 or norm2 == 0: raise ValueError("Input matrices must contain >1 unique points") # change scaling of data (in rows) such that trace(mtx*mtx') = 1 mtx1 /= norm1 mtx2 /= norm2 # transform mtx2 to minimize disparity R, s = orthogonal_procrustes(mtx1, mtx2) mtx2 = np.dot(mtx2, R.T) * s # measure the dissimilarity between the two datasets disparity = np.sum(np.square(mtx1 - mtx2)) return mtx1, mtx2, disparity

mit

yonglehou/scikit-learn

benchmarks/bench_plot_svd.py

322

2899

"""Benchmarks of Singular Value Decomposition (Exact and Approximate) The data is mostly low rank but is a fat infinite tail. """ import gc from time import time import numpy as np from collections import defaultdict from scipy.linalg import svd from sklearn.utils.extmath import randomized_svd from sklearn.datasets.samples_generator import make_low_rank_matrix def compute_bench(samples_range, features_range, n_iter=3, rank=50): it = 0 results = defaultdict(lambda: []) max_it = len(samples_range) * len(features_range) for n_samples in samples_range: for n_features in features_range: it += 1 print('====================') print('Iteration %03d of %03d' % (it, max_it)) print('====================') X = make_low_rank_matrix(n_samples, n_features, effective_rank=rank, tail_strength=0.2) gc.collect() print("benchmarking scipy svd: ") tstart = time() svd(X, full_matrices=False) results['scipy svd'].append(time() - tstart) gc.collect() print("benchmarking scikit-learn randomized_svd: n_iter=0") tstart = time() randomized_svd(X, rank, n_iter=0) results['scikit-learn randomized_svd (n_iter=0)'].append( time() - tstart) gc.collect() print("benchmarking scikit-learn randomized_svd: n_iter=%d " % n_iter) tstart = time() randomized_svd(X, rank, n_iter=n_iter) results['scikit-learn randomized_svd (n_iter=%d)' % n_iter].append(time() - tstart) return results if __name__ == '__main__': from mpl_toolkits.mplot3d import axes3d # register the 3d projection import matplotlib.pyplot as plt samples_range = np.linspace(2, 1000, 4).astype(np.int) features_range = np.linspace(2, 1000, 4).astype(np.int) results = compute_bench(samples_range, features_range) label = 'scikit-learn singular value decomposition benchmark results' fig = plt.figure(label) ax = fig.gca(projection='3d') for c, (label, timings) in zip('rbg', sorted(results.iteritems())): X, Y = np.meshgrid(samples_range, features_range) Z = np.asarray(timings).reshape(samples_range.shape[0], features_range.shape[0]) # plot the actual surface ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3, color=c) # dummy point plot to stick the legend to since surface plot do not # support legends (yet?) ax.plot([1], [1], [1], color=c, label=label) ax.set_xlabel('n_samples') ax.set_ylabel('n_features') ax.set_zlabel('Time (s)') ax.legend() plt.show()

bsd-3-clause

nickgentoo/scikit-learn-graph

scripts/Online_PassiveAggressive_ReservoirHashKernels_notanh.py

1

10477

# -*- coding: utf-8 -*- """ python -m scripts/Online_PassiveAggressive_countmeansketch LMdata 3 1 a ODDST 0.01 Created on Fri Mar 13 13:02:41 2015 Copyright 2015 Nicolo' Navarin This file is part of scikit-learn-graph. scikit-learn-graph is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. scikit-learn-graph is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with scikit-learn-graph. If not, see <http://www.gnu.org/licenses/>. """ from copy import copy import os,sys,inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0,parentdir) import sys from skgraph.feature_extraction.graph.ODDSTVectorizer import ODDSTVectorizer from skgraph.feature_extraction.graph.WLVectorizer import WLVectorizer from sklearn.linear_model import PassiveAggressiveClassifier as PAC from skgraph.datasets import load_graph_datasets import numpy as np from scipy.sparse import csc_matrix from sklearn.utils import compute_class_weight from scipy.sparse import csr_matrix from skgraph.utils.countminsketch_randomprojection_notanh import CountMinSketch from itertools import izip import time if __name__=='__main__': start_time = time.time() if len(sys.argv)<1: sys.exit("python ODDKernel_example.py dataset r l filename kernel C m seed") dataset=sys.argv[1] max_radius=int(sys.argv[2]) la=float(sys.argv[3]) #hashs=int(sys.argv[3]) njobs=1 name=str(sys.argv[4]) kernel=sys.argv[5] C=float(sys.argv[6]) m=int(sys.argv[7]) rs=int(sys.argv[8]) #lr=float(sys.argv[7]) #FIXED PARAMETERS normalization=False #working with Chemical g_it=load_graph_datasets.dispatch(dataset) f=open(name,'w') #At this point, one_hot_encoding contains the encoding for each symbol in the alphabet if kernel=="WL": print "Lambda ignored" print "Using WL fast subtree kernel" Vectorizer=WLVectorizer(r=max_radius,normalization=normalization) elif kernel=="ODDST": print "Using ST kernel" Vectorizer=ODDSTVectorizer(r=max_radius,l=la,normalization=normalization) elif kernel=="NSPDK": print "Using NSPDK kernel, lambda parameter interpreted as d" Vectorizer=NSPDKVectorizer(r=max_radius,d=int(la),normalization=normalization) else: print "Unrecognized kernel" #TODO the C parameter should probably be optimized #print zip(_letters, _one_hot) #exit() features=Vectorizer.transform(g_it.graphs) #Parallel ,njobs print "examples, features", features.shape features_time=time.time() print("Computed features in %s seconds ---" % (features_time - start_time)) errors=0 tp=0 fp=0 tn=0 fn=0 predictions=[0]*50 correct=[0]*50 #print ESN #netDataSet=[] #netTargetSet=[] #netKeyList=[] BERtotal=[] bintargets=[1,-1] #print features #print list_for_deep.keys() tp = 0 fp = 0 fn = 0 tn = 0 part_plus=0 part_minus=0 sizes=[5000]*50 transformer=CountMinSketch(m,features.shape[1],rs) WCMS=np.zeros(shape=(m,1)) cms_creation=0.0 for i in xrange(features.shape[0]): time1=time.time() ex=features[i][0].T exCMS=transformer.transform(ex) #print "exCMS", type(exCMS), exCMS.shape target=g_it.target[i] #W=csr_matrix(ex) #dot=0.0 module=np.dot(exCMS.T,exCMS)[0,0] #print "module", module time2=time.time() cms_creation+=time2 - time1 dot=np.dot(WCMS.T,exCMS) #print "dot", dot #print "dot:", dot, "dotCMS:",dot1 if (np.sign(dot) != target ): #print "error on example",i, "predicted:", dot, "correct:", target errors+=1 if target==1: fn+=1 else: fp+=1 else: #print "correct classification", target if target==1: tp+=1 else: tn+=1 if(target==1): coef=(part_minus+1.0)/(part_plus+part_minus+1.0) part_plus+=1 else: coef=(part_plus+1.0)/(part_plus+part_minus+1.0) part_minus+=1 tao = min (C, max (0.0,( (1.0 - target*dot )*coef) / module ) ); if (tao > 0.0): WCMS+=(exCMS*(tao*target)) # for row,col in zip(rows,cols): # ((row,col), ex[row,col]) # #print col, ex[row,col] # WCMS.add(col,target*tao*ex[row,col]) #print "Correct prediction example",i, "pred", score, "target",target if i%50==0 and i!=0: #output performance statistics every 50 examples if (tn+fp) > 0: pos_part= float(fp) / (tn+fp) else: pos_part=0 if (tp+fn) > 0: neg_part=float(fn) / (tp+fn) else: neg_part=0 BER = 0.5 * ( pos_part + neg_part) print "1-BER Window esempio ",i, (1.0 - BER) f.write("1-BER Window esempio "+str(i)+" "+str(1.0 - BER)+"\n") #print>>f,"1-BER Window esempio "+str(i)+" "+str(1.0 - BER) BERtotal.append(1.0 - BER) tp = 0 fp = 0 fn = 0 tn = 0 part_plus=0 part_minus=0 end_time=time.time() print("Learning phase time %s seconds ---" % (end_time - features_time )) #- cms_creation print("Total time %s seconds ---" % (end_time - start_time)) print "BER AVG", str(np.average(BERtotal)),"std", np.std(BERtotal) f.write("BER AVG "+ str(np.average(BERtotal))+" std "+str(np.std(BERtotal))+"\n") f.close() #print "N_features", ex.shape #generate explicit W from CountMeanSketch #print W #raw_input("W (output)") #============================================================================== # # tao = /*(double)labels->get_label(idx_a) **/ min (C, max (0.0,(1.0 - (((double)labels->get_label(idx_a))*(classe_mod) )) * c_plus ) / modulo_test); # # #W=W_old #dump line # # # #set the weights of PA to the predicted values # PassiveAggressive.coef_=W # pred=PassiveAggressive.predict(ex) # # score=PassiveAggressive.decision_function(ex) # # bintargets.append(target) # if pred!=target: # errors+=1 # print "Error",errors," on example",i, "pred", score, "target",target # if target==1: # fn+=1 # else: # fp+=1 # # else: # if target==1: # tp+=1 # else: # tn+=1 # #print "Correct prediction example",i, "pred", score, "target",target # # else: # #first example is always an error! # pred=0 # score=0 # errors+=1 # print "Error",errors," on example",i # if g_it.target[i]==1: # fn+=1 # else: # fp+=1 # #print i # if i%50==0 and i!=0: # #output performance statistics every 50 examples # if (tn+fp) > 0: # pos_part= float(fp) / (tn+fp) # else: # pos_part=0 # if (tp+fn) > 0: # neg_part=float(fn) / (tp+fn) # else: # neg_part=0 # BER = 0.5 * ( pos_part + neg_part) # print "1-BER Window esempio ",i, (1.0 - BER) # print>>f,"1-BER Window esempio "+str(i)+" "+str(1.0 - BER) # BERtotal.append(1.0 - BER) # tp = 0 # fp = 0 # fn = 0 # tn = 0 # bintargets=[1,-1] # #print features[0][i] # #print features[0][i].shape # #f=features[0][i,:] # #print f.shape # #print f.shape # #print g_it.target[i] # #third parameter is compulsory just for the first call # print "prediction", pred, score # #print "intecept",PassiveAggressive.intercept_ # #raw_input() # if abs(score)<1.0 or pred!=g_it.target[i]: # # ClassWeight=compute_class_weight('auto',np.asarray([1,-1]),bintargets) # #print "class weights", {1:ClassWeight[0],-1:ClassWeight[1]} # PassiveAggressive.class_weight={1:ClassWeight[0],-1:ClassWeight[1]} # # PassiveAggressive.partial_fit(ex,np.array([g_it.target[i]]),np.unique(g_it.target)) # #PassiveAggressive.partial_fit(ex,np.array([g_it.target[i]]),np.unique(g_it.target)) # W_old=PassiveAggressive.coef_ # # # #ESN target---# # netTargetSet=[] # for key,rowDict in list_for_deep[i].iteritems(): # # # target=np.asarray( [np.asarray([W_old[0,key]])]*len(rowDict)) # # # netTargetSet.append(target) # # # # # #------------ESN TargetSetset--------------------# # # ESN Training # # #for ftDataset,ftTargetSet in zip(netDataSet,netTargetSet): # #print "Input" # #print netDataSet # #raw_input("Output") # #print netTargetSet # #raw_input("Target") # model.OnlineTrain(netDataSet,netTargetSet,lr) # #raw_input("TR") # #calcolo statistiche # # print "BER AVG", sum(BERtotal) / float(len(BERtotal)) # print>>f,"BER AVG "+str(sum(BERtotal) / float(len(BERtotal))) # f.close() #==============================================================================

gpl-3.0

pkruskal/scikit-learn

sklearn/linear_model/bayes.py

219

15248

""" Various bayesian regression """ from __future__ import print_function # Authors: V. Michel, F. Pedregosa, A. Gramfort # License: BSD 3 clause from math import log import numpy as np from scipy import linalg from .base import LinearModel from ..base import RegressorMixin from ..utils.extmath import fast_logdet, pinvh from ..utils import check_X_y ############################################################################### # BayesianRidge regression class BayesianRidge(LinearModel, RegressorMixin): """Bayesian ridge regression Fit a Bayesian ridge model and optimize the regularization parameters lambda (precision of the weights) and alpha (precision of the noise). Read more in the :ref:`User Guide <bayesian_regression>`. Parameters ---------- n_iter : int, optional Maximum number of iterations. Default is 300. tol : float, optional Stop the algorithm if w has converged. Default is 1.e-3. alpha_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6 alpha_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. lambda_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. lambda_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6 compute_score : boolean, optional If True, compute the objective function at each step of the model. Default is False fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True. normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. verbose : boolean, optional, default False Verbose mode when fitting the model. Attributes ---------- coef_ : array, shape = (n_features) Coefficients of the regression model (mean of distribution) alpha_ : float estimated precision of the noise. lambda_ : array, shape = (n_features) estimated precisions of the weights. scores_ : float if computed, value of the objective function (to be maximized) Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.BayesianRidge() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... # doctest: +NORMALIZE_WHITESPACE BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, normalize=False, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.]) Notes ----- See examples/linear_model/plot_bayesian_ridge.py for an example. """ def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False, fit_intercept=True, normalize=False, copy_X=True, verbose=False): self.n_iter = n_iter self.tol = tol self.alpha_1 = alpha_1 self.alpha_2 = alpha_2 self.lambda_1 = lambda_1 self.lambda_2 = lambda_2 self.compute_score = compute_score self.fit_intercept = fit_intercept self.normalize = normalize self.copy_X = copy_X self.verbose = verbose def fit(self, X, y): """Fit the model Parameters ---------- X : numpy array of shape [n_samples,n_features] Training data y : numpy array of shape [n_samples] Target values Returns ------- self : returns an instance of self. """ X, y = check_X_y(X, y, dtype=np.float64, y_numeric=True) X, y, X_mean, y_mean, X_std = self._center_data( X, y, self.fit_intercept, self.normalize, self.copy_X) n_samples, n_features = X.shape ### Initialization of the values of the parameters alpha_ = 1. / np.var(y) lambda_ = 1. verbose = self.verbose lambda_1 = self.lambda_1 lambda_2 = self.lambda_2 alpha_1 = self.alpha_1 alpha_2 = self.alpha_2 self.scores_ = list() coef_old_ = None XT_y = np.dot(X.T, y) U, S, Vh = linalg.svd(X, full_matrices=False) eigen_vals_ = S ** 2 ### Convergence loop of the bayesian ridge regression for iter_ in range(self.n_iter): ### Compute mu and sigma # sigma_ = lambda_ / alpha_ * np.eye(n_features) + np.dot(X.T, X) # coef_ = sigma_^-1 * XT * y if n_samples > n_features: coef_ = np.dot(Vh.T, Vh / (eigen_vals_ + lambda_ / alpha_)[:, None]) coef_ = np.dot(coef_, XT_y) if self.compute_score: logdet_sigma_ = - np.sum( np.log(lambda_ + alpha_ * eigen_vals_)) else: coef_ = np.dot(X.T, np.dot( U / (eigen_vals_ + lambda_ / alpha_)[None, :], U.T)) coef_ = np.dot(coef_, y) if self.compute_score: logdet_sigma_ = lambda_ * np.ones(n_features) logdet_sigma_[:n_samples] += alpha_ * eigen_vals_ logdet_sigma_ = - np.sum(np.log(logdet_sigma_)) ### Update alpha and lambda rmse_ = np.sum((y - np.dot(X, coef_)) ** 2) gamma_ = (np.sum((alpha_ * eigen_vals_) / (lambda_ + alpha_ * eigen_vals_))) lambda_ = ((gamma_ + 2 * lambda_1) / (np.sum(coef_ ** 2) + 2 * lambda_2)) alpha_ = ((n_samples - gamma_ + 2 * alpha_1) / (rmse_ + 2 * alpha_2)) ### Compute the objective function if self.compute_score: s = lambda_1 * log(lambda_) - lambda_2 * lambda_ s += alpha_1 * log(alpha_) - alpha_2 * alpha_ s += 0.5 * (n_features * log(lambda_) + n_samples * log(alpha_) - alpha_ * rmse_ - (lambda_ * np.sum(coef_ ** 2)) - logdet_sigma_ - n_samples * log(2 * np.pi)) self.scores_.append(s) ### Check for convergence if iter_ != 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol: if verbose: print("Convergence after ", str(iter_), " iterations") break coef_old_ = np.copy(coef_) self.alpha_ = alpha_ self.lambda_ = lambda_ self.coef_ = coef_ self._set_intercept(X_mean, y_mean, X_std) return self ############################################################################### # ARD (Automatic Relevance Determination) regression class ARDRegression(LinearModel, RegressorMixin): """Bayesian ARD regression. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization) Read more in the :ref:`User Guide <bayesian_regression>`. Parameters ---------- n_iter : int, optional Maximum number of iterations. Default is 300 tol : float, optional Stop the algorithm if w has converged. Default is 1.e-3. alpha_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. alpha_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6. lambda_1 : float, optional Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. lambda_2 : float, optional Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6. compute_score : boolean, optional If True, compute the objective function at each step of the model. Default is False. threshold_lambda : float, optional threshold for removing (pruning) weights with high precision from the computation. Default is 1.e+4. fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). Default is True. normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True. If True, X will be copied; else, it may be overwritten. verbose : boolean, optional, default False Verbose mode when fitting the model. Attributes ---------- coef_ : array, shape = (n_features) Coefficients of the regression model (mean of distribution) alpha_ : float estimated precision of the noise. lambda_ : array, shape = (n_features) estimated precisions of the weights. sigma_ : array, shape = (n_features, n_features) estimated variance-covariance matrix of the weights scores_ : float if computed, value of the objective function (to be maximized) Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.ARDRegression() >>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2]) ... # doctest: +NORMALIZE_WHITESPACE ARDRegression(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, normalize=False, threshold_lambda=10000.0, tol=0.001, verbose=False) >>> clf.predict([[1, 1]]) array([ 1.]) Notes -------- See examples/linear_model/plot_ard.py for an example. """ def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6, lambda_1=1.e-6, lambda_2=1.e-6, compute_score=False, threshold_lambda=1.e+4, fit_intercept=True, normalize=False, copy_X=True, verbose=False): self.n_iter = n_iter self.tol = tol self.fit_intercept = fit_intercept self.normalize = normalize self.alpha_1 = alpha_1 self.alpha_2 = alpha_2 self.lambda_1 = lambda_1 self.lambda_2 = lambda_2 self.compute_score = compute_score self.threshold_lambda = threshold_lambda self.copy_X = copy_X self.verbose = verbose def fit(self, X, y): """Fit the ARDRegression model according to the given training data and parameters. Iterative procedure to maximize the evidence Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y : array, shape = [n_samples] Target values (integers) Returns ------- self : returns an instance of self. """ X, y = check_X_y(X, y, dtype=np.float64, y_numeric=True) n_samples, n_features = X.shape coef_ = np.zeros(n_features) X, y, X_mean, y_mean, X_std = self._center_data( X, y, self.fit_intercept, self.normalize, self.copy_X) ### Launch the convergence loop keep_lambda = np.ones(n_features, dtype=bool) lambda_1 = self.lambda_1 lambda_2 = self.lambda_2 alpha_1 = self.alpha_1 alpha_2 = self.alpha_2 verbose = self.verbose ### Initialization of the values of the parameters alpha_ = 1. / np.var(y) lambda_ = np.ones(n_features) self.scores_ = list() coef_old_ = None ### Iterative procedure of ARDRegression for iter_ in range(self.n_iter): ### Compute mu and sigma (using Woodbury matrix identity) sigma_ = pinvh(np.eye(n_samples) / alpha_ + np.dot(X[:, keep_lambda] * np.reshape(1. / lambda_[keep_lambda], [1, -1]), X[:, keep_lambda].T)) sigma_ = np.dot(sigma_, X[:, keep_lambda] * np.reshape(1. / lambda_[keep_lambda], [1, -1])) sigma_ = - np.dot(np.reshape(1. / lambda_[keep_lambda], [-1, 1]) * X[:, keep_lambda].T, sigma_) sigma_.flat[::(sigma_.shape[1] + 1)] += 1. / lambda_[keep_lambda] coef_[keep_lambda] = alpha_ * np.dot( sigma_, np.dot(X[:, keep_lambda].T, y)) ### Update alpha and lambda rmse_ = np.sum((y - np.dot(X, coef_)) ** 2) gamma_ = 1. - lambda_[keep_lambda] * np.diag(sigma_) lambda_[keep_lambda] = ((gamma_ + 2. * lambda_1) / ((coef_[keep_lambda]) ** 2 + 2. * lambda_2)) alpha_ = ((n_samples - gamma_.sum() + 2. * alpha_1) / (rmse_ + 2. * alpha_2)) ### Prune the weights with a precision over a threshold keep_lambda = lambda_ < self.threshold_lambda coef_[~keep_lambda] = 0 ### Compute the objective function if self.compute_score: s = (lambda_1 * np.log(lambda_) - lambda_2 * lambda_).sum() s += alpha_1 * log(alpha_) - alpha_2 * alpha_ s += 0.5 * (fast_logdet(sigma_) + n_samples * log(alpha_) + np.sum(np.log(lambda_))) s -= 0.5 * (alpha_ * rmse_ + (lambda_ * coef_ ** 2).sum()) self.scores_.append(s) ### Check for convergence if iter_ > 0 and np.sum(np.abs(coef_old_ - coef_)) < self.tol: if verbose: print("Converged after %s iterations" % iter_) break coef_old_ = np.copy(coef_) self.coef_ = coef_ self.alpha_ = alpha_ self.sigma_ = sigma_ self.lambda_ = lambda_ self._set_intercept(X_mean, y_mean, X_std) return self

bsd-3-clause

Leminen/project_template_deeplearning

src/data/datasets/psd.py

1

9623

""" Methods for downloading and converting the MNIST dataset to TF-records implementation is heavily inspired by the slim.datasets implementation (https://github.com/tensorflow/models/tree/master/research/slim/datasets) """ import os import sys import numpy as np from six.moves import urllib import gzip import zipfile import tensorflow as tf import src.utils as utils import src.data.util_data as util_data # The URLs where the PSD data can be downloaded. _DATA_URL = 'https://vision.eng.au.dk/?download=/data/WeedData/' _NONSEGMENTED = 'NonsegmentedV2.zip' _SEGMENTED = 'Segmented.zip' _DATA_URL_NONSEGMENTED = 'https://vision.eng.au.dk/?download=/data/WeedData/NonsegmentedV2.zip' _DATA_URL_SEGMENTED = 'https://vision.eng.au.dk/?download=/data/WeedData/Segmented.zip' # Local directories to store the dataset _DIR_RAW = 'data/raw/PSD' _DIR_PROCESSED = 'data/processed/PSD' _DIR_RAW_NONSEGMENTED = 'data/raw/PSD_Nonsegmented/NonsegmentedV2.zip' _DIR_PROCESSED_NONSEGMENTED = 'data/processed/PSD_Nonsegmented/' _DIR_RAW_SEGMENTED = 'data/raw/PSD_Segmented/Segmented.zip' _DIR_PROCESSED_SEGMENTED = 'data/processed/PSD_Segmented/' _EXCLUDED_GRASSES = True _EXCLUDE_LARGE_IMAGES = True _LARGE_IMAGE_DIM = 400 _NUM_SHARDS = 10 def chunkify(lst,n): return [lst[i::n] for i in iter(range(n))] class ImageReader(object): """Helper class that provides TensorFlow image coding utilities.""" def __init__(self): # Initializes function that decodes RGB PNG data. self._decode_png_data = tf.placeholder(dtype=tf.string) self._decode_png = tf.image.decode_png(self._decode_png_data, channels=3) self._encode_png = tf.image.encode_png(self._decode_png) def truncate_image(self, sess, image_data): image, reencoded_image = sess.run( [self._decode_png, self._encode_png], feed_dict={self._decode_png_data: image_data}) assert len(image.shape) == 3 assert image.shape[2] == 3 return reencoded_image, image.shape[0], image.shape[1], image.shape[2] def read_image_dims(self, sess, image_data): image = self.decode_png(sess, image_data) return image.shape[0], image.shape[1], image.shape[2] def decode_png(self, sess, image_data): image = sess.run(self._decode_png, feed_dict={self._decode_png_data: image_data}) assert len(image.shape) == 3 assert image.shape[2] == 3 return image def encode_png(self, sess, image_data): image_data = sess.run(self._encode_png, feed_dict={self._decode_png_data: image_data}) def _get_filenames_and_classes(dataset_dir, setname, exclude_list): """Returns a list of filenames and inferred class names. Args: dataset_dir: A directory containing a set of subdirectories representing class names. Each subdirectory should contain PNG or JPG encoded images. Returns: A list of image file paths, relative to `dataset_dir` and the list of subdirectories, representing class names. """ data_root = os.path.join(dataset_dir, *setname) directories = [] class_names = [] for filename in os.listdir(data_root): path = os.path.join(data_root, filename) if os.path.isdir(path): if not any(x in filename for x in exclude_list): directories.append(path) class_names.append(filename) photo_filenames = [] photo_filenames2 = [] for _ in range(_NUM_SHARDS): photo_filenames2.append([]) for directory in directories: if not any(x in directory for x in exclude_list): filenames = os.listdir(directory) paths = [os.path.join(directory, filename) for filename in filenames] paths_split = chunkify(paths,_NUM_SHARDS) for shard_n in range(_NUM_SHARDS): photo_filenames2[shard_n].extend(paths_split[shard_n]) for filename in filenames: path = os.path.join(directory, filename) photo_filenames.append(path) return photo_filenames2, sorted(class_names) def _convert_to_tfrecord(filenames, class_dict, tfrecord_writer): """Loads data from the binary MNIST files and writes files to a TFRecord. Args: data_filename: The filename of the MNIST images. labels_filename: The filename of the MNIST labels. num_images: The number of images in the dataset. tfrecord_writer: The TFRecord writer to use for writing. """ num_images = len(filenames) image_reader = ImageReader() with tf.Session('') as sess: for i in range(num_images): sys.stdout.write('\r>> Converting image %d/%d' % (i + 1, num_images)) sys.stdout.flush() # Read the filename: encoded_img = tf.gfile.FastGFile(filenames[i], 'rb').read() encoded_img, height, width, channels = image_reader.truncate_image(sess, encoded_img) if _EXCLUDE_LARGE_IMAGES and (height > _LARGE_IMAGE_DIM or width > _LARGE_IMAGE_DIM): pass else: class_name = os.path.basename(os.path.dirname(filenames[i])) label = class_dict[class_name] example = util_data.encode_image( image_data = encoded_img, image_format = 'png'.encode(), class_lbl = label, class_text = class_name.encode(), height = height, width = width, channels = channels, origin = filenames[i].encode() ) tfrecord_writer.write(example.SerializeToString()) def _get_output_filename(dataset_dir, shard_id): """Creates the output filename. Args: dataset_dir: The directory where the temporary files are stored. split_name: The name of the train/test split. Returns: An absolute file path. """ return '%s/PSD-data_%03d-of-%03d.tfrecord' % (dataset_dir, shard_id+1, _NUM_SHARDS) def download(dataset_part): """Downloads PSD locally """ if dataset_part == 'Nonsegmented': _data_url = _DATA_URL_NONSEGMENTED filepath = os.path.join(_DIR_RAW_NONSEGMENTED) else: _data_url = _DATA_URL_SEGMENTED filepath = os.path.join(_DIR_RAW_SEGMENTED) if not os.path.exists(filepath): print('Downloading dataset...') def _progress(count, block_size, total_size): sys.stdout.write('\r>> Downloading %.1f%%' % ( float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() filepath, _ = urllib.request.urlretrieve(_data_url, filepath, _progress) print() with tf.gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', size, 'bytes.') def process(dataset_part): """Runs the download and conversion operation. Args: dataset_dir: The dataset directory where the dataset is stored. """ if dataset_part == 'Nonsegmented': _dir_raw = _DIR_RAW_NONSEGMENTED _dir_processed = _DIR_PROCESSED_NONSEGMENTED setname = 'Nonsegmented' #training_filename = _get_output_filename(_DIR_PROCESSED_NONSEGMENTED, 'train') # testing_filename = _get_output_filename(_DIR_PROCESSED_NONSEGMENTED, 'test') else: _dir_raw = _DIR_RAW_SEGMENTED _dir_processed = _DIR_PROCESSED_SEGMENTED setname = 'Segmented' #training_filename = _get_output_filename(_DIR_PROCESSED_SEGMENTED, 'train') # testing_filename = _get_output_filename(_DIR_PROCESSED_SEGMENTED, 'test') #if tf.gfile.Exists(training_filename): #and tf.gfile.Exists(testing_filename): # print('Dataset files already exist. Exiting without re-creating them.') # return if _EXCLUDED_GRASSES: exclude_list = ['Black-grass', 'Common wheat', 'Loose Silky-bent'] else: exclude_list = [] # First, process training data: data_filename = os.path.join(_dir_raw) archive = zipfile.ZipFile(data_filename) archive.extractall(_dir_processed) filenames, class_names = _get_filenames_and_classes(_dir_processed, [setname], exclude_list) class_dict = dict(zip(class_names, range(len(class_names)))) utils.save_dict(class_dict, _dir_processed, 'class_dict.json') for shard_n in range(_NUM_SHARDS): utils.show_message('Processing shard %d/%d' % (shard_n+1,_NUM_SHARDS)) tf_filename = _get_output_filename(_dir_processed, shard_n) with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer: _convert_to_tfrecord(filenames[shard_n], class_dict, tfrecord_writer) tmp_dir = os.path.join(_dir_processed, setname) tf.gfile.DeleteRecursively(tmp_dir) # # First, process test data: # with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer: # data_filename = os.path.join(_dir_raw) # archive = zipfile.ZipFile(data_filename) # archive.extractall(_dir_processed) # # filenames, class_names = _get_filenames_and_classes(_dir_processed, [setname, 'test'], exclude_list) # class_dict = dict(zip(class_names, range(len(class_names)))) # _convert_to_tfrecord(filenames, class_dict, tfrecord_writer) # tmp_dir = os.path.join(_dir_processed, setname) # tf.gfile.DeleteRecursively(tmp_dir) print('\nFinished converting the PSD %s dataset!' % setname)

mit

yonglehou/scikit-learn

examples/linear_model/plot_multi_task_lasso_support.py

248

2211

#!/usr/bin/env python """ ============================================= Joint feature selection with multi-task Lasso ============================================= The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example simulates sequential measurements, each task is a time instant, and the relevant features vary in amplitude over time while being the same. The multi-task lasso imposes that features that are selected at one time point are select for all time point. This makes feature selection by the Lasso more stable. """ print(__doc__) # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import MultiTaskLasso, Lasso rng = np.random.RandomState(42) # Generate some 2D coefficients with sine waves with random frequency and phase n_samples, n_features, n_tasks = 100, 30, 40 n_relevant_features = 5 coef = np.zeros((n_tasks, n_features)) times = np.linspace(0, 2 * np.pi, n_tasks) for k in range(n_relevant_features): coef[:, k] = np.sin((1. + rng.randn(1)) * times + 3 * rng.randn(1)) X = rng.randn(n_samples, n_features) Y = np.dot(X, coef.T) + rng.randn(n_samples, n_tasks) coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T]) coef_multi_task_lasso_ = MultiTaskLasso(alpha=1.).fit(X, Y).coef_ ############################################################################### # Plot support and time series fig = plt.figure(figsize=(8, 5)) plt.subplot(1, 2, 1) plt.spy(coef_lasso_) plt.xlabel('Feature') plt.ylabel('Time (or Task)') plt.text(10, 5, 'Lasso') plt.subplot(1, 2, 2) plt.spy(coef_multi_task_lasso_) plt.xlabel('Feature') plt.ylabel('Time (or Task)') plt.text(10, 5, 'MultiTaskLasso') fig.suptitle('Coefficient non-zero location') feature_to_plot = 0 plt.figure() plt.plot(coef[:, feature_to_plot], 'k', label='Ground truth') plt.plot(coef_lasso_[:, feature_to_plot], 'g', label='Lasso') plt.plot(coef_multi_task_lasso_[:, feature_to_plot], 'r', label='MultiTaskLasso') plt.legend(loc='upper center') plt.axis('tight') plt.ylim([-1.1, 1.1]) plt.show()

bsd-3-clause

ammarkhann/FinalSeniorCode

lib/python2.7/site-packages/IPython/core/prefilter.py

9

25512

# encoding: utf-8 """ Prefiltering components. Prefilters transform user input before it is exec'd by Python. These transforms are used to implement additional syntax such as !ls and %magic. """ # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. from keyword import iskeyword import re from IPython.core.autocall import IPyAutocall from traitlets.config.configurable import Configurable from IPython.core.inputsplitter import ( ESC_MAGIC, ESC_QUOTE, ESC_QUOTE2, ESC_PAREN, ) from IPython.core.macro import Macro from IPython.core.splitinput import LineInfo from traitlets import ( List, Integer, Unicode, Bool, Instance, CRegExp ) #----------------------------------------------------------------------------- # Global utilities, errors and constants #----------------------------------------------------------------------------- class PrefilterError(Exception): pass # RegExp to identify potential function names re_fun_name = re.compile(r'[a-zA-Z_]([a-zA-Z0-9_.]*) *$') # RegExp to exclude strings with this start from autocalling. In # particular, all binary operators should be excluded, so that if foo is # callable, foo OP bar doesn't become foo(OP bar), which is invalid. The # characters '!=()' don't need to be checked for, as the checkPythonChars # routine explicitely does so, to catch direct calls and rebindings of # existing names. # Warning: the '-' HAS TO BE AT THE END of the first group, otherwise # it affects the rest of the group in square brackets. re_exclude_auto = re.compile(r'^[,&^\|\*/\+-]' r'|^is |^not |^in |^and |^or ') # try to catch also methods for stuff in lists/tuples/dicts: off # (experimental). For this to work, the line_split regexp would need # to be modified so it wouldn't break things at '['. That line is # nasty enough that I shouldn't change it until I can test it _well_. #self.re_fun_name = re.compile (r'[a-zA-Z_]([a-zA-Z0-9_.\[\]]*) ?$') # Handler Check Utilities def is_shadowed(identifier, ip): """Is the given identifier defined in one of the namespaces which shadow the alias and magic namespaces? Note that an identifier is different than ifun, because it can not contain a '.' character.""" # This is much safer than calling ofind, which can change state return (identifier in ip.user_ns \ or identifier in ip.user_global_ns \ or identifier in ip.ns_table['builtin']\ or iskeyword(identifier)) #----------------------------------------------------------------------------- # Main Prefilter manager #----------------------------------------------------------------------------- class PrefilterManager(Configurable): """Main prefilter component. The IPython prefilter is run on all user input before it is run. The prefilter consumes lines of input and produces transformed lines of input. The iplementation consists of two phases: 1. Transformers 2. Checkers and handlers Over time, we plan on deprecating the checkers and handlers and doing everything in the transformers. The transformers are instances of :class:`PrefilterTransformer` and have a single method :meth:`transform` that takes a line and returns a transformed line. The transformation can be accomplished using any tool, but our current ones use regular expressions for speed. After all the transformers have been run, the line is fed to the checkers, which are instances of :class:`PrefilterChecker`. The line is passed to the :meth:`check` method, which either returns `None` or a :class:`PrefilterHandler` instance. If `None` is returned, the other checkers are tried. If an :class:`PrefilterHandler` instance is returned, the line is passed to the :meth:`handle` method of the returned handler and no further checkers are tried. Both transformers and checkers have a `priority` attribute, that determines the order in which they are called. Smaller priorities are tried first. Both transformers and checkers also have `enabled` attribute, which is a boolean that determines if the instance is used. Users or developers can change the priority or enabled attribute of transformers or checkers, but they must call the :meth:`sort_checkers` or :meth:`sort_transformers` method after changing the priority. """ multi_line_specials = Bool(True).tag(config=True) shell = Instance('IPython.core.interactiveshell.InteractiveShellABC', allow_none=True) def __init__(self, shell=None, **kwargs): super(PrefilterManager, self).__init__(shell=shell, **kwargs) self.shell = shell self.init_transformers() self.init_handlers() self.init_checkers() #------------------------------------------------------------------------- # API for managing transformers #------------------------------------------------------------------------- def init_transformers(self): """Create the default transformers.""" self._transformers = [] for transformer_cls in _default_transformers: transformer_cls( shell=self.shell, prefilter_manager=self, parent=self ) def sort_transformers(self): """Sort the transformers by priority. This must be called after the priority of a transformer is changed. The :meth:`register_transformer` method calls this automatically. """ self._transformers.sort(key=lambda x: x.priority) @property def transformers(self): """Return a list of checkers, sorted by priority.""" return self._transformers def register_transformer(self, transformer): """Register a transformer instance.""" if transformer not in self._transformers: self._transformers.append(transformer) self.sort_transformers() def unregister_transformer(self, transformer): """Unregister a transformer instance.""" if transformer in self._transformers: self._transformers.remove(transformer) #------------------------------------------------------------------------- # API for managing checkers #------------------------------------------------------------------------- def init_checkers(self): """Create the default checkers.""" self._checkers = [] for checker in _default_checkers: checker( shell=self.shell, prefilter_manager=self, parent=self ) def sort_checkers(self): """Sort the checkers by priority. This must be called after the priority of a checker is changed. The :meth:`register_checker` method calls this automatically. """ self._checkers.sort(key=lambda x: x.priority) @property def checkers(self): """Return a list of checkers, sorted by priority.""" return self._checkers def register_checker(self, checker): """Register a checker instance.""" if checker not in self._checkers: self._checkers.append(checker) self.sort_checkers() def unregister_checker(self, checker): """Unregister a checker instance.""" if checker in self._checkers: self._checkers.remove(checker) #------------------------------------------------------------------------- # API for managing handlers #------------------------------------------------------------------------- def init_handlers(self): """Create the default handlers.""" self._handlers = {} self._esc_handlers = {} for handler in _default_handlers: handler( shell=self.shell, prefilter_manager=self, parent=self ) @property def handlers(self): """Return a dict of all the handlers.""" return self._handlers def register_handler(self, name, handler, esc_strings): """Register a handler instance by name with esc_strings.""" self._handlers[name] = handler for esc_str in esc_strings: self._esc_handlers[esc_str] = handler def unregister_handler(self, name, handler, esc_strings): """Unregister a handler instance by name with esc_strings.""" try: del self._handlers[name] except KeyError: pass for esc_str in esc_strings: h = self._esc_handlers.get(esc_str) if h is handler: del self._esc_handlers[esc_str] def get_handler_by_name(self, name): """Get a handler by its name.""" return self._handlers.get(name) def get_handler_by_esc(self, esc_str): """Get a handler by its escape string.""" return self._esc_handlers.get(esc_str) #------------------------------------------------------------------------- # Main prefiltering API #------------------------------------------------------------------------- def prefilter_line_info(self, line_info): """Prefilter a line that has been converted to a LineInfo object. This implements the checker/handler part of the prefilter pipe. """ # print "prefilter_line_info: ", line_info handler = self.find_handler(line_info) return handler.handle(line_info) def find_handler(self, line_info): """Find a handler for the line_info by trying checkers.""" for checker in self.checkers: if checker.enabled: handler = checker.check(line_info) if handler: return handler return self.get_handler_by_name('normal') def transform_line(self, line, continue_prompt): """Calls the enabled transformers in order of increasing priority.""" for transformer in self.transformers: if transformer.enabled: line = transformer.transform(line, continue_prompt) return line def prefilter_line(self, line, continue_prompt=False): """Prefilter a single input line as text. This method prefilters a single line of text by calling the transformers and then the checkers/handlers. """ # print "prefilter_line: ", line, continue_prompt # All handlers *must* return a value, even if it's blank (''). # save the line away in case we crash, so the post-mortem handler can # record it self.shell._last_input_line = line if not line: # Return immediately on purely empty lines, so that if the user # previously typed some whitespace that started a continuation # prompt, he can break out of that loop with just an empty line. # This is how the default python prompt works. return '' # At this point, we invoke our transformers. if not continue_prompt or (continue_prompt and self.multi_line_specials): line = self.transform_line(line, continue_prompt) # Now we compute line_info for the checkers and handlers line_info = LineInfo(line, continue_prompt) # the input history needs to track even empty lines stripped = line.strip() normal_handler = self.get_handler_by_name('normal') if not stripped: return normal_handler.handle(line_info) # special handlers are only allowed for single line statements if continue_prompt and not self.multi_line_specials: return normal_handler.handle(line_info) prefiltered = self.prefilter_line_info(line_info) # print "prefiltered line: %r" % prefiltered return prefiltered def prefilter_lines(self, lines, continue_prompt=False): """Prefilter multiple input lines of text. This is the main entry point for prefiltering multiple lines of input. This simply calls :meth:`prefilter_line` for each line of input. This covers cases where there are multiple lines in the user entry, which is the case when the user goes back to a multiline history entry and presses enter. """ llines = lines.rstrip('\n').split('\n') # We can get multiple lines in one shot, where multiline input 'blends' # into one line, in cases like recalling from the readline history # buffer. We need to make sure that in such cases, we correctly # communicate downstream which line is first and which are continuation # ones. if len(llines) > 1: out = '\n'.join([self.prefilter_line(line, lnum>0) for lnum, line in enumerate(llines) ]) else: out = self.prefilter_line(llines[0], continue_prompt) return out #----------------------------------------------------------------------------- # Prefilter transformers #----------------------------------------------------------------------------- class PrefilterTransformer(Configurable): """Transform a line of user input.""" priority = Integer(100).tag(config=True) # Transformers don't currently use shell or prefilter_manager, but as we # move away from checkers and handlers, they will need them. shell = Instance('IPython.core.interactiveshell.InteractiveShellABC', allow_none=True) prefilter_manager = Instance('IPython.core.prefilter.PrefilterManager', allow_none=True) enabled = Bool(True).tag(config=True) def __init__(self, shell=None, prefilter_manager=None, **kwargs): super(PrefilterTransformer, self).__init__( shell=shell, prefilter_manager=prefilter_manager, **kwargs ) self.prefilter_manager.register_transformer(self) def transform(self, line, continue_prompt): """Transform a line, returning the new one.""" return None def __repr__(self): return "<%s(priority=%r, enabled=%r)>" % ( self.__class__.__name__, self.priority, self.enabled) #----------------------------------------------------------------------------- # Prefilter checkers #----------------------------------------------------------------------------- class PrefilterChecker(Configurable): """Inspect an input line and return a handler for that line.""" priority = Integer(100).tag(config=True) shell = Instance('IPython.core.interactiveshell.InteractiveShellABC', allow_none=True) prefilter_manager = Instance('IPython.core.prefilter.PrefilterManager', allow_none=True) enabled = Bool(True).tag(config=True) def __init__(self, shell=None, prefilter_manager=None, **kwargs): super(PrefilterChecker, self).__init__( shell=shell, prefilter_manager=prefilter_manager, **kwargs ) self.prefilter_manager.register_checker(self) def check(self, line_info): """Inspect line_info and return a handler instance or None.""" return None def __repr__(self): return "<%s(priority=%r, enabled=%r)>" % ( self.__class__.__name__, self.priority, self.enabled) class EmacsChecker(PrefilterChecker): priority = Integer(100).tag(config=True) enabled = Bool(False).tag(config=True) def check(self, line_info): "Emacs ipython-mode tags certain input lines." if line_info.line.endswith('# PYTHON-MODE'): return self.prefilter_manager.get_handler_by_name('emacs') else: return None class MacroChecker(PrefilterChecker): priority = Integer(250).tag(config=True) def check(self, line_info): obj = self.shell.user_ns.get(line_info.ifun) if isinstance(obj, Macro): return self.prefilter_manager.get_handler_by_name('macro') else: return None class IPyAutocallChecker(PrefilterChecker): priority = Integer(300).tag(config=True) def check(self, line_info): "Instances of IPyAutocall in user_ns get autocalled immediately" obj = self.shell.user_ns.get(line_info.ifun, None) if isinstance(obj, IPyAutocall): obj.set_ip(self.shell) return self.prefilter_manager.get_handler_by_name('auto') else: return None class AssignmentChecker(PrefilterChecker): priority = Integer(600).tag(config=True) def check(self, line_info): """Check to see if user is assigning to a var for the first time, in which case we want to avoid any sort of automagic / autocall games. This allows users to assign to either alias or magic names true python variables (the magic/alias systems always take second seat to true python code). E.g. ls='hi', or ls,that=1,2""" if line_info.the_rest: if line_info.the_rest[0] in '=,': return self.prefilter_manager.get_handler_by_name('normal') else: return None class AutoMagicChecker(PrefilterChecker): priority = Integer(700).tag(config=True) def check(self, line_info): """If the ifun is magic, and automagic is on, run it. Note: normal, non-auto magic would already have been triggered via '%' in check_esc_chars. This just checks for automagic. Also, before triggering the magic handler, make sure that there is nothing in the user namespace which could shadow it.""" if not self.shell.automagic or not self.shell.find_magic(line_info.ifun): return None # We have a likely magic method. Make sure we should actually call it. if line_info.continue_prompt and not self.prefilter_manager.multi_line_specials: return None head = line_info.ifun.split('.',1)[0] if is_shadowed(head, self.shell): return None return self.prefilter_manager.get_handler_by_name('magic') class PythonOpsChecker(PrefilterChecker): priority = Integer(900).tag(config=True) def check(self, line_info): """If the 'rest' of the line begins with a function call or pretty much any python operator, we should simply execute the line (regardless of whether or not there's a possible autocall expansion). This avoids spurious (and very confusing) geattr() accesses.""" if line_info.the_rest and line_info.the_rest[0] in '!=()<>,+*/%^&|': return self.prefilter_manager.get_handler_by_name('normal') else: return None class AutocallChecker(PrefilterChecker): priority = Integer(1000).tag(config=True) function_name_regexp = CRegExp(re_fun_name, help="RegExp to identify potential function names." ).tag(config=True) exclude_regexp = CRegExp(re_exclude_auto, help="RegExp to exclude strings with this start from autocalling." ).tag(config=True) def check(self, line_info): "Check if the initial word/function is callable and autocall is on." if not self.shell.autocall: return None oinfo = line_info.ofind(self.shell) # This can mutate state via getattr if not oinfo['found']: return None if callable(oinfo['obj']) \ and (not self.exclude_regexp.match(line_info.the_rest)) \ and self.function_name_regexp.match(line_info.ifun): return self.prefilter_manager.get_handler_by_name('auto') else: return None #----------------------------------------------------------------------------- # Prefilter handlers #----------------------------------------------------------------------------- class PrefilterHandler(Configurable): handler_name = Unicode('normal') esc_strings = List([]) shell = Instance('IPython.core.interactiveshell.InteractiveShellABC', allow_none=True) prefilter_manager = Instance('IPython.core.prefilter.PrefilterManager', allow_none=True) def __init__(self, shell=None, prefilter_manager=None, **kwargs): super(PrefilterHandler, self).__init__( shell=shell, prefilter_manager=prefilter_manager, **kwargs ) self.prefilter_manager.register_handler( self.handler_name, self, self.esc_strings ) def handle(self, line_info): # print "normal: ", line_info """Handle normal input lines. Use as a template for handlers.""" # With autoindent on, we need some way to exit the input loop, and I # don't want to force the user to have to backspace all the way to # clear the line. The rule will be in this case, that either two # lines of pure whitespace in a row, or a line of pure whitespace but # of a size different to the indent level, will exit the input loop. line = line_info.line continue_prompt = line_info.continue_prompt if (continue_prompt and self.shell.autoindent and line.isspace() and 0 < abs(len(line) - self.shell.indent_current_nsp) <= 2): line = '' return line def __str__(self): return "<%s(name=%s)>" % (self.__class__.__name__, self.handler_name) class MacroHandler(PrefilterHandler): handler_name = Unicode("macro") def handle(self, line_info): obj = self.shell.user_ns.get(line_info.ifun) pre_space = line_info.pre_whitespace line_sep = "\n" + pre_space return pre_space + line_sep.join(obj.value.splitlines()) class MagicHandler(PrefilterHandler): handler_name = Unicode('magic') esc_strings = List([ESC_MAGIC]) def handle(self, line_info): """Execute magic functions.""" ifun = line_info.ifun the_rest = line_info.the_rest cmd = '%sget_ipython().magic(%r)' % (line_info.pre_whitespace, (ifun + " " + the_rest)) return cmd class AutoHandler(PrefilterHandler): handler_name = Unicode('auto') esc_strings = List([ESC_PAREN, ESC_QUOTE, ESC_QUOTE2]) def handle(self, line_info): """Handle lines which can be auto-executed, quoting if requested.""" line = line_info.line ifun = line_info.ifun the_rest = line_info.the_rest esc = line_info.esc continue_prompt = line_info.continue_prompt obj = line_info.ofind(self.shell)['obj'] # This should only be active for single-line input! if continue_prompt: return line force_auto = isinstance(obj, IPyAutocall) # User objects sometimes raise exceptions on attribute access other # than AttributeError (we've seen it in the past), so it's safest to be # ultra-conservative here and catch all. try: auto_rewrite = obj.rewrite except Exception: auto_rewrite = True if esc == ESC_QUOTE: # Auto-quote splitting on whitespace newcmd = '%s("%s")' % (ifun,'", "'.join(the_rest.split()) ) elif esc == ESC_QUOTE2: # Auto-quote whole string newcmd = '%s("%s")' % (ifun,the_rest) elif esc == ESC_PAREN: newcmd = '%s(%s)' % (ifun,",".join(the_rest.split())) else: # Auto-paren. if force_auto: # Don't rewrite if it is already a call. do_rewrite = not the_rest.startswith('(') else: if not the_rest: # We only apply it to argument-less calls if the autocall # parameter is set to 2. do_rewrite = (self.shell.autocall >= 2) elif the_rest.startswith('[') and hasattr(obj, '__getitem__'): # Don't autocall in this case: item access for an object # which is BOTH callable and implements __getitem__. do_rewrite = False else: do_rewrite = True # Figure out the rewritten command if do_rewrite: if the_rest.endswith(';'): newcmd = '%s(%s);' % (ifun.rstrip(),the_rest[:-1]) else: newcmd = '%s(%s)' % (ifun.rstrip(), the_rest) else: normal_handler = self.prefilter_manager.get_handler_by_name('normal') return normal_handler.handle(line_info) # Display the rewritten call if auto_rewrite: self.shell.auto_rewrite_input(newcmd) return newcmd class EmacsHandler(PrefilterHandler): handler_name = Unicode('emacs') esc_strings = List([]) def handle(self, line_info): """Handle input lines marked by python-mode.""" # Currently, nothing is done. Later more functionality can be added # here if needed. # The input cache shouldn't be updated return line_info.line #----------------------------------------------------------------------------- # Defaults #----------------------------------------------------------------------------- _default_transformers = [ ] _default_checkers = [ EmacsChecker, MacroChecker, IPyAutocallChecker, AssignmentChecker, AutoMagicChecker, PythonOpsChecker, AutocallChecker ] _default_handlers = [ PrefilterHandler, MacroHandler, MagicHandler, AutoHandler, EmacsHandler ]

mit

pkruskal/scikit-learn

examples/linear_model/plot_multi_task_lasso_support.py

248

2211

#!/usr/bin/env python """ ============================================= Joint feature selection with multi-task Lasso ============================================= The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example simulates sequential measurements, each task is a time instant, and the relevant features vary in amplitude over time while being the same. The multi-task lasso imposes that features that are selected at one time point are select for all time point. This makes feature selection by the Lasso more stable. """ print(__doc__) # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import MultiTaskLasso, Lasso rng = np.random.RandomState(42) # Generate some 2D coefficients with sine waves with random frequency and phase n_samples, n_features, n_tasks = 100, 30, 40 n_relevant_features = 5 coef = np.zeros((n_tasks, n_features)) times = np.linspace(0, 2 * np.pi, n_tasks) for k in range(n_relevant_features): coef[:, k] = np.sin((1. + rng.randn(1)) * times + 3 * rng.randn(1)) X = rng.randn(n_samples, n_features) Y = np.dot(X, coef.T) + rng.randn(n_samples, n_tasks) coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T]) coef_multi_task_lasso_ = MultiTaskLasso(alpha=1.).fit(X, Y).coef_ ############################################################################### # Plot support and time series fig = plt.figure(figsize=(8, 5)) plt.subplot(1, 2, 1) plt.spy(coef_lasso_) plt.xlabel('Feature') plt.ylabel('Time (or Task)') plt.text(10, 5, 'Lasso') plt.subplot(1, 2, 2) plt.spy(coef_multi_task_lasso_) plt.xlabel('Feature') plt.ylabel('Time (or Task)') plt.text(10, 5, 'MultiTaskLasso') fig.suptitle('Coefficient non-zero location') feature_to_plot = 0 plt.figure() plt.plot(coef[:, feature_to_plot], 'k', label='Ground truth') plt.plot(coef_lasso_[:, feature_to_plot], 'g', label='Lasso') plt.plot(coef_multi_task_lasso_[:, feature_to_plot], 'r', label='MultiTaskLasso') plt.legend(loc='upper center') plt.axis('tight') plt.ylim([-1.1, 1.1]) plt.show()

bsd-3-clause

yonglehou/scikit-learn

sklearn/linear_model/tests/test_ridge.py

129

22974

import numpy as np import scipy.sparse as sp from scipy import linalg from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_raise_message from sklearn.utils.testing import ignore_warnings from sklearn import datasets from sklearn.metrics import mean_squared_error from sklearn.metrics import make_scorer from sklearn.metrics import get_scorer from sklearn.linear_model.base import LinearRegression from sklearn.linear_model.ridge import ridge_regression from sklearn.linear_model.ridge import Ridge from sklearn.linear_model.ridge import _RidgeGCV from sklearn.linear_model.ridge import RidgeCV from sklearn.linear_model.ridge import RidgeClassifier from sklearn.linear_model.ridge import RidgeClassifierCV from sklearn.linear_model.ridge import _solve_cholesky from sklearn.linear_model.ridge import _solve_cholesky_kernel from sklearn.grid_search import GridSearchCV from sklearn.cross_validation import KFold diabetes = datasets.load_diabetes() X_diabetes, y_diabetes = diabetes.data, diabetes.target ind = np.arange(X_diabetes.shape[0]) rng = np.random.RandomState(0) rng.shuffle(ind) ind = ind[:200] X_diabetes, y_diabetes = X_diabetes[ind], y_diabetes[ind] iris = datasets.load_iris() X_iris = sp.csr_matrix(iris.data) y_iris = iris.target DENSE_FILTER = lambda X: X SPARSE_FILTER = lambda X: sp.csr_matrix(X) def test_ridge(): # Ridge regression convergence test using score # TODO: for this test to be robust, we should use a dataset instead # of np.random. rng = np.random.RandomState(0) alpha = 1.0 for solver in ("svd", "sparse_cg", "cholesky", "lsqr"): # With more samples than features n_samples, n_features = 6, 5 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_equal(ridge.coef_.shape, (X.shape[1], )) assert_greater(ridge.score(X, y), 0.47) if solver == "cholesky": # Currently the only solver to support sample_weight. ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.47) # With more features than samples n_samples, n_features = 5, 10 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=alpha, solver=solver) ridge.fit(X, y) assert_greater(ridge.score(X, y), .9) if solver == "cholesky": # Currently the only solver to support sample_weight. ridge.fit(X, y, sample_weight=np.ones(n_samples)) assert_greater(ridge.score(X, y), 0.9) def test_primal_dual_relationship(): y = y_diabetes.reshape(-1, 1) coef = _solve_cholesky(X_diabetes, y, alpha=[1e-2]) K = np.dot(X_diabetes, X_diabetes.T) dual_coef = _solve_cholesky_kernel(K, y, alpha=[1e-2]) coef2 = np.dot(X_diabetes.T, dual_coef).T assert_array_almost_equal(coef, coef2) def test_ridge_singular(): # test on a singular matrix rng = np.random.RandomState(0) n_samples, n_features = 6, 6 y = rng.randn(n_samples // 2) y = np.concatenate((y, y)) X = rng.randn(n_samples // 2, n_features) X = np.concatenate((X, X), axis=0) ridge = Ridge(alpha=0) ridge.fit(X, y) assert_greater(ridge.score(X, y), 0.9) def test_ridge_sample_weights(): rng = np.random.RandomState(0) for solver in ("cholesky", ): for n_samples, n_features in ((6, 5), (5, 10)): for alpha in (1.0, 1e-2): y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) sample_weight = 1 + rng.rand(n_samples) coefs = ridge_regression(X, y, alpha=alpha, sample_weight=sample_weight, solver=solver) # Sample weight can be implemented via a simple rescaling # for the square loss. coefs2 = ridge_regression( X * np.sqrt(sample_weight)[:, np.newaxis], y * np.sqrt(sample_weight), alpha=alpha, solver=solver) assert_array_almost_equal(coefs, coefs2) # Test for fit_intercept = True est = Ridge(alpha=alpha, solver=solver) est.fit(X, y, sample_weight=sample_weight) # Check using Newton's Method # Quadratic function should be solved in a single step. # Initialize sample_weight = np.sqrt(sample_weight) X_weighted = sample_weight[:, np.newaxis] * ( np.column_stack((np.ones(n_samples), X))) y_weighted = y * sample_weight # Gradient is (X*coef-y)*X + alpha*coef_[1:] # Remove coef since it is initialized to zero. grad = -np.dot(y_weighted, X_weighted) # Hessian is (X.T*X) + alpha*I except that the first # diagonal element should be zero, since there is no # penalization of intercept. diag = alpha * np.ones(n_features + 1) diag[0] = 0. hess = np.dot(X_weighted.T, X_weighted) hess.flat[::n_features + 2] += diag coef_ = - np.dot(linalg.inv(hess), grad) assert_almost_equal(coef_[0], est.intercept_) assert_array_almost_equal(coef_[1:], est.coef_) def test_ridge_shapes(): # Test shape of coef_ and intercept_ rng = np.random.RandomState(0) n_samples, n_features = 5, 10 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) Y1 = y[:, np.newaxis] Y = np.c_[y, 1 + y] ridge = Ridge() ridge.fit(X, y) assert_equal(ridge.coef_.shape, (n_features,)) assert_equal(ridge.intercept_.shape, ()) ridge.fit(X, Y1) assert_equal(ridge.coef_.shape, (1, n_features)) assert_equal(ridge.intercept_.shape, (1, )) ridge.fit(X, Y) assert_equal(ridge.coef_.shape, (2, n_features)) assert_equal(ridge.intercept_.shape, (2, )) def test_ridge_intercept(): # Test intercept with multiple targets GH issue #708 rng = np.random.RandomState(0) n_samples, n_features = 5, 10 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) Y = np.c_[y, 1. + y] ridge = Ridge() ridge.fit(X, y) intercept = ridge.intercept_ ridge.fit(X, Y) assert_almost_equal(ridge.intercept_[0], intercept) assert_almost_equal(ridge.intercept_[1], intercept + 1.) def test_toy_ridge_object(): # Test BayesianRegression ridge classifier # TODO: test also n_samples > n_features X = np.array([[1], [2]]) Y = np.array([1, 2]) clf = Ridge(alpha=0.0) clf.fit(X, Y) X_test = [[1], [2], [3], [4]] assert_almost_equal(clf.predict(X_test), [1., 2, 3, 4]) assert_equal(len(clf.coef_.shape), 1) assert_equal(type(clf.intercept_), np.float64) Y = np.vstack((Y, Y)).T clf.fit(X, Y) X_test = [[1], [2], [3], [4]] assert_equal(len(clf.coef_.shape), 2) assert_equal(type(clf.intercept_), np.ndarray) def test_ridge_vs_lstsq(): # On alpha=0., Ridge and OLS yield the same solution. rng = np.random.RandomState(0) # we need more samples than features n_samples, n_features = 5, 4 y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) ridge = Ridge(alpha=0., fit_intercept=False) ols = LinearRegression(fit_intercept=False) ridge.fit(X, y) ols.fit(X, y) assert_almost_equal(ridge.coef_, ols.coef_) ridge.fit(X, y) ols.fit(X, y) assert_almost_equal(ridge.coef_, ols.coef_) def test_ridge_individual_penalties(): # Tests the ridge object using individual penalties rng = np.random.RandomState(42) n_samples, n_features, n_targets = 20, 10, 5 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples, n_targets) penalties = np.arange(n_targets) coef_cholesky = np.array([ Ridge(alpha=alpha, solver="cholesky").fit(X, target).coef_ for alpha, target in zip(penalties, y.T)]) coefs_indiv_pen = [ Ridge(alpha=penalties, solver=solver, tol=1e-6).fit(X, y).coef_ for solver in ['svd', 'sparse_cg', 'lsqr', 'cholesky']] for coef_indiv_pen in coefs_indiv_pen: assert_array_almost_equal(coef_cholesky, coef_indiv_pen) # Test error is raised when number of targets and penalties do not match. ridge = Ridge(alpha=penalties[:3]) assert_raises(ValueError, ridge.fit, X, y) def _test_ridge_loo(filter_): # test that can work with both dense or sparse matrices n_samples = X_diabetes.shape[0] ret = [] ridge_gcv = _RidgeGCV(fit_intercept=False) ridge = Ridge(alpha=1.0, fit_intercept=False) # generalized cross-validation (efficient leave-one-out) decomp = ridge_gcv._pre_compute(X_diabetes, y_diabetes) errors, c = ridge_gcv._errors(1.0, y_diabetes, *decomp) values, c = ridge_gcv._values(1.0, y_diabetes, *decomp) # brute-force leave-one-out: remove one example at a time errors2 = [] values2 = [] for i in range(n_samples): sel = np.arange(n_samples) != i X_new = X_diabetes[sel] y_new = y_diabetes[sel] ridge.fit(X_new, y_new) value = ridge.predict([X_diabetes[i]])[0] error = (y_diabetes[i] - value) ** 2 errors2.append(error) values2.append(value) # check that efficient and brute-force LOO give same results assert_almost_equal(errors, errors2) assert_almost_equal(values, values2) # generalized cross-validation (efficient leave-one-out, # SVD variation) decomp = ridge_gcv._pre_compute_svd(X_diabetes, y_diabetes) errors3, c = ridge_gcv._errors_svd(ridge.alpha, y_diabetes, *decomp) values3, c = ridge_gcv._values_svd(ridge.alpha, y_diabetes, *decomp) # check that efficient and SVD efficient LOO give same results assert_almost_equal(errors, errors3) assert_almost_equal(values, values3) # check best alpha ridge_gcv.fit(filter_(X_diabetes), y_diabetes) alpha_ = ridge_gcv.alpha_ ret.append(alpha_) # check that we get same best alpha with custom loss_func f = ignore_warnings scoring = make_scorer(mean_squared_error, greater_is_better=False) ridge_gcv2 = RidgeCV(fit_intercept=False, scoring=scoring) f(ridge_gcv2.fit)(filter_(X_diabetes), y_diabetes) assert_equal(ridge_gcv2.alpha_, alpha_) # check that we get same best alpha with custom score_func func = lambda x, y: -mean_squared_error(x, y) scoring = make_scorer(func) ridge_gcv3 = RidgeCV(fit_intercept=False, scoring=scoring) f(ridge_gcv3.fit)(filter_(X_diabetes), y_diabetes) assert_equal(ridge_gcv3.alpha_, alpha_) # check that we get same best alpha with a scorer scorer = get_scorer('mean_squared_error') ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer) ridge_gcv4.fit(filter_(X_diabetes), y_diabetes) assert_equal(ridge_gcv4.alpha_, alpha_) # check that we get same best alpha with sample weights ridge_gcv.fit(filter_(X_diabetes), y_diabetes, sample_weight=np.ones(n_samples)) assert_equal(ridge_gcv.alpha_, alpha_) # simulate several responses Y = np.vstack((y_diabetes, y_diabetes)).T ridge_gcv.fit(filter_(X_diabetes), Y) Y_pred = ridge_gcv.predict(filter_(X_diabetes)) ridge_gcv.fit(filter_(X_diabetes), y_diabetes) y_pred = ridge_gcv.predict(filter_(X_diabetes)) assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=5) return ret def _test_ridge_cv(filter_): n_samples = X_diabetes.shape[0] ridge_cv = RidgeCV() ridge_cv.fit(filter_(X_diabetes), y_diabetes) ridge_cv.predict(filter_(X_diabetes)) assert_equal(len(ridge_cv.coef_.shape), 1) assert_equal(type(ridge_cv.intercept_), np.float64) cv = KFold(n_samples, 5) ridge_cv.set_params(cv=cv) ridge_cv.fit(filter_(X_diabetes), y_diabetes) ridge_cv.predict(filter_(X_diabetes)) assert_equal(len(ridge_cv.coef_.shape), 1) assert_equal(type(ridge_cv.intercept_), np.float64) def _test_ridge_diabetes(filter_): ridge = Ridge(fit_intercept=False) ridge.fit(filter_(X_diabetes), y_diabetes) return np.round(ridge.score(filter_(X_diabetes), y_diabetes), 5) def _test_multi_ridge_diabetes(filter_): # simulate several responses Y = np.vstack((y_diabetes, y_diabetes)).T n_features = X_diabetes.shape[1] ridge = Ridge(fit_intercept=False) ridge.fit(filter_(X_diabetes), Y) assert_equal(ridge.coef_.shape, (2, n_features)) Y_pred = ridge.predict(filter_(X_diabetes)) ridge.fit(filter_(X_diabetes), y_diabetes) y_pred = ridge.predict(filter_(X_diabetes)) assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3) def _test_ridge_classifiers(filter_): n_classes = np.unique(y_iris).shape[0] n_features = X_iris.shape[1] for clf in (RidgeClassifier(), RidgeClassifierCV()): clf.fit(filter_(X_iris), y_iris) assert_equal(clf.coef_.shape, (n_classes, n_features)) y_pred = clf.predict(filter_(X_iris)) assert_greater(np.mean(y_iris == y_pred), .79) n_samples = X_iris.shape[0] cv = KFold(n_samples, 5) clf = RidgeClassifierCV(cv=cv) clf.fit(filter_(X_iris), y_iris) y_pred = clf.predict(filter_(X_iris)) assert_true(np.mean(y_iris == y_pred) >= 0.8) def _test_tolerance(filter_): ridge = Ridge(tol=1e-5) ridge.fit(filter_(X_diabetes), y_diabetes) score = ridge.score(filter_(X_diabetes), y_diabetes) ridge2 = Ridge(tol=1e-3) ridge2.fit(filter_(X_diabetes), y_diabetes) score2 = ridge2.score(filter_(X_diabetes), y_diabetes) assert_true(score >= score2) def test_dense_sparse(): for test_func in (_test_ridge_loo, _test_ridge_cv, _test_ridge_diabetes, _test_multi_ridge_diabetes, _test_ridge_classifiers, _test_tolerance): # test dense matrix ret_dense = test_func(DENSE_FILTER) # test sparse matrix ret_sparse = test_func(SPARSE_FILTER) # test that the outputs are the same if ret_dense is not None and ret_sparse is not None: assert_array_almost_equal(ret_dense, ret_sparse, decimal=3) def test_ridge_cv_sparse_svd(): X = sp.csr_matrix(X_diabetes) ridge = RidgeCV(gcv_mode="svd") assert_raises(TypeError, ridge.fit, X) def test_ridge_sparse_svd(): X = sp.csc_matrix(rng.rand(100, 10)) y = rng.rand(100) ridge = Ridge(solver='svd') assert_raises(TypeError, ridge.fit, X, y) def test_class_weights(): # Test class weights. X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0], [1.0, 0.0]]) y = [1, 1, 1, -1, -1] clf = RidgeClassifier(class_weight=None) clf.fit(X, y) assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1])) # we give a small weights to class 1 clf = RidgeClassifier(class_weight={1: 0.001}) clf.fit(X, y) # now the hyperplane should rotate clock-wise and # the prediction on this point should shift assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1])) # check if class_weight = 'balanced' can handle negative labels. clf = RidgeClassifier(class_weight='balanced') clf.fit(X, y) assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1])) # class_weight = 'balanced', and class_weight = None should return # same values when y has equal number of all labels X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0]]) y = [1, 1, -1, -1] clf = RidgeClassifier(class_weight=None) clf.fit(X, y) clfa = RidgeClassifier(class_weight='balanced') clfa.fit(X, y) assert_equal(len(clfa.classes_), 2) assert_array_almost_equal(clf.coef_, clfa.coef_) assert_array_almost_equal(clf.intercept_, clfa.intercept_) def test_class_weight_vs_sample_weight(): """Check class_weights resemble sample_weights behavior.""" for clf in (RidgeClassifier, RidgeClassifierCV): # Iris is balanced, so no effect expected for using 'balanced' weights clf1 = clf() clf1.fit(iris.data, iris.target) clf2 = clf(class_weight='balanced') clf2.fit(iris.data, iris.target) assert_almost_equal(clf1.coef_, clf2.coef_) # Inflate importance of class 1, check against user-defined weights sample_weight = np.ones(iris.target.shape) sample_weight[iris.target == 1] *= 100 class_weight = {0: 1., 1: 100., 2: 1.} clf1 = clf() clf1.fit(iris.data, iris.target, sample_weight) clf2 = clf(class_weight=class_weight) clf2.fit(iris.data, iris.target) assert_almost_equal(clf1.coef_, clf2.coef_) # Check that sample_weight and class_weight are multiplicative clf1 = clf() clf1.fit(iris.data, iris.target, sample_weight ** 2) clf2 = clf(class_weight=class_weight) clf2.fit(iris.data, iris.target, sample_weight) assert_almost_equal(clf1.coef_, clf2.coef_) def test_class_weights_cv(): # Test class weights for cross validated ridge classifier. X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0], [1.0, 1.0], [1.0, 0.0]]) y = [1, 1, 1, -1, -1] clf = RidgeClassifierCV(class_weight=None, alphas=[.01, .1, 1]) clf.fit(X, y) # we give a small weights to class 1 clf = RidgeClassifierCV(class_weight={1: 0.001}, alphas=[.01, .1, 1, 10]) clf.fit(X, y) assert_array_equal(clf.predict([[-.2, 2]]), np.array([-1])) def test_ridgecv_store_cv_values(): # Test _RidgeCV's store_cv_values attribute. rng = rng = np.random.RandomState(42) n_samples = 8 n_features = 5 x = rng.randn(n_samples, n_features) alphas = [1e-1, 1e0, 1e1] n_alphas = len(alphas) r = RidgeCV(alphas=alphas, store_cv_values=True) # with len(y.shape) == 1 y = rng.randn(n_samples) r.fit(x, y) assert_equal(r.cv_values_.shape, (n_samples, n_alphas)) # with len(y.shape) == 2 n_responses = 3 y = rng.randn(n_samples, n_responses) r.fit(x, y) assert_equal(r.cv_values_.shape, (n_samples, n_responses, n_alphas)) def test_ridgecv_sample_weight(): rng = np.random.RandomState(0) alphas = (0.1, 1.0, 10.0) # There are different algorithms for n_samples > n_features # and the opposite, so test them both. for n_samples, n_features in ((6, 5), (5, 10)): y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) sample_weight = 1 + rng.rand(n_samples) cv = KFold(n_samples, 5) ridgecv = RidgeCV(alphas=alphas, cv=cv) ridgecv.fit(X, y, sample_weight=sample_weight) # Check using GridSearchCV directly parameters = {'alpha': alphas} fit_params = {'sample_weight': sample_weight} gs = GridSearchCV(Ridge(), parameters, fit_params=fit_params, cv=cv) gs.fit(X, y) assert_equal(ridgecv.alpha_, gs.best_estimator_.alpha) assert_array_almost_equal(ridgecv.coef_, gs.best_estimator_.coef_) def test_raises_value_error_if_sample_weights_greater_than_1d(): # Sample weights must be either scalar or 1D n_sampless = [2, 3] n_featuress = [3, 2] rng = np.random.RandomState(42) for n_samples, n_features in zip(n_sampless, n_featuress): X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) sample_weights_OK = rng.randn(n_samples) ** 2 + 1 sample_weights_OK_1 = 1. sample_weights_OK_2 = 2. sample_weights_not_OK = sample_weights_OK[:, np.newaxis] sample_weights_not_OK_2 = sample_weights_OK[np.newaxis, :] ridge = Ridge(alpha=1) # make sure the "OK" sample weights actually work ridge.fit(X, y, sample_weights_OK) ridge.fit(X, y, sample_weights_OK_1) ridge.fit(X, y, sample_weights_OK_2) def fit_ridge_not_ok(): ridge.fit(X, y, sample_weights_not_OK) def fit_ridge_not_ok_2(): ridge.fit(X, y, sample_weights_not_OK_2) assert_raise_message(ValueError, "Sample weights must be 1D array or scalar", fit_ridge_not_ok) assert_raise_message(ValueError, "Sample weights must be 1D array or scalar", fit_ridge_not_ok_2) def test_sparse_design_with_sample_weights(): # Sample weights must work with sparse matrices n_sampless = [2, 3] n_featuress = [3, 2] rng = np.random.RandomState(42) sparse_matrix_converters = [sp.coo_matrix, sp.csr_matrix, sp.csc_matrix, sp.lil_matrix, sp.dok_matrix ] sparse_ridge = Ridge(alpha=1., fit_intercept=False) dense_ridge = Ridge(alpha=1., fit_intercept=False) for n_samples, n_features in zip(n_sampless, n_featuress): X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) sample_weights = rng.randn(n_samples) ** 2 + 1 for sparse_converter in sparse_matrix_converters: X_sparse = sparse_converter(X) sparse_ridge.fit(X_sparse, y, sample_weight=sample_weights) dense_ridge.fit(X, y, sample_weight=sample_weights) assert_array_almost_equal(sparse_ridge.coef_, dense_ridge.coef_, decimal=6) def test_raises_value_error_if_solver_not_supported(): # Tests whether a ValueError is raised if a non-identified solver # is passed to ridge_regression wrong_solver = "This is not a solver (MagritteSolveCV QuantumBitcoin)" exception = ValueError message = "Solver %s not understood" % wrong_solver def func(): X = np.eye(3) y = np.ones(3) ridge_regression(X, y, alpha=1., solver=wrong_solver) assert_raise_message(exception, message, func) def test_sparse_cg_max_iter(): reg = Ridge(solver="sparse_cg", max_iter=1) reg.fit(X_diabetes, y_diabetes) assert_equal(reg.coef_.shape[0], X_diabetes.shape[1])

bsd-3-clause

cpacker/GraphZip

test/test_expr.py

1

23327

""" Test file that """ import cProfile import os import unittest import sys import time from operator import itemgetter from pstats import Stats from timeit import default_timer as timer try: import cPickle as pickle except: import pickle from compressor.compress import Compressor from .utils import import_insts, parse_subdue_output DEBUG = True # enable for debug print output SAVE = False # save the SVGs from each example PROFILE = False GRAPH_DIR = "data/" # root dir for graph (eg. *.g, *.graph) files IMAGE_DIR = "images/" # root dir for SVG images SUBDUE_DIR = "../SUBDUE/subdue-5.2.2/bin/" # location of SUBDUE exe SUBGEN_DIR = "data/SUBGEN/" # location of SUBGEN .graph and .insts files def get_gt_patterns_found(groundtruth, patterns): """ Returns an error metric using the groundtruth and returned patterns Error = #gt_patterns missed / total #gt_patterns """ hits = [0 for g in groundtruth] # 1 if hit, 0 if miss (on gt) # For each ground_truth pattern, check if we found it with our algorithm for i, gt in enumerate(groundtruth): c1 = gt.vs["label"] c1_edge = gt.es["label"] for p in patterns: if len(p.es) == 0: continue c2 = p.vs["label"] c2_edge = p.es["label"] if len(c1) != len(c2) or len(c1_edge) != len(c2_edge): continue try: if gt.isomorphic_vf2(p, color1=c1, color2=c2, edge_color1=c1_edge, edge_color2=c2_edge): if(hits[i] >= 1): print("Warning: ground-truth pattern already found") else: hits[i] = 1 # print("hit:",p) break except: print('Error') print(c1_edge) print(c2_edge) return (sum(hits), len(hits)) # hits, total def get_patterns_also_in_gt(groundtruth, patterns): """ Returns an error metric using the groundtruth and returned patterns Error = #patterns not in gt / total #patterns """ hits = [0 for p in patterns] # 1 if hit, 0 if miss # For each ground_truth pattern, check if we found it with our algorithm for i, p in enumerate(patterns): if len(p.es) == 0: continue c1 = p.vs["label"] c1_edge = p.es["label"] for gt in groundtruth: c2 = gt.vs["label"] c2_edge = gt.es["label"] if len(c1) != len(c2) or len(c1_edge) != len(c2_edge): continue if gt.isomorphic_vf2(p, color1=c1, color2=c2, edge_color1=c1_edge, edge_color2=c2_edge): if(hits[i] >= 1): print("Warning: ground-truth pattern already found") else: hits[i] = 1 break # consider multiple instances of same pattern? return (sum(hits), len(hits)) # hits,total def print_top_n_graphs(C, n): """ Print (repr) the iGraph representation and count of the top-N patterns Args: C (Compressor object): Has member field p (list of (Graph,c) tuples) n (int): Number of patterns to print """ ps = sorted(C.P, key=itemgetter(2), reverse=True) for i in range(n): if i >= len(ps): break p, c, s = ps[i] print(p) print("Appeared %d times" % c) class TestExamples(unittest.TestCase): """ Test the main compression algorithm using small example graphs The following tests only test that (>0) patterns are captured, ensuring basic functionality of the compressor """ def setUp(self): batch_size = 50 dict_size = 5000 if DEBUG: print("Setting up compressor with batch_size=%d, dict_size=%d ..." % (batch_size, dict_size)) self.c = Compressor(batch_size, dict_size) if PROFILE: self.pr = cProfile.Profile() self.pr.enable() def tearDown(self): if PROFILE: p = Stats(self.pr) p.strip_dirs() p.sort_stats('cumtime') p.print_stats() if DEBUG: print('\n{}>>>'.format('-'*77)) def test_nonempty_basic(self): if DEBUG: print("Running compression on basic1.graph ...") self.c.compress_file(GRAPH_DIR + "basic1.graph") # No "correct" patterns, however we should extract at least ONE pattern self.assertNotEqual(self.c.P, []) def test_nonempty_groups(self): if DEBUG: print("Running compression on groups.graph ...") self.c.compress_file(GRAPH_DIR + "groups.graph") self.assertNotEqual(self.c.P, []) def test_nonempty_diabetes(self): if DEBUG: print("Running compression on diabetes_0.graph ...") self.c.compress_file(GRAPH_DIR + "diabetes_0.graph") self.assertNotEqual(self.c.P, []) # @unittest.skip("Non-standard test") class TestGraphZipSubgen(unittest.TestCase): """ Test GraphZip on graphs and ground-truth files created via Subgen Idea is to test GraphZip on synthetic graphs containing: cliques, cycles, paths and trees The synthetic graphs are created via Subgen. Each Subgen graph has a certain pattern embedded in it (i.e. 3-cliques) and the specific instances of those patterns are specified in the *.insts files Subgen generates. For each test, we check how many of the patterns from the .insts file were captured in the compressor dictionary. """ def setUp(self): batch_size = 10 dict_size = 1000 if DEBUG: print("Setting up compressor with batch_size=%d, dict_size=%d ..." % (batch_size, dict_size)) self.c = Compressor(batch_size, dict_size) self.c.add_implicit_vertices = True # since batch_size < file_size if PROFILE: self.pr = cProfile.Profile() self.pr.enable() def tearDown(self): if DEBUG: print("\nCompression was run on a total of %d times\n" % self.c._compress_count) if PROFILE: p = Stats(self.pr) p.strip_dirs() p.sort_stats('cumtime') p.print_stats() if(DEBUG): print('\n{}>>>'.format('-'*77)) def _test_graphzip_subgen(self, fin_graphzip, fin_insts, n=None): """ Run compress on the Subgen file, then checks against GT """ print('Running compression on %s...' % fin_graphzip) start = time.perf_counter() # run compression to get pattern dictionary self.c.compress_file(fin_graphzip) elapsed = time.perf_counter()-start print('Compression took:') print(elapsed) # collect y and y_hat gt_gs = import_insts(fin_insts) graphzip_gs = [g for (g, _, _) in self.c.P] # trim the pattern dictionary e.g. to match the #patterns Subdue found if n is not None: graphzip_gs = graphzip_gs[:n] print('Succesfully imported %d graphs from the pattern dictionary' % len(graphzip_gs)) # error metric 1 hits1, total1 = get_gt_patterns_found(gt_gs, graphzip_gs) print('%d/%d GT patterns in the insts file were found by GraphZip.' % (hits1, total1)) # error metric 2 hits2, total2 = get_patterns_also_in_gt(gt_gs, graphzip_gs) print('%d/%d patterns in the dictionary were in the insts file.' % (hits2, total2)) def _test_multiple(self, fin_graphzip, fin_insts, T, n=None): """ Run the test multiple times to get iterative pattern growth """ for t in range(T+1)[1:]: self._test_graphzip_subgen(fin_graphzip, fin_insts, n) def test_3CLIQ_20(self): print('20pc coverage:') self._test_multiple("%s3CLIQ/3CLIQ_1_5_20cx.graph" % SUBGEN_DIR, "%s3CLIQ/3CLIQ_1_5_20c.insts" % SUBGEN_DIR, 1) def test_3CLIQ_50(self): print('50pc coverage:') self._test_multiple("%s3CLIQ/3CLIQ_1_5_50cx.graph" % SUBGEN_DIR, "%s3CLIQ/3CLIQ_1_5_50c.insts" % SUBGEN_DIR, 1) def test_3CLIQ_80(self): print('80pc coverage:') self._test_multiple("%s3CLIQ/3CLIQ_1_5_80cx.graph" % SUBGEN_DIR, "%s3CLIQ/3CLIQ_1_5_80c.insts" % SUBGEN_DIR, 1) def test_4PATH_20(self): print('20pc coverage:') self._test_multiple("%s4PATH/4PATH_1_5_20cx.graph" % SUBGEN_DIR, "%s4PATH/4PATH_1_5_20c.insts" % SUBGEN_DIR, 1) def test_4PATH_50(self): print('50pc coverage:') self._test_multiple("%s4PATH/4PATH_1_5_50cx.graph" % SUBGEN_DIR, "%s4PATH/4PATH_1_5_50c.insts" % SUBGEN_DIR, 1) def test_4PATH_80(self): print('80pc coverage:') self._test_multiple("%s4PATH/4PATH_1_5_80cx.graph" % SUBGEN_DIR, "%s4PATH/4PATH_1_5_80c.insts" % SUBGEN_DIR, 1) def test_4STAR_20(self): print('20pc coverage:') self._test_multiple("%s4STAR/4STAR_1_5_20cx.graph" % SUBGEN_DIR, "%s4STAR/4STAR_1_5_20c.insts" % SUBGEN_DIR, 1) def test_4STAR_50(self): print('50pc coverage:') self._test_multiple("%s4STAR/4STAR_1_5_50cx.graph" % SUBGEN_DIR, "%s4STAR/4STAR_1_5_50c.insts" % SUBGEN_DIR, 1) def test_4STAR_80(self): print('80pc coverage:') self._test_multiple("%s4STAR/4STAR_1_5_80cx.graph" % SUBGEN_DIR, "%s4STAR/4STAR_1_5_80c.insts" % SUBGEN_DIR, 1) def test_5PATH_20(self): print('20pc coverage:') self._test_multiple("%s5PATH/5PATH_1_5_20cx.graph" % SUBGEN_DIR, "%s5PATH/5PATH_1_5_20c.insts" % SUBGEN_DIR, 1) def test_5PATH_50(self): print('50pc coverage:') self._test_multiple("%s5PATH/5PATH_1_5_50cx.graph" % SUBGEN_DIR, "%s5PATH/5PATH_1_5_50c.insts" % SUBGEN_DIR, 1) def test_5PATH_80(self): print('80pc coverage:') self._test_multiple("%s5PATH/5PATH_1_5_80cx.graph" % SUBGEN_DIR, "%s5PATH/5PATH_1_5_80c.insts" % SUBGEN_DIR, 1) def test_8TREE_20(self): print('20pc coverage:') self._test_multiple("%s8TREE/8TREE_1_5_20cx.graph" % SUBGEN_DIR, "%s8TREE/8TREE_1_5_20c.insts" % SUBGEN_DIR, 1) def test_8TREE_50(self): print('50pc coverage:') self._test_multiple("%s8TREE/8TREE_1_5_50cx.graph" % SUBGEN_DIR, "%s8TREE/8TREE_1_5_50c.insts" % SUBGEN_DIR, 1) def test_8TREE_80(self): print('80pc coverage:') self._test_multiple("%s8TREE/8TREE_1_5_80cx.graph" % SUBGEN_DIR, "%s8TREE/8TREE_1_5_80c.insts" % SUBGEN_DIR, 1) def test_4CLIQ_20(self): print('20pc coverage:') self._test_multiple("%s4CLIQ/4CLIQ_1_5_20cx.graph" % SUBGEN_DIR, "%s4CLIQ/4CLIQ_1_5_20c.insts" % SUBGEN_DIR, 1) def test_4CLIQ_50(self): print('50pc coverage:') self._test_multiple("%s4CLIQ/4CLIQ_1_5_50cx.graph" % SUBGEN_DIR, "%s4CLIQ/4CLIQ_1_5_50c.insts" % SUBGEN_DIR, 1) def test_4CLIQ_80(self): print('80pc coverage:') self._test_multiple("%s4CLIQ/4CLIQ_1_5_80cx.graph" % SUBGEN_DIR, "%s4CLIQ/4CLIQ_1_5_80c.insts" % SUBGEN_DIR, 1) @unittest.skip("Non-standard test") class TestSubdueSubgen(unittest.TestCase): """ Test SUBDUE on graphs and ground-truth files created via Subgen """ def _test_subdue_subgen(self, fin_subdue, fin_insts, n=100): """ Use GraphZip and Subdue on the same example graph to compare the error rates and runtime on each When comparing run-time, we only count the runtime of the compression part, as opposed to the overall time for the entire test (reported by the profiler). This means starting and stopping the clock before and after the Compressor.compress_file() method and the `./subdue' system call. Subdue outputs its patterns to a file in the .graph format. After Subdue is finished running we can parse the file into iGraph objects then compare with the ground-truth using the same functions. Iterate once (no compression) to find bottom-level structures in the graph """ if not SUBDUE_DIR: pass # fout = "subdue_patterns_output_latest.out" fout = "subdue_patterns_output_{}.out".format(fin_subdue[-20:-6]) # XXX change to subprocess.call # e.g. './subdue -nsubs 100 ../data/3clique.graph > example_out.txt' cmd = "{}subdue -nsubs {} {} > {}".format( SUBDUE_DIR, n, GRAPH_DIR + fin_subdue, fout) print(cmd) start = time.perf_counter() status = os.system(cmd) # run cmd elapsed = time.perf_counter()-start if status: raise Exception("Error occured while attempting to run Subdue") print(elapsed) gt_gs = import_insts(GRAPH_DIR+fin_insts) subdue_gs = parse_subdue_output(fout) # error metric 1 hits1, total1 = get_gt_patterns_found(gt_gs, subdue_gs) print('%d/%d GT patterns in the insts file were found by Subdue.' % (hits1, total1)) # error metric 2 hits2, total2 = get_patterns_also_in_gt(gt_gs, subdue_gs) print('%d/%d patterns found by Subdue were in the insts file.' % (hits2, total2)) def test_3CLIQ_20(self): print('20pc coverage:') self._test_subdue_subgen("%s3CLIQ/3CLIQ_1_5_20cx.graph" % SUBGEN_DIR, "%s3CLIQ/3CLIQ_1_5_20c.insts" % SUBGEN_DIR) def test_3CLIQ_50(self): print('50pc coverage:') self._test_subdue_subgen("%s3CLIQ/3CLIQ_1_5_50cx.graph" % SUBGEN_DIR, "%s3CLIQ/3CLIQ_1_5_50c.insts" % SUBGEN_DIR) def test_3CLIQ_80(self): print('80pc coverage:') self._test_subdue_subgen("%s3CLIQ/3CLIQ_1_5_80cx.graph" % SUBGEN_DIR, "%s3CLIQ/3CLIQ_1_5_80c.insts" % SUBGEN_DIR) def test_4PATH_20(self): print('20pc coverage:') self._test_subdue_subgen("%s4PATH/4PATH_1_5_20cx.graph" % SUBGEN_DIR, "%s4PATH/4PATH_1_5_20c.insts" % SUBGEN_DIR) def test_4PATH_50(self): print('50pc coverage:') self._test_subdue_subgen("%s4PATH/4PATH_1_5_50cx.graph" % SUBGEN_DIR, "%s4PATH/4PATH_1_5_50c.insts" % SUBGEN_DIR) def test_4PATH_80(self): print('80pc coverage:') self._test_subdue_subgen("%s4PATH/4PATH_1_5_80cx.graph" % SUBGEN_DIR, "%s4PATH/4PATH_1_5_80c.insts" % SUBGEN_DIR) def test_4STAR_20(self): print('20pc coverage:') self._test_subdue_subgen("%s4STAR/4STAR_1_5_20cx.graph" % SUBGEN_DIR, "%s4STAR/4STAR_1_5_20c.insts" % SUBGEN_DIR) def test_4STAR_50(self): print('50pc coverage:') self._test_subdue_subgen("%s4STAR/4STAR_1_5_50cx.graph" % SUBGEN_DIR, "%s4STAR/4STAR_1_5_50c.insts" % SUBGEN_DIR) def test_4STAR_80(self): print('80pc coverage:') self._test_subdue_subgen("%s4STAR/4STAR_1_5_80cx.graph" % SUBGEN_DIR, "%s4STAR/4STAR_1_5_80c.insts" % SUBGEN_DIR) def test_5PATH_20(self): print('20pc coverage:') self._test_subdue_subgen("%s5PATH/5PATH_1_5_20cx.graph" % SUBGEN_DIR, "%s5PATH/5PATH_1_5_20c.insts" % SUBGEN_DIR) def test_5PATH_50(self): print('50pc coverage:') self._test_subdue_subgen("%s5PATH/5PATH_1_5_50cx.graph" % SUBGEN_DIR, "%s5PATH/5PATH_1_5_50c.insts" % SUBGEN_DIR) def test_5PATH_80(self): print('80pc coverage:') self._test_subdue_subgen("%s5PATH/5PATH_1_5_80cx.graph" % SUBGEN_DIR, "%s5PATH/5PATH_1_5_80c.insts" % SUBGEN_DIR) def test_8TREE_20(self): print('20pc coverage:') self._test_subdue_subgen("%s8TREE/8TREE_1_5_20cx.graph" % SUBGEN_DIR, "%s8TREE/8TREE_1_5_20c.insts" % SUBGEN_DIR) def test_8TREE_50(self): print('50pc coverage:') self._test_subdue_subgen("%s8TREE/8TREE_1_5_50cx.graph" % SUBGEN_DIR, "%s8TREE/8TREE_1_5_50c.insts" % SUBGEN_DIR) def test_8TREE_80(self): print('80pc coverage:') self._test_subdue_subgen("%s8TREE/8TREE_1_5_80cx.graph" % SUBGEN_DIR, "%s8TREE/8TREE_1_5_80c.insts" % SUBGEN_DIR) def test_4CLIQ_20(self): print('20pc coverage:') self._test_subdue_subgen("%s4CLIQ/4CLIQ_1_5_20cx.graph" % SUBGEN_DIR, "%s4CLIQ/4CLIQ_1_5_20c.insts" % SUBGEN_DIR) def test_4CLIQ_50(self): print('50pc coverage:') self._test_subdue_subgen("%s4CLIQ/4CLIQ_1_5_50cx.graph" % SUBGEN_DIR, "%s4CLIQ/4CLIQ_1_5_50c.insts" % SUBGEN_DIR) def test_4CLIQ_80(self): print('80pc coverage:') self._test_subdue_subgen("%s4CLIQ/4CLIQ_1_5_80cx.graph" % SUBGEN_DIR, "%s4CLIQ/4CLIQ_1_5_80c.insts" % SUBGEN_DIR) @unittest.skip("Non-standard test") class TestLarge(unittest.TestCase): """ GraphZip with larger (real-world) .graph datasets """ def setUp(self): batch_size = 5 dict_size = 50 if DEBUG: print("Setting up compressor with batch_size=%d, dict_size=%d ..." % (batch_size, dict_size)) self.c = Compressor(batch_size, dict_size) if PROFILE: self.pr = cProfile.Profile() self.pr.enable() def tearDown(self): if DEBUG: print("\nCompression was run a total of %d times\n" % self.c._compress_count) print("%d lines read" % self.c._lines_read) print("Dictionary trimmed %d times" % self.c._dict_trimmed) print("Compressor: batch_size=%d, dict_size=%d ..." % (self.c.batch_size, self.c.dict_size)) if PROFILE: p = Stats(self.pr) p.strip_dirs() p.sort_stats('cumtime') p.print_stats() if(DEBUG): print('\n{}>>>'.format('-'*77)) def testHetRec(self): times = [] try: for i in range(1, 99): f = '%sHetRec/hetrec_year_vfirst/%d.graph' % (GRAPH_DIR, i) start = timer() self.c.compress_file(f) end = timer() elapsed = end - start times.append((i, elapsed)) print('\nTook %.2f seconds' % elapsed) finally: self.c.P = sorted(self.c.P, key=itemgetter(2), reverse=True) print('Printing top 50 patterns for reference:') for g, c, s in self.c.P[:49]: print('\ncount: %d, score: %d\n' % (c, s)) print(g) print(g.vs['label']) # Save the dictionary, etc. print('Saving the latest state of GraphZip..') self.c.save_state('latest_HetRec_state.p') # Save the time measurements for plotting print('Saving time measurements..') print(times) with open('latest_HetRec_times.p', 'wb') as pfile: pickle.dump((times), pfile) def testHiggs(self): times = [] try: for i in range(1, 169): # f = '../datasets/Twitter_Higgs/higgs_hour_vfirst/%d.g' % i f = '%sTwitter_Higgs/higgs_hour_vfirst_unilabel/%d.g' %\ (GRAPH_DIR, i) start = timer() self.c.compress_file(f) end = timer() elapsed = end - start times.append((i, elapsed)) print('\nTook %.2f seconds' % elapsed) finally: self.c.P = sorted(self.c.P, key=itemgetter(2), reverse=True) print('Printing top 50 patterns for reference:') for g, c, s in self.c.P[:49]: print('\ncount: %d, score: %d\n' % (c, s)) print(g) print('Vertex labels:') print(g.vs['label']) print('Edge labels:') print(g.es['label']) # Save the dictionary, etc print('Saving the latest state of GraphZip..') self.c.save_state('latest_Higgs_state.p') # Save the time measurements for plotting print('Saving time measurements..') print(times) with open('latest_Higgs_times.p', 'wb') as pfile: pickle.dump((times), pfile) def testNBER(self): times = [] try: for i in range(1, 301): f = '%sNBER/cite75_99_month_clabels/%d.graph' % (GRAPH_DIR, i) #f = '../datasets/NBER/cite75_99_month_clabels_v0/%d.graph' % i # f = '../datasets/NBER/cite75_99_month_clabels_v0_vfirst/%d.graph' % i start = timer() self.c.compress_file(f) end = timer() elapsed = end - start times.append((i, elapsed)) print('\nTook %d seconds' % elapsed) finally: self.c.P = sorted(self.c.P, key=itemgetter(2), reverse=True) for g, c, s in self.c.P[:49]: print('\ncount: %d, score: %d\n' % (c, s)) print(g) print(g.vs['label']) # Save the dictionary, etc print('Saving the latest state of GraphZip..') self.c.save_state('latest_NBER_state.p') # Save the time measurements for plotting print('Saving time measurements..') print(times) with open('latest_NBER_times.p', 'wb') as pfile: pickle.dump((times), pfile) def main(out=sys.stderr, verbosity=2): loader = unittest.TestLoader() suite = loader.loadTestsFromModule(sys.modules[__name__]) unittest.TextTestRunner(out, verbosity=verbosity).run(suite) if __name__ == '__main__': with open('testing.out', 'w') as f: main(f)

mit

ageron/tensorflow

tensorflow/contrib/data/python/ops/enumerate_ops.py

20

1903

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Enumerate dataset transformations.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.data.experimental.ops import enumerate_ops from tensorflow.python.util import deprecation @deprecation.deprecated(None, "Use `tf.data.experimental.enumerate_dataset(...)`.") def enumerate_dataset(start=0): """A transformation that enumerate the elements of a dataset. It is Similar to python's `enumerate`. For example: ```python # NOTE: The following examples use `{ ... }` to represent the # contents of a dataset. a = { 1, 2, 3 } b = { (7, 8), (9, 10) } # The nested structure of the `datasets` argument determines the # structure of elements in the resulting dataset. a.apply(tf.contrib.data.enumerate(start=5)) == { (5, 1), (6, 2), (7, 3) } b.apply(tf.contrib.data.enumerate()) == { (0, (7, 8)), (1, (9, 10)) } ``` Args: start: A `tf.int64` scalar `tf.Tensor`, representing the start value for enumeration. Returns: A `Dataset` transformation function, which can be passed to `tf.data.Dataset.apply`. """ return enumerate_ops.enumerate_dataset(start)

apache-2.0

ageron/tensorflow

tensorflow/python/debug/examples/debug_tflearn_iris.py

17

7249

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Debug the tf-learn iris example, based on the tf-learn tutorial.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys import tempfile from six.moves import urllib import tensorflow as tf from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.python import debug as tf_debug # URLs to download data sets from, if necessary. IRIS_TRAINING_DATA_URL = "https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/monitors/iris_training.csv" IRIS_TEST_DATA_URL = "https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/monitors/iris_test.csv" def maybe_download_data(data_dir): """Download data sets if necessary. Args: data_dir: Path to where data should be downloaded. Returns: Paths to the training and test data files. """ if not os.path.isdir(data_dir): os.makedirs(data_dir) training_data_path = os.path.join(data_dir, os.path.basename(IRIS_TRAINING_DATA_URL)) if not os.path.isfile(training_data_path): train_file = open(training_data_path, "wt") urllib.request.urlretrieve(IRIS_TRAINING_DATA_URL, train_file.name) train_file.close() print("Training data are downloaded to %s" % train_file.name) test_data_path = os.path.join(data_dir, os.path.basename(IRIS_TEST_DATA_URL)) if not os.path.isfile(test_data_path): test_file = open(test_data_path, "wt") urllib.request.urlretrieve(IRIS_TEST_DATA_URL, test_file.name) test_file.close() print("Test data are downloaded to %s" % test_file.name) return training_data_path, test_data_path _IRIS_INPUT_DIM = 4 def iris_input_fn(): iris = base.load_iris() features = tf.reshape(tf.constant(iris.data), [-1, _IRIS_INPUT_DIM]) labels = tf.reshape(tf.constant(iris.target), [-1]) return features, labels def main(_): # Load datasets. if FLAGS.fake_data: def training_input_fn(): return ({"features": tf.random_normal([128, 4])}, tf.random_uniform([128], minval=0, maxval=3, dtype=tf.int32)) def test_input_fn(): return ({"features": tf.random_normal([32, 4])}, tf.random_uniform([32], minval=0, maxval=3, dtype=tf.int32)) feature_columns = [ tf.feature_column.numeric_column("features", shape=(4,))] else: training_data_path, test_data_path = maybe_download_data(FLAGS.data_dir) column_names = [ "sepal_length", "sepal_width", "petal_length", "petal_width", "label"] batch_size = 32 def training_input_fn(): return tf.data.experimental.make_csv_dataset([training_data_path], batch_size, column_names=column_names, label_name="label") def test_input_fn(): return tf.data.experimental.make_csv_dataset([test_data_path], batch_size, column_names=column_names, label_name="label") feature_columns = [tf.feature_column.numeric_column(feature) for feature in column_names[:-1]] # Build 3 layer DNN with 10, 20, 10 units respectively. model_dir = FLAGS.model_dir or tempfile.mkdtemp(prefix="debug_tflearn_iris_") classifier = tf.estimator.DNNClassifier( feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3, model_dir=model_dir) if FLAGS.debug and FLAGS.tensorboard_debug_address: raise ValueError( "The --debug and --tensorboard_debug_address flags are mutually " "exclusive.") hooks = [] if FLAGS.debug: hooks.append(tf_debug.LocalCLIDebugHook(ui_type=FLAGS.ui_type, dump_root=FLAGS.dump_root)) elif FLAGS.tensorboard_debug_address: hooks.append(tf_debug.TensorBoardDebugHook(FLAGS.tensorboard_debug_address)) # Train model, using tfdbg hook. classifier.train(training_input_fn, steps=FLAGS.train_steps, hooks=hooks) # Evaluate accuracy, using tfdbg hook. accuracy_score = classifier.evaluate(test_input_fn, steps=FLAGS.eval_steps, hooks=hooks)["accuracy"] print("After training %d steps, Accuracy = %f" % (FLAGS.train_steps, accuracy_score)) # Make predictions, using tfdbg hook. predict_results = classifier.predict(test_input_fn, hooks=hooks) print("A prediction result: %s" % next(predict_results)) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--data_dir", type=str, default="/tmp/iris_data", help="Directory to save the training and test data in.") parser.add_argument( "--model_dir", type=str, default="", help="Directory to save the trained model in.") parser.add_argument( "--train_steps", type=int, default=10, help="Number of steps to run training for.") parser.add_argument( "--eval_steps", type=int, default=1, help="Number of steps to run evaluation foir.") parser.add_argument( "--ui_type", type=str, default="curses", help="Command-line user interface type (curses | readline)") parser.add_argument( "--fake_data", type="bool", nargs="?", const=True, default=False, help="Use fake MNIST data for unit testing") parser.add_argument( "--debug", type="bool", nargs="?", const=True, default=False, help="Use debugger to track down bad values during training. " "Mutually exclusive with the --tensorboard_debug_address flag.") parser.add_argument( "--dump_root", type=str, default="", help="Optional custom root directory for temporary debug dump data") parser.add_argument( "--tensorboard_debug_address", type=str, default=None, help="Connect to the TensorBoard Debugger Plugin backend specified by " "the gRPC address (e.g., localhost:1234). Mutually exclusive with the " "--debug flag.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

apache-2.0

yonglehou/scikit-learn

sklearn/svm/tests/test_svm.py

115

31653

""" Testing for Support Vector Machine module (sklearn.svm) TODO: remove hard coded numerical results when possible """ import numpy as np import itertools from numpy.testing import assert_array_equal, assert_array_almost_equal from numpy.testing import assert_almost_equal from scipy import sparse from nose.tools import assert_raises, assert_true, assert_equal, assert_false from sklearn.base import ChangedBehaviorWarning from sklearn import svm, linear_model, datasets, metrics, base from sklearn.cross_validation import train_test_split from sklearn.datasets import make_classification, make_blobs from sklearn.metrics import f1_score from sklearn.metrics.pairwise import rbf_kernel from sklearn.utils import check_random_state from sklearn.utils import ConvergenceWarning from sklearn.utils.validation import NotFittedError from sklearn.utils.testing import assert_greater, assert_in, assert_less from sklearn.utils.testing import assert_raises_regexp, assert_warns from sklearn.utils.testing import assert_warns_message, assert_raise_message from sklearn.utils.testing import ignore_warnings # toy sample X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]] Y = [1, 1, 1, 2, 2, 2] T = [[-1, -1], [2, 2], [3, 2]] true_result = [1, 2, 2] # also load the iris dataset iris = datasets.load_iris() rng = check_random_state(42) perm = rng.permutation(iris.target.size) iris.data = iris.data[perm] iris.target = iris.target[perm] def test_libsvm_parameters(): # Test parameters on classes that make use of libsvm. clf = svm.SVC(kernel='linear').fit(X, Y) assert_array_equal(clf.dual_coef_, [[-0.25, .25]]) assert_array_equal(clf.support_, [1, 3]) assert_array_equal(clf.support_vectors_, (X[1], X[3])) assert_array_equal(clf.intercept_, [0.]) assert_array_equal(clf.predict(X), Y) def test_libsvm_iris(): # Check consistency on dataset iris. # shuffle the dataset so that labels are not ordered for k in ('linear', 'rbf'): clf = svm.SVC(kernel=k).fit(iris.data, iris.target) assert_greater(np.mean(clf.predict(iris.data) == iris.target), 0.9) assert_array_equal(clf.classes_, np.sort(clf.classes_)) # check also the low-level API model = svm.libsvm.fit(iris.data, iris.target.astype(np.float64)) pred = svm.libsvm.predict(iris.data, *model) assert_greater(np.mean(pred == iris.target), .95) model = svm.libsvm.fit(iris.data, iris.target.astype(np.float64), kernel='linear') pred = svm.libsvm.predict(iris.data, *model, kernel='linear') assert_greater(np.mean(pred == iris.target), .95) pred = svm.libsvm.cross_validation(iris.data, iris.target.astype(np.float64), 5, kernel='linear', random_seed=0) assert_greater(np.mean(pred == iris.target), .95) # If random_seed >= 0, the libsvm rng is seeded (by calling `srand`), hence # we should get deteriministic results (assuming that there is no other # thread calling this wrapper calling `srand` concurrently). pred2 = svm.libsvm.cross_validation(iris.data, iris.target.astype(np.float64), 5, kernel='linear', random_seed=0) assert_array_equal(pred, pred2) def test_single_sample_1d(): # Test whether SVCs work on a single sample given as a 1-d array clf = svm.SVC().fit(X, Y) clf.predict(X[0]) clf = svm.LinearSVC(random_state=0).fit(X, Y) clf.predict(X[0]) def test_precomputed(): # SVC with a precomputed kernel. # We test it with a toy dataset and with iris. clf = svm.SVC(kernel='precomputed') # Gram matrix for train data (square matrix) # (we use just a linear kernel) K = np.dot(X, np.array(X).T) clf.fit(K, Y) # Gram matrix for test data (rectangular matrix) KT = np.dot(T, np.array(X).T) pred = clf.predict(KT) assert_raises(ValueError, clf.predict, KT.T) assert_array_equal(clf.dual_coef_, [[-0.25, .25]]) assert_array_equal(clf.support_, [1, 3]) assert_array_equal(clf.intercept_, [0]) assert_array_almost_equal(clf.support_, [1, 3]) assert_array_equal(pred, true_result) # Gram matrix for test data but compute KT[i,j] # for support vectors j only. KT = np.zeros_like(KT) for i in range(len(T)): for j in clf.support_: KT[i, j] = np.dot(T[i], X[j]) pred = clf.predict(KT) assert_array_equal(pred, true_result) # same as before, but using a callable function instead of the kernel # matrix. kernel is just a linear kernel kfunc = lambda x, y: np.dot(x, y.T) clf = svm.SVC(kernel=kfunc) clf.fit(X, Y) pred = clf.predict(T) assert_array_equal(clf.dual_coef_, [[-0.25, .25]]) assert_array_equal(clf.intercept_, [0]) assert_array_almost_equal(clf.support_, [1, 3]) assert_array_equal(pred, true_result) # test a precomputed kernel with the iris dataset # and check parameters against a linear SVC clf = svm.SVC(kernel='precomputed') clf2 = svm.SVC(kernel='linear') K = np.dot(iris.data, iris.data.T) clf.fit(K, iris.target) clf2.fit(iris.data, iris.target) pred = clf.predict(K) assert_array_almost_equal(clf.support_, clf2.support_) assert_array_almost_equal(clf.dual_coef_, clf2.dual_coef_) assert_array_almost_equal(clf.intercept_, clf2.intercept_) assert_almost_equal(np.mean(pred == iris.target), .99, decimal=2) # Gram matrix for test data but compute KT[i,j] # for support vectors j only. K = np.zeros_like(K) for i in range(len(iris.data)): for j in clf.support_: K[i, j] = np.dot(iris.data[i], iris.data[j]) pred = clf.predict(K) assert_almost_equal(np.mean(pred == iris.target), .99, decimal=2) clf = svm.SVC(kernel=kfunc) clf.fit(iris.data, iris.target) assert_almost_equal(np.mean(pred == iris.target), .99, decimal=2) def test_svr(): # Test Support Vector Regression diabetes = datasets.load_diabetes() for clf in (svm.NuSVR(kernel='linear', nu=.4, C=1.0), svm.NuSVR(kernel='linear', nu=.4, C=10.), svm.SVR(kernel='linear', C=10.), svm.LinearSVR(C=10.), svm.LinearSVR(C=10.), ): clf.fit(diabetes.data, diabetes.target) assert_greater(clf.score(diabetes.data, diabetes.target), 0.02) # non-regression test; previously, BaseLibSVM would check that # len(np.unique(y)) < 2, which must only be done for SVC svm.SVR().fit(diabetes.data, np.ones(len(diabetes.data))) svm.LinearSVR().fit(diabetes.data, np.ones(len(diabetes.data))) def test_linearsvr(): # check that SVR(kernel='linear') and LinearSVC() give # comparable results diabetes = datasets.load_diabetes() lsvr = svm.LinearSVR(C=1e3).fit(diabetes.data, diabetes.target) score1 = lsvr.score(diabetes.data, diabetes.target) svr = svm.SVR(kernel='linear', C=1e3).fit(diabetes.data, diabetes.target) score2 = svr.score(diabetes.data, diabetes.target) assert np.linalg.norm(lsvr.coef_ - svr.coef_) / np.linalg.norm(svr.coef_) < .1 assert np.abs(score1 - score2) < 0.1 def test_svr_errors(): X = [[0.0], [1.0]] y = [0.0, 0.5] # Bad kernel clf = svm.SVR(kernel=lambda x, y: np.array([[1.0]])) clf.fit(X, y) assert_raises(ValueError, clf.predict, X) def test_oneclass(): # Test OneClassSVM clf = svm.OneClassSVM() clf.fit(X) pred = clf.predict(T) assert_array_almost_equal(pred, [-1, -1, -1]) assert_array_almost_equal(clf.intercept_, [-1.008], decimal=3) assert_array_almost_equal(clf.dual_coef_, [[0.632, 0.233, 0.633, 0.234, 0.632, 0.633]], decimal=3) assert_raises(ValueError, lambda: clf.coef_) def test_oneclass_decision_function(): # Test OneClassSVM decision function clf = svm.OneClassSVM() rnd = check_random_state(2) # Generate train data X = 0.3 * rnd.randn(100, 2) X_train = np.r_[X + 2, X - 2] # Generate some regular novel observations X = 0.3 * rnd.randn(20, 2) X_test = np.r_[X + 2, X - 2] # Generate some abnormal novel observations X_outliers = rnd.uniform(low=-4, high=4, size=(20, 2)) # fit the model clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1) clf.fit(X_train) # predict things y_pred_test = clf.predict(X_test) assert_greater(np.mean(y_pred_test == 1), .9) y_pred_outliers = clf.predict(X_outliers) assert_greater(np.mean(y_pred_outliers == -1), .9) dec_func_test = clf.decision_function(X_test) assert_array_equal((dec_func_test > 0).ravel(), y_pred_test == 1) dec_func_outliers = clf.decision_function(X_outliers) assert_array_equal((dec_func_outliers > 0).ravel(), y_pred_outliers == 1) def test_tweak_params(): # Make sure some tweaking of parameters works. # We change clf.dual_coef_ at run time and expect .predict() to change # accordingly. Notice that this is not trivial since it involves a lot # of C/Python copying in the libsvm bindings. # The success of this test ensures that the mapping between libsvm and # the python classifier is complete. clf = svm.SVC(kernel='linear', C=1.0) clf.fit(X, Y) assert_array_equal(clf.dual_coef_, [[-.25, .25]]) assert_array_equal(clf.predict([[-.1, -.1]]), [1]) clf._dual_coef_ = np.array([[.0, 1.]]) assert_array_equal(clf.predict([[-.1, -.1]]), [2]) def test_probability(): # Predict probabilities using SVC # This uses cross validation, so we use a slightly bigger testing set. for clf in (svm.SVC(probability=True, random_state=0, C=1.0), svm.NuSVC(probability=True, random_state=0)): clf.fit(iris.data, iris.target) prob_predict = clf.predict_proba(iris.data) assert_array_almost_equal( np.sum(prob_predict, 1), np.ones(iris.data.shape[0])) assert_true(np.mean(np.argmax(prob_predict, 1) == clf.predict(iris.data)) > 0.9) assert_almost_equal(clf.predict_proba(iris.data), np.exp(clf.predict_log_proba(iris.data)), 8) def test_decision_function(): # Test decision_function # Sanity check, test that decision_function implemented in python # returns the same as the one in libsvm # multi class: clf = svm.SVC(kernel='linear', C=0.1, decision_function_shape='ovo').fit(iris.data, iris.target) dec = np.dot(iris.data, clf.coef_.T) + clf.intercept_ assert_array_almost_equal(dec, clf.decision_function(iris.data)) # binary: clf.fit(X, Y) dec = np.dot(X, clf.coef_.T) + clf.intercept_ prediction = clf.predict(X) assert_array_almost_equal(dec.ravel(), clf.decision_function(X)) assert_array_almost_equal( prediction, clf.classes_[(clf.decision_function(X) > 0).astype(np.int)]) expected = np.array([-1., -0.66, -1., 0.66, 1., 1.]) assert_array_almost_equal(clf.decision_function(X), expected, 2) # kernel binary: clf = svm.SVC(kernel='rbf', gamma=1, decision_function_shape='ovo') clf.fit(X, Y) rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma) dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_ assert_array_almost_equal(dec.ravel(), clf.decision_function(X)) def test_decision_function_shape(): # check that decision_function_shape='ovr' gives # correct shape and is consistent with predict clf = svm.SVC(kernel='linear', C=0.1, decision_function_shape='ovr').fit(iris.data, iris.target) dec = clf.decision_function(iris.data) assert_equal(dec.shape, (len(iris.data), 3)) assert_array_equal(clf.predict(iris.data), np.argmax(dec, axis=1)) # with five classes: X, y = make_blobs(n_samples=80, centers=5, random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) clf = svm.SVC(kernel='linear', C=0.1, decision_function_shape='ovr').fit(X_train, y_train) dec = clf.decision_function(X_test) assert_equal(dec.shape, (len(X_test), 5)) assert_array_equal(clf.predict(X_test), np.argmax(dec, axis=1)) # check shape of ovo_decition_function=True clf = svm.SVC(kernel='linear', C=0.1, decision_function_shape='ovo').fit(X_train, y_train) dec = clf.decision_function(X_train) assert_equal(dec.shape, (len(X_train), 10)) # check deprecation warning clf.decision_function_shape = None msg = "change the shape of the decision function" dec = assert_warns_message(ChangedBehaviorWarning, msg, clf.decision_function, X_train) assert_equal(dec.shape, (len(X_train), 10)) def test_svr_decision_function(): # Test SVR's decision_function # Sanity check, test that decision_function implemented in python # returns the same as the one in libsvm X = iris.data y = iris.target # linear kernel reg = svm.SVR(kernel='linear', C=0.1).fit(X, y) dec = np.dot(X, reg.coef_.T) + reg.intercept_ assert_array_almost_equal(dec.ravel(), reg.decision_function(X).ravel()) # rbf kernel reg = svm.SVR(kernel='rbf', gamma=1).fit(X, y) rbfs = rbf_kernel(X, reg.support_vectors_, gamma=reg.gamma) dec = np.dot(rbfs, reg.dual_coef_.T) + reg.intercept_ assert_array_almost_equal(dec.ravel(), reg.decision_function(X).ravel()) def test_weight(): # Test class weights clf = svm.SVC(class_weight={1: 0.1}) # we give a small weights to class 1 clf.fit(X, Y) # so all predicted values belong to class 2 assert_array_almost_equal(clf.predict(X), [2] * 6) X_, y_ = make_classification(n_samples=200, n_features=10, weights=[0.833, 0.167], random_state=2) for clf in (linear_model.LogisticRegression(), svm.LinearSVC(random_state=0), svm.SVC()): clf.set_params(class_weight={0: .1, 1: 10}) clf.fit(X_[:100], y_[:100]) y_pred = clf.predict(X_[100:]) assert_true(f1_score(y_[100:], y_pred) > .3) def test_sample_weights(): # Test weights on individual samples # TODO: check on NuSVR, OneClass, etc. clf = svm.SVC() clf.fit(X, Y) assert_array_equal(clf.predict(X[2]), [1.]) sample_weight = [.1] * 3 + [10] * 3 clf.fit(X, Y, sample_weight=sample_weight) assert_array_equal(clf.predict(X[2]), [2.]) # test that rescaling all samples is the same as changing C clf = svm.SVC() clf.fit(X, Y) dual_coef_no_weight = clf.dual_coef_ clf.set_params(C=100) clf.fit(X, Y, sample_weight=np.repeat(0.01, len(X))) assert_array_almost_equal(dual_coef_no_weight, clf.dual_coef_) def test_auto_weight(): # Test class weights for imbalanced data from sklearn.linear_model import LogisticRegression # We take as dataset the two-dimensional projection of iris so # that it is not separable and remove half of predictors from # class 1. # We add one to the targets as a non-regression test: class_weight="balanced" # used to work only when the labels where a range [0..K). from sklearn.utils import compute_class_weight X, y = iris.data[:, :2], iris.target + 1 unbalanced = np.delete(np.arange(y.size), np.where(y > 2)[0][::2]) classes = np.unique(y[unbalanced]) class_weights = compute_class_weight('balanced', classes, y[unbalanced]) assert_true(np.argmax(class_weights) == 2) for clf in (svm.SVC(kernel='linear'), svm.LinearSVC(random_state=0), LogisticRegression()): # check that score is better when class='balanced' is set. y_pred = clf.fit(X[unbalanced], y[unbalanced]).predict(X) clf.set_params(class_weight='balanced') y_pred_balanced = clf.fit(X[unbalanced], y[unbalanced],).predict(X) assert_true(metrics.f1_score(y, y_pred, average='weighted') <= metrics.f1_score(y, y_pred_balanced, average='weighted')) def test_bad_input(): # Test that it gives proper exception on deficient input # impossible value of C assert_raises(ValueError, svm.SVC(C=-1).fit, X, Y) # impossible value of nu clf = svm.NuSVC(nu=0.0) assert_raises(ValueError, clf.fit, X, Y) Y2 = Y[:-1] # wrong dimensions for labels assert_raises(ValueError, clf.fit, X, Y2) # Test with arrays that are non-contiguous. for clf in (svm.SVC(), svm.LinearSVC(random_state=0)): Xf = np.asfortranarray(X) assert_false(Xf.flags['C_CONTIGUOUS']) yf = np.ascontiguousarray(np.tile(Y, (2, 1)).T) yf = yf[:, -1] assert_false(yf.flags['F_CONTIGUOUS']) assert_false(yf.flags['C_CONTIGUOUS']) clf.fit(Xf, yf) assert_array_equal(clf.predict(T), true_result) # error for precomputed kernelsx clf = svm.SVC(kernel='precomputed') assert_raises(ValueError, clf.fit, X, Y) # sample_weight bad dimensions clf = svm.SVC() assert_raises(ValueError, clf.fit, X, Y, sample_weight=range(len(X) - 1)) # predict with sparse input when trained with dense clf = svm.SVC().fit(X, Y) assert_raises(ValueError, clf.predict, sparse.lil_matrix(X)) Xt = np.array(X).T clf.fit(np.dot(X, Xt), Y) assert_raises(ValueError, clf.predict, X) clf = svm.SVC() clf.fit(X, Y) assert_raises(ValueError, clf.predict, Xt) def test_sparse_precomputed(): clf = svm.SVC(kernel='precomputed') sparse_gram = sparse.csr_matrix([[1, 0], [0, 1]]) try: clf.fit(sparse_gram, [0, 1]) assert not "reached" except TypeError as e: assert_in("Sparse precomputed", str(e)) def test_linearsvc_parameters(): # Test possible parameter combinations in LinearSVC # Generate list of possible parameter combinations losses = ['hinge', 'squared_hinge', 'logistic_regression', 'foo'] penalties, duals = ['l1', 'l2', 'bar'], [True, False] X, y = make_classification(n_samples=5, n_features=5) for loss, penalty, dual in itertools.product(losses, penalties, duals): clf = svm.LinearSVC(penalty=penalty, loss=loss, dual=dual) if ((loss, penalty) == ('hinge', 'l1') or (loss, penalty, dual) == ('hinge', 'l2', False) or (penalty, dual) == ('l1', True) or loss == 'foo' or penalty == 'bar'): assert_raises_regexp(ValueError, "Unsupported set of arguments.*penalty='%s.*" "loss='%s.*dual=%s" % (penalty, loss, dual), clf.fit, X, y) else: clf.fit(X, y) # Incorrect loss value - test if explicit error message is raised assert_raises_regexp(ValueError, ".*loss='l3' is not supported.*", svm.LinearSVC(loss="l3").fit, X, y) # FIXME remove in 1.0 def test_linearsvx_loss_penalty_deprecations(): X, y = [[0.0], [1.0]], [0, 1] msg = ("loss='%s' has been deprecated in favor of " "loss='%s' as of 0.16. Backward compatibility" " for the %s will be removed in %s") # LinearSVC # loss l1/L1 --> hinge assert_warns_message(DeprecationWarning, msg % ("l1", "hinge", "loss='l1'", "1.0"), svm.LinearSVC(loss="l1").fit, X, y) # loss l2/L2 --> squared_hinge assert_warns_message(DeprecationWarning, msg % ("L2", "squared_hinge", "loss='L2'", "1.0"), svm.LinearSVC(loss="L2").fit, X, y) # LinearSVR # loss l1/L1 --> epsilon_insensitive assert_warns_message(DeprecationWarning, msg % ("L1", "epsilon_insensitive", "loss='L1'", "1.0"), svm.LinearSVR(loss="L1").fit, X, y) # loss l2/L2 --> squared_epsilon_insensitive assert_warns_message(DeprecationWarning, msg % ("l2", "squared_epsilon_insensitive", "loss='l2'", "1.0"), svm.LinearSVR(loss="l2").fit, X, y) # FIXME remove in 0.18 def test_linear_svx_uppercase_loss_penalty(): # Check if Upper case notation is supported by _fit_liblinear # which is called by fit X, y = [[0.0], [1.0]], [0, 1] msg = ("loss='%s' has been deprecated in favor of " "loss='%s' as of 0.16. Backward compatibility" " for the uppercase notation will be removed in %s") # loss SQUARED_hinge --> squared_hinge assert_warns_message(DeprecationWarning, msg % ("SQUARED_hinge", "squared_hinge", "0.18"), svm.LinearSVC(loss="SQUARED_hinge").fit, X, y) # penalty L2 --> l2 assert_warns_message(DeprecationWarning, msg.replace("loss", "penalty") % ("L2", "l2", "0.18"), svm.LinearSVC(penalty="L2").fit, X, y) # loss EPSILON_INSENSITIVE --> epsilon_insensitive assert_warns_message(DeprecationWarning, msg % ("EPSILON_INSENSITIVE", "epsilon_insensitive", "0.18"), svm.LinearSVR(loss="EPSILON_INSENSITIVE").fit, X, y) def test_linearsvc(): # Test basic routines using LinearSVC clf = svm.LinearSVC(random_state=0).fit(X, Y) # by default should have intercept assert_true(clf.fit_intercept) assert_array_equal(clf.predict(T), true_result) assert_array_almost_equal(clf.intercept_, [0], decimal=3) # the same with l1 penalty clf = svm.LinearSVC(penalty='l1', loss='squared_hinge', dual=False, random_state=0).fit(X, Y) assert_array_equal(clf.predict(T), true_result) # l2 penalty with dual formulation clf = svm.LinearSVC(penalty='l2', dual=True, random_state=0).fit(X, Y) assert_array_equal(clf.predict(T), true_result) # l2 penalty, l1 loss clf = svm.LinearSVC(penalty='l2', loss='hinge', dual=True, random_state=0) clf.fit(X, Y) assert_array_equal(clf.predict(T), true_result) # test also decision function dec = clf.decision_function(T) res = (dec > 0).astype(np.int) + 1 assert_array_equal(res, true_result) def test_linearsvc_crammer_singer(): # Test LinearSVC with crammer_singer multi-class svm ovr_clf = svm.LinearSVC(random_state=0).fit(iris.data, iris.target) cs_clf = svm.LinearSVC(multi_class='crammer_singer', random_state=0) cs_clf.fit(iris.data, iris.target) # similar prediction for ovr and crammer-singer: assert_true((ovr_clf.predict(iris.data) == cs_clf.predict(iris.data)).mean() > .9) # classifiers shouldn't be the same assert_true((ovr_clf.coef_ != cs_clf.coef_).all()) # test decision function assert_array_equal(cs_clf.predict(iris.data), np.argmax(cs_clf.decision_function(iris.data), axis=1)) dec_func = np.dot(iris.data, cs_clf.coef_.T) + cs_clf.intercept_ assert_array_almost_equal(dec_func, cs_clf.decision_function(iris.data)) def test_crammer_singer_binary(): # Test Crammer-Singer formulation in the binary case X, y = make_classification(n_classes=2, random_state=0) for fit_intercept in (True, False): acc = svm.LinearSVC(fit_intercept=fit_intercept, multi_class="crammer_singer", random_state=0).fit(X, y).score(X, y) assert_greater(acc, 0.9) def test_linearsvc_iris(): # Test that LinearSVC gives plausible predictions on the iris dataset # Also, test symbolic class names (classes_). target = iris.target_names[iris.target] clf = svm.LinearSVC(random_state=0).fit(iris.data, target) assert_equal(set(clf.classes_), set(iris.target_names)) assert_greater(np.mean(clf.predict(iris.data) == target), 0.8) dec = clf.decision_function(iris.data) pred = iris.target_names[np.argmax(dec, 1)] assert_array_equal(pred, clf.predict(iris.data)) def test_dense_liblinear_intercept_handling(classifier=svm.LinearSVC): # Test that dense liblinear honours intercept_scaling param X = [[2, 1], [3, 1], [1, 3], [2, 3]] y = [0, 0, 1, 1] clf = classifier(fit_intercept=True, penalty='l1', loss='squared_hinge', dual=False, C=4, tol=1e-7, random_state=0) assert_true(clf.intercept_scaling == 1, clf.intercept_scaling) assert_true(clf.fit_intercept) # when intercept_scaling is low the intercept value is highly "penalized" # by regularization clf.intercept_scaling = 1 clf.fit(X, y) assert_almost_equal(clf.intercept_, 0, decimal=5) # when intercept_scaling is sufficiently high, the intercept value # is not affected by regularization clf.intercept_scaling = 100 clf.fit(X, y) intercept1 = clf.intercept_ assert_less(intercept1, -1) # when intercept_scaling is sufficiently high, the intercept value # doesn't depend on intercept_scaling value clf.intercept_scaling = 1000 clf.fit(X, y) intercept2 = clf.intercept_ assert_array_almost_equal(intercept1, intercept2, decimal=2) def test_liblinear_set_coef(): # multi-class case clf = svm.LinearSVC().fit(iris.data, iris.target) values = clf.decision_function(iris.data) clf.coef_ = clf.coef_.copy() clf.intercept_ = clf.intercept_.copy() values2 = clf.decision_function(iris.data) assert_array_almost_equal(values, values2) # binary-class case X = [[2, 1], [3, 1], [1, 3], [2, 3]] y = [0, 0, 1, 1] clf = svm.LinearSVC().fit(X, y) values = clf.decision_function(X) clf.coef_ = clf.coef_.copy() clf.intercept_ = clf.intercept_.copy() values2 = clf.decision_function(X) assert_array_equal(values, values2) def test_immutable_coef_property(): # Check that primal coef modification are not silently ignored svms = [ svm.SVC(kernel='linear').fit(iris.data, iris.target), svm.NuSVC(kernel='linear').fit(iris.data, iris.target), svm.SVR(kernel='linear').fit(iris.data, iris.target), svm.NuSVR(kernel='linear').fit(iris.data, iris.target), svm.OneClassSVM(kernel='linear').fit(iris.data), ] for clf in svms: assert_raises(AttributeError, clf.__setattr__, 'coef_', np.arange(3)) assert_raises((RuntimeError, ValueError), clf.coef_.__setitem__, (0, 0), 0) def test_linearsvc_verbose(): # stdout: redirect import os stdout = os.dup(1) # save original stdout os.dup2(os.pipe()[1], 1) # replace it # actual call clf = svm.LinearSVC(verbose=1) clf.fit(X, Y) # stdout: restore os.dup2(stdout, 1) # restore original stdout def test_svc_clone_with_callable_kernel(): # create SVM with callable linear kernel, check that results are the same # as with built-in linear kernel svm_callable = svm.SVC(kernel=lambda x, y: np.dot(x, y.T), probability=True, random_state=0, decision_function_shape='ovr') # clone for checking clonability with lambda functions.. svm_cloned = base.clone(svm_callable) svm_cloned.fit(iris.data, iris.target) svm_builtin = svm.SVC(kernel='linear', probability=True, random_state=0, decision_function_shape='ovr') svm_builtin.fit(iris.data, iris.target) assert_array_almost_equal(svm_cloned.dual_coef_, svm_builtin.dual_coef_) assert_array_almost_equal(svm_cloned.intercept_, svm_builtin.intercept_) assert_array_equal(svm_cloned.predict(iris.data), svm_builtin.predict(iris.data)) assert_array_almost_equal(svm_cloned.predict_proba(iris.data), svm_builtin.predict_proba(iris.data), decimal=4) assert_array_almost_equal(svm_cloned.decision_function(iris.data), svm_builtin.decision_function(iris.data)) def test_svc_bad_kernel(): svc = svm.SVC(kernel=lambda x, y: x) assert_raises(ValueError, svc.fit, X, Y) def test_timeout(): a = svm.SVC(kernel=lambda x, y: np.dot(x, y.T), probability=True, random_state=0, max_iter=1) assert_warns(ConvergenceWarning, a.fit, X, Y) def test_unfitted(): X = "foo!" # input validation not required when SVM not fitted clf = svm.SVC() assert_raises_regexp(Exception, r".*\bSVC\b.*\bnot\b.*\bfitted\b", clf.predict, X) clf = svm.NuSVR() assert_raises_regexp(Exception, r".*\bNuSVR\b.*\bnot\b.*\bfitted\b", clf.predict, X) # ignore convergence warnings from max_iter=1 @ignore_warnings def test_consistent_proba(): a = svm.SVC(probability=True, max_iter=1, random_state=0) proba_1 = a.fit(X, Y).predict_proba(X) a = svm.SVC(probability=True, max_iter=1, random_state=0) proba_2 = a.fit(X, Y).predict_proba(X) assert_array_almost_equal(proba_1, proba_2) def test_linear_svc_convergence_warnings(): # Test that warnings are raised if model does not converge lsvc = svm.LinearSVC(max_iter=2, verbose=1) assert_warns(ConvergenceWarning, lsvc.fit, X, Y) assert_equal(lsvc.n_iter_, 2) def test_svr_coef_sign(): # Test that SVR(kernel="linear") has coef_ with the right sign. # Non-regression test for #2933. X = np.random.RandomState(21).randn(10, 3) y = np.random.RandomState(12).randn(10) for svr in [svm.SVR(kernel='linear'), svm.NuSVR(kernel='linear'), svm.LinearSVR()]: svr.fit(X, y) assert_array_almost_equal(svr.predict(X), np.dot(X, svr.coef_.ravel()) + svr.intercept_) def test_linear_svc_intercept_scaling(): # Test that the right error message is thrown when intercept_scaling <= 0 for i in [-1, 0]: lsvc = svm.LinearSVC(intercept_scaling=i) msg = ('Intercept scaling is %r but needs to be greater than 0.' ' To disable fitting an intercept,' ' set fit_intercept=False.' % lsvc.intercept_scaling) assert_raise_message(ValueError, msg, lsvc.fit, X, Y) def test_lsvc_intercept_scaling_zero(): # Test that intercept_scaling is ignored when fit_intercept is False lsvc = svm.LinearSVC(fit_intercept=False) lsvc.fit(X, Y) assert_equal(lsvc.intercept_, 0.) def test_hasattr_predict_proba(): # Method must be (un)available before or after fit, switched by # `probability` param G = svm.SVC(probability=True) assert_true(hasattr(G, 'predict_proba')) G.fit(iris.data, iris.target) assert_true(hasattr(G, 'predict_proba')) G = svm.SVC(probability=False) assert_false(hasattr(G, 'predict_proba')) G.fit(iris.data, iris.target) assert_false(hasattr(G, 'predict_proba')) # Switching to `probability=True` after fitting should make # predict_proba available, but calling it must not work: G.probability = True assert_true(hasattr(G, 'predict_proba')) msg = "predict_proba is not available when fitted with probability=False" assert_raise_message(NotFittedError, msg, G.predict_proba, iris.data)

bsd-3-clause

mxjl620/scikit-learn

examples/linear_model/plot_omp.py

379

2263

""" =========================== Orthogonal Matching Pursuit =========================== Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary """ print(__doc__) import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import OrthogonalMatchingPursuit from sklearn.linear_model import OrthogonalMatchingPursuitCV from sklearn.datasets import make_sparse_coded_signal n_components, n_features = 512, 100 n_nonzero_coefs = 17 # generate the data ################### # y = Xw # |x|_0 = n_nonzero_coefs y, X, w = make_sparse_coded_signal(n_samples=1, n_components=n_components, n_features=n_features, n_nonzero_coefs=n_nonzero_coefs, random_state=0) idx, = w.nonzero() # distort the clean signal ########################## y_noisy = y + 0.05 * np.random.randn(len(y)) # plot the sparse signal ######################## plt.figure(figsize=(7, 7)) plt.subplot(4, 1, 1) plt.xlim(0, 512) plt.title("Sparse signal") plt.stem(idx, w[idx]) # plot the noise-free reconstruction #################################### omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs) omp.fit(X, y) coef = omp.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 2) plt.xlim(0, 512) plt.title("Recovered signal from noise-free measurements") plt.stem(idx_r, coef[idx_r]) # plot the noisy reconstruction ############################### omp.fit(X, y_noisy) coef = omp.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 3) plt.xlim(0, 512) plt.title("Recovered signal from noisy measurements") plt.stem(idx_r, coef[idx_r]) # plot the noisy reconstruction with number of non-zeros set by CV ################################################################## omp_cv = OrthogonalMatchingPursuitCV() omp_cv.fit(X, y_noisy) coef = omp_cv.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 4) plt.xlim(0, 512) plt.title("Recovered signal from noisy measurements with CV") plt.stem(idx_r, coef[idx_r]) plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38) plt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit', fontsize=16) plt.show()

bsd-3-clause

dimkal/mne-python

examples/inverse/plot_compute_mne_inverse_raw_in_label.py

19

1614

""" ============================================= Compute sLORETA inverse solution on raw data ============================================= Compute sLORETA inverse solution on raw dataset restricted to a brain label and stores the solution in stc files for visualisation. """ # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # # License: BSD (3-clause) import matplotlib.pyplot as plt import mne from mne.datasets import sample from mne.io import Raw from mne.minimum_norm import apply_inverse_raw, read_inverse_operator print(__doc__) data_path = sample.data_path() fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif' label_name = 'Aud-lh' fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name snr = 1.0 # use smaller SNR for raw data lambda2 = 1.0 / snr ** 2 method = "sLORETA" # use sLORETA method (could also be MNE or dSPM) # Load data raw = Raw(fname_raw) inverse_operator = read_inverse_operator(fname_inv) label = mne.read_label(fname_label) start, stop = raw.time_as_index([0, 15]) # read the first 15s of data # Compute inverse solution stc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label, start, stop, pick_ori=None) # Save result in stc files stc.save('mne_%s_raw_inverse_%s' % (method, label_name)) ############################################################################### # View activation time-series plt.plot(1e3 * stc.times, stc.data[::100, :].T) plt.xlabel('time (ms)') plt.ylabel('%s value' % method) plt.show()

bsd-3-clause

hammerlab/immuno_research

Feb7_tumor_specific_antigens.py

1

9469

import numpy as np import sklearn import sklearn.cross_validation import sklearn.ensemble import sklearn.linear_model from epitopes import cri_tumor_antigens, iedb, features, reduced_alphabet import eval_dataset cancer_peptides = cri_tumor_antigens.load_peptides(mhc_class = 1) def run(x,y, f): x_test = f.transform(cancer_peptides) y_test = np.array([True] * len(cancer_peptides)) eval_dataset.eval_split(x,y,x_test,y_test) ASSAY = 'cytotoxicity' print print "---" print "aromatic unigram" X, Y, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 1, reduced_alphabet= reduced_alphabet.aromatic2, return_transformer = True) eval_dataset.eval_cv(X, Y) print "Tumor-specific antigens" run(X,Y,f) print print "---" print "aromatic bigram" X, Y, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 2, reduced_alphabet= reduced_alphabet.aromatic2, return_transformer = True) eval_dataset.eval_cv(X, Y) print "Tumor-specific antigens" run(X, Y, f) print print "---" print "aromatic trigram" X, Y, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 3, reduced_alphabet= reduced_alphabet.aromatic2, return_transformer = True) eval_dataset.eval_cv(X, Y) print "Tumor-specific antigens" run(X, Y, f) print print "---" print "6-letter unigram" X6, Y6, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 1, reduced_alphabet= reduced_alphabet.alex6, return_transformer = True) eval_dataset.eval_cv(X6, Y6) print "Tumor-specific antigens" run(X6,Y6,f) print print "---" print "6-letter bigram" X6, Y6, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 2, reduced_alphabet= reduced_alphabet.alex6, return_transformer = True) eval_dataset.eval_cv(X6, Y6) print "Tumor-specific antigens" run(X6, Y6, f) print print "---" print "6-letter trigram" X6, Y6, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 3, reduced_alphabet= reduced_alphabet.alex6, return_transformer = True) eval_dataset.eval_cv(X6, Y6) print "Tumor-specific antigens" run(X6, Y6, f) print print "---" print "2-letter unigram" X2, Y2, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 1, reduced_alphabet = reduced_alphabet.hp2, return_transformer = True) eval_dataset.eval_cv(X2, Y2) print "Tumor-specific antigens" run(X2, Y2, f) print print "---" print "2-letter bigram" X2, Y2, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 2, reduced_alphabet = reduced_alphabet.hp2, return_transformer = True) eval_dataset.eval_cv(X2, Y2) print "Tumor-specific antigens" run(X2, Y2, f) print print "---" print "2-letter trigram" X2, Y2, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 3, reduced_alphabet = reduced_alphabet.hp2, return_transformer = True) eval_dataset.eval_cv(X2, Y2) print "Tumor-specific antigens" run(X2, Y2, f) print print "---" print "2-letter 4-gram" X2, Y2, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 4, reduced_alphabet = reduced_alphabet.hp2, return_transformer = True) eval_dataset.eval_cv(X2, Y2) print "Tumor-specific antigens" run(X2, Y2, f) print print "---" print "3-letter unigram" X3, Y3, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 1, reduced_alphabet= reduced_alphabet.gbmr4, return_transformer = True) eval_dataset.eval_cv(X3, Y3) print "Tumor-specific antigens" run(X3, Y3, f) print print "---" print "3-letter bigram" X3, Y3, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 2, reduced_alphabet= reduced_alphabet.gbmr4, return_transformer = True) eval_dataset.eval_cv(X3, Y3) print "Tumor-specific antigens" run(X3, Y3, f) print print "---" print "3-letter trigram" X3, Y3, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 3, reduced_alphabet= reduced_alphabet.gbmr4, return_transformer = True) eval_dataset.eval_cv(X3, Y3) print "Tumor-specific antigens" run(X3, Y3, f) print print "---" print "3-letter 4-gram" X3, Y3, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 4, reduced_alphabet= reduced_alphabet.gbmr4, return_transformer = True) eval_dataset.eval_cv(X3, Y3) print "Tumor-specific antigens" run(X3, Y3, f) print print "---" print "12-letter unigram" X12, Y12, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 1, reduced_alphabet= reduced_alphabet.sdm12, return_transformer = True) eval_dataset.eval_cv(X12, Y12) print "Tumor-specific antigens" run(X12, Y12, f) print print "---" print "12-letter bigram" X12, Y12, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 2, reduced_alphabet= reduced_alphabet.sdm12, return_transformer = True) eval_dataset.eval_cv(X12, Y12) print "Tumor-specific antigens" run(X12, Y12, f) print print "---" print "17-letter unigram" X17, Y17, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 1, reduced_alphabet= reduced_alphabet.hsdm17, return_transformer = True) eval_dataset.eval_cv(X17, Y17) print "Tumor-specific antigens" run(X17, Y17, f) print print "---" print "17-letter bigram" X17, Y17, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 2, reduced_alphabet= reduced_alphabet.hsdm17, return_transformer = True) eval_dataset.eval_cv(X17, Y17) print "Tumor-specific antigens" run(X17, Y17, f) print print "---" print "AA unigram" X, Y, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 1, reduced_alphabet= None, return_transformer = True) eval_dataset.eval_cv(X, Y) print "Tumor-specific antigens" run(X,Y,f) print print "---" print "AA bigram" X, Y, f = iedb.load_tcell_ngrams( noisy_labels = 'majority', assay_group = ASSAY, subsample_bigger_class = True, human = True, mhc_class = 1, max_ngram = 2, reduced_alphabet= None, return_transformer = True) eval_dataset.eval_cv(X, Y) print "Tumor-specific antigens" run(X, Y, f)

gpl-2.0

GoogleCloudPlatform/public-datasets-pipelines

datasets/covid19_govt_response/pipelines/oxford_policy_tracker/oxford_policy_tracker_dag.py

2

22798

# Copyright 2021 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from airflow import DAG from airflow.providers.cncf.kubernetes.operators import kubernetes_pod from airflow.providers.google.cloud.transfers import gcs_to_bigquery default_args = { "owner": "Google", "depends_on_past": False, "start_date": "2021-03-01", } with DAG( dag_id="covid19_govt_response.oxford_policy_tracker", default_args=default_args, max_active_runs=1, schedule_interval="@daily", catchup=False, default_view="graph", ) as dag: # Run CSV transform within kubernetes pod oxford_policy_tracker_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="oxford_policy_tracker_transform_csv", startup_timeout_seconds=600, name="oxford_policy_tracker", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.covid19_govt_response.container_registry.run_csv_transform_kub }}", env_vars={ "SOURCE_URL": "https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest_withnotes.csv", "SOURCE_FILE": "files/data.csv", "TARGET_FILE": "files/data_output.csv", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "data/covid19_govt_response/oxford_policy_tracker/data_output.csv", "PIPELINE_NAME": "oxford_policy_tracker", "CSV_HEADERS": '["country_name","alpha_3_code","region_name","region_code","date","school_closing","school_closing_flag","school_closing_notes","workplace_closing","workplace_closing_flag","workplace_closing_notes","cancel_public_events","cancel_public_events_flag","cancel_public_events_notes","restrictions_on_gatherings","restrictions_on_gatherings_flag","restrictions_on_gatherings_notes","close_public_transit","close_public_transit_flag","close_public_transit_notes","stay_at_home_requirements","stay_at_home_requirements_flag","stay_at_home_requirements_notes","restrictions_on_internal_movement","restrictions_on_internal_movement_flag","restrictions_on_internal_movement_notes","international_travel_controls","international_travel_controls_notes","income_support","income_support_flag","income_support_notes","debt_contract_relief","debt_contract_relief_notes","fiscal_measures","fiscal_measures_notes","international_support","international_support_notes","public_information_campaigns","public_information_campaigns_flag","public_information_campaigns_notes","testing_policy","testing_policy_notes","contact_tracing","contact_tracing_notes","emergency_healthcare_investment","emergency_healthcare_investment_notes","vaccine_investment","vaccine_investment_notes","misc_wildcard","misc_wildcard_notes","confirmed_cases","deaths","strintgency_index"]', "RENAME_MAPPINGS": '{"CountryName":"country_name","CountryCode":"alpha_3_code","RegionName":"region_name","RegionCode":"region_code","Date":"date","C1_School closing":"school_closing","C1_Flag":"school_closing_flag","C1_Notes":"school_closing_notes","C2_Workplace closing":"workplace_closing","C2_Flag":"workplace_closing_flag","C2_Notes":"workplace_closing_notes","C3_Cancel public events":"cancel_public_events","C3_Flag":"cancel_public_events_flag","C3_Notes":"cancel_public_events_notes","C4_Restrictions on gatherings":"restrictions_on_gatherings","C4_Flag":"restrictions_on_gatherings_flag","C4_Notes":"restrictions_on_gatherings_notes","C5_Close public transport":"close_public_transit","C5_Flag":"close_public_transit_flag","C5_Notes":"close_public_transit_notes","C6_Stay at home requirements":"stay_at_home_requirements","C6_Flag":"stay_at_home_requirements_flag","C6_Notes":"stay_at_home_requirements_notes","C7_Restrictions on internal movement":"restrictions_on_internal_movement","C7_Flag":"restrictions_on_internal_movement_flag","C7_Notes":"restrictions_on_internal_movement_notes","C8_International travel controls":"international_travel_controls","C8_Notes":"international_travel_controls_notes","E1_Income support":"income_support","E1_Flag":"income_support_flag","E1_Notes":"income_support_notes","E2_Debt/contract relief":"debt_contract_relief","E2_Notes":"debt_contract_relief_notes","E3_Fiscal measures":"fiscal_measures","E3_Notes":"fiscal_measures_notes","E4_International support":"international_support","E4_Notes":"international_support_notes","H1_Public information campaigns":"public_information_campaigns","H1_Flag":"public_information_campaigns_flag","H1_Notes":"public_information_campaigns_notes","H2_Testing policy":"testing_policy","H2_Notes":"testing_policy_notes","H3_Contact tracing":"contact_tracing","H3_Notes":"contact_tracing_notes","H4_Emergency investment in healthcare":"emergency_healthcare_investment","H4_Notes":"emergency_healthcare_investment_notes","H5_Investment in vaccines":"vaccine_investment","H5_Notes":"vaccine_investment_notes","M1_Wildcard":"misc_wildcard","M1_Notes":"misc_wildcard_notes","ConfirmedCases":"confirmed_cases","ConfirmedDeaths":"deaths","StringencyIndexForDisplay":"strintgency_index"}', }, resources={"request_memory": "2G", "request_cpu": "1"}, ) # Task to load CSV data to a BigQuery table load_oxford_policy_tracker_to_bq = gcs_to_bigquery.GCSToBigQueryOperator( task_id="load_oxford_policy_tracker_to_bq", bucket="{{ var.value.composer_bucket }}", source_objects=[ "data/covid19_govt_response/oxford_policy_tracker/data_output.csv" ], source_format="CSV", destination_project_dataset_table="covid19_govt_response.oxford_policy_tracker", skip_leading_rows=1, allow_quoted_newlines=True, write_disposition="WRITE_TRUNCATE", schema_fields=[ { "name": "country_name", "type": "string", "description": "Name of the country", "mode": "nullable", }, { "name": "alpha_3_code", "type": "string", "description": "3-letter alpha code abbreviation of the country/region. See `bigquery-public-data.utility_us.country_code_iso` for more details", "mode": "nullable", }, { "name": "region_name", "type": "string", "description": "Name of the region within the country", "mode": "nullable", }, { "name": "region_code", "type": "string", "description": "Code of the region within the country", "mode": "nullable", }, { "name": "date", "type": "date", "description": "Date of the measured policy action status", "mode": "nullable", }, { "name": "school_closing", "type": "string", "description": "C1 - Ordinal scale record closings of schools and universities; 0 - No measures 1 - recommend closing 2 - Require closing (only some levels or categories eg just high school or just public schools) 3 - Require closing all levels No data - blank", "mode": "nullable", }, { "name": "school_closing_flag", "type": "string", "description": "Are C1 actions targeted at specific areas or general:0 - Targeted 1- General No data - blank", "mode": "nullable", }, { "name": "school_closing_notes", "type": "string", "description": "Additional details about C1 policy actions", "mode": "nullable", }, { "name": "workplace_closing", "type": "string", "description": "C2 - Ordinal scale record closings of workplace; 0 - No measures 1 - recommend closing (or work from home) 2 - require closing (or work from home) for some sectors or categories of workers 3 - require closing (or work from home) all-but-essential workplaces (eg grocery stores doctors) No data - blank", "mode": "nullable", }, { "name": "workplace_closing_flag", "type": "string", "description": "Are C2 actions targeted at specific areas or general:0 - Targeted 1- General No data - blank", "mode": "nullable", }, { "name": "workplace_closing_notes", "type": "string", "description": "Additional details about C2 policy actions", "mode": "nullable", }, { "name": "cancel_public_events", "type": "string", "description": "C3 - Ordinal scale record cancellations of public events;0- No measures 1 - Recommend cancelling 2 - Require cancelling No data - blank", "mode": "nullable", }, { "name": "cancel_public_events_flag", "type": "string", "description": "Are C3 actions targeted at specific areas or general:0 - Targeted 1- General No data - blank", "mode": "nullable", }, { "name": "cancel_public_events_notes", "type": "string", "description": "Additional details about C3 policy actions", "mode": "nullable", }, { "name": "restrictions_on_gatherings", "type": "string", "description": "C4 - Ordinal scale to record the cut-off size for bans on private gatherings; 0 - No restrictions 1 - Restrictions on very large gatherings (the limit is above 1000 people) 2 - Restrictions on gatherings between 100-1000 people 3 - Restrictions on gatherings between 10-100 people 4 - Restrictions on gatherings of less than 10 people No data - blank", "mode": "nullable", }, { "name": "restrictions_on_gatherings_flag", "type": "string", "description": "Are C4 actions targeted at specific areas or general:0 - Targeted 1- General No data - blank", "mode": "nullable", }, { "name": "restrictions_on_gatherings_notes", "type": "string", "description": "Additional details about C4 policy actions", "mode": "nullable", }, { "name": "close_public_transit", "type": "string", "description": "C5 - Ordinal scale to record closing of public transportation; 0 - No measures 1 - Recommend closing (or significantly reduce volume/route/means of transport available) 2 - Require closing (or prohibit most citizens from using it)", "mode": "nullable", }, { "name": "close_public_transit_flag", "type": "string", "description": "Are C5 actions targeted at specific areas or general:0 - Targeted 1- General No data - blank", "mode": "nullable", }, { "name": "close_public_transit_notes", "type": "string", "description": "Additional details about C5 policy actions", "mode": "nullable", }, { "name": "stay_at_home_requirements", "type": "string", "description": "C6 - Ordinal scale record of orders to “shelter-in- place” and otherwise confine to home.", "mode": "nullable", }, { "name": "stay_at_home_requirements_flag", "type": "string", "description": 'Are C6 actions targeted at specific areas or general:0 - Targeted 1- General No data - blank"\\', "mode": "nullable", }, { "name": "stay_at_home_requirements_notes", "type": "string", "description": "Additional details about C6 policy actions", "mode": "nullable", }, { "name": "restrictions_on_internal_movement", "type": "string", "description": "C7 - Ordinal scale of restrictions on internal movement; 0 - No measures 1 - Recommend closing (or significantly reduce volume/route/means of transport) 2 - Require closing (or prohibit most people from using it)", "mode": "nullable", }, { "name": "restrictions_on_internal_movement_flag", "type": "string", "description": "Are C7 actions targeted at specific areas or general:0 - Targeted 1- General No data - blank", "mode": "nullable", }, { "name": "restrictions_on_internal_movement_notes", "type": "string", "description": "Additional details about C7 policy actions", "mode": "nullable", }, { "name": "international_travel_controls", "type": "string", "description": "C8 - Ordinal scale record of restrictions on international travel; 0 - No measures 1 - Screening 2 - Quarantine arrivals from high-risk regions 3 - Ban on high-risk regions 4 - Total border closure No data - blank", "mode": "nullable", }, { "name": "international_travel_controls_notes", "type": "string", "description": "Additional details about C8 policy actions", "mode": "nullable", }, { "name": "income_support", "type": "string", "description": "E1 - Ordinal scale record if the government is covering the salaries or providing direct cash payments universal basic income or similar of people who lose their jobs or cannot work. (Includes payments to firms if explicitly linked to payroll/ salaries)", "mode": "nullable", }, { "name": "income_support_flag", "type": "string", "description": "Sector scope of E1 actions; 0 - formal sector workers only 1 - transfers to informal sector workers too No data - blank", "mode": "nullable", }, { "name": "income_support_notes", "type": "string", "description": "Additional details about E1 policy actions", "mode": "nullable", }, { "name": "debt_contract_relief", "type": "string", "description": "E2 - Record if govt. is freezing financial obligations (eg stopping loan repayments preventing services like water from stopping or banning evictions)", "mode": "nullable", }, { "name": "debt_contract_relief_notes", "type": "string", "description": "Additional details about E2 policy actions", "mode": "nullable", }, { "name": "fiscal_measures", "type": "float", "description": "E3 - What economic stimulus policies are adopted (in USD); Record monetary value USD of fiscal stimuli including spending or tax cuts NOT included in S10 (see below) -If none enter 0 No data - blank Please use the exchange rate of the date you are coding not the current date.", "mode": "nullable", }, { "name": "fiscal_measures_notes", "type": "string", "description": "Additional details about E3 policy actions", "mode": "nullable", }, { "name": "international_support", "type": "float", "description": "E4 - Announced offers of COVID-19 related aid spending to other countries (in USD); Record monetary value announced if additional to previously announced spending -if none enter 0 No data - blank Please use the exchange rate of the date you are coding not the current date.", "mode": "nullable", }, { "name": "international_support_notes", "type": "string", "description": "Additional details about E4 policy actions", "mode": "nullable", }, { "name": "public_information_campaigns", "type": "string", "description": "H1 - Ordinal scale record presence of public info campaigns; 0 -No COVID-19 public information campaign 1 - public officials urging caution about COVID-19 2 - coordinated public information campaign (e.g. across traditional and social media) No data - blank", "mode": "nullable", }, { "name": "public_information_campaigns_flag", "type": "string", "description": "Sector scope of H1 actions; 0 - formal sector workers only 1 - transfers to informal sector workers too No data - blank", "mode": "nullable", }, { "name": "public_information_campaigns_notes", "type": "string", "description": "Additional details about H1 policy actions", "mode": "nullable", }, { "name": "testing_policy", "type": "string", "description": "H2 - Ordinal scale record of who can get tested; 0 – No testing policy 1 – Only those who both (a) have symptoms AND (b) meet specific criteria (eg key workers admitted to hospital came into contact with a known case returned from overseas) 2 – testing of anyone showing COVID-19 symptoms 3 – open public testing (eg “drive through” testing available to asymptomatic people) No data Nb we are looking for policies about testing for having an infection (PCR tests) - not for policies about testing for immunity (antibody tests).", "mode": "nullable", }, { "name": "testing_policy_notes", "type": "string", "description": "Additional details about H2 policy actions", "mode": "nullable", }, { "name": "contact_tracing", "type": "string", "description": "H3 - Ordinal scale record if governments doing contact tracing; 0 - No contact tracing 1 - Limited contact tracing - not done for all cases 2 - Comprehensive contact tracing - done for all cases No data", "mode": "nullable", }, { "name": "contact_tracing_notes", "type": "string", "description": "Additional details about H3 policy actions", "mode": "nullable", }, { "name": "emergency_healthcare_investment", "type": "float", "description": "H4 - Short-term spending on e.g hospitals masks etc in USD; Record monetary value in USD of new short-term spending on health. If none enter 0. No data - blank Please use the exchange rate of the date you are coding not the current date.", "mode": "nullable", }, { "name": "emergency_healthcare_investment_notes", "type": "string", "description": "Additional details about H4 policy actions", "mode": "nullable", }, { "name": "vaccine_investment", "type": "float", "description": "H5 - Announced public spending on vaccine development in USD; Record monetary value in USD of new short-term spending on health. If none enter 0. No data - blank Please use the exchange rate of the date you are coding not the current date.", "mode": "nullable", }, { "name": "vaccine_investment_notes", "type": "string", "description": "Additional details about H5 policy actions", "mode": "nullable", }, { "name": "misc_wildcard", "type": "string", "description": "M1 - Record policy announcements that do not fit anywhere else", "mode": "nullable", }, { "name": "misc_wildcard_notes", "type": "string", "description": "Additional details about M1 policy actions", "mode": "nullable", }, { "name": "confirmed_cases", "type": "integer", "description": "Number of confirmed COVID-19 cases", "mode": "nullable", }, { "name": "deaths", "type": "integer", "description": "Number of confirmed COVID-19 deaths", "mode": "nullable", }, { "name": "stringency_index", "type": "float", "description": "Used after April 28 2020. Nine-point aggregation of the eight containment and closure indicators as well as H1 (public information campaigns). It reports a number between 0 to 100 that reflects the overall stringency of the governments response. This is a measure of how many of the these nine indicators (mostly around social isolation) a government has acted upon and to what degree.", "mode": "nullable", }, ], ) oxford_policy_tracker_transform_csv >> load_oxford_policy_tracker_to_bq

apache-2.0

CI-WATER/tethys

tethys_apps/models.py

1

32674

""" ******************************************************************************** * Name: models.py * Author: Nathan Swain * Created On: 2014 * Copyright: (c) Brigham Young University 2014 * License: BSD 2-Clause ******************************************************************************** """ import sqlalchemy import logging import uuid import django.dispatch from django.db import models from django.core.exceptions import ValidationError from model_utils.managers import InheritanceManager from tethys_apps.exceptions import TethysAppSettingNotAssigned, PersistentStorePermissionError, \ PersistentStoreInitializerError from django.contrib.postgres.fields import ArrayField from sqlalchemy.orm import sessionmaker from tethys_apps.base.mixins import TethysBaseMixin from tethys_services.models import validate_url from tethys_sdk.testing import is_testing_environment, get_test_db_name from tethys_apps.base.function_extractor import TethysFunctionExtractor log = logging.getLogger('tethys') try: from tethys_services.models import (DatasetService, SpatialDatasetService, WebProcessingService, PersistentStoreService) except RuntimeError: # pragma: no cover log.exception('An error occurred while trying to import tethys service models.') class TethysApp(models.Model, TethysBaseMixin): """ DB Model for Tethys Apps """ # The package is enforced to be unique by the file system package = models.CharField(max_length=200, unique=True, default='') # Portal admin first attributes name = models.CharField(max_length=200, default='') description = models.TextField(max_length=1000, blank=True, default='') enable_feedback = models.BooleanField(default=False) feedback_emails = ArrayField( models.CharField(max_length=200, null=True, blank=True), default=list, ) tags = models.CharField(max_length=200, blank=True, default='') # Developer first attributes index = models.CharField(max_length=200, default='') icon = models.CharField(max_length=200, default='') root_url = models.CharField(max_length=200, default='') color = models.CharField(max_length=10, default='') # Portal admin only attributes enabled = models.BooleanField(default=True) show_in_apps_library = models.BooleanField(default=True) class Meta: verbose_name = 'Tethys App' verbose_name_plural = 'Installed Apps' def __str__(self): return self.name def add_settings(self, setting_list): """ Associate setting with app in database """ if setting_list is not None: for setting in setting_list: # Don't add the same setting twice if self.settings_set.filter(name=setting.name): return # Associate setting with this app setting.tethys_app = self setting.save() @property def settings(self): return self.settings_set.select_subclasses() @property def custom_settings(self): return self.settings_set.exclude(customsetting__isnull=True) \ .select_subclasses('customsetting') @property def dataset_service_settings(self): return self.settings_set.exclude(datasetservicesetting__isnull=True) \ .select_subclasses('datasetservicesetting') @property def spatial_dataset_service_settings(self): return self.settings_set.exclude(spatialdatasetservicesetting__isnull=True) \ .select_subclasses('spatialdatasetservicesetting') @property def wps_services_settings(self): return self.settings_set.exclude(webprocessingservicesetting__isnull=True) \ .select_subclasses('webprocessingservicesetting') @property def persistent_store_connection_settings(self): return self.settings_set.exclude(persistentstoreconnectionsetting__isnull=True) \ .select_subclasses('persistentstoreconnectionsetting') @property def persistent_store_database_settings(self): return self.settings_set.exclude(persistentstoredatabasesetting__isnull=True) \ .select_subclasses('persistentstoredatabasesetting') @property def configured(self): required_settings = [s for s in self.settings if s.required] for setting in required_settings: try: setting.get_value() except TethysAppSettingNotAssigned: return False return True class TethysExtension(models.Model, TethysBaseMixin): """ DB Model for Tethys Extension """ # The package is enforced to be unique by the file system package = models.CharField(max_length=200, unique=True, default='') # Portal admin first attributes name = models.CharField(max_length=200, default='') description = models.TextField(max_length=1000, blank=True, default='') # Developer first attributes root_url = models.CharField(max_length=200, default='') # Portal admin only attributes enabled = models.BooleanField(default=True) class Meta: verbose_name = 'Tethys Extension' verbose_name_plural = 'Installed Extensions' def __str__(self): return self.name class TethysAppSetting(models.Model): """ DB Model for Tethys App Settings """ objects = InheritanceManager() tethys_app = models.ForeignKey(TethysApp, on_delete=models.CASCADE, related_name='settings_set') name = models.CharField(max_length=200, default='') description = models.TextField(max_length=1000, blank=True, default='') required = models.BooleanField(default=True) initializer = models.CharField(max_length=1000, default='') initialized = models.BooleanField(default=False) def __str__(self): return self.name @property def initializer_function(self): """ The function pointed to by the initializer attribute. Returns: A handle to a Python function that will initialize the database or None if function is not valid. """ func_ext = TethysFunctionExtractor(self.initializer) return func_ext.function def initialize(self): """ Initialize. """ self.initializer_function(self.initialized) self.initialized = True def get_value(self, *args, **kwargs): raise NotImplementedError() class CustomSetting(TethysAppSetting): """ Used to define a Custom Setting. Attributes: name(str): Unique name used to identify the setting. type(enum): The type of the custom setting. Either CustomSetting.TYPE_STRING, CustomSetting.TYPE_INTEGER, CustomSetting.TYPE_FLOAT, CustomSetting.TYPE_BOOLEAN, CustomSetting.TYPE_UUID description(str): Short description of the setting. required(bool): A value will be required if True. default(str): Value as a string that may be provided as a default. **Example:** :: from tethys_sdk.app_settings import CustomSetting default_name_setting = CustomSetting( name='default_name', type=CustomSetting.TYPE_STRING description='Default model name.', required=True, default="Name_123" ) max_count_setting = CustomSetting( name='max_count', type=CustomSetting.TYPE_INTEGER, description='Maximum allowed count in a method.', required=False ) change_factor_setting = CustomSetting( name='change_factor', type=CustomSetting.TYPE_FLOAT, description='Change factor that is applied to some process.', required=True ) enable_feature_setting = CustomSetting( name='enable_feature', type=CustomSetting.TYPE_BOOLEAN, description='Enable this feature when True.', required=True ) feature_id_setting = CustomSetting( name='feature_id', type=CustomSetting.TYPE_UUID, description='Feature ID.', required=True ) """ # noqa: E501 TYPE_STRING = 'STRING' TYPE_INTEGER = 'INTEGER' TYPE_FLOAT = 'FLOAT' TYPE_BOOLEAN = 'BOOLEAN' TYPE_UUID = 'UUID' VALID_TYPES = (TYPE_STRING, TYPE_INTEGER, TYPE_FLOAT, TYPE_BOOLEAN, TYPE_UUID) VALID_BOOL_STRINGS = ('true', 'false', 'yes', 'no', 't', 'f', 'y', 'n', '1', '0') TRUTHY_BOOL_STRINGS = ('true', 'yes', 't', 'y', '1') TYPE_CHOICES = ( (TYPE_STRING, 'String'), (TYPE_INTEGER, 'Integer'), (TYPE_FLOAT, 'Float'), (TYPE_BOOLEAN, 'Boolean'), (TYPE_UUID, 'UUID'), ) value = models.CharField(max_length=1024, blank=True, default='') default = models.CharField(max_length=1024, blank=True, default='') type = models.CharField(max_length=200, choices=TYPE_CHOICES, default=TYPE_STRING) def clean(self): """ Validate prior to saving changes. """ if self.default != '': if self.value == '': self.value = self.default else: if self.value == '' and self.required: raise ValidationError('Required.') if self.value != '' and self.type == self.TYPE_FLOAT: try: float(self.value) except Exception: raise ValidationError('Value must be a float.') elif self.value != '' and self.type == self.TYPE_INTEGER: try: int(self.value) except Exception: raise ValidationError('Value must be an integer.') elif self.value != '' and self.type == self.TYPE_BOOLEAN: if self.value.lower() not in self.VALID_BOOL_STRINGS: raise ValidationError('Value must be a boolean.') elif self.value != '' and self.type == self.TYPE_UUID: try: uuid.UUID(self.value) except Exception: raise ValidationError('Value must be a uuid.') def get_value(self): """ Get the value, automatically casting it to the correct type. """ if self.default != '': if self.value == '': self.value = self.default if self.value == '' or self.value is None: if self.required: raise TethysAppSettingNotAssigned( f'The required setting "{self.name}" for app "{self.tethys_app.package}":' f'has not been assigned.') # None is a valid value to return in the case the value has not been set for this setting type return None if self.type == self.TYPE_STRING: return self.value if self.type == self.TYPE_FLOAT: return float(self.value) if self.type == self.TYPE_INTEGER: return int(self.value) if self.type == self.TYPE_BOOLEAN: return self.value.lower() in self.TRUTHY_BOOL_STRINGS if self.type == self.TYPE_UUID: return uuid.UUID(self.value) @django.dispatch.receiver(models.signals.post_init, sender=CustomSetting) def set_default_value(sender, instance, *args, **kwargs): """ Set the default value for `value` on the `instance` of Setting. This signal receiver will process it as soon as the object is created for use Attributes: sender(CustomSetting): The `CustomSetting` class that sent the signal. instance(CustomSetting): The `CustomSetting` instance that is being initialised. Returns: None """ if not instance.value or instance.value == '': instance.value = instance.default class DatasetServiceSetting(TethysAppSetting): """ Used to define a Dataset Service Setting. Attributes: name(str): Unique name used to identify the setting. description(str): Short description of the setting. engine(enum): Either DatasetServiceSetting.CKAN or DatasetServiceSetting.HYDROSHARE required(bool): A value will be required if True. **Example:** :: from tethys_sdk.app_settings import DatasetServiceSetting primary_ckan_setting = DatasetServiceSetting( name='primary_ckan', description='Primary CKAN service for app to use.', engine=DatasetServiceSetting.CKAN, required=True, ) hydroshare_setting = DatasetServiceSetting( name='hydroshare', description='HydroShare service for app to use.', engine=DatasetServiceSetting.HYDROSHARE, required=False ) """ CKAN = DatasetService.CKAN HYDROSHARE = DatasetService.HYDROSHARE dataset_service = models.ForeignKey(DatasetService, on_delete=models.CASCADE, blank=True, null=True) engine = models.CharField(max_length=200, choices=DatasetService.ENGINE_CHOICES, default=DatasetService.CKAN) def clean(self): """ Validate prior to saving changes. """ if not self.dataset_service and self.required: raise ValidationError('Required.') def get_value(self, as_public_endpoint=False, as_endpoint=False, as_engine=False): if not self.dataset_service: raise TethysAppSettingNotAssigned(f'Cannot create engine or endpoint for DatasetServiceSetting ' f'"{self.name}" for app "{self.tethys_app.package}": ' f'no DatasetService assigned.') # Order here matters. Think carefully before changing. if as_engine: return self.dataset_service.get_engine() if as_endpoint: return self.dataset_service.endpoint if as_public_endpoint: return self.dataset_service.public_endpoint return self.dataset_service class SpatialDatasetServiceSetting(TethysAppSetting): """ Used to define a Spatial Dataset Service Setting. Attributes: name(str): Unique name used to identify the setting. description(str): Short description of the setting. engine(enum): One of SpatialDatasetServiceSetting.GEOSERVER or SpatialDatasetServiceSetting.THREDDS at this time. required(bool): A value will be required if True. **Example:** :: from tethys_sdk.app_settings import SpatialDatasetServiceSetting primary_geoserver_setting = SpatialDatasetServiceSetting( name='primary_geoserver', description='spatial dataset service for app to use', engine=SpatialDatasetServiceSetting.GEOSERVER, required=True, ) """ # noqa: E501 GEOSERVER = SpatialDatasetService.GEOSERVER THREDDS = SpatialDatasetService.THREDDS spatial_dataset_service = models.ForeignKey(SpatialDatasetService, on_delete=models.CASCADE, blank=True, null=True) engine = models.CharField(max_length=200, choices=SpatialDatasetService.ENGINE_CHOICES, default=SpatialDatasetService.GEOSERVER) def clean(self): """ Validate prior to saving changes. """ if not self.spatial_dataset_service and self.required: raise ValidationError('Required.') def get_value(self, as_public_endpoint=False, as_endpoint=False, as_wms=False, as_wfs=False, as_engine=False, as_wcs=False): if not self.spatial_dataset_service: raise TethysAppSettingNotAssigned(f'Cannot create engine or endpoint for SpatialDatasetServiceSetting ' f'"{self.name}" for app "{self.tethys_app.package}": ' f'no SpatialDatasetService assigned.') # Order here matters. Think carefully before changing. if as_engine: return self.spatial_dataset_service.get_engine() if as_endpoint: return self.spatial_dataset_service.endpoint if as_public_endpoint: return self.spatial_dataset_service.public_endpoint if self.engine == self.GEOSERVER: if as_wms: return self.spatial_dataset_service.endpoint.split('/rest')[0] + '/wms' if as_wfs: return self.spatial_dataset_service.endpoint.split('/rest')[0] + '/ows' if as_wcs: return self.spatial_dataset_service.endpoint.split('/rest')[0] + '/wcs' elif self.engine == self.THREDDS: if as_wms: return self.spatial_dataset_service.endpoint.rstrip('/') + '/wms' if as_wcs: return self.spatial_dataset_service.endpoint.rstrip('/') + '/wcs' if as_wfs: raise ValueError('THREDDS does not support the WFS interface.') return self.spatial_dataset_service class WebProcessingServiceSetting(TethysAppSetting): """ Used to define a Web Processing Service Setting. Attributes: name(str): Unique name used to identify the setting. description(str): Short description of the setting. required(bool): A value will be required if True. **Example:** :: from tethys_sdk.app_settings import WebProcessingServiceSetting primary_52n_setting = WebProcessingServiceSetting( name='primary_52n', description='WPS service for app to use', required=True, ) """ web_processing_service = models.ForeignKey(WebProcessingService, on_delete=models.CASCADE, blank=True, null=True) def clean(self): """ Validate prior to saving changes. """ if not self.web_processing_service and self.required: raise ValidationError('Required.') def get_value(self, as_public_endpoint=False, as_endpoint=False, as_engine=False): wps_service = self.web_processing_service if not wps_service: raise TethysAppSettingNotAssigned(f'Cannot create engine or endpoint for WebProcessingServiceSetting ' f'"{self.name}" for app "{self.tethys_app.package}": ' f'no WebProcessingService assigned.') # Order here matters. Think carefully before changing. if as_engine: return wps_service.get_engine() if as_endpoint: return wps_service.endpoint if as_public_endpoint: return wps_service.public_endpoint return wps_service class PersistentStoreConnectionSetting(TethysAppSetting): """ Used to define a Peristent Store Connection Setting. Attributes: name(str): Unique name used to identify the setting. description(str): Short description of the setting. required(bool): A value will be required if True. **Example:** :: from tethys_sdk.app_settings import PersistentStoreConnectionSetting primary_db_conn_setting = PersistentStoreConnectionSetting( name='primary', description='Connection with superuser role needed.', required=True ) """ persistent_store_service = models.ForeignKey( PersistentStoreService, on_delete=models.CASCADE, blank=True, null=True) def clean(self): """ Validate prior to saving changes. """ if not self.persistent_store_service and self.required: raise ValidationError('Required.') def get_value(self, as_url=False, as_sessionmaker=False, as_engine=False): """ Get the SQLAlchemy engine from the connected persistent store service """ ps_service = self.persistent_store_service # Validate connection service if ps_service is None: raise TethysAppSettingNotAssigned(f'Cannot create engine or endpoint for PersistentStoreConnectionSetting ' f'"{self.name}" for app "{self.tethys_app.package}": ' f'no PersistentStoreService assigned.') # Order here matters. Think carefully before changing. if as_engine: return ps_service.get_engine() if as_sessionmaker: return sessionmaker(bind=ps_service.get_engine()) if as_url: return ps_service.get_url() return ps_service class PersistentStoreDatabaseSetting(TethysAppSetting): """ Used to define a Peristent Store Database Setting. Attributes: name(str): Unique name used to identify the setting. description(str): Short description of the setting. initializer(str): Dot-notation path to function used to initialize the database. spatial(bool): Enable the PostGIS extension on the database during creation when True. required(bool): A value will be required if True. **Example:** :: from tethys_sdk.app_settings import PersistentStoreDatabaseSetting spatial_db_setting = PersistentStoreDatabaseSetting( name='spatial_db', description='for storing important spatial stuff', required=True, initializer='appsettings.init_stores.init_spatial_db', spatial=True, ), temp_db_setting = PersistentStoreDatabaseSetting( name='temp_db', description='for storing temporary stuff', required=False, initializer='appsettings.init_stores.init_temp_db', spatial=False, ) """ spatial = models.BooleanField(default=False) dynamic = models.BooleanField(default=False) persistent_store_service = models.ForeignKey( PersistentStoreService, on_delete=models.CASCADE, blank=True, null=True) def clean(self): """ Validate prior to saving changes. """ if not self.persistent_store_service and self.required: raise ValidationError('Required.') def initialize(self): """ Initialize persistent store database setting. """ self.create_persistent_store_database() def get_namespaced_persistent_store_name(self): """ Return the namespaced persistent store database name (e.g. my_first_app_db). """ # Convert name given by user to database safe name safe_name = self.name.lower().replace(' ', '_') # If testing environment, the engine for the "test" version of the persistent store should be fetched if is_testing_environment(): safe_name = get_test_db_name(safe_name) return '_'.join((self.tethys_app.package, safe_name)) def get_value(self, with_db=False, as_url=False, as_sessionmaker=False, as_engine=False): """ Get the SQLAlchemy engine from the connected persistent store service """ ps_service = self.persistent_store_service # Validate connection service if ps_service is None: raise TethysAppSettingNotAssigned(f'Cannot create engine or endpoint for PersistentStoreDatabaseSetting ' f'"{self.name}" for app "{self.tethys_app.package}": ' f'no PersistentStoreService assigned.') if with_db: ps_service.database = self.get_namespaced_persistent_store_name() # Order here matters. Think carefully before changing. if as_engine: return ps_service.get_engine() if as_sessionmaker: return sessionmaker(bind=ps_service.get_engine()) if as_url: return ps_service.get_url() return ps_service def persistent_store_database_exists(self): """ Returns True if the persistent store database exists. """ # Get the database engine engine = self.get_value(as_engine=True) namespaced_name = self.get_namespaced_persistent_store_name() # Cannot create databases in a transaction: connect and commit to close transaction connection = engine.connect() # Check for Database existing_query = """ SELECT d.datname as name FROM pg_catalog.pg_database d LEFT JOIN pg_catalog.pg_user u ON d.datdba = u.usesysid WHERE d.datname = '{0}'; """.format(namespaced_name) existing_dbs = connection.execute(existing_query) connection.close() for existing_db in existing_dbs: if existing_db.name == namespaced_name: return True return False def drop_persistent_store_database(self): """ Drop the persistent store database. """ if not self.persistent_store_database_exists(): return # Provide update for user log = logging.getLogger('tethys') log.info('Dropping database "{0}" for app "{1}"...'.format( self.name, self.tethys_app.package )) # Get the database engine engine = self.get_value(as_engine=True) # Connection drop_connection = None namespaced_ps_name = self.get_namespaced_persistent_store_name() # Drop db drop_db_statement = 'DROP DATABASE IF EXISTS "{0}"'.format(namespaced_ps_name) try: drop_connection = engine.connect() drop_connection.execute('commit') drop_connection.execute(drop_db_statement) except Exception as e: if 'being accessed by other users' in str(e): # Force disconnect all other connections to the database disconnect_sessions_statement = ''' SELECT pg_terminate_backend(pg_stat_activity.pid) FROM pg_stat_activity WHERE pg_stat_activity.datname = '{0}' AND pg_stat_activity.pid <> pg_backend_pid(); '''.format(namespaced_ps_name) if drop_connection: drop_connection.execute(disconnect_sessions_statement) # Try again to drop the database drop_connection.execute('commit') drop_connection.execute(drop_db_statement) else: raise e finally: drop_connection and drop_connection.close() def create_persistent_store_database(self, refresh=False, force_first_time=False): """ Provision all persistent stores for all apps or for only the app name given. """ # Get looger log = logging.getLogger('tethys') # Connection engine url = self.get_value(as_url=True) engine = self.get_value(as_engine=True) namespaced_ps_name = self.get_namespaced_persistent_store_name() db_exists = self.persistent_store_database_exists() # -------------------------------------------------------------------------------------------------------------# # 1. Drop database if refresh option is included # -------------------------------------------------------------------------------------------------------------# if db_exists and refresh: self.drop_persistent_store_database() self.initialized = False self.save() db_exists = False # -------------------------------------------------------------------------------------------------------------# # 2. Create the database if it does not already exist # -------------------------------------------------------------------------------------------------------------# if not db_exists: # Provide Update for User log.info('Creating database "{0}" for app "{1}"...'.format( self.name, self.tethys_app.package )) # Cannot create databases in a transaction: connect and commit to close transaction create_connection = engine.connect() # Create db create_db_statement = ''' CREATE DATABASE "{0}" WITH OWNER {1} TEMPLATE template0 ENCODING 'UTF8' '''.format(namespaced_ps_name, url.username) # Close transaction first and then execute create_connection.execute('commit') try: create_connection.execute(create_db_statement) except sqlalchemy.exc.ProgrammingError: raise PersistentStorePermissionError('Database user "{0}" has insufficient permissions to create ' 'the persistent store database "{1}": must have CREATE DATABASES ' 'permission at a minimum.'.format(url.username, self.name)) finally: create_connection.close() # -------------------------------------------------------------------------------------------------------------# # 3. Enable PostGIS extension # -------------------------------------------------------------------------------------------------------------# if self.spatial: # Connect to new database new_db_engine = self.get_value(with_db=True, as_engine=True) new_db_connection = new_db_engine.connect() # Notify user log.info('Enabling PostGIS on database "{0}" for app "{1}"...'.format( self.name, self.tethys_app.package, )) enable_postgis_statement = 'CREATE EXTENSION IF NOT EXISTS postgis' # Execute postgis statement try: new_db_connection.execute(enable_postgis_statement) except sqlalchemy.exc.ProgrammingError: raise PersistentStorePermissionError('Database user "{0}" has insufficient permissions to enable ' 'spatial extension on persistent store database "{1}": must be a ' 'superuser.'.format(url.username, self.name)) finally: new_db_connection.close() # -------------------------------------------------------------------------------------------------------------# # 4. Run initialization function # -------------------------------------------------------------------------------------------------------------# if self.initializer: log.info('Initializing database "{0}" for app "{1}" with initializer "{2}"...'.format( self.name, self.tethys_app.package, self.initializer )) try: if force_first_time: self.initializer_function(self.get_value(with_db=True, as_engine=True), True) else: self.initializer_function(self.get_value(with_db=True, as_engine=True), not self.initialized) except Exception as e: raise PersistentStoreInitializerError(e) # Update initialization self.initialized = True self.save() class ProxyApp(models.Model): """ DB model for Proxy Apps which allows you to redirect an app to another host. """ name = models.CharField(max_length=100, unique=True) endpoint = models.CharField(max_length=1024, validators=[validate_url]) logo_url = models.CharField(max_length=100, validators=[validate_url], blank=True) description = models.TextField(max_length=2048, blank=True) tags = models.CharField(max_length=200, blank=True, default='') enabled = models.BooleanField(default=True) show_in_apps_library = models.BooleanField(default=True) class Meta: verbose_name = 'Proxy App' verbose_name_plural = 'Proxy Apps' def __str__(self): return self.name

bsd-2-clause

davidam/python-examples

scikit/pca-choosing-components.py

1

1682

#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright (C) 2019 David Arroyo Menéndez # Author: David Arroyo Menéndez <davidam@gnu.org> # Maintainer: David Arroyo Menéndez <davidam@gnu.org> # This file is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3, or (at your option) # any later version. # This file is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with GNU Emacs; see the file COPYING. If not, write to # the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, # Boston, MA 02110-1301 USA, import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt import argparse parser = argparse.ArgumentParser() parser.add_argument('--csv') args = parser.parse_args() #filepath = 'pulsar_stars.csv' #your path here data = np.genfromtxt(args.csv, delimiter=',', dtype='float64') scaler = MinMaxScaler(feature_range=[0, 1]) data_rescaled = scaler.fit_transform(data[1:, 0:8]) #Fitting the PCA algorithm with our Data pca = PCA().fit(data_rescaled) #Plotting the Cumulative Summation of the Explained Variance plt.figure() plt.plot(np.cumsum(pca.explained_variance_ratio_)) plt.xlabel('Number of Components') plt.ylabel('Variance (%)') #for each component plt.title('Dataset Explained Variance') plt.show()

gpl-3.0

mxjl620/scikit-learn

examples/ensemble/plot_adaboost_hastie_10_2.py

352

3576

""" ============================= Discrete versus Real AdaBoost ============================= This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates the difference in performance between the discrete SAMME [2] boosting algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluated on a binary classification task where the target Y is a non-linear function of 10 input features. Discrete SAMME AdaBoost adapts based on errors in predicted class labels whereas real SAMME.R uses the predicted class probabilities. .. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical Learning Ed. 2", Springer, 2009. .. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009. """ print(__doc__) # Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>, # Noel Dawe <noel.dawe@gmail.com> # # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import zero_one_loss from sklearn.ensemble import AdaBoostClassifier n_estimators = 400 # A learning rate of 1. may not be optimal for both SAMME and SAMME.R learning_rate = 1. X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1) X_test, y_test = X[2000:], y[2000:] X_train, y_train = X[:2000], y[:2000] dt_stump = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1) dt_stump.fit(X_train, y_train) dt_stump_err = 1.0 - dt_stump.score(X_test, y_test) dt = DecisionTreeClassifier(max_depth=9, min_samples_leaf=1) dt.fit(X_train, y_train) dt_err = 1.0 - dt.score(X_test, y_test) ada_discrete = AdaBoostClassifier( base_estimator=dt_stump, learning_rate=learning_rate, n_estimators=n_estimators, algorithm="SAMME") ada_discrete.fit(X_train, y_train) ada_real = AdaBoostClassifier( base_estimator=dt_stump, learning_rate=learning_rate, n_estimators=n_estimators, algorithm="SAMME.R") ada_real.fit(X_train, y_train) fig = plt.figure() ax = fig.add_subplot(111) ax.plot([1, n_estimators], [dt_stump_err] * 2, 'k-', label='Decision Stump Error') ax.plot([1, n_estimators], [dt_err] * 2, 'k--', label='Decision Tree Error') ada_discrete_err = np.zeros((n_estimators,)) for i, y_pred in enumerate(ada_discrete.staged_predict(X_test)): ada_discrete_err[i] = zero_one_loss(y_pred, y_test) ada_discrete_err_train = np.zeros((n_estimators,)) for i, y_pred in enumerate(ada_discrete.staged_predict(X_train)): ada_discrete_err_train[i] = zero_one_loss(y_pred, y_train) ada_real_err = np.zeros((n_estimators,)) for i, y_pred in enumerate(ada_real.staged_predict(X_test)): ada_real_err[i] = zero_one_loss(y_pred, y_test) ada_real_err_train = np.zeros((n_estimators,)) for i, y_pred in enumerate(ada_real.staged_predict(X_train)): ada_real_err_train[i] = zero_one_loss(y_pred, y_train) ax.plot(np.arange(n_estimators) + 1, ada_discrete_err, label='Discrete AdaBoost Test Error', color='red') ax.plot(np.arange(n_estimators) + 1, ada_discrete_err_train, label='Discrete AdaBoost Train Error', color='blue') ax.plot(np.arange(n_estimators) + 1, ada_real_err, label='Real AdaBoost Test Error', color='orange') ax.plot(np.arange(n_estimators) + 1, ada_real_err_train, label='Real AdaBoost Train Error', color='green') ax.set_ylim((0.0, 0.5)) ax.set_xlabel('n_estimators') ax.set_ylabel('error rate') leg = ax.legend(loc='upper right', fancybox=True) leg.get_frame().set_alpha(0.7) plt.show()

bsd-3-clause

TNick/pyl2extra

pyl2extra/scripts/datasets/tests/test_imagenet.py

1

13084

""" Tests for adjusters. """ __authors__ = "Nicu Tofan" __copyright__ = "Copyright 2015, Nicu Tofan" __credits__ = ["Nicu Tofan"] __license__ = "3-clause BSD" __maintainer__ = "Nicu Tofan" __email__ = "nicu.tofan@gmail.com" import functools import unittest from mock import patch, Mock import os import shutil import tempfile from xml.dom import minidom from xml.parsers.expat import ExpatError from pyl2extra.scripts.datasets import imagenet TEST_SYNSETS = """ n04386664 n10731013 n03002948 n07609632 n03003091 n10562968 n07586179 n09929577 n07933530 n04136161 n03602194 n03703075 n12990597 """ RELEASE_STATUS_SAMPLE = """<ReleaseStatus> <releaseData>fall2011</releaseData> <images> <synsetInfos> <synset wnid="n10801802" released="1" version="winter11" numImages="269"/> <synset wnid="n10772937" released="1" version="winter11" numImages="58"/> <synset wnid="n10028541" released="1" version="winter11" numImages="201"/> <synset wnid="n10712374" released="1" version="winter11" numImages="175"/> <synset wnid="n09878921" released="1" version="winter11" numImages="46"/> <synset wnid="n10789415" released="1" version="winter11" numImages="48"/> <synset wnid="n10370955" released="1" version="winter11" numImages="502"/> </synsetInfos> </images> </ReleaseStatus>""" GET_MAPPING_SAMPLE = """ n02109150_5962 http://1.jpg n02109150_5969 http://2.jpg n02109150_5976 http://3.jpg n02109150_5981 http://4.jpg n02109150_307 http://www.scbeacon.com/beacon_issues/03_09_18/images/Guidedog_pjh_091803.jpg n02109150_323 http://www.braille.be/content/lig_braille/rapport_2005/img_05.jpg """ @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestListFromUrl(unittest.TestCase): """ Tests for list_from_url(). """ def test_simple(self, mock_urlopen): """ testing list_from_url(). """ mok = Mock() mok.read.side_effect = ['resp1', 'resp1\nresp2', '', ' a '] mock_urlopen.return_value = mok lst = imagenet.list_from_url('some_url') self.assertListEqual(lst, ['resp1']) lst = imagenet.list_from_url('some_url') self.assertListEqual(lst, ['resp1', 'resp2']) lst = imagenet.list_from_url('some_url') self.assertListEqual(lst, ['']) lst = imagenet.list_from_url('some_url') self.assertListEqual(lst, [' a ']) @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestDenseListFromUrl(unittest.TestCase): """ Tests for dense_list_from_url(). """ def test_simple(self, mock_urlopen): """ testing dense_list_from_url(). """ mok = Mock() mok.read.side_effect = ['resp1', 'resp1\nresp2', '', ' ', ' a ', ' a \n b \n c ', '\n\na\n\nb\n\n c'] mock_urlopen.return_value = mok lst = imagenet.dense_list_from_url('some_url') self.assertListEqual(lst, ['resp1']) lst = imagenet.dense_list_from_url('some_url') self.assertListEqual(lst, ['resp1', 'resp2']) lst = imagenet.dense_list_from_url('some_url') self.assertListEqual(lst, []) lst = imagenet.dense_list_from_url('some_url') self.assertListEqual(lst, []) lst = imagenet.dense_list_from_url('some_url') self.assertListEqual(lst, ['a']) lst = imagenet.dense_list_from_url('some_url') self.assertListEqual(lst, ['a', 'b', 'c']) lst = imagenet.dense_list_from_url('some_url') self.assertListEqual(lst, ['a', 'b', 'c']) class TestXmlElemByPath(unittest.TestCase): """ Tests for xml_elem_by_path(). """ @functools.wraps(unittest.TestCase.setUp) def setUp(self): self.doc = minidom.Document() root = self.doc.createElement('root') self.doc.appendChild(root) self.lv1 = self.doc.createElement('level1-1') root.appendChild(self.lv1) self.lv11 = self.doc.createElement('level2-1') self.lv1.appendChild(self.lv11) lv111 = self.doc.createElement('level3-1') self.lv11.appendChild(lv111) root.appendChild(self.doc.createElement('level1-2')) root.appendChild(self.doc.createElement('level1-3')) lv4 = self.doc.createElement('level1-4') root.appendChild(lv4) @functools.wraps(unittest.TestCase.tearDown) def tearDown(self): del self.doc def test_simple(self): """ testing xml_elem_by_path(). """ elm = imagenet.xml_elem_by_path(self.doc, []) self.assertEqual(elm, self.doc.documentElement) self.assertRaises(IndexError, imagenet.xml_elem_by_path, self.doc, ['nonexisting']) self.assertRaises(IndexError, imagenet.xml_elem_by_path, self.doc, ['level1-1', 'nonexisting']) elm = imagenet.xml_elem_by_path(self.doc, ['level1-1']) self.assertEqual(elm, self.lv1) elm = imagenet.xml_elem_by_path(self.doc, ['level1-1', 'level2-1']) self.assertEqual(elm, self.lv11) @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestXmlFromUrl(unittest.TestCase): """ Tests for xml_from_url(). """ def test_simple(self, mock_urlopen): """ testing xml_from_url(). """ mok = Mock() mok.read.side_effect = ['<root></root>', '<root><el>test text</el></root>', '', ' a '] mock_urlopen.return_value = mok doc = imagenet.xml_from_url('some_url') self.assertEqual(doc.documentElement.tagName, 'root') doc = imagenet.xml_from_url('some_url') self.assertEqual(doc.documentElement.tagName, 'root') self.assertRaises(ExpatError, imagenet.xml_from_url, 'some_url') self.assertRaises(ExpatError, imagenet.xml_from_url, 'some_url') @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestGetSynsets(unittest.TestCase): """ Tests for get_synsets(). """ def test_simple(self, mock_urlopen): """ testing get_synsets(). """ mok = Mock() mok.read.side_effect = [TEST_SYNSETS] mock_urlopen.return_value = mok lst = imagenet.get_synsets() self.assertListEqual(lst, ['n04386664', 'n10731013', 'n03002948', 'n07609632', 'n03003091', 'n10562968', 'n07586179', 'n09929577', 'n07933530', 'n04136161', 'n03602194', 'n03703075', 'n12990597']) @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestGetWords(unittest.TestCase): """ Tests for get_words(). """ def test_simple(self, mock_urlopen): """ testing get_words(). """ mok = Mock() mok.read.side_effect = ["chickeree\nDouglas squirrel\n" "Tamiasciurus douglasi"] mock_urlopen.return_value = mok lst = imagenet.get_words('some_url/%s', 'n07609632') self.assertListEqual(lst, ['chickeree', 'Douglas squirrel', 'Tamiasciurus douglasi']) @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestGetHypos(unittest.TestCase): """ Tests for get_hypos(). """ @functools.wraps(unittest.TestCase.setUp) def setUp(self): pass @functools.wraps(unittest.TestCase.tearDown) def tearDown(self): pass def test_simple(self, mock_urlopen): """ testing get_hypos(). """ mok = Mock() mok.read.side_effect = [TEST_SYNSETS] mock_urlopen.return_value = mok lst = imagenet.get_hypos('some_url/%s-%s', 'n07609632', True) self.assertListEqual(lst, ['n04386664', 'n10731013', 'n03002948', 'n07609632', 'n03003091', 'n10562968', 'n07586179', 'n09929577', 'n07933530', 'n04136161', 'n03602194', 'n03703075', 'n12990597']) @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestGetImageCount(unittest.TestCase): """ Tests for get_image_count(). """ @functools.wraps(unittest.TestCase.setUp) def setUp(self): self.sample = RELEASE_STATUS_SAMPLE @functools.wraps(unittest.TestCase.tearDown) def tearDown(self): pass def test_simple(self, mock_urlopen): """ testing get_image_count(). """ mok = Mock() mok.read.side_effect = [self.sample] mock_urlopen.return_value = mok lst = imagenet.get_image_count('some_url', True) self.assertDictEqual(lst, {'n10801802': 269, 'n10772937': 58, 'n10028541': 201, 'n10712374': 175, 'n09878921': 46, 'n10789415': 48, 'n10370955': 502}) @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestGetImageSynsets(unittest.TestCase): """ Tests for get_image_synsets(). """ @functools.wraps(unittest.TestCase.setUp) def setUp(self): self.sample = RELEASE_STATUS_SAMPLE @functools.wraps(unittest.TestCase.tearDown) def tearDown(self): pass def test_simple(self, mock_urlopen): """ testing get_image_synsets(). """ mok = Mock() mok.read.side_effect = [self.sample] mock_urlopen.return_value = mok lst = imagenet.get_image_synsets('some_url', True) self.assertListEqual(lst, ['n10801802', 'n10772937', 'n10028541', 'n10712374', 'n09878921', 'n10789415', 'n10370955']) @patch('pyl2extra.scripts.datasets.imagenet.urllib2.urlopen') class TestGetImageUrls(unittest.TestCase): """ Tests for get_image_urls(). """ @functools.wraps(unittest.TestCase.setUp) def setUp(self): pass @functools.wraps(unittest.TestCase.tearDown) def tearDown(self): pass def test_simple(self, mock_urlopen): """ testing get_image_urls(). """ mok = Mock() mok.read.side_effect = [GET_MAPPING_SAMPLE] mock_urlopen.return_value = mok lst = imagenet.get_image_urls('some_url/%s', 'n02109150') self.assertDictEqual(lst, {'n02109150_5962': 'http://1.jpg', 'n02109150_5969': 'http://2.jpg', 'n02109150_5976': 'http://3.jpg', 'n02109150_5981': 'http://4.jpg', 'n02109150_307': 'http://www.scbeacon.com/beacon_issues/03_09_18/images/Guidedog_pjh_091803.jpg', 'n02109150_323': 'http://www.braille.be/content/lig_braille/rapport_2005/img_05.jpg'}) class TestHashFile(unittest.TestCase): """ Tests for hashfile(). """ @functools.wraps(unittest.TestCase.setUp) def setUp(self): self.tmp_dir = tempfile.mkdtemp() self.file_empty = os.path.join(self.tmp_dir, 'file_empty.txt') with open(self.file_empty, 'wt') as fhnd: fhnd.write('') self.file_a = os.path.join(self.tmp_dir, 'file_a.txt') with open(self.file_a, 'wt') as fhnd: fhnd.write('a') self.file_line = os.path.join(self.tmp_dir, 'file_line.txt') with open(self.file_line, 'wt') as fhnd: fhnd.write('abcdefghij') self.file_mlines = os.path.join(self.tmp_dir, 'file_mlines.txt') with open(self.file_mlines, 'wt') as fhnd: fhnd.write('abcdefghij\nabcdefghij\nabcdefghij\n') @functools.wraps(unittest.TestCase.tearDown) def tearDown(self): shutil.rmtree(self.tmp_dir) del self.tmp_dir def test_simple(self): """ testing hashfile(). """ self.assertEqual(imagenet.hashfile(self.file_empty), 'd41d8cd98f00b204e9800998ecf8427e') self.assertEqual(imagenet.hashfile(self.file_a), '0cc175b9c0f1b6a831c399e269772661') self.assertEqual(imagenet.hashfile(self.file_line), 'a925576942e94b2ef57a066101b48876') self.assertEqual(imagenet.hashfile(self.file_mlines), 'f90932f561733ea4558ada7ac7d27527') if __name__ == '__main__': unittest.main()

bsd-3-clause

pkruskal/scikit-learn

sklearn/linear_model/stochastic_gradient.py

129

50966

# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author) # Mathieu Blondel (partial_fit support) # # License: BSD 3 clause """Classification and regression using Stochastic Gradient Descent (SGD).""" import numpy as np import scipy.sparse as sp from abc import ABCMeta, abstractmethod from ..externals.joblib import Parallel, delayed from .base import LinearClassifierMixin, SparseCoefMixin from ..base import BaseEstimator, RegressorMixin from ..feature_selection.from_model import _LearntSelectorMixin from ..utils import (check_array, check_random_state, check_X_y, deprecated) from ..utils.extmath import safe_sparse_dot from ..utils.multiclass import _check_partial_fit_first_call from ..utils.validation import check_is_fitted from ..externals import six from .sgd_fast import plain_sgd, average_sgd from ..utils.fixes import astype from ..utils.seq_dataset import ArrayDataset, CSRDataset from ..utils import compute_class_weight from .sgd_fast import Hinge from .sgd_fast import SquaredHinge from .sgd_fast import Log from .sgd_fast import ModifiedHuber from .sgd_fast import SquaredLoss from .sgd_fast import Huber from .sgd_fast import EpsilonInsensitive from .sgd_fast import SquaredEpsilonInsensitive LEARNING_RATE_TYPES = {"constant": 1, "optimal": 2, "invscaling": 3, "pa1": 4, "pa2": 5} PENALTY_TYPES = {"none": 0, "l2": 2, "l1": 1, "elasticnet": 3} SPARSE_INTERCEPT_DECAY = 0.01 """For sparse data intercept updates are scaled by this decay factor to avoid intercept oscillation.""" DEFAULT_EPSILON = 0.1 """Default value of ``epsilon`` parameter. """ class BaseSGD(six.with_metaclass(ABCMeta, BaseEstimator, SparseCoefMixin)): """Base class for SGD classification and regression.""" def __init__(self, loss, penalty='l2', alpha=0.0001, C=1.0, l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate="optimal", eta0=0.0, power_t=0.5, warm_start=False, average=False): self.loss = loss self.penalty = penalty self.learning_rate = learning_rate self.epsilon = epsilon self.alpha = alpha self.C = C self.l1_ratio = l1_ratio self.fit_intercept = fit_intercept self.n_iter = n_iter self.shuffle = shuffle self.random_state = random_state self.verbose = verbose self.eta0 = eta0 self.power_t = power_t self.warm_start = warm_start self.average = average self._validate_params() self.coef_ = None if self.average > 0: self.standard_coef_ = None self.average_coef_ = None # iteration count for learning rate schedule # must not be int (e.g. if ``learning_rate=='optimal'``) self.t_ = None def set_params(self, *args, **kwargs): super(BaseSGD, self).set_params(*args, **kwargs) self._validate_params() return self @abstractmethod def fit(self, X, y): """Fit model.""" def _validate_params(self): """Validate input params. """ if not isinstance(self.shuffle, bool): raise ValueError("shuffle must be either True or False") if self.n_iter <= 0: raise ValueError("n_iter must be > zero") if not (0.0 <= self.l1_ratio <= 1.0): raise ValueError("l1_ratio must be in [0, 1]") if self.alpha < 0.0: raise ValueError("alpha must be >= 0") if self.learning_rate in ("constant", "invscaling"): if self.eta0 <= 0.0: raise ValueError("eta0 must be > 0") # raises ValueError if not registered self._get_penalty_type(self.penalty) self._get_learning_rate_type(self.learning_rate) if self.loss not in self.loss_functions: raise ValueError("The loss %s is not supported. " % self.loss) def _get_loss_function(self, loss): """Get concrete ``LossFunction`` object for str ``loss``. """ try: loss_ = self.loss_functions[loss] loss_class, args = loss_[0], loss_[1:] if loss in ('huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'): args = (self.epsilon, ) return loss_class(*args) except KeyError: raise ValueError("The loss %s is not supported. " % loss) def _get_learning_rate_type(self, learning_rate): try: return LEARNING_RATE_TYPES[learning_rate] except KeyError: raise ValueError("learning rate %s " "is not supported. " % learning_rate) def _get_penalty_type(self, penalty): penalty = str(penalty).lower() try: return PENALTY_TYPES[penalty] except KeyError: raise ValueError("Penalty %s is not supported. " % penalty) def _validate_sample_weight(self, sample_weight, n_samples): """Set the sample weight array.""" if sample_weight is None: # uniform sample weights sample_weight = np.ones(n_samples, dtype=np.float64, order='C') else: # user-provided array sample_weight = np.asarray(sample_weight, dtype=np.float64, order="C") if sample_weight.shape[0] != n_samples: raise ValueError("Shapes of X and sample_weight do not match.") return sample_weight def _allocate_parameter_mem(self, n_classes, n_features, coef_init=None, intercept_init=None): """Allocate mem for parameters; initialize if provided.""" if n_classes > 2: # allocate coef_ for multi-class if coef_init is not None: coef_init = np.asarray(coef_init, order="C") if coef_init.shape != (n_classes, n_features): raise ValueError("Provided ``coef_`` does not match dataset. ") self.coef_ = coef_init else: self.coef_ = np.zeros((n_classes, n_features), dtype=np.float64, order="C") # allocate intercept_ for multi-class if intercept_init is not None: intercept_init = np.asarray(intercept_init, order="C") if intercept_init.shape != (n_classes, ): raise ValueError("Provided intercept_init " "does not match dataset.") self.intercept_ = intercept_init else: self.intercept_ = np.zeros(n_classes, dtype=np.float64, order="C") else: # allocate coef_ for binary problem if coef_init is not None: coef_init = np.asarray(coef_init, dtype=np.float64, order="C") coef_init = coef_init.ravel() if coef_init.shape != (n_features,): raise ValueError("Provided coef_init does not " "match dataset.") self.coef_ = coef_init else: self.coef_ = np.zeros(n_features, dtype=np.float64, order="C") # allocate intercept_ for binary problem if intercept_init is not None: intercept_init = np.asarray(intercept_init, dtype=np.float64) if intercept_init.shape != (1,) and intercept_init.shape != (): raise ValueError("Provided intercept_init " "does not match dataset.") self.intercept_ = intercept_init.reshape(1,) else: self.intercept_ = np.zeros(1, dtype=np.float64, order="C") # initialize average parameters if self.average > 0: self.standard_coef_ = self.coef_ self.standard_intercept_ = self.intercept_ self.average_coef_ = np.zeros(self.coef_.shape, dtype=np.float64, order="C") self.average_intercept_ = np.zeros(self.standard_intercept_.shape, dtype=np.float64, order="C") def _make_dataset(X, y_i, sample_weight): """Create ``Dataset`` abstraction for sparse and dense inputs. This also returns the ``intercept_decay`` which is different for sparse datasets. """ if sp.issparse(X): dataset = CSRDataset(X.data, X.indptr, X.indices, y_i, sample_weight) intercept_decay = SPARSE_INTERCEPT_DECAY else: dataset = ArrayDataset(X, y_i, sample_weight) intercept_decay = 1.0 return dataset, intercept_decay def _prepare_fit_binary(est, y, i): """Initialization for fit_binary. Returns y, coef, intercept. """ y_i = np.ones(y.shape, dtype=np.float64, order="C") y_i[y != est.classes_[i]] = -1.0 average_intercept = 0 average_coef = None if len(est.classes_) == 2: if not est.average: coef = est.coef_.ravel() intercept = est.intercept_[0] else: coef = est.standard_coef_.ravel() intercept = est.standard_intercept_[0] average_coef = est.average_coef_.ravel() average_intercept = est.average_intercept_[0] else: if not est.average: coef = est.coef_[i] intercept = est.intercept_[i] else: coef = est.standard_coef_[i] intercept = est.standard_intercept_[i] average_coef = est.average_coef_[i] average_intercept = est.average_intercept_[i] return y_i, coef, intercept, average_coef, average_intercept def fit_binary(est, i, X, y, alpha, C, learning_rate, n_iter, pos_weight, neg_weight, sample_weight): """Fit a single binary classifier. The i'th class is considered the "positive" class. """ # if average is not true, average_coef, and average_intercept will be # unused y_i, coef, intercept, average_coef, average_intercept = \ _prepare_fit_binary(est, y, i) assert y_i.shape[0] == y.shape[0] == sample_weight.shape[0] dataset, intercept_decay = _make_dataset(X, y_i, sample_weight) penalty_type = est._get_penalty_type(est.penalty) learning_rate_type = est._get_learning_rate_type(learning_rate) # XXX should have random_state_! random_state = check_random_state(est.random_state) # numpy mtrand expects a C long which is a signed 32 bit integer under # Windows seed = random_state.randint(0, np.iinfo(np.int32).max) if not est.average: return plain_sgd(coef, intercept, est.loss_function, penalty_type, alpha, C, est.l1_ratio, dataset, n_iter, int(est.fit_intercept), int(est.verbose), int(est.shuffle), seed, pos_weight, neg_weight, learning_rate_type, est.eta0, est.power_t, est.t_, intercept_decay) else: standard_coef, standard_intercept, average_coef, \ average_intercept = average_sgd(coef, intercept, average_coef, average_intercept, est.loss_function, penalty_type, alpha, C, est.l1_ratio, dataset, n_iter, int(est.fit_intercept), int(est.verbose), int(est.shuffle), seed, pos_weight, neg_weight, learning_rate_type, est.eta0, est.power_t, est.t_, intercept_decay, est.average) if len(est.classes_) == 2: est.average_intercept_[0] = average_intercept else: est.average_intercept_[i] = average_intercept return standard_coef, standard_intercept class BaseSGDClassifier(six.with_metaclass(ABCMeta, BaseSGD, LinearClassifierMixin)): loss_functions = { "hinge": (Hinge, 1.0), "squared_hinge": (SquaredHinge, 1.0), "perceptron": (Hinge, 0.0), "log": (Log, ), "modified_huber": (ModifiedHuber, ), "squared_loss": (SquaredLoss, ), "huber": (Huber, DEFAULT_EPSILON), "epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON), "squared_epsilon_insensitive": (SquaredEpsilonInsensitive, DEFAULT_EPSILON), } @abstractmethod def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None, learning_rate="optimal", eta0=0.0, power_t=0.5, class_weight=None, warm_start=False, average=False): super(BaseSGDClassifier, self).__init__(loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio, fit_intercept=fit_intercept, n_iter=n_iter, shuffle=shuffle, verbose=verbose, epsilon=epsilon, random_state=random_state, learning_rate=learning_rate, eta0=eta0, power_t=power_t, warm_start=warm_start, average=average) self.class_weight = class_weight self.classes_ = None self.n_jobs = int(n_jobs) def _partial_fit(self, X, y, alpha, C, loss, learning_rate, n_iter, classes, sample_weight, coef_init, intercept_init): X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C") n_samples, n_features = X.shape self._validate_params() _check_partial_fit_first_call(self, classes) n_classes = self.classes_.shape[0] # Allocate datastructures from input arguments self._expanded_class_weight = compute_class_weight(self.class_weight, self.classes_, y) sample_weight = self._validate_sample_weight(sample_weight, n_samples) if self.coef_ is None or coef_init is not None: self._allocate_parameter_mem(n_classes, n_features, coef_init, intercept_init) elif n_features != self.coef_.shape[-1]: raise ValueError("Number of features %d does not match previous data %d." % (n_features, self.coef_.shape[-1])) self.loss_function = self._get_loss_function(loss) if self.t_ is None: self.t_ = 1.0 # delegate to concrete training procedure if n_classes > 2: self._fit_multiclass(X, y, alpha=alpha, C=C, learning_rate=learning_rate, sample_weight=sample_weight, n_iter=n_iter) elif n_classes == 2: self._fit_binary(X, y, alpha=alpha, C=C, learning_rate=learning_rate, sample_weight=sample_weight, n_iter=n_iter) else: raise ValueError("The number of class labels must be " "greater than one.") return self def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None, intercept_init=None, sample_weight=None): if hasattr(self, "classes_"): self.classes_ = None X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C") n_samples, n_features = X.shape # labels can be encoded as float, int, or string literals # np.unique sorts in asc order; largest class id is positive class classes = np.unique(y) if self.warm_start and self.coef_ is not None: if coef_init is None: coef_init = self.coef_ if intercept_init is None: intercept_init = self.intercept_ else: self.coef_ = None self.intercept_ = None if self.average > 0: self.standard_coef_ = self.coef_ self.standard_intercept_ = self.intercept_ self.average_coef_ = None self.average_intercept_ = None # Clear iteration count for multiple call to fit. self.t_ = None self._partial_fit(X, y, alpha, C, loss, learning_rate, self.n_iter, classes, sample_weight, coef_init, intercept_init) return self def _fit_binary(self, X, y, alpha, C, sample_weight, learning_rate, n_iter): """Fit a binary classifier on X and y. """ coef, intercept = fit_binary(self, 1, X, y, alpha, C, learning_rate, n_iter, self._expanded_class_weight[1], self._expanded_class_weight[0], sample_weight) self.t_ += n_iter * X.shape[0] # need to be 2d if self.average > 0: if self.average <= self.t_ - 1: self.coef_ = self.average_coef_.reshape(1, -1) self.intercept_ = self.average_intercept_ else: self.coef_ = self.standard_coef_.reshape(1, -1) self.standard_intercept_ = np.atleast_1d(intercept) self.intercept_ = self.standard_intercept_ else: self.coef_ = coef.reshape(1, -1) # intercept is a float, need to convert it to an array of length 1 self.intercept_ = np.atleast_1d(intercept) def _fit_multiclass(self, X, y, alpha, C, learning_rate, sample_weight, n_iter): """Fit a multi-class classifier by combining binary classifiers Each binary classifier predicts one class versus all others. This strategy is called OVA: One Versus All. """ # Use joblib to fit OvA in parallel. result = Parallel(n_jobs=self.n_jobs, backend="threading", verbose=self.verbose)( delayed(fit_binary)(self, i, X, y, alpha, C, learning_rate, n_iter, self._expanded_class_weight[i], 1., sample_weight) for i in range(len(self.classes_))) for i, (_, intercept) in enumerate(result): self.intercept_[i] = intercept self.t_ += n_iter * X.shape[0] if self.average > 0: if self.average <= self.t_ - 1.0: self.coef_ = self.average_coef_ self.intercept_ = self.average_intercept_ else: self.coef_ = self.standard_coef_ self.standard_intercept_ = np.atleast_1d(intercept) self.intercept_ = self.standard_intercept_ def partial_fit(self, X, y, classes=None, sample_weight=None): """Fit linear model with Stochastic Gradient Descent. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Subset of the training data y : numpy array, shape (n_samples,) Subset of the target values classes : array, shape (n_classes,) Classes across all calls to partial_fit. Can be obtained by via `np.unique(y_all)`, where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn't need to contain all labels in `classes`. sample_weight : array-like, shape (n_samples,), optional Weights applied to individual samples. If not provided, uniform weights are assumed. Returns ------- self : returns an instance of self. """ if self.class_weight in ['balanced', 'auto']: raise ValueError("class_weight '{0}' is not supported for " "partial_fit. In order to use 'balanced' weights, " "use compute_class_weight('{0}', classes, y). " "In place of y you can us a large enough sample " "of the full training set target to properly " "estimate the class frequency distributions. " "Pass the resulting weights as the class_weight " "parameter.".format(self.class_weight)) return self._partial_fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss, learning_rate=self.learning_rate, n_iter=1, classes=classes, sample_weight=sample_weight, coef_init=None, intercept_init=None) def fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None): """Fit linear model with Stochastic Gradient Descent. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data y : numpy array, shape (n_samples,) Target values coef_init : array, shape (n_classes, n_features) The initial coefficients to warm-start the optimization. intercept_init : array, shape (n_classes,) The initial intercept to warm-start the optimization. sample_weight : array-like, shape (n_samples,), optional Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the contructor) if class_weight is specified Returns ------- self : returns an instance of self. """ return self._fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss, learning_rate=self.learning_rate, coef_init=coef_init, intercept_init=intercept_init, sample_weight=sample_weight) class SGDClassifier(BaseSGDClassifier, _LearntSelectorMixin): """Linear classifiers (SVM, logistic regression, a.o.) with SGD training. This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning, see the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance. This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM). The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection. Read more in the :ref:`User Guide <sgd>`. Parameters ---------- loss : str, 'hinge', 'log', 'modified_huber', 'squared_hinge',\ 'perceptron', or a regression loss: 'squared_loss', 'huber',\ 'epsilon_insensitive', or 'squared_epsilon_insensitive' The loss function to be used. Defaults to 'hinge', which gives a linear SVM. The 'log' loss gives logistic regression, a probabilistic classifier. 'modified_huber' is another smooth loss that brings tolerance to outliers as well as probability estimates. 'squared_hinge' is like hinge but is quadratically penalized. 'perceptron' is the linear loss used by the perceptron algorithm. The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a description. penalty : str, 'none', 'l2', 'l1', or 'elasticnet' The penalty (aka regularization term) to be used. Defaults to 'l2' which is the standard regularizer for linear SVM models. 'l1' and 'elasticnet' might bring sparsity to the model (feature selection) not achievable with 'l2'. alpha : float Constant that multiplies the regularization term. Defaults to 0.0001 l1_ratio : float The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Defaults to 0.15. fit_intercept : bool Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True. n_iter : int, optional The number of passes over the training data (aka epochs). The number of iterations is set to 1 if using partial_fit. Defaults to 5. shuffle : bool, optional Whether or not the training data should be shuffled after each epoch. Defaults to True. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. verbose : integer, optional The verbosity level epsilon : float Epsilon in the epsilon-insensitive loss functions; only if `loss` is 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'. For 'huber', determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold. n_jobs : integer, optional The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. -1 means 'all CPUs'. Defaults to 1. learning_rate : string, optional The learning rate schedule: constant: eta = eta0 optimal: eta = 1.0 / (t + t0) [default] invscaling: eta = eta0 / pow(t, power_t) where t0 is chosen by a heuristic proposed by Leon Bottou. eta0 : double The initial learning rate for the 'constant' or 'invscaling' schedules. The default value is 0.0 as eta0 is not used by the default schedule 'optimal'. power_t : double The exponent for inverse scaling learning rate [default 0.5]. class_weight : dict, {class_label: weight} or "balanced" or None, optional Preset for the class_weight fit parameter. Weights associated with classes. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` warm_start : bool, optional When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. average : bool or int, optional When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples. Attributes ---------- coef_ : array, shape (1, n_features) if n_classes == 2 else (n_classes,\ n_features) Weights assigned to the features. intercept_ : array, shape (1,) if n_classes == 2 else (n_classes,) Constants in decision function. Examples -------- >>> import numpy as np >>> from sklearn import linear_model >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) >>> Y = np.array([1, 1, 2, 2]) >>> clf = linear_model.SGDClassifier() >>> clf.fit(X, Y) ... #doctest: +NORMALIZE_WHITESPACE SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15, learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5, random_state=None, shuffle=True, verbose=0, warm_start=False) >>> print(clf.predict([[-0.8, -1]])) [1] See also -------- LinearSVC, LogisticRegression, Perceptron """ def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None, learning_rate="optimal", eta0=0.0, power_t=0.5, class_weight=None, warm_start=False, average=False): super(SGDClassifier, self).__init__( loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio, fit_intercept=fit_intercept, n_iter=n_iter, shuffle=shuffle, verbose=verbose, epsilon=epsilon, n_jobs=n_jobs, random_state=random_state, learning_rate=learning_rate, eta0=eta0, power_t=power_t, class_weight=class_weight, warm_start=warm_start, average=average) def _check_proba(self): check_is_fitted(self, "t_") if self.loss not in ("log", "modified_huber"): raise AttributeError("probability estimates are not available for" " loss=%r" % self.loss) @property def predict_proba(self): """Probability estimates. This method is only available for log loss and modified Huber loss. Multiclass probability estimates are derived from binary (one-vs.-rest) estimates by simple normalization, as recommended by Zadrozny and Elkan. Binary probability estimates for loss="modified_huber" are given by (clip(decision_function(X), -1, 1) + 1) / 2. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Returns ------- array, shape (n_samples, n_classes) Returns the probability of the sample for each class in the model, where classes are ordered as they are in `self.classes_`. References ---------- Zadrozny and Elkan, "Transforming classifier scores into multiclass probability estimates", SIGKDD'02, http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf The justification for the formula in the loss="modified_huber" case is in the appendix B in: http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf """ self._check_proba() return self._predict_proba def _predict_proba(self, X): if self.loss == "log": return self._predict_proba_lr(X) elif self.loss == "modified_huber": binary = (len(self.classes_) == 2) scores = self.decision_function(X) if binary: prob2 = np.ones((scores.shape[0], 2)) prob = prob2[:, 1] else: prob = scores np.clip(scores, -1, 1, prob) prob += 1. prob /= 2. if binary: prob2[:, 0] -= prob prob = prob2 else: # the above might assign zero to all classes, which doesn't # normalize neatly; work around this to produce uniform # probabilities prob_sum = prob.sum(axis=1) all_zero = (prob_sum == 0) if np.any(all_zero): prob[all_zero, :] = 1 prob_sum[all_zero] = len(self.classes_) # normalize prob /= prob_sum.reshape((prob.shape[0], -1)) return prob else: raise NotImplementedError("predict_(log_)proba only supported when" " loss='log' or loss='modified_huber' " "(%r given)" % self.loss) @property def predict_log_proba(self): """Log of probability estimates. This method is only available for log loss and modified Huber loss. When loss="modified_huber", probability estimates may be hard zeros and ones, so taking the logarithm is not possible. See ``predict_proba`` for details. Parameters ---------- X : array-like, shape (n_samples, n_features) Returns ------- T : array-like, shape (n_samples, n_classes) Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in `self.classes_`. """ self._check_proba() return self._predict_log_proba def _predict_log_proba(self, X): return np.log(self.predict_proba(X)) class BaseSGDRegressor(BaseSGD, RegressorMixin): loss_functions = { "squared_loss": (SquaredLoss, ), "huber": (Huber, DEFAULT_EPSILON), "epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON), "squared_epsilon_insensitive": (SquaredEpsilonInsensitive, DEFAULT_EPSILON), } @abstractmethod def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, random_state=None, learning_rate="invscaling", eta0=0.01, power_t=0.25, warm_start=False, average=False): super(BaseSGDRegressor, self).__init__(loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio, fit_intercept=fit_intercept, n_iter=n_iter, shuffle=shuffle, verbose=verbose, epsilon=epsilon, random_state=random_state, learning_rate=learning_rate, eta0=eta0, power_t=power_t, warm_start=warm_start, average=average) def _partial_fit(self, X, y, alpha, C, loss, learning_rate, n_iter, sample_weight, coef_init, intercept_init): X, y = check_X_y(X, y, "csr", copy=False, order='C', dtype=np.float64) y = astype(y, np.float64, copy=False) n_samples, n_features = X.shape self._validate_params() # Allocate datastructures from input arguments sample_weight = self._validate_sample_weight(sample_weight, n_samples) if self.coef_ is None: self._allocate_parameter_mem(1, n_features, coef_init, intercept_init) elif n_features != self.coef_.shape[-1]: raise ValueError("Number of features %d does not match previous data %d." % (n_features, self.coef_.shape[-1])) if self.average > 0 and self.average_coef_ is None: self.average_coef_ = np.zeros(n_features, dtype=np.float64, order="C") self.average_intercept_ = np.zeros(1, dtype=np.float64, order="C") self._fit_regressor(X, y, alpha, C, loss, learning_rate, sample_weight, n_iter) return self def partial_fit(self, X, y, sample_weight=None): """Fit linear model with Stochastic Gradient Descent. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Subset of training data y : numpy array of shape (n_samples,) Subset of target values sample_weight : array-like, shape (n_samples,), optional Weights applied to individual samples. If not provided, uniform weights are assumed. Returns ------- self : returns an instance of self. """ return self._partial_fit(X, y, self.alpha, C=1.0, loss=self.loss, learning_rate=self.learning_rate, n_iter=1, sample_weight=sample_weight, coef_init=None, intercept_init=None) def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None, intercept_init=None, sample_weight=None): if self.warm_start and self.coef_ is not None: if coef_init is None: coef_init = self.coef_ if intercept_init is None: intercept_init = self.intercept_ else: self.coef_ = None self.intercept_ = None if self.average > 0: self.standard_intercept_ = self.intercept_ self.standard_coef_ = self.coef_ self.average_coef_ = None self.average_intercept_ = None # Clear iteration count for multiple call to fit. self.t_ = None return self._partial_fit(X, y, alpha, C, loss, learning_rate, self.n_iter, sample_weight, coef_init, intercept_init) def fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None): """Fit linear model with Stochastic Gradient Descent. Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training data y : numpy array, shape (n_samples,) Target values coef_init : array, shape (n_features,) The initial coefficients to warm-start the optimization. intercept_init : array, shape (1,) The initial intercept to warm-start the optimization. sample_weight : array-like, shape (n_samples,), optional Weights applied to individual samples (1. for unweighted). Returns ------- self : returns an instance of self. """ return self._fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss, learning_rate=self.learning_rate, coef_init=coef_init, intercept_init=intercept_init, sample_weight=sample_weight) @deprecated(" and will be removed in 0.19.") def decision_function(self, X): """Predict using the linear model Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Returns ------- array, shape (n_samples,) Predicted target values per element in X. """ return self._decision_function(X) def _decision_function(self, X): """Predict using the linear model Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Returns ------- array, shape (n_samples,) Predicted target values per element in X. """ check_is_fitted(self, ["t_", "coef_", "intercept_"], all_or_any=all) X = check_array(X, accept_sparse='csr') scores = safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_ return scores.ravel() def predict(self, X): """Predict using the linear model Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Returns ------- array, shape (n_samples,) Predicted target values per element in X. """ return self._decision_function(X) def _fit_regressor(self, X, y, alpha, C, loss, learning_rate, sample_weight, n_iter): dataset, intercept_decay = _make_dataset(X, y, sample_weight) loss_function = self._get_loss_function(loss) penalty_type = self._get_penalty_type(self.penalty) learning_rate_type = self._get_learning_rate_type(learning_rate) if self.t_ is None: self.t_ = 1.0 random_state = check_random_state(self.random_state) # numpy mtrand expects a C long which is a signed 32 bit integer under # Windows seed = random_state.randint(0, np.iinfo(np.int32).max) if self.average > 0: self.standard_coef_, self.standard_intercept_, \ self.average_coef_, self.average_intercept_ =\ average_sgd(self.standard_coef_, self.standard_intercept_[0], self.average_coef_, self.average_intercept_[0], loss_function, penalty_type, alpha, C, self.l1_ratio, dataset, n_iter, int(self.fit_intercept), int(self.verbose), int(self.shuffle), seed, 1.0, 1.0, learning_rate_type, self.eta0, self.power_t, self.t_, intercept_decay, self.average) self.average_intercept_ = np.atleast_1d(self.average_intercept_) self.standard_intercept_ = np.atleast_1d(self.standard_intercept_) self.t_ += n_iter * X.shape[0] if self.average <= self.t_ - 1.0: self.coef_ = self.average_coef_ self.intercept_ = self.average_intercept_ else: self.coef_ = self.standard_coef_ self.intercept_ = self.standard_intercept_ else: self.coef_, self.intercept_ = \ plain_sgd(self.coef_, self.intercept_[0], loss_function, penalty_type, alpha, C, self.l1_ratio, dataset, n_iter, int(self.fit_intercept), int(self.verbose), int(self.shuffle), seed, 1.0, 1.0, learning_rate_type, self.eta0, self.power_t, self.t_, intercept_decay) self.t_ += n_iter * X.shape[0] self.intercept_ = np.atleast_1d(self.intercept_) class SGDRegressor(BaseSGDRegressor, _LearntSelectorMixin): """Linear model fitted by minimizing a regularized empirical loss with SGD SGD stands for Stochastic Gradient Descent: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value because of the regularizer, the update is truncated to 0.0 to allow for learning sparse models and achieve online feature selection. This implementation works with data represented as dense numpy arrays of floating point values for the features. Read more in the :ref:`User Guide <sgd>`. Parameters ---------- loss : str, 'squared_loss', 'huber', 'epsilon_insensitive', \ or 'squared_epsilon_insensitive' The loss function to be used. Defaults to 'squared_loss' which refers to the ordinary least squares fit. 'huber' modifies 'squared_loss' to focus less on getting outliers correct by switching from squared to linear loss past a distance of epsilon. 'epsilon_insensitive' ignores errors less than epsilon and is linear past that; this is the loss function used in SVR. 'squared_epsilon_insensitive' is the same but becomes squared loss past a tolerance of epsilon. penalty : str, 'none', 'l2', 'l1', or 'elasticnet' The penalty (aka regularization term) to be used. Defaults to 'l2' which is the standard regularizer for linear SVM models. 'l1' and 'elasticnet' might bring sparsity to the model (feature selection) not achievable with 'l2'. alpha : float Constant that multiplies the regularization term. Defaults to 0.0001 l1_ratio : float The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Defaults to 0.15. fit_intercept : bool Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True. n_iter : int, optional The number of passes over the training data (aka epochs). The number of iterations is set to 1 if using partial_fit. Defaults to 5. shuffle : bool, optional Whether or not the training data should be shuffled after each epoch. Defaults to True. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. verbose : integer, optional The verbosity level. epsilon : float Epsilon in the epsilon-insensitive loss functions; only if `loss` is 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'. For 'huber', determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold. learning_rate : string, optional The learning rate: constant: eta = eta0 optimal: eta = 1.0/(alpha * t) invscaling: eta = eta0 / pow(t, power_t) [default] eta0 : double, optional The initial learning rate [default 0.01]. power_t : double, optional The exponent for inverse scaling learning rate [default 0.25]. warm_start : bool, optional When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. average : bool or int, optional When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So ``average=10 will`` begin averaging after seeing 10 samples. Attributes ---------- coef_ : array, shape (n_features,) Weights assigned to the features. intercept_ : array, shape (1,) The intercept term. average_coef_ : array, shape (n_features,) Averaged weights assigned to the features. average_intercept_ : array, shape (1,) The averaged intercept term. Examples -------- >>> import numpy as np >>> from sklearn import linear_model >>> n_samples, n_features = 10, 5 >>> np.random.seed(0) >>> y = np.random.randn(n_samples) >>> X = np.random.randn(n_samples, n_features) >>> clf = linear_model.SGDRegressor() >>> clf.fit(X, y) ... #doctest: +NORMALIZE_WHITESPACE SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling', loss='squared_loss', n_iter=5, penalty='l2', power_t=0.25, random_state=None, shuffle=True, verbose=0, warm_start=False) See also -------- Ridge, ElasticNet, Lasso, SVR """ def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, random_state=None, learning_rate="invscaling", eta0=0.01, power_t=0.25, warm_start=False, average=False): super(SGDRegressor, self).__init__(loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio, fit_intercept=fit_intercept, n_iter=n_iter, shuffle=shuffle, verbose=verbose, epsilon=epsilon, random_state=random_state, learning_rate=learning_rate, eta0=eta0, power_t=power_t, warm_start=warm_start, average=average)

bsd-3-clause

deniszgonjanin/ckanext-bcgov

ckanext/bcgov/controllers/user.py

2

8360

# Copyright 2015, Province of British Columbia # License: https://github.com/bcgov/ckanext-bcgov/blob/master/license import logging from ckan.controllers.user import UserController from ckan.common import OrderedDict,_,g, request import ckan.lib.base as base import ckan.model as model import ckan.logic as logic import ckan.lib.helpers as h from urllib import urlencode from ckan.logic import get_action import ckan.lib.maintain as maintain import ckan.plugins.toolkit as toolkit from ckanext.bcgov.util.util import (get_user_orgs, get_user_toporgs) c = toolkit.c render = base.render abort = base.abort redirect = base.redirect check_access = logic.check_access NotAuthorized = logic.NotAuthorized render = base.render log = logging.getLogger('ckanext.edc_schema') def _encode_params(params): return [(k, v.encode('utf-8') if isinstance(v, basestring) else str(v)) for k, v in params] class EDCUserController(UserController): def dashboard_unpublished(self): user_id = c.userobj.id fq = ' +edc_state:("DRAFT" OR "PENDING PUBLISH" OR "REJECTED")' #Get the list of organizations that this user is the admin if not c.userobj.sysadmin : user_orgs = ['"' + org.id + '"' for org in get_user_orgs(user_id, 'admin')] user_orgs += ['"' + org.id + '"' for org in get_user_orgs(user_id, 'editor')] if len(user_orgs) > 0 : fq += ' +owner_org:(' + ' OR '.join(user_orgs) + ')' self._user_datasets('dashboard_unpublished', c.userobj.id, fq) return render('user/dashboard_unpublished.html') def dashboard_datasets(self): fq = ' +author:("%s")' % (c.userobj.id) self._user_datasets('dashboard_datasets', c.userobj.id, fq) return render('user/dashboard_datasets.html') def read(self, id=None): if c.userobj and c.userobj.sysadmin == True: fq = '' else: fq = ' +(edc_state:("PUBLISHED" OR "PENDING ARCHIVE")' if c.userobj: user_id = c.userobj.id user_orgs = ['"' + org.id + '"' for org in get_user_orgs(user_id, 'admin')] user_orgs += ['"' + org.id + '"' for org in get_user_orgs(user_id, 'editor')] if len(user_orgs) > 0: fq += ' OR owner_org:(' + ' OR '.join(user_orgs) + ')' fq += ')' self._user_datasets('read',id, fq) return render('user/read.html') def _user_datasets(self, action, id=None, filter_query=None): from ckan.lib.search import SearchError context = {'model': model, 'session': model.Session, 'user': c.user or c.author, 'auth_user_obj': c.userobj, 'for_view': True} user_dict = {'id': id, 'user_obj': c.userobj} # unicode format (decoded from utf8) q = c.q = request.params.get('q', u'') # q += ' author:"%s"' %c.userobj.id context['return_query'] = True try: page = int(request.params.get('page', 1)) except ValueError, e: abort(400, ('"page" parameter must be an integer')) limit = g.datasets_per_page # most search operations should reset the page counter: params_nopage = [(k, v) for k, v in request.params.items() if k != 'page'] sort_by = request.params.get('sort', None) def search_url(params): if action == 'read': url = h.url_for(controller='user', action=action, id=id) else: url = h.url_for(controller='user', action=action) params = [(k, v.encode('utf-8') if isinstance(v, basestring) else str(v)) for k, v in params] return url + u'?' + urlencode(params) def drill_down_url(alternative_url=None, **by): return h.add_url_param(alternative_url=alternative_url, controller='user', action=action, extras=dict(id=c.userobj.id), new_params=by) c.drill_down_url = drill_down_url def remove_field(key, value=None, replace=None): return h.remove_url_param(key, value=value, replace=replace, controller='user', action=action, extras=dict(id=c.userobj.id)) c.remove_field = remove_field def pager_url(q=None, page=None): params = list(params_nopage) params.append(('page', page)) return search_url(params) try: c.fields = [] search_extras = {} for (param, value) in request.params.items(): if param not in ['q', 'page', 'sort'] \ and len(value) and not param.startswith('_'): if not param.startswith('ext_'): c.fields.append((param, value)) q += ' %s:"%s"' % (param, value) else: search_extras[param] = value facets = OrderedDict() default_facet_titles = { 'organization': _('Organizations'), 'edc_state': _('States'), 'tags': _('Tags'), 'res_format': _('Formats'), } for facet in default_facet_titles: facets[facet] = default_facet_titles[facet] c.facet_titles = facets fq = filter_query or '' data_dict = { 'q': q, 'fq': fq.strip(), 'facet.field': facets.keys(), 'rows': limit, 'start': (page - 1) * limit, 'sort': sort_by, 'extras': search_extras } query = get_action('package_search')(context, data_dict) c.page = h.Page( collection=query['results'], page=page, url=pager_url, item_count=query['count'], items_per_page=limit ) user_dict['package_count'] = query['count'] c.facets = query['facets'] maintain.deprecate_context_item('facets', 'Use `c.search_facets` instead.') c.search_facets = query['search_facets'] c.search_facets_limits = {} for facet in c.facets.keys(): limit = int(request.params.get('_%s_limit' % facet, g.facets_default_number)) c.search_facets_limits[facet] = limit c.page.items = query['results'] c.sort_by_selected = sort_by except SearchError, se: log.error('User search error: %r', se.args) c.query_error = True c.facets = {} c.page = h.Page(collection=[]) self._setup_template_variables(context, user_dict) def dashboard_organizations(self): context = {'model': model, 'session': model.Session, 'for_view': True, 'user': c.user or c.author, 'auth_user_obj': c.userobj} data_dict = {'user_obj': c.userobj} self._setup_template_variables(context, data_dict) (user_orgs, usr_suborgs) = get_user_toporgs(c.userobj.id) facets = OrderedDict() #Add the organization facet to get the number of records for each organization facets['organization'] = _('Organizations') data_dict = { 'facet.field': facets.keys(), } query = get_action('package_search')(context, data_dict) c.org_pkg_count = query['facets'].get('organization') c.top_orgs_items = user_orgs c.suborgs_items = usr_suborgs return render('user/dashboard_organizations.html')

agpl-3.0

benoitsteiner/tensorflow-xsmm

tensorflow/contrib/learn/python/learn/datasets/base_test.py

132

3072

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.learn.python.learn.datasets import base from tensorflow.python.platform import test mock = test.mock _TIMEOUT = IOError(110, "timeout") class BaseTest(test.TestCase): """Test load csv functions.""" def testUrlretrieveRetriesOnIOError(self): with mock.patch.object(base, "time") as mock_time: with mock.patch.object(base, "urllib") as mock_urllib: mock_urllib.request.urlretrieve.side_effect = [ _TIMEOUT, _TIMEOUT, _TIMEOUT, _TIMEOUT, _TIMEOUT, None ] base.urlretrieve_with_retry("http://dummy.com", "/tmp/dummy") # Assert full backoff was tried actual_list = [arg[0][0] for arg in mock_time.sleep.call_args_list] expected_list = [1, 2, 4, 8, 16] for actual, expected in zip(actual_list, expected_list): self.assertLessEqual(abs(actual - expected), 0.25 * expected) self.assertEquals(len(actual_list), len(expected_list)) def testUrlretrieveRaisesAfterRetriesAreExhausted(self): with mock.patch.object(base, "time") as mock_time: with mock.patch.object(base, "urllib") as mock_urllib: mock_urllib.request.urlretrieve.side_effect = [ _TIMEOUT, _TIMEOUT, _TIMEOUT, _TIMEOUT, _TIMEOUT, _TIMEOUT, ] with self.assertRaises(IOError): base.urlretrieve_with_retry("http://dummy.com", "/tmp/dummy") # Assert full backoff was tried actual_list = [arg[0][0] for arg in mock_time.sleep.call_args_list] expected_list = [1, 2, 4, 8, 16] for actual, expected in zip(actual_list, expected_list): self.assertLessEqual(abs(actual - expected), 0.25 * expected) self.assertEquals(len(actual_list), len(expected_list)) def testUrlretrieveRaisesOnNonRetriableErrorWithoutRetry(self): with mock.patch.object(base, "time") as mock_time: with mock.patch.object(base, "urllib") as mock_urllib: mock_urllib.request.urlretrieve.side_effect = [ IOError(2, "No such file or directory"), ] with self.assertRaises(IOError): base.urlretrieve_with_retry("http://dummy.com", "/tmp/dummy") # Assert no retries self.assertFalse(mock_time.called) if __name__ == "__main__": test.main()

apache-2.0

yonglehou/scikit-learn

sklearn/metrics/regression.py

174

16953

"""Metrics to assess performance on regression task Functions named as ``*_score`` return a scalar value to maximize: the higher the better Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: the lower the better """ # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Mathieu Blondel <mathieu@mblondel.org> # Olivier Grisel <olivier.grisel@ensta.org> # Arnaud Joly <a.joly@ulg.ac.be> # Jochen Wersdorfer <jochen@wersdoerfer.de> # Lars Buitinck <L.J.Buitinck@uva.nl> # Joel Nothman <joel.nothman@gmail.com> # Noel Dawe <noel@dawe.me> # Manoj Kumar <manojkumarsivaraj334@gmail.com> # Michael Eickenberg <michael.eickenberg@gmail.com> # Konstantin Shmelkov <konstantin.shmelkov@polytechnique.edu> # License: BSD 3 clause from __future__ import division import numpy as np from ..utils.validation import check_array, check_consistent_length from ..utils.validation import column_or_1d import warnings __ALL__ = [ "mean_absolute_error", "mean_squared_error", "median_absolute_error", "r2_score", "explained_variance_score" ] def _check_reg_targets(y_true, y_pred, multioutput): """Check that y_true and y_pred belong to the same regression task Parameters ---------- y_true : array-like, y_pred : array-like, multioutput : array-like or string in ['raw_values', uniform_average', 'variance_weighted'] or None None is accepted due to backward compatibility of r2_score(). Returns ------- type_true : one of {'continuous', continuous-multioutput'} The type of the true target data, as output by 'utils.multiclass.type_of_target' y_true : array-like of shape = (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples, n_outputs) Estimated target values. multioutput : array-like of shape = (n_outputs) or string in ['raw_values', uniform_average', 'variance_weighted'] or None Custom output weights if ``multioutput`` is array-like or just the corresponding argument if ``multioutput`` is a correct keyword. """ check_consistent_length(y_true, y_pred) y_true = check_array(y_true, ensure_2d=False) y_pred = check_array(y_pred, ensure_2d=False) if y_true.ndim == 1: y_true = y_true.reshape((-1, 1)) if y_pred.ndim == 1: y_pred = y_pred.reshape((-1, 1)) if y_true.shape[1] != y_pred.shape[1]: raise ValueError("y_true and y_pred have different number of output " "({0}!={1})".format(y_true.shape[1], y_pred.shape[1])) n_outputs = y_true.shape[1] multioutput_options = (None, 'raw_values', 'uniform_average', 'variance_weighted') if multioutput not in multioutput_options: multioutput = check_array(multioutput, ensure_2d=False) if n_outputs == 1: raise ValueError("Custom weights are useful only in " "multi-output cases.") elif n_outputs != len(multioutput): raise ValueError(("There must be equally many custom weights " "(%d) as outputs (%d).") % (len(multioutput), n_outputs)) y_type = 'continuous' if n_outputs == 1 else 'continuous-multioutput' return y_type, y_true, y_pred, multioutput def mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average'): """Mean absolute error regression loss Read more in the :ref:`User Guide <mean_absolute_error>`. Parameters ---------- y_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs) Estimated target values. sample_weight : array-like of shape = (n_samples), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average'] or array-like of shape (n_outputs) Defines aggregating of multiple output values. Array-like value defines weights used to average errors. 'raw_values' : Returns a full set of errors in case of multioutput input. 'uniform_average' : Errors of all outputs are averaged with uniform weight. Returns ------- loss : float or ndarray of floats If multioutput is 'raw_values', then mean absolute error is returned for each output separately. If multioutput is 'uniform_average' or an ndarray of weights, then the weighted average of all output errors is returned. MAE output is non-negative floating point. The best value is 0.0. Examples -------- >>> from sklearn.metrics import mean_absolute_error >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_absolute_error(y_true, y_pred) 0.5 >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> mean_absolute_error(y_true, y_pred) 0.75 >>> mean_absolute_error(y_true, y_pred, multioutput='raw_values') array([ 0.5, 1. ]) >>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7]) ... # doctest: +ELLIPSIS 0.849... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) output_errors = np.average(np.abs(y_pred - y_true), weights=sample_weight, axis=0) if multioutput == 'raw_values': return output_errors elif multioutput == 'uniform_average': # pass None as weights to np.average: uniform mean multioutput = None return np.average(output_errors, weights=multioutput) def mean_squared_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average'): """Mean squared error regression loss Read more in the :ref:`User Guide <mean_squared_error>`. Parameters ---------- y_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs) Estimated target values. sample_weight : array-like of shape = (n_samples), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average'] or array-like of shape (n_outputs) Defines aggregating of multiple output values. Array-like value defines weights used to average errors. 'raw_values' : Returns a full set of errors in case of multioutput input. 'uniform_average' : Errors of all outputs are averaged with uniform weight. Returns ------- loss : float or ndarray of floats A non-negative floating point value (the best value is 0.0), or an array of floating point values, one for each individual target. Examples -------- >>> from sklearn.metrics import mean_squared_error >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_squared_error(y_true, y_pred) 0.375 >>> y_true = [[0.5, 1],[-1, 1],[7, -6]] >>> y_pred = [[0, 2],[-1, 2],[8, -5]] >>> mean_squared_error(y_true, y_pred) # doctest: +ELLIPSIS 0.708... >>> mean_squared_error(y_true, y_pred, multioutput='raw_values') ... # doctest: +ELLIPSIS array([ 0.416..., 1. ]) >>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7]) ... # doctest: +ELLIPSIS 0.824... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) if multioutput == 'raw_values': return output_errors elif multioutput == 'uniform_average': # pass None as weights to np.average: uniform mean multioutput = None return np.average(output_errors, weights=multioutput) def median_absolute_error(y_true, y_pred): """Median absolute error regression loss Read more in the :ref:`User Guide <median_absolute_error>`. Parameters ---------- y_true : array-like of shape = (n_samples) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) Estimated target values. Returns ------- loss : float A positive floating point value (the best value is 0.0). Examples -------- >>> from sklearn.metrics import median_absolute_error >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> median_absolute_error(y_true, y_pred) 0.5 """ y_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred, 'uniform_average') if y_type == 'continuous-multioutput': raise ValueError("Multioutput not supported in median_absolute_error") return np.median(np.abs(y_pred - y_true)) def explained_variance_score(y_true, y_pred, sample_weight=None, multioutput='uniform_average'): """Explained variance regression score function Best possible score is 1.0, lower values are worse. Read more in the :ref:`User Guide <explained_variance_score>`. Parameters ---------- y_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs) Estimated target values. sample_weight : array-like of shape = (n_samples), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average', \ 'variance_weighted'] or array-like of shape (n_outputs) Defines aggregating of multiple output scores. Array-like value defines weights used to average scores. 'raw_values' : Returns a full set of scores in case of multioutput input. 'uniform_average' : Scores of all outputs are averaged with uniform weight. 'variance_weighted' : Scores of all outputs are averaged, weighted by the variances of each individual output. Returns ------- score : float or ndarray of floats The explained variance or ndarray if 'multioutput' is 'raw_values'. Notes ----- This is not a symmetric function. Examples -------- >>> from sklearn.metrics import explained_variance_score >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> explained_variance_score(y_true, y_pred) # doctest: +ELLIPSIS 0.957... >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> explained_variance_score(y_true, y_pred, multioutput='uniform_average') ... # doctest: +ELLIPSIS 0.983... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) y_diff_avg = np.average(y_true - y_pred, weights=sample_weight, axis=0) numerator = np.average((y_true - y_pred - y_diff_avg) ** 2, weights=sample_weight, axis=0) y_true_avg = np.average(y_true, weights=sample_weight, axis=0) denominator = np.average((y_true - y_true_avg) ** 2, weights=sample_weight, axis=0) nonzero_numerator = numerator != 0 nonzero_denominator = denominator != 0 valid_score = nonzero_numerator & nonzero_denominator output_scores = np.ones(y_true.shape[1]) output_scores[valid_score] = 1 - (numerator[valid_score] / denominator[valid_score]) output_scores[nonzero_numerator & ~nonzero_denominator] = 0. if multioutput == 'raw_values': # return scores individually return output_scores elif multioutput == 'uniform_average': # passing to np.average() None as weights results is uniform mean avg_weights = None elif multioutput == 'variance_weighted': avg_weights = denominator else: avg_weights = multioutput return np.average(output_scores, weights=avg_weights) def r2_score(y_true, y_pred, sample_weight=None, multioutput=None): """R^2 (coefficient of determination) regression score function. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0. Read more in the :ref:`User Guide <r2_score>`. Parameters ---------- y_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs) Estimated target values. sample_weight : array-like of shape = (n_samples), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average', 'variance_weighted'] or None or array-like of shape (n_outputs) Defines aggregating of multiple output scores. Array-like value defines weights used to average scores. Default value correponds to 'variance_weighted', but will be changed to 'uniform_average' in next versions. 'raw_values' : Returns a full set of scores in case of multioutput input. 'uniform_average' : Scores of all outputs are averaged with uniform weight. 'variance_weighted' : Scores of all outputs are averaged, weighted by the variances of each individual output. Returns ------- z : float or ndarray of floats The R^2 score or ndarray of scores if 'multioutput' is 'raw_values'. Notes ----- This is not a symmetric function. Unlike most other scores, R^2 score may be negative (it need not actually be the square of a quantity R). References ---------- .. [1] `Wikipedia entry on the Coefficient of determination <http://en.wikipedia.org/wiki/Coefficient_of_determination>`_ Examples -------- >>> from sklearn.metrics import r2_score >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> r2_score(y_true, y_pred) # doctest: +ELLIPSIS 0.948... >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> r2_score(y_true, y_pred, multioutput='variance_weighted') # doctest: +ELLIPSIS 0.938... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) if sample_weight is not None: sample_weight = column_or_1d(sample_weight) weight = sample_weight[:, np.newaxis] else: weight = 1. numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0, dtype=np.float64) denominator = (weight * (y_true - np.average( y_true, axis=0, weights=sample_weight)) ** 2).sum(axis=0, dtype=np.float64) nonzero_denominator = denominator != 0 nonzero_numerator = numerator != 0 valid_score = nonzero_denominator & nonzero_numerator output_scores = np.ones([y_true.shape[1]]) output_scores[valid_score] = 1 - (numerator[valid_score] / denominator[valid_score]) # arbitrary set to zero to avoid -inf scores, having a constant # y_true is not interesting for scoring a regression anyway output_scores[nonzero_numerator & ~nonzero_denominator] = 0. if multioutput is None and y_true.shape[1] != 1: # @FIXME change in 0.18 warnings.warn("Default 'multioutput' behavior now corresponds to " "'variance_weighted' value, it will be changed " "to 'uniform_average' in 0.18.", DeprecationWarning) multioutput = 'variance_weighted' if multioutput == 'raw_values': # return scores individually return output_scores elif multioutput == 'uniform_average': # passing None as weights results is uniform mean avg_weights = None elif multioutput == 'variance_weighted': avg_weights = denominator # avoid fail on constant y or one-element arrays if not np.any(nonzero_denominator): if not np.any(nonzero_numerator): return 1.0 else: return 0.0 else: avg_weights = multioutput return np.average(output_scores, weights=avg_weights)

bsd-3-clause

andrewcmyers/tensorflow

tensorflow/contrib/keras/api/keras/datasets/__init__.py

129

1271

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Keras built-in datasets.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.contrib.keras.api.keras.datasets import boston_housing from tensorflow.contrib.keras.api.keras.datasets import cifar10 from tensorflow.contrib.keras.api.keras.datasets import cifar100 from tensorflow.contrib.keras.api.keras.datasets import imdb from tensorflow.contrib.keras.api.keras.datasets import mnist from tensorflow.contrib.keras.api.keras.datasets import reuters del absolute_import del division del print_function

apache-2.0

pkruskal/scikit-learn

sklearn/tests/test_learning_curve.py

224

10791

# Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de> # # License: BSD 3 clause import sys from sklearn.externals.six.moves import cStringIO as StringIO import numpy as np import warnings from sklearn.base import BaseEstimator from sklearn.learning_curve import learning_curve, validation_curve from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_warns from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.datasets import make_classification from sklearn.cross_validation import KFold from sklearn.linear_model import PassiveAggressiveClassifier class MockImprovingEstimator(BaseEstimator): """Dummy classifier to test the learning curve""" def __init__(self, n_max_train_sizes): self.n_max_train_sizes = n_max_train_sizes self.train_sizes = 0 self.X_subset = None def fit(self, X_subset, y_subset=None): self.X_subset = X_subset self.train_sizes = X_subset.shape[0] return self def predict(self, X): raise NotImplementedError def score(self, X=None, Y=None): # training score becomes worse (2 -> 1), test error better (0 -> 1) if self._is_training_data(X): return 2. - float(self.train_sizes) / self.n_max_train_sizes else: return float(self.train_sizes) / self.n_max_train_sizes def _is_training_data(self, X): return X is self.X_subset class MockIncrementalImprovingEstimator(MockImprovingEstimator): """Dummy classifier that provides partial_fit""" def __init__(self, n_max_train_sizes): super(MockIncrementalImprovingEstimator, self).__init__(n_max_train_sizes) self.x = None def _is_training_data(self, X): return self.x in X def partial_fit(self, X, y=None, **params): self.train_sizes += X.shape[0] self.x = X[0] class MockEstimatorWithParameter(BaseEstimator): """Dummy classifier to test the validation curve""" def __init__(self, param=0.5): self.X_subset = None self.param = param def fit(self, X_subset, y_subset): self.X_subset = X_subset self.train_sizes = X_subset.shape[0] return self def predict(self, X): raise NotImplementedError def score(self, X=None, y=None): return self.param if self._is_training_data(X) else 1 - self.param def _is_training_data(self, X): return X is self.X_subset def test_learning_curve(): X, y = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockImprovingEstimator(20) with warnings.catch_warnings(record=True) as w: train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=3, train_sizes=np.linspace(0.1, 1.0, 10)) if len(w) > 0: raise RuntimeError("Unexpected warning: %r" % w[0].message) assert_equal(train_scores.shape, (10, 3)) assert_equal(test_scores.shape, (10, 3)) assert_array_equal(train_sizes, np.linspace(2, 20, 10)) assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10)) assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10)) def test_learning_curve_unsupervised(): X, _ = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockImprovingEstimator(20) train_sizes, train_scores, test_scores = learning_curve( estimator, X, y=None, cv=3, train_sizes=np.linspace(0.1, 1.0, 10)) assert_array_equal(train_sizes, np.linspace(2, 20, 10)) assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10)) assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10)) def test_learning_curve_verbose(): X, y = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockImprovingEstimator(20) old_stdout = sys.stdout sys.stdout = StringIO() try: train_sizes, train_scores, test_scores = \ learning_curve(estimator, X, y, cv=3, verbose=1) finally: out = sys.stdout.getvalue() sys.stdout.close() sys.stdout = old_stdout assert("[learning_curve]" in out) def test_learning_curve_incremental_learning_not_possible(): X, y = make_classification(n_samples=2, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) # The mockup does not have partial_fit() estimator = MockImprovingEstimator(1) assert_raises(ValueError, learning_curve, estimator, X, y, exploit_incremental_learning=True) def test_learning_curve_incremental_learning(): X, y = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockIncrementalImprovingEstimator(20) train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=3, exploit_incremental_learning=True, train_sizes=np.linspace(0.1, 1.0, 10)) assert_array_equal(train_sizes, np.linspace(2, 20, 10)) assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10)) assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10)) def test_learning_curve_incremental_learning_unsupervised(): X, _ = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockIncrementalImprovingEstimator(20) train_sizes, train_scores, test_scores = learning_curve( estimator, X, y=None, cv=3, exploit_incremental_learning=True, train_sizes=np.linspace(0.1, 1.0, 10)) assert_array_equal(train_sizes, np.linspace(2, 20, 10)) assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10)) assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10)) def test_learning_curve_batch_and_incremental_learning_are_equal(): X, y = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) train_sizes = np.linspace(0.2, 1.0, 5) estimator = PassiveAggressiveClassifier(n_iter=1, shuffle=False) train_sizes_inc, train_scores_inc, test_scores_inc = \ learning_curve( estimator, X, y, train_sizes=train_sizes, cv=3, exploit_incremental_learning=True) train_sizes_batch, train_scores_batch, test_scores_batch = \ learning_curve( estimator, X, y, cv=3, train_sizes=train_sizes, exploit_incremental_learning=False) assert_array_equal(train_sizes_inc, train_sizes_batch) assert_array_almost_equal(train_scores_inc.mean(axis=1), train_scores_batch.mean(axis=1)) assert_array_almost_equal(test_scores_inc.mean(axis=1), test_scores_batch.mean(axis=1)) def test_learning_curve_n_sample_range_out_of_bounds(): X, y = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockImprovingEstimator(20) assert_raises(ValueError, learning_curve, estimator, X, y, cv=3, train_sizes=[0, 1]) assert_raises(ValueError, learning_curve, estimator, X, y, cv=3, train_sizes=[0.0, 1.0]) assert_raises(ValueError, learning_curve, estimator, X, y, cv=3, train_sizes=[0.1, 1.1]) assert_raises(ValueError, learning_curve, estimator, X, y, cv=3, train_sizes=[0, 20]) assert_raises(ValueError, learning_curve, estimator, X, y, cv=3, train_sizes=[1, 21]) def test_learning_curve_remove_duplicate_sample_sizes(): X, y = make_classification(n_samples=3, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockImprovingEstimator(2) train_sizes, _, _ = assert_warns( RuntimeWarning, learning_curve, estimator, X, y, cv=3, train_sizes=np.linspace(0.33, 1.0, 3)) assert_array_equal(train_sizes, [1, 2]) def test_learning_curve_with_boolean_indices(): X, y = make_classification(n_samples=30, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) estimator = MockImprovingEstimator(20) cv = KFold(n=30, n_folds=3) train_sizes, train_scores, test_scores = learning_curve( estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10)) assert_array_equal(train_sizes, np.linspace(2, 20, 10)) assert_array_almost_equal(train_scores.mean(axis=1), np.linspace(1.9, 1.0, 10)) assert_array_almost_equal(test_scores.mean(axis=1), np.linspace(0.1, 1.0, 10)) def test_validation_curve(): X, y = make_classification(n_samples=2, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) param_range = np.linspace(0, 1, 10) with warnings.catch_warnings(record=True) as w: train_scores, test_scores = validation_curve( MockEstimatorWithParameter(), X, y, param_name="param", param_range=param_range, cv=2 ) if len(w) > 0: raise RuntimeError("Unexpected warning: %r" % w[0].message) assert_array_almost_equal(train_scores.mean(axis=1), param_range) assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)

bsd-3-clause

pkruskal/scikit-learn

sklearn/feature_selection/tests/test_feature_select.py

142

22295

""" Todo: cross-check the F-value with stats model """ from __future__ import division import itertools import warnings import numpy as np from scipy import stats, sparse from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_not_in from sklearn.utils.testing import assert_less from sklearn.utils.testing import assert_warns from sklearn.utils.testing import ignore_warnings from sklearn.utils.testing import assert_warns_message from sklearn.utils.testing import assert_greater from sklearn.utils.testing import assert_greater_equal from sklearn.utils import safe_mask from sklearn.datasets.samples_generator import (make_classification, make_regression) from sklearn.feature_selection import (chi2, f_classif, f_oneway, f_regression, SelectPercentile, SelectKBest, SelectFpr, SelectFdr, SelectFwe, GenericUnivariateSelect) ############################################################################## # Test the score functions def test_f_oneway_vs_scipy_stats(): # Test that our f_oneway gives the same result as scipy.stats rng = np.random.RandomState(0) X1 = rng.randn(10, 3) X2 = 1 + rng.randn(10, 3) f, pv = stats.f_oneway(X1, X2) f2, pv2 = f_oneway(X1, X2) assert_true(np.allclose(f, f2)) assert_true(np.allclose(pv, pv2)) def test_f_oneway_ints(): # Smoke test f_oneway on integers: that it does raise casting errors # with recent numpys rng = np.random.RandomState(0) X = rng.randint(10, size=(10, 10)) y = np.arange(10) fint, pint = f_oneway(X, y) # test that is gives the same result as with float f, p = f_oneway(X.astype(np.float), y) assert_array_almost_equal(f, fint, decimal=4) assert_array_almost_equal(p, pint, decimal=4) def test_f_classif(): # Test whether the F test yields meaningful results # on a simple simulated classification problem X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) F, pv = f_classif(X, y) F_sparse, pv_sparse = f_classif(sparse.csr_matrix(X), y) assert_true((F > 0).all()) assert_true((pv > 0).all()) assert_true((pv < 1).all()) assert_true((pv[:5] < 0.05).all()) assert_true((pv[5:] > 1.e-4).all()) assert_array_almost_equal(F_sparse, F) assert_array_almost_equal(pv_sparse, pv) def test_f_regression(): # Test whether the F test yields meaningful results # on a simple simulated regression problem X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) F, pv = f_regression(X, y) assert_true((F > 0).all()) assert_true((pv > 0).all()) assert_true((pv < 1).all()) assert_true((pv[:5] < 0.05).all()) assert_true((pv[5:] > 1.e-4).all()) # again without centering, compare with sparse F, pv = f_regression(X, y, center=False) F_sparse, pv_sparse = f_regression(sparse.csr_matrix(X), y, center=False) assert_array_almost_equal(F_sparse, F) assert_array_almost_equal(pv_sparse, pv) def test_f_regression_input_dtype(): # Test whether f_regression returns the same value # for any numeric data_type rng = np.random.RandomState(0) X = rng.rand(10, 20) y = np.arange(10).astype(np.int) F1, pv1 = f_regression(X, y) F2, pv2 = f_regression(X, y.astype(np.float)) assert_array_almost_equal(F1, F2, 5) assert_array_almost_equal(pv1, pv2, 5) def test_f_regression_center(): # Test whether f_regression preserves dof according to 'center' argument # We use two centered variates so we have a simple relationship between # F-score with variates centering and F-score without variates centering. # Create toy example X = np.arange(-5, 6).reshape(-1, 1) # X has zero mean n_samples = X.size Y = np.ones(n_samples) Y[::2] *= -1. Y[0] = 0. # have Y mean being null F1, _ = f_regression(X, Y, center=True) F2, _ = f_regression(X, Y, center=False) assert_array_almost_equal(F1 * (n_samples - 1.) / (n_samples - 2.), F2) assert_almost_equal(F2[0], 0.232558139) # value from statsmodels OLS def test_f_classif_multi_class(): # Test whether the F test yields meaningful results # on a simple simulated classification problem X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) F, pv = f_classif(X, y) assert_true((F > 0).all()) assert_true((pv > 0).all()) assert_true((pv < 1).all()) assert_true((pv[:5] < 0.05).all()) assert_true((pv[5:] > 1.e-4).all()) def test_select_percentile_classif(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the percentile heuristic X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) univariate_filter = SelectPercentile(f_classif, percentile=25) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect(f_classif, mode='percentile', param=25).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) def test_select_percentile_classif_sparse(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the percentile heuristic X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) X = sparse.csr_matrix(X) univariate_filter = SelectPercentile(f_classif, percentile=25) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect(f_classif, mode='percentile', param=25).fit(X, y).transform(X) assert_array_equal(X_r.toarray(), X_r2.toarray()) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) X_r2inv = univariate_filter.inverse_transform(X_r2) assert_true(sparse.issparse(X_r2inv)) support_mask = safe_mask(X_r2inv, support) assert_equal(X_r2inv.shape, X.shape) assert_array_equal(X_r2inv[:, support_mask].toarray(), X_r.toarray()) # Check other columns are empty assert_equal(X_r2inv.getnnz(), X_r.getnnz()) ############################################################################## # Test univariate selection in classification settings def test_select_kbest_classif(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the k best heuristic X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) univariate_filter = SelectKBest(f_classif, k=5) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect( f_classif, mode='k_best', param=5).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) def test_select_kbest_all(): # Test whether k="all" correctly returns all features. X, y = make_classification(n_samples=20, n_features=10, shuffle=False, random_state=0) univariate_filter = SelectKBest(f_classif, k='all') X_r = univariate_filter.fit(X, y).transform(X) assert_array_equal(X, X_r) def test_select_kbest_zero(): # Test whether k=0 correctly returns no features. X, y = make_classification(n_samples=20, n_features=10, shuffle=False, random_state=0) univariate_filter = SelectKBest(f_classif, k=0) univariate_filter.fit(X, y) support = univariate_filter.get_support() gtruth = np.zeros(10, dtype=bool) assert_array_equal(support, gtruth) X_selected = assert_warns_message(UserWarning, 'No features were selected', univariate_filter.transform, X) assert_equal(X_selected.shape, (20, 0)) def test_select_heuristics_classif(): # Test whether the relative univariate feature selection # gets the correct items in a simple classification problem # with the fdr, fwe and fpr heuristics X, y = make_classification(n_samples=200, n_features=20, n_informative=3, n_redundant=2, n_repeated=0, n_classes=8, n_clusters_per_class=1, flip_y=0.0, class_sep=10, shuffle=False, random_state=0) univariate_filter = SelectFwe(f_classif, alpha=0.01) X_r = univariate_filter.fit(X, y).transform(X) gtruth = np.zeros(20) gtruth[:5] = 1 for mode in ['fdr', 'fpr', 'fwe']: X_r2 = GenericUnivariateSelect( f_classif, mode=mode, param=0.01).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() assert_array_almost_equal(support, gtruth) ############################################################################## # Test univariate selection in regression settings def assert_best_scores_kept(score_filter): scores = score_filter.scores_ support = score_filter.get_support() assert_array_equal(np.sort(scores[support]), np.sort(scores)[-support.sum():]) def test_select_percentile_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the percentile heuristic X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) univariate_filter = SelectPercentile(f_regression, percentile=25) X_r = univariate_filter.fit(X, y).transform(X) assert_best_scores_kept(univariate_filter) X_r2 = GenericUnivariateSelect( f_regression, mode='percentile', param=25).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) X_2 = X.copy() X_2[:, np.logical_not(support)] = 0 assert_array_equal(X_2, univariate_filter.inverse_transform(X_r)) # Check inverse_transform respects dtype assert_array_equal(X_2.astype(bool), univariate_filter.inverse_transform(X_r.astype(bool))) def test_select_percentile_regression_full(): # Test whether the relative univariate feature selection # selects all features when '100%' is asked. X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) univariate_filter = SelectPercentile(f_regression, percentile=100) X_r = univariate_filter.fit(X, y).transform(X) assert_best_scores_kept(univariate_filter) X_r2 = GenericUnivariateSelect( f_regression, mode='percentile', param=100).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.ones(20) assert_array_equal(support, gtruth) def test_invalid_percentile(): X, y = make_regression(n_samples=10, n_features=20, n_informative=2, shuffle=False, random_state=0) assert_raises(ValueError, SelectPercentile(percentile=-1).fit, X, y) assert_raises(ValueError, SelectPercentile(percentile=101).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='percentile', param=-1).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='percentile', param=101).fit, X, y) def test_select_kbest_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the k best heuristic X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0, noise=10) univariate_filter = SelectKBest(f_regression, k=5) X_r = univariate_filter.fit(X, y).transform(X) assert_best_scores_kept(univariate_filter) X_r2 = GenericUnivariateSelect( f_regression, mode='k_best', param=5).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support, gtruth) def test_select_heuristics_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the fpr, fdr or fwe heuristics X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0, noise=10) univariate_filter = SelectFpr(f_regression, alpha=0.01) X_r = univariate_filter.fit(X, y).transform(X) gtruth = np.zeros(20) gtruth[:5] = 1 for mode in ['fdr', 'fpr', 'fwe']: X_r2 = GenericUnivariateSelect( f_regression, mode=mode, param=0.01).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() assert_array_equal(support[:5], np.ones((5, ), dtype=np.bool)) assert_less(np.sum(support[5:] == 1), 3) def test_select_fdr_regression(): # Test that fdr heuristic actually has low FDR. def single_fdr(alpha, n_informative, random_state): X, y = make_regression(n_samples=150, n_features=20, n_informative=n_informative, shuffle=False, random_state=random_state, noise=10) with warnings.catch_warnings(record=True): # Warnings can be raised when no features are selected # (low alpha or very noisy data) univariate_filter = SelectFdr(f_regression, alpha=alpha) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect( f_regression, mode='fdr', param=alpha).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() num_false_positives = np.sum(support[n_informative:] == 1) num_true_positives = np.sum(support[:n_informative] == 1) if num_false_positives == 0: return 0. false_discovery_rate = (num_false_positives / (num_true_positives + num_false_positives)) return false_discovery_rate for alpha in [0.001, 0.01, 0.1]: for n_informative in [1, 5, 10]: # As per Benjamini-Hochberg, the expected false discovery rate # should be lower than alpha: # FDR = E(FP / (TP + FP)) <= alpha false_discovery_rate = np.mean([single_fdr(alpha, n_informative, random_state) for random_state in range(30)]) assert_greater_equal(alpha, false_discovery_rate) # Make sure that the empirical false discovery rate increases # with alpha: if false_discovery_rate != 0: assert_greater(false_discovery_rate, alpha / 10) def test_select_fwe_regression(): # Test whether the relative univariate feature selection # gets the correct items in a simple regression problem # with the fwe heuristic X, y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0) univariate_filter = SelectFwe(f_regression, alpha=0.01) X_r = univariate_filter.fit(X, y).transform(X) X_r2 = GenericUnivariateSelect( f_regression, mode='fwe', param=0.01).fit(X, y).transform(X) assert_array_equal(X_r, X_r2) support = univariate_filter.get_support() gtruth = np.zeros(20) gtruth[:5] = 1 assert_array_equal(support[:5], np.ones((5, ), dtype=np.bool)) assert_less(np.sum(support[5:] == 1), 2) def test_selectkbest_tiebreaking(): # Test whether SelectKBest actually selects k features in case of ties. # Prior to 0.11, SelectKBest would return more features than requested. Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]] y = [1] dummy_score = lambda X, y: (X[0], X[0]) for X in Xs: sel = SelectKBest(dummy_score, k=1) X1 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X1.shape[1], 1) assert_best_scores_kept(sel) sel = SelectKBest(dummy_score, k=2) X2 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X2.shape[1], 2) assert_best_scores_kept(sel) def test_selectpercentile_tiebreaking(): # Test if SelectPercentile selects the right n_features in case of ties. Xs = [[0, 1, 1], [0, 0, 1], [1, 0, 0], [1, 1, 0]] y = [1] dummy_score = lambda X, y: (X[0], X[0]) for X in Xs: sel = SelectPercentile(dummy_score, percentile=34) X1 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X1.shape[1], 1) assert_best_scores_kept(sel) sel = SelectPercentile(dummy_score, percentile=67) X2 = ignore_warnings(sel.fit_transform)([X], y) assert_equal(X2.shape[1], 2) assert_best_scores_kept(sel) def test_tied_pvalues(): # Test whether k-best and percentiles work with tied pvalues from chi2. # chi2 will return the same p-values for the following features, but it # will return different scores. X0 = np.array([[10000, 9999, 9998], [1, 1, 1]]) y = [0, 1] for perm in itertools.permutations((0, 1, 2)): X = X0[:, perm] Xt = SelectKBest(chi2, k=2).fit_transform(X, y) assert_equal(Xt.shape, (2, 2)) assert_not_in(9998, Xt) Xt = SelectPercentile(chi2, percentile=67).fit_transform(X, y) assert_equal(Xt.shape, (2, 2)) assert_not_in(9998, Xt) def test_tied_scores(): # Test for stable sorting in k-best with tied scores. X_train = np.array([[0, 0, 0], [1, 1, 1]]) y_train = [0, 1] for n_features in [1, 2, 3]: sel = SelectKBest(chi2, k=n_features).fit(X_train, y_train) X_test = sel.transform([0, 1, 2]) assert_array_equal(X_test[0], np.arange(3)[-n_features:]) def test_nans(): # Assert that SelectKBest and SelectPercentile can handle NaNs. # First feature has zero variance to confuse f_classif (ANOVA) and # make it return a NaN. X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]] y = [1, 0, 1] for select in (SelectKBest(f_classif, 2), SelectPercentile(f_classif, percentile=67)): ignore_warnings(select.fit)(X, y) assert_array_equal(select.get_support(indices=True), np.array([1, 2])) def test_score_func_error(): X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]] y = [1, 0, 1] for SelectFeatures in [SelectKBest, SelectPercentile, SelectFwe, SelectFdr, SelectFpr, GenericUnivariateSelect]: assert_raises(TypeError, SelectFeatures(score_func=10).fit, X, y) def test_invalid_k(): X = [[0, 1, 0], [0, -1, -1], [0, .5, .5]] y = [1, 0, 1] assert_raises(ValueError, SelectKBest(k=-1).fit, X, y) assert_raises(ValueError, SelectKBest(k=4).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='k_best', param=-1).fit, X, y) assert_raises(ValueError, GenericUnivariateSelect(mode='k_best', param=4).fit, X, y) def test_f_classif_constant_feature(): # Test that f_classif warns if a feature is constant throughout. X, y = make_classification(n_samples=10, n_features=5) X[:, 0] = 2.0 assert_warns(UserWarning, f_classif, X, y) def test_no_feature_selected(): rng = np.random.RandomState(0) # Generate random uncorrelated data: a strict univariate test should # rejects all the features X = rng.rand(40, 10) y = rng.randint(0, 4, size=40) strict_selectors = [ SelectFwe(alpha=0.01).fit(X, y), SelectFdr(alpha=0.01).fit(X, y), SelectFpr(alpha=0.01).fit(X, y), SelectPercentile(percentile=0).fit(X, y), SelectKBest(k=0).fit(X, y), ] for selector in strict_selectors: assert_array_equal(selector.get_support(), np.zeros(10)) X_selected = assert_warns_message( UserWarning, 'No features were selected', selector.transform, X) assert_equal(X_selected.shape, (40, 0))

bsd-3-clause

yonglehou/scikit-learn

examples/manifold/plot_manifold_sphere.py

257

5101

#!/usr/bin/python # -*- coding: utf-8 -*- """ ============================================= Manifold Learning methods on a severed sphere ============================================= An application of the different :ref:`manifold` techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold learning methods. Regarding the dataset, the poles are cut from the sphere, as well as a thin slice down its side. This enables the manifold learning techniques to 'spread it open' whilst projecting it onto two dimensions. For a similar example, where the methods are applied to the S-curve dataset, see :ref:`example_manifold_plot_compare_methods.py` Note that the purpose of the :ref:`MDS <multidimensional_scaling>` is to find a low-dimensional representation of the data (here 2D) in which the distances respect well the distances in the original high-dimensional space, unlike other manifold-learning algorithms, it does not seeks an isotropic representation of the data in the low-dimensional space. Here the manifold problem matches fairly that of representing a flat map of the Earth, as with `map projection <http://en.wikipedia.org/wiki/Map_projection>`_ """ # Author: Jaques Grobler <jaques.grobler@inria.fr> # License: BSD 3 clause print(__doc__) from time import time import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib.ticker import NullFormatter from sklearn import manifold from sklearn.utils import check_random_state # Next line to silence pyflakes. Axes3D # Variables for manifold learning. n_neighbors = 10 n_samples = 1000 # Create our sphere. random_state = check_random_state(0) p = random_state.rand(n_samples) * (2 * np.pi - 0.55) t = random_state.rand(n_samples) * np.pi # Sever the poles from the sphere. indices = ((t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))) colors = p[indices] x, y, z = np.sin(t[indices]) * np.cos(p[indices]), \ np.sin(t[indices]) * np.sin(p[indices]), \ np.cos(t[indices]) # Plot our dataset. fig = plt.figure(figsize=(15, 8)) plt.suptitle("Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14) ax = fig.add_subplot(251, projection='3d') ax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow) try: # compatibility matplotlib < 1.0 ax.view_init(40, -10) except: pass sphere_data = np.array([x, y, z]).T # Perform Locally Linear Embedding Manifold learning methods = ['standard', 'ltsa', 'hessian', 'modified'] labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE'] for i, method in enumerate(methods): t0 = time() trans_data = manifold\ .LocallyLinearEmbedding(n_neighbors, 2, method=method).fit_transform(sphere_data).T t1 = time() print("%s: %.2g sec" % (methods[i], t1 - t0)) ax = fig.add_subplot(252 + i) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("%s (%.2g sec)" % (labels[i], t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform Isomap Manifold learning. t0 = time() trans_data = manifold.Isomap(n_neighbors, n_components=2)\ .fit_transform(sphere_data).T t1 = time() print("%s: %.2g sec" % ('ISO', t1 - t0)) ax = fig.add_subplot(257) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("%s (%.2g sec)" % ('Isomap', t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform Multi-dimensional scaling. t0 = time() mds = manifold.MDS(2, max_iter=100, n_init=1) trans_data = mds.fit_transform(sphere_data).T t1 = time() print("MDS: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(258) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("MDS (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform Spectral Embedding. t0 = time() se = manifold.SpectralEmbedding(n_components=2, n_neighbors=n_neighbors) trans_data = se.fit_transform(sphere_data).T t1 = time() print("Spectral Embedding: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(259) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("Spectral Embedding (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform t-distributed stochastic neighbor embedding. t0 = time() tsne = manifold.TSNE(n_components=2, init='pca', random_state=0) trans_data = tsne.fit_transform(sphere_data).T t1 = time() print("t-SNE: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(250) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("t-SNE (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') plt.show()

bsd-3-clause

mxjl620/scikit-learn

examples/manifold/plot_manifold_sphere.py

257

5101

#!/usr/bin/python # -*- coding: utf-8 -*- """ ============================================= Manifold Learning methods on a severed sphere ============================================= An application of the different :ref:`manifold` techniques on a spherical data-set. Here one can see the use of dimensionality reduction in order to gain some intuition regarding the manifold learning methods. Regarding the dataset, the poles are cut from the sphere, as well as a thin slice down its side. This enables the manifold learning techniques to 'spread it open' whilst projecting it onto two dimensions. For a similar example, where the methods are applied to the S-curve dataset, see :ref:`example_manifold_plot_compare_methods.py` Note that the purpose of the :ref:`MDS <multidimensional_scaling>` is to find a low-dimensional representation of the data (here 2D) in which the distances respect well the distances in the original high-dimensional space, unlike other manifold-learning algorithms, it does not seeks an isotropic representation of the data in the low-dimensional space. Here the manifold problem matches fairly that of representing a flat map of the Earth, as with `map projection <http://en.wikipedia.org/wiki/Map_projection>`_ """ # Author: Jaques Grobler <jaques.grobler@inria.fr> # License: BSD 3 clause print(__doc__) from time import time import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib.ticker import NullFormatter from sklearn import manifold from sklearn.utils import check_random_state # Next line to silence pyflakes. Axes3D # Variables for manifold learning. n_neighbors = 10 n_samples = 1000 # Create our sphere. random_state = check_random_state(0) p = random_state.rand(n_samples) * (2 * np.pi - 0.55) t = random_state.rand(n_samples) * np.pi # Sever the poles from the sphere. indices = ((t < (np.pi - (np.pi / 8))) & (t > ((np.pi / 8)))) colors = p[indices] x, y, z = np.sin(t[indices]) * np.cos(p[indices]), \ np.sin(t[indices]) * np.sin(p[indices]), \ np.cos(t[indices]) # Plot our dataset. fig = plt.figure(figsize=(15, 8)) plt.suptitle("Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14) ax = fig.add_subplot(251, projection='3d') ax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow) try: # compatibility matplotlib < 1.0 ax.view_init(40, -10) except: pass sphere_data = np.array([x, y, z]).T # Perform Locally Linear Embedding Manifold learning methods = ['standard', 'ltsa', 'hessian', 'modified'] labels = ['LLE', 'LTSA', 'Hessian LLE', 'Modified LLE'] for i, method in enumerate(methods): t0 = time() trans_data = manifold\ .LocallyLinearEmbedding(n_neighbors, 2, method=method).fit_transform(sphere_data).T t1 = time() print("%s: %.2g sec" % (methods[i], t1 - t0)) ax = fig.add_subplot(252 + i) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("%s (%.2g sec)" % (labels[i], t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform Isomap Manifold learning. t0 = time() trans_data = manifold.Isomap(n_neighbors, n_components=2)\ .fit_transform(sphere_data).T t1 = time() print("%s: %.2g sec" % ('ISO', t1 - t0)) ax = fig.add_subplot(257) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("%s (%.2g sec)" % ('Isomap', t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform Multi-dimensional scaling. t0 = time() mds = manifold.MDS(2, max_iter=100, n_init=1) trans_data = mds.fit_transform(sphere_data).T t1 = time() print("MDS: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(258) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("MDS (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform Spectral Embedding. t0 = time() se = manifold.SpectralEmbedding(n_components=2, n_neighbors=n_neighbors) trans_data = se.fit_transform(sphere_data).T t1 = time() print("Spectral Embedding: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(259) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("Spectral Embedding (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') # Perform t-distributed stochastic neighbor embedding. t0 = time() tsne = manifold.TSNE(n_components=2, init='pca', random_state=0) trans_data = tsne.fit_transform(sphere_data).T t1 = time() print("t-SNE: %.2g sec" % (t1 - t0)) ax = fig.add_subplot(250) plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow) plt.title("t-SNE (%.2g sec)" % (t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) plt.axis('tight') plt.show()

bsd-3-clause

yonglehou/scikit-learn

examples/cluster/plot_lena_segmentation.py

269

2444

""" ========================================= Segmenting the picture of Lena in regions ========================================= This example uses :ref:`spectral_clustering` on a graph created from voxel-to-voxel difference on an image to break this image into multiple partly-homogeneous regions. This procedure (spectral clustering on an image) is an efficient approximate solution for finding normalized graph cuts. There are two options to assign labels: * with 'kmeans' spectral clustering will cluster samples in the embedding space using a kmeans algorithm * whereas 'discrete' will iteratively search for the closest partition space to the embedding space. """ print(__doc__) # Author: Gael Varoquaux <gael.varoquaux@normalesup.org>, Brian Cheung # License: BSD 3 clause import time import numpy as np import scipy as sp import matplotlib.pyplot as plt from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering lena = sp.misc.lena() # Downsample the image by a factor of 4 lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(lena) # Take a decreasing function of the gradient: an exponential # The smaller beta is, the more independent the segmentation is of the # actual image. For beta=1, the segmentation is close to a voronoi beta = 5 eps = 1e-6 graph.data = np.exp(-beta * graph.data / lena.std()) + eps # Apply spectral clustering (this step goes much faster if you have pyamg # installed) N_REGIONS = 11 ############################################################################### # Visualize the resulting regions for assign_labels in ('kmeans', 'discretize'): t0 = time.time() labels = spectral_clustering(graph, n_clusters=N_REGIONS, assign_labels=assign_labels, random_state=1) t1 = time.time() labels = labels.reshape(lena.shape) plt.figure(figsize=(5, 5)) plt.imshow(lena, cmap=plt.cm.gray) for l in range(N_REGIONS): plt.contour(labels == l, contours=1, colors=[plt.cm.spectral(l / float(N_REGIONS)), ]) plt.xticks(()) plt.yticks(()) plt.title('Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))) plt.show()

bsd-3-clause

dimkal/mne-python

examples/decoding/plot_linear_model_patterns.py

13

3098

""" =============================================================== Linear classifier on sensor data with plot patterns and filters =============================================================== Decoding, a.k.a MVPA or supervised machine learning applied to MEG and EEG data in sensor space. Fit a linear classifier with the LinearModel object providing topographical patterns which are more neurophysiologically interpretable [1] than the classifier filters (weight vectors). The patterns explain how the MEG and EEG data were generated from the discriminant neural sources which are extracted by the filters. Note patterns/filters in MEG data are more similar than EEG data because the noise is less spatially correlated in MEG than EEG. [1] Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.-D., Blankertz, B., & Bießmann, F. (2014). On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage, 87, 96–110. doi:10.1016/j.neuroimage.2013.10.067 """ # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # Romain Trachel <trachelr@gmail.com> # # License: BSD (3-clause) import mne from mne import io from mne.datasets import sample from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression # import a linear classifier from mne.decoding from mne.decoding import LinearModel print(__doc__) data_path = sample.data_path() ############################################################################### # Set parameters raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' tmin, tmax = -0.2, 0.5 event_id = dict(aud_l=1, vis_l=3) # Setup for reading the raw data raw = io.Raw(raw_fname, preload=True) raw.filter(2, None, method='iir') # replace baselining with high-pass events = mne.read_events(event_fname) # Read epochs epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, decim=4, baseline=None, preload=True) labels = epochs.events[:, -1] # get MEG and EEG data meg_epochs = epochs.pick_types(meg=True, eeg=False, copy=True) meg_data = meg_epochs.get_data().reshape(len(labels), -1) eeg_epochs = epochs.pick_types(meg=False, eeg=True, copy=True) eeg_data = eeg_epochs.get_data().reshape(len(labels), -1) ############################################################################### # Decoding in sensor space using a LogisticRegression classifier clf = LogisticRegression() sc = StandardScaler() # create a linear model with LogisticRegression model = LinearModel(clf) # fit the classifier on MEG data X = sc.fit_transform(meg_data) model.fit(X, labels) # plot patterns and filters model.plot_patterns(meg_epochs.info, title='MEG Patterns') model.plot_filters(meg_epochs.info, title='MEG Filters') # fit the classifier on EEG data X = sc.fit_transform(eeg_data) model.fit(X, labels) # plot patterns and filters model.plot_patterns(eeg_epochs.info, title='EEG Patterns') model.plot_filters(eeg_epochs.info, title='EEG Filters')

bsd-3-clause

benoitsteiner/tensorflow-xsmm

tensorflow/contrib/factorization/python/ops/kmeans.py

12

20349

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """A canned Estimator for k-means clustering.""" # TODO(ccolby): Move clustering_ops.py into this file and streamline the code. from __future__ import absolute_import from __future__ import division from __future__ import print_function import time from tensorflow.contrib.factorization.python.ops import clustering_ops from tensorflow.python.estimator import estimator from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator.export import export_output from tensorflow.python.feature_column import feature_column as fc from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics from tensorflow.python.ops import state_ops from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.summary import summary from tensorflow.python.training import session_run_hook from tensorflow.python.training import training_util class _LossRelativeChangeHook(session_run_hook.SessionRunHook): """Stops when the change in loss goes below a tolerance.""" def __init__(self, loss_tensor, tolerance): """Creates a _LossRelativeChangeHook. Args: loss_tensor: A scalar tensor of the loss value. tolerance: A relative tolerance of loss change between iterations. """ self._loss_tensor = loss_tensor self._tolerance = tolerance self._prev_loss = None def before_run(self, run_context): del run_context # unused return session_run_hook.SessionRunArgs(self._loss_tensor) def after_run(self, run_context, run_values): loss = run_values.results assert loss is not None if self._prev_loss: relative_change = ( abs(loss - self._prev_loss) / (1 + abs(self._prev_loss))) if relative_change < self._tolerance: run_context.request_stop() self._prev_loss = loss class _InitializeClustersHook(session_run_hook.SessionRunHook): """Initializes the cluster centers. The chief repeatedly invokes an initialization op until all cluster centers are initialized. The workers wait for the initialization phase to complete. """ def __init__(self, init_op, is_initialized_var, is_chief): """Creates an _InitializeClustersHook. Args: init_op: An op that, when run, will choose some initial cluster centers. This op may need to be run multiple times to choose all the centers. is_initialized_var: A boolean variable reporting whether all initial centers have been chosen. is_chief: A boolean specifying whether this task is the chief. """ self._init_op = init_op self._is_initialized_var = is_initialized_var self._is_chief = is_chief def after_create_session(self, session, coord): del coord # unused assert self._init_op.graph is ops.get_default_graph() assert self._is_initialized_var.graph is self._init_op.graph while True: try: if session.run(self._is_initialized_var): break elif self._is_chief: session.run(self._init_op) else: time.sleep(1) except RuntimeError as e: logging.info(e) def _parse_features_if_necessary(features, feature_columns): """Helper function to convert the input points into a usable format. Args: features: The input features. feature_columns: An optionable iterable containing all the feature columns used by the model. All items in the set should be feature column instances that can be passed to `tf.feature_column.input_layer`. If this is None, all features will be used. Returns: If `features` is a dict of `k` features (optionally filtered by `feature_columns`), each of which is a vector of `n` scalars, the return value is a Tensor of shape `(n, k)` representing `n` input points, where the items in the `k` dimension are sorted lexicographically by `features` key. If `features` is not a dict, it is returned unmodified. """ if not isinstance(features, dict): return features if feature_columns: return fc.input_layer(features, feature_columns) keys = sorted(features.keys()) with ops.colocate_with(features[keys[0]]): return array_ops.concat([features[k] for k in keys], axis=1) class _ModelFn(object): """Model function for the estimator.""" def __init__(self, num_clusters, initial_clusters, distance_metric, random_seed, use_mini_batch, mini_batch_steps_per_iteration, kmeans_plus_plus_num_retries, relative_tolerance, feature_columns): self._num_clusters = num_clusters self._initial_clusters = initial_clusters self._distance_metric = distance_metric self._random_seed = random_seed self._use_mini_batch = use_mini_batch self._mini_batch_steps_per_iteration = mini_batch_steps_per_iteration self._kmeans_plus_plus_num_retries = kmeans_plus_plus_num_retries self._relative_tolerance = relative_tolerance self._feature_columns = feature_columns def model_fn(self, features, mode, config): """Model function for the estimator. Note that this does not take a `labels` arg. This works, but `input_fn` must return either `features` or, equivalently, `(features, None)`. Args: features: The input points. See @{tf.estimator.Estimator}. mode: See @{tf.estimator.Estimator}. config: See @{tf.estimator.Estimator}. Returns: A @{tf.estimator.EstimatorSpec} (see @{tf.estimator.Estimator}) specifying this behavior: * `train_op`: Execute one mini-batch or full-batch run of Lloyd's algorithm. * `loss`: The sum of the squared distances from each input point to its closest center. * `eval_metric_ops`: Maps `SCORE` to `loss`. * `predictions`: Maps `ALL_DISTANCES` to the distance from each input point to each cluster center; maps `CLUSTER_INDEX` to the index of the closest cluster center for each input point. """ # input_points is a single Tensor. Therefore, the sharding functionality # in clustering_ops is unused, and some of the values below are lists of a # single item. input_points = _parse_features_if_necessary(features, self._feature_columns) # Let N = the number of input_points. # all_distances: A list of one matrix of shape (N, num_clusters). Each value # is the distance from an input point to a cluster center. # model_predictions: A list of one vector of shape (N). Each value is the # cluster id of an input point. # losses: Similar to cluster_idx but provides the distance to the cluster # center. # is_initialized: scalar indicating whether the initial cluster centers # have been chosen; see init_op. # cluster_centers_var: a Variable containing the cluster centers. # init_op: an op to choose the initial cluster centers. A single worker # repeatedly executes init_op until is_initialized becomes True. # training_op: an op that runs an iteration of training, either an entire # Lloyd iteration or a mini-batch of a Lloyd iteration. Multiple workers # may execute this op, but only after is_initialized becomes True. (all_distances, model_predictions, losses, is_initialized, init_op, training_op) = clustering_ops.KMeans( inputs=input_points, num_clusters=self._num_clusters, initial_clusters=self._initial_clusters, distance_metric=self._distance_metric, use_mini_batch=self._use_mini_batch, mini_batch_steps_per_iteration=self._mini_batch_steps_per_iteration, random_seed=self._random_seed, kmeans_plus_plus_num_retries=self._kmeans_plus_plus_num_retries ).training_graph() loss = math_ops.reduce_sum(losses) summary.scalar('loss/raw', loss) incr_step = state_ops.assign_add(training_util.get_global_step(), 1) training_op = control_flow_ops.with_dependencies([training_op, incr_step], loss) training_hooks = [ _InitializeClustersHook(init_op, is_initialized, config.is_chief) ] if self._relative_tolerance is not None: training_hooks.append( _LossRelativeChangeHook(loss, self._relative_tolerance)) export_outputs = { KMeansClustering.ALL_DISTANCES: export_output.PredictOutput(all_distances[0]), KMeansClustering.CLUSTER_INDEX: export_output.PredictOutput(model_predictions[0]), signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: export_output.PredictOutput(model_predictions[0]) } return model_fn_lib.EstimatorSpec( mode=mode, predictions={ KMeansClustering.ALL_DISTANCES: all_distances[0], KMeansClustering.CLUSTER_INDEX: model_predictions[0], }, loss=loss, train_op=training_op, eval_metric_ops={KMeansClustering.SCORE: metrics.mean(loss)}, training_hooks=training_hooks, export_outputs=export_outputs) # TODO(agarwal,ands): support sharded input. class KMeansClustering(estimator.Estimator): """An Estimator for K-Means clustering. Example: ``` import numpy as np import tensorflow as tf num_points = 100 dimensions = 2 points = np.random.uniform(0, 1000, [num_points, dimensions]) def input_fn(): return tf.train.limit_epochs( tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1) num_clusters = 5 kmeans = tf.contrib.factorization.KMeansClustering( num_clusters=num_clusters, use_mini_batch=False) # train num_iterations = 10 previous_centers = None for _ in xrange(num_iterations): kmeans.train(input_fn) cluster_centers = kmeans.cluster_centers() if previous_centers is not None: print 'delta:', cluster_centers - previous_centers previous_centers = cluster_centers print 'score:', kmeans.score(input_fn) print 'cluster centers:', cluster_centers # map the input points to their clusters cluster_indices = list(kmeans.predict_cluster_index(input_fn)) for i, point in enumerate(points): cluster_index = cluster_indices[i] center = cluster_centers[cluster_index] print 'point:', point, 'is in cluster', cluster_index, 'centered at', center ``` The `SavedModel` saved by the `export_savedmodel` method does not include the cluster centers. However, the cluster centers may be retrieved by the latest checkpoint saved during training. Specifically, ``` kmeans.cluster_centers() ``` is equivalent to ``` tf.train.load_variable( kmeans.model_dir, KMeansClustering.CLUSTER_CENTERS_VAR_NAME) ``` """ # Valid values for the distance_metric constructor argument. SQUARED_EUCLIDEAN_DISTANCE = clustering_ops.SQUARED_EUCLIDEAN_DISTANCE COSINE_DISTANCE = clustering_ops.COSINE_DISTANCE # Values for initial_clusters constructor argument. RANDOM_INIT = clustering_ops.RANDOM_INIT KMEANS_PLUS_PLUS_INIT = clustering_ops.KMEANS_PLUS_PLUS_INIT # Metric returned by evaluate(): The sum of the squared distances from each # input point to its closest center. SCORE = 'score' # Keys returned by predict(). # ALL_DISTANCES: The distance from each input point to each cluster center. # CLUSTER_INDEX: The index of the closest cluster center for each input point. CLUSTER_INDEX = 'cluster_index' ALL_DISTANCES = 'all_distances' # Variable name used by cluster_centers(). CLUSTER_CENTERS_VAR_NAME = clustering_ops.CLUSTERS_VAR_NAME def __init__(self, num_clusters, model_dir=None, initial_clusters=RANDOM_INIT, distance_metric=SQUARED_EUCLIDEAN_DISTANCE, random_seed=0, use_mini_batch=True, mini_batch_steps_per_iteration=1, kmeans_plus_plus_num_retries=2, relative_tolerance=None, config=None, feature_columns=None): """Creates an Estimator for running KMeans training and inference. This Estimator implements the following variants of the K-means algorithm: If `use_mini_batch` is False, it runs standard full batch K-means. Each training step runs a single iteration of K-Means and must process the full input at once. To run in this mode, the `input_fn` passed to `train` must return the entire input dataset. If `use_mini_batch` is True, it runs a generalization of the mini-batch K-means algorithm. It runs multiple iterations, where each iteration is composed of `mini_batch_steps_per_iteration` steps. Each training step accumulates the contribution from one mini-batch into temporary storage. Every `mini_batch_steps_per_iteration` steps, the cluster centers are updated and the temporary storage cleared for the next iteration. Note that: * If `mini_batch_steps_per_iteration=1`, the algorithm reduces to the standard K-means mini-batch algorithm. * If `mini_batch_steps_per_iteration = num_inputs / batch_size`, the algorithm becomes an asynchronous version of the full-batch algorithm. However, there is no guarantee by this implementation that each input is seen exactly once per iteration. Also, different updates are applied asynchronously without locking. So this asynchronous version may not behave exactly like a full-batch version. Args: num_clusters: An integer tensor specifying the number of clusters. This argument is ignored if `initial_clusters` is a tensor or numpy array. model_dir: The directory to save the model results and log files. initial_clusters: Specifies how the initial cluster centers are chosen. One of the following: * a tensor or numpy array with the initial cluster centers. * a callable `f(inputs, k)` that selects and returns up to `k` centers from an input batch. `f` is free to return any number of centers from `0` to `k`. It will be invoked on successive input batches as necessary until all `num_clusters` centers are chosen. * `KMeansClustering.RANDOM_INIT`: Choose centers randomly from an input batch. If the batch size is less than `num_clusters` then the entire batch is chosen to be initial cluster centers and the remaining centers are chosen from successive input batches. * `KMeansClustering.KMEANS_PLUS_PLUS_INIT`: Use kmeans++ to choose centers from the first input batch. If the batch size is less than `num_clusters`, a TensorFlow runtime error occurs. distance_metric: The distance metric used for clustering. One of: * `KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE`: Euclidean distance between vectors `u` and `v` is defined as \\(||u - v||_2\\) which is the square root of the sum of the absolute squares of the elements' difference. * `KMeansClustering.COSINE_DISTANCE`: Cosine distance between vectors `u` and `v` is defined as \\(1 - (u . v) / (||u||_2 ||v||_2)\\). random_seed: Python integer. Seed for PRNG used to initialize centers. use_mini_batch: A boolean specifying whether to use the mini-batch k-means algorithm. See explanation above. mini_batch_steps_per_iteration: The number of steps after which the updated cluster centers are synced back to a master copy. Used only if `use_mini_batch=True`. See explanation above. kmeans_plus_plus_num_retries: For each point that is sampled during kmeans++ initialization, this parameter specifies the number of additional points to draw from the current distribution before selecting the best. If a negative value is specified, a heuristic is used to sample `O(log(num_to_sample))` additional points. Used only if `initial_clusters=KMeansClustering.KMEANS_PLUS_PLUS_INIT`. relative_tolerance: A relative tolerance of change in the loss between iterations. Stops learning if the loss changes less than this amount. This may not work correctly if `use_mini_batch=True`. config: See @{tf.estimator.Estimator}. feature_columns: An optionable iterable containing all the feature columns used by the model. All items in the set should be feature column instances that can be passed to `tf.feature_column.input_layer`. If this is None, all features will be used. Raises: ValueError: An invalid argument was passed to `initial_clusters` or `distance_metric`. """ if isinstance(initial_clusters, str) and initial_clusters not in [ KMeansClustering.RANDOM_INIT, KMeansClustering.KMEANS_PLUS_PLUS_INIT ]: raise ValueError( "Unsupported initialization algorithm '%s'" % initial_clusters) if distance_metric not in [ KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE, KMeansClustering.COSINE_DISTANCE ]: raise ValueError("Unsupported distance metric '%s'" % distance_metric) super(KMeansClustering, self).__init__( model_fn=_ModelFn( num_clusters, initial_clusters, distance_metric, random_seed, use_mini_batch, mini_batch_steps_per_iteration, kmeans_plus_plus_num_retries, relative_tolerance, feature_columns).model_fn, model_dir=model_dir, config=config) def _predict_one_key(self, input_fn, predict_key): for result in self.predict(input_fn=input_fn, predict_keys=[predict_key]): yield result[predict_key] def predict_cluster_index(self, input_fn): """Finds the index of the closest cluster center to each input point. Args: input_fn: Input points. See @{tf.estimator.Estimator.predict}. Yields: The index of the closest cluster center for each input point. """ for index in self._predict_one_key(input_fn, KMeansClustering.CLUSTER_INDEX): yield index def score(self, input_fn): """Returns the sum of squared distances to nearest clusters. Note that this function is different from the corresponding one in sklearn which returns the negative sum. Args: input_fn: Input points. See @{tf.estimator.Estimator.evaluate}. Only one batch is retrieved. Returns: The sum of the squared distance from each point in the first batch of inputs to its nearest cluster center. """ return self.evaluate(input_fn=input_fn, steps=1)[KMeansClustering.SCORE] def transform(self, input_fn): """Transforms each input point to its distances to all cluster centers. Note that if `distance_metric=KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE`, this function returns the squared Euclidean distance while the corresponding sklearn function returns the Euclidean distance. Args: input_fn: Input points. See @{tf.estimator.Estimator.predict}. Yields: The distances from each input point to each cluster center. """ for distances in self._predict_one_key(input_fn, KMeansClustering.ALL_DISTANCES): yield distances def cluster_centers(self): """Returns the cluster centers.""" return self.get_variable_value(KMeansClustering.CLUSTER_CENTERS_VAR_NAME)

apache-2.0

yonglehou/scikit-learn

sklearn/linear_model/omp.py

126

30417

"""Orthogonal matching pursuit algorithms """ # Author: Vlad Niculae # # License: BSD 3 clause import warnings from distutils.version import LooseVersion import numpy as np from scipy import linalg from scipy.linalg.lapack import get_lapack_funcs from .base import LinearModel, _pre_fit from ..base import RegressorMixin from ..utils import as_float_array, check_array, check_X_y from ..cross_validation import check_cv from ..externals.joblib import Parallel, delayed import scipy solve_triangular_args = {} if LooseVersion(scipy.__version__) >= LooseVersion('0.12'): # check_finite=False is an optimization available only in scipy >=0.12 solve_triangular_args = {'check_finite': False} premature = """ Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. """ def _cholesky_omp(X, y, n_nonzero_coefs, tol=None, copy_X=True, return_path=False): """Orthogonal Matching Pursuit step using the Cholesky decomposition. Parameters ---------- X : array, shape (n_samples, n_features) Input dictionary. Columns are assumed to have unit norm. y : array, shape (n_samples,) Input targets n_nonzero_coefs : int Targeted number of non-zero elements tol : float Targeted squared error, if not None overrides n_nonzero_coefs. copy_X : bool, optional Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway. return_path : bool, optional. Default: False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. Returns ------- gamma : array, shape (n_nonzero_coefs,) Non-zero elements of the solution idx : array, shape (n_nonzero_coefs,) Indices of the positions of the elements in gamma within the solution vector coef : array, shape (n_features, n_nonzero_coefs) The first k values of column k correspond to the coefficient value for the active features at that step. The lower left triangle contains garbage. Only returned if ``return_path=True``. n_active : int Number of active features at convergence. """ if copy_X: X = X.copy('F') else: # even if we are allowed to overwrite, still copy it if bad order X = np.asfortranarray(X) min_float = np.finfo(X.dtype).eps nrm2, swap = linalg.get_blas_funcs(('nrm2', 'swap'), (X,)) potrs, = get_lapack_funcs(('potrs',), (X,)) alpha = np.dot(X.T, y) residual = y gamma = np.empty(0) n_active = 0 indices = np.arange(X.shape[1]) # keeping track of swapping max_features = X.shape[1] if tol is not None else n_nonzero_coefs if solve_triangular_args: # new scipy, don't need to initialize because check_finite=False L = np.empty((max_features, max_features), dtype=X.dtype) else: # old scipy, we need the garbage upper triangle to be non-Inf L = np.zeros((max_features, max_features), dtype=X.dtype) L[0, 0] = 1. if return_path: coefs = np.empty_like(L) while True: lam = np.argmax(np.abs(np.dot(X.T, residual))) if lam < n_active or alpha[lam] ** 2 < min_float: # atom already selected or inner product too small warnings.warn(premature, RuntimeWarning, stacklevel=2) break if n_active > 0: # Updates the Cholesky decomposition of X' X L[n_active, :n_active] = np.dot(X[:, :n_active].T, X[:, lam]) linalg.solve_triangular(L[:n_active, :n_active], L[n_active, :n_active], trans=0, lower=1, overwrite_b=True, **solve_triangular_args) v = nrm2(L[n_active, :n_active]) ** 2 if 1 - v <= min_float: # selected atoms are dependent warnings.warn(premature, RuntimeWarning, stacklevel=2) break L[n_active, n_active] = np.sqrt(1 - v) X.T[n_active], X.T[lam] = swap(X.T[n_active], X.T[lam]) alpha[n_active], alpha[lam] = alpha[lam], alpha[n_active] indices[n_active], indices[lam] = indices[lam], indices[n_active] n_active += 1 # solves LL'x = y as a composition of two triangular systems gamma, _ = potrs(L[:n_active, :n_active], alpha[:n_active], lower=True, overwrite_b=False) if return_path: coefs[:n_active, n_active - 1] = gamma residual = y - np.dot(X[:, :n_active], gamma) if tol is not None and nrm2(residual) ** 2 <= tol: break elif n_active == max_features: break if return_path: return gamma, indices[:n_active], coefs[:, :n_active], n_active else: return gamma, indices[:n_active], n_active def _gram_omp(Gram, Xy, n_nonzero_coefs, tol_0=None, tol=None, copy_Gram=True, copy_Xy=True, return_path=False): """Orthogonal Matching Pursuit step on a precomputed Gram matrix. This function uses the the Cholesky decomposition method. Parameters ---------- Gram : array, shape (n_features, n_features) Gram matrix of the input data matrix Xy : array, shape (n_features,) Input targets n_nonzero_coefs : int Targeted number of non-zero elements tol_0 : float Squared norm of y, required if tol is not None. tol : float Targeted squared error, if not None overrides n_nonzero_coefs. copy_Gram : bool, optional Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway. copy_Xy : bool, optional Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten. return_path : bool, optional. Default: False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. Returns ------- gamma : array, shape (n_nonzero_coefs,) Non-zero elements of the solution idx : array, shape (n_nonzero_coefs,) Indices of the positions of the elements in gamma within the solution vector coefs : array, shape (n_features, n_nonzero_coefs) The first k values of column k correspond to the coefficient value for the active features at that step. The lower left triangle contains garbage. Only returned if ``return_path=True``. n_active : int Number of active features at convergence. """ Gram = Gram.copy('F') if copy_Gram else np.asfortranarray(Gram) if copy_Xy: Xy = Xy.copy() min_float = np.finfo(Gram.dtype).eps nrm2, swap = linalg.get_blas_funcs(('nrm2', 'swap'), (Gram,)) potrs, = get_lapack_funcs(('potrs',), (Gram,)) indices = np.arange(len(Gram)) # keeping track of swapping alpha = Xy tol_curr = tol_0 delta = 0 gamma = np.empty(0) n_active = 0 max_features = len(Gram) if tol is not None else n_nonzero_coefs if solve_triangular_args: # new scipy, don't need to initialize because check_finite=False L = np.empty((max_features, max_features), dtype=Gram.dtype) else: # old scipy, we need the garbage upper triangle to be non-Inf L = np.zeros((max_features, max_features), dtype=Gram.dtype) L[0, 0] = 1. if return_path: coefs = np.empty_like(L) while True: lam = np.argmax(np.abs(alpha)) if lam < n_active or alpha[lam] ** 2 < min_float: # selected same atom twice, or inner product too small warnings.warn(premature, RuntimeWarning, stacklevel=3) break if n_active > 0: L[n_active, :n_active] = Gram[lam, :n_active] linalg.solve_triangular(L[:n_active, :n_active], L[n_active, :n_active], trans=0, lower=1, overwrite_b=True, **solve_triangular_args) v = nrm2(L[n_active, :n_active]) ** 2 if 1 - v <= min_float: # selected atoms are dependent warnings.warn(premature, RuntimeWarning, stacklevel=3) break L[n_active, n_active] = np.sqrt(1 - v) Gram[n_active], Gram[lam] = swap(Gram[n_active], Gram[lam]) Gram.T[n_active], Gram.T[lam] = swap(Gram.T[n_active], Gram.T[lam]) indices[n_active], indices[lam] = indices[lam], indices[n_active] Xy[n_active], Xy[lam] = Xy[lam], Xy[n_active] n_active += 1 # solves LL'x = y as a composition of two triangular systems gamma, _ = potrs(L[:n_active, :n_active], Xy[:n_active], lower=True, overwrite_b=False) if return_path: coefs[:n_active, n_active - 1] = gamma beta = np.dot(Gram[:, :n_active], gamma) alpha = Xy - beta if tol is not None: tol_curr += delta delta = np.inner(gamma, beta[:n_active]) tol_curr -= delta if abs(tol_curr) <= tol: break elif n_active == max_features: break if return_path: return gamma, indices[:n_active], coefs[:, :n_active], n_active else: return gamma, indices[:n_active], n_active def orthogonal_mp(X, y, n_nonzero_coefs=None, tol=None, precompute=False, copy_X=True, return_path=False, return_n_iter=False): """Orthogonal Matching Pursuit (OMP) Solves n_targets Orthogonal Matching Pursuit problems. An instance of the problem has the form: When parametrized by the number of non-zero coefficients using `n_nonzero_coefs`: argmin ||y - X\gamma||^2 subject to ||\gamma||_0 <= n_{nonzero coefs} When parametrized by error using the parameter `tol`: argmin ||\gamma||_0 subject to ||y - X\gamma||^2 <= tol Read more in the :ref:`User Guide <omp>`. Parameters ---------- X : array, shape (n_samples, n_features) Input data. Columns are assumed to have unit norm. y : array, shape (n_samples,) or (n_samples, n_targets) Input targets n_nonzero_coefs : int Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. tol : float Maximum norm of the residual. If not None, overrides n_nonzero_coefs. precompute : {True, False, 'auto'}, Whether to perform precomputations. Improves performance when n_targets or n_samples is very large. copy_X : bool, optional Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway. return_path : bool, optional. Default: False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. return_n_iter : bool, optional default False Whether or not to return the number of iterations. Returns ------- coef : array, shape (n_features,) or (n_features, n_targets) Coefficients of the OMP solution. If `return_path=True`, this contains the whole coefficient path. In this case its shape is (n_features, n_features) or (n_features, n_targets, n_features) and iterating over the last axis yields coefficients in increasing order of active features. n_iters : array-like or int Number of active features across every target. Returned only if `return_n_iter` is set to True. See also -------- OrthogonalMatchingPursuit orthogonal_mp_gram lars_path decomposition.sparse_encode Notes ----- Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf) This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf """ X = check_array(X, order='F', copy=copy_X) copy_X = False if y.ndim == 1: y = y.reshape(-1, 1) y = check_array(y) if y.shape[1] > 1: # subsequent targets will be affected copy_X = True if n_nonzero_coefs is None and tol is None: # default for n_nonzero_coefs is 0.1 * n_features # but at least one. n_nonzero_coefs = max(int(0.1 * X.shape[1]), 1) if tol is not None and tol < 0: raise ValueError("Epsilon cannot be negative") if tol is None and n_nonzero_coefs <= 0: raise ValueError("The number of atoms must be positive") if tol is None and n_nonzero_coefs > X.shape[1]: raise ValueError("The number of atoms cannot be more than the number " "of features") if precompute == 'auto': precompute = X.shape[0] > X.shape[1] if precompute: G = np.dot(X.T, X) G = np.asfortranarray(G) Xy = np.dot(X.T, y) if tol is not None: norms_squared = np.sum((y ** 2), axis=0) else: norms_squared = None return orthogonal_mp_gram(G, Xy, n_nonzero_coefs, tol, norms_squared, copy_Gram=copy_X, copy_Xy=False, return_path=return_path) if return_path: coef = np.zeros((X.shape[1], y.shape[1], X.shape[1])) else: coef = np.zeros((X.shape[1], y.shape[1])) n_iters = [] for k in range(y.shape[1]): out = _cholesky_omp( X, y[:, k], n_nonzero_coefs, tol, copy_X=copy_X, return_path=return_path) if return_path: _, idx, coefs, n_iter = out coef = coef[:, :, :len(idx)] for n_active, x in enumerate(coefs.T): coef[idx[:n_active + 1], k, n_active] = x[:n_active + 1] else: x, idx, n_iter = out coef[idx, k] = x n_iters.append(n_iter) if y.shape[1] == 1: n_iters = n_iters[0] if return_n_iter: return np.squeeze(coef), n_iters else: return np.squeeze(coef) def orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=None, tol=None, norms_squared=None, copy_Gram=True, copy_Xy=True, return_path=False, return_n_iter=False): """Gram Orthogonal Matching Pursuit (OMP) Solves n_targets Orthogonal Matching Pursuit problems using only the Gram matrix X.T * X and the product X.T * y. Read more in the :ref:`User Guide <omp>`. Parameters ---------- Gram : array, shape (n_features, n_features) Gram matrix of the input data: X.T * X Xy : array, shape (n_features,) or (n_features, n_targets) Input targets multiplied by X: X.T * y n_nonzero_coefs : int Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. tol : float Maximum norm of the residual. If not None, overrides n_nonzero_coefs. norms_squared : array-like, shape (n_targets,) Squared L2 norms of the lines of y. Required if tol is not None. copy_Gram : bool, optional Whether the gram matrix must be copied by the algorithm. A false value is only helpful if it is already Fortran-ordered, otherwise a copy is made anyway. copy_Xy : bool, optional Whether the covariance vector Xy must be copied by the algorithm. If False, it may be overwritten. return_path : bool, optional. Default: False Whether to return every value of the nonzero coefficients along the forward path. Useful for cross-validation. return_n_iter : bool, optional default False Whether or not to return the number of iterations. Returns ------- coef : array, shape (n_features,) or (n_features, n_targets) Coefficients of the OMP solution. If `return_path=True`, this contains the whole coefficient path. In this case its shape is (n_features, n_features) or (n_features, n_targets, n_features) and iterating over the last axis yields coefficients in increasing order of active features. n_iters : array-like or int Number of active features across every target. Returned only if `return_n_iter` is set to True. See also -------- OrthogonalMatchingPursuit orthogonal_mp lars_path decomposition.sparse_encode Notes ----- Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf) This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf """ Gram = check_array(Gram, order='F', copy=copy_Gram) Xy = np.asarray(Xy) if Xy.ndim > 1 and Xy.shape[1] > 1: # or subsequent target will be affected copy_Gram = True if Xy.ndim == 1: Xy = Xy[:, np.newaxis] if tol is not None: norms_squared = [norms_squared] if n_nonzero_coefs is None and tol is None: n_nonzero_coefs = int(0.1 * len(Gram)) if tol is not None and norms_squared is None: raise ValueError('Gram OMP needs the precomputed norms in order ' 'to evaluate the error sum of squares.') if tol is not None and tol < 0: raise ValueError("Epsilon cannot be negative") if tol is None and n_nonzero_coefs <= 0: raise ValueError("The number of atoms must be positive") if tol is None and n_nonzero_coefs > len(Gram): raise ValueError("The number of atoms cannot be more than the number " "of features") if return_path: coef = np.zeros((len(Gram), Xy.shape[1], len(Gram))) else: coef = np.zeros((len(Gram), Xy.shape[1])) n_iters = [] for k in range(Xy.shape[1]): out = _gram_omp( Gram, Xy[:, k], n_nonzero_coefs, norms_squared[k] if tol is not None else None, tol, copy_Gram=copy_Gram, copy_Xy=copy_Xy, return_path=return_path) if return_path: _, idx, coefs, n_iter = out coef = coef[:, :, :len(idx)] for n_active, x in enumerate(coefs.T): coef[idx[:n_active + 1], k, n_active] = x[:n_active + 1] else: x, idx, n_iter = out coef[idx, k] = x n_iters.append(n_iter) if Xy.shape[1] == 1: n_iters = n_iters[0] if return_n_iter: return np.squeeze(coef), n_iters else: return np.squeeze(coef) class OrthogonalMatchingPursuit(LinearModel, RegressorMixin): """Orthogonal Matching Pursuit model (OMP) Parameters ---------- n_nonzero_coefs : int, optional Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features. tol : float, optional Maximum norm of the residual. If not None, overrides n_nonzero_coefs. fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional If False, the regressors X are assumed to be already normalized. precompute : {True, False, 'auto'}, default 'auto' Whether to use a precomputed Gram and Xy matrix to speed up calculations. Improves performance when `n_targets` or `n_samples` is very large. Note that if you already have such matrices, you can pass them directly to the fit method. Read more in the :ref:`User Guide <omp>`. Attributes ---------- coef_ : array, shape (n_features,) or (n_features, n_targets) parameter vector (w in the formula) intercept_ : float or array, shape (n_targets,) independent term in decision function. n_iter_ : int or array-like Number of active features across every target. Notes ----- Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415. (http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf) This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad, M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit Technical Report - CS Technion, April 2008. http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf See also -------- orthogonal_mp orthogonal_mp_gram lars_path Lars LassoLars decomposition.sparse_encode """ def __init__(self, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize=True, precompute='auto'): self.n_nonzero_coefs = n_nonzero_coefs self.tol = tol self.fit_intercept = fit_intercept self.normalize = normalize self.precompute = precompute def fit(self, X, y): """Fit the model using X, y as training data. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data. y : array-like, shape (n_samples,) or (n_samples, n_targets) Target values. Returns ------- self : object returns an instance of self. """ X, y = check_X_y(X, y, multi_output=True, y_numeric=True) n_features = X.shape[1] X, y, X_mean, y_mean, X_std, Gram, Xy = \ _pre_fit(X, y, None, self.precompute, self.normalize, self.fit_intercept, copy=True) if y.ndim == 1: y = y[:, np.newaxis] if self.n_nonzero_coefs is None and self.tol is None: # default for n_nonzero_coefs is 0.1 * n_features # but at least one. self.n_nonzero_coefs_ = max(int(0.1 * n_features), 1) else: self.n_nonzero_coefs_ = self.n_nonzero_coefs if Gram is False: coef_, self.n_iter_ = orthogonal_mp( X, y, self.n_nonzero_coefs_, self.tol, precompute=False, copy_X=True, return_n_iter=True) else: norms_sq = np.sum(y ** 2, axis=0) if self.tol is not None else None coef_, self.n_iter_ = orthogonal_mp_gram( Gram, Xy=Xy, n_nonzero_coefs=self.n_nonzero_coefs_, tol=self.tol, norms_squared=norms_sq, copy_Gram=True, copy_Xy=True, return_n_iter=True) self.coef_ = coef_.T self._set_intercept(X_mean, y_mean, X_std) return self def _omp_path_residues(X_train, y_train, X_test, y_test, copy=True, fit_intercept=True, normalize=True, max_iter=100): """Compute the residues on left-out data for a full LARS path Parameters ----------- X_train : array, shape (n_samples, n_features) The data to fit the LARS on y_train : array, shape (n_samples) The target variable to fit LARS on X_test : array, shape (n_samples, n_features) The data to compute the residues on y_test : array, shape (n_samples) The target variable to compute the residues on copy : boolean, optional Whether X_train, X_test, y_train and y_test should be copied. If False, they may be overwritten. fit_intercept : boolean whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. max_iter : integer, optional Maximum numbers of iterations to perform, therefore maximum features to include. 100 by default. Returns ------- residues: array, shape (n_samples, max_features) Residues of the prediction on the test data """ if copy: X_train = X_train.copy() y_train = y_train.copy() X_test = X_test.copy() y_test = y_test.copy() if fit_intercept: X_mean = X_train.mean(axis=0) X_train -= X_mean X_test -= X_mean y_mean = y_train.mean(axis=0) y_train = as_float_array(y_train, copy=False) y_train -= y_mean y_test = as_float_array(y_test, copy=False) y_test -= y_mean if normalize: norms = np.sqrt(np.sum(X_train ** 2, axis=0)) nonzeros = np.flatnonzero(norms) X_train[:, nonzeros] /= norms[nonzeros] coefs = orthogonal_mp(X_train, y_train, n_nonzero_coefs=max_iter, tol=None, precompute=False, copy_X=False, return_path=True) if coefs.ndim == 1: coefs = coefs[:, np.newaxis] if normalize: coefs[nonzeros] /= norms[nonzeros][:, np.newaxis] return np.dot(coefs.T, X_test.T) - y_test class OrthogonalMatchingPursuitCV(LinearModel, RegressorMixin): """Cross-validated Orthogonal Matching Pursuit model (OMP) Parameters ---------- copy : bool, optional Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway. fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional If False, the regressors X are assumed to be already normalized. max_iter : integer, optional Maximum numbers of iterations to perform, therefore maximum features to include. 10% of ``n_features`` but at least 5 if available. cv : cross-validation generator, optional see :mod:`sklearn.cross_validation`. If ``None`` is passed, default to a 5-fold strategy n_jobs : integer, optional Number of CPUs to use during the cross validation. If ``-1``, use all the CPUs verbose : boolean or integer, optional Sets the verbosity amount Read more in the :ref:`User Guide <omp>`. Attributes ---------- intercept_ : float or array, shape (n_targets,) Independent term in decision function. coef_ : array, shape (n_features,) or (n_features, n_targets) Parameter vector (w in the problem formulation). n_nonzero_coefs_ : int Estimated number of non-zero coefficients giving the best mean squared error over the cross-validation folds. n_iter_ : int or array-like Number of active features across every target for the model refit with the best hyperparameters got by cross-validating across all folds. See also -------- orthogonal_mp orthogonal_mp_gram lars_path Lars LassoLars OrthogonalMatchingPursuit LarsCV LassoLarsCV decomposition.sparse_encode """ def __init__(self, copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=None, n_jobs=1, verbose=False): self.copy = copy self.fit_intercept = fit_intercept self.normalize = normalize self.max_iter = max_iter self.cv = cv self.n_jobs = n_jobs self.verbose = verbose def fit(self, X, y): """Fit the model using X, y as training data. Parameters ---------- X : array-like, shape [n_samples, n_features] Training data. y : array-like, shape [n_samples] Target values. Returns ------- self : object returns an instance of self. """ X, y = check_X_y(X, y, y_numeric=True) X = as_float_array(X, copy=False, force_all_finite=False) cv = check_cv(self.cv, X, y, classifier=False) max_iter = (min(max(int(0.1 * X.shape[1]), 5), X.shape[1]) if not self.max_iter else self.max_iter) cv_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)( delayed(_omp_path_residues)( X[train], y[train], X[test], y[test], self.copy, self.fit_intercept, self.normalize, max_iter) for train, test in cv) min_early_stop = min(fold.shape[0] for fold in cv_paths) mse_folds = np.array([(fold[:min_early_stop] ** 2).mean(axis=1) for fold in cv_paths]) best_n_nonzero_coefs = np.argmin(mse_folds.mean(axis=0)) + 1 self.n_nonzero_coefs_ = best_n_nonzero_coefs omp = OrthogonalMatchingPursuit(n_nonzero_coefs=best_n_nonzero_coefs, fit_intercept=self.fit_intercept, normalize=self.normalize) omp.fit(X, y) self.coef_ = omp.coef_ self.intercept_ = omp.intercept_ self.n_iter_ = omp.n_iter_ return self

bsd-3-clause

opendatadurban/scoda

scoda/public.py

1

45401

import itertools import operator from sqlalchemy_searchable import search from scoda.app import app from flask import request, url_for, redirect, flash, make_response, session, render_template, jsonify, Response, \ send_file from flask_security import current_user from itertools import zip_longest from sqlalchemy.sql import select from sqlalchemy import func, extract, desc,cast,Date from .models import db from .models import * from .models.user import UserAnalysis from .models.datasets import ExploreForm from .models.maps import MapForm, NightFormETH, NightFormJHB from pandas import read_sql_query import gviz_api import geojson, json import pandas as pd from .app import csrf from werkzeug.datastructures import MultiDict from urllib.parse import urlencode, urlparse, parse_qsl, urlsplit, parse_qs def grouper(iterable, n, fillvalue=None): "Collect data into fixed-length chunks or blocks" # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx" args = [iter(iterable)] * n return zip_longest(*args, fillvalue=fillvalue) @app.route('/help') def help(): return render_template('help/help.html') @app.route('/demographics/<region_id>/<city_ward_code>/download', methods=['GET']) def demographics_download(region_id, city_ward_code): region = Region.query.get(region_id).re_name if city_ward_code == 'None': query = db.session.query(Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == region_id).all() df = pd.DataFrame() df['Year'] = range(1996, 2031) for g in query: df['%s - Ward %s' % (region, g[1])] = list(g[0]) else: query = db.session.query(Ward.data, Ward.city_ward_code) \ .filter(Ward.city_ward_code == city_ward_code) \ .filter(Ward.region_id == region_id).all() df = pd.DataFrame() df['Year'] = range(1996, 2031) for g in query: df['%s - Ward %s' % (region, g[1])] = list(g[0]) return Response(df.to_csv(index=False), mimetype="text/csv", headers={"Content-disposition": "attachment; filename=demographics.csv"}) @app.route('/demographics', methods=['GET', 'POST']) def demographics(): analyses = [] if current_user.is_authenticated: query = db.session.query(UserAnalysis.id, UserAnalysis.ds_name, UserAnalysis.description) \ .filter(UserAnalysis.user_id == current_user.id).order_by(UserAnalysis.id.desc()) analyses = [] for i in grouper(query, 4): analyses.append(i) session['demo'] = [] if 'maps' not in session.keys(): session['maps'] = {0: {}, 1: {}} form1 = MapForm(prefix='form1', region_id='1', year=1) print(form1.city_ward_code.choices) status = 200 tour = 1 geometries1 = {} forms = [form1] if request.method == 'POST': if all(f.validate() for f in forms): for f, F in enumerate(forms): for field in F: if str(field.data) == 'None': field.data = session['maps'][str(f)][field.name[6:]] else: session['maps'][str(f)][field.name[6:]] = field.data tour = 0 # query = db.session.query(Area.geom.ST_AsGeoJSON(), Area.data) year1 = int(form1.year.data) year_ind1 = range(1996, 2031) if form1.city_ward_code.data == '': query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == form1.region_id.data) geometries1 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) if year1 == 0: flow = 0 else: flow = round(g[1][year1] - g[1][year1 - 1]) geometries1['features'].append({"type": "Feature", "properties": {"density": round(g[1][year1]), "flow": flow, "name": 'Ward %s' % g[2], "year": year_ind1[year1]}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == form1.region_id.data).all() region = db.session.query(Region.re_name).filter(Region.id == form1.region_id.data).first() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', '%s' % str(region[0])]] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m1 = 1.05 * max(df.sum(axis=0).tolist()) else: query = db.session.query(Area.geom.ST_AsGeoJSON(), Area.data, Area.city_ward_code) \ .filter(Area.city_ward_code == form1.city_ward_code.data) \ .filter(Area.region_id == form1.region_id.data) geometries1 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) if year1 == 0: flow = 0 else: flow = round(g[1][year1] - g[1][year1 - 1]) geometries1['features'].append( {"type": "Feature", "properties": {"density": round(g[1][year1]), "flow": flow, "name": 'Area %s' % g[2], "year": year_ind1[year1]}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.city_ward_code == form1.city_ward_code.data). \ filter(Ward.region_id == form1.region_id.data).first() region = db.session.query(Region.re_name).filter(Region.id == form1.region_id.data).first() region2 = db.session.query(Ward.city_ward_code).filter(Ward.city_ward_code == form1.city_ward_code.data) \ .first() results = [] for r in query: row = [val for val in list(r)] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', '%s - Ward %s' % (str(region[0]), str(region2[0]))]] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m1 = 1.05 * max(df.sum(axis=0).tolist()) query = db.session.query(Ward.city_ward_code).filter(Ward.region_id == form1.region_id.data).order_by( Ward.city_ward_code).distinct() form1.city_ward_code.choices = [[str(i), 'Ward %s' % row.city_ward_code] for i, row in enumerate(query.all() , start=1)] form1.city_ward_code.choices.insert(0, ('', 'View All')) return render_template('demographics/demographics.html', form1=form1, geometries1=geometries1, table1=table1, tour=tour, max1=m1, region1=form1.region_id.data, ward1=form1.city_ward_code.data, analyses=analyses) else: if request.is_xhr: status = 412 else: flash('Please correct the problems below and try again.', 'warning') else: session['maps'][0] = {'city_ward_code': '', 'region_id': 1, 'year': 1} session['maps'][1] = {'city_ward_code': '', 'region_id': 4, 'year': 1} query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == 1) geometries1 = {"type": "FeatureCollection", "features": []} geometries2 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries1['features'].append({"type": "Feature", "properties": {"density": round(g[1][1]), "flow": round(g[1][1] - g[1][0]), "name": 'Ward %s' % g[2], "year": 1997}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == 1).all() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', 'Johannesburg']] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m = 1.05 * max(df.sum(axis=0).tolist()) query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == 4) for g in query: d = json.loads(g[0]) geometries2['features'].append({"type": "Feature", "properties": {"density": round(g[1][1]), "flow": round(g[1][1] - g[1][0]), "name": 'Ward %s' % g[2], "year": 1997}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == 4).all() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table2 = [['Year', 'EThekwini']] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table2.append([str(y), val]) m2 = 1.05 * max(df.sum(axis=0).tolist()) return render_template('demographics/demographics.html', form1=form1, geometries1=geometries1, tour=tour, table1=table1, max1=m, region1=1, ward1=None, ward2=None, analyses=analyses ) if not request.is_xhr: query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == 1) geometries1 = {"type": "FeatureCollection", "features": []} geometries2 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries1['features'].append( {"type": "Feature", "properties": {"density": round(g[1][0]), "flow": 0, "name": g[2], "year": 1996}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) geometries2['features'].append( {"type": "Feature", "properties": {"density": round(g[1][0]), "flow": 0, "name": g[2], "year": 1996}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == 1).all() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', 'Johannesburg']] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m = 1.05 * max(df.sum(axis=0).tolist()) resp = make_response(render_template('demographics/demographics.html', form1=form1, geometries1=geometries1, table1=table1, tour=tour, max1=m, region1=1, ward1=None, analyses=analyses)) else: resp = '' return (resp, status, # ensure the browser refreshes the page when Back is pressed {'Cache-Control': 'no-cache, no-store, must-revalidate'}) @app.route('/api/demographics', methods=['GET', 'POST']) @csrf.exempt def api_demographics(): analyses = [] session['demo'] = [] if 'maps' not in session.keys(): session['maps'] = {0: {}, 1: {}} form1 = MapForm(prefix='form1', region_id='1', year=1) geometries1 = {} if request.method == 'POST': data = request.get_json() print(data) #data = request.data.decode('utf-8') #object = parse_qs(urlsplit('?' + data).query) #object = {key: str(value[0]) for key, value in object.items()} #if 'csrf_token' in object: del object['csrf_token'] #form1 = MapForm(MultiDict(object)) form1 = data print(form1['year']) #if form1.validate(): if form1: tour = 0 # query = db.session.query(Area.geom.ST_AsGeoJSON(), Area.data) #year1 = int(form1.year) year1 = int(form1['year']) year_ind1 = range(1996, 2031) #if form1.city_ward_code.data == '': if form1['city_ward_code'] == '': query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == form1['region_id']) geometries1 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) if year1 == 0: flow = 0 else: flow = round(g[1][year1] - g[1][year1 - 1]) geometries1['features'].append({"type": "Feature", "properties": {"density": round(g[1][year1]), "flow": flow, "name": 'Ward %s' % g[2], "year": year_ind1[year1]}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == form1['region_id']).all() region = db.session.query(Region.re_name).filter(Region.id == form1['region_id']).first() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', '%s' % str(region[0])]] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m1 = 1.05 * max(df.sum(axis=0).tolist()) else: query = db.session.query(Area.geom.ST_AsGeoJSON(), Area.data, Area.city_ward_code) \ .filter(Area.city_ward_code == int(form1['city_ward_code'])) \ .filter(Area.region_id == int(form1['region_id'])) geometries1 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) if year1 == 0: flow = 0 else: flow = round(g[1][year1] - g[1][year1 - 1]) geometries1['features'].append( {"type": "Feature", "properties": {"density": round(g[1][year1]), "flow": flow, "name": 'Area %s' % g[2], "year": year_ind1[year1]}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.city_ward_code == int(form1['city_ward_code'])). \ filter(Ward.region_id == int(form1['region_id'])).first() region = db.session.query(Region.re_name).filter(Region.id == int(form1['region_id'])).first() region2 = db.session.query(Ward.city_ward_code).filter(Ward.city_ward_code == int(form1['city_ward_code'])) \ .first() results = [] for r in query: row = [val for val in list(r)] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', '%s - Ward %s' % (str(region[0]), str(region2[0]))]] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m1 = 1.05 * max(df.sum(axis=0).tolist()) query = db.session.query(Ward.city_ward_code).filter(Ward.region_id == int(form1['region_id'])).order_by( Ward.city_ward_code).distinct() #form1.city_ward_code.choices = [[str(i), 'Ward %s' % row.city_ward_code] for i, row in enumerate(query.all() #, start=1)] #form1.city_ward_code.choices.insert(0, ('', 'View All')) resp = jsonify({'success': True, 'geometries1': geometries1,'table1':table1, 'tour':tour, 'max1':m1, 'region1':form1['region_id'],'ward1':form1['city_ward_code']}) resp.status_code = 200 return resp else: message = 'Please correct the problems below and try again.' resp = jsonify(message=message) resp.status_code = 500 return resp else: session['maps'][0] = {'city_ward_code': '', 'region_id': 1, 'year': 1} session['maps'][1] = {'city_ward_code': '', 'region_id': 4, 'year': 1} query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == 1) geometries1 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries1['features'].append({"type": "Feature", "properties": {"density": round(g[1][1]), "flow": round(g[1][1] - g[1][0]), "name": 'Ward %s' % g[2], "year": 1997}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == 1).all() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', 'Johannesburg']] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m = 1.05 * max(df.sum(axis=0).tolist()) resp = jsonify({'success': True, 'table1': table1, 'max1': m, 'region1': 1, 'ward1': None,'ward2':None, 'geometries1': geometries1, 'form_year':form1.year.choices,'form_ward':form1.city_ward_code.choices,'form_city':form1.region_id.choices}) resp.status_code = 200 return resp @app.route('/nightlights_jhb', methods=['GET', 'POST']) def demographics_night_jhb(): analyses = [] if current_user.is_authenticated: query = db.session.query(UserAnalysis.id, UserAnalysis.ds_name, UserAnalysis.description) \ .filter(UserAnalysis.user_id == current_user.id).order_by(UserAnalysis.id.desc()) analyses = [] for i in grouper(query, 4): analyses.append(i) session['night'] = [] form = NightFormJHB() status = 200 tour = 1 if request.method == 'POST': if form.validate(): tour = 0 if form.city_ward_code.data == '': query = db.session.query(Grid.geom.ST_AsGeoJSON(), Grid.data, Grid.city_grid_id, Grid.reference). \ filter(Grid.region_id == 1) geometries = {"type": "FeatureCollection", "features": []} bias_ind = [x / 10.0 for x in range(5, 21, 1)].index(float(form.grid_bias.data)) for g in query: d = json.loads(g[0]) geometries['features'].append({"type": "Feature", "properties": {"density": round(g[1][bias_ind] - g[3]), "name": 'Grid %s' % g[2], "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.city_ward_code).filter(Ward.region_id == 1).order_by( Ward.city_ward_code).distinct() form.city_ward_code.choices = [[str(i), 'Ward %s' % row.city_ward_code] for i, row in enumerate(query.all() , start=1)] form.city_ward_code.choices.insert(0, ('', 'View All')) return render_template('demographics/demographics_night.html', form=form, geometries=geometries, bias_val=form.grid_bias.data) else: w = db.session.query(Ward.id).filter(Ward.city_ward_code == form.city_ward_code.data)\ .filter(Ward.region_id == 1).first() w = Ward.query.get(w[0]) query = db.session.query(Grid.geom.ST_AsGeoJSON(), Grid.data, Grid.city_grid_id, Grid.reference) \ .filter(Grid.geom.intersects(w.geom)) geometries = {"type": "FeatureCollection", "features": []} bias_ind = [x / 10.0 for x in range(5, 21, 1)].index(float(form.grid_bias.data)) for g in query: d = json.loads(g[0]) geometries['features'].append( {"type": "Feature", "properties": {"density": round(g[1][bias_ind] - g[3]), "name": 'Grid %s' % g[2], "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code)\ .filter(Ward.city_ward_code == form.city_ward_code.data).filter(Ward.region_id == 1) geometries2 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries2['features'].append( {"type": "Feature", "properties": {"density": 0, "name": 'Ward %s' % form.city_ward_code.data, "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.city_ward_code).filter(Ward.region_id == 1).order_by( Ward.city_ward_code).distinct() form.city_ward_code.choices = [[str(i), 'Ward %s' % row.city_ward_code] for i, row in enumerate(query.all() , start=1)] form.city_ward_code.choices.insert(0, ('', 'View All')) return render_template('demographics/demographics_night.html', form=form, geometries=geometries, bias_val=form.grid_bias.data, geometries2=geometries2, ward=form.city_ward_code.data) else: if request.is_xhr: status = 412 else: flash('Please correct the problems below and try again.', 'warning') else: query = db.session.query(Grid.geom.ST_AsGeoJSON(), Grid.data, Grid.city_grid_id, Grid.reference). \ filter(Grid.region_id == 1) geometries = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries['features'].append({"type": "Feature", "properties": {"density": g[1][0] - g[3], "name": 'Grid %s' % g[2], "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) return render_template('demographics/demographics_night.html', form=form, bias_val=0.5, geometries=geometries, analyses=analyses) if not request.is_xhr: query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == 1) geometries1 = {"type": "FeatureCollection", "features": []} geometries2 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries1['features'].append( {"type": "Feature", "properties": {"density": round(g[1][0]), "flow": 0, "name": g[2], "year": 1996}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) geometries2['features'].append( {"type": "Feature", "properties": {"density": round(g[1][0]), "flow": 0, "name": g[2], "year": 1996}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == 1).all() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', 'Johannesburg']] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m = 1.05 * max(df.sum(axis=0).tolist()) resp = make_response(render_template('demographics/demographics.html', form1=form1, form2=form2, geometries1=geometries1, geometries2=geometries2, table1=table1, table2=table1, tour=tour, max1=m, max2=m, region1=1, region2=1, ward1=None, ward2=None, analyses=analyses)) else: resp = '' return (resp, status, # ensure the browser refreshes the page when Back is pressed {'Cache-Control': 'no-cache, no-store, must-revalidate'}) @app.route('/nightlights_eth', methods=['GET', 'POST']) def demographics_night_eth(): analyses = [] if current_user.is_authenticated: query = db.session.query(UserAnalysis.id, UserAnalysis.ds_name, UserAnalysis.description) \ .filter(UserAnalysis.user_id == current_user.id).order_by(UserAnalysis.id.desc()) analyses = [] for i in grouper(query, 4): analyses.append(i) session['night'] = [] form = NightFormETH() status = 200 tour = 1 if request.method == 'POST': if form.validate(): tour = 0 if form.city_ward_code.data == '': query = db.session.query(Grid.geom.ST_AsGeoJSON(), Grid.data, Grid.city_grid_id, Grid.reference). \ filter(Grid.region_id == 4) geometries = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries['features'].append({"type": "Feature", "properties": {"density": round(g[1][0]-g[3]), "name": 'Grid %s' % g[2], "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.city_ward_code).filter(Ward.region_id == 4).order_by( Ward.city_ward_code).distinct() form.city_ward_code.choices = [[str(i), 'Ward %s' % row.city_ward_code] for i, row in enumerate(query.all() , start=1)] form.city_ward_code.choices.insert(0, ('', 'View All')) return render_template('demographics/demographics_night_ETH.html', form=form, geometries=geometries) else: w = db.session.query(Ward.id).filter(Ward.city_ward_code == form.city_ward_code.data)\ .filter(Ward.region_id == 4).first() w = Ward.query.get(w[0]) query = db.session.query(Grid.geom.ST_AsGeoJSON(), Grid.data, Grid.city_grid_id, Grid.reference) \ .filter(Grid.geom.intersects(w.geom)).filter(Grid.region_id == 4) geometries = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries['features'].append( {"type": "Feature", "properties": {"density": round(g[1][0]-g[3]), "name": 'Grid %s' % g[2], "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code)\ .filter(Ward.city_ward_code == form.city_ward_code.data).filter(Ward.region_id == 4) geometries2 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries2['features'].append( {"type": "Feature", "properties": {"density": 0, "name": 'Ward %s' % form.city_ward_code.data, "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.city_ward_code).filter(Ward.region_id == 4).order_by( Ward.city_ward_code).distinct() form.city_ward_code.choices = [[str(i), 'Ward %s' % row.city_ward_code] for i, row in enumerate(query.all() , start=1)] form.city_ward_code.choices.insert(0, ('', 'View All')) return render_template('demographics/demographics_night_ETH.html', form=form, geometries=geometries, geometries2=geometries2, ward=form.city_ward_code.data) else: if request.is_xhr: status = 412 else: flash('Please correct the problems below and try again.', 'warning') else: query = db.session.query(Grid.geom.ST_AsGeoJSON(), Grid.data, Grid.city_grid_id, Grid.reference). \ filter(Grid.region_id == 4) geometries = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries['features'].append({"type": "Feature", "properties": {"density": round(g[1][0]-g[3]), "name": 'Grid %s' % g[2], "year": 2016}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) return render_template('demographics/demographics_night_ETH.html', form=form, geometries=geometries, analyses=analyses) if not request.is_xhr: query = db.session.query(Ward.geom.ST_AsGeoJSON(), Ward.data, Ward.city_ward_code). \ filter(Ward.region_id == 1) geometries1 = {"type": "FeatureCollection", "features": []} geometries2 = {"type": "FeatureCollection", "features": []} for g in query: d = json.loads(g[0]) geometries1['features'].append( {"type": "Feature", "properties": {"density": round(g[1][0]), "flow": 0, "name": g[2], "year": 1996}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) geometries2['features'].append( {"type": "Feature", "properties": {"density": round(g[1][0]), "flow": 0, "name": g[2], "year": 1996}, "geometry": {"type": "Polygon", "coordinates": d['coordinates']}}) query = db.session.query(Ward.data).filter(Ward.region_id == 1).all() results = [] for r in query: row = [val for val in list(r)[0]] results.append(row) df = pd.DataFrame(results).fillna(value=0) table1 = [['Year', 'Johannesburg']] for y, val in zip(range(1996, 2031), df.sum(axis=0).tolist()): table1.append([str(y), val]) m = 1.05 * max(df.sum(axis=0).tolist()) resp = make_response(render_template('demographics/demographics.html', form1=form1, form2=form2, geometries1=geometries1, geometries2=geometries2, table1=table1, table2=table1, tour=tour, max1=m, max2=m, region1=1, region2=1, ward1=None, ward2=None, analyses=analyses)) else: resp = '' return (resp, status, # ensure the browser refreshes the page when Back is pressed {'Cache-Control': 'no-cache, no-store, must-revalidate'}) @app.route('/return-land/') def land_gen(): return send_file('data/Ethekwini_Region_Data.xlsx', as_attachment=True) @app.route('/_parse_data', methods=['GET']) def parse_data(): kwargs = {} for i in ['dataset_id', 'indicator_id', 'region_id', 'type_id', 'theme_id', 'year']: param = request.args.get(i) if (i == 'year'): if (str(param) != 'Empty') and (param is not None) and (str(param) != ''): kwargs[i] = int(param) else: pass elif (param is not None) and (str(param) != ''): kwargs[i] = param session['explore'] = [i for i in kwargs] datasets = db.session.query(DataPoint.dataset_id).filter_by(**kwargs).distinct() indicators = db.session.query(DataPoint.indicator_id).filter_by(**kwargs).distinct() regions = db.session.query(DataPoint.region_id).filter_by(**kwargs).distinct() types = db.session.query(DataPoint.type_id).filter_by(**kwargs).distinct() themes = db.session.query(DataPoint.theme_id).filter_by(**kwargs).distinct() years = db.session.query(DataPoint.year).filter_by(**kwargs).distinct() response = {} remove_list = ['Poverty rate', 'Gini Coefficient', 'Gross Value Add', 'Exports', 'Multiple deprivation index', 'Human Development Index'] dataset_list = [(i[0], str(DataSet.query.filter_by(id=i).first().ds_name)) for i in datasets if str(DataSet.query.filter_by(id=i).first().ds_name) not in remove_list] if 'dataset_id' not in session['explore']: dataset_list.insert(0, ('', 'Empty')) else: dataset_list.insert(1, ('', 'Empty')) response['dataset'] = dataset_list indicator_list = [[i[0], str(Indicator.query.filter_by(id=i).first().in_name)] for i in indicators if str(Indicator.query.filter_by(id=i).first().in_name) not in remove_list] if 'indicator_id' not in session['explore']: indicator_list.insert(0, ('', 'Empty')) response['ind_ready'] = 0 else: indicator_list.insert(1, ('', 'Empty')) response['ind_ready'] = 1 response['indicator'] = indicator_list region_list = [(i[0], str(Region.query.filter_by(id=i).first().re_name)) for i in regions] if 'region_id' not in session['explore']: region_list.insert(0, ('', 'Empty')) else: region_list.insert(1, ('', 'Empty')) response['region'] = region_list type_list = [(i[0], str(Type.query.filter_by(id=i).first().ty_name)) for i in types] if 'type_id' not in session['explore']: type_list.insert(0, ('', 'Empty')) else: type_list.insert(1, ('', 'Empty')) response['type'] = type_list theme_list = [(i[0], str(Theme.query.filter_by(id=i).first().th_name)) for i in themes] if 'theme_id' not in session['explore']: theme_list.insert(0, ('', 'Empty')) else: theme_list.insert(1, ('', 'Empty')) response['theme'] = theme_list year_list = [(str(i), str(y[0])) for i, y in enumerate(sorted(years))] if 'year' not in session['explore']: year_list.insert(0, ('', 'Empty')) else: year_list.insert(1, ('', 'Empty')) response['year'] = year_list return jsonify(response) @app.route('/_parse_demo', methods=['GET']) def parse_demo(): kwargs = {} for i in ['region_id', 'ward_id']: param = request.args.get(i) if (param is not None) and (str(param) != ''): kwargs[i] = param session['demo'] = [i for i in kwargs] wards = db.session.query(Ward.city_ward_code).filter_by(**kwargs).distinct().order_by(Ward.city_ward_code) response = {} ward_list = [(str(i[0]), 'Ward %s' % Ward.query.filter_by(id=i).first().city_ward_code) for i in wards] if 'ward_id' not in session['demo']: ward_list.insert(0, ('', 'View All')) else: ward_list.insert(1, ('', 'View All')) response['wards'] = ward_list return jsonify(response) @app.route('/api/codebook', methods=['GET', 'POST']) @app.route('/api/codebook/<int:page>', methods=['GET', 'POST']) @csrf.exempt def api_codebook(page=1): query = db.session.query(CbIndicator). \ outerjoin(CbTheme, CbTheme.id == CbIndicator.theme_id). \ outerjoin(CbSource, CbSource.id == CbIndicator.source_id). \ outerjoin(CbUnit, CbUnit.id == CbIndicator.unit_id) if request.method == 'POST': data = request.get_json() print(f'data: {data}') if data['c88']: query = query.filter(CbIndicator.c88_theme.in_(data['c88'])) if data['socr']: query = query.filter(CbIndicator.socr_theme.in_(data['socr'])) if data['sdg']: query = query.filter(CbIndicator.sdg_theme.in_(data['sdg'])) if data['search']: query = search(query, data['search'], sort=True) # else: # query = query.limit(150).offset((page - 1) * 20) row_count = query.count() query = query.all() # query.sort(key=lambda x: x.code) result_list = [row_count] for day, dicts_for_group_code in itertools.groupby(query, key=lambda x:x.group_code): dicts_for_group_code = list(dicts_for_group_code) day_dict = { "id": str(dicts_for_group_code[0].id), "varCode": dicts_for_group_code[0].code, "groupCode": dicts_for_group_code[0].group_code, "indicator": dicts_for_group_code[0].name, "c88": dicts_for_group_code[0].c88_theme, "socr": dicts_for_group_code[0].socr_theme, "sdg": dicts_for_group_code[0].sdg_theme, "definition": dicts_for_group_code[0].definition, "source": dicts_for_group_code[0].source.name if dicts_for_group_code[0].source else None, "reportingResponsibility": dicts_for_group_code[0].reporting_responsibility, "notesOnCalculation": dicts_for_group_code[0].notes_on_calculation, "variableType": dicts_for_group_code[0].unit.name if dicts_for_group_code[0].unit else None, "frequencyOfCollection": dicts_for_group_code[0].frequency_of_collection, "automatibility": dicts_for_group_code[0].automatable, "granulity": dicts_for_group_code[0].granularity, "gathering_method": dicts_for_group_code[0].gathering_method, "expandability": dicts_for_group_code[0].expandable, "period": dicts_for_group_code[0].period, "unit_of_measurement": dicts_for_group_code[0].unit.name if dicts_for_group_code[0].unit else None, "source_link": dicts_for_group_code[0].url_link, "data_check":True if dicts_for_group_code[0].indicator_data else False } children = [] dicts_for_group_code.pop(0) for d in dicts_for_group_code: child = { "id": str(d.id), "varCode": d.code, "groupCode": d.group_code, "indicator": d.name, "c88": d.c88_theme, "socr": d.socr_theme, "sdg": d.sdg_theme, "definition": d.definition, "source": d.source.name if d.source else None, "reportingResponsibility": d.reporting_responsibility, "notesOnCalculation": d.notes_on_calculation, "variableType": d.unit.name if d.unit else None, "frequencyOfCollection": d.frequency_of_collection, "automatibility": d.automatable, "granulity": d.granularity, "gathering_method": d.gathering_method, "expandability": d.expandable, "period": d.period, "unit_of_measurement": d.unit.name if d.unit else None, "source_link": d.url_link, "data_check": bool(d.indicator_data), } children.append(child) day_dict.update({"children": children}) result_list.append(day_dict) return jsonify(result_list)

apache-2.0

mxjl620/scikit-learn

sklearn/datasets/tests/test_base.py

204

5878

import os import shutil import tempfile import warnings import nose import numpy from pickle import loads from pickle import dumps from sklearn.datasets import get_data_home from sklearn.datasets import clear_data_home from sklearn.datasets import load_files from sklearn.datasets import load_sample_images from sklearn.datasets import load_sample_image from sklearn.datasets import load_digits from sklearn.datasets import load_diabetes from sklearn.datasets import load_linnerud from sklearn.datasets import load_iris from sklearn.datasets import load_boston from sklearn.datasets.base import Bunch from sklearn.externals.six import b, u from sklearn.utils.testing import assert_false from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_raises DATA_HOME = tempfile.mkdtemp(prefix="scikit_learn_data_home_test_") LOAD_FILES_ROOT = tempfile.mkdtemp(prefix="scikit_learn_load_files_test_") TEST_CATEGORY_DIR1 = "" TEST_CATEGORY_DIR2 = "" def _remove_dir(path): if os.path.isdir(path): shutil.rmtree(path) def teardown_module(): """Test fixture (clean up) run once after all tests of this module""" for path in [DATA_HOME, LOAD_FILES_ROOT]: _remove_dir(path) def setup_load_files(): global TEST_CATEGORY_DIR1 global TEST_CATEGORY_DIR2 TEST_CATEGORY_DIR1 = tempfile.mkdtemp(dir=LOAD_FILES_ROOT) TEST_CATEGORY_DIR2 = tempfile.mkdtemp(dir=LOAD_FILES_ROOT) sample_file = tempfile.NamedTemporaryFile(dir=TEST_CATEGORY_DIR1, delete=False) sample_file.write(b("Hello World!\n")) sample_file.close() def teardown_load_files(): _remove_dir(TEST_CATEGORY_DIR1) _remove_dir(TEST_CATEGORY_DIR2) def test_data_home(): # get_data_home will point to a pre-existing folder data_home = get_data_home(data_home=DATA_HOME) assert_equal(data_home, DATA_HOME) assert_true(os.path.exists(data_home)) # clear_data_home will delete both the content and the folder it-self clear_data_home(data_home=data_home) assert_false(os.path.exists(data_home)) # if the folder is missing it will be created again data_home = get_data_home(data_home=DATA_HOME) assert_true(os.path.exists(data_home)) def test_default_empty_load_files(): res = load_files(LOAD_FILES_ROOT) assert_equal(len(res.filenames), 0) assert_equal(len(res.target_names), 0) assert_equal(res.DESCR, None) @nose.tools.with_setup(setup_load_files, teardown_load_files) def test_default_load_files(): res = load_files(LOAD_FILES_ROOT) assert_equal(len(res.filenames), 1) assert_equal(len(res.target_names), 2) assert_equal(res.DESCR, None) assert_equal(res.data, [b("Hello World!\n")]) @nose.tools.with_setup(setup_load_files, teardown_load_files) def test_load_files_w_categories_desc_and_encoding(): category = os.path.abspath(TEST_CATEGORY_DIR1).split('/').pop() res = load_files(LOAD_FILES_ROOT, description="test", categories=category, encoding="utf-8") assert_equal(len(res.filenames), 1) assert_equal(len(res.target_names), 1) assert_equal(res.DESCR, "test") assert_equal(res.data, [u("Hello World!\n")]) @nose.tools.with_setup(setup_load_files, teardown_load_files) def test_load_files_wo_load_content(): res = load_files(LOAD_FILES_ROOT, load_content=False) assert_equal(len(res.filenames), 1) assert_equal(len(res.target_names), 2) assert_equal(res.DESCR, None) assert_equal(res.get('data'), None) def test_load_sample_images(): try: res = load_sample_images() assert_equal(len(res.images), 2) assert_equal(len(res.filenames), 2) assert_true(res.DESCR) except ImportError: warnings.warn("Could not load sample images, PIL is not available.") def test_load_digits(): digits = load_digits() assert_equal(digits.data.shape, (1797, 64)) assert_equal(numpy.unique(digits.target).size, 10) def test_load_digits_n_class_lt_10(): digits = load_digits(9) assert_equal(digits.data.shape, (1617, 64)) assert_equal(numpy.unique(digits.target).size, 9) def test_load_sample_image(): try: china = load_sample_image('china.jpg') assert_equal(china.dtype, 'uint8') assert_equal(china.shape, (427, 640, 3)) except ImportError: warnings.warn("Could not load sample images, PIL is not available.") def test_load_missing_sample_image_error(): have_PIL = True try: try: from scipy.misc import imread except ImportError: from scipy.misc.pilutil import imread except ImportError: have_PIL = False if have_PIL: assert_raises(AttributeError, load_sample_image, 'blop.jpg') else: warnings.warn("Could not load sample images, PIL is not available.") def test_load_diabetes(): res = load_diabetes() assert_equal(res.data.shape, (442, 10)) assert_true(res.target.size, 442) def test_load_linnerud(): res = load_linnerud() assert_equal(res.data.shape, (20, 3)) assert_equal(res.target.shape, (20, 3)) assert_equal(len(res.target_names), 3) assert_true(res.DESCR) def test_load_iris(): res = load_iris() assert_equal(res.data.shape, (150, 4)) assert_equal(res.target.size, 150) assert_equal(res.target_names.size, 3) assert_true(res.DESCR) def test_load_boston(): res = load_boston() assert_equal(res.data.shape, (506, 13)) assert_equal(res.target.size, 506) assert_equal(res.feature_names.size, 13) assert_true(res.DESCR) def test_loads_dumps_bunch(): bunch = Bunch(x="x") bunch_from_pkl = loads(dumps(bunch)) bunch_from_pkl.x = "y" assert_equal(bunch_from_pkl['x'], bunch_from_pkl.x)

bsd-3-clause

Borisvl/amazon-dsstne

benchmarks/tf/autoencoder.py

3

7363

#!/usr/bin/env python """Trains sparse auto-encoder """ import argparse import math import numpy as np import sys import tensorflow as tf import time class FeedForwardNetwork(object): """Constructs a basic multi-layer neural network. """ def __init__(self, dim_x, dim_y, hidden_units, layers, gpu_mrr=True, activation=tf.nn.sigmoid): with tf.variable_scope("FFN"): # Create input/output variables self.x = x = tf.placeholder("float", shape=[None, dim_x]) self.y_ = y_ = tf.placeholder("float", shape=[None, dim_y]) # Create model: parameterized for k deep FF layers Hsize = [dim_x] + [hidden_units]*layers + [dim_y] print "Layers: %s" % str(Hsize) k = len(Hsize)-1 Wall = [None] * k ball = [None] * k for (layer, d1) in enumerate(Hsize[:-1]): d2 = Hsize[layer+1] Wall[layer] = tf.Variable(tf.random_normal(shape=[d1,d2],stddev=0.1)) ball[layer] = tf.Variable(tf.constant(0.1,shape=[d2])) Hact = [None] * (k+1) Hact[0] = x for layer in range(k): Hact[layer+1] = activation(tf.matmul(Hact[layer],Wall[layer]) + ball[layer]) # output is the last activation self.output = y = Hact[k] # Loss: numerically stable cross-entropy self.loss = loss = -tf.reduce_mean(y_*tf.log(y) + (tf.sub(1.0,y_)*tf.log(tf.sub(1.000001,y)))) # Optimizer self.lr = tf.Variable(1e-4, trainable=False) #self.train_step = tf.train.MomentumOptimizer(self.lr,momentum=0.9).minimize(loss) # Momentum gives very poor results in my experience here. #self.train_step = tf.train.AdamOptimizer(self.lr).minimize(loss) self.train_step = tf.train.RMSPropOptimizer(self.lr,decay=0.9).minimize(loss) self.avgloss = tf.reduce_mean(loss) class DataManager(object): """Encapsulates low-level data loading. """ def __init__(self, width): """Initialize loader width: the possible number of bits, which is the dimensionality of the vectors """ self.width = width # number of dimensions self.word_assignments = {} # maps from word to vector index self.W = None def index_for_word(self, word): """returns a list of k indices into the output vector corresponding to the bits for this word """ if not self.word_assignments.has_key(word): idx = len(self.word_assignments) self.word_assignments[word] = idx return self.word_assignments[word] def set_bit(self,row,word): bit = self.index_for_word(word) row[0,bit] = 1 return row def parse_line_into_words(self, line): """This is specific to the ReMo AIV format, but can be overridden """ line = line.split("\t")[1] # strip first column, which is customer id words = [x[:x.find(",")] for x in line.split(":")] return words def parse_cust_id(self, line): cust_id = line.split("\t")[0] return cust_id def load(self, filename): W_list = [] with open(filename,"r") as f: for line in f.readlines(): words = self.parse_line_into_words(line) row = np.zeros((1,self.width)) for word in words: row = self.set_bit(row,word) W_list.append(row) self.W = np.concatenate(W_list) return self.W class MiniBatcher(object): """Iterable set of input/output matrices for training or testing """ def __init__(self, x, y): self.batch_pos = 0 self.x = x self.y = y self.size = x.shape[0] if y.shape[0] != self.size: raise RuntimeError("X & Y must have same number of entries") def next(self, n): """Generates the next minibatch of n items. Returns a tuple of (x,y) """ if self.batch_pos + n > self.size: # We could be cleaner about wrapping self.batch_pos = 0 b = [] p1 = self.batch_pos p2 = p1 + n b.append( self.x[p1:p2] ) b.append( self.y[p1:p2] ) self.batch_pos = p2 return b class AutoencoderParser(object): """Responsible for loading a directory of data files (train/validate,input/output/etc). """ def __init__(self, cmd): """Takes a argparse command as configuration. Loads data, and makes it accessible as member variables: Accessible members: train: MiniBatcher object for training """ # Parse config from command dims = cmd.vocab_size # Set up loader mgr = DataManager(dims) # Load train data train_x = mgr.load(cmd.datafile) train_y = train_x self.train = MiniBatcher(train_x,train_y) def main(cmd): print("Loading datasets") all_data = AutoencoderParser(cmd) dims = cmd.vocab_size print("Constructing neural network") dnn = FeedForwardNetwork(dims, dims, cmd.hidden_units, cmd.layers) print("Initializing TensorFlow") # train the model sess = tf.Session() sess.run(tf.initialize_all_variables()) print("Starting training") with sess.as_default(): start_time = time.time() for i in range(cmd.max_iters): batch = all_data.train.next(cmd.batch_size) train_dict = { dnn.x: batch[0], dnn.y_: batch[1], dnn.lr: cmd.learning_rate, } dnn.train_step.run(feed_dict=train_dict) if i%cmd.eval_iters == 0: spd = (i+1) / (time.time() - start_time) print("Iter %d. %giter/s" % (i,spd)) print "Done training\n" def get_parser(): parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawTextHelpFormatter) parser.add_argument("-l","--layers", help="Number of hidden layers", type=int, default=1) parser.add_argument("--vocab_size", help="Number of unique items to auto-encode", type=int, default=30000) parser.add_argument("-u","--hidden_units", help="Size of hidden layer", type=int, default=8192) parser.add_argument("-i","--max_iters", help="Maximum number of iterations", type=int, default=1000) parser.add_argument("-b","--batch_size", help="minibatch size", type=int, default=512) parser.add_argument("-f","--datafile", help="file with input/output data for autoencoder", required=True) parser.add_argument("-v","--eval_iters", help="how often to print speed", type=int, default=5) parser.add_argument("--learning_rate", help="learning rate", type=float, default=1e-4) return parser if __name__ == "__main__": cmd = get_parser().parse_args() main(cmd)

apache-2.0

LucidAi/nlcd

husky/textutil.py

1

10826

# coding: utf-8 # Author: Vova Zaytsev <zaytsev@usc.edu> import re import lxml import nltk import ftfy import string import langid import difflib import logging import newspaper import readability import textblob.tokenizers from lxml.etree import ParserError from readability.readability import Unparseable from husky.markup import Markup from husky.markup import MarkupChunk from husky.markup import EntityReference class TextUtil(object): """ TODO """ RE_MULTIPLE_SPACE = re.compile(u"\s+", re.UNICODE) RE_WHITESPACE = re.compile(u" +", re.UNICODE) RE_EMPTY_STR = re.compile(u"^\s*$", re.UNICODE) RE_HTML_SPECIAL_CHARS = re.compile(u"&#?[a-z0-9]+;", re.UNICODE) RE_QUOTED_PHRASE = re.compile(u"\"([^\"]*)\"", re.UNICODE) RE_L_SPACE = re.compile(u"^\s+", re.UNICODE) RE_R_SPACE = re.compile(u"\s+$", re.UNICODE) RE_LQ = re.compile(u"^\s*\"\s*", re.UNICODE) RE_RQ = re.compile(u"\s*\"\s*$", re.UNICODE) def __init__(self): self.np_config = newspaper.configuration.Configuration() self.np_config.fetch_images = False self.seq_matcher = difflib.SequenceMatcher(None) def simplified_text(self, text, remove_punct=True): if text is None: return None text = text.lower() if isinstance(text, unicode): text = text.encode("utf-8") if remove_punct: text = text.translate(None, string.punctuation) text = self.RE_MULTIPLE_SPACE.sub(" ", text) text = " ".join(nltk.word_tokenize(text)) return text def get_pretty_markup(self, html): clean_html = document = readability.Document(html).summary() paragraphs = lxml.html.fromstring(clean_html).xpath("//p") return [p.text_content() for p in paragraphs] def extract_body(self, url, html): try: document = readability.Document(html) summary = document.summary() lang_id, _ = langid.classify(summary) try: doc_title = document.title() except TypeError: doc_title = None except ParserError: lang_id = "en" document = None summary = "" doc_title = None except Unparseable: lang_id = "en" document = None summary = "" doc_title = None try: article = newspaper.Article(url, language=lang_id, config=self.np_config) article.set_html(html) article.parse() except IOError: # If language is not found, try to use English parser. article = newspaper.Article(url, language="en", config=self.np_config) article.set_html(html) article.parse() a_text = "" if article.text is None or len(article.text) == 0 else article.text r_text = "" if summary is None or len(summary) == 0 else summary r_text = nltk.clean_html(r_text) if len(a_text) > len(r_text): text = a_text else: text = r_text a_title = "" if article.title is None or len(article.title) == 0 else article.title r_title = "" if doc_title is None or len(doc_title) == 0 else doc_title if len(r_title) == 0: title = a_title else: title = r_title if len(title) > 0: title = self.norm_sentence(title) if title[-1] not in string.punctuation: title += "." text = title + "\n" + text text = text.replace(".\n", " ") try: text = ftfy.fix_text(text, fix_entities=True, remove_terminal_escapes=True, uncurl_quotes=True, fix_line_breaks=True) except UnicodeError: logging.error("Error while parsing HTML from %r" % url) return "", "en" return text, lang_id def sent_tokenize(self, text): lines = text.split("\n") sentences = [] for line in lines: sentences.extend(textblob.tokenizers.sent_tokenize(line)) sentences = [sent for sent in sentences if not self.RE_EMPTY_STR.match(sent)] return [self.RE_WHITESPACE.sub(" ", sent) for sent in sentences] def extract_quoted(self, text): return self.RE_QUOTED_PHRASE.findall(text) def norm_sentence(self, sentence): sentence = self.RE_L_SPACE.sub("", sentence) sentence = self.RE_R_SPACE.sub("", sentence) return sentence def remove_lr_quotes(self, sentence): sentence = self.RE_LQ.sub("", sentence) sentence = self.RE_RQ.sub("", sentence) return sentence def words_count(self, sentence): if isinstance(sentence, unicode): sentence = sentence.encode("utf-8") no_punct = sentence.translate(None, string.punctuation) tokens = nltk.word_tokenize(no_punct) return len(tokens) def select_segments(self, sentences, quoted, min_length=10, min_size=5): sentences = map(self.remove_lr_quotes, map(self.norm_sentence, sentences)) quoted = map(self.remove_lr_quotes, map(self.norm_sentence, quoted)) segments = set() for segm in sentences + quoted: if len(segm) < min_length: continue if segm in segments: continue if self.words_count(segm) < min_size: continue segments.add(segm.replace("\"", "")) return list(segments) def compile_fuzzy_patterns(self, queries): patterns = {} for query_text in queries: query_re = self.compile_fuzzy_pattern(query_text) patterns[query_text] = query_re return patterns def compile_fuzzy_pattern(self, query_text): query_tokens = query_text.split(" ") query_re_tokens = ["(?:%s)?" % re.escape(query_tokens[0])] for i in xrange(1, len(query_tokens)): re_token = "(?: %s)?" % re.escape(query_tokens[i]) query_re_tokens.append(re_token) query_re = "(%s)" % ".*?".join(query_re_tokens) query_re = re.compile(query_re, re.UNICODE | re.IGNORECASE) return query_re def ffs(self, text, query_text, fuzzy_pattern, min_threshold=0.5, max_threshold=1.5, min_m_size=5, min_ratio=0.5): if query_text in text: return True min_len = max(len(query_text) * min_threshold, min_m_size) max_len = len(query_text) * max_threshold matches = [m for m in fuzzy_pattern.findall(text) if min_len < len(m) < max_len] if len(matches) == 0: return False self.seq_matcher.set_seq1(query_text) for m in matches: self.seq_matcher.set_seq2(m) ratio = self.seq_matcher.ratio() if ratio > min_ratio: return True return False def fuzzy_search(self, text, query_text, fuzzy_pattern, min_threshold=0.5, max_threshold=1.5, min_m_size=5): min_len = max(len(query_text) * min_threshold, min_m_size) max_len = len(query_text) * max_threshold mm = fuzzy_pattern.findall(text) matches = [m for m in mm if min_len < len(m) < max_len] if len(matches) == 0: return 0.0, None best_ratio = 0.0 best_match = None self.seq_matcher.set_seq1(query_text) for m in matches: self.seq_matcher.set_seq2(m) ratio = self.seq_matcher.ratio() if ratio > best_ratio: best_ratio = ratio best_match = m return best_ratio, best_match def fuzzy_group_search(self, text, query_text, fuzzy_pattern, min_threshold=0.5, max_threshold=1.5, min_m_size=5): min_len = max(len(query_text) * min_threshold, min_m_size) max_len = len(query_text) * max_threshold match_groups = [m for m in fuzzy_pattern.finditer(text)] matches = [] for m_g in match_groups: if min_len < len(m_g.group()) < max_len: matches.append(m_g) if len(match_groups) == 0: return 0.0, None best_ratio = 0.0 best_match = None self.seq_matcher.set_seq1(query_text) for m in matches: self.seq_matcher.set_seq2(m.group()) ratio = self.seq_matcher.ratio() if ratio > best_ratio: best_ratio = ratio best_match = m return best_ratio, best_match def generate_markup(self, title, text, paragraphs, bodies, trim=32, min_ratio=0.00): paragraphs = [title] + paragraphs markup = Markup.blank() for i, paragraph in enumerate(paragraphs): paragraph_markup = MarkupChunk(text=paragraph) if i == 0: markup.set_title(paragraph_markup) else: markup.add_body_element(paragraph_markup) sentences = self.sent_tokenize(paragraph) quotes = self.extract_quoted(paragraph) segments = self.select_segments(sentences, quotes, min_size=5) segments = [self.simplified_text(s) for s in segments] if trim is not None and trim > 0: for i in xrange(len(segments)): segments[i] = " ".join(segments[i].split()[:trim]) triggered_regexes = [] for segment in segments: fuzzy_pattern = self.compile_fuzzy_pattern(segment) found_refs = [] for body, body_id in bodies: if self.ffs(body, segment, fuzzy_pattern, min_ratio=0.3): found_refs.append(body_id) if len(found_refs) > 0: triggered_regexes.append((segment, fuzzy_pattern, found_refs)) for segment, fuzzy_pattern, found_refs in triggered_regexes: ratio, match = self.fuzzy_group_search(paragraph, segment, fuzzy_pattern, min_threshold=0, max_threshold=10) if ratio >= min_ratio: ref_object = EntityReference(span=match.span(), match=match.group(), references=found_refs, extra_attr={"ratio": ratio}) paragraph_markup.add_ref(ref_object) return markup

mit

bdoner/SickRage

lib/guessit/transfo/guess_video_rexps.py

40

3082

#!/usr/bin/env python # -*- coding: utf-8 -*- # # GuessIt - A library for guessing information from filenames # Copyright (c) 2013 Nicolas Wack <wackou@gmail.com> # # GuessIt is free software; you can redistribute it and/or modify it under # the terms of the Lesser GNU General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # GuessIt is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # Lesser GNU General Public License for more details. # # You should have received a copy of the Lesser GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # from __future__ import absolute_import, division, print_function, \ unicode_literals from guessit.patterns import _psep from guessit.containers import PropertiesContainer from guessit.plugins.transformers import Transformer from guessit.matcher import GuessFinder from guessit.patterns.numeral import parse_numeral class GuessVideoRexps(Transformer): def __init__(self): Transformer.__init__(self, 25) self.container = PropertiesContainer(canonical_from_pattern=False) self.container.register_property(None, 'cd' + _psep + '(?P<cdNumber>[0-9])(?:' + _psep + 'of' + _psep + '(?P<cdNumberTotal>[0-9]))?', confidence=1.0, enhance=False, global_span=True, formatter=parse_numeral) self.container.register_property('cdNumberTotal', '([1-9])' + _psep + 'cds?', confidence=0.9, enhance=False, formatter=parse_numeral) self.container.register_property('bonusNumber', 'x([0-9]{1,2})', enhance=False, global_span=True, formatter=parse_numeral) self.container.register_property('filmNumber', 'f([0-9]{1,2})', enhance=False, global_span=True, formatter=parse_numeral) self.container.register_property('edition', 'collector', 'collector-edition', 'edition-collector', canonical_form='Collector Edition') self.container.register_property('edition', 'special-edition', 'edition-special', canonical_form='Special Edition') self.container.register_property('edition', 'criterion', 'criterion-edition', 'edition-criterion', canonical_form='Criterion Edition') self.container.register_property('edition', 'deluxe', 'cdeluxe-edition', 'edition-deluxe', canonical_form='Deluxe Edition') self.container.register_property('edition', 'director\'?s?-cut', 'director\'?s?-cut-edition', 'edition-director\'?s?-cut', canonical_form='Director\'s cut') def supported_properties(self): return self.container.get_supported_properties() def guess_video_rexps(self, string, node=None, options=None): found = self.container.find_properties(string, node, options) return self.container.as_guess(found, string) def process(self, mtree, options=None): GuessFinder(self.guess_video_rexps, None, self.log, options).process_nodes(mtree.unidentified_leaves())

gpl-3.0

ageron/tensorflow

tensorflow/contrib/learn/python/learn/estimators/_sklearn.py

12

6775

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """sklearn cross-support (deprecated).""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os import numpy as np import six def _pprint(d): return ', '.join(['%s=%s' % (key, str(value)) for key, value in d.items()]) class _BaseEstimator(object): """This is a cross-import when sklearn is not available. Adopted from sklearn.BaseEstimator implementation. https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py """ def get_params(self, deep=True): """Get parameters for this estimator. Args: deep: boolean, optional If `True`, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values. """ out = dict() param_names = [name for name in self.__dict__ if not name.startswith('_')] for key in param_names: value = getattr(self, key, None) if isinstance(value, collections.Callable): continue # XXX: should we rather test if instance of estimator? if deep and hasattr(value, 'get_params'): deep_items = value.get_params().items() out.update((key + '__' + k, val) for k, val in deep_items) out[key] = value return out def set_params(self, **params): """Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names. """ if not params: # Simple optimisation to gain speed (inspect is slow) return self valid_params = self.get_params(deep=True) for key, value in six.iteritems(params): split = key.split('__', 1) if len(split) > 1: # nested objects case name, sub_name = split if name not in valid_params: raise ValueError('Invalid parameter %s for estimator %s. ' 'Check the list of available parameters ' 'with `estimator.get_params().keys()`.' % (name, self)) sub_object = valid_params[name] sub_object.set_params(**{sub_name: value}) else: # simple objects case if key not in valid_params: raise ValueError('Invalid parameter %s for estimator %s. ' 'Check the list of available parameters ' 'with `estimator.get_params().keys()`.' % (key, self.__class__.__name__)) setattr(self, key, value) return self def __repr__(self): class_name = self.__class__.__name__ return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False)),) # pylint: disable=old-style-class class _ClassifierMixin(): """Mixin class for all classifiers.""" pass class _RegressorMixin(): """Mixin class for all regression estimators.""" pass class _TransformerMixin(): """Mixin class for all transformer estimators.""" class NotFittedError(ValueError, AttributeError): """Exception class to raise if estimator is used before fitting. USE OF THIS EXCEPTION IS DEPRECATED. This class inherits from both ValueError and AttributeError to help with exception handling and backward compatibility. Examples: >>> from sklearn.svm import LinearSVC >>> from sklearn.exceptions import NotFittedError >>> try: ... LinearSVC().predict([[1, 2], [2, 3], [3, 4]]) ... except NotFittedError as e: ... print(repr(e)) ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS NotFittedError('This LinearSVC instance is not fitted yet',) Copied from https://github.com/scikit-learn/scikit-learn/master/sklearn/exceptions.py """ # pylint: enable=old-style-class def _accuracy_score(y_true, y_pred): score = y_true == y_pred return np.average(score) def _mean_squared_error(y_true, y_pred): if len(y_true.shape) > 1: y_true = np.squeeze(y_true) if len(y_pred.shape) > 1: y_pred = np.squeeze(y_pred) return np.average((y_true - y_pred)**2) def _train_test_split(*args, **options): # pylint: disable=missing-docstring test_size = options.pop('test_size', None) train_size = options.pop('train_size', None) random_state = options.pop('random_state', None) if test_size is None and train_size is None: train_size = 0.75 elif train_size is None: train_size = 1 - test_size train_size = int(train_size * args[0].shape[0]) np.random.seed(random_state) indices = np.random.permutation(args[0].shape[0]) train_idx, test_idx = indices[:train_size], indices[train_size:] result = [] for x in args: result += [x.take(train_idx, axis=0), x.take(test_idx, axis=0)] return tuple(result) # If "TENSORFLOW_SKLEARN" flag is defined then try to import from sklearn. TRY_IMPORT_SKLEARN = os.environ.get('TENSORFLOW_SKLEARN', False) if TRY_IMPORT_SKLEARN: # pylint: disable=g-import-not-at-top,g-multiple-import,unused-import from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin from sklearn.metrics import accuracy_score, log_loss, mean_squared_error from sklearn.model_selection import train_test_split try: from sklearn.exceptions import NotFittedError except ImportError: try: from sklearn.utils.validation import NotFittedError except ImportError: pass else: # Naive implementations of sklearn classes and functions. BaseEstimator = _BaseEstimator ClassifierMixin = _ClassifierMixin RegressorMixin = _RegressorMixin TransformerMixin = _TransformerMixin accuracy_score = _accuracy_score log_loss = None mean_squared_error = _mean_squared_error train_test_split = _train_test_split

apache-2.0

IONISx/edx-platform

lms/djangoapps/course_blocks/transformers/tests/test_helpers.py

9

12710

""" Test helpers for testing course block transformers. """ from student.tests.factories import CourseEnrollmentFactory, UserFactory from xmodule.modulestore.django import modulestore from xmodule.modulestore.tests.factories import CourseFactory, ItemFactory from xmodule.modulestore.tests.django_utils import ModuleStoreTestCase from lms.djangoapps.courseware.access import has_access from ...api import get_course_blocks class CourseStructureTestCase(ModuleStoreTestCase): """ Helper for test cases that need to build course structures. """ def setUp(self): """ Create users. """ super(CourseStructureTestCase, self).setUp() # Set up users. self.password = 'test' self.user = UserFactory.create(password=self.password) self.staff = UserFactory.create(password=self.password, is_staff=True) def create_block_id(self, block_type, block_ref): """ Returns the block id (display name) that is used in the test course structures for the given block type and block reference string. """ return '{}_{}'.format(block_type, block_ref) def build_xblock(self, block_hierarchy, block_map, parent): """ Build an XBlock, add it to block_map, and call build_xblock on the children defined in block_dict. Arguments: block_hierarchy (BlockStructureDict): Definition of hierarchy, from this block down. block_map (dict[str: XBlock]): Mapping from '#ref' values to their XBlocks. parent (XBlock): Parent block for this xBlock. """ block_type = block_hierarchy['#type'] block_ref = block_hierarchy['#ref'] factory = (CourseFactory if block_type == 'course' else ItemFactory) kwargs = {key: value for key, value in block_hierarchy.iteritems() if key[0] != '#'} if block_type != 'course': kwargs['category'] = block_type if parent: kwargs['parent'] = parent xblock = factory.create( display_name=self.create_block_id(block_type, block_ref), publish_item=True, **kwargs ) block_map[block_ref] = xblock for child_hierarchy in block_hierarchy.get('#children', []): self.build_xblock(child_hierarchy, block_map, xblock) def add_parents(self, block_hierarchy, block_map): """ Recursively traverse the block_hierarchy and add additional parents. This method is expected to be called only after all blocks have been created. The additional parents are obtained from the '#parents' field and is expected to be a list of '#ref' values of the parents. Note: if a '#parents' field is found, the block is removed from the course block since it is expected to not belong to the root. If the block is meant to be a direct child of the course as well, the course should be explicitly listed in '#parents'. Arguments: block_hierarchy (BlockStructureDict): Definition of block hierarchy. block_map (dict[str: XBlock]): Mapping from '#ref' values to their XBlocks. """ parents = block_hierarchy.get('#parents', []) if parents: block_key = block_map[block_hierarchy['#ref']].location # First remove the block from the course. # It would be re-added to the course if the course was # explicitly listed in parents. course = modulestore().get_item(block_map['course'].location) course.children.remove(block_key) block_map['course'] = update_block(course) # Add this to block to each listed parent. for parent_ref in parents: parent_block = modulestore().get_item(block_map[parent_ref].location) parent_block.children.append(block_key) block_map[parent_ref] = update_block(parent_block) # recursively call the children for child_hierarchy in block_hierarchy.get('#children', []): self.add_parents(child_hierarchy, block_map) def build_course(self, course_hierarchy): """ Build a hierarchy of XBlocks. Arguments: course_hierarchy (BlockStructureDict): Definition of course hierarchy. where a BlockStructureDict is a list of dicts in the form { 'key1': 'value1', ... 'keyN': 'valueN', '#type': block_type, '#ref': short_string_for_referencing_block, '#children': list[BlockStructureDict], '#parents': list['#ref' values] } Special keys start with '#'; the rest just get passed as kwargs to Factory.create. Note: the caller has a choice of whether to create (1) a nested block structure with children blocks embedded within their parents, or (2) a flat block structure with children blocks defined alongside their parents and attached via the #parents field, or (3) a combination of both #1 and #2 used for whichever blocks. Note 2: When the #parents field is used in addition to the nested pattern for a block, it specifies additional parents that aren't already implied by having the block exist within another block's #children field. Returns: dict[str: XBlock]: Mapping from '#ref' values to their XBlocks. """ block_map = {} # build the course tree for block_hierarchy in course_hierarchy: self.build_xblock(block_hierarchy, block_map, parent=None) # add additional parents if the course is a DAG or built # linearly (without specifying '#children' values) for block_hierarchy in course_hierarchy: self.add_parents(block_hierarchy, block_map) return block_map def get_block_key_set(self, blocks, *refs): """ Gets the set of usage keys that correspond to the list of #ref values as defined on blocks. Returns: set[UsageKey] """ xblocks = (blocks[ref] for ref in refs) return set([xblock.location for xblock in xblocks]) class BlockParentsMapTestCase(ModuleStoreTestCase): """ Test helper class for creating a test course of a graph of vertical blocks based on a parents_map. """ # Tree formed by parent_map: # 0 # / \ # 1 2 # / \ / \ # 3 4 / 5 # \ / # 6 # Note the parents must always have lower indices than their # children. parents_map = [[], [0], [0], [1], [1], [2], [2, 4]] def setUp(self, **kwargs): super(BlockParentsMapTestCase, self).setUp(**kwargs) # create the course self.course = CourseFactory.create() # an ordered list of block locations, where the index # corresponds to the block's index in the parents_map. self.xblock_keys = [self.course.location] # create all other blocks in the course for i, parents_index in enumerate(self.parents_map): if i == 0: continue # course already created # create the block as a vertical self.xblock_keys.append( ItemFactory.create( parent=self.get_block(parents_index[0]), category="vertical", ).location ) # add additional parents if len(parents_index) > 1: for index in range(1, len(parents_index)): parent_index = parents_index[index] parent_block = self.get_block(parent_index) parent_block.children.append(self.xblock_keys[i]) update_block(parent_block) self.password = 'test' self.student = UserFactory.create(is_staff=False, username='test_student', password=self.password) self.staff = UserFactory.create(is_staff=True, username='test_staff', password=self.password) CourseEnrollmentFactory.create(is_active=True, mode='honor', user=self.student, course_id=self.course.id) def assert_transform_results( self, test_user, expected_user_accessible_blocks, blocks_with_differing_access, transformers=None, ): """ Verifies the results of transforming the blocks in the course. Arguments: test_user (User): The non-staff user that is being tested. For example, self.student. expected_user_accessible_blocks (set(int)): Set of blocks (indices) that a student user is expected to have access to after the transformers are executed. blocks_with_differing_access (set(int)): Set of blocks (indices) whose access will differ from the transformers result and the current implementation of has_access. transformers (BlockStructureTransformer): An optional list of transformer that are to be executed. If not provided, the default value used by get_course_blocks is used. """ def check_results(user, expected_accessible_blocks, blocks_with_differing_access): """ Verifies the results of transforming the blocks in the course for the given user. """ self.client.login(username=user.username, password=self.password) block_structure = get_course_blocks(user, self.course.location, transformers=transformers) # Enumerate through all the blocks that were created in the # course for i, xblock_key in enumerate(self.xblock_keys): # verify existence of the block block_structure_result = block_structure.has_block(xblock_key) has_access_result = bool(has_access(user, 'load', self.get_block(i), course_key=self.course.id)) # compare with expected value self.assertEquals( block_structure_result, i in expected_accessible_blocks, "block_structure return value {0} not equal to expected value for block {1} for user {2}".format( block_structure_result, i, user.username ) ) # compare with has_access result if i in blocks_with_differing_access: self.assertNotEqual( block_structure_result, has_access_result, "block structure ({0}) & has_access ({1}) results are equal for block {2} for user {3}".format( block_structure_result, has_access_result, i, user.username ) ) else: self.assertEquals( block_structure_result, has_access_result, "block structure ({0}) & has_access ({1}) results not equal for block {2} for user {3}".format( block_structure_result, has_access_result, i, user.username ) ) self.client.logout() # verify given test user has access to expected blocks check_results( test_user, expected_user_accessible_blocks, blocks_with_differing_access ) # verify staff has access to all blocks check_results(self.staff, set(range(len(self.parents_map))), {}) def get_block(self, block_index): """ Helper method to retrieve the requested block (index) from the modulestore """ return modulestore().get_item(self.xblock_keys[block_index]) def update_block(block): """ Helper method to update the block in the modulestore """ return modulestore().update_item(block, 'test_user') def create_location(org, course, run, block_type, block_id): """ Returns the usage key for the given key parameters using the default modulestore """ return modulestore().make_course_key(org, course, run).make_usage_key(block_type, block_id)

agpl-3.0

pkruskal/scikit-learn

sklearn/linear_model/randomized_l1.py

33

23358

""" Randomized Lasso/Logistic: feature selection based on Lasso and sparse Logistic Regression """ # Author: Gael Varoquaux, Alexandre Gramfort # # License: BSD 3 clause import itertools from abc import ABCMeta, abstractmethod import warnings import numpy as np from scipy.sparse import issparse from scipy import sparse from scipy.interpolate import interp1d from .base import center_data from ..base import BaseEstimator, TransformerMixin from ..externals import six from ..externals.joblib import Memory, Parallel, delayed from ..utils import (as_float_array, check_random_state, check_X_y, check_array, safe_mask, ConvergenceWarning) from ..utils.validation import check_is_fitted from .least_angle import lars_path, LassoLarsIC from .logistic import LogisticRegression ############################################################################### # Randomized linear model: feature selection def _resample_model(estimator_func, X, y, scaling=.5, n_resampling=200, n_jobs=1, verbose=False, pre_dispatch='3*n_jobs', random_state=None, sample_fraction=.75, **params): random_state = check_random_state(random_state) # We are generating 1 - weights, and not weights n_samples, n_features = X.shape if not (0 < scaling < 1): raise ValueError( "'scaling' should be between 0 and 1. Got %r instead." % scaling) scaling = 1. - scaling scores_ = 0.0 for active_set in Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)( delayed(estimator_func)( X, y, weights=scaling * random_state.random_integers( 0, 1, size=(n_features,)), mask=(random_state.rand(n_samples) < sample_fraction), verbose=max(0, verbose - 1), **params) for _ in range(n_resampling)): scores_ += active_set scores_ /= n_resampling return scores_ class BaseRandomizedLinearModel(six.with_metaclass(ABCMeta, BaseEstimator, TransformerMixin)): """Base class to implement randomized linear models for feature selection This implements the strategy by Meinshausen and Buhlman: stability selection with randomized sampling, and random re-weighting of the penalty. """ @abstractmethod def __init__(self): pass _center_data = staticmethod(center_data) def fit(self, X, y): """Fit the model using X, y as training data. Parameters ---------- X : array-like, sparse matrix shape = [n_samples, n_features] Training data. y : array-like, shape = [n_samples] Target values. Returns ------- self : object Returns an instance of self. """ X, y = check_X_y(X, y, ['csr', 'csc'], y_numeric=True) X = as_float_array(X, copy=False) n_samples, n_features = X.shape X, y, X_mean, y_mean, X_std = self._center_data(X, y, self.fit_intercept, self.normalize) estimator_func, params = self._make_estimator_and_params(X, y) memory = self.memory if isinstance(memory, six.string_types): memory = Memory(cachedir=memory) scores_ = memory.cache( _resample_model, ignore=['verbose', 'n_jobs', 'pre_dispatch'] )( estimator_func, X, y, scaling=self.scaling, n_resampling=self.n_resampling, n_jobs=self.n_jobs, verbose=self.verbose, pre_dispatch=self.pre_dispatch, random_state=self.random_state, sample_fraction=self.sample_fraction, **params) if scores_.ndim == 1: scores_ = scores_[:, np.newaxis] self.all_scores_ = scores_ self.scores_ = np.max(self.all_scores_, axis=1) return self def _make_estimator_and_params(self, X, y): """Return the parameters passed to the estimator""" raise NotImplementedError def get_support(self, indices=False): """Return a mask, or list, of the features/indices selected.""" check_is_fitted(self, 'scores_') mask = self.scores_ > self.selection_threshold return mask if not indices else np.where(mask)[0] # XXX: the two function below are copy/pasted from feature_selection, # Should we add an intermediate base class? def transform(self, X): """Transform a new matrix using the selected features""" mask = self.get_support() X = check_array(X) if len(mask) != X.shape[1]: raise ValueError("X has a different shape than during fitting.") return check_array(X)[:, safe_mask(X, mask)] def inverse_transform(self, X): """Transform a new matrix using the selected features""" support = self.get_support() if X.ndim == 1: X = X[None, :] Xt = np.zeros((X.shape[0], support.size)) Xt[:, support] = X return Xt ############################################################################### # Randomized lasso: regression settings def _randomized_lasso(X, y, weights, mask, alpha=1., verbose=False, precompute=False, eps=np.finfo(np.float).eps, max_iter=500): X = X[safe_mask(X, mask)] y = y[mask] # Center X and y to avoid fit the intercept X -= X.mean(axis=0) y -= y.mean() alpha = np.atleast_1d(np.asarray(alpha, dtype=np.float)) X = (1 - weights) * X with warnings.catch_warnings(): warnings.simplefilter('ignore', ConvergenceWarning) alphas_, _, coef_ = lars_path(X, y, Gram=precompute, copy_X=False, copy_Gram=False, alpha_min=np.min(alpha), method='lasso', verbose=verbose, max_iter=max_iter, eps=eps) if len(alpha) > 1: if len(alphas_) > 1: # np.min(alpha) < alpha_min interpolator = interp1d(alphas_[::-1], coef_[:, ::-1], bounds_error=False, fill_value=0.) scores = (interpolator(alpha) != 0.0) else: scores = np.zeros((X.shape[1], len(alpha)), dtype=np.bool) else: scores = coef_[:, -1] != 0.0 return scores class RandomizedLasso(BaseRandomizedLinearModel): """Randomized Lasso. Randomized Lasso works by resampling the train data and computing a Lasso on each resampling. In short, the features selected more often are good features. It is also known as stability selection. Read more in the :ref:`User Guide <randomized_l1>`. Parameters ---------- alpha : float, 'aic', or 'bic', optional The regularization parameter alpha parameter in the Lasso. Warning: this is not the alpha parameter in the stability selection article which is scaling. scaling : float, optional The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. sample_fraction : float, optional The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used. n_resampling : int, optional Number of randomized models. selection_threshold: float, optional The score above which features should be selected. fit_intercept : boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default True If True, the regressors X will be normalized before regression. precompute : True | False | 'auto' Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument. max_iter : integer, optional Maximum number of iterations to perform in the Lars algorithm. eps : float, optional The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the 'tol' parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization. n_jobs : integer, optional Number of CPUs to use during the resampling. If '-1', use all the CPUs random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A string, giving an expression as a function of n_jobs, as in '2*n_jobs' memory : Instance of joblib.Memory or string Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory. Attributes ---------- scores_ : array, shape = [n_features] Feature scores between 0 and 1. all_scores_ : array, shape = [n_features, n_reg_parameter] Feature scores between 0 and 1 for all values of the regularization \ parameter. The reference article suggests ``scores_`` is the max of \ ``all_scores_``. Examples -------- >>> from sklearn.linear_model import RandomizedLasso >>> randomized_lasso = RandomizedLasso() Notes ----- See examples/linear_model/plot_sparse_recovery.py for an example. References ---------- Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417-473, September 2010 DOI: 10.1111/j.1467-9868.2010.00740.x See also -------- RandomizedLogisticRegression, LogisticRegression """ def __init__(self, alpha='aic', scaling=.5, sample_fraction=.75, n_resampling=200, selection_threshold=.25, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=np.finfo(np.float).eps, random_state=None, n_jobs=1, pre_dispatch='3*n_jobs', memory=Memory(cachedir=None, verbose=0)): self.alpha = alpha self.scaling = scaling self.sample_fraction = sample_fraction self.n_resampling = n_resampling self.fit_intercept = fit_intercept self.max_iter = max_iter self.verbose = verbose self.normalize = normalize self.precompute = precompute self.eps = eps self.random_state = random_state self.n_jobs = n_jobs self.selection_threshold = selection_threshold self.pre_dispatch = pre_dispatch self.memory = memory def _make_estimator_and_params(self, X, y): assert self.precompute in (True, False, None, 'auto') alpha = self.alpha if alpha in ('aic', 'bic'): model = LassoLarsIC(precompute=self.precompute, criterion=self.alpha, max_iter=self.max_iter, eps=self.eps) model.fit(X, y) self.alpha_ = alpha = model.alpha_ return _randomized_lasso, dict(alpha=alpha, max_iter=self.max_iter, eps=self.eps, precompute=self.precompute) ############################################################################### # Randomized logistic: classification settings def _randomized_logistic(X, y, weights, mask, C=1., verbose=False, fit_intercept=True, tol=1e-3): X = X[safe_mask(X, mask)] y = y[mask] if issparse(X): size = len(weights) weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size)) X = X * weight_dia else: X *= (1 - weights) C = np.atleast_1d(np.asarray(C, dtype=np.float)) scores = np.zeros((X.shape[1], len(C)), dtype=np.bool) for this_C, this_scores in zip(C, scores.T): # XXX : would be great to do it with a warm_start ... clf = LogisticRegression(C=this_C, tol=tol, penalty='l1', dual=False, fit_intercept=fit_intercept) clf.fit(X, y) this_scores[:] = np.any( np.abs(clf.coef_) > 10 * np.finfo(np.float).eps, axis=0) return scores class RandomizedLogisticRegression(BaseRandomizedLinearModel): """Randomized Logistic Regression Randomized Regression works by resampling the train data and computing a LogisticRegression on each resampling. In short, the features selected more often are good features. It is also known as stability selection. Read more in the :ref:`User Guide <randomized_l1>`. Parameters ---------- C : float, optional, default=1 The regularization parameter C in the LogisticRegression. scaling : float, optional, default=0.5 The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. sample_fraction : float, optional, default=0.75 The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used. n_resampling : int, optional, default=200 Number of randomized models. selection_threshold : float, optional, default=0.25 The score above which features should be selected. fit_intercept : boolean, optional, default=True whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). verbose : boolean or integer, optional Sets the verbosity amount normalize : boolean, optional, default=True If True, the regressors X will be normalized before regression. tol : float, optional, default=1e-3 tolerance for stopping criteria of LogisticRegression n_jobs : integer, optional Number of CPUs to use during the resampling. If '-1', use all the CPUs random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A string, giving an expression as a function of n_jobs, as in '2*n_jobs' memory : Instance of joblib.Memory or string Used for internal caching. By default, no caching is done. If a string is given, it is the path to the caching directory. Attributes ---------- scores_ : array, shape = [n_features] Feature scores between 0 and 1. all_scores_ : array, shape = [n_features, n_reg_parameter] Feature scores between 0 and 1 for all values of the regularization \ parameter. The reference article suggests ``scores_`` is the max \ of ``all_scores_``. Examples -------- >>> from sklearn.linear_model import RandomizedLogisticRegression >>> randomized_logistic = RandomizedLogisticRegression() Notes ----- See examples/linear_model/plot_sparse_recovery.py for an example. References ---------- Stability selection Nicolai Meinshausen, Peter Buhlmann Journal of the Royal Statistical Society: Series B Volume 72, Issue 4, pages 417-473, September 2010 DOI: 10.1111/j.1467-9868.2010.00740.x See also -------- RandomizedLasso, Lasso, ElasticNet """ def __init__(self, C=1, scaling=.5, sample_fraction=.75, n_resampling=200, selection_threshold=.25, tol=1e-3, fit_intercept=True, verbose=False, normalize=True, random_state=None, n_jobs=1, pre_dispatch='3*n_jobs', memory=Memory(cachedir=None, verbose=0)): self.C = C self.scaling = scaling self.sample_fraction = sample_fraction self.n_resampling = n_resampling self.fit_intercept = fit_intercept self.verbose = verbose self.normalize = normalize self.tol = tol self.random_state = random_state self.n_jobs = n_jobs self.selection_threshold = selection_threshold self.pre_dispatch = pre_dispatch self.memory = memory def _make_estimator_and_params(self, X, y): params = dict(C=self.C, tol=self.tol, fit_intercept=self.fit_intercept) return _randomized_logistic, params def _center_data(self, X, y, fit_intercept, normalize=False): """Center the data in X but not in y""" X, _, Xmean, _, X_std = center_data(X, y, fit_intercept, normalize=normalize) return X, y, Xmean, y, X_std ############################################################################### # Stability paths def _lasso_stability_path(X, y, mask, weights, eps): "Inner loop of lasso_stability_path" X = X * weights[np.newaxis, :] X = X[safe_mask(X, mask), :] y = y[mask] alpha_max = np.max(np.abs(np.dot(X.T, y))) / X.shape[0] alpha_min = eps * alpha_max # set for early stopping in path with warnings.catch_warnings(): warnings.simplefilter('ignore', ConvergenceWarning) alphas, _, coefs = lars_path(X, y, method='lasso', verbose=False, alpha_min=alpha_min) # Scale alpha by alpha_max alphas /= alphas[0] # Sort alphas in assending order alphas = alphas[::-1] coefs = coefs[:, ::-1] # Get rid of the alphas that are too small mask = alphas >= eps # We also want to keep the first one: it should be close to the OLS # solution mask[0] = True alphas = alphas[mask] coefs = coefs[:, mask] return alphas, coefs def lasso_stability_path(X, y, scaling=0.5, random_state=None, n_resampling=200, n_grid=100, sample_fraction=0.75, eps=4 * np.finfo(np.float).eps, n_jobs=1, verbose=False): """Stabiliy path based on randomized Lasso estimates Read more in the :ref:`User Guide <randomized_l1>`. Parameters ---------- X : array-like, shape = [n_samples, n_features] training data. y : array-like, shape = [n_samples] target values. scaling : float, optional, default=0.5 The alpha parameter in the stability selection article used to randomly scale the features. Should be between 0 and 1. random_state : integer or numpy.random.RandomState, optional The generator used to randomize the design. n_resampling : int, optional, default=200 Number of randomized models. n_grid : int, optional, default=100 Number of grid points. The path is linearly reinterpolated on a grid between 0 and 1 before computing the scores. sample_fraction : float, optional, default=0.75 The fraction of samples to be used in each randomized design. Should be between 0 and 1. If 1, all samples are used. eps : float, optional Smallest value of alpha / alpha_max considered n_jobs : integer, optional Number of CPUs to use during the resampling. If '-1', use all the CPUs verbose : boolean or integer, optional Sets the verbosity amount Returns ------- alphas_grid : array, shape ~ [n_grid] The grid points between 0 and 1: alpha/alpha_max scores_path : array, shape = [n_features, n_grid] The scores for each feature along the path. Notes ----- See examples/linear_model/plot_sparse_recovery.py for an example. """ rng = check_random_state(random_state) if not (0 < scaling < 1): raise ValueError("Parameter 'scaling' should be between 0 and 1." " Got %r instead." % scaling) n_samples, n_features = X.shape paths = Parallel(n_jobs=n_jobs, verbose=verbose)( delayed(_lasso_stability_path)( X, y, mask=rng.rand(n_samples) < sample_fraction, weights=1. - scaling * rng.random_integers(0, 1, size=(n_features,)), eps=eps) for k in range(n_resampling)) all_alphas = sorted(list(set(itertools.chain(*[p[0] for p in paths])))) # Take approximately n_grid values stride = int(max(1, int(len(all_alphas) / float(n_grid)))) all_alphas = all_alphas[::stride] if not all_alphas[-1] == 1: all_alphas.append(1.) all_alphas = np.array(all_alphas) scores_path = np.zeros((n_features, len(all_alphas))) for alphas, coefs in paths: if alphas[0] != 0: alphas = np.r_[0, alphas] coefs = np.c_[np.ones((n_features, 1)), coefs] if alphas[-1] != all_alphas[-1]: alphas = np.r_[alphas, all_alphas[-1]] coefs = np.c_[coefs, np.zeros((n_features, 1))] scores_path += (interp1d(alphas, coefs, kind='nearest', bounds_error=False, fill_value=0, axis=-1)(all_alphas) != 0) scores_path /= n_resampling return all_alphas, scores_path

bsd-3-clause

GoogleCloudPlatform/public-datasets-pipelines

datasets/irs_990/pipelines/irs_990_ez_2014/irs_990_ez_2014_dag.py

1

20827

# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from airflow import DAG from airflow.providers.cncf.kubernetes.operators import kubernetes_pod from airflow.providers.google.cloud.transfers import gcs_to_bigquery default_args = { "owner": "Google", "depends_on_past": False, "start_date": "2021-03-01", } with DAG( dag_id="irs_990.irs_990_ez_2014", default_args=default_args, max_active_runs=1, schedule_interval="@daily", catchup=False, default_view="graph", ) as dag: # Run CSV transform within kubernetes pod irs_990_ez_2014_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="irs_990_ez_2014_transform_csv", startup_timeout_seconds=600, name="irs_990_ez_2014", service_account_name="datasets", namespace="composer", image_pull_policy="Always", image="{{ var.json.irs_990.container_registry.run_csv_transform_kub }}", env_vars={ "SOURCE_URL": "https://www.irs.gov/pub/irs-soi/14eofinextract990ez.zip", "SOURCE_FILE": "files/data.dat", "TARGET_FILE": "files/data_output.csv", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "data/irs_990/irs_990_ez_2014/data_output.csv", "PIPELINE_NAME": "irs_990_ez_2014", "CSV_HEADERS": '["ein","tax_pd","subseccd","totcntrbs","prgmservrev","duesassesmnts","othrinvstinc","grsamtsalesastothr","basisalesexpnsothr","gnsaleofastothr","grsincgaming","grsrevnuefndrsng","direxpns","netincfndrsng","grsalesminusret","costgoodsold","grsprft","othrevnue","totrevnue","totexpns","totexcessyr","othrchgsnetassetfnd","networthend","totassetsend","totliabend","totnetassetsend","actvtynotprevrptcd","chngsinorgcd","unrelbusincd","filedf990tcd","contractioncd","politicalexpend","filedf1120polcd","loanstoofficerscd","loanstoofficers","initiationfee","grspublicrcpts","s4958excessbenefcd","prohibtdtxshltrcd","nonpfrea","totnooforgscnt","totsupport","gftgrntsrcvd170","txrevnuelevied170","srvcsval170","pubsuppsubtot170","exceeds2pct170","pubsupplesspct170","samepubsuppsubtot170","grsinc170","netincunreltd170","othrinc170","totsupp170","grsrcptsrelated170","totgftgrntrcvd509","grsrcptsadmissn509","grsrcptsactivities509","txrevnuelevied509","srvcsval509","pubsuppsubtot509","rcvdfrmdisqualsub509","exceeds1pct509","subtotpub509","pubsupplesub509","samepubsuppsubtot509","grsinc509","unreltxincls511tx509","subtotsuppinc509","netincunrelatd509","othrinc509","totsupp509"]', "RENAME_MAPPINGS": '{"EIN": "ein","a_tax_prd": "tax_pd","taxpd": "tax_pd","taxprd": "tax_pd","subseccd": "subseccd","prgmservrev": "prgmservrev","duesassesmnts": "duesassesmnts","othrinvstinc": "othrinvstinc","grsamtsalesastothr": "grsamtsalesastothr","basisalesexpnsothr": "basisalesexpnsothr","gnsaleofastothr": "gnsaleofastothr","grsincgaming": "grsincgaming","grsrevnuefndrsng": "grsrevnuefndrsng","direxpns": "direxpns","netincfndrsng": "netincfndrsng","grsalesminusret": "grsalesminusret","costgoodsold": "costgoodsold","grsprft": "grsprft","othrevnue": "othrevnue","totrevnue": "totrevnue","totexpns": "totexpns","totexcessyr": "totexcessyr","othrchgsnetassetfnd": "othrchgsnetassetfnd","networthend": "networthend","totassetsend": "totassetsend","totliabend": "totliabend","totnetassetsend": "totnetassetsend","actvtynotprevrptcd": "actvtynotprevrptcd","chngsinorgcd": "chngsinorgcd","unrelbusincd": "unrelbusincd","filedf990tcd": "filedf990tcd","contractioncd": "contractioncd","politicalexpend": "politicalexpend","filedfYYN0polcd": "filedf1120polcd","loanstoofficerscd": "loanstoofficerscd","loanstoofficers": "loanstoofficers","initiationfee": "initiationfee","grspublicrcpts": "grspublicrcpts","s4958excessbenefcd": "s4958excessbenefcd","prohibtdtxshltrcd": "prohibtdtxshltrcd","nonpfrea": "nonpfrea","totnoforgscnt": "totnooforgscnt","totsupport": "totsupport","gftgrntrcvd170": "gftgrntsrcvd170","txrevnuelevied170": "txrevnuelevied170","srvcsval170": "srvcsval170","pubsuppsubtot170": "pubsuppsubtot170","excds2pct170": "exceeds2pct170","pubsupplesspct170": "pubsupplesspct170","samepubsuppsubtot170": "samepubsuppsubtot170","grsinc170": "grsinc170","netincunrelatd170": "netincunreltd170","othrinc170": "othrinc170","totsupport170": "totsupp170","grsrcptsrelatd170": "grsrcptsrelated170","totgftgrntrcvd509": "totgftgrntrcvd509","grsrcptsadmiss509": "grsrcptsadmissn509","grsrcptsactvts509": "grsrcptsactivities509","txrevnuelevied509": "txrevnuelevied509","srvcsval509": "srvcsval509","pubsuppsubtot509": "pubsuppsubtot509","rcvdfrmdisqualsub509": "rcvdfrmdisqualsub509","excds1pct509": "exceeds1pct509","subtotpub509": "subtotpub509","pubsupplesssub509": "pubsupplesub509","samepubsuppsubtot509": "samepubsuppsubtot509","grsinc509": "grsinc509","unreltxincls511tx509": "unreltxincls511tx509","subtotsuppinc509": "subtotsuppinc509","netincunreltd509": "netincunrelatd509","othrinc509": "othrinc509","totsupp509": "totsupp509","elf": "elf","totcntrbs": "totcntrbs"}', }, resources={"request_memory": "2G", "request_cpu": "1"}, ) # Task to load CSV data to a BigQuery table load_irs_990_ez_2014_to_bq = gcs_to_bigquery.GCSToBigQueryOperator( task_id="load_irs_990_ez_2014_to_bq", bucket="{{ var.value.composer_bucket }}", source_objects=["data/irs_990/irs_990_ez_2014/data_output.csv"], source_format="CSV", destination_project_dataset_table="irs_990.irs_990_ez_2014", skip_leading_rows=1, write_disposition="WRITE_TRUNCATE", schema_fields=[ { "name": "ein", "type": "string", "description": "Employer Identification Number", "mode": "required", }, { "name": "tax_pd", "type": "integer", "description": "Tax period", "mode": "nullable", }, { "name": "subseccd", "type": "integer", "description": "Subsection code", "mode": "nullable", }, { "name": "totcntrbs", "type": "integer", "description": "Contributions gifts grants etc received", "mode": "nullable", }, { "name": "prgmservrev", "type": "integer", "description": "Program service revenue", "mode": "nullable", }, { "name": "duesassesmnts", "type": "integer", "description": "Membership dues and assessments", "mode": "nullable", }, { "name": "othrinvstinc", "type": "integer", "description": "Investment income", "mode": "nullable", }, { "name": "grsamtsalesastothr", "type": "integer", "description": "Gross amount from sale of assets", "mode": "nullable", }, { "name": "basisalesexpnsothr", "type": "integer", "description": "Cost or other basis and sales expenses", "mode": "nullable", }, { "name": "gnsaleofastothr", "type": "integer", "description": "Gain or (loss) from sale of assets", "mode": "nullable", }, { "name": "grsincgaming", "type": "integer", "description": "Gross income from gaming", "mode": "nullable", }, { "name": "grsrevnuefndrsng", "type": "integer", "description": "Special events gross revenue", "mode": "nullable", }, { "name": "direxpns", "type": "integer", "description": "Special events direct expenses", "mode": "nullable", }, { "name": "netincfndrsng", "type": "integer", "description": "Special events net income (or loss)", "mode": "nullable", }, { "name": "grsalesminusret", "type": "integer", "description": "Gross sales of inventory", "mode": "nullable", }, { "name": "costgoodsold", "type": "integer", "description": "Less: cost of goods sold", "mode": "nullable", }, { "name": "grsprft", "type": "integer", "description": "Gross profit (or loss) from sales of inventory", "mode": "nullable", }, { "name": "othrevnue", "type": "integer", "description": "Other revenue - total", "mode": "nullable", }, { "name": "totrevnue", "type": "integer", "description": "Total revenue", "mode": "nullable", }, { "name": "totexpns", "type": "integer", "description": "Total expenses", "mode": "nullable", }, { "name": "totexcessyr", "type": "integer", "description": "Excess or deficit", "mode": "nullable", }, { "name": "othrchgsnetassetfnd", "type": "integer", "description": "Other changes in net assets", "mode": "nullable", }, { "name": "networthend", "type": "integer", "description": "Net assets EOY", "mode": "nullable", }, { "name": "totassetsend", "type": "integer", "description": "Total assets e-o-y", "mode": "nullable", }, { "name": "totliabend", "type": "integer", "description": "Total liabilities e-o-y", "mode": "nullable", }, { "name": "totnetassetsend", "type": "integer", "description": "Total net worth e-o-y", "mode": "nullable", }, { "name": "actvtynotprevrptcd", "type": "string", "description": "Activity not previously reported?", "mode": "nullable", }, { "name": "chngsinorgcd", "type": "string", "description": "Significant changes to governing docs?", "mode": "nullable", }, { "name": "unrelbusincd", "type": "string", "description": "UBI over $1000?", "mode": "nullable", }, { "name": "filedf990tcd", "type": "string", "description": "Organization Filed 990T", "mode": "nullable", }, { "name": "contractioncd", "type": "string", "description": "Liquidation dissolution termination or contraction", "mode": "nullable", }, { "name": "politicalexpend", "type": "integer", "description": "Direct or indirect political expenditures", "mode": "nullable", }, { "name": "filedf1120polcd", "type": "string", "description": "File Form 1120-POL?", "mode": "nullable", }, { "name": "loanstoofficerscd", "type": "string", "description": "Loans to/from officers directors or trustees?", "mode": "nullable", }, { "name": "loanstoofficers", "type": "integer", "description": "Amount of loans to/from officers", "mode": "nullable", }, { "name": "initiationfee", "type": "integer", "description": "Initiation fees and capital contributions", "mode": "nullable", }, { "name": "grspublicrcpts", "type": "integer", "description": "Gross receipts for public use of club facilities", "mode": "nullable", }, { "name": "s4958excessbenefcd", "type": "string", "description": "Section 4958 excess benefit transactions?", "mode": "nullable", }, { "name": "prohibtdtxshltrcd", "type": "string", "description": "Party to a prohibited tax shelter transaction?", "mode": "nullable", }, { "name": "nonpfrea", "type": "integer", "description": "Reason for non-PF status", "mode": "nullable", }, { "name": "totnooforgscnt", "type": "integer", "description": "Number of organizations supported", "mode": "nullable", }, { "name": "totsupport", "type": "integer", "description": "Sum of amounts of support", "mode": "nullable", }, { "name": "gftgrntsrcvd170", "type": "integer", "description": "Gifts grants membership fees received (170)", "mode": "nullable", }, { "name": "txrevnuelevied170", "type": "integer", "description": "Tax revenues levied (170)", "mode": "nullable", }, { "name": "srvcsval170", "type": "integer", "description": "Services or facilities furnished by gov (170)", "mode": "nullable", }, { "name": "pubsuppsubtot170", "type": "integer", "description": "Public support subtotal (170)", "mode": "nullable", }, { "name": "exceeds2pct170", "type": "integer", "description": "Amount support exceeds total (170)", "mode": "nullable", }, { "name": "pubsupplesspct170", "type": "integer", "description": "Public support (170)", "mode": "nullable", }, { "name": "samepubsuppsubtot170", "type": "integer", "description": "Public support from line 4 (170)", "mode": "nullable", }, { "name": "grsinc170", "type": "integer", "description": "Gross income from interest etc (170)", "mode": "nullable", }, { "name": "netincunreltd170", "type": "integer", "description": "Net UBI (170)", "mode": "nullable", }, { "name": "othrinc170", "type": "integer", "description": "Other income (170)", "mode": "nullable", }, { "name": "totsupp170", "type": "integer", "description": "Total support (170)", "mode": "nullable", }, { "name": "grsrcptsrelated170", "type": "integer", "description": "Gross receipts from related activities (170)", "mode": "nullable", }, { "name": "totgftgrntrcvd509", "type": "integer", "description": "Gifts grants membership fees received (509)", "mode": "nullable", }, { "name": "grsrcptsadmissn509", "type": "integer", "description": "Receipts from admissions merchandise etc (509)", "mode": "nullable", }, { "name": "grsrcptsactivities509", "type": "integer", "description": "Gross receipts from related activities (509)", "mode": "nullable", }, { "name": "txrevnuelevied509", "type": "integer", "description": "Tax revenues levied (509)", "mode": "nullable", }, { "name": "srvcsval509", "type": "integer", "description": "Services or facilities furnished by gov (509)", "mode": "nullable", }, { "name": "pubsuppsubtot509", "type": "integer", "description": "Public support subtotal (509)", "mode": "nullable", }, { "name": "rcvdfrmdisqualsub509", "type": "integer", "description": "Amounts from disqualified persons (509)", "mode": "nullable", }, { "name": "exceeds1pct509", "type": "integer", "description": "Amount support exceeds total (509)", "mode": "nullable", }, { "name": "subtotpub509", "type": "integer", "description": "Public support subtotal (509)", "mode": "nullable", }, { "name": "pubsupplesub509", "type": "integer", "description": "Public support (509)", "mode": "nullable", }, { "name": "samepubsuppsubtot509", "type": "integer", "description": "Public support from line 6 (509)", "mode": "nullable", }, { "name": "grsinc509", "type": "integer", "description": "Gross income from interest etc (509)", "mode": "nullable", }, { "name": "unreltxincls511tx509", "type": "integer", "description": "Net UBI (509)", "mode": "nullable", }, { "name": "subtotsuppinc509", "type": "integer", "description": "Subtotal total support (509)", "mode": "nullable", }, { "name": "netincunrelatd509", "type": "integer", "description": "Net income from UBI not in 10b (509)", "mode": "nullable", }, { "name": "othrinc509", "type": "integer", "description": "Other income (509)", "mode": "nullable", }, { "name": "totsupp509", "type": "integer", "description": "Total support (509)", "mode": "nullable", }, ], ) irs_990_ez_2014_transform_csv >> load_irs_990_ez_2014_to_bq

apache-2.0

mekhod/Pandas-Multi-Colomn-Processor

MultiColProcessor/MultiColProcessor.py

1

4999

import pandas as pd import numpy as np from sklearn import preprocessing ## class MultiColomnLabelEncoder: ## def __init__(self): self.dataTypes = {} self.__catColumns = [] self.__MultiLE = {} ## Later, self.dataTypes will be used to convert dtypes to the original ones. def __Get_Dtypes(self, data=pd.DataFrame()): ##to get original data datatypes for colomn in data.columns: self.dataTypes[colomn] = data[colomn].dtypes return self ## def fit(self, data): ## self.__Get_Dtypes(data) ## self.__catColumns = [cat for cat in self.dataTypes.keys() if (self.dataTypes[cat].name == 'category')] ## for col in self.__catColumns: le = preprocessing.LabelEncoder() le.fit(data.loc[:, col]) self.__MultiLE[col] = le ## return self ## def transform(self, data): ## catData = data[self.__catColumns] data = data.drop(self.__catColumns, axis=1) ## def Transform_Rec(dta=catData): ## nCol = dta.shape[1] ## if nCol == 1: col = dta.columns[0] le = self.__MultiLE[col] transformed = le.transform(dta.iloc[:, 0]) transformed = pd.DataFrame({col: transformed}) ## return transformed else: ## if (nCol % 2 == 0): middle_index = int(nCol / 2) else: middle_index = int(nCol / 2 - 0.5) ## left = dta.iloc[:, :middle_index] right = dta.iloc[:, middle_index:] ## return pd.concat([Transform_Rec(dta=left), Transform_Rec(dta=right)], axis=1) ## catData = Transform_Rec(dta=catData) catData.set_index(data.index, inplace=True) ## data = pd.concat([data, catData], axis=1) ## for i, j in self.dataTypes.items(): try: data[i] = data[i].astype(j) except: pass ## return data ## class MultiColomnOneHotEncoder: ## def __init__(self): self.__catColumns = [] self.__MultiOHE = {} ## def __getCategoryColomns(self, data=pd.DataFrame()): catColumns = [] for i, j in enumerate(data): if (data.dtypes[i].name == 'category'): catColumns.append(j) else: continue ## self.__catColumns = catColumns ## return ## def fit(self, data): ## self.__getCategoryColomns(data) ## for col in self.__catColumns: OneHotEncoder = preprocessing.OneHotEncoder(sparse=False) OneHotEncoder.fit(np.array(data.loc[:, col]).reshape(-1, 1)) self.__MultiOHE[col] = OneHotEncoder ## return self def transform(self, data): ## catData = data[self.__catColumns] data = data.drop(self.__catColumns, axis=1) ## def Transform_Rec(dta=catData): ## nCol = dta.shape[1] ## if nCol == 1: ## col = dta.columns[0] OneHotEncoder = self.__MultiOHE[col] transformed = OneHotEncoder.transform(np.array(dta.loc[:, col]).reshape(-1, 1)) transformed = pd.DataFrame(transformed) transformed.columns = [str(col) + '_' + str(c) for c in transformed.columns] ## return transformed else: ## if (nCol % 2 == 0): middle_index = int(nCol / 2) else: middle_index = int(nCol / 2 - 0.5) ## left = dta.iloc[:, :middle_index] right = dta.iloc[:, middle_index:] ## return pd.concat([Transform_Rec(dta=left), Transform_Rec(dta=right)], axis=1) ## transformedCatData = Transform_Rec(dta=catData) transformedCatData.set_index(data.index, inplace=True) ## return pd.concat([data, transformedCatData], axis=1) ## class MultiColomnScaler: ## def __init__(self): self.scaler = object() ## def fit(self, data): ## self.scaler = preprocessing.MinMaxScaler(feature_range=(0, 1)) self.scaler.fit(data) ## return self ## def transform(self, data): ## columns = data.columns.tolist() ## data = pd.DataFrame(self.scaler.transform(data.as_matrix())) ## data.columns = columns ## return data

mit

dimkal/mne-python

mne/time_frequency/tfr.py

1

51415

"""A module which implements the time frequency estimation. Morlet code inspired by Matlab code from Sheraz Khan & Brainstorm & SPM """ # Authors : Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # Hari Bharadwaj <hari@nmr.mgh.harvard.edu> # # License : BSD (3-clause) import warnings from math import sqrt from copy import deepcopy import numpy as np from scipy import linalg from scipy.fftpack import fftn, ifftn from ..fixes import partial from ..baseline import rescale from ..parallel import parallel_func from ..utils import logger, verbose, _time_mask from ..channels.channels import ContainsMixin, UpdateChannelsMixin from ..io.pick import pick_info, pick_types from ..utils import check_fname from .multitaper import dpss_windows from ..viz.utils import figure_nobar from ..externals.h5io import write_hdf5, read_hdf5 def _get_data(inst, return_itc): """Get data from Epochs or Evoked instance as epochs x ch x time""" from ..epochs import _BaseEpochs from ..evoked import Evoked if not isinstance(inst, (_BaseEpochs, Evoked)): raise TypeError('inst must be Epochs or Evoked') if isinstance(inst, _BaseEpochs): data = inst.get_data() else: if return_itc: raise ValueError('return_itc must be False for evoked data') data = inst.data[np.newaxis, ...].copy() return data def morlet(sfreq, freqs, n_cycles=7, sigma=None, zero_mean=False): """Compute Wavelets for the given frequency range Parameters ---------- sfreq : float Sampling Frequency freqs : array frequency range of interest (1 x Frequencies) n_cycles: float | array of float Number of cycles. Fixed number or one per frequency. sigma : float, (optional) It controls the width of the wavelet ie its temporal resolution. If sigma is None the temporal resolution is adapted with the frequency like for all wavelet transform. The higher the frequency the shorter is the wavelet. If sigma is fixed the temporal resolution is fixed like for the short time Fourier transform and the number of oscillations increases with the frequency. zero_mean : bool Make sure the wavelet is zero mean Returns ------- Ws : list of array Wavelets time series See Also -------- mne.time_frequency.cwt_morlet : Compute time-frequency decomposition with Morlet wavelets """ Ws = list() n_cycles = np.atleast_1d(n_cycles) if (n_cycles.size != 1) and (n_cycles.size != len(freqs)): raise ValueError("n_cycles should be fixed or defined for " "each frequency.") for k, f in enumerate(freqs): if len(n_cycles) != 1: this_n_cycles = n_cycles[k] else: this_n_cycles = n_cycles[0] # fixed or scale-dependent window if sigma is None: sigma_t = this_n_cycles / (2.0 * np.pi * f) else: sigma_t = this_n_cycles / (2.0 * np.pi * sigma) # this scaling factor is proportional to (Tallon-Baudry 98): # (sigma_t*sqrt(pi))^(-1/2); t = np.arange(0., 5. * sigma_t, 1.0 / sfreq) t = np.r_[-t[::-1], t[1:]] oscillation = np.exp(2.0 * 1j * np.pi * f * t) gaussian_enveloppe = np.exp(-t ** 2 / (2.0 * sigma_t ** 2)) if zero_mean: # to make it zero mean real_offset = np.exp(- 2 * (np.pi * f * sigma_t) ** 2) oscillation -= real_offset W = oscillation * gaussian_enveloppe W /= sqrt(0.5) * linalg.norm(W.ravel()) Ws.append(W) return Ws def _dpss_wavelet(sfreq, freqs, n_cycles=7, time_bandwidth=4.0, zero_mean=False): """Compute Wavelets for the given frequency range Parameters ---------- sfreq : float Sampling Frequency. freqs : ndarray, shape (n_freqs,) The frequencies in Hz. n_cycles : float | ndarray, shape (n_freqs,) The number of cycles globally or for each frequency. Defaults to 7. time_bandwidth : float, (optional) Time x Bandwidth product. The number of good tapers (low-bias) is chosen automatically based on this to equal floor(time_bandwidth - 1). Default is 4.0, giving 3 good tapers. Returns ------- Ws : list of array Wavelets time series """ Ws = list() if time_bandwidth < 2.0: raise ValueError("time_bandwidth should be >= 2.0 for good tapers") n_taps = int(np.floor(time_bandwidth - 1)) n_cycles = np.atleast_1d(n_cycles) if n_cycles.size != 1 and n_cycles.size != len(freqs): raise ValueError("n_cycles should be fixed or defined for " "each frequency.") for m in range(n_taps): Wm = list() for k, f in enumerate(freqs): if len(n_cycles) != 1: this_n_cycles = n_cycles[k] else: this_n_cycles = n_cycles[0] t_win = this_n_cycles / float(f) t = np.arange(0., t_win, 1.0 / sfreq) # Making sure wavelets are centered before tapering oscillation = np.exp(2.0 * 1j * np.pi * f * (t - t_win / 2.)) # Get dpss tapers tapers, conc = dpss_windows(t.shape[0], time_bandwidth / 2., n_taps) Wk = oscillation * tapers[m] if zero_mean: # to make it zero mean real_offset = Wk.mean() Wk -= real_offset Wk /= sqrt(0.5) * linalg.norm(Wk.ravel()) Wm.append(Wk) Ws.append(Wm) return Ws def _centered(arr, newsize): """Aux Function to center data""" # Return the center newsize portion of the array. newsize = np.asarray(newsize) currsize = np.array(arr.shape) startind = (currsize - newsize) // 2 endind = startind + newsize myslice = [slice(startind[k], endind[k]) for k in range(len(endind))] return arr[tuple(myslice)] def _cwt_fft(X, Ws, mode="same"): """Compute cwt with fft based convolutions Return a generator over signals. """ X = np.asarray(X) # Precompute wavelets for given frequency range to save time n_signals, n_times = X.shape n_freqs = len(Ws) Ws_max_size = max(W.size for W in Ws) size = n_times + Ws_max_size - 1 # Always use 2**n-sized FFT fsize = 2 ** int(np.ceil(np.log2(size))) # precompute FFTs of Ws fft_Ws = np.empty((n_freqs, fsize), dtype=np.complex128) for i, W in enumerate(Ws): if len(W) > n_times: raise ValueError('Wavelet is too long for such a short signal. ' 'Reduce the number of cycles.') fft_Ws[i] = fftn(W, [fsize]) for k, x in enumerate(X): if mode == "full": tfr = np.zeros((n_freqs, fsize), dtype=np.complex128) elif mode == "same" or mode == "valid": tfr = np.zeros((n_freqs, n_times), dtype=np.complex128) fft_x = fftn(x, [fsize]) for i, W in enumerate(Ws): ret = ifftn(fft_x * fft_Ws[i])[:n_times + W.size - 1] if mode == "valid": sz = abs(W.size - n_times) + 1 offset = (n_times - sz) / 2 tfr[i, offset:(offset + sz)] = _centered(ret, sz) else: tfr[i, :] = _centered(ret, n_times) yield tfr def _cwt_convolve(X, Ws, mode='same'): """Compute time freq decomposition with temporal convolutions Return a generator over signals. """ X = np.asarray(X) n_signals, n_times = X.shape n_freqs = len(Ws) # Compute convolutions for x in X: tfr = np.zeros((n_freqs, n_times), dtype=np.complex128) for i, W in enumerate(Ws): ret = np.convolve(x, W, mode=mode) if len(W) > len(x): raise ValueError('Wavelet is too long for such a short ' 'signal. Reduce the number of cycles.') if mode == "valid": sz = abs(W.size - n_times) + 1 offset = (n_times - sz) / 2 tfr[i, offset:(offset + sz)] = ret else: tfr[i] = ret yield tfr def cwt_morlet(X, sfreq, freqs, use_fft=True, n_cycles=7.0, zero_mean=False): """Compute time freq decomposition with Morlet wavelets This function operates directly on numpy arrays. Consider using `tfr_morlet` to process `Epochs` or `Evoked` instances. Parameters ---------- X : array of shape [n_signals, n_times] signals (one per line) sfreq : float sampling Frequency freqs : array Array of frequencies of interest use_fft : bool Compute convolution with FFT or temoral convolution. n_cycles: float | array of float Number of cycles. Fixed number or one per frequency. zero_mean : bool Make sure the wavelets are zero mean. Returns ------- tfr : 3D array Time Frequency Decompositions (n_signals x n_frequencies x n_times) See Also -------- tfr.cwt : Compute time-frequency decomposition with user-provided wavelets """ mode = 'same' # mode = "valid" n_signals, n_times = X.shape n_frequencies = len(freqs) # Precompute wavelets for given frequency range to save time Ws = morlet(sfreq, freqs, n_cycles=n_cycles, zero_mean=zero_mean) if use_fft: coefs = _cwt_fft(X, Ws, mode) else: coefs = _cwt_convolve(X, Ws, mode) tfrs = np.empty((n_signals, n_frequencies, n_times), dtype=np.complex) for k, tfr in enumerate(coefs): tfrs[k] = tfr return tfrs def cwt(X, Ws, use_fft=True, mode='same', decim=1): """Compute time freq decomposition with continuous wavelet transform Parameters ---------- X : array of shape [n_signals, n_times] signals (one per line) Ws : list of array Wavelets time series use_fft : bool Use FFT for convolutions mode : 'same' | 'valid' | 'full' Convention for convolution decim : int Temporal decimation factor Returns ------- tfr : 3D array Time Frequency Decompositions (n_signals x n_frequencies x n_times) See Also -------- mne.time_frequency.cwt_morlet : Compute time-frequency decomposition with Morlet wavelets """ n_signals, n_times = X[:, ::decim].shape n_frequencies = len(Ws) if use_fft: coefs = _cwt_fft(X, Ws, mode) else: coefs = _cwt_convolve(X, Ws, mode) tfrs = np.empty((n_signals, n_frequencies, n_times), dtype=np.complex) for k, tfr in enumerate(coefs): tfrs[k] = tfr[..., ::decim] return tfrs def _time_frequency(X, Ws, use_fft, decim): """Aux of time_frequency for parallel computing over channels """ n_epochs, n_times = X.shape n_times = n_times // decim + bool(n_times % decim) n_frequencies = len(Ws) psd = np.zeros((n_frequencies, n_times)) # PSD plf = np.zeros((n_frequencies, n_times), np.complex) # phase lock mode = 'same' if use_fft: tfrs = _cwt_fft(X, Ws, mode) else: tfrs = _cwt_convolve(X, Ws, mode) for tfr in tfrs: tfr = tfr[:, ::decim] tfr_abs = np.abs(tfr) psd += tfr_abs ** 2 plf += tfr / tfr_abs psd /= n_epochs plf = np.abs(plf) / n_epochs return psd, plf @verbose def single_trial_power(data, sfreq, frequencies, use_fft=True, n_cycles=7, baseline=None, baseline_mode='ratio', times=None, decim=1, n_jobs=1, zero_mean=False, verbose=None): """Compute time-frequency power on single epochs Parameters ---------- data : array of shape [n_epochs, n_channels, n_times] The epochs sfreq : float Sampling rate frequencies : array-like The frequencies use_fft : bool Use the FFT for convolutions or not. n_cycles : float | array of float Number of cycles in the Morlet wavelet. Fixed number or one per frequency. baseline : None (default) or tuple of length 2 The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal ot (None, None) all the time interval is used. baseline_mode : None | 'ratio' | 'zscore' Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)) times : array Required to define baseline decim : int Temporal decimation factor n_jobs : int The number of epochs to process at the same time zero_mean : bool Make sure the wavelets are zero mean. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose). Returns ------- power : 4D array Power estimate (Epochs x Channels x Frequencies x Timepoints). """ mode = 'same' n_frequencies = len(frequencies) n_epochs, n_channels, n_times = data[:, :, ::decim].shape # Precompute wavelets for given frequency range to save time Ws = morlet(sfreq, frequencies, n_cycles=n_cycles, zero_mean=zero_mean) parallel, my_cwt, _ = parallel_func(cwt, n_jobs) logger.info("Computing time-frequency power on single epochs...") power = np.empty((n_epochs, n_channels, n_frequencies, n_times), dtype=np.float) # Package arguments for `cwt` here to minimize omissions where only one of # the two calls below is updated with new function arguments. cwt_kw = dict(Ws=Ws, use_fft=use_fft, mode=mode, decim=decim) if n_jobs == 1: for k, e in enumerate(data): x = cwt(e, **cwt_kw) power[k] = (x * x.conj()).real else: # Precompute tf decompositions in parallel tfrs = parallel(my_cwt(e, **cwt_kw) for e in data) for k, tfr in enumerate(tfrs): power[k] = (tfr * tfr.conj()).real # Run baseline correction. Be sure to decimate the times array as well if # needed. if times is not None: times = times[::decim] power = rescale(power, times, baseline, baseline_mode, copy=False) return power def _induced_power_cwt(data, sfreq, frequencies, use_fft=True, n_cycles=7, decim=1, n_jobs=1, zero_mean=False): """Compute time induced power and inter-trial phase-locking factor The time frequency decomposition is done with Morlet wavelets Parameters ---------- data : array 3D array of shape [n_epochs, n_channels, n_times] sfreq : float sampling Frequency frequencies : array Array of frequencies of interest use_fft : bool Compute transform with fft based convolutions or temporal convolutions. n_cycles : float | array of float Number of cycles. Fixed number or one per frequency. decim: int Temporal decimation factor n_jobs : int The number of CPUs used in parallel. All CPUs are used in -1. Requires joblib package. zero_mean : bool Make sure the wavelets are zero mean. Returns ------- power : 2D array Induced power (Channels x Frequencies x Timepoints). Squared amplitude of time-frequency coefficients. phase_lock : 2D array Phase locking factor in [0, 1] (Channels x Frequencies x Timepoints) """ n_frequencies = len(frequencies) n_epochs, n_channels, n_times = data[:, :, ::decim].shape # Precompute wavelets for given frequency range to save time Ws = morlet(sfreq, frequencies, n_cycles=n_cycles, zero_mean=zero_mean) psd = np.empty((n_channels, n_frequencies, n_times)) plf = np.empty((n_channels, n_frequencies, n_times)) # Separate to save memory for n_jobs=1 parallel, my_time_frequency, _ = parallel_func(_time_frequency, n_jobs) psd_plf = parallel(my_time_frequency(data[:, c, :], Ws, use_fft, decim) for c in range(n_channels)) for c, (psd_c, plf_c) in enumerate(psd_plf): psd[c, :, :], plf[c, :, :] = psd_c, plf_c return psd, plf def _preproc_tfr(data, times, freqs, tmin, tmax, fmin, fmax, mode, baseline, vmin, vmax, dB): """Aux Function to prepare tfr computation""" from ..viz.utils import _setup_vmin_vmax if mode is not None and baseline is not None: logger.info("Applying baseline correction '%s' during %s" % (mode, baseline)) data = rescale(data.copy(), times, baseline, mode) # crop time itmin, itmax = None, None idx = np.where(_time_mask(times, tmin, tmax))[0] if tmin is not None: itmin = idx[0] if tmax is not None: itmax = idx[-1] + 1 times = times[itmin:itmax] # crop freqs ifmin, ifmax = None, None idx = np.where(_time_mask(freqs, fmin, fmax))[0] if fmin is not None: ifmin = idx[0] if fmax is not None: ifmax = idx[-1] + 1 freqs = freqs[ifmin:ifmax] # crop data data = data[:, ifmin:ifmax, itmin:itmax] times *= 1e3 if dB: data = 10 * np.log10((data * data.conj()).real) vmin, vmax = _setup_vmin_vmax(data, vmin, vmax) return data, times, freqs, vmin, vmax class AverageTFR(ContainsMixin, UpdateChannelsMixin): """Container for Time-Frequency data Can for example store induced power at sensor level or intertrial coherence. Parameters ---------- info : Info The measurement info. data : ndarray, shape (n_channels, n_freqs, n_times) The data. times : ndarray, shape (n_times,) The time values in seconds. freqs : ndarray, shape (n_freqs,) The frequencies in Hz. nave : int The number of averaged TFRs. comment : str | None Comment on the data, e.g., the experimental condition. Defaults to None. method : str | None Comment on the method used to compute the data, e.g., morlet wavelet. Defaults to None. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose). Attributes ---------- ch_names : list The names of the channels. """ @verbose def __init__(self, info, data, times, freqs, nave, comment=None, method=None, verbose=None): self.info = info if data.ndim != 3: raise ValueError('data should be 3d. Got %d.' % data.ndim) n_channels, n_freqs, n_times = data.shape if n_channels != len(info['chs']): raise ValueError("Number of channels and data size don't match" " (%d != %d)." % (n_channels, len(info['chs']))) if n_freqs != len(freqs): raise ValueError("Number of frequencies and data size don't match" " (%d != %d)." % (n_freqs, len(freqs))) if n_times != len(times): raise ValueError("Number of times and data size don't match" " (%d != %d)." % (n_times, len(times))) self.data = data self.times = times self.freqs = freqs self.nave = nave self.comment = comment self.method = method @property def ch_names(self): return self.info['ch_names'] def crop(self, tmin=None, tmax=None, copy=False): """Crop data to a given time interval Parameters ---------- tmin : float | None Start time of selection in seconds. tmax : float | None End time of selection in seconds. copy : bool If False epochs is cropped in place. """ inst = self if not copy else self.copy() mask = _time_mask(inst.times, tmin, tmax) inst.times = inst.times[mask] inst.data = inst.data[..., mask] return inst @verbose def plot(self, picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, cmap='RdBu_r', dB=False, colorbar=True, show=True, title=None, axes=None, layout=None, verbose=None): """Plot TFRs in a topography with images Parameters ---------- picks : array-like of int | None The indices of the channels to plot. baseline : None (default) or tuple of length 2 The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal ot (None, None) all the time interval is used. mode : None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)). If None no baseline correction is applied. tmin : None | float The first time instant to display. If None the first time point available is used. tmax : None | float The last time instant to display. If None the last time point available is used. fmin : None | float The first frequency to display. If None the first frequency available is used. fmax : None | float The last frequency to display. If None the last frequency available is used. vmin : float | None The mininum value an the color scale. If vmin is None, the data minimum value is used. vmax : float | None The maxinum value an the color scale. If vmax is None, the data maximum value is used. cmap : matplotlib colormap | str The colormap to use. Defaults to 'RdBu_r'. dB : bool If True, 20*log10 is applied to the data to get dB. colorbar : bool If true, colorbar will be added to the plot. For user defined axes, the colorbar cannot be drawn. Defaults to True. show : bool Call pyplot.show() at the end. title : str | None String for title. Defaults to None (blank/no title). axes : instance of Axes | list | None The axes to plot to. If list, the list must be a list of Axes of the same length as the number of channels. If instance of Axes, there must be only one channel plotted. layout : Layout | None Layout instance specifying sensor positions. Used for interactive plotting of topographies on rectangle selection. If possible, the correct layout is inferred from the data. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose). Returns ------- fig : matplotlib.figure.Figure The figure containing the topography. """ from ..viz.topo import _imshow_tfr import matplotlib.pyplot as plt times, freqs = self.times.copy(), self.freqs.copy() data = self.data[picks] data, times, freqs, vmin, vmax = \ _preproc_tfr(data, times, freqs, tmin, tmax, fmin, fmax, mode, baseline, vmin, vmax, dB) tmin, tmax = times[0], times[-1] if isinstance(axes, plt.Axes): axes = [axes] if isinstance(axes, list) or isinstance(axes, np.ndarray): if len(axes) != len(picks): raise RuntimeError('There must be an axes for each picked ' 'channel.') for idx in range(len(data)): if axes is None: fig = plt.figure() ax = fig.add_subplot(111) else: ax = axes[idx] fig = ax.get_figure() onselect_callback = partial(self._onselect, baseline=baseline, mode=mode, layout=layout) _imshow_tfr(ax, 0, tmin, tmax, vmin, vmax, onselect_callback, ylim=None, tfr=data[idx: idx + 1], freq=freqs, x_label='Time (ms)', y_label='Frequency (Hz)', colorbar=colorbar, picker=False, cmap=cmap) if title: fig.suptitle(title) colorbar = False # only one colorbar for multiple axes if show: plt.show() return fig def _onselect(self, eclick, erelease, baseline, mode, layout): """Callback function called by rubber band selector in channel tfr.""" import matplotlib.pyplot as plt from ..viz import plot_tfr_topomap if abs(eclick.x - erelease.x) < .1 or abs(eclick.y - erelease.y) < .1: return plt.ion() # turn interactive mode on tmin = round(min(eclick.xdata, erelease.xdata) / 1000., 5) # ms to s tmax = round(max(eclick.xdata, erelease.xdata) / 1000., 5) fmin = round(min(eclick.ydata, erelease.ydata), 5) # Hz fmax = round(max(eclick.ydata, erelease.ydata), 5) tmin = min(self.times, key=lambda x: abs(x - tmin)) # find closest tmax = min(self.times, key=lambda x: abs(x - tmax)) fmin = min(self.freqs, key=lambda x: abs(x - fmin)) fmax = min(self.freqs, key=lambda x: abs(x - fmax)) if tmin == tmax or fmin == fmax: logger.info('The selected area is too small. ' 'Select a larger time-frequency window.') return types = list() if 'eeg' in self: types.append('eeg') if 'mag' in self: types.append('mag') if 'grad' in self: types.append('grad') fig = figure_nobar() fig.suptitle('{:.2f} s - {:.2f} s, {:.2f} Hz - {:.2f} Hz'.format(tmin, tmax, fmin, fmax), y=0.04) for idx, ch_type in enumerate(types): ax = plt.subplot(1, len(types), idx + 1) plot_tfr_topomap(self, ch_type=ch_type, tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, layout=layout, baseline=baseline, mode=mode, cmap=None, title=ch_type, vmin=None, vmax=None, axes=ax) def plot_topo(self, picks=None, baseline=None, mode='mean', tmin=None, tmax=None, fmin=None, fmax=None, vmin=None, vmax=None, layout=None, cmap='RdBu_r', title=None, dB=False, colorbar=True, layout_scale=0.945, show=True, border='none', fig_facecolor='k', font_color='w'): """Plot TFRs in a topography with images Parameters ---------- picks : array-like of int | None The indices of the channels to plot. If None all available channels are displayed. baseline : None (default) or tuple of length 2 The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal ot (None, None) all the time interval is used. mode : None | 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)). If None no baseline correction is applied. tmin : None | float The first time instant to display. If None the first time point available is used. tmax : None | float The last time instant to display. If None the last time point available is used. fmin : None | float The first frequency to display. If None the first frequency available is used. fmax : None | float The last frequency to display. If None the last frequency available is used. vmin : float | None The mininum value an the color scale. If vmin is None, the data minimum value is used. vmax : float | None The maxinum value an the color scale. If vmax is None, the data maximum value is used. layout : Layout | None Layout instance specifying sensor positions. If possible, the correct layout is inferred from the data. cmap : matplotlib colormap | str The colormap to use. Defaults to 'RdBu_r'. title : str Title of the figure. dB : bool If True, 20*log10 is applied to the data to get dB. colorbar : bool If true, colorbar will be added to the plot layout_scale : float Scaling factor for adjusting the relative size of the layout on the canvas. show : bool Call pyplot.show() at the end. border : str matplotlib borders style to be used for each sensor plot. fig_facecolor : str | obj The figure face color. Defaults to black. font_color: str | obj The color of tick labels in the colorbar. Defaults to white. Returns ------- fig : matplotlib.figure.Figure The figure containing the topography. """ from ..viz.topo import _imshow_tfr, _plot_topo import matplotlib.pyplot as plt times = self.times.copy() freqs = self.freqs data = self.data info = self.info if picks is not None: data = data[picks] info = pick_info(info, picks) data, times, freqs, vmin, vmax = \ _preproc_tfr(data, times, freqs, tmin, tmax, fmin, fmax, mode, baseline, vmin, vmax, dB) if layout is None: from mne import find_layout layout = find_layout(self.info) onselect_callback = partial(self._onselect, baseline=baseline, mode=mode, layout=layout) imshow = partial(_imshow_tfr, tfr=data, freq=freqs, cmap=cmap, onselect=onselect_callback) fig = _plot_topo(info=info, times=times, show_func=imshow, layout=layout, colorbar=colorbar, vmin=vmin, vmax=vmax, cmap=cmap, layout_scale=layout_scale, title=title, border=border, x_label='Time (ms)', y_label='Frequency (Hz)', fig_facecolor=fig_facecolor, font_color=font_color) if show: plt.show() return fig def _check_compat(self, tfr): """checks that self and tfr have the same time-frequency ranges""" assert np.all(tfr.times == self.times) assert np.all(tfr.freqs == self.freqs) def __add__(self, tfr): self._check_compat(tfr) out = self.copy() out.data += tfr.data return out def __iadd__(self, tfr): self._check_compat(tfr) self.data += tfr.data return self def __sub__(self, tfr): self._check_compat(tfr) out = self.copy() out.data -= tfr.data return out def __isub__(self, tfr): self._check_compat(tfr) self.data -= tfr.data return self def copy(self): """Return a copy of the instance.""" return deepcopy(self) def __repr__(self): s = "time : [%f, %f]" % (self.times[0], self.times[-1]) s += ", freq : [%f, %f]" % (self.freqs[0], self.freqs[-1]) s += ", nave : %d" % self.nave s += ', channels : %d' % self.data.shape[0] return "<AverageTFR | %s>" % s def apply_baseline(self, baseline, mode='mean'): """Baseline correct the data Parameters ---------- baseline : tuple or list of length 2 The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. mode : 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' Do baseline correction with ratio (power is divided by mean power during baseline) or z-score (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)) If None, baseline no correction will be performed. """ self.data = rescale(self.data, self.times, baseline, mode, copy=False) def plot_topomap(self, tmin=None, tmax=None, fmin=None, fmax=None, ch_type=None, baseline=None, mode='mean', layout=None, vmin=None, vmax=None, cmap=None, sensors=True, colorbar=True, unit=None, res=64, size=2, cbar_fmt='%1.1e', show_names=False, title=None, axes=None, show=True, outlines='head', head_pos=None): """Plot topographic maps of time-frequency intervals of TFR data Parameters ---------- tmin : None | float The first time instant to display. If None the first time point available is used. tmax : None | float The last time instant to display. If None the last time point available is used. fmin : None | float The first frequency to display. If None the first frequency available is used. fmax : None | float The last frequency to display. If None the last frequency available is used. ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None The channel type to plot. For 'grad', the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then channels are chosen in the order given above. baseline : tuple or list of length 2 The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. mode : 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent' Do baseline correction with ratio (power is divided by mean power during baseline) or z-score (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)) If None, baseline no correction will be performed. layout : None | Layout Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout file is inferred from the data; if no appropriate layout file was found, the layout is automatically generated from the sensor locations. vmin : float | callable | None The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data) or in case data contains only positive values 0. If callable, the output equals vmin(data). Defaults to None. vmax : float | callable | None The value specifying the upper bound of the color range. If None, the maximum value is used. If callable, the output equals vmax(data). Defaults to None. cmap : matplotlib colormap | None Colormap. If None and the plotted data is all positive, defaults to 'Reds'. If None and data contains also negative values, defaults to 'RdBu_r'. Defaults to None. sensors : bool | str Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., 'r+' for red plusses). If True, a circle will be used (via .add_artist). Defaults to True. colorbar : bool Plot a colorbar. unit : dict | str | None The unit of the channel type used for colorbar label. If scale is None the unit is automatically determined. res : int The resolution of the topomap image (n pixels along each side). size : float Side length per topomap in inches. cbar_fmt : str String format for colorbar values. show_names : bool | callable If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix 'MEG ' from all channel names, pass the function lambda x: x.replace('MEG ', ''). If `mask` is not None, only significant sensors will be shown. title : str | None Title. If None (default), no title is displayed. axes : instance of Axes | None The axes to plot to. If None the axes is defined automatically. show : bool Call pyplot.show() at the end. outlines : 'head' | 'skirt' | dict | None The outlines to be drawn. If 'head', the default head scheme will be drawn. If 'skirt' the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in 'mask_pos' will serve as image mask, and the 'autoshrink' (bool) field will trigger automated shrinking of the positions due to points outside the outline. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to 'head'. head_pos : dict | None If None (default), the sensors are positioned such that they span the head circle. If dict, can have entries 'center' (tuple) and 'scale' (tuple) for what the center and scale of the head should be relative to the electrode locations. Returns ------- fig : matplotlib.figure.Figure The figure containing the topography. """ from ..viz import plot_tfr_topomap return plot_tfr_topomap(self, tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax, ch_type=ch_type, baseline=baseline, mode=mode, layout=layout, vmin=vmin, vmax=vmax, cmap=cmap, sensors=sensors, colorbar=colorbar, unit=unit, res=res, size=size, cbar_fmt=cbar_fmt, show_names=show_names, title=title, axes=axes, show=show, outlines=outlines, head_pos=head_pos) def save(self, fname, overwrite=False): """Save TFR object to hdf5 file Parameters ---------- fname : str The file name, which should end with -tfr.h5 . overwrite : bool If True, overwrite file (if it exists). Defaults to false """ write_tfrs(fname, self, overwrite=overwrite) def _prepare_write_tfr(tfr, condition): """Aux function""" return (condition, dict(times=tfr.times, freqs=tfr.freqs, data=tfr.data, info=tfr.info, nave=tfr.nave, comment=tfr.comment, method=tfr.method)) def write_tfrs(fname, tfr, overwrite=False): """Write a TFR dataset to hdf5. Parameters ---------- fname : string The file name, which should end with -tfr.h5 tfr : AverageTFR instance, or list of AverageTFR instances The TFR dataset, or list of TFR datasets, to save in one file. Note. If .comment is not None, a name will be generated on the fly, based on the order in which the TFR objects are passed overwrite : bool If True, overwrite file (if it exists). Defaults to False. See Also -------- read_tfrs Notes ----- .. versionadded:: 0.9.0 """ out = [] if not isinstance(tfr, (list, tuple)): tfr = [tfr] for ii, tfr_ in enumerate(tfr): comment = ii if tfr_.comment is None else tfr_.comment out.append(_prepare_write_tfr(tfr_, condition=comment)) write_hdf5(fname, out, overwrite=overwrite, title='mnepython') def read_tfrs(fname, condition=None): """ Read TFR datasets from hdf5 file. Parameters ---------- fname : string The file name, which should end with -tfr.h5 . condition : int or str | list of int or str | None The condition to load. If None, all conditions will be returned. Defaults to None. See Also -------- write_tfrs Returns ------- tfrs : list of instances of AverageTFR | instance of AverageTFR Depending on `condition` either the TFR object or a list of multiple TFR objects. Notes ----- .. versionadded:: 0.9.0 """ check_fname(fname, 'tfr', ('-tfr.h5',)) logger.info('Reading %s ...' % fname) tfr_data = read_hdf5(fname, title='mnepython') if condition is not None: tfr_dict = dict(tfr_data) if condition not in tfr_dict: keys = ['%s' % k for k in tfr_dict] raise ValueError('Cannot find condition ("{0}") in this file. ' 'I can give you "{1}""' .format(condition, " or ".join(keys))) out = AverageTFR(**tfr_dict[condition]) else: out = [AverageTFR(**d) for d in list(zip(*tfr_data))[1]] return out def tfr_morlet(inst, freqs, n_cycles, use_fft=False, return_itc=True, decim=1, n_jobs=1): """Compute Time-Frequency Representation (TFR) using Morlet wavelets Parameters ---------- inst : Epochs | Evoked The epochs or evoked object. freqs : ndarray, shape (n_freqs,) The frequencies in Hz. n_cycles : float | ndarray, shape (n_freqs,) The number of cycles globally or for each frequency. use_fft : bool The fft based convolution or not. return_itc : bool Return intertrial coherence (ITC) as well as averaged power. Must be ``False`` for evoked data. decim : int The decimation factor on the time axis. To reduce memory usage. n_jobs : int The number of jobs to run in parallel. Returns ------- power : instance of AverageTFR The averaged power. itc : instance of AverageTFR The intertrial coherence (ITC). Only returned if return_itc is True. See Also -------- tfr_multitaper, tfr_stockwell """ data = _get_data(inst, return_itc) picks = pick_types(inst.info, meg=True, eeg=True) info = pick_info(inst.info, picks) data = data[:, picks, :] power, itc = _induced_power_cwt(data, sfreq=info['sfreq'], frequencies=freqs, n_cycles=n_cycles, n_jobs=n_jobs, use_fft=use_fft, decim=decim, zero_mean=True) times = inst.times[::decim].copy() nave = len(data) out = AverageTFR(info, power, times, freqs, nave, method='morlet-power') if return_itc: out = (out, AverageTFR(info, itc, times, freqs, nave, method='morlet-itc')) return out @verbose def _induced_power_mtm(data, sfreq, frequencies, time_bandwidth=4.0, use_fft=True, n_cycles=7, decim=1, n_jobs=1, zero_mean=True, verbose=None): """Compute time induced power and inter-trial phase-locking factor The time frequency decomposition is done with DPSS wavelets Parameters ---------- data : np.ndarray, shape (n_epochs, n_channels, n_times) The input data. sfreq : float sampling Frequency frequencies : np.ndarray, shape (n_frequencies,) Array of frequencies of interest time_bandwidth : float Time x (Full) Bandwidth product. The number of good tapers (low-bias) is chosen automatically based on this to equal floor(time_bandwidth - 1). Default is 4.0 (3 tapers). use_fft : bool Compute transform with fft based convolutions or temporal convolutions. Defaults to True. n_cycles : float | np.ndarray shape (n_frequencies,) Number of cycles. Fixed number or one per frequency. Defaults to 7. decim: int Temporal decimation factor. Defaults to 1. n_jobs : int The number of CPUs used in parallel. All CPUs are used in -1. Requires joblib package. Defaults to 1. zero_mean : bool Make sure the wavelets are zero mean. Defaults to True. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose). Returns ------- power : np.ndarray, shape (n_channels, n_frequencies, n_times) Induced power. Squared amplitude of time-frequency coefficients. itc : np.ndarray, shape (n_channels, n_frequencies, n_times) Phase locking value. """ n_epochs, n_channels, n_times = data[:, :, ::decim].shape logger.info('Data is %d trials and %d channels', n_epochs, n_channels) n_frequencies = len(frequencies) logger.info('Multitaper time-frequency analysis for %d frequencies', n_frequencies) # Precompute wavelets for given frequency range to save time Ws = _dpss_wavelet(sfreq, frequencies, n_cycles=n_cycles, time_bandwidth=time_bandwidth, zero_mean=zero_mean) n_taps = len(Ws) logger.info('Using %d tapers', n_taps) n_times_wavelets = Ws[0][0].shape[0] if n_times <= n_times_wavelets: warnings.warn("Time windows are as long or longer than the epoch. " "Consider reducing n_cycles.") psd = np.zeros((n_channels, n_frequencies, n_times)) itc = np.zeros((n_channels, n_frequencies, n_times)) parallel, my_time_frequency, _ = parallel_func(_time_frequency, n_jobs) for m in range(n_taps): psd_itc = parallel(my_time_frequency(data[:, c, :], Ws[m], use_fft, decim) for c in range(n_channels)) for c, (psd_c, itc_c) in enumerate(psd_itc): psd[c, :, :] += psd_c itc[c, :, :] += itc_c psd /= n_taps itc /= n_taps return psd, itc def tfr_multitaper(inst, freqs, n_cycles, time_bandwidth=4.0, use_fft=True, return_itc=True, decim=1, n_jobs=1): """Compute Time-Frequency Representation (TFR) using DPSS wavelets Parameters ---------- inst : Epochs | Evoked The epochs or evoked object. freqs : ndarray, shape (n_freqs,) The frequencies in Hz. n_cycles : float | ndarray, shape (n_freqs,) The number of cycles globally or for each frequency. The time-window length is thus T = n_cycles / freq. time_bandwidth : float, (optional) Time x (Full) Bandwidth product. Should be >= 2.0. Choose this along with n_cycles to get desired frequency resolution. The number of good tapers (least leakage from far away frequencies) is chosen automatically based on this to floor(time_bandwidth - 1). Default is 4.0 (3 good tapers). E.g., With freq = 20 Hz and n_cycles = 10, we get time = 0.5 s. If time_bandwidth = 4., then frequency smoothing is (4 / time) = 8 Hz. use_fft : bool The fft based convolution or not. Defaults to True. return_itc : bool Return intertrial coherence (ITC) as well as averaged power. Defaults to True. decim : int The decimation factor on the time axis. To reduce memory usage. Note than this is brute force decimation, no anti-aliasing is done. Defaults to 1. n_jobs : int The number of jobs to run in parallel. Defaults to 1. Returns ------- power : AverageTFR The averaged power. itc : AverageTFR The intertrial coherence (ITC). Only returned if return_itc is True. See Also -------- tfr_multitaper, tfr_stockwell Notes ----- .. versionadded:: 0.9.0 """ data = _get_data(inst, return_itc) picks = pick_types(inst.info, meg=True, eeg=True) info = pick_info(inst.info, picks) data = data[:, picks, :] power, itc = _induced_power_mtm(data, sfreq=info['sfreq'], frequencies=freqs, n_cycles=n_cycles, time_bandwidth=time_bandwidth, use_fft=use_fft, decim=decim, n_jobs=n_jobs, zero_mean=True, verbose='INFO') times = inst.times[::decim].copy() nave = len(data) out = AverageTFR(info, power, times, freqs, nave, method='mutlitaper-power') if return_itc: out = (out, AverageTFR(info, itc, times, freqs, nave, method='mutlitaper-itc')) return out

bsd-3-clause

ageron/tensorflow

tensorflow/contrib/learn/python/learn/datasets/synthetic.py

40

7451

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Synthetic dataset generators (deprecated). This module and all its submodules are deprecated. See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) for migration instructions. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.contrib.learn.python.learn.datasets.base import Dataset from tensorflow.python.util.deprecation import deprecated @deprecated(None, 'Consider using synthetic datasets from scikits.learn.') def circles(n_samples=100, noise=None, seed=None, factor=0.8, n_classes=2, *args, **kwargs): """Create circles separated by some value Args: n_samples: int, number of datapoints to generate noise: float or None, standard deviation of the Gaussian noise added seed: int or None, seed for the noise factor: float, size factor of the inner circles with respect to the outer ones n_classes: int, number of classes to generate Returns: Shuffled features and labels for 'circles' synthetic dataset of type `base.Dataset` Note: The multi-class support might not work as expected if `noise` is enabled TODO: - Generation of unbalanced data Credit goes to (under BSD 3 clause): B. Thirion, G. Varoquaux, A. Gramfort, V. Michel, O. Grisel, G. Louppe, J. Nothman """ if seed is not None: np.random.seed(seed) # Algo: 1) Generate initial circle, 2) For ever class generate a smaller radius circle linspace = np.linspace(0, 2 * np.pi, n_samples // n_classes) circ_x = np.empty(0, dtype=np.int32) circ_y = np.empty(0, dtype=np.int32) base_cos = np.cos(linspace) base_sin = np.sin(linspace) y = np.empty(0, dtype=np.int32) for label in range(n_classes): circ_x = np.append(circ_x, base_cos) circ_y = np.append(circ_y, base_sin) base_cos *= factor base_sin *= factor y = np.append(y, label * np.ones(n_samples // n_classes, dtype=np.int32)) # Add more points if n_samples is not divisible by n_classes (unbalanced!) extras = n_samples % n_classes circ_x = np.append(circ_x, np.cos(np.random.rand(extras) * 2 * np.pi)) circ_y = np.append(circ_y, np.sin(np.random.rand(extras) * 2 * np.pi)) y = np.append(y, np.zeros(extras, dtype=np.int32)) # Reshape the features/labels X = np.vstack((circ_x, circ_y)).T y = np.hstack(y) # Shuffle the data indices = np.random.permutation(range(n_samples)) if noise is not None: X += np.random.normal(scale=noise, size=X.shape) return Dataset(data=X[indices], target=y[indices]) @deprecated(None, 'Consider using synthetic datasets from scikits.learn.') def spirals(n_samples=100, noise=None, seed=None, mode='archimedes', n_loops=2, *args, **kwargs): """Create spirals Currently only binary classification is supported for spiral generation Args: n_samples: int, number of datapoints to generate noise: float or None, standard deviation of the Gaussian noise added seed: int or None, seed for the noise n_loops: int, number of spiral loops, doesn't play well with 'bernoulli' mode: str, how the spiral should be generated. Current implementations: 'archimedes': a spiral with equal distances between branches 'bernoulli': logarithmic spiral with branch distances increasing 'fermat': a spiral with branch distances decreasing (sqrt) Returns: Shuffled features and labels for 'spirals' synthetic dataset of type `base.Dataset` Raises: ValueError: If the generation `mode` is not valid TODO: - Generation of unbalanced data """ n_classes = 2 # I am not sure how to make it multiclass _modes = { 'archimedes': _archimedes_spiral, 'bernoulli': _bernoulli_spiral, 'fermat': _fermat_spiral } if mode is None or mode not in _modes: raise ValueError('Cannot generate spiral with mode %s' % mode) if seed is not None: np.random.seed(seed) linspace = np.linspace(0, 2 * n_loops * np.pi, n_samples // n_classes) spir_x = np.empty(0, dtype=np.int32) spir_y = np.empty(0, dtype=np.int32) y = np.empty(0, dtype=np.int32) for label in range(n_classes): base_cos, base_sin = _modes[mode](linspace, label * np.pi, *args, **kwargs) spir_x = np.append(spir_x, base_cos) spir_y = np.append(spir_y, base_sin) y = np.append(y, label * np.ones(n_samples // n_classes, dtype=np.int32)) # Add more points if n_samples is not divisible by n_classes (unbalanced!) extras = n_samples % n_classes if extras > 0: x_extra, y_extra = _modes[mode](np.random.rand(extras) * 2 * np.pi, *args, **kwargs) spir_x = np.append(spir_x, x_extra) spir_y = np.append(spir_y, y_extra) y = np.append(y, np.zeros(extras, dtype=np.int32)) # Reshape the features/labels X = np.vstack((spir_x, spir_y)).T y = np.hstack(y) # Shuffle the data indices = np.random.permutation(range(n_samples)) if noise is not None: X += np.random.normal(scale=noise, size=X.shape) return Dataset(data=X[indices], target=y[indices]) def _archimedes_spiral(theta, theta_offset=0., *args, **kwargs): """Return Archimedes spiral Args: theta: array-like, angles from polar coordinates to be converted theta_offset: float, angle offset in radians (2*pi = 0) """ x, y = theta * np.cos(theta + theta_offset), theta * np.sin( theta + theta_offset) x_norm = np.max(np.abs(x)) y_norm = np.max(np.abs(y)) x, y = x / x_norm, y / y_norm return x, y def _bernoulli_spiral(theta, theta_offset=0., *args, **kwargs): """Return Equiangular (Bernoulli's) spiral Args: theta: array-like, angles from polar coordinates to be converted theta_offset: float, angle offset in radians (2*pi = 0) Kwargs: exp_scale: growth rate of the exponential """ exp_scale = kwargs.pop('exp_scale', 0.1) x, y = np.exp(exp_scale * theta) * np.cos(theta + theta_offset), np.exp( exp_scale * theta) * np.sin(theta + theta_offset) x_norm = np.max(np.abs(x)) y_norm = np.max(np.abs(y)) x, y = x / x_norm, y / y_norm return x, y def _fermat_spiral(theta, theta_offset=0., *args, **kwargs): """Return Parabolic (Fermat's) spiral Args: theta: array-like, angles from polar coordinates to be converted theta_offset: float, angle offset in radians (2*pi = 0) """ x, y = np.sqrt(theta) * np.cos(theta + theta_offset), np.sqrt(theta) * np.sin( theta + theta_offset) x_norm = np.max(np.abs(x)) y_norm = np.max(np.abs(y)) x, y = x / x_norm, y / y_norm return x, y

apache-2.0

dimkal/mne-python

examples/datasets/plot_brainstorm_data.py

8

2004

""" ============================ Brainstorm tutorial datasets ============================ Here we compute the evoked from raw for the Brainstorm tutorial dataset. For comparison, see: http://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf References ---------- .. [1] Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: A User-Friendly Application for MEG/EEG Analysis. Computational Intelligence and Neuroscience, vol. 2011, Article ID 879716, 13 pages, 2011. doi:10.1155/2011/879716 """ # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr> # # License: BSD (3-clause) import numpy as np import mne from mne.datasets.brainstorm import bst_raw from mne.io import Raw print(__doc__) tmin, tmax, event_id = -0.1, 0.3, 2 # take right-hand somato reject = dict(mag=4e-12, eog=250e-6) data_path = bst_raw.data_path() raw_fname = data_path + '/MEG/bst_raw/' + \ 'subj001_somatosensory_20111109_01_AUX-f_raw.fif' raw = Raw(raw_fname, preload=True) raw.plot() # set EOG channel raw.set_channel_types({'EEG058': 'eog'}) # show power line interference and remove it raw.plot_psd() raw.notch_filter(np.arange(60, 181, 60)) events = mne.find_events(raw, stim_channel='UPPT001') # pick MEG channels picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True, exclude='bads') # Compute epochs epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=reject, preload=False) # compute evoked evoked = epochs.average() # remove physiological artifacts (eyeblinks, heartbeats) using SSP on baseline evoked.add_proj(mne.compute_proj_evoked(evoked.crop(tmax=0, copy=True))) evoked.apply_proj() # fix stim artifact mne.preprocessing.fix_stim_artifact(evoked) # correct delays due to hardware (stim artifact is at 4 ms) evoked.shift_time(-0.004) # plot the result evoked.plot() # show topomaps evoked.plot_topomap(times=np.array([0.016, 0.030, 0.060, 0.070]))

bsd-3-clause

yingzha/blocks-examples

markov_chain/dataset.py

9

1480

"""Defines the dataset for a Markov chain. Has to be in a separate module from the main script in order to be unpicklable by a third party. """ import numpy import copy from fuel.datasets import Dataset class MarkovChainDataset(Dataset): """Training data generator.""" num_states = 3 trans_prob = numpy.array([[0.1, 0.5, 0.4], [0.1, 0.9, 0.0], [0.3, 0.3, 0.4]]) values, vectors = numpy.linalg.eig(trans_prob.T) equilibrium = vectors[:, values.argmax()] equilibrium = equilibrium / equilibrium.sum() trans_entropy = trans_prob * numpy.log(trans_prob + 1e-6) entropy = equilibrium.dot(trans_entropy).sum() provides_sources = ("data",) def __init__(self, rng, seq_len, **kwargs): self.rng = rng self.seq_len = seq_len super(MarkovChainDataset, self).__init__(**kwargs) def open(self): return copy.deepcopy(self.rng) def _next_single(self, rng): states = [0] while len(states) != self.seq_len: states.append(rng.multinomial( 1, self.trans_prob[states[-1]]).argmax()) return states def get_data(self, state, request): """Generate random sequences from the family.""" assert isinstance(request, int) x = numpy.zeros((self.seq_len, request), dtype='int64') for i in range(request): x[:, i] = self._next_single(state) return (x,)

mit

AnonymousBee/bitexchange

libs/coinkit/coinkit/words.py

11

726962

# -*- coding: utf-8 -*- """ Coinkit ~~~~~ :copyright: (c) 2014 by Halfmoon Labs :license: MIT, see LICENSE for more details. """ TOP_ENGLISH_WORDS = ["the", "of", "and", "to", "a", "in", "for", "is", "on", "that", "by", "this", "with", "i", "you", "it", "not", "or", "be", "are", "from", "at", "as", "your", "all", "have", "new", "more", "an", "was", "we", "will", "home", "can", "us", "about", "if", "page", "my", "has", "search", "free", "but", "our", "one", "other", "do", "no", "information", "time", "they", "site", "he", "up", "may", "what", "which", "their", "news", "out", "use", "any", "there", "see", "only", "so", "his", "when", "contact", "here", "business", "who", "web", "also", "now", "help", "get", "view", "online", "c", "e", "first", "am", "been", "would", "how", "were", "me", "s", "services", "some", "these", "click", "its", "like", "service", "x", "than", "find", "price", "date", "back", "top", "people", "had", "list", "name", "just", "over", "state", "year", "day", "into", "email", "two", "health", "n", "world", "re", "next", "used", "go", "b", "work", "last", "most", "products", "music", "buy", "data", "make", "them", "should", "product", "system", "post", "her", "city", "t", "add", "policy", "number", "such", "please", "available", "copyright", "support", "message", "after", "best", "software", "then", "jan", "good", "well", "d", "where", "rights", "public", "books", "high", "school", "through", "m", "each", "links", "she", "review", "years", "order", "very", "privacy", "book", "items", "company", "r", "read", "group", "sex", "need", "many", "user", "said", "de", "does", "set", "under", "general", "research", "university", "january", "mail", "full", "map", "reviews", "program", "life", "know", "games", "way", "days", "management", "p", "part", "could", "great", "united", "hotel", "real", "f", "item", "international", "center", "must", "store", "travel", "comments", "made", "development", "report", "off", "member", "details", "line", "terms", "before", "hotels", "did", "send", "right", "type", "because", "local", "those", "using", "results", "office", "education", "national", "car", "design", "take", "posted", "internet", "address", "community", "within", "states", "area", "want", "phone", "shipping", "reserved", "subject", "between", "forum", "family", "l", "long", "based", "w", "code", "show", "o", "even", "black", "check", "special", "prices", "index", "being", "women", "much", "sign", "file", "link", "open", "today", "technology", "south", "case", "project", "same", "pages", "uk", "version", "section", "own", "found", "sports", "house", "related", "security", "both", "g", "county", "american", "photo", "game", "members", "power", "while", "care", "network", "down", "computer", "systems", "three", "total", "place", "end", "following", "download", "h", "him", "without", "per", "access", "think", "north", "resources", "current", "posts", "big", "media", "law", "control", "water", "history", "pictures", "size", "art", "personal", "since", "including", "guide", "shop", "directory", "board", "location", "change", "white", "text", "small", "rating", "rate", "government", "children", "during", "usa", "return", "students", "v", "shopping", "account", "times", "sites", "level", "digital", "profile", "previous", "form", "events", "love", "old", "john", "main", "call", "hours", "image", "department", "title", "description", "non", "k", "y", "insurance", "another", "why", "shall", "property", "class", "cd", "still", "money", "quality", "every", "listing", "content", "country", "private", "little", "visit", "save", "tools", "low", "reply", "customer", "december", "compare", "movies", "include", "college", "value", "article", "york", "man", "card", "jobs", "provide", "j", "food", "source", "author", "different", "press", "u", "learn", "sale", "around", "print", "course", "job", "canada", "process", "teen", "room", "stock", "training", "too", "credit", "point", "join", "science", "men", "categories", "advanced", "west", "sales", "look", "english", "left", "team", "estate", "box", "conditions", "select", "windows", "gay", "thread", "week", "category", "note", "live", "large", "gallery", "table", "register", "however", "june", "october", "november", "market", "library", "really", "action", "start", "series", "model", "features", "air", "industry", "plan", "human", "provided", "tv", "yes", "required", "second", "hot", "accessories", "cost", "movie", "march", "la", "september", "better", "say", "questions", "july", "going", "medical", "test", "friend", "come", "dec", "study", "application", "cart", "staff", "articles", "san", "again", "play", "looking", "issues", "april", "never", "users", "complete", "street", "topic", "comment", "financial", "things", "working", "against", "standard", "tax", "person", "below", "mobile", "less", "got", "party", "payment", "equipment", "login", "student", "let", "programs", "offers", "legal", "above", "recent", "park", "stores", "side", "act", "problem", "red", "give", "memory", "performance", "social", "q", "august", "quote", "language", "story", "sell", "experience", "rates", "create", "key", "body", "young", "america", "important", "field", "few", "east", "paper", "single", "ii", "age", "activities", "club", "example", "girls", "additional", "password", "z", "latest", "something", "road", "gift", "question", "changes", "night", "ca", "hard", "texas", "oct", "pay", "four", "poker", "status", "browse", "issue", "range", "building", "seller", "court", "february", "always", "result", "light", "write", "war", "nov", "offer", "blue", "groups", "al", "easy", "given", "files", "event", "release", "analysis", "request", "china", "making", "picture", "needs", "possible", "might", "professional", "yet", "month", "major", "star", "areas", "future", "space", "committee", "hand", "sun", "cards", "problems", "london", "washington", "meeting", "become", "interest", "id", "child", "keep", "enter", "california", "share", "similar", "garden", "schools", "million", "added", "reference", "companies", "listed", "baby", "learning", "energy", "run", "delivery", "net", "popular", "term", "film", "stories", "put", "computers", "journal", "reports", "co", "try", "welcome", "central", "images", "president", "notice", "god", "original", "head", "radio", "until", "cell", "color", "self", "council", "away", "includes", "track", "australia", "discussion", "archive", "once", "others", "entertainment", "agreement", "format", "least", "society", "months", "log", "safety", "friends", "sure", "trade", "edition", "cars", "messages", "marketing", "tell", "further", "updated", "association", "able", "having", "provides", "david", "fun", "already", "green", "studies", "close", "common", "drive", "specific", "several", "gold", "feb", "living", "collection", "called", "short", "arts", "lot", "ask", "display", "limited", "solutions", "means", "director", "daily", "beach", "past", "natural", "whether", "due", "et", "five", "upon", "period", "planning", "says", "official", "weather", "mar", "land", "average", "done", "technical", "window", "france", "pro", "region", "island", "record", "direct", "conference", "environment", "records", "st", "district", "calendar", "costs", "style", "front", "statement", "parts", "aug", "ever", "early", "miles", "sound", "resource", "present", "applications", "either", "ago", "document", "word", "works", "material", "bill", "written", "talk", "federal", "rules", "final", "adult", "tickets", "thing", "centre", "requirements", "via", "cheap", "nude", "kids", "finance", "true", "minutes", "else", "mark", "third", "rock", "gifts", "europe", "reading", "topics", "bad", "individual", "tips", "plus", "auto", "cover", "usually", "edit", "together", "percent", "fast", "function", "fact", "unit", "getting", "global", "meet", "far", "economic", "en", "player", "projects", "lyrics", "often", "subscribe", "submit", "germany", "amount", "watch", "included", "feel", "though", "bank", "risk", "thanks", "everything", "deals", "various", "words", "jul", "production", "commercial", "james", "weight", "town", "heart", "advertising", "received", "choose", "treatment", "newsletter", "archives", "points", "knowledge", "magazine", "error", "camera", "girl", "currently", "construction", "toys", "registered", "clear", "golf", "receive", "domain", "methods", "chapter", "makes", "protection", "policies", "loan", "wide", "beauty", "manager", "india", "position", "taken", "sort", "models", "michael", "known", "half", "cases", "step", "engineering", "florida", "simple", "quick", "none", "wireless", "license", "paul", "friday", "lake", "whole", "annual", "published", "later", "basic", "shows", "corporate", "church", "method", "purchase", "customers", "active", "response", "practice", "hardware", "figure", "materials", "fire", "holiday", "chat", "enough", "designed", "along", "among", "death", "writing", "speed", "html", "countries", "loss", "face", "brand", "discount", "higher", "effects", "created", "remember", "standards", "oil", "bit", "yellow", "political", "increase", "advertise", "kingdom", "base", "near", "thought", "stuff", "french", 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"callsign", "martinis", "envisions", "flexibly", "nakd", "natwest", "wilsons", "ccn", "reposition", "msci", "orginal", "hobbyists", "anat", "fleshbot", "weta", "sindh", "pcf", "glick", "obsoletes", "mammogram", "sani", "webcasting", "soggy", "apha", "ecologist", "ararat", "narrowband", "bph", "webstore", "maus", "reinstalling", "gendered", "relateddiagram", "kingsland", "ssid", "rackets", "litigants", "shimon", "ducted", "ebsq", "crisps", "modelle", "wristwatches", "xenadrine", "linac", "identifications", "dressy", "authenticator", "arash", "cristobal", "stewie", "depositories", "pcre", "setpoint", "rockdale", "evita", "ballmer", "hemphill", "taormina", "plath", "pickers", "boardgamegeek", "serbo", "oci", "noviembre", "mappoint", "surn", "minisd", "madmums", "mosher", "digitallife", "grahame", "forecasters", "linoleum", "shearling", "stockster", "firstcall", "dorint", "wmc", "culverts", "cuticle", "codebase", "rdfs", "lter", "pimples", "hdb", "shorted", "loghi", "spunky", "razz", "komatsu", "bietet", "madisonville", "readies", "jovenes", "deuterium", "totalitarianism", "trigonometric", "selmer", "popcap", "verbosity", "aashto", "pavarotti", "syncing", "vanden", "majeure", "beret", "fallbrook", "audiovideo", "muay", "longshot", "rollaway", "yor", "nonstandard", "tbr", "manoa", "laundries", "whoo", "tefal", "tothe", "crv", "amx", "falign", "goleta", "holst", "ebola", "redbook", "rangel", "consolidates", "disaggregated", "chromatographic", "supersport", "golly", "flumotion", "seagrass", "congratulates", "anais", "grievant", "reinstalled", "entreprises", "clemons", "eurovision", "airplus", "panchkula", "shahid", "phospholipids", "elsinore", "opendocument", "ankeny", "canzoni", "wakeman", "moana", "wobbly", "seagulls", "megawatts", "denning", "temas", "illuminator", "marylebone", "symbolically", "erotico", "linx", "randle", "nhu", "unsubstantiated", "centroid", "monogrammed", "gambian", "tailgating", "colville", "vpu", "russische", "sgp", "soccernet", "zing", "downunder", "snips", "allawi", "lockup", "cholinergic", "lhr", "barthelemy", "babymint", "benning", "implantable", "ligo", "haddad", "univariate", "katia", "motorcross", "sangha", "shn", "myfonts", "usuarios", "caml", "resiliency", "barossa", "astrobiology", "disinfectants", "kawai", "uktv", "dreamtime", "berkshires", "inhumane", "trobe", "unlocks", "auctex", "pogues", "panicked", "developerworks", "bullitt", "toed", "smartcard", "kushner", "hardcoresex", "crump", "gunderson", "paramus", "cepr", "lma", "politica", "randomization", "rinsing", "reschedule", "tob", "hostal", "preempt", "resold", "cyclo", "phosphor", "frontenac", "wipeout", "mambots", "unscented", "ipfw", "ergonomically", "roosters", "homologues", "loring", "ionosphere", "belvidere", "trotsky", "airworthiness", "sistemas", "devsource", "retroviral", "llnl", "keyloggers", "amgen", "marci", "willey", "yau", "groucho", "foreshore", "gusset", "dissapointed", "dtds", "mibs", "metalwork", "refering", "punting", "triphasil", "scab", "bhavnagar", "creedence", "musee", "wellstone", "lleol", "gpib", "tidbit", "allyson", "teriyaki", "impoundment", "interrelationships", "gres", "coffeecup", "maru", "joon", "josephus", "ulong", "maputo", "chev", "krispy", "dogtown", "abernathy", "raz", "fermion", "weltweit", "fluor", "bergstrom", "inoperable", "esrc", "asdf", "gollum", "ceus", "macintyre", "srd", "cyclonic", "cft", "unsubscribing", "shawna", "pinyin", "ipac", "ramone", "fethiye", "multipath", "hakusho", "tein", "treeview", "atd", "wonderswan", "eugenics", "dustjacket", "emmanuelle", "dlocaledir", "molotov", "sandpaper", "hbc", "fannin", "interscope", "eba", "melayu", "hardiness", "liss", "phew", "furuno", "moynihan", "johnsons", "heng", "dro", "carbonated", "waives", "wraparound", "jfs", "ejackulation", "reboots", "headliner", "sqr", "bustin", "powernetworker", "vul", "superposition", "supremes", "insite", "fanzine", "laney", "purportedly", "antigenic", "rurouni", "dietetics", "assembles", "veracruz", "hausfrauen", "wsf", "benzo", "vietcong", "chairwoman", "petrochemicals", "pata", "cntr", "nettime", "techies", "bentyxxo", "xango", "radish", "gatto", "checkmate", "gantt", "valli", "tuv", "starlets", "plavix", "roomba", "aficionado", "motivator", "bijan", "riv", "storrs", "tabula", "reigate", "emmons", "sandstorm", "laci", "taoist", "nameplate", "axp", "wcb", "mothering", "billard", "chrysanthemum", "reconstructions", "innodb", "sunspot", "aisha", "fluorine", "healdsburg", "retype", "fishin", "likud", "cyberread", "pme", "rothwell", "kmf", "creationist", "wth", "setlist", "scrollbars", "bocelli", "zuckerman", "vtd", "ampicillin", "arcy", "wasn", "cowbell", "rater", "everson", "angebot", "cezanne", "tamagotchi", "earpiece", "franca", "thymidine", "disa", "gearlog", "tranche", "volum", "prsp", "openvpn", "mcentire", "londra", "kaur", "unconstrained", "datadirect", "souter", "redfern", "tulum", "nyy", "pagesize", "osteopathy", "stavanger", "cated", "autry", "fip", "rooftops", "findpage", "discourages", "benitez", "boater", "shackleton", "weirdo", "congresswoman", "dalek", "tass", "itrip", "myob", "helloween", "reperfusion", "fieldhouse", "manukau", "libname", "eucharistic", "mong", "homeware", "ckt", "winmx", "mobic", "farts", "rourke", "lackawanna", "villiers", "comercio", "huy", "brooksville", "falwell", "gwb", "donwload", "wrth", "attrs", "knockoffs", "esm", "bionicle", "hygienist", "nichole", "quidditch", "dartmoor", "rowlett", "stapled", "gardenweb", "butternut", "nummer", "groban", "asw", "arora", "yatsura", "warr", "hainan", "esg", "logoff", "cockroach", "xanadu", "computable", "occup", "playgroup", "tintin", "ethnicities", "webposition", "crafter", "roby", "disassemble", "boltzmann", "caos", "abidjan", "anise", "grainy", "hospitalizations", "notizie", "zoek", "sepultura", "walkabout", "pepperoni", "optimising", "cityreview", "boathouse", "katt", "weissman", "siri", "herkimer", "namecite", "refreshingly", "aph", "ryland", "sculptural", "neurophysiology", "gsk", "hermanus", "mocldy", "ngage", "annexure", "ipchains", "yosef", "tlds", "gozo", "pso", "helton", "outflows", "saas", "asthmatic", "guillemot", "realizations", "linguistically", "jaco", "mckinsey", "dezember", "hylafax", "reconstitution", "amateurwebcam", "lumberton", "interviewee", "intereco", "portola", "hematologic", "sgc", "rebbe", "pinup", "transcendence", "surah", "brendon", "farberware", "statisticians", "swatches", "perioperative", "maoist", "henkel", "lilangeni", "trapeze", "lemmings", "extents", "spams", "omagh", "workcentre", "sunbird", "cellophane", "deland", "blevins", "sacha", "cardholders", "dddd", "accessori", "qo", "araujo", "mylist", "pcu", "kloczek", "enet", "seperated", "clusty", "rolfe", "cuttack", "provantage", "dominio", "hyperbaric", "nannofossil", "logansport", "bulldozer", "blacksonblondes", "subprime", "overpayments", "sharpie", "modutils", "whitehaven", "whaley", "currier", "taproot", "topsite", "delorme", "rayner", "aio", "rossum", "urbanism", "colloquia", "ewr", "capillaries", "mountainside", "menthol", "blackouts", "starkey", "eves", "hpux", "canby", "dragonflies", "montrail", "findfont", "aigner", "urusei", "soundblaster", "beatle", "webzine", "propranolol", "inescapable", "swabs", "absorbance", "lbw", "audiofile", "simba", "mohd", "redgoldfish", "cornbread", "jcaho", "appendixes", "aod", "crestview", "keynotes", "fotolia", "subnets", "cau", "espanola", "busnes", "froggy", "decarboxylase", "elfman", "throughs", "prioritise", "oreck", "schottland", "bagpipe", "terns", "erythematosus", "ftrs", "excitatory", "mcevoy", "fujita", "niagra", "yq", "dribble", "hardwired", "hosta", "grambling", "exten", "seeger", "ringgold", "sondheim", "interconnecting", "inkjets", "ebv", "underpinnings", "lazar", "laxatives", "mythos", "soname", "colloid", "hiked", "defrag", "zanesville", "oxidant", "umbra", "poppin", "trebuchet", "pyrite", "partido", "drunks", "submitters", "branes", "mahdi", "agoura", "manchesteronline", "blunkett", "lapd", "kidder", "hotkey", "tirupur", "parkville", "crediting", "tmo"]

gpl-3.0

andrewcmyers/tensorflow

tensorflow/contrib/data/python/kernel_tests/zip_dataset_op_test.py

39

4394

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for the experimental input pipeline ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.contrib.data.python.ops import dataset_ops from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.ops import array_ops from tensorflow.python.platform import test class ZipDatasetTest(test.TestCase): def testZipDataset(self): component_placeholders = [ array_ops.placeholder(dtypes.int64), array_ops.placeholder(dtypes.int64), array_ops.placeholder(dtypes.float64) ] datasets = tuple([ dataset_ops.Dataset.from_tensor_slices(component_placeholder) for component_placeholder in component_placeholders ]) zipped = dataset_ops.Dataset.zip(datasets) iterator = zipped.make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() with self.test_session() as sess: equal_length_components = [ np.tile(np.array([[1], [2], [3], [4]]), 20), np.tile(np.array([[12], [13], [14], [15]]), 22), np.array([37.0, 38.0, 39.0, 40.0]) ] sess.run(init_op, feed_dict={ph: value for ph, value in zip( component_placeholders, equal_length_components)}) for i in range(4): results = sess.run(get_next) for component, result_component in zip( equal_length_components, results): self.assertAllEqual(component[i], result_component) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) variable_length_components = [[1, 2, 3, 4], [1, 2, 3, 4, 5], [1.0, 2.0]] sess.run(init_op, feed_dict={ph: value for ph, value in zip( component_placeholders, variable_length_components)}) for i in range(2): results = sess.run(get_next) for component, result_component in zip( variable_length_components, results): self.assertAllEqual(component[i], result_component) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) def testNestedZipDataset(self): component_placeholders = [ array_ops.placeholder(dtypes.int64, shape=[4, 20]), array_ops.placeholder(dtypes.int64, shape=[4, 22]), array_ops.placeholder(dtypes.float64, shape=[4]) ] datasets = [ dataset_ops.Dataset.from_tensor_slices(component_placeholder) for component_placeholder in component_placeholders ] zipped = dataset_ops.Dataset.zip((datasets[0], (datasets[1], datasets[2]))) iterator = zipped.make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() self.assertEqual([20], get_next[0].shape) self.assertEqual([22], get_next[1][0].shape) self.assertEqual([], get_next[1][1].shape) with self.test_session() as sess: equal_length_components = [ np.tile(np.array([[1], [2], [3], [4]]), 20), np.tile(np.array([[12], [13], [14], [15]]), 22), np.array([37.0, 38.0, 39.0, 40.0]) ] sess.run(init_op, feed_dict={ph: value for ph, value in zip( component_placeholders, equal_length_components)}) for i in range(4): result1, (result2, result3) = sess.run(get_next) self.assertAllEqual(equal_length_components[0][i], result1) self.assertAllEqual(equal_length_components[1][i], result2) self.assertAllEqual(equal_length_components[2][i], result3) with self.assertRaises(errors.OutOfRangeError): sess.run(get_next) if __name__ == "__main__": test.main()

apache-2.0

GoogleCloudPlatform/public-datasets-pipelines

datasets/fec/pipelines/other_committee_tx_2018/other_committee_tx_2018_dag.py

1

7056

# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from airflow import DAG from airflow.providers.cncf.kubernetes.operators import kubernetes_pod from airflow.providers.google.cloud.transfers import gcs_to_bigquery default_args = { "owner": "Google", "depends_on_past": False, "start_date": "2021-03-01", } with DAG( dag_id="fec.other_committee_tx_2018", default_args=default_args, max_active_runs=1, schedule_interval="@daily", catchup=False, default_view="graph", ) as dag: # Run CSV transform within kubernetes pod other_committee_tx_2018_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="other_committee_tx_2018_transform_csv", startup_timeout_seconds=600, name="other_committee_tx_2018", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.fec.container_registry.run_csv_transform_kub }}", env_vars={ "SOURCE_URL": "https://cg-519a459a-0ea3-42c2-b7bc-fa1143481f74.s3-us-gov-west-1.amazonaws.com/bulk-downloads/2018/oth18.zip", "SOURCE_FILE_ZIP_FILE": "files/zip_file.zip", "SOURCE_FILE_PATH": "files/", "SOURCE_FILE": "files/itoth.txt", "TARGET_FILE": "files/data_output.csv", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "data/fec/other_committee_tx_2018/data_output.csv", "PIPELINE_NAME": "other_committee_tx_2018", "CSV_HEADERS": '["cmte_id","amndt_ind","rpt_tp","transaction_pgi","image_num","transaction_tp","entity_tp","name","city","state", "zip_code","employer","occupation","transaction_dt","transaction_amt","other_id","tran_id" ,"file_num", "memo_cd","memo_text","sub_id"]', }, resources={ "request_memory": "3G", "request_cpu": "1", "request_ephemeral_storage": "5G", }, ) # Task to load CSV data to a BigQuery table load_other_committee_tx_2018_to_bq = gcs_to_bigquery.GCSToBigQueryOperator( task_id="load_other_committee_tx_2018_to_bq", bucket="{{ var.value.composer_bucket }}", source_objects=["data/fec/other_committee_tx_2018/data_output.csv"], source_format="CSV", destination_project_dataset_table="fec.other_committee_tx_2018", skip_leading_rows=1, allow_quoted_newlines=True, write_disposition="WRITE_TRUNCATE", schema_fields=[ { "name": "cmte_id", "type": "string", "description": "Filer Identification Number", "mode": "nullable", }, { "name": "amndt_ind", "type": "string", "description": "Amendment Indicator", "mode": "nullable", }, { "name": "rpt_tp", "type": "string", "description": "Report Type", "mode": "nullable", }, { "name": "transaction_pgi", "type": "string", "description": "Primary-General Indicator", "mode": "nullable", }, { "name": "image_num", "type": "integer", "description": "Image Number", "mode": "nullable", }, { "name": "transaction_tp", "type": "string", "description": "Transaction Type", "mode": "nullable", }, { "name": "entity_tp", "type": "string", "description": "Entity Type", "mode": "nullable", }, { "name": "name", "type": "string", "description": "Contributor/Lender/Transfer Name", "mode": "nullable", }, { "name": "city", "type": "string", "description": "City/Town", "mode": "nullable", }, { "name": "state", "type": "string", "description": "State", "mode": "nullable", }, { "name": "zip_code", "type": "string", "description": "Zip Code", "mode": "nullable", }, { "name": "employer", "type": "string", "description": "Employer", "mode": "nullable", }, { "name": "occupation", "type": "string", "description": "Occupation", "mode": "nullable", }, { "name": "transaction_dt", "type": "date", "description": "Transaction Date(MMDDYYYY)", "mode": "nullable", }, { "name": "transaction_amt", "type": "float", "description": "Transaction Amount", "mode": "nullable", }, { "name": "other_id", "type": "string", "description": "Other Identification Number", "mode": "nullable", }, { "name": "tran_id", "type": "string", "description": "Transaction ID", "mode": "nullable", }, { "name": "file_num", "type": "integer", "description": "File Number / Report ID", "mode": "nullable", }, { "name": "memo_cd", "type": "string", "description": "Memo Code", "mode": "nullable", }, { "name": "memo_text", "type": "string", "description": "Memo Text", "mode": "nullable", }, { "name": "sub_id", "type": "integer", "description": "FEC Record Number", "mode": "nullable", }, ], ) other_committee_tx_2018_transform_csv >> load_other_committee_tx_2018_to_bq

apache-2.0

pkruskal/scikit-learn

sklearn/utils/setup.py

294

2884

import os from os.path import join from sklearn._build_utils import get_blas_info def configuration(parent_package='', top_path=None): import numpy from numpy.distutils.misc_util import Configuration config = Configuration('utils', parent_package, top_path) config.add_subpackage('sparsetools') cblas_libs, blas_info = get_blas_info() cblas_compile_args = blas_info.pop('extra_compile_args', []) cblas_includes = [join('..', 'src', 'cblas'), numpy.get_include(), blas_info.pop('include_dirs', [])] libraries = [] if os.name == 'posix': libraries.append('m') cblas_libs.append('m') config.add_extension('sparsefuncs_fast', sources=['sparsefuncs_fast.c'], libraries=libraries) config.add_extension('arrayfuncs', sources=['arrayfuncs.c'], depends=[join('src', 'cholesky_delete.h')], libraries=cblas_libs, include_dirs=cblas_includes, extra_compile_args=cblas_compile_args, **blas_info ) config.add_extension( 'murmurhash', sources=['murmurhash.c', join('src', 'MurmurHash3.cpp')], include_dirs=['src']) config.add_extension('lgamma', sources=['lgamma.c', join('src', 'gamma.c')], include_dirs=['src'], libraries=libraries) config.add_extension('graph_shortest_path', sources=['graph_shortest_path.c'], include_dirs=[numpy.get_include()]) config.add_extension('fast_dict', sources=['fast_dict.cpp'], language="c++", include_dirs=[numpy.get_include()], libraries=libraries) config.add_extension('seq_dataset', sources=['seq_dataset.c'], include_dirs=[numpy.get_include()]) config.add_extension('weight_vector', sources=['weight_vector.c'], include_dirs=cblas_includes, libraries=cblas_libs, **blas_info) config.add_extension("_random", sources=["_random.c"], include_dirs=[numpy.get_include()], libraries=libraries) config.add_extension("_logistic_sigmoid", sources=["_logistic_sigmoid.c"], include_dirs=[numpy.get_include()], libraries=libraries) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(**configuration(top_path='').todict())

bsd-3-clause

blackye/luscan-devel

thirdparty_libs/nltk/tokenize/regexp.py

12

8204

# Natural Language Toolkit: Tokenizers # # Copyright (C) 2001-2012 NLTK Project # Author: Edward Loper <edloper@gradient.cis.upenn.edu> # Steven Bird <sb@csse.unimelb.edu.au> # Trevor Cohn <tacohn@csse.unimelb.edu.au> # URL: <http://nltk.sourceforge.net> # For license information, see LICENSE.TXT r""" Regular-Expression Tokenizers A ``RegexpTokenizer`` splits a string into substrings using a regular expression. For example, the following tokenizer forms tokens out of alphabetic sequences, money expressions, and any other non-whitespace sequences: >>> from nltk.tokenize import RegexpTokenizer >>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks." >>> tokenizer = RegexpTokenizer('\w+|\$[\d\.]+|\S+') >>> tokenizer.tokenize(s) ['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.'] A ``RegexpTokenizer`` can use its regexp to match delimiters instead: >>> tokenizer = RegexpTokenizer('\s+', gaps=True) >>> tokenizer.tokenize(s) ['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York.', 'Please', 'buy', 'me', 'two', 'of', 'them.', 'Thanks.'] Note that empty tokens are not returned when the delimiter appears at the start or end of the string. The material between the tokens is discarded. For example, the following tokenizer selects just the capitalized words: >>> capword_tokenizer = RegexpTokenizer('[A-Z]\w+') >>> capword_tokenizer.tokenize(s) ['Good', 'New', 'York', 'Please', 'Thanks'] This module contains several subclasses of ``RegexpTokenizer`` that use pre-defined regular expressions. >>> from nltk.tokenize import BlanklineTokenizer >>> # Uses '\s*\n\s*\n\s*': >>> BlanklineTokenizer().tokenize(s) ['Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.', 'Thanks.'] All of the regular expression tokenizers are also available as functions: >>> from nltk.tokenize import regexp_tokenize, wordpunct_tokenize, blankline_tokenize >>> regexp_tokenize(s, pattern='\w+|\$[\d\.]+|\S+') ['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.'] >>> wordpunct_tokenize(s) ['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.'] >>> blankline_tokenize(s) ['Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.', 'Thanks.'] Caution: The function ``regexp_tokenize()`` takes the text as its first argument, and the regular expression pattern as its second argument. This differs from the conventions used by Python's ``re`` functions, where the pattern is always the first argument. (This is for consistency with the other NLTK tokenizers.) """ import re import sre_constants from nltk.internals import convert_regexp_to_nongrouping from nltk.tokenize.api import TokenizerI from nltk.tokenize.util import regexp_span_tokenize class RegexpTokenizer(TokenizerI): """ A tokenizer that splits a string using a regular expression, which matches either the tokens or the separators between tokens. >>> tokenizer = RegexpTokenizer('\w+|\$[\d\.]+|\S+') :type pattern: str :param pattern: The pattern used to build this tokenizer. (This pattern may safely contain grouping parentheses.) :type gaps: bool :param gaps: True if this tokenizer's pattern should be used to find separators between tokens; False if this tokenizer's pattern should be used to find the tokens themselves. :type discard_empty: bool :param discard_empty: True if any empty tokens `''` generated by the tokenizer should be discarded. Empty tokens can only be generated if `_gaps == True`. :type flags: int :param flags: The regexp flags used to compile this tokenizer's pattern. By default, the following flags are used: `re.UNICODE | re.MULTILINE | re.DOTALL`. """ def __init__(self, pattern, gaps=False, discard_empty=True, flags=re.UNICODE | re.MULTILINE | re.DOTALL): # If they gave us a regexp object, extract the pattern. pattern = getattr(pattern, 'pattern', pattern) self._pattern = pattern self._gaps = gaps self._discard_empty = discard_empty self._flags = flags self._regexp = None # Remove grouping parentheses -- if the regexp contains any # grouping parentheses, then the behavior of re.findall and # re.split will change. nongrouping_pattern = convert_regexp_to_nongrouping(pattern) try: self._regexp = re.compile(nongrouping_pattern, flags) except re.error, e: raise ValueError('Error in regular expression %r: %s' % (pattern, e)) def tokenize(self, text): # If our regexp matches gaps, use re.split: if self._gaps: if self._discard_empty: return [tok for tok in self._regexp.split(text) if tok] else: return self._regexp.split(text) # If our regexp matches tokens, use re.findall: else: return self._regexp.findall(text) def span_tokenize(self, text): if self._gaps: for left, right in regexp_span_tokenize(text, self._regexp): if not (self._discard_empty and left == right): yield left, right else: for m in re.finditer(self._regexp, text): yield m.span() def __repr__(self): return ('%s(pattern=%r, gaps=%r, discard_empty=%r, flags=%r)' % (self.__class__.__name__, self._pattern, self._gaps, self._discard_empty, self._flags)) class WhitespaceTokenizer(RegexpTokenizer): r""" Tokenize a string on whitespace (space, tab, newline). In general, users should use the string ``split()`` method instead. >>> from nltk.tokenize import WhitespaceTokenizer >>> s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks." >>> WhitespaceTokenizer().tokenize(s) ['Good', 'muffins', 'cost', '$3.88', 'in', 'New', 'York.', 'Please', 'buy', 'me', 'two', 'of', 'them.', 'Thanks.'] """ def __init__(self): RegexpTokenizer.__init__(self, r'\s+', gaps=True) class BlanklineTokenizer(RegexpTokenizer): """ Tokenize a string, treating any sequence of blank lines as a delimiter. Blank lines are defined as lines containing no characters, except for space or tab characters. """ def __init__(self): RegexpTokenizer.__init__(self, r'\s*\n\s*\n\s*', gaps=True) class WordPunctTokenizer(RegexpTokenizer): """ Tokenize a text into a sequence of alphabetic and non-alphabetic characters, using the regexp ``\w+|[^\w\s]+``. >>> from nltk.tokenize import WordPunctTokenizer >>> s = "Good muffins cost $3.88\\nin New York. Please buy me\\ntwo of them.\\n\\nThanks." >>> WordPunctTokenizer().tokenize(s) ['Good', 'muffins', 'cost', '$', '3', '.', '88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.'] """ def __init__(self): RegexpTokenizer.__init__(self, r'\w+|[^\w\s]+') ###################################################################### #{ Tokenization Functions ###################################################################### def regexp_tokenize(text, pattern, gaps=False, discard_empty=True, flags=re.UNICODE | re.MULTILINE | re.DOTALL): """ Return a tokenized copy of *text*. See :class:`.RegexpTokenizer` for descriptions of the arguments. """ tokenizer = RegexpTokenizer(pattern, gaps, discard_empty, flags) return tokenizer.tokenize(text) blankline_tokenize = BlanklineTokenizer().tokenize wordpunct_tokenize = WordPunctTokenizer().tokenize if __name__ == "__main__": import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)

gpl-2.0

crigroup/criros

src/criros/raveutils.py

1

17736

#! /usr/bin/env python import copy import criros import itertools import numpy as np import scipy.spatial import sklearn.cluster import openravepy as orpy # Transformations import tf.transformations as tr # Logger from criros.utils import TextColors logger = TextColors() # Supported IK types iktype5D = orpy.IkParameterization.Type.TranslationDirection5D iktype6D = orpy.IkParameterization.Type.Transform6D SUPPORTED_IK_TYPES = [iktype5D, iktype6D] class Hole(object): def __init__(self, position, direction, depth): self.position = np.array(position) self.direction = tr.unit_vector(direction) self.depth = abs(depth) def __repr__(self): printoptions = np.get_printoptions() np.set_printoptions(precision=4, suppress=True) text = '<Hole(pos: {0} dir: {1} depth: {2})>'.format(self.position, self.direction, self.depth) np.set_printoptions(**printoptions) return text def __str__(self): return self.__repr__() def get_ray(self): return orpy.Ray(self.position, self.direction) def transform(self, T): ray = self.get_ray() Thole = criros.conversions.from_ray(ray) Tnew = np.dot(T, Thole) newray = criros.conversions.to_ray(Tnew) self.position = newray.pos() self.direction = newray.dir() def compute_bounding_box_corners(body, Tbody=None, scale=1.0): """ Computes the bounding box corners (8 corners) for the given body. If C{Tbody} is given (not None), the corners are transformed. The {scale} parameters is a factor used to scale the extents of the bounding box. @type body: orpy.KinBody @param body: The OpenRAVE body @type Tbody: np.array @param Tbody: homogeneous transformation to transform the corners. If None, the corners are given using the current position of the body in OpenRAVE. @type scale: factor @param scale: the scale factor to modify the extents of the bounding box. @rtype: list @return: list containing the 8 box corners. Each corner is a XYZ point of type C{np.array}. """ # Create a dummy body an use OpenRAVE to get the corners env = body.GetEnv() dummy = orpy.RaveCreateKinBody(env, '') dummy.Clone(body, 0) if Tbody is not None: with env: dummy.SetTransform(Tbody) aabb = dummy.ComputeAABB() corners = [] for k in itertools.product([-1,1],[-1,1],[-1,1]): corners.append(aabb.pos() + np.array(k)*aabb.extents()*scale) return corners def enable_body(body, enable): """ Enables all the links of a body. @type body: orpy.KinBody @param body: The OpenRAVE body @type enable: bool @param enable: If true, will enable all the links. """ env = body.GetEnv() with env: for link in body.GetLinks(): link.Enable(enable) def environment_from_dict(config, env=None, logger=TextColors()): """ Loads and configures and OpenRAVE environment from a configuration dictionary. This approach allows to encapsulate additional information that would be tedious to include if we only used the OpenRAVE XML specification. @type config: dict @param config: The configuration dictionary @rtype: orpy.Environment @return: The OpenRAVE environment loaded """ if not isinstance(config, dict): logger.logwarn('config is not a dict') return None # Check required fields are in the config dict required_fields = ['world'] if not criros.utils.has_keys(config, required_fields): logger.logwarn( 'config dict does not have the required fields: {0}'.format(required_fields) ) return None if env is None: env = orpy.Environment() if not env.Load(config['world']): env = None return None # OPTIONAL parameters # Viewer parameters if config.has_key('viewer'): viewer_name = config['viewer']['name'] if viewer_name == 'default': env.SetDefaultViewer() else: env.SetViewer(viewer_name) # The camera where we look the viewer from if config['viewer'].has_key('camera'): transform_dict = config['viewer']['camera'] camera_fields = ['rotation','translation'] if not criros.utils.has_keys(transform_dict, camera_fields): logger.logwarn('camera dict does not have the required fields: {0}'.format(camera_fields)) elif env.GetViewer() is not None: Tcam = criros.conversions.from_dict(transform_dict) env.GetViewer().SetCamera(Tcam) # Return configured environment return env def destroy_env(env): """ Dummy function that destroys properly an OpenRAVE environment. @note: Useful when working with C{IPython} + QtCoin viewer. @type env: orpy.Environment @param env: The OpenRAVE environment """ env.Reset() env.Destroy() def find_body_holes(body, radius, absolute=True): import trimesh mesh_holes = dict() body_holes = dict() Tbody = body.GetTransform() for link in body.GetLinks(): link_holes = [] Tlink = link.GetTransform() for geometry in link.GetGeometries(): if geometry.GetType() == orpy.GeometryType.Trimesh: filename = geometry.GetRenderFilename() scale = geometry.GetRenderScale() pose = geometry.GetTransformPose() if filename not in mesh_holes: mesh = trimesh.load(filename) mesh_holes[filename] = find_mesh_holes(mesh.vertices, mesh.faces, radius, scale) for h in mesh_holes[filename]: hole = copy.deepcopy(h) Tmesh = np.dot(Tlink, orpy.matrixFromPose(pose)) if absolute: hole.transform(np.dot(Tbody,Tmesh)) else: hole.transform(Tmesh) link_holes.append(hole) if len(link_holes) > 0: body_holes[str(link.GetName())] = link_holes return body_holes def find_mesh_holes(vert, faces, radius, scale=1., fitplane_eps=1e-8, fitplane_attempts=10): vertices = np.array(vert)*scale # Circles have lots of vertices. Use clustering to locate them eps = radius + 1e-3 db = sklearn.cluster.DBSCAN(eps=eps, min_samples=10).fit(vertices) unique_labels = set(db.labels_) circles_info = [] for k in unique_labels: if k == -1: # Unknown cluster continue points = vertices[db.labels_==k] for _ in range(fitplane_attempts): seed = np.zeros(4) seed[:3] = tr.unit_vector(tr.random_vector(3)) res = criros.spalg.fit_plane_optimize(points, seed=seed) equation=res[0] fit_error = res[2] if fit_error < fitplane_eps: break data = dict() data['center'] = np.mean(points, axis=0) data['plane'] = criros.spalg.Plane(equation=res[0]) circles_info.append(data) if fit_error > fitplane_eps: # Report circles that weren't fitted properly print 'Circle planefit error above threshold: {0}'.format(fit_error) # One hole is composed by two circles, pair them holes = [] num_circles = len(circles_info) found = set() for i in range(num_circles): if i in found: continue # Skip already paired circles for j in range(1, num_circles): if (j in found) or (i == j): continue # Skip already paired circles plane_i = circles_info[i]['plane'] plane_j = circles_info[j]['plane'] ni = plane_i.normal nj = plane_j.normal parallel = np.isclose(abs(np.dot(ni,nj)), 1.) center_i = circles_info[i]['center'] center_j = circles_info[j]['center'] pi = center_i pj = plane_i.project(center_j) if parallel and np.allclose(pi, pj): found.add(i) found.add(j) position = center_j diff = center_i - center_j direction = tr.unit_vector(diff) depth = np.linalg.norm(diff) holes.append(Hole(position, direction, depth)) return holes def generate_convex_decomposition_model(robot, padding): cdmodel = orpy.databases.convexdecomposition.ConvexDecompositionModel(robot, padding=padding) cdmodel.generate(padding=padding, minTriangleConvexHullThresh=12000, skinWidth=0, decompositionDepth=8, maxHullVertices=256, concavityThresholdPercent=10, mergeThresholdPercent=30, volumeSplitThresholdPercent=15) cdmodel.save() if cdmodel.load(): return cdmodel else: return None def get_arm_length_estimate(robot): """ The best estimate of arm length is to sum up the distances of the anchors of all the points in between the chain """ manip = robot.GetActiveManipulator() armjoints = [j for j in robot.GetDependencyOrderedJoints() if j.GetJointIndex() in manip.GetArmIndices()] baseanchor = armjoints[0].GetAnchor() eetrans = manip.GetEndEffectorTransform()[0:3,3] armlength = 0 for j in armjoints[::-1]: armlength += np.sqrt(np.sum((eetrans-j.GetAnchor())**2)) eetrans = j.GetAnchor() return armlength def get_enabled_bodies(env): """ Returns a C{set} with the names of the bodies enabled in the given environment @type env: orpy.Environment @param env: The OpenRAVE environment @rtype: set @return: The names of the enabled bodies """ enabled_bodies = [] with env: for body in env.GetBodies(): if body.IsEnabled(): enabled_bodies.append(body.GetName()) return set(enabled_bodies) def get_robot_iktypes(robot): """ Returns a dict with the manipulator:[iktypes] pairs of available iksolvers . @type refbody: orpy.Robot @param refbody: The OpenRAVE robot @rtype: orpy.Environment @return: The dict with the manipname:[iktypes] pairs. """ robot_iktypes = dict() for manip in robot.GetManipulators(): iktypes = [] for iktype in SUPPORTED_IK_TYPES: ikmodel = orpy.databases.inversekinematics.InverseKinematicsModel(iktype=iktype, manip=manip) if ikmodel.load(): iktypes.append(iktype) if iktypes: robot_iktypes[manip.GetName()] = list(iktypes) return robot_iktypes def move_origin_to_body(refbody): """ Moves everything in the OpenRAVE scene so that the C{refbody} ends-up at the origin. @type refbody: orpy.KinBody @param refbody: The body that will be at the origin """ env = refbody.GetEnv() Toffset = criros.spalg.transform_inv( refbody.GetTransform() ) grabbed_names = [body.GetName() for robot in env.GetRobots() for body in robot.GetGrabbed()] with env: for body in env.GetBodies(): # Dont move Grabbed bodies. They will move once we move the robot grabbing them. if body.GetName() in grabbed_names: continue Tbody = body.GetTransform() body.SetTransform( np.dot(Toffset, Tbody) ) def move_out_of_collision(env, body, max_displacement=0.005): """ Moves an OpenRAVE body out of collision in the opposite direction to the penetration direction. @type env: orpy.Environment @param env: The OpenRAVE environment. @type body: orpy.KinBody @param body: The OpenRAVE body. @type max_displacement: float @param max_displacement: The maximum displacement we can apply to the body. """ if not env.CheckCollision(body): # Not in collision return True # Need to use pqp collision checker previous_cc = env.GetCollisionChecker() checker = orpy.RaveCreateCollisionChecker(env, 'pqp') checker.SetCollisionOptions(orpy.CollisionOptions.Distance|orpy.CollisionOptions.Contacts) env.SetCollisionChecker(checker) # Collision report report = orpy.CollisionReport() env.CheckCollision(body, report) # Restore previous collision checker env.SetCollisionChecker(previous_cc) # Get the direction we should push the object positions = [] normals = [] occurrences = [] for c in report.contacts: positions.append(c.pos) if len(normals) == 0: normals.append(c.norm) occurrences.append(1) continue found = False for i,normal in enumerate(normals): if np.allclose(c.norm, normal): occurrences[i] += 1 found = True break if not found: normals.append(c.norm) occurrences.append(1) push_direction = tr.unit_vector(normals[np.argmax(occurrences)]) # Get the distance we should push the object Tbody = body.GetTransform() Tnew = np.array(Tbody) push_distance = 0 while env.CheckCollision(body): push_distance += 0.001 Tnew[:3,3] = Tbody[:3,3] + push_distance*push_direction body.SetTransform(Tnew) if push_distance > max_displacement: print 'push_distance: {0}'.format(push_distance) body.SetTransform(Tbody) return False return True def random_joint_positions(robot): """ Generates random joint positions within joint limits for the given robot. @type robot: orpy.Robot @param robot: The OpenRAVE robot @rtype: np.array @return: """ # Get the limits of the active DOFs lower, upper = robot.GetActiveDOFLimits() positions = lower + np.random.rand(len(lower))*(upper-lower) return positions def remove_bodies(env, remove=None, keep=None): """ Removes the specified bodies from the OpenRAVE environment. You can specify the bodies to be removed or kept. @type env: orpy.Environment @param env: The OpenRAVE environment @type remove: list @param remove: list of objects to remove @type keep: list @param keep: list of objects to keep """ # Check that one of the lists is None if (remove is None) and (type(keep) is list): case = 1 elif (keep is None) and (type(remove) is list): case = 2 else: return for body in env.GetBodies(): remove_body = False name = body.GetName() if case == 1: remove_body = name not in keep if case == 2: remove_body = name in remove if remove_body: with env: env.Remove(body) def remove_body_padding(body): """ Restores the collision meshes of the body. The original collision meshes are store as C{UserData} by the C{set_body_padding} function. @type body: orpy.KinBody @param body: The OpenRAVE body @rtype: bool @return: True if succeeded, False otherwise """ if not body.IsRobot(): raise Exception('Not implemented yet for bodies') robot = body original_collision_meshes = robot.GetUserData() if original_collision_meshes is None: logger.logerr('Robot user data is empty: {0}'.format(robot.GetName())) return False for name,meshes in original_collision_meshes.items(): link = robot.GetLink(name) for geom,mesh in itertools.izip(link.GetGeometries(), meshes): if mesh is not None: geom.SetCollisionMesh(mesh) return True def set_body_padding(body, padding, generate=False, links=[]): """ Sets the padding for the specified links. If C{links} is empty, the padding will be done for ALL the links. @type body: orpy.KinBody @param body: The OpenRAVE body @type padding: float @param padding: The padding value. @type generate: bool @param generate: If set, the ConvexDecompositionModel will be generated if it doesn't exist already. @type links: list @param links: The list of links to be padded. If it is empty, the padding will be done for ALL the links. @rtype: bool @return: True if succeeded, False otherwise """ if not body.IsRobot(): raise Exception('Not implemented yet for bodies') robot = body cdmodel = orpy.databases.convexdecomposition.ConvexDecompositionModel(robot, padding=padding) if not cdmodel.load(): if generate: cmodel = generate_convex_decomposition_model(robot, padding) if cdmodel is None: logger.logerr('Failed to generate ConvexDecompositionModel: {0}'.format(robot.GetName())) return False else: logger.logerr('ConvexDecompositionModel database for robot {0} with padding {1:.3f} not found'.format(robot.GetName(), padding)) return False if len(links) == 0: # Do it for all the links links = [l.GetName() for l in robot.GetLinks()] original_collision_meshes = robot.GetUserData() if original_collision_meshes is None: original_collision_meshes = dict() env = robot.GetEnv() with env: for link, linkcd in itertools.izip(robot.GetLinks(), cdmodel.linkgeometry): if link.GetName() not in links: continue make_a_copy = link.GetName() not in original_collision_meshes if make_a_copy: original_collision_meshes[link.GetName()] = [None] * len(link.GetGeometries()) for ig,hulls in linkcd: geom = link.GetGeometries()[ig] if geom.IsModifiable(): if make_a_copy: # Keep a copy of the original collision meshes original_collision_meshes[link.GetName()][ig] = geom.GetCollisionMesh() # Set the padded mesh geom.SetCollisionMesh(cdmodel.GenerateTrimeshFromHulls(hulls)) robot.SetUserData(original_collision_meshes) return True def set_body_transparency(body, transparency=0.0, links=None): """ Sets the transparency value of a body recursively. @type body: orpy.KinBody @param body: The OpenRAVE body @type transparency: float @param transparency: The transparency value. If it's out of range [0.0, 1.0], it'll be clipped. @type links: list @param links: Links to be changed. By default all the links are changed """ transparency = np.clip(transparency, 0.0, 1.0) env = body.GetEnv() with env: for link in body.GetLinks(): if type(links) == list: if link.GetName() not in links: continue for geom in link.GetGeometries(): geom.SetTransparency(transparency) def trimesh_from_point_cloud(cloud): """ Converts a PCL point cloud into a OpenRAVE trimesh @type cloud: pcl.Cloud @param cloud: The PCL cloud @rtype: orpy.Trimesh @return: The OpenRAVE trimesh """ points = np.asarray(cloud) hull = scipy.spatial.ConvexHull(points) hull = scipy.spatial.ConvexHull(points[hull.vertices]) criros.spalg.counterclockwise_hull(hull) return orpy.TriMesh(hull.points, hull.simplices)

bsd-3-clause

davidam/python-examples

scikit/plot_nested_cross_validation_iris.py

19

4413

""" ========================================= Nested versus non-nested cross-validation ========================================= This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters also need to be optimized. Nested CV estimates the generalization error of the underlying model and its (hyper)parameter search. Choosing the parameters that maximize non-nested CV biases the model to the dataset, yielding an overly-optimistic score. Model selection without nested CV uses the same data to tune model parameters and evaluate model performance. Information may thus "leak" into the model and overfit the data. The magnitude of this effect is primarily dependent on the size of the dataset and the stability of the model. See Cawley and Talbot [1]_ for an analysis of these issues. To avoid this problem, nested CV effectively uses a series of train/validation/test set splits. In the inner loop (here executed by :class:`GridSearchCV <sklearn.model_selection.GridSearchCV>`), the score is approximately maximized by fitting a model to each training set, and then directly maximized in selecting (hyper)parameters over the validation set. In the outer loop (here in :func:`cross_val_score <sklearn.model_selection.cross_val_score>`), generalization error is estimated by averaging test set scores over several dataset splits. The example below uses a support vector classifier with a non-linear kernel to build a model with optimized hyperparameters by grid search. We compare the performance of non-nested and nested CV strategies by taking the difference between their scores. .. topic:: See Also: - :ref:`cross_validation` - :ref:`grid_search` .. topic:: References: .. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res 2010,11, 2079-2107. <http://jmlr.csail.mit.edu/papers/volume11/cawley10a/cawley10a.pdf>`_ """ from sklearn.datasets import load_iris from matplotlib import pyplot as plt from sklearn.svm import SVC from sklearn.model_selection import GridSearchCV, cross_val_score, KFold import numpy as np print(__doc__) # Number of random trials NUM_TRIALS = 30 # Load the dataset iris = load_iris() X_iris = iris.data y_iris = iris.target # Set up possible values of parameters to optimize over p_grid = {"C": [1, 10, 100], "gamma": [.01, .1]} # We will use a Support Vector Classifier with "rbf" kernel svm = SVC(kernel="rbf") # Arrays to store scores non_nested_scores = np.zeros(NUM_TRIALS) nested_scores = np.zeros(NUM_TRIALS) # Loop for each trial for i in range(NUM_TRIALS): # Choose cross-validation techniques for the inner and outer loops, # independently of the dataset. # E.g "GroupKFold", "LeaveOneOut", "LeaveOneGroupOut", etc. inner_cv = KFold(n_splits=4, shuffle=True, random_state=i) outer_cv = KFold(n_splits=4, shuffle=True, random_state=i) # Non_nested parameter search and scoring clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv) clf.fit(X_iris, y_iris) non_nested_scores[i] = clf.best_score_ # Nested CV with parameter optimization nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv) nested_scores[i] = nested_score.mean() score_difference = non_nested_scores - nested_scores print("Average difference of {:6f} with std. dev. of {:6f}." .format(score_difference.mean(), score_difference.std())) # Plot scores on each trial for nested and non-nested CV plt.figure() plt.subplot(211) non_nested_scores_line, = plt.plot(non_nested_scores, color='r') nested_line, = plt.plot(nested_scores, color='b') plt.ylabel("score", fontsize="14") plt.legend([non_nested_scores_line, nested_line], ["Non-Nested CV", "Nested CV"], bbox_to_anchor=(0, .4, .5, 0)) plt.title("Non-Nested and Nested Cross Validation on Iris Dataset", x=.5, y=1.1, fontsize="15") # Plot bar chart of the difference. plt.subplot(212) difference_plot = plt.bar(range(NUM_TRIALS), score_difference) plt.xlabel("Individual Trial #") plt.legend([difference_plot], ["Non-Nested CV - Nested CV Score"], bbox_to_anchor=(0, 1, .8, 0)) plt.ylabel("score difference", fontsize="14") plt.show()

gpl-3.0

sigopt/sigopt_sklearn

sigopt_sklearn/search.py

1

20866

from __future__ import absolute_import, print_function import math import os from multiprocessing import TimeoutError import sys import time import warnings import collections import sigopt import numpy from joblib import Parallel, delayed from joblib.func_inspect import getfullargspec try: # For scikit-learn >= 0.18 from sklearn.model_selection import check_cv as base_check_cv def our_check_cv(cv, X, y, classifier): ret = base_check_cv(cv, y, classifier) return ret.n_splits, list(ret.split(X, y=y)) from sklearn.model_selection._search import BaseSearchCV from sklearn.model_selection._validation import _fit_and_score except ImportError: # For scikit-learn < 0.18 from sklearn.grid_search import BaseSearchCV from sklearn.cross_validation import check_cv as base_check_cv, _fit_and_score def our_check_cv(cv, X, y, classifier): ret = base_check_cv(cv, X, y, classifier) return len(ret), list(iter(ret)) from sklearn.metrics.scorer import check_scoring from sklearn.utils.validation import _num_samples, indexable from sklearn.base import is_classifier, clone HANDLES_UNICODE = sys.version_info[0] >= 3 class SigOptSearchCV(BaseSearchCV): """SigOpt powered search on hyper parameters. SigOptSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is chosen from the specified domains. The number of parameter settings that are tried is given by n_iter. Parameters ---------- estimator : estimator object. A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. param_domains : dict Dictionary with parameters names (string) as keys and domains as lists of parameter ranges to try. Domains are either lists of categorical (string) values or 2 element lists specifying a min and max for integer or float parameters n_iter : int, default=10 Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. n_sug : int, default=1 Number of suggestions to retrieve from SigOpt for evaluation in parallel client_token : string, optional SigOpt API client token, find yours here: https://sigopt.com/tokens. This field is required except when the ``sigopt_connection`` argument is present or when the ``SIGOPT_API_TOKEN`` environment variable is set. We recommend using this instead of ``sigopt_connection``. sigopt_connection : sigopt.interface.Connection, optional SigOpt API Connection object. If present, this object will be used to connect to SigOpt in lieu of the client token. We recommend using the ``client_token`` option instead of this one. opt_timeout : float, optional Max time for entire optimization process cv_timeout : float, optional Max time each CV fold objective evaluation can take scoring : string, callable or None, default=None A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``. If ``None``, the ``score`` method of the estimator is used. fit_params : dict, optional Parameters to pass to the fit method. n_jobs : int, default=1 Number of jobs to run in parallel. pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A string, giving an expression as a function of n_jobs, as in '2*n_jobs' iid : boolean, default=True If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross validation, - integer, to specify the number of folds in a `(Stratified)KFold`, - An object to be used as a cross-validation generator. - An iterable yielding train, test splits. For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` used. In all other cases, :class:`KFold` is used. Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here. refit : boolean, default=True Refit the best estimator with the entire dataset. If "False", it is impossible to make predictions using this RandomizedSearchCV instance after fitting. verbose : integer Controls the verbosity: the higher, the more messages. error_score : 'raise' (default) or numeric Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error. Attributes ---------- best_estimator_ : estimator Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False. best_score_ : float Score of best_estimator on the left out data. best_params_ : dict Parameter setting that gave the best results on the hold out data. Notes ----- The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter. If `n_jobs` was set to a value higher than one, the data is copied for each parameter setting(and not `n_jobs` times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set `pre_dispatch`. Then, the memory is copied only `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 * n_jobs`. """ def __init__(self, estimator, param_domains, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, n_sug=1, pre_dispatch='2*n_jobs', error_score='raise', cv_timeout=None, opt_timeout=None, client_token=None, sigopt_connection=None, experiment=None): self.param_domains = param_domains self.n_iter = n_iter self.n_sug = n_sug self.cv_timeout = cv_timeout self.opt_timeout = opt_timeout self.verbose = verbose # Stores the mappings between categorical strings to Python values. The keys correspond to parameter names and # values correspond to the string-to-value mappings themselves. self.categorical_mappings_ = {} self.scorer_ = None self.our_best_params_ = None self.our_best_score_ = None self.our_best_estimator_ = None self.experiment = experiment # Set up sigopt_connection found_token = client_token or os.environ.get('SIGOPT_API_TOKEN') if (not found_token) and (not sigopt_connection): raise ValueError( 'Please set the `SIGOPT_API_TOKEN` environment variable, pass the ``client_token`` parameter, or pass ' 'the ``sigopt_connection`` parameter. You can find your client token here: ' 'https://sigopt.com/tokens.') else: self.sigopt_connection = (sigopt_connection if sigopt_connection else sigopt.Connection(client_token=found_token)) super(SigOptSearchCV, self).__init__( estimator=estimator, scoring=scoring, fit_params=fit_params, n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose, pre_dispatch=pre_dispatch, error_score=error_score) def _transform_param_domains(self, param_domains): def _transform_param(param_name, param_bounds): """Transform a parameter name and its bounds into a form that can be sent to the API layer.""" def _check_bounds(): """Check that min/max bounds are well formed.""" if len(param_bounds) != 2: raise Exception('Parameter bounds must be specified with two numbers! Not sure what to do with {}.' .format(param_bounds)) if not isinstance(param_bounds, tuple): warnings.warn('Parameter bounds should be specified as a tuple in the form (min, max).') # Check that param bounds is either iterable (range/categoricals) or a dict (categoricals) if not isinstance(param_bounds, (collections.Iterable, dict)): raise Exception('Parameter bounds must be iterable or dicts! The range {} isn\'t friendly!' .format(param_bounds)) param_dict = {'name': param_name} if isinstance(param_bounds, dict): # This is a categorical with mappings between strings and values param_dict['type'] = 'categorical' param_dict['categorical_values'] = [{'name': k} for k in param_bounds.keys()] # Add this mapping to our set of categorical string mappings self.categorical_mappings_[param_name] = param_bounds elif all(isinstance(x, str) for x in param_bounds): # This is a categorical with a list of strings naming each category param_dict['type'] = 'categorical' param_dict['categorical_values'] = [{'name': k} for k in param_bounds] elif all(isinstance(x, int) for x in param_bounds): # This is an integer parameter _check_bounds() param_dict['type'] = 'int' param_dict['bounds'] = {'min': param_bounds[0], 'max': param_bounds[1]} elif any(isinstance(x, float) for x in param_bounds): # This is a continuous parameter. Note that we use `any` since the user may pass some combination of # float and integer parameters, e.g. (0, 0.1). _check_bounds() param_dict['type'] = 'double' param_dict['bounds'] = {'min': param_bounds[0], 'max': param_bounds[1]} else: # Not sure what the user gave us here raise Exception('Bad parameter range {}.'.format(param_bounds)) return param_dict # generate sigopt experiment parameters return [_transform_param(name, bounds) for (name, bounds) in param_domains.items()] def _create_sigopt_exp(self, conn): est_name = self.estimator.__class__.__name__ exp_name = est_name + ' (sklearn)' if len(exp_name) > 50: exp_name = est_name if self.verbose > 0: print('Creating SigOpt experiment: ', exp_name) # create sigopt experiment experiment = conn.experiments().create( name=exp_name, parameters=self._transform_param_domains(self.param_domains), observation_budget=self.n_iter, ) if self.verbose > 0: exp_url = 'https://sigopt.com/experiment/{0}'.format(self.experiment.id) print('Experiment progress available at :', exp_url) return experiment # NOTE(patrick): SVM can't handle unicode, so we need to convert those to string. def _convert_unicode(self, data): if HANDLES_UNICODE: return data # pylint: disable=undefined-variable if isinstance(data, basestring): return str(data) # pylint: enable=undefined-variable if isinstance(data, collections.Mapping): return dict(map(self._convert_unicode, data.items())) if isinstance(data, collections.Iterable): return type(data)(map(self._convert_unicode, data)) return data def _convert_log_params(self, param_dict): # searches through names for params and converts params with __log__ names log_converted_dict = {} for pname in param_dict: pval = param_dict[pname] if '__log__' in pname: pval = math.exp(pval) pname = pname.replace('__log__', '') log_converted_dict[pname] = pval return log_converted_dict def _convert_nonstring_categoricals(self, param_dict): """Apply the self.categorical_mappings_ mappings where necessary.""" return {name: (self.categorical_mappings_[name][val] if name in self.categorical_mappings_ else val) for (name, val) in param_dict.items()} def _convert_sigopt_api_to_sklearn_assignments(self, param_dict): return self._convert_nonstring_categoricals(self._convert_log_params(self._convert_unicode(param_dict))) # pylint: disable=unused-argument def _run_search(self, evaluate_candidates): # NOTE(patrick): scikit-learn 0.20.0 checks for the existence of this method, since # the default implementation of `_fit` calls it. However, to maintain compatibility # with older versions, we completely override _fit, so this method is unused. But # we make sure it exists, so that the class can be instantiated raise NotImplementedError('_run_search not used in this implementation') # pylint: enable=unused-argument def _fit(self, X, y, groups=None, parameter_iterable=None, **fit_params): if groups is not None: raise NotImplementedError('The groups argument is not supported.') if parameter_iterable is not None: raise NotImplementedError('The parameter_iterable argument is not supported.') if self.fit_params is not None: fit_params = self.fit_params # Actual fitting, performing the search over parameters. estimator = self.estimator cv = self.cv self.scorer_ = check_scoring(self.estimator, scoring=self.scoring) n_samples = _num_samples(X) X, y = indexable(X, y) if y is not None: if len(y) != n_samples: raise ValueError('Target variable (y) has a different number of samples (%i) than data (X: %i samples)' % (len(y), n_samples)) n_folds, cv_iter = our_check_cv(cv, X, y, classifier=is_classifier(estimator)) base_estimator = clone(self.estimator) pre_dispatch = self.pre_dispatch # setup SigOpt experiment and run optimization if not self.experiment: self.experiment = self._create_sigopt_exp(self.sigopt_connection) # start tracking time to optimize estimator opt_start_time = time.time() for jk in range(0, self.n_iter, self.n_sug): # check for opt timeout, ensuring at least 1 observation # TODO : handling failure observations if ( self.opt_timeout is not None and time.time() - opt_start_time > self.opt_timeout and jk >= 1 ): # break out of loop and refit model with best params so far break suggestions = [] parameter_configs = [] for _ in range(self.n_sug): suggestion = self.sigopt_connection.experiments(self.experiment.id).suggestions().create() parameters = self._convert_sigopt_api_to_sklearn_assignments(suggestion.assignments.to_json()) suggestions.append(suggestion) parameter_configs.append(parameters) if self.verbose > 0: print('Evaluating params : ', parameter_configs) # do CV folds in parallel using joblib # returns scores on test set obs_timed_out = False try: par_kwargs = {'n_jobs': self.n_jobs, 'verbose': self.verbose, 'pre_dispatch': pre_dispatch} # add timeout kwarg if version of joblib supports it if 'timeout' in getfullargspec(Parallel.__init__).args: par_kwargs['timeout'] = self.cv_timeout out = Parallel( **par_kwargs )( delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_, train, test, self.verbose, parameters, fit_params, return_parameters=True, error_score=self.error_score) for parameters in parameter_configs for train, test in cv_iter) except TimeoutError: obs_timed_out = True if not obs_timed_out: # grab scores from results for sidx, suggestion in enumerate(suggestions): out_idx = sidx * n_folds scores = [o[0] for o in out[out_idx:out_idx+n_folds]] self.sigopt_connection.experiments(self.experiment.id).observations().create( suggestion=suggestion.id, value=numpy.mean(scores), value_stddev=numpy.std(scores) ) else: # obsevation timed out so report a failure self.sigopt_connection.experiments(self.experiment.id).observations().create( suggestion=suggestion.id, failed=True) # return best SigOpt assignments so far best_assignments = self.sigopt_connection.experiments(self.experiment.id).best_assignments().fetch().data if not best_assignments: raise RuntimeError( 'No valid observations found. ' 'Make sure opt_timeout and cv_timeout provide sufficient time for observations to be reported.') self.our_best_params_ = self._convert_sigopt_api_to_sklearn_assignments( best_assignments[0].assignments.to_json()) self.our_best_score_ = best_assignments[0].value if self.refit: # fit the best estimator using the entire dataset # clone first to work around broken estimators best_estimator = clone(base_estimator).set_params(**self.best_params_) if y is not None: best_estimator.fit(X, y, **fit_params) else: best_estimator.fit(X, **fit_params) self.our_best_estimator_ = best_estimator return self @property def best_params_(self): return self.our_best_params_ @property def best_score_(self): return self.our_best_score_ @property def best_estimator_(self): return self.our_best_estimator_ def fit(self, X, y=None, groups=None, **fit_params): """ Run fit on the estimator with parameters chosen sequentially by SigOpt. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. """ return self._fit(X, y=y, groups=groups, **fit_params)

mit

dimkal/mne-python

tutorials/plot_introduction.py

9

13464

""" .. _intro_tutorial: Basic MEG and EEG data processing ================================= MNE-Python reimplements most of MNE-C's (the original MNE command line utils) functionality and offers transparent scripting. On top of that it extends MNE-C's functionality considerably (customize events, compute contrasts, group statistics, time-frequency analysis, EEG-sensor space analyses , etc.) It uses the same files as standard MNE unix commands: no need to convert your files to a new system or database. What you can do with MNE Python ------------------------------- - **Raw data visualization** to visualize recordings, can also use *mne_browse_raw* for extended functionality (see :ref:`ch_browse`) - **Epoching**: Define epochs, baseline correction, handle conditions etc. - **Averaging** to get Evoked data - **Compute SSP pojectors** to remove ECG and EOG artifacts - **Compute ICA** to remove artifacts or select latent sources. - **Boundary Element Modeling**: single and three-layer BEM model creation and solution computation. - **Forward modeling**: BEM computation and mesh creation (see :ref:`ch_forward`) - **Linear inverse solvers** (dSPM, sLORETA, MNE, LCMV, DICS) - **Sparse inverse solvers** (L1/L2 mixed norm MxNE, Gamma Map, Time-Frequency MxNE) - **Connectivity estimation** in sensor and source space - **Visualization of sensor and source space data** - **Time-frequency** analysis with Morlet wavelets (induced power, intertrial coherence, phase lock value) also in the source space - **Spectrum estimation** using multi-taper method - **Mixed Source Models** combining cortical and subcortical structures - **Dipole Fitting** - **Decoding** multivariate pattern analyis of M/EEG topographies - **Compute contrasts** between conditions, between sensors, across subjects etc. - **Non-parametric statistics** in time, space and frequency (including cluster-level) - **Scripting** (batch and parallel computing) What you're not supposed to do with MNE Python ---------------------------------------------- - **Brain and head surface segmentation** for use with BEM models -- use Freesurfer. .. note:: Package based on the FIF file format from Neuromag. It can read and convert CTF, BTI/4D, KIT and various EEG formats to FIF. Installation of the required materials --------------------------------------- See :ref:`getting_started` with Python. .. note:: The expected location for the MNE-sample data is my-path-to/mne-python/examples. If you downloaded data and an example asks you whether to download it again, make sure the data reside in the examples directory and you run the script from its current directory. From IPython e.g. say:: cd examples/preprocessing %run plot_find_ecg_artifacts.py From raw data to evoked data ---------------------------- .. _ipython: http://ipython.scipy.org/ Now, launch `ipython`_ (Advanced Python shell) using the QT backend which best supported across systems:: $ ipython --pylab -qt First, load the mne package: """ import mne ############################################################################## # If you'd like to turn information status messages off: mne.set_log_level('WARNING') ############################################################################## # But it's generally a good idea to leave them on: mne.set_log_level('INFO') ############################################################################## # You can set the default level by setting the environment variable # "MNE_LOGGING_LEVEL", or by having mne-python write preferences to a file: mne.set_config('MNE_LOGGING_LEVEL','WARNING') ############################################################################## # Note that the location of the mne-python preferences file (for easier manual # editing) can be found using: mne.get_config_path() ############################################################################## # By default logging messages print to the console, but look at # mne.set_log_file() to save output to a file. # # Access raw data # ^^^^^^^^^^^^^^^ from mne.datasets import sample data_path = sample.data_path() raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' print(raw_fname) ############################################################################## # .. note:: The MNE sample dataset should be downloaded automatically but be # patient (approx. 2GB) # # Read data from file: raw = mne.io.Raw(raw_fname) print(raw) print(raw.info) ############################################################################## # Look at the channels in raw: print(raw.ch_names) ############################################################################## # Read and plot a segment of raw data start, stop = raw.time_as_index([100, 115]) # 100 s to 115 s data segment data, times = raw[:, start:stop] print(data.shape) print(times.shape) data, times = raw[2:20:3, start:stop] # access underlying data raw.plot() ############################################################################## # Save a segment of 150s of raw data (MEG only): picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True, exclude='bads') raw.save('sample_audvis_meg_raw.fif', tmin=0, tmax=150, picks=picks, overwrite=True) ############################################################################## # Define and read epochs # ^^^^^^^^^^^^^^^^^^^^^^ # # First extract events: events = mne.find_events(raw, stim_channel='STI 014') print(events[:5]) ############################################################################## # Note that, by default, we use stim_channel='STI 014'. If you have a different # system (e.g., a newer system that uses channel 'STI101' by default), you can # use the following to set the default stim channel to use for finding events: mne.set_config('MNE_STIM_CHANNEL', 'STI101') ############################################################################## # Events are stored as 2D numpy array where the first column is the time # instant and the last one is the event number. It is therefore easy to # manipulate. # # Define epochs parameters: event_id = dict(aud_l=1, aud_r=2) # event trigger and conditions tmin = -0.2 # start of each epoch (200ms before the trigger) tmax = 0.5 # end of each epoch (500ms after the trigger) ############################################################################## # Exclude some channels (original bads + 2 more): raw.info['bads'] += ['MEG 2443', 'EEG 053'] ############################################################################## # The variable raw.info['bads'] is just a python list. # # Pick the good channels, excluding raw.info['bads']: picks = mne.pick_types(raw.info, meg=True, eeg=True, eog=True, stim=False, exclude='bads') ############################################################################## # Alternatively one can restrict to magnetometers or gradiometers with: mag_picks = mne.pick_types(raw.info, meg='mag', eog=True, exclude='bads') grad_picks = mne.pick_types(raw.info, meg='grad', eog=True, exclude='bads') ############################################################################## # Define the baseline period: baseline = (None, 0) # means from the first instant to t = 0 ############################################################################## # Define peak-to-peak rejection parameters for gradiometers, magnetometers and EOG: reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6) ############################################################################## # Read epochs: epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline=baseline, preload=False, reject=reject) print(epochs) ############################################################################## # Get single epochs for one condition: epochs_data = epochs['aud_l'].get_data() print(epochs_data.shape) ############################################################################## # epochs_data is a 3D array of dimension (55 epochs, 365 channels, 106 time # instants). # # Scipy supports read and write of matlab files. You can save your single # trials with: from scipy import io io.savemat('epochs_data.mat', dict(epochs_data=epochs_data), oned_as='row') ############################################################################## # or if you want to keep all the information about the data you can save your # epochs in a fif file: epochs.save('sample-epo.fif') ############################################################################## # and read them later with: saved_epochs = mne.read_epochs('sample-epo.fif') ############################################################################## # Compute evoked responses for auditory responses by averaging and plot it: evoked = epochs['aud_l'].average() print(evoked) evoked.plot() ############################################################################## # .. topic:: Exercise # # 1. Extract the max value of each epoch max_in_each_epoch = [e.max() for e in epochs['aud_l']] # doctest:+ELLIPSIS print(max_in_each_epoch[:4]) # doctest:+ELLIPSIS ############################################################################## # It is also possible to read evoked data stored in a fif file: evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif' evoked1 = mne.read_evokeds( evoked_fname, condition='Left Auditory', baseline=(None, 0), proj=True) ############################################################################## # Or another one stored in the same file: evoked2 = mne.read_evokeds( evoked_fname, condition='Right Auditory', baseline=(None, 0), proj=True) ############################################################################## # Compute a contrast: contrast = evoked1 - evoked2 print(contrast) ############################################################################## # Time-Frequency: Induced power and inter trial coherence # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Define parameters: import numpy as np n_cycles = 2 # number of cycles in Morlet wavelet freqs = np.arange(7, 30, 3) # frequencies of interest ############################################################################## # Compute induced power and phase-locking values and plot gradiometers: from mne.time_frequency import tfr_morlet power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles, return_itc=True, decim=3, n_jobs=1) # power.plot() ############################################################################## # Inverse modeling: MNE and dSPM on evoked and raw data # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Import the required functions: from mne.minimum_norm import apply_inverse, read_inverse_operator ############################################################################## # Read the inverse operator: fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif' inverse_operator = read_inverse_operator(fname_inv) ############################################################################## # Define the inverse parameters: snr = 3.0 lambda2 = 1.0 / snr ** 2 method = "dSPM" ############################################################################## # Compute the inverse solution: stc = apply_inverse(evoked, inverse_operator, lambda2, method) ############################################################################## # Save the source time courses to disk: stc.save('mne_dSPM_inverse') ############################################################################## # Now, let's compute dSPM on a raw file within a label: fname_label = data_path + '/MEG/sample/labels/Aud-lh.label' label = mne.read_label(fname_label) ############################################################################## # Compute inverse solution during the first 15s: from mne.minimum_norm import apply_inverse_raw start, stop = raw.time_as_index([0, 15]) # read the first 15s of data stc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label, start, stop) ############################################################################## # Save result in stc files: stc.save('mne_dSPM_raw_inverse_Aud') ############################################################################## # What else can you do? # ^^^^^^^^^^^^^^^^^^^^^ # # - detect heart beat QRS component # - detect eye blinks and EOG artifacts # - compute SSP projections to remove ECG or EOG artifacts # - compute Independent Component Analysis (ICA) to remove artifacts or # select latent sources # - estimate noise covariance matrix from Raw and Epochs # - visualize cross-trial response dynamics using epochs images # - compute forward solutions # - estimate power in the source space # - estimate connectivity in sensor and source space # - morph stc from one brain to another for group studies # - compute mass univariate statistics base on custom contrasts # - visualize source estimates # - export raw, epochs, and evoked data to other python data analysis # libraries e.g. pandas # - and many more things ... # # Want to know more ? # ^^^^^^^^^^^^^^^^^^^ # # Browse :ref:`examples-index` gallery. print("Done!")

bsd-3-clause

pkruskal/scikit-learn

examples/exercises/plot_iris_exercise.py

320

1602

""" ================================ SVM Exercise ================================ A tutorial exercise for using different SVM kernels. This exercise is used in the :ref:`using_kernels_tut` part of the :ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, svm iris = datasets.load_iris() X = iris.data y = iris.target X = X[y != 0, :2] y = y[y != 0] n_sample = len(X) np.random.seed(0) order = np.random.permutation(n_sample) X = X[order] y = y[order].astype(np.float) X_train = X[:.9 * n_sample] y_train = y[:.9 * n_sample] X_test = X[.9 * n_sample:] y_test = y[.9 * n_sample:] # fit the model for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')): clf = svm.SVC(kernel=kernel, gamma=10) clf.fit(X_train, y_train) plt.figure(fig_num) plt.clf() plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired) # Circle out the test data plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10) plt.axis('tight') x_min = X[:, 0].min() x_max = X[:, 0].max() y_min = X[:, 1].min() y_max = X[:, 1].max() XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j] Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) # Put the result into a color plot Z = Z.reshape(XX.shape) plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[-.5, 0, .5]) plt.title(kernel) plt.show()

bsd-3-clause

dimkal/mne-python

mne/io/fiff/tests/test_raw.py

3

43394

from __future__ import print_function # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # Denis Engemann <denis.engemann@gmail.com> # # License: BSD (3-clause) import os import os.path as op import glob from copy import deepcopy import warnings import itertools as itt import numpy as np from numpy.testing import (assert_array_almost_equal, assert_array_equal, assert_allclose, assert_equal) from nose.tools import assert_true, assert_raises, assert_not_equal from mne.datasets import testing from mne.io.constants import FIFF from mne.io import Raw, RawArray, concatenate_raws, read_raw_fif from mne.io.tests.test_raw import _test_concat from mne import (concatenate_events, find_events, equalize_channels, compute_proj_raw, pick_types, pick_channels, create_info) from mne.utils import (_TempDir, requires_pandas, slow_test, requires_mne, run_subprocess, run_tests_if_main) from mne.externals.six.moves import zip, cPickle as pickle from mne.io.proc_history import _get_sss_rank from mne.io.pick import _picks_by_type warnings.simplefilter('always') # enable b/c these tests throw warnings data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample') fif_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif') base_dir = op.join(op.dirname(__file__), '..', '..', 'tests', 'data') test_fif_fname = op.join(base_dir, 'test_raw.fif') test_fif_gz_fname = op.join(base_dir, 'test_raw.fif.gz') ctf_fname = op.join(base_dir, 'test_ctf_raw.fif') ctf_comp_fname = op.join(base_dir, 'test_ctf_comp_raw.fif') fif_bad_marked_fname = op.join(base_dir, 'test_withbads_raw.fif') bad_file_works = op.join(base_dir, 'test_bads.txt') bad_file_wrong = op.join(base_dir, 'test_wrong_bads.txt') hp_fname = op.join(base_dir, 'test_chpi_raw_hp.txt') hp_fif_fname = op.join(base_dir, 'test_chpi_raw_sss.fif') def test_concat(): """Test RawFIF concatenation""" # we trim the file to save lots of memory and some time tempdir = _TempDir() raw = read_raw_fif(test_fif_fname) raw.crop(0, 2., copy=False) test_name = op.join(tempdir, 'test_raw.fif') raw.save(test_name) # now run the standard test _test_concat(read_raw_fif, test_name) @testing.requires_testing_data def test_hash_raw(): """Test hashing raw objects """ raw = read_raw_fif(fif_fname) assert_raises(RuntimeError, raw.__hash__) raw = Raw(fif_fname).crop(0, 0.5, False) raw.preload_data() raw_2 = Raw(fif_fname).crop(0, 0.5, False) raw_2.preload_data() assert_equal(hash(raw), hash(raw_2)) # do NOT use assert_equal here, failing output is terrible assert_equal(pickle.dumps(raw), pickle.dumps(raw_2)) raw_2._data[0, 0] -= 1 assert_not_equal(hash(raw), hash(raw_2)) @testing.requires_testing_data def test_subject_info(): """Test reading subject information """ tempdir = _TempDir() raw = Raw(fif_fname).crop(0, 1, False) assert_true(raw.info['subject_info'] is None) # fake some subject data keys = ['id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex', 'hand'] vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1] subject_info = dict() for key, val in zip(keys, vals): subject_info[key] = val raw.info['subject_info'] = subject_info out_fname = op.join(tempdir, 'test_subj_info_raw.fif') raw.save(out_fname, overwrite=True) raw_read = Raw(out_fname) for key in keys: assert_equal(subject_info[key], raw_read.info['subject_info'][key]) raw_read.anonymize() assert_true(raw_read.info.get('subject_info') is None) out_fname_anon = op.join(tempdir, 'test_subj_info_anon_raw.fif') raw_read.save(out_fname_anon, overwrite=True) raw_read = Raw(out_fname_anon) assert_true(raw_read.info.get('subject_info') is None) @testing.requires_testing_data def test_copy_append(): """Test raw copying and appending combinations """ raw = Raw(fif_fname, preload=True).copy() raw_full = Raw(fif_fname) raw_full.append(raw) data = raw_full[:, :][0] assert_equal(data.shape[1], 2 * raw._data.shape[1]) @slow_test @testing.requires_testing_data def test_rank_estimation(): """Test raw rank estimation """ iter_tests = itt.product( [fif_fname, hp_fif_fname], # sss ['norm', dict(mag=1e11, grad=1e9, eeg=1e5)] ) for fname, scalings in iter_tests: raw = Raw(fname) (_, picks_meg), (_, picks_eeg) = _picks_by_type(raw.info, meg_combined=True) n_meg = len(picks_meg) n_eeg = len(picks_eeg) raw = Raw(fname, preload=True) if 'proc_history' not in raw.info: expected_rank = n_meg + n_eeg else: mf = raw.info['proc_history'][0]['max_info'] expected_rank = _get_sss_rank(mf) + n_eeg assert_array_equal(raw.estimate_rank(scalings=scalings), expected_rank) assert_array_equal(raw.estimate_rank(picks=picks_eeg, scalings=scalings), n_eeg) raw = Raw(fname, preload=False) if 'sss' in fname: tstart, tstop = 0., 30. raw.add_proj(compute_proj_raw(raw)) raw.apply_proj() else: tstart, tstop = 10., 20. raw.apply_proj() n_proj = len(raw.info['projs']) assert_array_equal(raw.estimate_rank(tstart=tstart, tstop=tstop, scalings=scalings), expected_rank - (1 if 'sss' in fname else n_proj)) @testing.requires_testing_data def test_output_formats(): """Test saving and loading raw data using multiple formats """ tempdir = _TempDir() formats = ['short', 'int', 'single', 'double'] tols = [1e-4, 1e-7, 1e-7, 1e-15] # let's fake a raw file with different formats raw = Raw(test_fif_fname).crop(0, 1, copy=False) temp_file = op.join(tempdir, 'raw.fif') for ii, (fmt, tol) in enumerate(zip(formats, tols)): # Let's test the overwriting error throwing while we're at it if ii > 0: assert_raises(IOError, raw.save, temp_file, fmt=fmt) raw.save(temp_file, fmt=fmt, overwrite=True) raw2 = Raw(temp_file) raw2_data = raw2[:, :][0] assert_allclose(raw2_data, raw[:, :][0], rtol=tol, atol=1e-25) assert_equal(raw2.orig_format, fmt) def _compare_combo(raw, new, times, n_times): for ti in times: # let's do a subset of points for speed orig = raw[:, ti % n_times][0] # these are almost_equals because of possible dtype differences assert_allclose(orig, new[:, ti][0]) @slow_test @testing.requires_testing_data def test_multiple_files(): """Test loading multiple files simultaneously """ # split file tempdir = _TempDir() raw = Raw(fif_fname).crop(0, 10, False) raw.preload_data() raw.preload_data() # test no operation split_size = 3. # in seconds sfreq = raw.info['sfreq'] nsamp = (raw.last_samp - raw.first_samp) tmins = np.round(np.arange(0., nsamp, split_size * sfreq)) tmaxs = np.concatenate((tmins[1:] - 1, [nsamp])) tmaxs /= sfreq tmins /= sfreq assert_equal(raw.n_times, len(raw.times)) # going in reverse order so the last fname is the first file (need later) raws = [None] * len(tmins) for ri in range(len(tmins) - 1, -1, -1): fname = op.join(tempdir, 'test_raw_split-%d_raw.fif' % ri) raw.save(fname, tmin=tmins[ri], tmax=tmaxs[ri]) raws[ri] = Raw(fname) events = [find_events(r, stim_channel='STI 014') for r in raws] last_samps = [r.last_samp for r in raws] first_samps = [r.first_samp for r in raws] # test concatenation of split file assert_raises(ValueError, concatenate_raws, raws, True, events[1:]) all_raw_1, events1 = concatenate_raws(raws, preload=False, events_list=events) assert_equal(raw.first_samp, all_raw_1.first_samp) assert_equal(raw.last_samp, all_raw_1.last_samp) assert_allclose(raw[:, :][0], all_raw_1[:, :][0]) raws[0] = Raw(fname) all_raw_2 = concatenate_raws(raws, preload=True) assert_allclose(raw[:, :][0], all_raw_2[:, :][0]) # test proper event treatment for split files events2 = concatenate_events(events, first_samps, last_samps) events3 = find_events(all_raw_2, stim_channel='STI 014') assert_array_equal(events1, events2) assert_array_equal(events1, events3) # test various methods of combining files raw = Raw(fif_fname, preload=True) n_times = raw.n_times # make sure that all our data match times = list(range(0, 2 * n_times, 999)) # add potentially problematic points times.extend([n_times - 1, n_times, 2 * n_times - 1]) raw_combo0 = Raw([fif_fname, fif_fname], preload=True) _compare_combo(raw, raw_combo0, times, n_times) raw_combo = Raw([fif_fname, fif_fname], preload=False) _compare_combo(raw, raw_combo, times, n_times) raw_combo = Raw([fif_fname, fif_fname], preload='memmap8.dat') _compare_combo(raw, raw_combo, times, n_times) assert_raises(ValueError, Raw, [fif_fname, ctf_fname]) assert_raises(ValueError, Raw, [fif_fname, fif_bad_marked_fname]) assert_equal(raw[:, :][0].shape[1] * 2, raw_combo0[:, :][0].shape[1]) assert_equal(raw_combo0[:, :][0].shape[1], raw_combo0.n_times) # with all data preloaded, result should be preloaded raw_combo = Raw(fif_fname, preload=True) raw_combo.append(Raw(fif_fname, preload=True)) assert_true(raw_combo.preload is True) assert_equal(raw_combo.n_times, raw_combo._data.shape[1]) _compare_combo(raw, raw_combo, times, n_times) # with any data not preloaded, don't set result as preloaded raw_combo = concatenate_raws([Raw(fif_fname, preload=True), Raw(fif_fname, preload=False)]) assert_true(raw_combo.preload is False) assert_array_equal(find_events(raw_combo, stim_channel='STI 014'), find_events(raw_combo0, stim_channel='STI 014')) _compare_combo(raw, raw_combo, times, n_times) # user should be able to force data to be preloaded upon concat raw_combo = concatenate_raws([Raw(fif_fname, preload=False), Raw(fif_fname, preload=True)], preload=True) assert_true(raw_combo.preload is True) _compare_combo(raw, raw_combo, times, n_times) raw_combo = concatenate_raws([Raw(fif_fname, preload=False), Raw(fif_fname, preload=True)], preload='memmap3.dat') _compare_combo(raw, raw_combo, times, n_times) raw_combo = concatenate_raws([Raw(fif_fname, preload=True), Raw(fif_fname, preload=True)], preload='memmap4.dat') _compare_combo(raw, raw_combo, times, n_times) raw_combo = concatenate_raws([Raw(fif_fname, preload=False), Raw(fif_fname, preload=False)], preload='memmap5.dat') _compare_combo(raw, raw_combo, times, n_times) # verify that combining raws with different projectors throws an exception raw.add_proj([], remove_existing=True) assert_raises(ValueError, raw.append, Raw(fif_fname, preload=True)) # now test event treatment for concatenated raw files events = [find_events(raw, stim_channel='STI 014'), find_events(raw, stim_channel='STI 014')] last_samps = [raw.last_samp, raw.last_samp] first_samps = [raw.first_samp, raw.first_samp] events = concatenate_events(events, first_samps, last_samps) events2 = find_events(raw_combo0, stim_channel='STI 014') assert_array_equal(events, events2) # check out the len method assert_equal(len(raw), raw.n_times) assert_equal(len(raw), raw.last_samp - raw.first_samp + 1) @testing.requires_testing_data def test_split_files(): """Test writing and reading of split raw files """ tempdir = _TempDir() raw_1 = Raw(fif_fname, preload=True) split_fname = op.join(tempdir, 'split_raw.fif') raw_1.save(split_fname, buffer_size_sec=1.0, split_size='10MB') raw_2 = Raw(split_fname) data_1, times_1 = raw_1[:, :] data_2, times_2 = raw_2[:, :] assert_array_equal(data_1, data_2) assert_array_equal(times_1, times_2) # test the case where the silly user specifies the split files fnames = [split_fname] fnames.extend(sorted(glob.glob(op.join(tempdir, 'split_raw-*.fif')))) with warnings.catch_warnings(record=True): warnings.simplefilter('always') raw_2 = Raw(fnames) data_2, times_2 = raw_2[:, :] assert_array_equal(data_1, data_2) assert_array_equal(times_1, times_2) def test_load_bad_channels(): """Test reading/writing of bad channels """ tempdir = _TempDir() # Load correctly marked file (manually done in mne_process_raw) raw_marked = Raw(fif_bad_marked_fname) correct_bads = raw_marked.info['bads'] raw = Raw(test_fif_fname) # Make sure it starts clean assert_array_equal(raw.info['bads'], []) # Test normal case raw.load_bad_channels(bad_file_works) # Write it out, read it in, and check raw.save(op.join(tempdir, 'foo_raw.fif')) raw_new = Raw(op.join(tempdir, 'foo_raw.fif')) assert_equal(correct_bads, raw_new.info['bads']) # Reset it raw.info['bads'] = [] # Test bad case assert_raises(ValueError, raw.load_bad_channels, bad_file_wrong) # Test forcing the bad case with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') raw.load_bad_channels(bad_file_wrong, force=True) n_found = sum(['1 bad channel' in str(ww.message) for ww in w]) assert_equal(n_found, 1) # there could be other irrelevant errors # write it out, read it in, and check raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True) raw_new = Raw(op.join(tempdir, 'foo_raw.fif')) assert_equal(correct_bads, raw_new.info['bads']) # Check that bad channels are cleared raw.load_bad_channels(None) raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True) raw_new = Raw(op.join(tempdir, 'foo_raw.fif')) assert_equal([], raw_new.info['bads']) @slow_test @testing.requires_testing_data def test_io_raw(): """Test IO for raw data (Neuromag + CTF + gz) """ tempdir = _TempDir() # test unicode io for chars in [b'\xc3\xa4\xc3\xb6\xc3\xa9', b'a']: with Raw(fif_fname) as r: assert_true('Raw' in repr(r)) desc1 = r.info['description'] = chars.decode('utf-8') temp_file = op.join(tempdir, 'raw.fif') r.save(temp_file, overwrite=True) with Raw(temp_file) as r2: desc2 = r2.info['description'] assert_equal(desc1, desc2) # Let's construct a simple test for IO first raw = Raw(fif_fname).crop(0, 3.5, False) raw.preload_data() # put in some data that we know the values of data = np.random.randn(raw._data.shape[0], raw._data.shape[1]) raw._data[:, :] = data # save it somewhere fname = op.join(tempdir, 'test_copy_raw.fif') raw.save(fname, buffer_size_sec=1.0) # read it in, make sure the whole thing matches raw = Raw(fname) assert_allclose(data, raw[:, :][0], rtol=1e-6, atol=1e-20) # let's read portions across the 1-sec tag boundary, too inds = raw.time_as_index([1.75, 2.25]) sl = slice(inds[0], inds[1]) assert_allclose(data[:, sl], raw[:, sl][0], rtol=1e-6, atol=1e-20) # now let's do some real I/O fnames_in = [fif_fname, test_fif_gz_fname, ctf_fname] fnames_out = ['raw.fif', 'raw.fif.gz', 'raw.fif'] for fname_in, fname_out in zip(fnames_in, fnames_out): fname_out = op.join(tempdir, fname_out) raw = Raw(fname_in) nchan = raw.info['nchan'] ch_names = raw.info['ch_names'] meg_channels_idx = [k for k in range(nchan) if ch_names[k][0] == 'M'] n_channels = 100 meg_channels_idx = meg_channels_idx[:n_channels] start, stop = raw.time_as_index([0, 5]) data, times = raw[meg_channels_idx, start:(stop + 1)] meg_ch_names = [ch_names[k] for k in meg_channels_idx] # Set up pick list: MEG + STI 014 - bad channels include = ['STI 014'] include += meg_ch_names picks = pick_types(raw.info, meg=True, eeg=False, stim=True, misc=True, ref_meg=True, include=include, exclude='bads') # Writing with drop_small_buffer True raw.save(fname_out, picks, tmin=0, tmax=4, buffer_size_sec=3, drop_small_buffer=True, overwrite=True) raw2 = Raw(fname_out) sel = pick_channels(raw2.ch_names, meg_ch_names) data2, times2 = raw2[sel, :] assert_true(times2.max() <= 3) # Writing raw.save(fname_out, picks, tmin=0, tmax=5, overwrite=True) if fname_in == fif_fname or fname_in == fif_fname + '.gz': assert_equal(len(raw.info['dig']), 146) raw2 = Raw(fname_out) sel = pick_channels(raw2.ch_names, meg_ch_names) data2, times2 = raw2[sel, :] assert_allclose(data, data2, rtol=1e-6, atol=1e-20) assert_allclose(times, times2) assert_allclose(raw.info['sfreq'], raw2.info['sfreq'], rtol=1e-5) # check transformations for trans in ['dev_head_t', 'dev_ctf_t', 'ctf_head_t']: if raw.info[trans] is None: assert_true(raw2.info[trans] is None) else: assert_array_equal(raw.info[trans]['trans'], raw2.info[trans]['trans']) # check transformation 'from' and 'to' if trans.startswith('dev'): from_id = FIFF.FIFFV_COORD_DEVICE else: from_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD if trans[4:8] == 'head': to_id = FIFF.FIFFV_COORD_HEAD else: to_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD for raw_ in [raw, raw2]: assert_equal(raw_.info[trans]['from'], from_id) assert_equal(raw_.info[trans]['to'], to_id) if fname_in == fif_fname or fname_in == fif_fname + '.gz': assert_allclose(raw.info['dig'][0]['r'], raw2.info['dig'][0]['r']) # test warnings on bad filenames with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") raw_badname = op.join(tempdir, 'test-bad-name.fif.gz') raw.save(raw_badname) Raw(raw_badname) assert_true(len(w) > 0) # len(w) should be 2 but Travis sometimes has more @testing.requires_testing_data def test_io_complex(): """Test IO with complex data types """ tempdir = _TempDir() dtypes = [np.complex64, np.complex128] raw = Raw(fif_fname, preload=True) picks = np.arange(5) start, stop = raw.time_as_index([0, 5]) data_orig, _ = raw[picks, start:stop] for di, dtype in enumerate(dtypes): imag_rand = np.array(1j * np.random.randn(data_orig.shape[0], data_orig.shape[1]), dtype) raw_cp = raw.copy() raw_cp._data = np.array(raw_cp._data, dtype) raw_cp._data[picks, start:stop] += imag_rand # this should throw an error because it's complex with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') raw_cp.save(op.join(tempdir, 'raw.fif'), picks, tmin=0, tmax=5, overwrite=True) # warning gets thrown on every instance b/c simplifilter('always') assert_equal(len(w), 1) raw2 = Raw(op.join(tempdir, 'raw.fif')) raw2_data, _ = raw2[picks, :] n_samp = raw2_data.shape[1] assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp]) # with preloading raw2 = Raw(op.join(tempdir, 'raw.fif'), preload=True) raw2_data, _ = raw2[picks, :] n_samp = raw2_data.shape[1] assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp]) @testing.requires_testing_data def test_getitem(): """Test getitem/indexing of Raw """ for preload in [False, True, 'memmap.dat']: raw = Raw(fif_fname, preload=preload) data, times = raw[0, :] data1, times1 = raw[0] assert_array_equal(data, data1) assert_array_equal(times, times1) data, times = raw[0:2, :] data1, times1 = raw[0:2] assert_array_equal(data, data1) assert_array_equal(times, times1) data1, times1 = raw[[0, 1]] assert_array_equal(data, data1) assert_array_equal(times, times1) @testing.requires_testing_data def test_proj(): """Test SSP proj operations """ tempdir = _TempDir() for proj in [True, False]: raw = Raw(fif_fname, preload=False, proj=proj) assert_true(all(p['active'] == proj for p in raw.info['projs'])) data, times = raw[0:2, :] data1, times1 = raw[0:2] assert_array_equal(data, data1) assert_array_equal(times, times1) # test adding / deleting proj if proj: assert_raises(ValueError, raw.add_proj, [], {'remove_existing': True}) assert_raises(ValueError, raw.del_proj, 0) else: projs = deepcopy(raw.info['projs']) n_proj = len(raw.info['projs']) raw.del_proj(0) assert_equal(len(raw.info['projs']), n_proj - 1) raw.add_proj(projs, remove_existing=False) assert_equal(len(raw.info['projs']), 2 * n_proj - 1) raw.add_proj(projs, remove_existing=True) assert_equal(len(raw.info['projs']), n_proj) # test apply_proj() with and without preload for preload in [True, False]: raw = Raw(fif_fname, preload=preload, proj=False) data, times = raw[:, 0:2] raw.apply_proj() data_proj_1 = np.dot(raw._projector, data) # load the file again without proj raw = Raw(fif_fname, preload=preload, proj=False) # write the file with proj. activated, make sure proj has been applied raw.save(op.join(tempdir, 'raw.fif'), proj=True, overwrite=True) raw2 = Raw(op.join(tempdir, 'raw.fif'), proj=False) data_proj_2, _ = raw2[:, 0:2] assert_allclose(data_proj_1, data_proj_2) assert_true(all(p['active'] for p in raw2.info['projs'])) # read orig file with proj. active raw2 = Raw(fif_fname, preload=preload, proj=True) data_proj_2, _ = raw2[:, 0:2] assert_allclose(data_proj_1, data_proj_2) assert_true(all(p['active'] for p in raw2.info['projs'])) # test that apply_proj works raw.apply_proj() data_proj_2, _ = raw[:, 0:2] assert_allclose(data_proj_1, data_proj_2) assert_allclose(data_proj_2, np.dot(raw._projector, data_proj_2)) tempdir = _TempDir() out_fname = op.join(tempdir, 'test_raw.fif') raw = read_raw_fif(test_fif_fname, preload=True).crop(0, 0.002, copy=False) raw.pick_types(meg=False, eeg=True) raw.info['projs'] = [raw.info['projs'][-1]] raw._data.fill(0) raw._data[-1] = 1. raw.save(out_fname) raw = read_raw_fif(out_fname, proj=True, preload=False) assert_allclose(raw[:, :][0][:1], raw[0, :][0]) @testing.requires_testing_data def test_preload_modify(): """Test preloading and modifying data """ tempdir = _TempDir() for preload in [False, True, 'memmap.dat']: raw = Raw(fif_fname, preload=preload) nsamp = raw.last_samp - raw.first_samp + 1 picks = pick_types(raw.info, meg='grad', exclude='bads') data = np.random.randn(len(picks), nsamp // 2) try: raw[picks, :nsamp // 2] = data except RuntimeError as err: if not preload: continue else: raise err tmp_fname = op.join(tempdir, 'raw.fif') raw.save(tmp_fname, overwrite=True) raw_new = Raw(tmp_fname) data_new, _ = raw_new[picks, :nsamp / 2] assert_allclose(data, data_new) @slow_test @testing.requires_testing_data def test_filter(): """Test filtering (FIR and IIR) and Raw.apply_function interface """ raw = Raw(fif_fname).crop(0, 7, False) raw.preload_data() sig_dec = 11 sig_dec_notch = 12 sig_dec_notch_fit = 12 picks_meg = pick_types(raw.info, meg=True, exclude='bads') picks = picks_meg[:4] raw_lp = raw.copy() raw_lp.filter(0., 4.0 - 0.25, picks=picks, n_jobs=2) raw_hp = raw.copy() raw_hp.filter(8.0 + 0.25, None, picks=picks, n_jobs=2) raw_bp = raw.copy() raw_bp.filter(4.0 + 0.25, 8.0 - 0.25, picks=picks) raw_bs = raw.copy() raw_bs.filter(8.0 + 0.25, 4.0 - 0.25, picks=picks, n_jobs=2) data, _ = raw[picks, :] lp_data, _ = raw_lp[picks, :] hp_data, _ = raw_hp[picks, :] bp_data, _ = raw_bp[picks, :] bs_data, _ = raw_bs[picks, :] assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec) assert_array_almost_equal(data, bp_data + bs_data, sig_dec) raw_lp_iir = raw.copy() raw_lp_iir.filter(0., 4.0, picks=picks, n_jobs=2, method='iir') raw_hp_iir = raw.copy() raw_hp_iir.filter(8.0, None, picks=picks, n_jobs=2, method='iir') raw_bp_iir = raw.copy() raw_bp_iir.filter(4.0, 8.0, picks=picks, method='iir') lp_data_iir, _ = raw_lp_iir[picks, :] hp_data_iir, _ = raw_hp_iir[picks, :] bp_data_iir, _ = raw_bp_iir[picks, :] summation = lp_data_iir + hp_data_iir + bp_data_iir assert_array_almost_equal(data[:, 100:-100], summation[:, 100:-100], sig_dec) # make sure we didn't touch other channels data, _ = raw[picks_meg[4:], :] bp_data, _ = raw_bp[picks_meg[4:], :] assert_array_equal(data, bp_data) bp_data_iir, _ = raw_bp_iir[picks_meg[4:], :] assert_array_equal(data, bp_data_iir) # do a very simple check on line filtering raw_bs = raw.copy() with warnings.catch_warnings(record=True): warnings.simplefilter('always') raw_bs.filter(60.0 + 0.5, 60.0 - 0.5, picks=picks, n_jobs=2) data_bs, _ = raw_bs[picks, :] raw_notch = raw.copy() raw_notch.notch_filter(60.0, picks=picks, n_jobs=2, method='fft') data_notch, _ = raw_notch[picks, :] assert_array_almost_equal(data_bs, data_notch, sig_dec_notch) # now use the sinusoidal fitting raw_notch = raw.copy() raw_notch.notch_filter(None, picks=picks, n_jobs=2, method='spectrum_fit') data_notch, _ = raw_notch[picks, :] data, _ = raw[picks, :] assert_array_almost_equal(data, data_notch, sig_dec_notch_fit) @testing.requires_testing_data def test_crop(): """Test cropping raw files """ # split a concatenated file to test a difficult case raw = Raw([fif_fname, fif_fname], preload=False) split_size = 10. # in seconds sfreq = raw.info['sfreq'] nsamp = (raw.last_samp - raw.first_samp + 1) # do an annoying case (off-by-one splitting) tmins = np.r_[1., np.round(np.arange(0., nsamp - 1, split_size * sfreq))] tmins = np.sort(tmins) tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1])) tmaxs /= sfreq tmins /= sfreq raws = [None] * len(tmins) for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)): raws[ri] = raw.crop(tmin, tmax, True) all_raw_2 = concatenate_raws(raws, preload=False) assert_equal(raw.first_samp, all_raw_2.first_samp) assert_equal(raw.last_samp, all_raw_2.last_samp) assert_array_equal(raw[:, :][0], all_raw_2[:, :][0]) tmins = np.round(np.arange(0., nsamp - 1, split_size * sfreq)) tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1])) tmaxs /= sfreq tmins /= sfreq # going in revere order so the last fname is the first file (need it later) raws = [None] * len(tmins) for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)): raws[ri] = raw.copy() raws[ri].crop(tmin, tmax, False) # test concatenation of split file all_raw_1 = concatenate_raws(raws, preload=False) all_raw_2 = raw.crop(0, None, True) for ar in [all_raw_1, all_raw_2]: assert_equal(raw.first_samp, ar.first_samp) assert_equal(raw.last_samp, ar.last_samp) assert_array_equal(raw[:, :][0], ar[:, :][0]) @testing.requires_testing_data def test_resample(): """Test resample (with I/O and multiple files) """ tempdir = _TempDir() raw = Raw(fif_fname).crop(0, 3, False) raw.preload_data() raw_resamp = raw.copy() sfreq = raw.info['sfreq'] # test parallel on upsample raw_resamp.resample(sfreq * 2, n_jobs=2) assert_equal(raw_resamp.n_times, len(raw_resamp.times)) raw_resamp.save(op.join(tempdir, 'raw_resamp-raw.fif')) raw_resamp = Raw(op.join(tempdir, 'raw_resamp-raw.fif'), preload=True) assert_equal(sfreq, raw_resamp.info['sfreq'] / 2) assert_equal(raw.n_times, raw_resamp.n_times / 2) assert_equal(raw_resamp._data.shape[1], raw_resamp.n_times) assert_equal(raw._data.shape[0], raw_resamp._data.shape[0]) # test non-parallel on downsample raw_resamp.resample(sfreq, n_jobs=1) assert_equal(raw_resamp.info['sfreq'], sfreq) assert_equal(raw._data.shape, raw_resamp._data.shape) assert_equal(raw.first_samp, raw_resamp.first_samp) assert_equal(raw.last_samp, raw.last_samp) # upsampling then downsampling doubles resampling error, but this still # works (hooray). Note that the stim channels had to be sub-sampled # without filtering to be accurately preserved # note we have to treat MEG and EEG+STIM channels differently (tols) assert_allclose(raw._data[:306, 200:-200], raw_resamp._data[:306, 200:-200], rtol=1e-2, atol=1e-12) assert_allclose(raw._data[306:, 200:-200], raw_resamp._data[306:, 200:-200], rtol=1e-2, atol=1e-7) # now check multiple file support w/resampling, as order of operations # (concat, resample) should not affect our data raw1 = raw.copy() raw2 = raw.copy() raw3 = raw.copy() raw4 = raw.copy() raw1 = concatenate_raws([raw1, raw2]) raw1.resample(10.) raw3.resample(10.) raw4.resample(10.) raw3 = concatenate_raws([raw3, raw4]) assert_array_equal(raw1._data, raw3._data) assert_array_equal(raw1._first_samps, raw3._first_samps) assert_array_equal(raw1._last_samps, raw3._last_samps) assert_array_equal(raw1._raw_lengths, raw3._raw_lengths) assert_equal(raw1.first_samp, raw3.first_samp) assert_equal(raw1.last_samp, raw3.last_samp) assert_equal(raw1.info['sfreq'], raw3.info['sfreq']) # test resampling of stim channel # basic decimation stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0] raw = RawArray([stim], create_info(1, len(stim), ['stim'])) assert_allclose(raw.resample(8.)._data, [[1, 1, 0, 0, 1, 1, 0, 0]]) # decimation of multiple stim channels raw = RawArray(2 * [stim], create_info(2, len(stim), 2 * ['stim'])) assert_allclose(raw.resample(8.)._data, [[1, 1, 0, 0, 1, 1, 0, 0], [1, 1, 0, 0, 1, 1, 0, 0]]) # decimation that could potentially drop events if the decimation is # done naively stim = [0, 0, 0, 1, 1, 0, 0, 0] raw = RawArray([stim], create_info(1, len(stim), ['stim'])) assert_allclose(raw.resample(4.)._data, [[0, 1, 1, 0]]) # two events are merged in this case (warning) stim = [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0] raw = RawArray([stim], create_info(1, len(stim), ['stim'])) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') raw.resample(8.) assert_true(len(w) == 1) # events are dropped in this case (warning) stim = [0, 1, 1, 0, 0, 1, 1, 0] raw = RawArray([stim], create_info(1, len(stim), ['stim'])) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') raw.resample(4.) assert_true(len(w) == 1) # test resampling events: this should no longer give a warning stim = [0, 1, 1, 0, 0, 1, 1, 0] raw = RawArray([stim], create_info(1, len(stim), ['stim'])) events = find_events(raw) raw, events = raw.resample(4., events=events) assert_equal(events, np.array([[0, 0, 1], [2, 0, 1]])) # test copy flag stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0] raw = RawArray([stim], create_info(1, len(stim), ['stim'])) raw_resampled = raw.resample(4., copy=True) assert_true(raw_resampled is not raw) raw_resampled = raw.resample(4., copy=False) assert_true(raw_resampled is raw) # resample should still work even when no stim channel is present raw = RawArray(np.random.randn(1, 100), create_info(1, 100, ['eeg'])) raw.resample(10) assert_true(len(raw) == 10) @testing.requires_testing_data def test_hilbert(): """Test computation of analytic signal using hilbert """ raw = Raw(fif_fname, preload=True) picks_meg = pick_types(raw.info, meg=True, exclude='bads') picks = picks_meg[:4] raw_filt = raw.copy() raw_filt.filter(10, 20) raw_filt_2 = raw_filt.copy() raw2 = raw.copy() raw3 = raw.copy() raw.apply_hilbert(picks) raw2.apply_hilbert(picks, envelope=True, n_jobs=2) # Test custom n_fft raw_filt.apply_hilbert(picks) raw_filt_2.apply_hilbert(picks, n_fft=raw_filt_2.n_times + 1000) assert_equal(raw_filt._data.shape, raw_filt_2._data.shape) assert_allclose(raw_filt._data[:, 50:-50], raw_filt_2._data[:, 50:-50], atol=1e-13, rtol=1e-2) assert_raises(ValueError, raw3.apply_hilbert, picks, n_fft=raw3.n_times - 100) env = np.abs(raw._data[picks, :]) assert_allclose(env, raw2._data[picks, :], rtol=1e-2, atol=1e-13) @testing.requires_testing_data def test_raw_copy(): """Test Raw copy """ raw = Raw(fif_fname, preload=True) data, _ = raw[:, :] copied = raw.copy() copied_data, _ = copied[:, :] assert_array_equal(data, copied_data) assert_equal(sorted(raw.__dict__.keys()), sorted(copied.__dict__.keys())) raw = Raw(fif_fname, preload=False) data, _ = raw[:, :] copied = raw.copy() copied_data, _ = copied[:, :] assert_array_equal(data, copied_data) assert_equal(sorted(raw.__dict__.keys()), sorted(copied.__dict__.keys())) @requires_pandas def test_to_data_frame(): """Test raw Pandas exporter""" raw = Raw(test_fif_fname, preload=True) _, times = raw[0, :10] df = raw.to_data_frame() assert_true((df.columns == raw.ch_names).all()) assert_array_equal(np.round(times * 1e3), df.index.values[:10]) df = raw.to_data_frame(index=None) assert_true('time' in df.index.names) assert_array_equal(df.values[:, 0], raw._data[0] * 1e13) assert_array_equal(df.values[:, 2], raw._data[2] * 1e15) @testing.requires_testing_data def test_raw_index_as_time(): """ Test index as time conversion""" raw = Raw(fif_fname, preload=True) t0 = raw.index_as_time([0], True)[0] t1 = raw.index_as_time([100], False)[0] t2 = raw.index_as_time([100], True)[0] assert_equal(t2 - t1, t0) # ensure we can go back and forth t3 = raw.index_as_time(raw.time_as_index([0], True), True) assert_array_almost_equal(t3, [0.0], 2) t3 = raw.index_as_time(raw.time_as_index(raw.info['sfreq'], True), True) assert_array_almost_equal(t3, [raw.info['sfreq']], 2) t3 = raw.index_as_time(raw.time_as_index(raw.info['sfreq'], False), False) assert_array_almost_equal(t3, [raw.info['sfreq']], 2) i0 = raw.time_as_index(raw.index_as_time([0], True), True) assert_equal(i0[0], 0) i1 = raw.time_as_index(raw.index_as_time([100], True), True) assert_equal(i1[0], 100) # Have to add small amount of time because we truncate via int casting i1 = raw.time_as_index(raw.index_as_time([100.0001], False), False) assert_equal(i1[0], 100) def test_add_channels(): """Test raw splitting / re-appending channel types """ raw = Raw(test_fif_fname).crop(0, 1).preload_data() raw_nopre = Raw(test_fif_fname, preload=False) raw_eeg_meg = raw.pick_types(meg=True, eeg=True, copy=True) raw_eeg = raw.pick_types(meg=False, eeg=True, copy=True) raw_meg = raw.pick_types(meg=True, eeg=False, copy=True) raw_stim = raw.pick_types(meg=False, eeg=False, stim=True, copy=True) raw_new = raw_meg.add_channels([raw_eeg, raw_stim], copy=True) assert_true(all(ch in raw_new.ch_names for ch in raw_stim.ch_names + raw_meg.ch_names)) raw_new = raw_meg.add_channels([raw_eeg], copy=True) assert_true(ch in raw_new.ch_names for ch in raw.ch_names) assert_array_equal(raw_new[:, :][0], raw_eeg_meg[:, :][0]) assert_array_equal(raw_new[:, :][1], raw[:, :][1]) assert_true(all(ch not in raw_new.ch_names for ch in raw_stim.ch_names)) # Now test errors raw_badsf = raw_eeg.copy() raw_badsf.info['sfreq'] = 3.1415927 raw_eeg = raw_eeg.crop(.5) assert_raises(AssertionError, raw_meg.add_channels, [raw_nopre]) assert_raises(RuntimeError, raw_meg.add_channels, [raw_badsf]) assert_raises(AssertionError, raw_meg.add_channels, [raw_eeg]) assert_raises(ValueError, raw_meg.add_channels, [raw_meg]) assert_raises(AssertionError, raw_meg.add_channels, raw_badsf) @testing.requires_testing_data def test_raw_time_as_index(): """ Test time as index conversion""" raw = Raw(fif_fname, preload=True) first_samp = raw.time_as_index([0], True)[0] assert_equal(raw.first_samp, -first_samp) @testing.requires_testing_data def test_save(): """ Test saving raw""" tempdir = _TempDir() raw = Raw(fif_fname, preload=False) # can't write over file being read assert_raises(ValueError, raw.save, fif_fname) raw = Raw(fif_fname, preload=True) # can't overwrite file without overwrite=True assert_raises(IOError, raw.save, fif_fname) # test abspath support new_fname = op.join(op.abspath(op.curdir), 'break-raw.fif') raw.save(op.join(tempdir, new_fname), overwrite=True) new_raw = Raw(op.join(tempdir, new_fname), preload=False) assert_raises(ValueError, new_raw.save, new_fname) # make sure we can overwrite the file we loaded when preload=True new_raw = Raw(op.join(tempdir, new_fname), preload=True) new_raw.save(op.join(tempdir, new_fname), overwrite=True) os.remove(new_fname) @testing.requires_testing_data def test_with_statement(): """ Test with statement """ for preload in [True, False]: with Raw(fif_fname, preload=preload) as raw_: print(raw_) def test_compensation_raw(): """Test Raw compensation """ tempdir = _TempDir() raw1 = Raw(ctf_comp_fname, compensation=None) assert_true(raw1.comp is None) data1, times1 = raw1[:, :] raw2 = Raw(ctf_comp_fname, compensation=3) data2, times2 = raw2[:, :] assert_true(raw2.comp is None) # unchanged (data come with grade 3) assert_array_equal(times1, times2) assert_array_equal(data1, data2) raw3 = Raw(ctf_comp_fname, compensation=1) data3, times3 = raw3[:, :] assert_true(raw3.comp is not None) assert_array_equal(times1, times3) # make sure it's different with a different compensation: assert_true(np.mean(np.abs(data1 - data3)) > 1e-12) assert_raises(ValueError, Raw, ctf_comp_fname, compensation=33) # Try IO with compensation temp_file = op.join(tempdir, 'raw.fif') raw1.save(temp_file, overwrite=True) raw4 = Raw(temp_file) data4, times4 = raw4[:, :] assert_array_equal(times1, times4) assert_array_equal(data1, data4) # Now save the file that has modified compensation # and make sure we can the same data as input ie. compensation # is undone raw3.save(temp_file, overwrite=True) raw5 = Raw(temp_file) data5, times5 = raw5[:, :] assert_array_equal(times1, times5) assert_allclose(data1, data5, rtol=1e-12, atol=1e-22) @requires_mne def test_compensation_raw_mne(): """Test Raw compensation by comparing with MNE """ tempdir = _TempDir() def compensate_mne(fname, grad): tmp_fname = op.join(tempdir, 'mne_ctf_test_raw.fif') cmd = ['mne_process_raw', '--raw', fname, '--save', tmp_fname, '--grad', str(grad), '--projoff', '--filteroff'] run_subprocess(cmd) return Raw(tmp_fname, preload=True) for grad in [0, 2, 3]: raw_py = Raw(ctf_comp_fname, preload=True, compensation=grad) raw_c = compensate_mne(ctf_comp_fname, grad) assert_allclose(raw_py._data, raw_c._data, rtol=1e-6, atol=1e-17) @testing.requires_testing_data def test_drop_channels_mixin(): """Test channels-dropping functionality """ raw = Raw(fif_fname, preload=True) drop_ch = raw.ch_names[:3] ch_names = raw.ch_names[3:] ch_names_orig = raw.ch_names dummy = raw.drop_channels(drop_ch, copy=True) assert_equal(ch_names, dummy.ch_names) assert_equal(ch_names_orig, raw.ch_names) assert_equal(len(ch_names_orig), raw._data.shape[0]) raw.drop_channels(drop_ch) assert_equal(ch_names, raw.ch_names) assert_equal(len(ch_names), len(raw._cals)) assert_equal(len(ch_names), raw._data.shape[0]) @testing.requires_testing_data def test_pick_channels_mixin(): """Test channel-picking functionality """ # preload is True raw = Raw(fif_fname, preload=True) ch_names = raw.ch_names[:3] ch_names_orig = raw.ch_names dummy = raw.pick_channels(ch_names, copy=True) # copy is True assert_equal(ch_names, dummy.ch_names) assert_equal(ch_names_orig, raw.ch_names) assert_equal(len(ch_names_orig), raw._data.shape[0]) raw.pick_channels(ch_names, copy=False) # copy is False assert_equal(ch_names, raw.ch_names) assert_equal(len(ch_names), len(raw._cals)) assert_equal(len(ch_names), raw._data.shape[0]) assert_raises(ValueError, raw.pick_channels, ch_names[0]) raw = Raw(fif_fname, preload=False) assert_raises(RuntimeError, raw.pick_channels, ch_names) assert_raises(RuntimeError, raw.drop_channels, ch_names) @testing.requires_testing_data def test_equalize_channels(): """Test equalization of channels """ raw1 = Raw(fif_fname, preload=True) raw2 = raw1.copy() ch_names = raw1.ch_names[2:] raw1.drop_channels(raw1.ch_names[:1]) raw2.drop_channels(raw2.ch_names[1:2]) my_comparison = [raw1, raw2] equalize_channels(my_comparison) for e in my_comparison: assert_equal(ch_names, e.ch_names) run_tests_if_main()

bsd-3-clause

pkruskal/scikit-learn

sklearn/cluster/tests/test_dbscan.py

113

11393

""" Tests for DBSCAN clustering algorithm """ import pickle import numpy as np from scipy.spatial import distance from scipy import sparse from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_in from sklearn.utils.testing import assert_not_in from sklearn.cluster.dbscan_ import DBSCAN from sklearn.cluster.dbscan_ import dbscan from sklearn.cluster.tests.common import generate_clustered_data from sklearn.metrics.pairwise import pairwise_distances n_clusters = 3 X = generate_clustered_data(n_clusters=n_clusters) def test_dbscan_similarity(): # Tests the DBSCAN algorithm with a similarity array. # Parameters chosen specifically for this task. eps = 0.15 min_samples = 10 # Compute similarities D = distance.squareform(distance.pdist(X)) D /= np.max(D) # Compute DBSCAN core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples) labels = db.fit(D).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_feature(): # Tests the DBSCAN algorithm with a feature vector array. # Parameters chosen specifically for this task. # Different eps to other test, because distance is not normalised. eps = 0.8 min_samples = 10 metric = 'euclidean' # Compute DBSCAN # parameters chosen for task core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples) labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_sparse(): core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X), eps=.8, min_samples=10) core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10) assert_array_equal(core_dense, core_sparse) assert_array_equal(labels_dense, labels_sparse) def test_dbscan_no_core_samples(): rng = np.random.RandomState(0) X = rng.rand(40, 10) X[X < .8] = 0 for X_ in [X, sparse.csr_matrix(X)]: db = DBSCAN(min_samples=6).fit(X_) assert_array_equal(db.components_, np.empty((0, X_.shape[1]))) assert_array_equal(db.labels_, -1) assert_equal(db.core_sample_indices_.shape, (0,)) def test_dbscan_callable(): # Tests the DBSCAN algorithm with a callable metric. # Parameters chosen specifically for this task. # Different eps to other test, because distance is not normalised. eps = 0.8 min_samples = 10 # metric is the function reference, not the string key. metric = distance.euclidean # Compute DBSCAN # parameters chosen for task core_samples, labels = dbscan(X, metric=metric, eps=eps, min_samples=min_samples, algorithm='ball_tree') # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) def test_dbscan_balltree(): # Tests the DBSCAN algorithm with balltree for neighbor calculation. eps = 0.8 min_samples = 10 D = pairwise_distances(X) core_samples, labels = dbscan(D, metric="precomputed", eps=eps, min_samples=min_samples) # number of clusters, ignoring noise if present n_clusters_1 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_1, n_clusters) db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_2 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_2, n_clusters) db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='kd_tree') labels = db.fit(X).labels_ n_clusters_3 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_3, n_clusters) db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_4 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_4, n_clusters) db = DBSCAN(leaf_size=20, eps=eps, min_samples=min_samples, algorithm='ball_tree') labels = db.fit(X).labels_ n_clusters_5 = len(set(labels)) - int(-1 in labels) assert_equal(n_clusters_5, n_clusters) def test_input_validation(): # DBSCAN.fit should accept a list of lists. X = [[1., 2.], [3., 4.]] DBSCAN().fit(X) # must not raise exception def test_dbscan_badargs(): # Test bad argument values: these should all raise ValueErrors assert_raises(ValueError, dbscan, X, eps=-1.0) assert_raises(ValueError, dbscan, X, algorithm='blah') assert_raises(ValueError, dbscan, X, metric='blah') assert_raises(ValueError, dbscan, X, leaf_size=-1) assert_raises(ValueError, dbscan, X, p=-1) def test_pickle(): obj = DBSCAN() s = pickle.dumps(obj) assert_equal(type(pickle.loads(s)), obj.__class__) def test_boundaries(): # ensure min_samples is inclusive of core point core, _ = dbscan([[0], [1]], eps=2, min_samples=2) assert_in(0, core) # ensure eps is inclusive of circumference core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2) assert_in(0, core) core, _ = dbscan([[0], [1], [1]], eps=.99, min_samples=2) assert_not_in(0, core) def test_weighted_dbscan(): # ensure sample_weight is validated assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2]) assert_raises(ValueError, dbscan, [[0], [1]], sample_weight=[2, 3, 4]) # ensure sample_weight has an effect assert_array_equal([], dbscan([[0], [1]], sample_weight=None, min_samples=6)[0]) assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 5], min_samples=6)[0]) assert_array_equal([0], dbscan([[0], [1]], sample_weight=[6, 5], min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 6], min_samples=6)[0]) # points within eps of each other: assert_array_equal([0, 1], dbscan([[0], [1]], eps=1.5, sample_weight=[5, 1], min_samples=6)[0]) # and effect of non-positive and non-integer sample_weight: assert_array_equal([], dbscan([[0], [1]], sample_weight=[5, 0], eps=1.5, min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[5.9, 0.1], eps=1.5, min_samples=6)[0]) assert_array_equal([0, 1], dbscan([[0], [1]], sample_weight=[6, 0], eps=1.5, min_samples=6)[0]) assert_array_equal([], dbscan([[0], [1]], sample_weight=[6, -1], eps=1.5, min_samples=6)[0]) # for non-negative sample_weight, cores should be identical to repetition rng = np.random.RandomState(42) sample_weight = rng.randint(0, 5, X.shape[0]) core1, label1 = dbscan(X, sample_weight=sample_weight) assert_equal(len(label1), len(X)) X_repeated = np.repeat(X, sample_weight, axis=0) core_repeated, label_repeated = dbscan(X_repeated) core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool) core_repeated_mask[core_repeated] = True core_mask = np.zeros(X.shape[0], dtype=bool) core_mask[core1] = True assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask) # sample_weight should work with precomputed distance matrix D = pairwise_distances(X) core3, label3 = dbscan(D, sample_weight=sample_weight, metric='precomputed') assert_array_equal(core1, core3) assert_array_equal(label1, label3) # sample_weight should work with estimator est = DBSCAN().fit(X, sample_weight=sample_weight) core4 = est.core_sample_indices_ label4 = est.labels_ assert_array_equal(core1, core4) assert_array_equal(label1, label4) est = DBSCAN() label5 = est.fit_predict(X, sample_weight=sample_weight) core5 = est.core_sample_indices_ assert_array_equal(core1, core5) assert_array_equal(label1, label5) assert_array_equal(label1, est.labels_) def test_dbscan_core_samples_toy(): X = [[0], [2], [3], [4], [6], [8], [10]] n_samples = len(X) for algorithm in ['brute', 'kd_tree', 'ball_tree']: # Degenerate case: every sample is a core sample, either with its own # cluster or including other close core samples. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=1) assert_array_equal(core_samples, np.arange(n_samples)) assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4]) # With eps=1 and min_samples=2 only the 3 samples from the denser area # are core samples. All other points are isolated and considered noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=2) assert_array_equal(core_samples, [1, 2, 3]) assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1]) # Only the sample in the middle of the dense area is core. Its two # neighbors are edge samples. Remaining samples are noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=3) assert_array_equal(core_samples, [2]) assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1]) # It's no longer possible to extract core samples with eps=1: # everything is noise. core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=4) assert_array_equal(core_samples, []) assert_array_equal(labels, -np.ones(n_samples)) def test_dbscan_precomputed_metric_with_degenerate_input_arrays(): # see https://github.com/scikit-learn/scikit-learn/issues/4641 for # more details X = np.ones((10, 2)) labels = DBSCAN(eps=0.5, metric='precomputed').fit(X).labels_ assert_equal(len(set(labels)), 1) X = np.zeros((10, 2)) labels = DBSCAN(eps=0.5, metric='precomputed').fit(X).labels_ assert_equal(len(set(labels)), 1)

bsd-3-clause

arthur-gouveia/DAT210x

Module3/just-playing-module3.py

1

1894

# -*- coding: utf-8 -*- """ Created on Fri Nov 4 13:18:57 2016 Module 3 on DAT210x course scripts More info on matplotlib histogram: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist @author: Arthur Gouveia """ import pandas as pd import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D from pandas.tools.plotting import parallel_coordinates from sklearn.datasets import load_iris MENU = ''' 1: Single Histogram 2: Multiple Histogram 3: Scatter plot 4: 3D Scatter plot 5: Parallel Plot Enter your choice: ''' def histplot(data, **kwargs): data.plot.hist(**kwargs) def scatter2D(data, x, y, **kwargs): data.plot.scatter(x=x, y=y, **kwargs) def scatter3D(data, **kwargs): fig = mpl.pyplot.figure() ax = fig.add_subplot(111, projection='3d') ax.set_xlabel('Final Grade') ax.set_ylabel('First Grade') ax.set_zlabel('Daily Alcohol') ax.scatter(data[0], data[1], data[2], **kwargs) mpl.pyplot.show() def menu(): return input(MENU) if __name__ == '__main__': mpl.style.use('ggplot') mpl.cm.cmapname = 'gray' student_dataset = pd.read_csv("Datasets/students.data", index_col=0) data = load_iris() iris = pd.DataFrame(data.data, columns=data.feature_names) iris['target_names'] = [data.target_names[i] for i in data.target] choice = menu() if choice == '1': histplot(student_dataset.G3, alpha=0.5, normed=True) elif choice == '2': histplot(student_dataset[['G3', 'G2', 'G1']], alpha=0.5) elif choice == '3': scatter2D(student_dataset[['G1', 'G3']], x='G1', y='G3') elif choice == '4': scatter3D([student_dataset.G1, student_dataset.G3, student_dataset['Dalc']], c='r', marker='o') elif choice == '5': parallel_coordinates(iris, 'target_names') else: print('Invalid option. Try again')

mit

yonglehou/scikit-learn

sklearn/decomposition/base.py

310

5647

"""Principal Component Analysis Base Classes""" # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Olivier Grisel <olivier.grisel@ensta.org> # Mathieu Blondel <mathieu@mblondel.org> # Denis A. Engemann <d.engemann@fz-juelich.de> # Kyle Kastner <kastnerkyle@gmail.com> # # License: BSD 3 clause import numpy as np from scipy import linalg from ..base import BaseEstimator, TransformerMixin from ..utils import check_array from ..utils.extmath import fast_dot from ..utils.validation import check_is_fitted from ..externals import six from abc import ABCMeta, abstractmethod class _BasePCA(six.with_metaclass(ABCMeta, BaseEstimator, TransformerMixin)): """Base class for PCA methods. Warning: This class should not be used directly. Use derived classes instead. """ def get_covariance(self): """Compute data covariance with the generative model. ``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)`` where S**2 contains the explained variances, and sigma2 contains the noise variances. Returns ------- cov : array, shape=(n_features, n_features) Estimated covariance of data. """ components_ = self.components_ exp_var = self.explained_variance_ if self.whiten: components_ = components_ * np.sqrt(exp_var[:, np.newaxis]) exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.) cov = np.dot(components_.T * exp_var_diff, components_) cov.flat[::len(cov) + 1] += self.noise_variance_ # modify diag inplace return cov def get_precision(self): """Compute data precision matrix with the generative model. Equals the inverse of the covariance but computed with the matrix inversion lemma for efficiency. Returns ------- precision : array, shape=(n_features, n_features) Estimated precision of data. """ n_features = self.components_.shape[1] # handle corner cases first if self.n_components_ == 0: return np.eye(n_features) / self.noise_variance_ if self.n_components_ == n_features: return linalg.inv(self.get_covariance()) # Get precision using matrix inversion lemma components_ = self.components_ exp_var = self.explained_variance_ if self.whiten: components_ = components_ * np.sqrt(exp_var[:, np.newaxis]) exp_var_diff = np.maximum(exp_var - self.noise_variance_, 0.) precision = np.dot(components_, components_.T) / self.noise_variance_ precision.flat[::len(precision) + 1] += 1. / exp_var_diff precision = np.dot(components_.T, np.dot(linalg.inv(precision), components_)) precision /= -(self.noise_variance_ ** 2) precision.flat[::len(precision) + 1] += 1. / self.noise_variance_ return precision @abstractmethod def fit(X, y=None): """Placeholder for fit. Subclasses should implement this method! Fit the model with X. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features. Returns ------- self : object Returns the instance itself. """ def transform(self, X, y=None): """Apply dimensionality reduction to X. X is projected on the first principal components previously extracted from a training set. Parameters ---------- X : array-like, shape (n_samples, n_features) New data, where n_samples is the number of samples and n_features is the number of features. Returns ------- X_new : array-like, shape (n_samples, n_components) Examples -------- >>> import numpy as np >>> from sklearn.decomposition import IncrementalPCA >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> ipca = IncrementalPCA(n_components=2, batch_size=3) >>> ipca.fit(X) IncrementalPCA(batch_size=3, copy=True, n_components=2, whiten=False) >>> ipca.transform(X) # doctest: +SKIP """ check_is_fitted(self, ['mean_', 'components_'], all_or_any=all) X = check_array(X) if self.mean_ is not None: X = X - self.mean_ X_transformed = fast_dot(X, self.components_.T) if self.whiten: X_transformed /= np.sqrt(self.explained_variance_) return X_transformed def inverse_transform(self, X, y=None): """Transform data back to its original space. In other words, return an input X_original whose transform would be X. Parameters ---------- X : array-like, shape (n_samples, n_components) New data, where n_samples is the number of samples and n_components is the number of components. Returns ------- X_original array-like, shape (n_samples, n_features) Notes ----- If whitening is enabled, inverse_transform will compute the exact inverse operation, which includes reversing whitening. """ if self.whiten: return fast_dot(X, np.sqrt(self.explained_variance_[:, np.newaxis]) * self.components_) + self.mean_ else: return fast_dot(X, self.components_) + self.mean_

bsd-3-clause

tclose/python-neo

neo/test/coretest/test_analogsignalarray.py

3

37391

# -*- coding: utf-8 -*- """ Tests of the neo.core.analogsignalarray.AnalogSignalArrayArray class """ import os import pickle try: import unittest2 as unittest except ImportError: import unittest import numpy as np import quantities as pq try: from IPython.lib.pretty import pretty except ImportError as err: HAVE_IPYTHON = False else: HAVE_IPYTHON = True from neo.core.analogsignalarray import AnalogSignalArray from neo.core import AnalogSignal, Segment, RecordingChannelGroup from neo.test.tools import (assert_arrays_almost_equal, assert_arrays_equal, assert_neo_object_is_compliant, assert_same_sub_schema) from neo.test.generate_datasets import (get_fake_value, get_fake_values, fake_neo, TEST_ANNOTATIONS) class Test__generate_datasets(unittest.TestCase): def setUp(self): np.random.seed(0) self.annotations = dict([(str(x), TEST_ANNOTATIONS[x]) for x in range(len(TEST_ANNOTATIONS))]) def test__get_fake_values(self): self.annotations['seed'] = 0 signal = get_fake_value('signal', pq.Quantity, seed=0, dim=2) sampling_rate = get_fake_value('sampling_rate', pq.Quantity, seed=1, dim=0) t_start = get_fake_value('t_start', pq.Quantity, seed=2, dim=0) channel_index = get_fake_value('channel_index', np.ndarray, seed=3, dim=1, dtype='i') name = get_fake_value('name', str, seed=4, obj=AnalogSignalArray) description = get_fake_value('description', str, seed=5, obj='AnalogSignalArray') file_origin = get_fake_value('file_origin', str) attrs1 = {'name': name, 'description': description, 'file_origin': file_origin} attrs2 = attrs1.copy() attrs2.update(self.annotations) res11 = get_fake_values(AnalogSignalArray, annotate=False, seed=0) res12 = get_fake_values('AnalogSignalArray', annotate=False, seed=0) res21 = get_fake_values(AnalogSignalArray, annotate=True, seed=0) res22 = get_fake_values('AnalogSignalArray', annotate=True, seed=0) assert_arrays_equal(res11.pop('signal'), signal) assert_arrays_equal(res12.pop('signal'), signal) assert_arrays_equal(res21.pop('signal'), signal) assert_arrays_equal(res22.pop('signal'), signal) assert_arrays_equal(res11.pop('sampling_rate'), sampling_rate) assert_arrays_equal(res12.pop('sampling_rate'), sampling_rate) assert_arrays_equal(res21.pop('sampling_rate'), sampling_rate) assert_arrays_equal(res22.pop('sampling_rate'), sampling_rate) assert_arrays_equal(res11.pop('t_start'), t_start) assert_arrays_equal(res12.pop('t_start'), t_start) assert_arrays_equal(res21.pop('t_start'), t_start) assert_arrays_equal(res22.pop('t_start'), t_start) assert_arrays_equal(res11.pop('channel_index'), channel_index) assert_arrays_equal(res12.pop('channel_index'), channel_index) assert_arrays_equal(res21.pop('channel_index'), channel_index) assert_arrays_equal(res22.pop('channel_index'), channel_index) self.assertEqual(res11, attrs1) self.assertEqual(res12, attrs1) self.assertEqual(res21, attrs2) self.assertEqual(res22, attrs2) def test__fake_neo__cascade(self): self.annotations['seed'] = None obj_type = 'AnalogSignalArray' cascade = True res = fake_neo(obj_type=obj_type, cascade=cascade) self.assertTrue(isinstance(res, AnalogSignalArray)) assert_neo_object_is_compliant(res) self.assertEqual(res.annotations, self.annotations) def test__fake_neo__nocascade(self): self.annotations['seed'] = None obj_type = AnalogSignalArray cascade = False res = fake_neo(obj_type=obj_type, cascade=cascade) self.assertTrue(isinstance(res, AnalogSignalArray)) assert_neo_object_is_compliant(res) self.assertEqual(res.annotations, self.annotations) class TestAnalogSignalArrayConstructor(unittest.TestCase): def test__create_from_list(self): data = [(i, i, i) for i in range(10)] # 3 signals each with 10 samples rate = 1000*pq.Hz signal = AnalogSignalArray(data, sampling_rate=rate, units="mV") assert_neo_object_is_compliant(signal) self.assertEqual(signal.shape, (10, 3)) self.assertEqual(signal.t_start, 0*pq.ms) self.assertEqual(signal.t_stop, len(data)/rate) self.assertEqual(signal[9, 0], 9000*pq.uV) def test__create_from_numpy_array(self): data = np.arange(20.0).reshape((10, 2)) rate = 1*pq.kHz signal = AnalogSignalArray(data, sampling_rate=rate, units="uV") assert_neo_object_is_compliant(signal) self.assertEqual(signal.t_start, 0*pq.ms) self.assertEqual(signal.t_stop, data.shape[0]/rate) self.assertEqual(signal[9, 0], 0.018*pq.mV) self.assertEqual(signal[9, 1], 19*pq.uV) def test__create_from_quantities_array(self): data = np.arange(20.0).reshape((10, 2)) * pq.mV rate = 5000*pq.Hz signal = AnalogSignalArray(data, sampling_rate=rate) assert_neo_object_is_compliant(signal) self.assertEqual(signal.t_start, 0*pq.ms) self.assertEqual(signal.t_stop, data.shape[0]/rate) self.assertEqual(signal[9, 0], 18000*pq.uV) def test__create_from_quantities_with_inconsistent_units_ValueError(self): data = np.arange(20.0).reshape((10, 2)) * pq.mV self.assertRaises(ValueError, AnalogSignalArray, data, sampling_rate=1*pq.kHz, units="nA") def test__create_with_copy_true_should_return_copy(self): data = np.arange(20.0).reshape((10, 2)) * pq.mV rate = 5000*pq.Hz signal = AnalogSignalArray(data, copy=True, sampling_rate=rate) assert_neo_object_is_compliant(signal) data[3, 0] = 0.099*pq.V self.assertNotEqual(signal[3, 0], 99*pq.mV) def test__create_with_copy_false_should_return_view(self): data = np.arange(20.0).reshape((10, 2)) * pq.mV rate = 5000*pq.Hz signal = AnalogSignalArray(data, copy=False, sampling_rate=rate) assert_neo_object_is_compliant(signal) data[3, 0] = 99*pq.mV self.assertEqual(signal[3, 0], 99000*pq.uV) # signal must not be 1D - should raise Exception if 1D class TestAnalogSignalArrayProperties(unittest.TestCase): def setUp(self): self.t_start = [0.0*pq.ms, 100*pq.ms, -200*pq.ms] self.rates = [1*pq.kHz, 420*pq.Hz, 999*pq.Hz] self.data = [np.arange(10.0).reshape((5, 2))*pq.nA, np.arange(-100.0, 100.0, 10.0).reshape((4, 5))*pq.mV, np.random.uniform(size=(100, 4))*pq.uV] self.signals = [AnalogSignalArray(D, sampling_rate=r, t_start=t) for r, D, t in zip(self.rates, self.data, self.t_start)] def test__compliant(self): for signal in self.signals: assert_neo_object_is_compliant(signal) def test__t_stop(self): for i, signal in enumerate(self.signals): targ = self.t_start[i] + self.data[i].shape[0]/self.rates[i] self.assertEqual(signal.t_stop, targ) def test__duration(self): for signal in self.signals: self.assertAlmostEqual(signal.duration, signal.t_stop - signal.t_start, delta=1e-15) def test__sampling_period(self): for signal, rate in zip(self.signals, self.rates): self.assertEqual(signal.sampling_period, 1/rate) def test__times(self): for i, signal in enumerate(self.signals): targ = np.arange(self.data[i].shape[0]) targ = targ/self.rates[i] + self.t_start[i] assert_arrays_almost_equal(signal.times, targ, 1e-12*pq.ms) def test__children(self): signal = self.signals[0] segment = Segment(name='seg1') segment.analogsignalarrays = [signal] segment.create_many_to_one_relationship() rcg = RecordingChannelGroup(name='rcg1') rcg.analogsignalarrays = [signal] rcg.create_many_to_one_relationship() self.assertEqual(signal._single_parent_objects, ('Segment', 'RecordingChannelGroup')) self.assertEqual(signal._multi_parent_objects, ()) self.assertEqual(signal._single_parent_containers, ('segment', 'recordingchannelgroup')) self.assertEqual(signal._multi_parent_containers, ()) self.assertEqual(signal._parent_objects, ('Segment', 'RecordingChannelGroup')) self.assertEqual(signal._parent_containers, ('segment', 'recordingchannelgroup')) self.assertEqual(len(signal.parents), 2) self.assertEqual(signal.parents[0].name, 'seg1') self.assertEqual(signal.parents[1].name, 'rcg1') assert_neo_object_is_compliant(signal) def test__repr(self): for i, signal in enumerate(self.signals): prepr = repr(signal) targ = '<AnalogSignalArray(%s, [%s, %s], sampling rate: %s)>' % \ (repr(self.data[i]), self.t_start[i], self.t_start[i] + len(self.data[i])/self.rates[i], self.rates[i]) self.assertEqual(prepr, targ) @unittest.skipUnless(HAVE_IPYTHON, "requires IPython") def test__pretty(self): for signal in self.signals: prepr = pretty(signal) targ = (('AnalogSignalArray in %s with %sx%s %s values\n' % (signal.units, signal.shape[0], signal.shape[1], signal.dtype)) + ('channel index: %s\n' % signal.channel_index) + ('sampling rate: %s\n' % signal.sampling_rate) + ('time: %s to %s' % (signal.t_start, signal.t_stop))) self.assertEqual(prepr, targ) class TestAnalogSignalArrayArrayMethods(unittest.TestCase): def setUp(self): self.data1 = np.arange(55.0).reshape((11, 5)) self.data1quant = self.data1 * pq.nA self.signal1 = AnalogSignalArray(self.data1quant, sampling_rate=1*pq.kHz, name='spam', description='eggs', file_origin='testfile.txt', arg1='test') self.data2 = np.array([[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]]).T self.data2quant = self.data2 * pq.mV self.signal2 = AnalogSignalArray(self.data2quant, sampling_rate=1.0*pq.Hz, name='spam', description='eggs', file_origin='testfile.txt', arg1='test') def test__compliant(self): assert_neo_object_is_compliant(self.signal1) self.assertEqual(self.signal1.name, 'spam') self.assertEqual(self.signal1.description, 'eggs') self.assertEqual(self.signal1.file_origin, 'testfile.txt') self.assertEqual(self.signal1.annotations, {'arg1': 'test'}) assert_neo_object_is_compliant(self.signal2) self.assertEqual(self.signal2.name, 'spam') self.assertEqual(self.signal2.description, 'eggs') self.assertEqual(self.signal2.file_origin, 'testfile.txt') self.assertEqual(self.signal2.annotations, {'arg1': 'test'}) def test__index_dim1_should_return_analogsignal(self): result = self.signal1[:, 0] self.assertIsInstance(result, AnalogSignal) assert_neo_object_is_compliant(result) self.assertEqual(result.name, None) self.assertEqual(result.description, None) self.assertEqual(result.file_origin, None) self.assertEqual(result.annotations, {}) self.assertEqual(result.t_stop, self.signal1.t_stop) self.assertEqual(result.t_start, self.signal1.t_start) self.assertEqual(result.sampling_rate, self.signal1.sampling_rate) assert_arrays_equal(result, self.data1[:, 0]) def test__index_dim1_and_slice_dim0_should_return_analogsignal(self): result = self.signal1[2:7, 0] self.assertIsInstance(result, AnalogSignal) assert_neo_object_is_compliant(result) self.assertEqual(result.name, None) self.assertEqual(result.description, None) self.assertEqual(result.file_origin, None) self.assertEqual(result.annotations, {}) self.assertEqual(result.t_start, self.signal1.t_start+2*self.signal1.sampling_period) self.assertEqual(result.t_stop, self.signal1.t_start+7*self.signal1.sampling_period) self.assertEqual(result.sampling_rate, self.signal1.sampling_rate) assert_arrays_equal(result, self.data1[2:7, 0]) def test__index_dim0_should_return_quantity_array(self): # i.e. values from all signals for a single point in time result = self.signal1[3, :] self.assertIsInstance(result, pq.Quantity) self.assertFalse(hasattr(result, 'name')) self.assertFalse(hasattr(result, 'description')) self.assertFalse(hasattr(result, 'file_origin')) self.assertFalse(hasattr(result, 'annotations')) self.assertEqual(result.shape, (5,)) self.assertFalse(hasattr(result, "t_start")) self.assertEqual(result.units, pq.nA) assert_arrays_equal(result, self.data1[3, :]) def test__index_dim0_and_slice_dim1_should_return_quantity_array(self): # i.e. values from a subset of signals for a single point in time result = self.signal1[3, 2:5] self.assertIsInstance(result, pq.Quantity) self.assertFalse(hasattr(result, 'name')) self.assertFalse(hasattr(result, 'description')) self.assertFalse(hasattr(result, 'file_origin')) self.assertFalse(hasattr(result, 'annotations')) self.assertEqual(result.shape, (3,)) self.assertFalse(hasattr(result, "t_start")) self.assertEqual(result.units, pq.nA) assert_arrays_equal(result, self.data1[3, 2:5]) def test__index_as_string_IndexError(self): self.assertRaises(IndexError, self.signal1.__getitem__, 5.) def test__slice_both_dimensions_should_return_analogsignalarray(self): result = self.signal1[0:3, 0:3] self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) targ = AnalogSignalArray([[0, 1, 2], [5, 6, 7], [10, 11, 12]], dtype=float, units="nA", sampling_rate=1*pq.kHz, name='spam', description='eggs', file_origin='testfile.txt', arg1='test') assert_neo_object_is_compliant(targ) self.assertEqual(result.t_stop, targ.t_stop) self.assertEqual(result.t_start, targ.t_start) self.assertEqual(result.sampling_rate, targ.sampling_rate) self.assertEqual(result.shape, targ.shape) assert_same_sub_schema(result, targ) assert_arrays_equal(result, self.data1[0:3, 0:3]) def test__slice_only_first_dimension_should_return_analogsignalarray(self): result = self.signal1[2:7] self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(result.shape, (5, 5)) self.assertEqual(result.t_start, self.signal1.t_start+2*self.signal1.sampling_period) self.assertEqual(result.t_stop, self.signal1.t_start+7*self.signal1.sampling_period) self.assertEqual(result.sampling_rate, self.signal1.sampling_rate) assert_arrays_equal(result, self.data1[2:7]) def test__getitem_should_return_single_quantity(self): # quantities drops the units in this case self.assertEqual(self.signal1[9, 3], 48000*pq.pA) self.assertEqual(self.signal1[9][3], self.signal1[9, 3]) self.assertTrue(hasattr(self.signal1[9, 3], 'units')) self.assertRaises(IndexError, self.signal1.__getitem__, (99, 73)) def test_comparison_operators(self): assert_arrays_equal(self.signal1[0:3, 0:3] >= 5*pq.nA, np.array([[False, False, False], [True, True, True], [True, True, True]])) assert_arrays_equal(self.signal1[0:3, 0:3] >= 5*pq.pA, np.array([[False, True, True], [True, True, True], [True, True, True]])) def test__comparison_with_inconsistent_units_should_raise_Exception(self): self.assertRaises(ValueError, self.signal1.__gt__, 5*pq.mV) def test__simple_statistics(self): self.assertEqual(self.signal1.max(), 54000*pq.pA) self.assertEqual(self.signal1.min(), 0*pq.nA) self.assertEqual(self.signal1.mean(), 27*pq.nA) self.assertEqual(self.signal1.std(), self.signal1.magnitude.std()*pq.nA) self.assertEqual(self.signal1.var(), self.signal1.magnitude.var()*pq.nA**2) def test__rescale_same(self): result = self.signal1.copy() result = result.rescale(pq.nA) self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(result.units, 1*pq.nA) assert_arrays_equal(result, self.data1) assert_same_sub_schema(result, self.signal1) def test__rescale_new(self): result = self.signal1.copy() result = result.rescale(pq.pA) self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(result.units, 1*pq.pA) assert_arrays_almost_equal(np.array(result), self.data1*1000., 1e-10) def test__time_slice(self): t_start = 2 * pq.s t_stop = 4 * pq.s result = self.signal2.time_slice(t_start, t_stop) self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) targ = AnalogSignalArray(np.array([[2., 3.], [2., 3.]]).T, sampling_rate=1.0*pq.Hz, units='mV', t_start=t_start, name='spam', description='eggs', file_origin='testfile.txt', arg1='test') assert_neo_object_is_compliant(result) self.assertEqual(result.t_stop, t_stop) self.assertEqual(result.t_start, t_start) self.assertEqual(result.sampling_rate, targ.sampling_rate) assert_arrays_equal(result, targ) assert_same_sub_schema(result, targ) def test__time_slice__out_of_bounds_ValueError(self): t_start_good = 2 * pq.s t_stop_good = 4 * pq.s t_start_bad = -2 * pq.s t_stop_bad = 40 * pq.s self.assertRaises(ValueError, self.signal2.time_slice, t_start_good, t_stop_bad) self.assertRaises(ValueError, self.signal2.time_slice, t_start_bad, t_stop_good) self.assertRaises(ValueError, self.signal2.time_slice, t_start_bad, t_stop_bad) def test__time_equal(self): t_start = 0 * pq.s t_stop = 6 * pq.s result = self.signal2.time_slice(t_start, t_stop) self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(result.t_stop, t_stop) self.assertEqual(result.t_start, t_start) assert_arrays_equal(result, self.signal2) assert_same_sub_schema(result, self.signal2) def test__time_slice__offset(self): self.signal2.t_start = 10.0 * pq.s assert_neo_object_is_compliant(self.signal2) t_start = 12 * pq.s t_stop = 14 * pq.s result = self.signal2.time_slice(t_start, t_stop) self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) targ = AnalogSignalArray(np.array([[2., 3.], [2., 3.]]).T, t_start=12.0*pq.ms, sampling_rate=1.0*pq.Hz, units='mV', name='spam', description='eggs', file_origin='testfile.txt', arg1='test') assert_neo_object_is_compliant(result) self.assertEqual(self.signal2.t_start, 10.0 * pq.s) self.assertEqual(result.t_stop, t_stop) self.assertEqual(result.t_start, t_start) self.assertEqual(result.sampling_rate, targ.sampling_rate) assert_arrays_equal(result, targ) assert_same_sub_schema(result, targ) def test__time_slice__different_units(self): self.signal2.t_start = 10.0 * pq.ms assert_neo_object_is_compliant(self.signal2) t_start = 2 * pq.s + 10.0 * pq.ms t_stop = 4 * pq.s + 10.0 * pq.ms result = self.signal2.time_slice(t_start, t_stop) self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) targ = AnalogSignalArray(np.array([[2., 3.], [2., 3.]]).T, t_start=t_start.rescale(pq.ms), sampling_rate=1.0*pq.Hz, units='mV', name='spam', description='eggs', file_origin='testfile.txt', arg1='test') assert_neo_object_is_compliant(result) assert_neo_object_is_compliant(self.signal2) self.assertEqual(self.signal2.t_start, 10.0 * pq.ms) self.assertAlmostEqual(result.t_stop, t_stop, delta=1e-12*pq.ms) self.assertAlmostEqual(result.t_start, t_start, delta=1e-12*pq.ms) assert_arrays_almost_equal(result.times, targ.times, 1e-12*pq.ms) self.assertEqual(result.sampling_rate, targ.sampling_rate) assert_arrays_equal(result, targ) assert_same_sub_schema(result, targ) def test__time_slice__no_explicit_time(self): self.signal2.t_start = 10.0 * pq.ms assert_neo_object_is_compliant(self.signal2) t1 = 2 * pq.s + 10.0 * pq.ms t2 = 4 * pq.s + 10.0 * pq.ms for t_start,t_stop in [(t1,None),(None,None),(None,t2)]: t_start_targ = t1 if t_start!=None else self.signal2.t_start t_stop_targ = t2 if t_stop!=None else self.signal2.t_stop result = self.signal2.time_slice(t_start, t_stop) self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) targ_ind = np.where((self.signal2.times >= t_start_targ) & (self.signal2.times < t_stop_targ)) targ_array = self.signal2.magnitude[targ_ind] targ = AnalogSignalArray(targ_array, t_start=t_start_targ.rescale(pq.ms), sampling_rate=1.0*pq.Hz, units='mV', name='spam', description='eggs', file_origin='testfile.txt', arg1='test') assert_neo_object_is_compliant(result) assert_neo_object_is_compliant(self.signal2) self.assertEqual(self.signal2.t_start, 10.0 * pq.ms) self.assertAlmostEqual(result.t_stop, t_stop_targ, delta=1e-12*pq.ms) self.assertAlmostEqual(result.t_start, t_start_targ, delta=1e-12*pq.ms) assert_arrays_almost_equal(result.times, targ.times, 1e-12*pq.ms) self.assertEqual(result.sampling_rate, targ.sampling_rate) assert_arrays_equal(result, targ) assert_same_sub_schema(result, targ) class TestAnalogSignalArrayEquality(unittest.TestCase): def test__signals_with_different_data_complement_should_be_not_equal(self): signal1 = AnalogSignalArray(np.arange(55.0).reshape((11, 5)), units="mV", sampling_rate=1*pq.kHz) signal2 = AnalogSignalArray(np.arange(55.0).reshape((11, 5)), units="mV", sampling_rate=2*pq.kHz) self.assertNotEqual(signal1, signal2) assert_neo_object_is_compliant(signal1) assert_neo_object_is_compliant(signal2) class TestAnalogSignalArrayCombination(unittest.TestCase): def setUp(self): self.data1 = np.arange(55.0).reshape((11, 5)) self.data1quant = self.data1 * pq.mV self.signal1 = AnalogSignalArray(self.data1quant, sampling_rate=1*pq.kHz, name='spam', description='eggs', file_origin='testfile.txt', arg1='test') self.data2 = np.arange(100.0, 155.0).reshape((11, 5)) self.data2quant = self.data2 * pq.mV self.signal2 = AnalogSignalArray(self.data2quant, sampling_rate=1*pq.kHz, name='spam', description='eggs', file_origin='testfile.txt', arg1='test') def test__compliant(self): assert_neo_object_is_compliant(self.signal1) self.assertEqual(self.signal1.name, 'spam') self.assertEqual(self.signal1.description, 'eggs') self.assertEqual(self.signal1.file_origin, 'testfile.txt') self.assertEqual(self.signal1.annotations, {'arg1': 'test'}) assert_neo_object_is_compliant(self.signal2) self.assertEqual(self.signal2.name, 'spam') self.assertEqual(self.signal2.description, 'eggs') self.assertEqual(self.signal2.file_origin, 'testfile.txt') self.assertEqual(self.signal2.annotations, {'arg1': 'test'}) def test__add_const_quantity_should_preserve_data_complement(self): result = self.signal1 + 0.065*pq.V self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) # time zero, signal index 4 assert_arrays_equal(result, self.data1 + 65) self.assertEqual(self.signal1[0, 4], 4*pq.mV) self.assertEqual(result[0, 4], 69000*pq.uV) self.assertEqual(self.signal1.t_start, result.t_start) self.assertEqual(self.signal1.sampling_rate, result.sampling_rate) def test__add_two_consistent_signals_should_preserve_data_complement(self): result = self.signal1 + self.signal2 self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) targdata = np.arange(100.0, 210.0, 2.0).reshape((11, 5)) targ = AnalogSignalArray(targdata, units="mV", sampling_rate=1*pq.kHz, name='spam', description='eggs', file_origin='testfile.txt', arg1='test') assert_neo_object_is_compliant(targ) assert_arrays_equal(result, targdata) assert_same_sub_schema(result, targ) def test__add_signals_with_inconsistent_data_complement_ValueError(self): self.signal2.sampling_rate = 0.5*pq.kHz assert_neo_object_is_compliant(self.signal2) self.assertRaises(ValueError, self.signal1.__add__, self.signal2) def test__subtract_const_should_preserve_data_complement(self): result = self.signal1 - 65*pq.mV self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(np.array(self.signal1[1, 4]), 9) self.assertEqual(np.array(result[1, 4]), -56) assert_arrays_equal(result, self.data1 - 65) self.assertEqual(self.signal1.sampling_rate, result.sampling_rate) def test__subtract_from_const_should_return_signal(self): result = 10*pq.mV - self.signal1 self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(np.array(self.signal1[1, 4]), 9) self.assertEqual(np.array(result[1, 4]), 1) assert_arrays_equal(result, 10 - self.data1) self.assertEqual(self.signal1.sampling_rate, result.sampling_rate) def test__mult_by_const_float_should_preserve_data_complement(self): result = self.signal1*2 self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(np.array(self.signal1[1, 4]), 9) self.assertEqual(np.array(result[1, 4]), 18) assert_arrays_equal(result, self.data1*2) self.assertEqual(self.signal1.sampling_rate, result.sampling_rate) def test__divide_by_const_should_preserve_data_complement(self): result = self.signal1/0.5 self.assertIsInstance(result, AnalogSignalArray) assert_neo_object_is_compliant(result) self.assertEqual(result.name, 'spam') self.assertEqual(result.description, 'eggs') self.assertEqual(result.file_origin, 'testfile.txt') self.assertEqual(result.annotations, {'arg1': 'test'}) self.assertEqual(np.array(self.signal1[1, 4]), 9) self.assertEqual(np.array(result[1, 4]), 18) assert_arrays_equal(result, self.data1/0.5) self.assertEqual(self.signal1.sampling_rate, result.sampling_rate) def test__merge(self): self.signal1.description = None self.signal1.file_origin = None assert_neo_object_is_compliant(self.signal1) data3 = np.arange(1000.0, 1066.0).reshape((11, 6)) * pq.uV data3scale = data3.rescale(self.data1quant.units) signal2 = AnalogSignalArray(self.data1quant, sampling_rate=1*pq.kHz, channel_index=np.arange(5), name='signal2', description='test signal', file_origin='testfile.txt') signal3 = AnalogSignalArray(data3, units="uV", sampling_rate=1*pq.kHz, channel_index=np.arange(5, 11), name='signal3', description='test signal', file_origin='testfile.txt') signal4 = AnalogSignalArray(data3, units="uV", sampling_rate=1*pq.kHz, name='signal4', description='test signal', file_origin='testfile.txt') merged13 = self.signal1.merge(signal3) merged23 = signal2.merge(signal3) merged24 = signal2.merge(signal4) mergeddata13 = np.array(merged13) mergeddata23 = np.array(merged23) mergeddata24 = np.array(merged24) targdata13 = np.hstack([self.data1quant, data3scale]) targdata23 = np.hstack([self.data1quant, data3scale]) targdata24 = np.hstack([self.data1quant, data3scale]) assert_neo_object_is_compliant(signal2) assert_neo_object_is_compliant(signal3) assert_neo_object_is_compliant(merged13) assert_neo_object_is_compliant(merged23) assert_neo_object_is_compliant(merged24) self.assertEqual(merged13[0, 4], 4*pq.mV) self.assertEqual(merged23[0, 4], 4*pq.mV) self.assertEqual(merged13[0, 5], 1*pq.mV) self.assertEqual(merged23[0, 5], 1*pq.mV) self.assertEqual(merged13[10, 10], 1.065*pq.mV) self.assertEqual(merged23[10, 10], 1.065*pq.mV) self.assertEqual(merged13.t_stop, self.signal1.t_stop) self.assertEqual(merged23.t_stop, self.signal1.t_stop) self.assertEqual(merged13.name, 'merge(spam, signal3)') self.assertEqual(merged23.name, 'merge(signal2, signal3)') self.assertEqual(merged13.description, 'merge(None, test signal)') self.assertEqual(merged23.description, 'test signal') self.assertEqual(merged13.file_origin, 'merge(None, testfile.txt)') self.assertEqual(merged23.file_origin, 'testfile.txt') assert_arrays_equal(mergeddata13, targdata13) assert_arrays_equal(mergeddata23, targdata23) assert_arrays_equal(mergeddata24, targdata24) assert_arrays_equal(merged13.channel_indexes, np.arange(5, 11)) assert_arrays_equal(merged23.channel_indexes, np.arange(11)) assert_arrays_equal(merged24.channel_indexes, np.arange(5)) class TestAnalogSignalArrayFunctions(unittest.TestCase): def test__pickle(self): signal1 = AnalogSignalArray(np.arange(55.0).reshape((11, 5)), units="mV", sampling_rate=1*pq.kHz, channel_index=np.arange(5)) fobj = open('./pickle', 'wb') pickle.dump(signal1, fobj) fobj.close() fobj = open('./pickle', 'rb') try: signal2 = pickle.load(fobj) except ValueError: signal2 = None assert_arrays_equal(signal1, signal2) assert_neo_object_is_compliant(signal1) assert_neo_object_is_compliant(signal2) self.assertEqual(list(signal1.channel_indexes), [0, 1, 2, 3, 4]) self.assertEqual(list(signal1.channel_indexes), list(signal2.channel_indexes)) fobj.close() os.remove('./pickle') if __name__ == "__main__": unittest.main()

bsd-3-clause

ageron/tensorflow

tensorflow/tools/docs/generate_lib.py

16

23300

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Generate docs for the TensorFlow Python API.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import fnmatch import os import shutil import tempfile import six from tensorflow.python.util import tf_inspect from tensorflow.tools.common import public_api from tensorflow.tools.common import traverse from tensorflow.tools.docs import doc_controls from tensorflow.tools.docs import doc_generator_visitor from tensorflow.tools.docs import parser from tensorflow.tools.docs import pretty_docs from tensorflow.tools.docs import py_guide_parser def write_docs(output_dir, parser_config, yaml_toc, root_title='TensorFlow', search_hints=True, site_api_path=''): """Write previously extracted docs to disk. Write a docs page for each symbol included in the indices of parser_config to a tree of docs at `output_dir`. Symbols with multiple aliases will have only one page written about them, which is referenced for all aliases. Args: output_dir: Directory to write documentation markdown files to. Will be created if it doesn't exist. parser_config: A `parser.ParserConfig` object, containing all the necessary indices. yaml_toc: Set to `True` to generate a "_toc.yaml" file. root_title: The title name for the root level index.md. search_hints: (bool) include meta-data search hints at the top of each output file. site_api_path: The output path relative to the site root. Used in the `_toc.yaml` and `_redirects.yaml` files. Raises: ValueError: if `output_dir` is not an absolute path """ # Make output_dir. if not os.path.isabs(output_dir): raise ValueError("'output_dir' must be an absolute path.\n" " output_dir='%s'" % output_dir) if not os.path.exists(output_dir): os.makedirs(output_dir) # These dictionaries are used for table-of-contents generation below # They will contain, after the for-loop below:: # - module name(string):classes and functions the module contains(list) module_children = {} # - symbol name(string):pathname (string) symbol_to_file = {} # Collect redirects for an api _redirects.yaml file. redirects = [] # Parse and write Markdown pages, resolving cross-links (@{symbol}). for full_name, py_object in six.iteritems(parser_config.index): parser_config.reference_resolver.current_doc_full_name = full_name if full_name in parser_config.duplicate_of: continue # Methods and some routines are documented only as part of their class. if not (tf_inspect.ismodule(py_object) or tf_inspect.isclass(py_object) or parser.is_free_function(py_object, full_name, parser_config.index)): continue sitepath = os.path.join('api_docs/python', parser.documentation_path(full_name)[:-3]) # For TOC, we need to store a mapping from full_name to the file # we're generating symbol_to_file[full_name] = sitepath # For a module, remember the module for the table-of-contents if tf_inspect.ismodule(py_object): if full_name in parser_config.tree: module_children.setdefault(full_name, []) # For something else that's documented, # figure out what module it lives in else: subname = str(full_name) while True: subname = subname[:subname.rindex('.')] if tf_inspect.ismodule(parser_config.index[subname]): module_children.setdefault(subname, []).append(full_name) break # Generate docs for `py_object`, resolving references. page_info = parser.docs_for_object(full_name, py_object, parser_config) path = os.path.join(output_dir, parser.documentation_path(full_name)) directory = os.path.dirname(path) try: if not os.path.exists(directory): os.makedirs(directory) # This function returns raw bytes in PY2 or unicode in PY3. if search_hints: content = [page_info.get_metadata_html()] else: content = [''] content.append(pretty_docs.build_md_page(page_info)) text = '\n'.join(content) if six.PY3: text = text.encode('utf-8') with open(path, 'wb') as f: f.write(text) except OSError: raise OSError( 'Cannot write documentation for %s to %s' % (full_name, directory)) duplicates = parser_config.duplicates.get(full_name, []) if not duplicates: continue duplicates = [item for item in duplicates if item != full_name] for dup in duplicates: from_path = os.path.join(site_api_path, dup.replace('.', '/')) to_path = os.path.join(site_api_path, full_name.replace('.', '/')) redirects.append(( os.path.join('/', from_path), os.path.join('/', to_path))) if redirects: redirects = sorted(redirects) template = ('- from: {}\n' ' to: {}\n') redirects = [template.format(f, t) for f, t in redirects] api_redirects_path = os.path.join(output_dir, '_redirects.yaml') with open(api_redirects_path, 'w') as redirect_file: redirect_file.write('redirects:\n') redirect_file.write(''.join(redirects)) if yaml_toc: # Generate table of contents # Put modules in alphabetical order, case-insensitive modules = sorted(module_children.keys(), key=lambda a: a.upper()) leftnav_path = os.path.join(output_dir, '_toc.yaml') with open(leftnav_path, 'w') as f: # Generate header f.write('# Automatically generated file; please do not edit\ntoc:\n') for module in modules: indent_num = module.count('.') # Don't list `tf.submodule` inside `tf` indent_num = max(indent_num, 1) indent = ' '*indent_num if indent_num > 1: # tf.contrib.baysflow.entropy will be under # tf.contrib->baysflow->entropy title = module.split('.')[-1] else: title = module header = [ '- title: ' + title, ' section:', ' - title: Overview', ' path: ' + os.path.join('/', site_api_path, symbol_to_file[module])] header = ''.join([indent+line+'\n' for line in header]) f.write(header) symbols_in_module = module_children.get(module, []) # Sort case-insensitive, if equal sort case sensitive (upper first) symbols_in_module.sort(key=lambda a: (a.upper(), a)) for full_name in symbols_in_module: item = [ ' - title: ' + full_name[len(module) + 1:], ' path: ' + os.path.join('/', site_api_path, symbol_to_file[full_name])] item = ''.join([indent+line+'\n' for line in item]) f.write(item) # Write a global index containing all full names with links. with open(os.path.join(output_dir, 'index.md'), 'w') as f: f.write( parser.generate_global_index(root_title, parser_config.index, parser_config.reference_resolver)) def add_dict_to_dict(add_from, add_to): for key in add_from: if key in add_to: add_to[key].extend(add_from[key]) else: add_to[key] = add_from[key] # Exclude some libraries in contrib from the documentation altogether. def _get_default_private_map(): return { 'tf.contrib.autograph': ['utils', 'operators'], 'tf.test': ['mock'], 'tf.compat': ['v1', 'v2'], 'tf.contrib.estimator': ['python'], } # Exclude members of some libraries. def _get_default_do_not_descend_map(): # TODO(markdaoust): Use docs_controls decorators, locally, instead. return { 'tf': ['cli', 'lib', 'wrappers'], 'tf.contrib': [ 'compiler', 'grid_rnn', # Block contrib.keras to de-clutter the docs 'keras', 'labeled_tensor', 'quantization', 'session_bundle', 'slim', 'solvers', 'specs', 'tensor_forest', 'tensorboard', 'testing', 'tfprof', ], 'tf.contrib.bayesflow': [ 'special_math', 'stochastic_gradient_estimators', 'stochastic_variables' ], 'tf.contrib.ffmpeg': ['ffmpeg_ops'], 'tf.contrib.graph_editor': [ 'edit', 'match', 'reroute', 'subgraph', 'transform', 'select', 'util' ], 'tf.contrib.keras': ['api', 'python'], 'tf.contrib.layers': ['feature_column', 'summaries'], 'tf.contrib.learn': [ 'datasets', 'head', 'graph_actions', 'io', 'models', 'monitors', 'ops', 'preprocessing', 'utils', ], 'tf.contrib.util': ['loader'], } class DocControlsAwareCrawler(public_api.PublicAPIVisitor): """A `docs_controls` aware API-crawler.""" def _is_private(self, path, name, obj): if doc_controls.should_skip(obj): return True return super(DocControlsAwareCrawler, self)._is_private(path, name, obj) def extract(py_modules, private_map, do_not_descend_map, visitor_cls=doc_generator_visitor.DocGeneratorVisitor): """Extract docs from tf namespace and write them to disk.""" # Traverse the first module. visitor = visitor_cls(py_modules[0][0]) api_visitor = DocControlsAwareCrawler(visitor) api_visitor.set_root_name(py_modules[0][0]) add_dict_to_dict(private_map, api_visitor.private_map) add_dict_to_dict(do_not_descend_map, api_visitor.do_not_descend_map) traverse.traverse(py_modules[0][1], api_visitor) # Traverse all py_modules after the first: for module_name, module in py_modules[1:]: visitor.set_root_name(module_name) api_visitor.set_root_name(module_name) traverse.traverse(module, api_visitor) return visitor class _GetMarkdownTitle(py_guide_parser.PyGuideParser): """Extract the title from a .md file.""" def __init__(self): self.title = None py_guide_parser.PyGuideParser.__init__(self) def process_title(self, _, title): if self.title is None: # only use the first title self.title = title class _DocInfo(object): """A simple struct for holding a doc's url and title.""" def __init__(self, url, title): self.url = url self.title = title def build_doc_index(src_dir): """Build an index from a keyword designating a doc to _DocInfo objects.""" doc_index = {} if not os.path.isabs(src_dir): raise ValueError("'src_dir' must be an absolute path.\n" " src_dir='%s'" % src_dir) if not os.path.exists(src_dir): raise ValueError("'src_dir' path must exist.\n" " src_dir='%s'" % src_dir) for dirpath, _, filenames in os.walk(src_dir): suffix = os.path.relpath(path=dirpath, start=src_dir) for base_name in filenames: if not base_name.endswith('.md'): continue title_parser = _GetMarkdownTitle() title_parser.process(os.path.join(dirpath, base_name)) if title_parser.title is None: msg = ('`{}` has no markdown title (# title)'.format( os.path.join(dirpath, base_name))) raise ValueError(msg) key_parts = os.path.join(suffix, base_name[:-3]).split('/') if key_parts[-1] == 'index': key_parts = key_parts[:-1] doc_info = _DocInfo(os.path.join(suffix, base_name), title_parser.title) doc_index[key_parts[-1]] = doc_info if len(key_parts) > 1: doc_index['/'.join(key_parts[-2:])] = doc_info return doc_index class _GuideRef(object): def __init__(self, base_name, title, section_title, section_tag): self.url = 'api_guides/python/' + (('%s#%s' % (base_name, section_tag)) if section_tag else base_name) self.link_text = (('%s > %s' % (title, section_title)) if section_title else title) def make_md_link(self, url_prefix): return '[%s](%s%s)' % (self.link_text, url_prefix, self.url) class _GenerateGuideIndex(py_guide_parser.PyGuideParser): """Turn guide files into an index from symbol name to a list of _GuideRefs.""" def __init__(self): self.index = {} py_guide_parser.PyGuideParser.__init__(self) def process(self, full_path, base_name): """Index a file, reading from `full_path`, with `base_name` as the link.""" self.full_path = full_path self.base_name = base_name self.title = None self.section_title = None self.section_tag = None py_guide_parser.PyGuideParser.process(self, full_path) def process_title(self, _, title): if self.title is None: # only use the first title self.title = title def process_section(self, _, section_title, tag): self.section_title = section_title self.section_tag = tag def process_line(self, _, line): """Index the file and section of each `symbol` reference.""" for match in parser.AUTO_REFERENCE_RE.finditer(line): val = self.index.get(match.group(1), []) val.append( _GuideRef(self.base_name, self.title, self.section_title, self.section_tag)) self.index[match.group(1)] = val def _build_guide_index(guide_src_dir): """Return dict: symbol name -> _GuideRef from the files in `guide_src_dir`.""" index_generator = _GenerateGuideIndex() if os.path.exists(guide_src_dir): for full_path, base_name in py_guide_parser.md_files_in_dir(guide_src_dir): index_generator.process(full_path, base_name) return index_generator.index class _UpdateTags(py_guide_parser.PyGuideParser): """Rewrites a Python guide so that each section has an explicit id tag. "section" here refers to blocks delimited by second level headings. """ def process_section(self, line_number, section_title, tag): self.replace_line(line_number, '<h2 id="%s">%s</h2>' % (tag, section_title)) def update_id_tags_inplace(src_dir): """Set explicit ids on all second-level headings to ensure back-links work. Args: src_dir: The directory of md-files to convert (inplace). """ tag_updater = _UpdateTags() for dirpath, _, filenames in os.walk(src_dir): for base_name in filenames: if not base_name.endswith('.md'): continue full_path = os.path.join(src_dir, dirpath, base_name) # Tag updater loads the file, makes the replacements, and returns the # modified file contents content = tag_updater.process(full_path) with open(full_path, 'w') as f: f.write(content) EXCLUDED = set(['__init__.py', 'OWNERS', 'README.txt']) def replace_refs(src_dir, output_dir, reference_resolver, file_pattern='*.md', api_docs_relpath='api_docs'): """Fix @{} references in all files under `src_dir` matching `file_pattern`. A matching directory structure, with the modified files is written to `output_dir`. `{"__init__.py","OWNERS","README.txt"}` are skipped. Files not matching `file_pattern` (using `fnmatch`) are copied with no change. Also, files in the `api_guides/python` directory get explicit ids set on all heading-2s to ensure back-links work. Args: src_dir: The directory to convert files from. output_dir: The root directory to write the resulting files to. reference_resolver: A `parser.ReferenceResolver` to make the replacements. file_pattern: Only replace references in files matching file_patters, using fnmatch. Non-matching files are copied unchanged. api_docs_relpath: Relative-path string to the api_docs, from the src_dir. """ # Iterate through all the source files and process them. for dirpath, _, filenames in os.walk(src_dir): depth = os.path.relpath(src_dir, start=dirpath) # How to get from `dirpath` to api_docs/python/ relative_path_to_root = os.path.join(depth, api_docs_relpath, 'python') # Make the directory under output_dir. new_dir = os.path.join(output_dir, os.path.relpath(path=dirpath, start=src_dir)) if not os.path.exists(new_dir): os.makedirs(new_dir) for base_name in filenames: if base_name in EXCLUDED: continue full_in_path = os.path.join(dirpath, base_name) # Set the `current_doc_full_name` so bad files can be reported on errors. reference_resolver.current_doc_full_name = full_in_path suffix = os.path.relpath(path=full_in_path, start=src_dir) full_out_path = os.path.join(output_dir, suffix) # Copy files that do not match the file_pattern, unmodified. if not fnmatch.fnmatch(base_name, file_pattern): if full_in_path != full_out_path: shutil.copyfile(full_in_path, full_out_path) continue with open(full_in_path, 'rb') as f: content = f.read().decode('utf-8') content = reference_resolver.replace_references(content, relative_path_to_root) with open(full_out_path, 'wb') as f: f.write(content.encode('utf-8')) class DocGenerator(object): """Main entry point for generating docs.""" def __init__(self): self.argument_parser = argparse.ArgumentParser() self._py_modules = None self._private_map = _get_default_private_map() self._do_not_descend_map = _get_default_do_not_descend_map() self.yaml_toc = True self.argument_parser.add_argument( '--no_search_hints', dest='search_hints', action='store_false', default=True) self.argument_parser.add_argument( '--site_api_path', type=str, default='', help='The path from the site-root to api_docs' 'directory for this project') self.argument_parser.add_argument( '--api_cache_out_path', type=str, default=None, help='Path to store a json-serialized api-index, so links can be ' 'inserted into docs without rebuilding the api_docs') def add_output_dir_argument(self): self.argument_parser.add_argument( '--output_dir', type=str, default=None, required=True, help='Directory to write docs to.') def add_src_dir_argument(self): self.argument_parser.add_argument( '--src_dir', type=str, default=tempfile.mkdtemp(), required=False, help='Optional directory of source docs to add api_docs links to') def add_base_dir_argument(self, default_base_dir): self.argument_parser.add_argument( '--base_dir', type=str, default=default_base_dir, help='Base directory to strip from file names referenced in docs.') def parse_known_args(self): flags, _ = self.argument_parser.parse_known_args() return flags def add_to_private_map(self, d): add_dict_to_dict(d, self._private_map) def add_to_do_not_descend_map(self, d): add_dict_to_dict(d, self._do_not_descend_map) def set_private_map(self, d): self._private_map = d def set_do_not_descend_map(self, d): self._do_not_descend_map = d def set_py_modules(self, py_modules): self._py_modules = py_modules def py_module_names(self): if self._py_modules is None: raise RuntimeError( 'Must call set_py_modules() before running py_module_names().') return [name for (name, _) in self._py_modules] def make_reference_resolver(self, visitor, doc_index): return parser.ReferenceResolver.from_visitor( visitor, doc_index, py_module_names=self.py_module_names()) def make_parser_config(self, visitor, reference_resolver, guide_index, base_dir): return parser.ParserConfig( reference_resolver=reference_resolver, duplicates=visitor.duplicates, duplicate_of=visitor.duplicate_of, tree=visitor.tree, index=visitor.index, reverse_index=visitor.reverse_index, guide_index=guide_index, base_dir=base_dir) def run_extraction(self): return extract(self._py_modules, self._private_map, self._do_not_descend_map) def build(self, flags): """Build all the docs. This produces two outputs python api docs: * generated from modules set with `set_py_modules`. * written to '{FLAGS.output_dir}/api_docs/python/' non-api docs: * Everything in '{FLAGS.src_dir}' is copied to '{FLAGS.output_dir}'. * '@{}' references in '.md' files are replaced with links. * '.md' files under 'api_guides/python' have explicit ids set for their second level headings. Args: flags: * src_dir: Where to fetch the non-api-docs. * base_dir: Base of the docs directory (Used to build correct relative links). * output_dir: Where to write the resulting docs. Returns: The number of errors encountered while processing. """ # Extract the python api from the _py_modules doc_index = build_doc_index(flags.src_dir) visitor = self.run_extraction() reference_resolver = self.make_reference_resolver(visitor, doc_index) if getattr(flags, 'api_cache_out_path', None): reference_resolver.to_json_file(flags.api_cache_out_path) # Build the guide_index for the api_docs back links. root_title = getattr(flags, 'root_title', 'TensorFlow') guide_index = _build_guide_index( os.path.join(flags.src_dir, 'api_guides/python')) # Write the api docs. parser_config = self.make_parser_config(visitor, reference_resolver, guide_index, flags.base_dir) output_dir = os.path.join(flags.output_dir, 'api_docs/python') write_docs( output_dir, parser_config, yaml_toc=self.yaml_toc, root_title=root_title, search_hints=getattr(flags, 'search_hints', True), site_api_path=getattr(flags, 'site_api_path', '')) # Replace all the @{} references in files under `FLAGS.src_dir` replace_refs(flags.src_dir, flags.output_dir, reference_resolver, '*.md') # Fix the tags in the guide dir. guide_dir = os.path.join(flags.output_dir, 'api_guides/python') if os.path.exists(guide_dir): update_id_tags_inplace(guide_dir) # Report all errors found by the reference resolver, and return the error # code. parser_config.reference_resolver.log_errors() return parser_config.reference_resolver.num_errors()

apache-2.0

mxjl620/scikit-learn

sklearn/tests/test_discriminant_analysis.py

35

11709

try: # Python 2 compat reload except NameError: # Regular Python 3+ import from importlib import reload import numpy as np from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_raises from sklearn.utils.testing import assert_raise_message from sklearn.utils.testing import assert_warns from sklearn.utils.testing import assert_greater from sklearn.utils.testing import ignore_warnings from sklearn.datasets import make_blobs from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis # Data is just 6 separable points in the plane X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]], dtype='f') y = np.array([1, 1, 1, 2, 2, 2]) y3 = np.array([1, 1, 2, 2, 3, 3]) # Degenerate data with only one feature (still should be separable) X1 = np.array([[-2, ], [-1, ], [-1, ], [1, ], [1, ], [2, ]], dtype='f') # Data is just 9 separable points in the plane X6 = np.array([[0, 0], [-2, -2], [-2, -1], [-1, -1], [-1, -2], [1, 3], [1, 2], [2, 1], [2, 2]]) y6 = np.array([1, 1, 1, 1, 1, 2, 2, 2, 2]) y7 = np.array([1, 2, 3, 2, 3, 1, 2, 3, 1]) # Degenerate data with 1 feature (still should be separable) X7 = np.array([[-3, ], [-2, ], [-1, ], [-1, ], [0, ], [1, ], [1, ], [2, ], [3, ]]) # Data that has zero variance in one dimension and needs regularization X2 = np.array([[-3, 0], [-2, 0], [-1, 0], [-1, 0], [0, 0], [1, 0], [1, 0], [2, 0], [3, 0]]) # One element class y4 = np.array([1, 1, 1, 1, 1, 1, 1, 1, 2]) # Data with less samples in a class than n_features X5 = np.c_[np.arange(8), np.zeros((8, 3))] y5 = np.array([0, 0, 0, 0, 0, 1, 1, 1]) solver_shrinkage = [('svd', None), ('lsqr', None), ('eigen', None), ('lsqr', 'auto'), ('lsqr', 0), ('lsqr', 0.43), ('eigen', 'auto'), ('eigen', 0), ('eigen', 0.43)] def test_lda_predict(): # Test LDA classification. # This checks that LDA implements fit and predict and returns correct # values for simple toy data. for test_case in solver_shrinkage: solver, shrinkage = test_case clf = LinearDiscriminantAnalysis(solver=solver, shrinkage=shrinkage) y_pred = clf.fit(X, y).predict(X) assert_array_equal(y_pred, y, 'solver %s' % solver) # Assert that it works with 1D data y_pred1 = clf.fit(X1, y).predict(X1) assert_array_equal(y_pred1, y, 'solver %s' % solver) # Test probability estimates y_proba_pred1 = clf.predict_proba(X1) assert_array_equal((y_proba_pred1[:, 1] > 0.5) + 1, y, 'solver %s' % solver) y_log_proba_pred1 = clf.predict_log_proba(X1) assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1, 8, 'solver %s' % solver) # Primarily test for commit 2f34950 -- "reuse" of priors y_pred3 = clf.fit(X, y3).predict(X) # LDA shouldn't be able to separate those assert_true(np.any(y_pred3 != y3), 'solver %s' % solver) # Test invalid shrinkages clf = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=-0.2231) assert_raises(ValueError, clf.fit, X, y) clf = LinearDiscriminantAnalysis(solver="eigen", shrinkage="dummy") assert_raises(ValueError, clf.fit, X, y) clf = LinearDiscriminantAnalysis(solver="svd", shrinkage="auto") assert_raises(NotImplementedError, clf.fit, X, y) # Test unknown solver clf = LinearDiscriminantAnalysis(solver="dummy") assert_raises(ValueError, clf.fit, X, y) def test_lda_priors(): # Test priors (negative priors) priors = np.array([0.5, -0.5]) clf = LinearDiscriminantAnalysis(priors=priors) msg = "priors must be non-negative" assert_raise_message(ValueError, msg, clf.fit, X, y) # Test that priors passed as a list are correctly handled (run to see if # failure) clf = LinearDiscriminantAnalysis(priors=[0.5, 0.5]) clf.fit(X, y) # Test that priors always sum to 1 priors = np.array([0.5, 0.6]) prior_norm = np.array([0.45, 0.55]) clf = LinearDiscriminantAnalysis(priors=priors) clf.fit(X, y) assert_array_almost_equal(clf.priors_, prior_norm, 2) def test_lda_coefs(): # Test if the coefficients of the solvers are approximately the same. n_features = 2 n_classes = 2 n_samples = 1000 X, y = make_blobs(n_samples=n_samples, n_features=n_features, centers=n_classes, random_state=11) clf_lda_svd = LinearDiscriminantAnalysis(solver="svd") clf_lda_lsqr = LinearDiscriminantAnalysis(solver="lsqr") clf_lda_eigen = LinearDiscriminantAnalysis(solver="eigen") clf_lda_svd.fit(X, y) clf_lda_lsqr.fit(X, y) clf_lda_eigen.fit(X, y) assert_array_almost_equal(clf_lda_svd.coef_, clf_lda_lsqr.coef_, 1) assert_array_almost_equal(clf_lda_svd.coef_, clf_lda_eigen.coef_, 1) assert_array_almost_equal(clf_lda_eigen.coef_, clf_lda_lsqr.coef_, 1) def test_lda_transform(): # Test LDA transform. clf = LinearDiscriminantAnalysis(solver="svd", n_components=1) X_transformed = clf.fit(X, y).transform(X) assert_equal(X_transformed.shape[1], 1) clf = LinearDiscriminantAnalysis(solver="eigen", n_components=1) X_transformed = clf.fit(X, y).transform(X) assert_equal(X_transformed.shape[1], 1) clf = LinearDiscriminantAnalysis(solver="lsqr", n_components=1) clf.fit(X, y) msg = "transform not implemented for 'lsqr'" assert_raise_message(NotImplementedError, msg, clf.transform, X) def test_lda_explained_variance_ratio(): # Test if the sum of the normalized eigen vectors values equals 1 n_features = 2 n_classes = 2 n_samples = 1000 X, y = make_blobs(n_samples=n_samples, n_features=n_features, centers=n_classes, random_state=11) clf_lda_eigen = LinearDiscriminantAnalysis(solver="eigen") clf_lda_eigen.fit(X, y) assert_almost_equal(clf_lda_eigen.explained_variance_ratio_.sum(), 1.0, 3) def test_lda_orthogonality(): # arrange four classes with their means in a kite-shaped pattern # the longer distance should be transformed to the first component, and # the shorter distance to the second component. means = np.array([[0, 0, -1], [0, 2, 0], [0, -2, 0], [0, 0, 5]]) # We construct perfectly symmetric distributions, so the LDA can estimate # precise means. scatter = np.array([[0.1, 0, 0], [-0.1, 0, 0], [0, 0.1, 0], [0, -0.1, 0], [0, 0, 0.1], [0, 0, -0.1]]) X = (means[:, np.newaxis, :] + scatter[np.newaxis, :, :]).reshape((-1, 3)) y = np.repeat(np.arange(means.shape[0]), scatter.shape[0]) # Fit LDA and transform the means clf = LinearDiscriminantAnalysis(solver="svd").fit(X, y) means_transformed = clf.transform(means) d1 = means_transformed[3] - means_transformed[0] d2 = means_transformed[2] - means_transformed[1] d1 /= np.sqrt(np.sum(d1 ** 2)) d2 /= np.sqrt(np.sum(d2 ** 2)) # the transformed within-class covariance should be the identity matrix assert_almost_equal(np.cov(clf.transform(scatter).T), np.eye(2)) # the means of classes 0 and 3 should lie on the first component assert_almost_equal(np.abs(np.dot(d1[:2], [1, 0])), 1.0) # the means of classes 1 and 2 should lie on the second component assert_almost_equal(np.abs(np.dot(d2[:2], [0, 1])), 1.0) def test_lda_scaling(): # Test if classification works correctly with differently scaled features. n = 100 rng = np.random.RandomState(1234) # use uniform distribution of features to make sure there is absolutely no # overlap between classes. x1 = rng.uniform(-1, 1, (n, 3)) + [-10, 0, 0] x2 = rng.uniform(-1, 1, (n, 3)) + [10, 0, 0] x = np.vstack((x1, x2)) * [1, 100, 10000] y = [-1] * n + [1] * n for solver in ('svd', 'lsqr', 'eigen'): clf = LinearDiscriminantAnalysis(solver=solver) # should be able to separate the data perfectly assert_equal(clf.fit(x, y).score(x, y), 1.0, 'using covariance: %s' % solver) def test_qda(): # QDA classification. # This checks that QDA implements fit and predict and returns # correct values for a simple toy dataset. clf = QuadraticDiscriminantAnalysis() y_pred = clf.fit(X6, y6).predict(X6) assert_array_equal(y_pred, y6) # Assure that it works with 1D data y_pred1 = clf.fit(X7, y6).predict(X7) assert_array_equal(y_pred1, y6) # Test probas estimates y_proba_pred1 = clf.predict_proba(X7) assert_array_equal((y_proba_pred1[:, 1] > 0.5) + 1, y6) y_log_proba_pred1 = clf.predict_log_proba(X7) assert_array_almost_equal(np.exp(y_log_proba_pred1), y_proba_pred1, 8) y_pred3 = clf.fit(X6, y7).predict(X6) # QDA shouldn't be able to separate those assert_true(np.any(y_pred3 != y7)) # Classes should have at least 2 elements assert_raises(ValueError, clf.fit, X6, y4) def test_qda_priors(): clf = QuadraticDiscriminantAnalysis() y_pred = clf.fit(X6, y6).predict(X6) n_pos = np.sum(y_pred == 2) neg = 1e-10 clf = QuadraticDiscriminantAnalysis(priors=np.array([neg, 1 - neg])) y_pred = clf.fit(X6, y6).predict(X6) n_pos2 = np.sum(y_pred == 2) assert_greater(n_pos2, n_pos) def test_qda_store_covariances(): # The default is to not set the covariances_ attribute clf = QuadraticDiscriminantAnalysis().fit(X6, y6) assert_true(not hasattr(clf, 'covariances_')) # Test the actual attribute: clf = QuadraticDiscriminantAnalysis(store_covariances=True).fit(X6, y6) assert_true(hasattr(clf, 'covariances_')) assert_array_almost_equal( clf.covariances_[0], np.array([[0.7, 0.45], [0.45, 0.7]]) ) assert_array_almost_equal( clf.covariances_[1], np.array([[0.33333333, -0.33333333], [-0.33333333, 0.66666667]]) ) def test_qda_regularization(): # the default is reg_param=0. and will cause issues # when there is a constant variable clf = QuadraticDiscriminantAnalysis() with ignore_warnings(): y_pred = clf.fit(X2, y6).predict(X2) assert_true(np.any(y_pred != y6)) # adding a little regularization fixes the problem clf = QuadraticDiscriminantAnalysis(reg_param=0.01) with ignore_warnings(): clf.fit(X2, y6) y_pred = clf.predict(X2) assert_array_equal(y_pred, y6) # Case n_samples_in_a_class < n_features clf = QuadraticDiscriminantAnalysis(reg_param=0.1) with ignore_warnings(): clf.fit(X5, y5) y_pred5 = clf.predict(X5) assert_array_equal(y_pred5, y5) def test_deprecated_lda_qda_deprecation(): def import_lda_module(): import sklearn.lda # ensure that we trigger DeprecationWarning even if the sklearn.lda # was loaded previously by another test. reload(sklearn.lda) return sklearn.lda lda = assert_warns(DeprecationWarning, import_lda_module) assert lda.LDA is LinearDiscriminantAnalysis def import_qda_module(): import sklearn.qda # ensure that we trigger DeprecationWarning even if the sklearn.qda # was loaded previously by another test. reload(sklearn.qda) return sklearn.qda qda = assert_warns(DeprecationWarning, import_qda_module) assert qda.QDA is QuadraticDiscriminantAnalysis

bsd-3-clause

ageron/tensorflow

tensorflow/contrib/tpu/python/tpu/keras_support.py

1

87984

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """*Experimental* support for running Keras models on the TPU. To use, wrap your model with the `keras_support.tpu_model` function. Example usage: ``` image = tf.keras.layers.Input(shape=(28, 28, 3), name='image') c1 = tf.keras.layers.Conv2D(filters=16, kernel_size=(3, 3))( image) flattened = tf.keras.layers.Flatten()(c1) logits = tf.keras.layers.Dense(10, activation='softmax')(flattened) model = tf.keras.Model(inputs=[image], outputs=[logits]) resolver = tf.contrib.cluster_resolver.TPUClusterResolver(tpu=tpu_name) strategy = keras_support.TPUDistributionStrategy(resolver) model = keras_support.tpu_model(model, strategy=strategy) # Only TF optimizers are currently supported. model.compile(optimizer=tf.train.AdamOptimizer(), ...) # `images` and `labels` should be Numpy arrays. Support for tensor input # (e.g. datasets) is planned. model.fit(images, labels) ``` """ # pylint: disable=protected-access from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import collections import contextlib import re import sys import time import numpy as np import six from tensorflow.contrib.cluster_resolver.python.training import tpu_cluster_resolver as tpu_cluster_resolver_lib from tensorflow.contrib.tpu.python.ops import tpu_ops from tensorflow.contrib.tpu.python.tpu import keras_tpu_variables from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.contrib.tpu.python.tpu import tpu_function from tensorflow.contrib.tpu.python.tpu import tpu_optimizer from tensorflow.contrib.tpu.python.tpu import tpu_system_metadata as tpu_system_metadata_lib from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf.tpu import compilation_result_pb2 as tpu_compilation_result from tensorflow.python import tf2 from tensorflow.python.client import session as tf_session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_spec from tensorflow.python.keras import backend as K from tensorflow.python.keras import callbacks as cbks from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras import models from tensorflow.python.keras import optimizers as keras_optimizers from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.engine import training_arrays from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.layers import embeddings from tensorflow.python.keras.utils.generic_utils import make_batches from tensorflow.python.keras.utils.generic_utils import slice_arrays from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util.deprecation import deprecated # TODO(b/114775106): temporary shim to optionally initialize the TPU # This increases the odds our session is initialized, but shouldn't be needed. _TEST_REWRITE_OP = None def _maybe_initialize_tpu(session): """Initialize the TPU if it has not already been initialized.""" global _TEST_REWRITE_OP try: # Try to use cached version to avoid another ground of graph optimization. test_rewrite_op = _TEST_REWRITE_OP if (test_rewrite_op is None or test_rewrite_op[0].graph != ops.get_default_graph()): def test_op(): return constant_op.constant(1) + constant_op.constant(1) test_rewrite_op = tpu.rewrite(test_op) _TEST_REWRITE_OP = test_rewrite_op session.run(test_rewrite_op) except errors.FailedPreconditionError as _: session.run(tpu.initialize_system()) @contextlib.contextmanager def _tpu_session_context(): """Initialize the TPU and cleans cache entries for bad sessions.""" try: _maybe_initialize_tpu(K.get_session()) yield except (errors.FailedPreconditionError, errors.AbortedError) as e: K.clear_session() raise Exception(""" An error occurred connecting or initializing your TPU. The session has been reset. re-run keras_to_tpu_model to create a new session. """ + str(e)) def setup_tpu_session(cluster_resolver): """Construct or return a `tf.Session` connected to the given cluster.""" master = cluster_resolver.master() # Use the existing session if we're already connected to this TPU # N.B K.get_session() is a non-trivial operation, and may fail if the remote # session has been reset. try: default_session = K.get_session() if (default_session._target == master and getattr(default_session, '_tpu_initialized', None)): return except errors.AbortedError as _: # We lost the remote session and need to re-initialize. logging.warning('Lost remote session: creating a new session.') cluster_spec = cluster_resolver.cluster_spec() config = config_pb2.ConfigProto(isolate_session_state=True) if cluster_spec: config.cluster_def.CopyFrom(cluster_spec.as_cluster_def()) tpu_session = tf_session.Session(target=master, config=config) tpu_session.run(tpu.initialize_system()) tpu_session._tpu_initialized = True # N.B. We have to call `K.set_session()` AND set our session as the # TF default. `K.get_session()` surprisingly does not return the value # supplied by K.set_session otherwise. K.set_session(tpu_session) try: from scipy.sparse import issparse # pylint: disable=g-import-not-at-top except ImportError: issparse = None def get_tpu_system_metadata(tpu_cluster_resolver): """Retrieves TPU system metadata given a TPUClusterResolver.""" master = tpu_cluster_resolver.master() # pylint: disable=protected-access cluster_spec = tpu_cluster_resolver.cluster_spec() cluster_def = cluster_spec.as_cluster_def() if cluster_spec else None tpu_system_metadata = ( tpu_system_metadata_lib._query_tpu_system_metadata( master, cluster_def=cluster_def, query_topology=False)) return tpu_system_metadata class TPUDistributionStrategy(object): """The strategy to run Keras model on TPU.""" def __init__(self, tpu_cluster_resolver=None, using_single_core=False): """Construct a TPUDistributionStrategy. Args: tpu_cluster_resolver: Any instance of `TPUClusterResolver`. If None, will create one with '' as master address. using_single_core: Bool. This is the debugging option, which might be removed in future once the model replication functionality is mature enough. If `False` (default behavior), the system automatically finds the best configuration, in terms of number of TPU cores, for the model replication, typically using all available TPU cores. If overwrites as `True`, force the model replication using single core, i.e., no replication. Raises: Exception: No TPU Found on the given worker. """ if tf2.enabled(): raise RuntimeError( 'Keras support is now deprecated in support of TPU Strategy. ' 'Please follow the distribution strategy guide on tensorflow.org ' 'to migrate to the 2.0 supported version.') else: logging.warning( 'Keras support is now deprecated in support of TPU Strategy. ' 'Please follow the distribution strategy guide on tensorflow.org ' 'to migrate to the 2.0 supported version.') if tpu_cluster_resolver is None: tpu_cluster_resolver = tpu_cluster_resolver_lib.TPUClusterResolver('') metadata = get_tpu_system_metadata(tpu_cluster_resolver) self._tpu_metadata = metadata self._tpu_cluster_resolver = tpu_cluster_resolver self._num_cores = 1 if using_single_core else metadata.num_cores # Walk device list to identify TPU worker for enqueue/dequeue operations. worker_re = re.compile('/job:([^/]+)') for device in metadata.devices: if 'TPU:0' in device.name: self._worker_name = worker_re.search(device.name).group(1) return raise Exception('No TPU found on given worker.') def _make_assignment_for_model(self, cpu_model): """Makes a `TPUAssignment` for the passed in `cpu_model`.""" num_cores = self._num_cores if num_cores > 1 and cpu_model.stateful: logging.warning( 'Model replication does not currently support stateful models. ' 'Degrading to a single core.') num_cores = 1 return TPUAssignment(worker_name=self._worker_name, num_cores=num_cores) class TPUAssignment(object): """This is object holding TPU resources assignment for the concrete model. `TPUDistributionStrategy` is responsible to create the instance of `TPUAssignment`, so, it can dynamically adjust the `num_cores` to use based on model and input batch sizes. """ def __init__(self, worker_name, num_cores): self._worker_name = worker_name self._num_cores = num_cores @property def worker_name(self): return self._worker_name @property def num_towers(self): # TODO(xiejw): Support automatically assign num_cores based on inputs. return self._num_cores class TPUEmbedding(embeddings.Embedding): """TPU compatible embedding layer. The default Keras layer is not TPU compatible. This layer is a drop-in replacement: it has the same behavior and will work on CPU and GPU devices. """ def build(self, input_shape): if input_shape[0] is None: raise ValueError( 'TPUEmbeddings must have a fixed input_length or input shape.') return super(TPUEmbedding, self).build(input_shape) def call(self, inputs): if K.dtype(inputs) != 'int32': inputs = math_ops.cast(inputs, 'int32') inputs = array_ops.one_hot(inputs, self.input_dim) return math_ops.tensordot(inputs, self.embeddings, 1) def _cross_replica_concat(tensor, core_id, num_cores, name): """Concatenate `tensor` across cores. Args: tensor: The tensor to be concatenated. Must be [int32 and float32]. core_id: Tensor indicating the current TPU core. num_cores: Python int. The total number of TPU cores in the system. name: The string name to print for debugging. Returns: The same concatenated Tensor on each core. """ input_dtype = tensor.dtype if input_dtype not in [dtypes.bfloat16, dtypes.float32, dtypes.int32]: raise TypeError('For model replication, only (bfloat16, float32 and int32) ' 'is supported for model outputs and targets. Got {} for ' '{}.'.format(input_dtype, name)) batch_size = tensor.shape[0] mask = math_ops.to_float( math_ops.equal(np.arange(num_cores, dtype=np.int32), core_id)) mask = array_ops.reshape(mask, [num_cores] + [1] * tensor.shape.ndims) result = mask * math_ops.to_float(tensor) local_tensor_with_holes = array_ops.reshape(result, [-1] + result.shape.as_list()[2:]) concat_tensor = tpu_ops.cross_replica_sum(local_tensor_with_holes) concat_tensor.set_shape((num_cores * batch_size,) + tuple(tensor.shape[1:])) if concat_tensor != input_dtype: concat_tensor = math_ops.cast(concat_tensor, input_dtype) return concat_tensor class KerasCrossShardOptimizer(keras_optimizers.Optimizer): """An optimizer that averages gradients across TPU shards.""" def __init__(self, opt, name='KerasCrossShardOptimizer'): """Construct a new cross-shard optimizer. Args: opt: An existing `Optimizer` to encapsulate. name: Optional name prefix for the operations created when applying gradients. Defaults to "KerasCrossShardOptimizer". Raises: ValueError: If reduction is not a valid cross-shard reduction. """ super(KerasCrossShardOptimizer, self).__init__() self._name = name self._opt = opt logging.info('KerasCrossShard: %s %s', self._opt, self._opt.weights) def get_updates(self, loss, params): self._opt.get_gradients = self.get_gradients return self._opt.get_updates(loss, params) def get_gradients(self, loss, params): num_shards = tpu_function.get_tpu_context().number_of_shards grads = super(KerasCrossShardOptimizer, self).get_gradients(loss, params) return [tpu_ops.cross_replica_sum(grad) / num_shards for grad in grads] def get_weights(self): return self._opt.get_weights() def get_config(self): return self._opt.get_config() # Defer remaining operations to the underlying optimizer def __getattr__(self, key): return getattr(self._opt, key) class TPUModelOp( collections.namedtuple('TPUModelOp', [ 'compile_op', 'execute_op', 'infeed_tensors', 'infeed_op', 'outfeed_op' ])): pass def _valid_name(tensor_name): """Return a valid tensor name (strips '/', ':', etc).""" return re.sub('[^a-zA-Z0-9_-]+', '', tensor_name) def _replicated_optimizer(opt): """Wrap the optimizer `opt` with CrossShardOptimizer if applicable.""" # Always wrap `opt` with CrossShardOptimizer, even if we are running on a # single core. This ensures Keras properly tracks and initializes optimizer # variables. if isinstance(opt, keras_optimizers.TFOptimizer): return tpu_optimizer.CrossShardOptimizer(opt.optimizer) else: return KerasCrossShardOptimizer(opt) def _clone_optimizer(optimizer, config=None, worker_name=None): """Returns a cloned optimizer with the provided optimizer.config or config.""" if not isinstance(optimizer, keras_optimizers.Optimizer): # In the first call to tpu_model(model), Keras may not have wrapped the TF # optimizer in the TFOptimizer helper, e.g., the given model isn't compiled # or optimizer isn't set, and later generated tpu_model compiles with a TF # optimizer. return optimizer if isinstance(optimizer, keras_optimizers.TFOptimizer): return keras_optimizers.TFOptimizer(optimizer.optimizer) if config is None: config = optimizer.get_config() logging.info('Cloning %s %s', optimizer.__class__.__name__, config) with ops.device( '%s/device:CPU:0' % ('/job:%s' % worker_name if worker_name else '')): # Explicitly put optimizer parameter variables on TPU worker. return optimizer.__class__.from_config(config) class TPURewriteContext(object): """Prepare the environment for a Keras model during `tpu.rewrite`. This overrides the default placeholder behaviour to instead refer to a preset input mapping. Placeholders are unsupported in TPU compiled code, and must be replaced with explicit inputs or values from the infeed queue. Instead of explicitly threading inputs all the way through the Keras codebase, we override the behavior of the placeholder while compiling and inject the Tensors from the infeed in place of the placeholder. Similarly, as we compile a new sub-graph for each unique shape and execution mode, we need to override the behavior of an embedded `name_scope` call in the base Keras layer code. This allows us to re-use the same weights across many compiles and share a single session/graph. """ def __init__(self, input_map): self._input_map = input_map self._default_placeholder = None self._default_name_scope = None def __enter__(self): def _placeholder(dtype, shape=None, name=None): # pylint: disable=unused-argument logging.info('Remapping placeholder for %s', name) if name in self._input_map: return self._input_map[name] else: logging.info('Default: %s', name) return self._default_placeholder(dtype, shape, name) def _name_scope(name, default_name=None, values=None): caller_frame = sys._getframe().f_back caller_obj = caller_frame.f_locals.get('self') if (caller_obj is not None and isinstance(caller_obj, base_layer.Layer) and name is not None): return variable_scope.variable_scope( name, default_name, values, reuse=variable_scope.AUTO_REUSE) return self._default_name_scope(name, default_name, values) self._default_placeholder = array_ops.placeholder self._default_name_scope = ops.name_scope self._default_make_variable = base_layer_utils.make_variable self._default_random_normal = random_ops.random_normal self._default_qr = gen_linalg_ops.qr array_ops.placeholder = _placeholder # Replace random_ops.random_normal with a dummy function because # `random_normal` isn't yet implemented on the TPU. Because these # initialized values are overwritten by the CPU values, this is okay. def random_normal(shape, mean=0.0, stddev=1.0, dtype=dtypes.float32, seed=None, name=None): del mean del stddev del seed return array_ops.zeros(shape, dtype=dtype, name=name) random_ops.random_normal = random_normal # Replace gen_linalg_ops.qr because QR decomposition is not yet implemented. # TODO(saeta): Remove qr override once we confirm the qr implementation is # ok. # pylint: disable=redefined-builtin def qr(input, full_matrices=False, name=None): """Dummy implementation of qr decomposition.""" del full_matrices # TODO(saeta): Properly handle the full matrix case. input_shape = input.shape if len(input_shape) < 2: raise ValueError('Invalid shape passed to qr: %s' % input_shape) p = min(input_shape[-1], input_shape[-2]) if len(input_shape) == 2: q = array_ops.zeros((p, p), name=name) r = array_ops.zeros(input_shape, name=name) return (r, q) elif len(input_shape) == 3: n = input_shape[0] q = array_ops.zeros((n, p, p), name=name) r = array_ops.zeros(input_shape, name=name) return (r, q) else: raise ValueError('Invalid shape passed to qr: %s' % input_shape) gen_linalg_ops.qr = qr ops.name_scope = _name_scope base_layer_utils.make_variable = variable_scope.get_variable logging.info('Overriding default placeholder.') return def __exit__(self, exc_type, exc_val, exc_tb): array_ops.placeholder = self._default_placeholder ops.name_scope = self._default_name_scope base_layer_utils.make_variable = self._default_make_variable random_ops.random_normal = self._default_random_normal gen_linalg_ops.qr = self._default_qr class SizedInfeed( collections.namedtuple('SizedInfeed', ['sharded_infeed_tensors', 'infeed_ops'])): """Represents an instantiation of the infeed ops for a concrete input shape. sharded_infeed_tensors: A data structure of Tensors used to represent the placeholder tensors that must be fed when using feed_dicts. infeed_ops: the set of ops that will be run to drive infeed for a single step. """ pass class TPUInfeedInstance(object): """TPUInfeedInstance represents the logic to manage feeding in a single step. See the comments on the `TPUInfeedManager` for a description for how infeed is managed. """ @abc.abstractmethod def make_input_specs(self, input_tensors): """Constructs the infeed_specs for the given Infeed instance. Args: input_tensors: The inputs to the model. Returns: A list of """ pass def make_feed_dict(self, tpu_model_op): """Constructs a feed_dict for this instance, given the tpu_model_op. Args: tpu_model_op: A `TPUModelOp` representing the TPU Model for this instance's input spec. Returns: A dictionary to use as the feed_dict of a `session.run` call. """ pass @six.add_metaclass(abc.ABCMeta) class TPUInfeedManager(object): """TPUInfeedManager manages the data infeeding of data to a TPU computation. Because there are multiple data sources (e.g. in-memory NumPy arrays, `tf.data.Dataset`s), we abstract the different logic behind a single interface: the `TPUInfeedManager`. (1) A `TPUFunction` is called with a set of inputs. Based on the inputs, `TPUFunction` retrieves the corresponding `TPUInfeedManager` (or constructs a new one if required). (2) The `TPUFunction` calls `make_infeed_instance` on the `TPUInfeedManager` which returns a `TPUInfeedInstance`. (3) The `TPUFunction` checks in the shape cache for a pre-compiled instance of the model based on the returned `input_specs` from `TPUInfeedInstance`. (4) [Optional.] If the model has not already been instantiated for the given input spec, the `TPUFunction` compiles the model for the input spec (using the `TPUInfeedManager`). (5) The `TPUInfeedInstance` constructs the session.run's feed_dict given the compiled model instance corresponding to its shape. """ @abc.abstractmethod def make_infeed_instance(self, inputs): """Given a single step's input, construct a `TPUInfeedInstance`. Args: inputs: The inputs to a given step. Returns: A subclass of `TPUInfeedInstance`. """ pass @abc.abstractmethod def build_infeed_from_input_specs(self, input_specs, execution_mode): """For a given input specification (size, type), construct the infeed ops. This is called only once for a given input specification and builds the graph ops. It does not have a pointer to the actual infeed data. Args: input_specs: TODO(saeta): Document me! execution_mode: TODO(saeta): Document me! Returns: A `SizedInfeed` instance. """ pass class TPUNumpyInfeedManager(TPUInfeedManager): """TPU Infeed manager for Numpy inputs.""" class NumpyInfeedInstance(TPUInfeedInstance): """Infeed instance for Numpy inputs.""" def __init__(self, sharded_inputs): self._sharded_inputs = sharded_inputs def make_input_specs(self, input_tensors): # Compute an input specification (used to generate infeed enqueue and # dequeue operations). We use the shape from our input array and the # dtype from our model. A user may pass in a float64 for a float32 # input: for model compatibility we still must generate a float32 infeed. input_specs = [] # We use the shape and dtype from the first shard to compute the input # metadata (`input_specs`); all replicas have the same type and shape. for tensor, ary in zip(input_tensors, self._sharded_inputs[0]): input_specs.append( tensor_spec.TensorSpec(ary.shape, tensor.dtype, _valid_name(tensor.name))) return input_specs def make_feed_dict(self, tpu_model_op): infeed_dict = {} for infeed_tensors, inputs in zip(tpu_model_op.infeed_tensors, self._sharded_inputs): for tensor, value in zip(infeed_tensors, inputs): infeed_dict[tensor] = value return infeed_dict def __init__(self, tpu_assignment): self._tpu_assignment = tpu_assignment def _split_tensors(self, inputs): """Split input data across shards. Each input is sliced along the batch axis. Args: inputs: List of Numpy arrays to run on the TPU. Returns: List of lists containing the input to feed to each TPU shard. """ if self._tpu_assignment.num_towers == 1: return [inputs] batch_size = inputs[0].shape[0] assert batch_size % self._tpu_assignment.num_towers == 0, ( 'batch_size must be divisible by the number of TPU cores in use (%s ' 'vs %s)' % (batch_size, self._tpu_assignment.num_towers)) shard_size = batch_size // self._tpu_assignment.num_towers input_list = [] for index in range(self._tpu_assignment.num_towers): shard_inputs = [ x[index * shard_size:(index + 1) * shard_size] for x in inputs ] input_list.append(shard_inputs) return input_list def make_infeed_instance(self, inputs): sharded_inputs = self._split_tensors(inputs) return self.NumpyInfeedInstance(sharded_inputs) def build_infeed_from_input_specs(self, input_specs, execution_mode): infeed_op = [] shard_infeed_tensors = [] for shard_id in range(self._tpu_assignment.num_towers): with ops.device( '/job:%s/device:CPU:0' % self._tpu_assignment.worker_name): infeed_tensors = [] with ops.device('/device:TPU:%d' % shard_id): for spec in input_specs: # Construct placeholders for each of the inputs. infeed_tensors.append( array_ops.placeholder( dtype=spec.dtype, shape=spec.shape, name='infeed-enqueue-%s-%d' % (spec.name, shard_id))) shard_infeed_tensors.append(infeed_tensors) infeed_op.append( tpu_ops.infeed_enqueue_tuple( infeed_tensors, [spec.shape for spec in input_specs], name='infeed-enqueue-%s-%d' % (execution_mode, shard_id), device_ordinal=shard_id)) return SizedInfeed( infeed_ops=infeed_op, sharded_infeed_tensors=shard_infeed_tensors) class TPUDatasetInfeedManager(TPUInfeedManager): """Manages infeed for a `tf.data.Dataset` into a TPU computation. """ class DatasetInfeedInstance(TPUInfeedInstance): """An instance of the TPU infeed.""" def __init__(self, input_specs): self._input_specs = input_specs def make_input_specs(self, input_tensors): # TODO(saeta): Do error checking here! return self._input_specs def make_feed_dict(self, tpu_model_op): # TODO(saeta): Verify tpu_model_op is as expected! return {} # pylint: disable=redefined-outer-name def __init__(self, dataset, tpu_assignment, mode): """Constructs a TPUDatasetInfeedManager. Args: dataset: A `tf.data.Dataset` to infeed. tpu_assignment: The `TPUAssignment` used to configure the Keras TPU model. mode: ModeKeys enum. """ self._verify_dataset_shape(dataset) self._dataset = dataset self._tpu_assignment = tpu_assignment dataset_output_shapes = dataset_ops.get_legacy_output_shapes(dataset) dummy_x_shape = dataset_output_shapes[0].as_list() dummy_x_shape[0] *= tpu_assignment.num_towers dummy_y_shape = dataset_output_shapes[1].as_list() dummy_y_shape[0] *= tpu_assignment.num_towers self._iterator = dataset_ops.make_initializable_iterator(dataset) K.get_session().run(self._iterator.initializer) self._get_next_ops = [] ctrl_deps = [] for i in range(tpu_assignment.num_towers): with ops.control_dependencies(ctrl_deps): # Ensure deterministic # TODO(saeta): Ensure correct placement! get_next_op = self._iterator.get_next() self._get_next_ops.append(get_next_op) ctrl_deps.extend(get_next_op) # Use dummy numpy inputs for the rest of Keras' shape checking. We # intercept them when building the model. dataset_output_types = dataset_ops.get_legacy_output_types(dataset) self._dummy_x = np.zeros( dummy_x_shape, dtype=dataset_output_types[0].as_numpy_dtype) self._dummy_y = np.zeros( dummy_y_shape, dtype=dataset_output_types[1].as_numpy_dtype) input_specs = [] iterator_output_shapes = dataset_ops.get_legacy_output_shapes( self._iterator) iterator_output_types = dataset_ops.get_legacy_output_types(self._iterator) if isinstance(iterator_output_shapes, tuple): assert isinstance(iterator_output_types, tuple) assert len(iterator_output_shapes) == len(iterator_output_types) for i in range(len(iterator_output_shapes)): spec = tensor_spec.TensorSpec(iterator_output_shapes[i], iterator_output_types[i]) input_specs.append(spec) elif isinstance(iterator_output_shapes, tensor_shape.TensorShape): spec = tensor_spec.TensorSpec(iterator_output_shapes, iterator_output_types) input_specs.append(spec) # Pre-process the inputs and get_next_ops before caching. input_specs, self._get_next_ops = ( _inject_tpu_inputs_for_dataset( tpu_assignment, mode, input_specs, self._get_next_ops)) self._infeed_instance = self.DatasetInfeedInstance(input_specs) def _verify_dataset_shape(self, dataset): """Verifies a dataset is of an appropriate shape for TPUs.""" dataset_output_shapes = dataset_ops.get_legacy_output_shapes(dataset) dataset_output_classes = dataset_ops.get_legacy_output_classes(dataset) if not isinstance(dataset, dataset_ops.DatasetV2): raise ValueError('The function passed as the `x` parameter did not ' 'return a `tf.data.Dataset`.') if not isinstance(dataset_output_classes, tuple): raise ValueError('The dataset must return a tuple of tf.Tensors, ' 'instead it returns: %s' % dataset_output_classes) if len(dataset_output_classes) != 2: raise ValueError('The dataset must return a 2-element tuple, got ' '%s output classes instead.' % (dataset_output_classes,)) for i, cls in enumerate(dataset_output_classes): if cls != ops.Tensor: raise ValueError('The dataset returned a non-Tensor type (%s) at ' 'index %d.' % (cls, i)) for i, shape in enumerate(dataset_output_shapes): if not shape: raise ValueError('The dataset returns a scalar tensor in ' 'tuple index %d. Did you forget to batch? ' '(Output shapes: %s).' % (i, dataset_output_shapes)) for j, dim in enumerate(shape): if dim.value is None: if j == 0: hint = (' Hint: did you use `ds.batch(BATCH_SIZE, ' 'drop_remainder=True)`?') else: hint = '' raise ValueError( 'The Keras-TPU integration for `tf.data` ' 'currently requires static shapes. The provided ' 'dataset only has a partially defined shape. ' '(Dimension %d of output tensor %d is not statically known ' 'for output shapes: %s.%s)' % (j, i, dataset_output_shapes, hint)) @property def dummy_x(self): return self._dummy_x @property def dummy_y(self): return self._dummy_y def make_infeed_instance(self, inputs): # TODO(saeta): Verify inputs is as expected. return self._infeed_instance def build_infeed_from_input_specs(self, input_specs, execution_mode): shard_infeed_tensors = self._get_next_ops assert len(shard_infeed_tensors) == self._tpu_assignment.num_towers infeed_ops = [] for shard_id in range(self._tpu_assignment.num_towers): with ops.device( '/job:%s/device:CPU:0' % self._tpu_assignment.worker_name): infeed_ops.append( tpu_ops.infeed_enqueue_tuple( shard_infeed_tensors[shard_id], [spec.shape for spec in input_specs], name='infeed-enqueue-%s-%d' % (execution_mode, shard_id), device_ordinal=shard_id)) return SizedInfeed( infeed_ops=infeed_ops, sharded_infeed_tensors=shard_infeed_tensors) def _inject_tpu_inputs_for_dataset(tpu_assignment, mode, input_specs, get_next_ops): """Append core information to the set of dataset inputs.""" # This is used during compilation to identify the current TPU core and enable # concatenation operations across cores. if mode not in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL]: return input_specs, get_next_ops # Dataset inputs operate on per core basis. per_core_batch_size = input_specs[0].shape.as_list()[0] # Insert, at head, the tensor for core_id. assert len(get_next_ops) == tpu_assignment.num_towers for i in range(tpu_assignment.num_towers): core_id_constant = constant_op.constant( np.array([i] * per_core_batch_size).astype('int32'), dtype=dtypes.int32, name='cord_id_constant') get_next_ops[i] = [core_id_constant] + list(get_next_ops[i]) # Insert the input spec at head also. input_specs = [tensor_spec.TensorSpec([per_core_batch_size], dtypes.int32) ] + input_specs return input_specs, get_next_ops def _inject_tpu_inputs_for_infeed(tpu_assignment, mode, core_id_place_holder, input_tensors, inputs): """Append core information to the set of inputs.""" # This is used during compilation to identify the current TPU core and enable # concatenation operations across cores. if mode not in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL]: return input_tensors, inputs # Puts a place holder in input spec. input_tensors = [core_id_place_holder] + input_tensors # Now fill the core id. For `num_cores` = 2, `batch_size` = 8, we fill the # core id inputs as [0, 0, 0, 0, 1, 1, 1, 1], so each core sees its core id # (duplicated). num_cores = tpu_assignment.num_towers per_core_batch_size = inputs[0].shape[0] // num_cores core_ids = np.arange(num_cores).repeat(per_core_batch_size) inputs = [core_ids] + inputs return input_tensors, inputs def _read_tpu_coreid_from_infeed(mode, infeed_tensors): """Popping out the core ids from infeed.""" if mode not in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL]: return None, infeed_tensors if len(infeed_tensors) <= 1: raise RuntimeError( 'The infeed tensors on TPU core has only {} tensors. ' 'This is not expected. Please report a bug.\nTensors: {}'.format( len(infeed_tensors), infeed_tensors)) core_id = infeed_tensors[0][0] # Pop out the scalar version. rest = infeed_tensors[1:] return core_id, rest class TPUFunction(object): """K.function compatible interface for invoking a TPU compiled function. Recompilation is triggered on-demand for each set of new inputs shapes: the results are cached for future execution. We expect most computations will be dominated by a standard batch-size, followed by a straggler batch for the end of training or evaluation. All `inputs` and `outputs` will be loaded via the infeed and outfeed queues instead of being injected as `feed_dict` items or fetches. """ def __init__(self, model, execution_mode, tpu_assignment): self.model = model self.execution_mode = execution_mode self._tpu_assignment = tpu_assignment self._compilation_cache = {} self._cloned_model = None self._cloned_optimizer = None # Create a placeholder for the TPU core ID. Cache the placeholder to avoid # modifying the graph for every batch. self._core_id_place_holder = array_ops.placeholder( dtype=dtypes.int32, shape=[1], name='core_id') def _specialize_model(self, input_specs, infeed_manager): """Specialize `self.model` (a Keras model) for the given input shapes.""" # Re-create our input and output layers inside our subgraph. They will be # attached to the true computation when we clone our model in `tpu_fn`. K.set_learning_phase(self.execution_mode == model_fn_lib.ModeKeys.TRAIN) # functools.partial and callable objects are not supported by tpu.rewrite def _model_fn(): """Compute fit/eval/predict for the TPU.""" is_training = self.execution_mode == model_fn_lib.ModeKeys.TRAIN is_test = self.execution_mode == model_fn_lib.ModeKeys.EVAL is_predict = self.execution_mode == model_fn_lib.ModeKeys.PREDICT # During train/eval, we infeed our features as well as labels. if is_training or is_test: infeed_layers = self.model._input_layers + self.model._output_layers else: infeed_layers = self.model._input_layers # Generate our infeed operation to read features & labels. infeed_tensors = tpu_ops.infeed_dequeue_tuple( dtypes=[spec.dtype for spec in input_specs], shapes=[spec.shape for spec in input_specs], name='infeed-%s' % self.execution_mode) core_id, infeed_tensors = ( _read_tpu_coreid_from_infeed( mode=self.execution_mode, infeed_tensors=infeed_tensors)) assert len(infeed_tensors) == len(infeed_layers), ( 'Infeed inputs did not match model: %s vs %s' % (infeed_layers, infeed_tensors)) tpu_targets = [] tpu_input_map = {} # Sort infeed outputs into inputs and labels for calling our Keras model. for tensor, layer in zip(infeed_tensors, infeed_layers): if layer in self.model._input_layers: tpu_input_map[layer.name] = tensor if layer in self.model._output_layers: tpu_targets.append(tensor) # Clone our CPU model, running within the TPU device context. # # We use the id of the original model as a key to avoid weight collisions # (if a user re-runs the same model multiple times, in e.g. Colab). with TPURewriteContext(tpu_input_map): with variable_scope.variable_scope('tpu_%s' % id(self.model)): with keras_tpu_variables.replicated_scope( self._tpu_assignment.num_towers): if not self._cloned_optimizer: self._cloned_optimizer = _clone_optimizer( self.model.cpu_optimizer, worker_name=self._tpu_assignment.worker_name) self._cloned_model = models.clone_model(self.model) # When running on more than one core, concatenate outputs at the end # of processing. In backprop stage, the gradients will be # calculated according to the local inputs as gradient of # cross-replica-concat being zero for any outputs other than those # from mlocal core so the loss calculation is identical. num_towers = self.model._tpu_assignment.num_towers if num_towers > 1 and (is_training or is_test): new_outputs = [ _cross_replica_concat( o, core_id, num_towers, name='model output ({})'.format(o.name)) for o in self._cloned_model.outputs ] # Recast all low precision outputs back to float32 since we only # casted the inputs to bfloat16 and not targets. This is done so # that we can preserve precision when calculating the loss value. if new_outputs and new_outputs[0].dtype == dtypes.bfloat16: new_outputs = [ math_ops.cast(o, dtypes.float32) for o in new_outputs] self._cloned_model.outputs = new_outputs tpu_targets = [ _cross_replica_concat( tensor, core_id, num_towers, name='model target ({})'.format(tensor.name)) for tensor in tpu_targets ] if is_training or is_test: with variable_scope.variable_scope( 'metrics', reuse=variable_scope.AUTO_REUSE): self._cloned_model.compile( optimizer=_replicated_optimizer(self._cloned_optimizer), loss=self.model.loss, loss_weights=self.model.loss_weights, metrics=metrics_module.clone_metrics( self.model._compile_metrics), weighted_metrics=metrics_module.clone_metrics( self.model._compile_weighted_metrics), target_tensors=tpu_targets, ) # Compute our outfeed depending on the execution mode if is_training: if not isinstance(self._cloned_optimizer, keras_optimizers.TFOptimizer): # For Keras optimizer, we try to place the variable weights on the TPU # device. Keras creates optimizer variables (e.g. momentum values for # the Momentum optimizer) when _make_train_function is invoked. with keras_tpu_variables.replicated_variable_for_optimizer( self._tpu_assignment.num_towers): self._cloned_model._make_fit_function() else: self._cloned_model._make_fit_function() self._outfeed_spec = [ tensor_spec.TensorSpec(tensor.shape, tensor.dtype, tensor.name) for tensor in self._cloned_model._fit_function.outputs ] return [ self._cloned_model._fit_function.updates_op, tpu_ops.outfeed_enqueue_tuple( self._cloned_model._fit_function.outputs, name='outfeed-enqueue-train') ] elif is_test: self._cloned_model._make_eval_function() self._outfeed_spec = [ tensor_spec.TensorSpec(tensor.shape, tensor.dtype, tensor.name) for tensor in self._cloned_model._eval_function.outputs ] return [ tpu_ops.outfeed_enqueue_tuple( self._cloned_model._eval_function.outputs, name='outfeed-enqueue-test') ] elif is_predict: self._cloned_model._make_predict_function() self._outfeed_spec = [ tensor_spec.TensorSpec(tensor.shape, tensor.dtype, tensor.name) for tensor in self._cloned_model.predict_function.outputs ] return [ tpu_ops.outfeed_enqueue_tuple( self._cloned_model.predict_function.outputs, name='outfeed-enqueue-predict', ) ] else: assert False, 'Unexpected execution mode: %s' % self.execution_mode # Capture outfeed metadata computed during the rewrite. self._outfeed_spec = None # Generate out TPU operations using `tpu.split_compile_and_replicate`. # `compile_op` can be used to test the TPU model compiles before execution. # `execute op` replicates `_model_fn` `num_replicas` times, with each shard # running on a different logical core. compile_op, execute_op = tpu.split_compile_and_replicate( _model_fn, inputs=[[] for _ in range(self._tpu_assignment.num_towers)]) # Generate CPU side operations to enqueue features/labels and dequeue # outputs from the model call. sized_infeed = infeed_manager.build_infeed_from_input_specs( input_specs, self.execution_mode) # Build output ops. outfeed_op = [] for shard_id in range(self._tpu_assignment.num_towers): with ops.device( '/job:%s/device:CPU:0' % self._tpu_assignment.worker_name): outfeed_op.extend( tpu_ops.outfeed_dequeue_tuple( dtypes=[spec.dtype for spec in self._outfeed_spec], shapes=[spec.shape for spec in self._outfeed_spec], name='outfeed-dequeue-%s-%d' % (self.execution_mode, shard_id), device_ordinal=shard_id)) return TPUModelOp( compile_op, execute_op, infeed_tensors=sized_infeed.sharded_infeed_tensors, infeed_op=sized_infeed.infeed_ops, outfeed_op=outfeed_op) def _test_model_compiles(self, tpu_model_ops): """Verifies that the given TPUModelOp can be compiled via XLA.""" logging.info('Started compiling') start_time = time.time() result = K.get_session().run(tpu_model_ops.compile_op) proto = tpu_compilation_result.CompilationResultProto() proto.ParseFromString(result) if proto.status_error_message: raise RuntimeError('Compilation failed: {}'.format( proto.status_error_message)) end_time = time.time() logging.info('Finished compiling. Time elapsed: %s secs', end_time - start_time) def _lookup_infeed_manager(self, inputs): """Return an existing manager, or construct a new InfeedManager for inputs. _lookup_infeed_manager will return an existing InfeedManager if one has been previously assigned for this model and input. If not, it will construct a new TPUNumpyInfeedManager. Args: inputs: A NumPy input to the model. Returns: A `TPUInfeedManager` object to manage infeeds for this input. """ if inputs is None: return None for x, mgr in self.model._numpy_to_infeed_manager_list: if inputs[0] is x: return mgr return TPUNumpyInfeedManager(self.model._tpu_assignment) def _tpu_model_ops_for_input_specs(self, input_specs, infeed_manager): """Looks up the corresponding `TPUModelOp` for a given `input_specs`. It instantiates a new copy of the model for each unique input shape. Args: input_specs: The specification of the inputs to train on. infeed_manager: The infeed manager responsible for feeding in data. Returns: A `TPUModelOp` instance that can be used to execute a step of the model. """ if input_specs is None or infeed_manager is None: # Note: this condition is possible during the prologue or epilogue of the # pipelined loop. return None # XLA requires every operation in the graph has a fixed shape. To # handle varying batch sizes we recompile a new sub-graph for each # unique input shape. shape_key = tuple([tuple(spec.shape.as_list()) for spec in input_specs]) if shape_key not in self._compilation_cache: logging.info( 'New input shapes; (re-)compiling: mode=%s ' '(# of cores %d), %s', self.execution_mode, self._tpu_assignment.num_towers, input_specs) new_tpu_model_ops = self._specialize_model(input_specs, infeed_manager) self._compilation_cache[shape_key] = new_tpu_model_ops self._test_model_compiles(new_tpu_model_ops) return self._compilation_cache[shape_key] def _construct_input_tensors_and_inputs(self, inputs): """Returns input tensors and numpy array inputs corresponding to `inputs`. Args: inputs: NumPy inputs. Returns: A tuple of `input_tensors`, and `inputs`. """ if inputs is None: # Note: this condition is possible during the prologue or epilogue of the # pipelined loop. return None, None if isinstance(inputs[-1], int): # Remove the learning_phase flag at the end. We currently hard code the # learning_phase in TPUFunction. inputs = inputs[:-1] if (self.execution_mode == model_fn_lib.ModeKeys.TRAIN or self.execution_mode == model_fn_lib.ModeKeys.EVAL): # Strip sample weight from inputs. input_tensors = self.model._feed_inputs + self.model._feed_targets else: input_tensors = self.model._feed_inputs inputs = inputs[:len(input_tensors)] input_tensors, inputs = ( _inject_tpu_inputs_for_infeed( self._tpu_assignment, self.execution_mode, self._core_id_place_holder, input_tensors, inputs)) return input_tensors, inputs def _process_outputs(self, outfeed_outputs): """Processes the outputs of a model function execution. Args: outfeed_outputs: The sharded outputs of the TPU computation. Returns: The aggregated outputs of the TPU computation to be used in the rest of the model execution. """ # TODO(xiejw): Decide how to reduce outputs, or discard all but first. if self.execution_mode == model_fn_lib.ModeKeys.PREDICT: outputs = [[] for _ in range(len(self._outfeed_spec))] outputs_per_replica = len(self._outfeed_spec) for i in range(self._tpu_assignment.num_towers): output_group = outfeed_outputs[i * outputs_per_replica:(i + 1) * outputs_per_replica] for j in range(outputs_per_replica): outputs[j].append(output_group[j]) return [np.concatenate(group) for group in outputs] else: return outfeed_outputs[:len(outfeed_outputs) // self._tpu_assignment.num_towers] def __call__(self, inputs): """__call__ executes the function on the computational hardware. It handles executing infeed, and preprocessing in addition to executing the model on the TPU hardware. Note: `__call__` has a sibling method `pipeline_run` which performs the same operations, but with software pipelining. Args: inputs: The inputs to use to train. Returns: The output of the computation for the given mode it is executed in. Raises: RuntimeError: If there is an inappropriate use of the function. """ assert isinstance(inputs, list) infeed_manager = self._lookup_infeed_manager(inputs) input_tensors, inputs = self._construct_input_tensors_and_inputs(inputs) infeed_instance = infeed_manager.make_infeed_instance(inputs) del inputs # To avoid accident usage. input_specs = infeed_instance.make_input_specs(input_tensors) tpu_model_ops = self._tpu_model_ops_for_input_specs(input_specs, infeed_manager) infeed_dict = infeed_instance.make_feed_dict(tpu_model_ops) # Initialize our TPU weights on the first compile. self.model._initialize_weights(self._cloned_model) _, _, outfeed_outputs = K.get_session().run([ tpu_model_ops.infeed_op, tpu_model_ops.execute_op, tpu_model_ops.outfeed_op ], infeed_dict) return self._process_outputs(outfeed_outputs) def pipeline_run(self, cur_step_inputs, next_step_inputs): """pipeline_run executes the function on the computational hardware. pipeline_run performs the same computation as __call__, however it runs the infeed in a software pipelined fashion compared to the on-device execution. Note: it is the responsibility of the caller to call `pipeline_run` in the following sequence: - Once with `cur_step_inputs=None` and `next_step_inputs=list(...)` - `n` times with `cur_step_inputs` and `next_step_inputs` as `list`s - Once with `cur_step_inputs=list(...)` and `next_step_inputs=None` Additionally, it is the responsibility of the caller to pass `next_step_inputs` as `cur_step_inputs` on the next invocation of `pipeline_run`. Args: cur_step_inputs: The current step's inputs. next_step_inputs: The next step's inputs. Returns: The output of the computation for the given mode it is executed in. Raises: RuntimeError: If there is an inappropriate use of the function. """ # Software pipelined case. next_step_infeed_manager = self._lookup_infeed_manager(next_step_inputs) cur_step_infeed_manager = self._lookup_infeed_manager(cur_step_inputs) if (next_step_infeed_manager is not None and cur_step_infeed_manager is not None): assert type(next_step_infeed_manager) is type(cur_step_infeed_manager) next_input_tensors, next_step_inputs = ( self._construct_input_tensors_and_inputs(next_step_inputs)) cur_input_tensors, cur_step_inputs = ( self._construct_input_tensors_and_inputs(cur_step_inputs)) cur_infeed_instance = None if cur_step_infeed_manager: cur_infeed_instance = cur_step_infeed_manager.make_infeed_instance( cur_step_inputs) next_infeed_instance = None if next_step_infeed_manager: next_infeed_instance = next_step_infeed_manager.make_infeed_instance( next_step_inputs) del cur_step_inputs # Avoid accidental re-use. del next_step_inputs # Avoid accidental re-use. cur_tpu_model_ops = None next_tpu_model_ops = None infeed_dict = None if cur_infeed_instance and cur_input_tensors and cur_step_infeed_manager: cur_input_specs = cur_infeed_instance.make_input_specs(cur_input_tensors) cur_tpu_model_ops = self._tpu_model_ops_for_input_specs( cur_input_specs, cur_step_infeed_manager) if (next_infeed_instance and next_input_tensors and next_step_infeed_manager): next_input_specs = next_infeed_instance.make_input_specs( next_input_tensors) next_tpu_model_ops = self._tpu_model_ops_for_input_specs( next_input_specs, next_step_infeed_manager) infeed_dict = next_infeed_instance.make_feed_dict(next_tpu_model_ops) # Initialize our TPU weights on the first compile. self.model._initialize_weights(self._cloned_model) if next_tpu_model_ops and cur_tpu_model_ops: _, _, outfeed_outputs = K.get_session().run([ next_tpu_model_ops.infeed_op, cur_tpu_model_ops.execute_op, cur_tpu_model_ops.outfeed_op ], infeed_dict) return self._process_outputs(outfeed_outputs) if cur_tpu_model_ops: _, outfeed_outputs = K.get_session().run( [cur_tpu_model_ops.execute_op, cur_tpu_model_ops.outfeed_op]) return self._process_outputs(outfeed_outputs) if next_tpu_model_ops: K.get_session().run(next_tpu_model_ops.infeed_op, infeed_dict) return None raise RuntimeError('Internal error: both current & next tpu_model_ops ' 'were None') class KerasTPUModel(models.Model): """TPU compatible Keras model wrapper.""" def __init__(self, cpu_model, strategy): super(models.Model, self).__init__( # pylint: disable=bad-super-call inputs=cpu_model.inputs, outputs=cpu_model.outputs, name=cpu_model.name, ) if tf2.enabled(): raise RuntimeError( 'Keras support is now deprecated in support of TPU Strategy. ' 'Please follow the distribution strategy guide on tensorflow.org ' 'to migrate to the 2.0 supported version.') else: logging.warning( 'Keras support is now deprecated in support of TPU Strategy. ' 'Please follow the distribution strategy guide on tensorflow.org ' 'to migrate to the 2.0 supported version.') # Create a mapping from numpy arrays to infeed managers. # Note: uses a list of tuples instead of a map because numpy arrays are # not hashable. self._numpy_to_infeed_manager_list = [] # Add distribution specific arguments since we don't call the Model init. self._distribution_strategy = None self._compile_distribution = None self.predict_function = None self.test_function = None self.train_function = None self._fit_function = None self._eval_function = None self._stateful_metric_functions = [] cluster_resolver = strategy._tpu_cluster_resolver self._tpu_name_or_address = cluster_resolver.get_master() self._cpu_model = cpu_model self._tpu_assignment = strategy._make_assignment_for_model(cpu_model) self._tpu_model = None self._tpu_weights_initialized = False # If the input CPU model has already been compiled, compile our TPU model # immediately. if self._cpu_model.optimizer: self.compile( self._cpu_model.optimizer, self._cpu_model.loss, self._cpu_model._compile_metrics, self._cpu_model.loss_weights, self._cpu_model.sample_weight_mode, self._cpu_model._compile_weighted_metrics, self._cpu_model.target_tensors, ) # This flag must be disabled upon model mutation, such as changing the model # layers or recompiling the model to use a different optimizer. New function # definitions are generated whenever this flag is disabled, ensuring that # internal graph functions are always using the current model structure. # # Requires declaration here because this constructor skips the # Model constructor. self._built_graph_functions = False def get_config(self): return { 'cpu_model': self._cpu_model, 'tpu_name_or_address': self._tpu_name_or_address, 'tpu_assignment': self._tpu_assignment, } def compile(self, optimizer, loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None, **kwargs): if sample_weight_mode: raise ValueError('sample_weight_mode not supported for TPU execution.') if weighted_metrics: raise ValueError('weighted_metrics not supported for TPU execution.') if target_tensors: raise ValueError('target_tensors is not supported for TPU execution.') self._cpu_model.compile( _clone_optimizer(optimizer), loss, metrics_module.clone_metrics(metrics), loss_weights, sample_weight_mode, metrics_module.clone_metrics(weighted_metrics), target_tensors, **kwargs) super(KerasTPUModel, self).compile(optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs) def fit(self, x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0., validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, **kwargs): if context.executing_eagerly(): raise EnvironmentError('KerasTPUModel currently does not support eager ' 'mode.') with _tpu_session_context(): assert not self._numpy_to_infeed_manager_list # Ensure empty. infeed_managers = [] # Managers to clean up at the end of the fit call. if isinstance(x, dataset_ops.DatasetV2): # TODO(b/111413240): Support taking a tf.data.Dataset directly. raise ValueError( 'Taking a Dataset directly is not yet supported. Please ' 'wrap your dataset construction code in a function and ' 'pass that to fit instead. For examples, see: ' 'https://github.com/tensorflow/tpu/tree/master/models/experimental' '/keras') if callable(x): with ops.device( '/job:%s/device:CPU:0' % self._tpu_assignment.worker_name): dataset = x() if steps_per_epoch is None: raise ValueError('When using tf.data as input to a model, you ' 'should specify the steps_per_epoch argument.') if y is not None: raise ValueError('When using tf.data as input to a model, y must ' 'be None') infeed_manager = TPUDatasetInfeedManager( dataset, self._tpu_assignment, model_fn_lib.ModeKeys.TRAIN) # Use dummy numpy inputs for the rest of Keras' shape checking. We # intercept them when building the model. x = infeed_manager.dummy_x y = infeed_manager.dummy_y infeed_managers.append((x, infeed_manager)) if isinstance(validation_data, dataset_ops.DatasetV2): # TODO(b/111413240): Support taking a tf.data.Dataset directly. raise ValueError( 'Taking a Dataset directly is not yet supported. Please ' 'wrap your dataset construction code in a function and ' 'pass that to fit instead. For examples, see: ' 'https://github.com/tensorflow/tpu/tree/master/models/experimental' '/keras') if callable(validation_data): dataset = validation_data() if validation_steps is None: raise ValueError('When using tf.data as validation for a model, you ' 'should specify the validation_steps argument.') infeed_manager = TPUDatasetInfeedManager(dataset, self._tpu_assignment, model_fn_lib.ModeKeys.EVAL) # Use dummy numpy inputs for the rest of Keras' shape checking. We # intercept them when building the model. val_x = infeed_manager.dummy_x val_y = infeed_manager.dummy_y infeed_managers.append((val_x, infeed_manager)) validation_data = (val_x, val_y) self._numpy_to_infeed_manager_list = infeed_managers try: pipeline = kwargs.get('_pipeline', True) if '_pipeline' in kwargs: kwargs.pop('_pipeline') if not pipeline: logging.info('Running non-pipelined training loop (`_pipeline=%s`).', pipeline) return super(KerasTPUModel, self).fit( x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs) return self._pipeline_fit(x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs) finally: self._numpy_to_infeed_manager_list = [] def evaluate(self, x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None): original_numpy_to_infeed_manager_list = [] if self._numpy_to_infeed_manager_list: # evaluate call may be executed as callbacks during the training. In this # case, _numpy_to_infeed_manager_list is not empty, so save it for # recovery at the end of evaluate call. original_numpy_to_infeed_manager_list = self._numpy_to_infeed_manager_list self._numpy_to_infeed_manager_list = [] with _tpu_session_context(): # Managers to clean up at the end of the evaluate call. infeed_managers = [] if isinstance(x, dataset_ops.DatasetV2): # TODO(b/111413240): Support taking a tf.data.Dataset directly. raise ValueError( 'Taking a Dataset directly is not yet supported. Please ' 'wrap your dataset construction code in a function and ' 'pass that to fit instead. For examples, see: ' 'https://github.com/tensorflow/tpu/tree/master/models/experimental' '/keras') if callable(x): dataset = x() if steps is None: raise ValueError('When using tf.data as input to a model, you ' 'should specify the steps argument.') if y is not None: raise ValueError('When using tf.data as input to a model, y must be ' 'None') infeed_manager = TPUDatasetInfeedManager(dataset, self._tpu_assignment, model_fn_lib.ModeKeys.EVAL) # Use dummy numpy inputs for the rest of Keras' shape checking. We # intercept them when building the model. x = infeed_manager.dummy_x y = infeed_manager.dummy_y infeed_managers.append((x, infeed_manager)) self._numpy_to_infeed_manager_list = infeed_managers try: return super(KerasTPUModel, self).evaluate(x, y, batch_size, verbose, sample_weight, steps) finally: self._numpy_to_infeed_manager_list = ( original_numpy_to_infeed_manager_list) def _pipeline_fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs): # Similar to super.fit(...), but modified to support software pipelining. # Backwards compatibility if batch_size is None and steps_per_epoch is None: batch_size = 32 # Legacy support if 'nb_epoch' in kwargs: logging.warning('The `nb_epoch` argument in `fit` has been renamed ' '`epochs`.') epochs = kwargs.pop('nb_epoch') if kwargs: raise TypeError('Unrecognized keyword arguments: ' + str(kwargs)) # Validate and standardize user data x, y, sample_weights = self._standardize_user_data( x, y, sample_weight=sample_weight, class_weight=class_weight, batch_size=batch_size, check_steps=True, steps_name='steps_per_epoch', steps=steps_per_epoch, validation_split=validation_split) # Prepare validation data val_x, val_y, val_sample_weights = self._prepare_validation_data( validation_data, validation_split, validation_steps, x, y, sample_weights, batch_size) return self._pipeline_fit_loop( x, y, sample_weights=sample_weights, batch_size=batch_size, epochs=epochs, verbose=verbose, callbacks=callbacks, val_inputs=val_x, val_targets=val_y, val_sample_weights=val_sample_weights, shuffle=shuffle, initial_epoch=initial_epoch, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps) def _pipeline_fit_loop(self, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps): self._make_train_function() sample_weights = sample_weights or [] val_sample_weights = val_sample_weights or [] if not isinstance(K.learning_phase(), int): ins = inputs + targets + sample_weights + [1] else: ins = inputs + targets + sample_weights do_validation = False if val_inputs: do_validation = True if (steps_per_epoch is None and verbose and inputs and hasattr(inputs[0], 'shape') and hasattr(val_inputs[0], 'shape')): print('Train on %d samples, validate on %d samples' % (inputs[0].shape[0], val_inputs[0].shape[0])) if validation_steps: do_validation = True if steps_per_epoch is None: raise ValueError('Can only use `validation_steps` when doing step-wise ' 'training, i.e. `steps_per_epoch` must be set.') num_training_samples = training_utils.check_num_samples( ins, batch_size, steps_per_epoch, 'steps_per_epoch') count_mode = 'steps' if steps_per_epoch else 'samples' callbacks = cbks.configure_callbacks( callbacks, self, do_validation=do_validation, batch_size=batch_size, epochs=epochs, steps_per_epoch=steps_per_epoch, samples=num_training_samples, verbose=verbose, count_mode=count_mode) if num_training_samples is not None: index_array = np.arange(num_training_samples) # To prevent a slowdown, we find beforehand the arrays that need conversion. feed = self._feed_inputs + self._feed_targets + self._feed_sample_weights indices_for_conversion_to_dense = [] for i in range(len(feed)): if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]): indices_for_conversion_to_dense.append(i) callbacks.on_train_begin() for epoch in range(initial_epoch, epochs): # Reset stateful metrics for m in self.metrics: m.reset_states() # Update callbacks callbacks.on_epoch_begin(epoch) epoch_logs = {} if steps_per_epoch is not None: # Step-wise fit loop. self._pipeline_fit_loop_step_wise( ins=ins, callbacks=callbacks, steps_per_epoch=steps_per_epoch, epochs=epochs, do_validation=do_validation, val_inputs=val_inputs, val_targets=val_targets, val_sample_weights=val_sample_weights, validation_steps=validation_steps, epoch_logs=epoch_logs) else: # Sample-wise fit loop. self._pipeline_fit_loop_sample_wise( ins=ins, callbacks=callbacks, index_array=index_array, shuffle=shuffle, batch_size=batch_size, num_training_samples=num_training_samples, indices_for_conversion_to_dense=indices_for_conversion_to_dense, do_validation=do_validation, val_inputs=val_inputs, val_targets=val_targets, val_sample_weights=val_sample_weights, validation_steps=validation_steps, epoch_logs=epoch_logs) callbacks.on_epoch_end(epoch, epoch_logs) if callbacks.model.stop_training: break callbacks.on_train_end() return self.history def _pipeline_fit_loop_sample_wise(self, ins, callbacks, index_array, shuffle, batch_size, num_training_samples, indices_for_conversion_to_dense, do_validation, val_inputs, val_targets, val_sample_weights, validation_steps, epoch_logs): f = self.train_function if shuffle == 'batch': index_array = training_utils.batch_shuffle(index_array, batch_size) elif shuffle: np.random.shuffle(index_array) batches = make_batches(num_training_samples, batch_size) ins_last_batch = None last_batch_logs = None batch_index = 0 for batch_index, (batch_start, batch_end) in enumerate(batches): batch_ids = index_array[batch_start:batch_end] try: if isinstance(ins[-1], int): # Do not slice the training phase flag. ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]] else: ins_batch = slice_arrays(ins, batch_ids) except TypeError: raise TypeError('TypeError while preparing batch. If using HDF5 ' 'input data, pass shuffle="batch".') # Pipeline batch logs next_batch_logs = {} next_batch_logs['batch'] = batch_index next_batch_logs['size'] = len(batch_ids) if batch_index > 0: # Callbacks operate one step behind in software pipeline. callbacks.on_batch_begin(batch_index - 1, last_batch_logs) for i in indices_for_conversion_to_dense: ins_batch[i] = ins_batch[i].toarray() outs = f.pipeline_run( cur_step_inputs=ins_last_batch, next_step_inputs=ins_batch) ins_last_batch = ins_batch if batch_index == 0: assert outs is None else: if not isinstance(outs, list): outs = [outs] for l, o in zip(self.metrics_names, outs): last_batch_logs[l] = o # pylint: disable=unsupported-assignment-operation callbacks.on_batch_end(batch_index - 1, last_batch_logs) if callbacks.model.stop_training: return last_batch_logs = next_batch_logs # Final batch callbacks.on_batch_begin(batch_index, last_batch_logs) outs = f.pipeline_run(cur_step_inputs=ins_last_batch, next_step_inputs=None) if not isinstance(outs, list): outs = [outs] for l, o in zip(self.metrics_names, outs): last_batch_logs[l] = o callbacks.on_batch_end(batch_index, last_batch_logs) if callbacks.model.stop_training: return if do_validation: val_outs = training_arrays.test_loop( self, val_inputs, val_targets, sample_weights=val_sample_weights, batch_size=batch_size, steps=validation_steps, verbose=0) if not isinstance(val_outs, list): val_outs = [val_outs] # Same labels assumed. for l, o in zip(self.metrics_names, val_outs): epoch_logs['val_' + l] = o def _pipeline_fit_loop_step_wise(self, ins, callbacks, steps_per_epoch, epochs, do_validation, val_inputs, val_targets, val_sample_weights, validation_steps, epoch_logs): f = self.train_function # Loop prologue try: outs = f.pipeline_run(cur_step_inputs=None, next_step_inputs=ins) assert outs is None # Function shouldn't return anything! except errors.OutOfRangeError: logging.warning('Your dataset iterator ran out of data on the first step ' 'of the epoch, preventing further training. Check to ' 'make sure your paths are correct and you have ' 'permissions to read the files. Skipping validation') for step_index in range(steps_per_epoch): batch_logs = {'batch': step_index, 'size': 1} callbacks.on_batch_begin(step_index, batch_logs) try: if step_index < steps_per_epoch - 1: next_step_inputs = ins else: next_step_inputs = None outs = f.pipeline_run( cur_step_inputs=ins, next_step_inputs=next_step_inputs) except errors.OutOfRangeError: logging.warning('Your dataset iterator ran out of data; ' 'interrupting training. Make sure that your ' 'dataset can generate at least `steps_per_batch * ' 'epochs` batches (in this case, %d batches). You ' 'may need to use the repeat() function when ' 'building your dataset.' % steps_per_epoch * epochs) break if not isinstance(outs, list): outs = [outs] for l, o in zip(self.metrics_names, outs): batch_logs[l] = o callbacks.on_batch_end(step_index, batch_logs) if callbacks.model.stop_training: break if do_validation: val_outs = training_arrays.test_loop( self, val_inputs, val_targets, sample_weights=val_sample_weights, steps=validation_steps, verbose=0) if not isinstance(val_outs, list): val_outs = [val_outs] # Same labels assumed. for l, o in zip(self.metrics_names, val_outs): epoch_logs['val_' + l] = o def _prepare_validation_data(self, validation_data, validation_split, validation_steps, x, y, sample_weights, batch_size): """Prepares the validation dataset. Args: validation_data: The validation data (if provided) validation_split: The validation split (if provided) validation_steps: The validation steps (if provided) x: The main training data x (if provided) y: The main training data y (if provided) sample_weights: The sample weights (if provided) batch_size: The training batch size (if provided) Returns: A 3-tuple of (val_x, val_y, val_sample_weights). Raises: ValueError: If the provided arguments are not compatible with `KerasTPUModel`. """ # Note: this is similar to a section of $tf/python/keras/engine/training.py # It differns in that tf.data objects are not allowed to be passed directly. # Additionally, it handles validating shapes & types appropriately for use # in TPUs. if validation_data: if (isinstance(validation_data, iterator_ops.Iterator) or isinstance(validation_data, iterator_ops.EagerIterator) or isinstance(validation_data, dataset_ops.DatasetV2)): raise ValueError('KerasTPUModel cannot handle a Dataset or Iterator ' 'for validation_data. Please instead pass a function ' 'that returns a `tf.data.Dataset`.') if len(validation_data) == 2: val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence val_sample_weight = None elif len(validation_data) == 3: val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence else: raise ValueError('When passing a `validation_data` argument, it must ' 'contain either 2 items (x_val, y_val), or 3 items ' '(x_val, y_val, val_sample_weights). However we ' 'received `validation_data=%s`' % validation_data) val_x, val_y, val_sample_weights = self._standardize_user_data( val_x, val_y, sample_weight=val_sample_weight, batch_size=batch_size, steps=validation_steps) elif validation_split and 0. < validation_split < 1.: if training_utils.has_symbolic_tensors(x): raise ValueError('If your data is in the form of symbolic tensors, you ' 'cannot use `validation_split`.') if hasattr(x[0], 'shape'): split_at = int(x[0].shape[0] * (1. - validation_split)) else: split_at = int(len(x[0]) * (1. - validation_split)) x, val_x = (slice_arrays(x, 0, split_at), slice_arrays(x, split_at)) y, val_y = (slice_arrays(y, 0, split_at), slice_arrays(y, split_at)) sample_weights, val_sample_weights = ( slice_arrays(sample_weights, 0, split_at), slice_arrays(sample_weights, split_at) ) elif validation_steps: val_x = [] val_y = [] val_sample_weights = [] else: val_x = None val_y = None val_sample_weights = None return val_x, val_y, val_sample_weights def predict(self, x, batch_size=None, verbose=0, steps=None, max_queue_size=10, workers=1, use_multiprocessing=False): with _tpu_session_context(): return super(KerasTPUModel, self).predict( x, batch_size=batch_size, verbose=verbose, steps=steps, max_queue_size=max_queue_size, workers=workers, use_multiprocessing=use_multiprocessing) @property def optimizer(self): if self._tpu_model: return self._tpu_model.optimizer return self._cpu_model.optimizer @optimizer.setter def optimizer(self, optimizer): self._optimizer = optimizer @property def metrics(self): if self._tpu_model: return self._tpu_model.metrics return self._stateful_metric_functions @metrics.setter def metrics(self, metrics): self._stateful_metric_functions = metrics def _make_train_function(self): if not self.train_function: self.train_function = TPUFunction( self, model_fn_lib.ModeKeys.TRAIN, tpu_assignment=self._tpu_assignment) return self.train_function def _make_test_function(self): if not self.test_function: self.test_function = TPUFunction( self, model_fn_lib.ModeKeys.EVAL, tpu_assignment=self._tpu_assignment) return self.test_function def _make_fit_function(self): if not self._fit_function: self._fit_function = TPUFunction( self, model_fn_lib.ModeKeys.TRAIN, tpu_assignment=self._tpu_assignment) return self._fit_function def _make_eval_function(self): if not self._eval_function: self._eval_function = TPUFunction( self, model_fn_lib.ModeKeys.EVAL, tpu_assignment=self._tpu_assignment) return self._eval_function def _make_predict_function(self): if not self.predict_function: self.predict_function = TPUFunction( self, model_fn_lib.ModeKeys.PREDICT, tpu_assignment=self._tpu_assignment) return self.predict_function def _initialize_weights(self, cloned_model): """Initialize TPU weights. This is called on the first compile of the TPU model (first call to fit/predict/evaluate). Args: cloned_model: `keras.Model`, TPU model to initialize. """ if self._tpu_weights_initialized: return self._tpu_model = cloned_model self._tpu_weights_initialized = True weights = self._cpu_model.get_weights() if isinstance(self.cpu_optimizer, keras_optimizers.TFOptimizer): cpu_optimizer_config = {} else: cpu_optimizer_config = self.cpu_optimizer.get_config() logging.info('Setting weights on TPU model.') cloned_model.set_weights(weights) if self._tpu_model.optimizer is None: # tpu_model may not be compiled, e.g., loading weights and then predict. return for k, v in six.iteritems(cpu_optimizer_config): if k == 'name': continue opt_var = getattr(self._tpu_model.optimizer, k) if isinstance(opt_var, variables.Variable): logging.info('CPU -> TPU %s: %s {%s}', k, v, K.get_value(opt_var)) K.get_session().run(opt_var.assign(v)) else: logging.warning('Cannot update non-variable config: %s', k) @property def cpu_optimizer(self): return self._cpu_model.optimizer def sync_to_cpu(self): """Copy weights from the CPU, returning a synchronized CPU model.""" if not self._tpu_weights_initialized: return self._cpu_model logging.info('Copying TPU weights to the CPU') tpu_weights = self._tpu_model.get_weights() # TFOptimizers have no configurable options if isinstance(self.cpu_optimizer, keras_optimizers.TFOptimizer): tpu_optimizer_config = {} else: tpu_optimizer_config = self._tpu_model.optimizer.get_config() self._cpu_model.set_weights(tpu_weights) for k, v in six.iteritems(tpu_optimizer_config): logging.info('TPU -> CPU %s: %s', k, v) if k == 'name': continue opt_var = getattr(self.cpu_optimizer, k) if isinstance(opt_var, variables.Variable): K.get_session().run(opt_var.assign(v)) else: logging.warning('Cannot update non-variable config: %s', k) return self._cpu_model def get_weights(self): return self.sync_to_cpu().get_weights() def save_weights(self, *args, **kw): return self.sync_to_cpu().save_weights(*args, **kw) def save(self, *args, **kw): return self.sync_to_cpu().save(*args, **kw) def set_weights(self, weights): # We may not have a TPU model available if we haven't run fit/predict, so # we can't directly set the TPU weights here. # Instead, reset CPU model weights and force TPU re-initialization at the # next call. self._cpu_model.set_weights(weights) self._tpu_weights_initialized = False def load_weights(self, filepath, by_name=False): self._cpu_model.load_weights(filepath, by_name) self._tpu_weights_initialized = False # pylint: disable=bad-continuation def _validate_shapes(model): """Validate that all layers in `model` have constant shape.""" for layer in model.layers: if isinstance(layer.input_shape, tuple): input_shapes = [layer.input_shape] else: input_shapes = layer.input_shape if isinstance(layer.output_shape, tuple): output_shapes = [layer.output_shape] else: output_shapes = layer.output_shape for shape in input_shapes + output_shapes: for dim in shape[1:]: if dim is None: raise ValueError( """ Layer %(layer)s has a variable shape in a non-batch dimension. TPU models must have constant shapes for all operations. You may have to specify `input_length` for RNN/TimeDistributed layers. Layer: %(layer)s Input shape: %(input_shape)s Output shape: %(output_shape)s """ % { 'layer': layer, 'input_shape': layer.input_shape, 'output_shape': layer.output_shape }) # pylint: enable=bad-continuation @deprecated( '2019-02-20', 'Switch to tf.contrib.distribute.TPUStrategy. ' 'https://www.tensorflow.org/api_docs/python/tf/contrib/distribute/DistributionStrategy' ) def tpu_model(model, strategy=None): """Copy `model` along with weights to the TPU. Returns a TPU model. Usage: ``` a = Input(shape=(32,)) b = Dense(32)(a) model = Model(inputs=a, outputs=b) # If `num_cores_per_host` is greater than one, batch parallelism will be used # to run on multiple TPU cores. strategy = keras_support.TPUDistributionStrategy(tpu_cluster_resolver) model = keras_support.tpu_model(model, strategy) model.compile( optimizer=tf.train.GradientDescentOptimizer(learning_rate=1.0), ...) ``` Args: model: A `tf.keras.Model` instance. strategy: `TPUDistributionStrategy`. The strategy to use for replicating model across multiple TPU cores. Returns: A new `KerasTPUModel` instance. """ _validate_shapes(model) # TODO(xiejw): Validate TPU model. TPUModel only? # TODO(xiejw): Validate replicas. Full or 1. Shall we allow subset? # TODO(xiejw): Adds reduction option. if strategy is None: strategy = TPUDistributionStrategy() else: if not isinstance(strategy, TPUDistributionStrategy): raise TypeError( '`strategy` must have type `tf.contrib.tpu.TPUDistributionStrategy`. ' 'Got: {}'.format(type(strategy))) # If the model has already been initialized, grab the optimizer configuration # and model weights before entering the TPU session. if model.optimizer: if (isinstance(model.optimizer, keras_optimizers.Optimizer) and not isinstance(model.optimizer, keras_optimizers.TFOptimizer)): optimizer_config = model.optimizer.get_config() else: optimizer_config = None model_weights = model.get_weights() else: model_weights = None setup_tpu_session(strategy._tpu_cluster_resolver) # Force initialization of the CPU model in the TPU session. cpu_model = models.clone_model(model) if model.optimizer: cpu_model.compile( _clone_optimizer(model.optimizer, optimizer_config), model.loss, metrics_module.clone_metrics(model._compile_metrics), model.loss_weights, model.sample_weight_mode, metrics_module.clone_metrics(model._compile_weighted_metrics), ) if model_weights: cpu_model.set_weights(model_weights) cpu_model.reset_states() return KerasTPUModel(cpu_model=cpu_model, strategy=strategy)

apache-2.0

zizouvb/deeplearning

image-classification/helper.py

155

5631

import pickle import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import LabelBinarizer def _load_label_names(): """ Load the label names from file """ return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] def load_cfar10_batch(cifar10_dataset_folder_path, batch_id): """ Load a batch of the dataset """ with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file: batch = pickle.load(file, encoding='latin1') features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1) labels = batch['labels'] return features, labels def display_stats(cifar10_dataset_folder_path, batch_id, sample_id): """ Display Stats of the the dataset """ batch_ids = list(range(1, 6)) if batch_id not in batch_ids: print('Batch Id out of Range. Possible Batch Ids: {}'.format(batch_ids)) return None features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_id) if not (0 <= sample_id < len(features)): print('{} samples in batch {}. {} is out of range.'.format(len(features), batch_id, sample_id)) return None print('\nStats of batch {}:'.format(batch_id)) print('Samples: {}'.format(len(features))) print('Label Counts: {}'.format(dict(zip(*np.unique(labels, return_counts=True))))) print('First 20 Labels: {}'.format(labels[:20])) sample_image = features[sample_id] sample_label = labels[sample_id] label_names = _load_label_names() print('\nExample of Image {}:'.format(sample_id)) print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max())) print('Image - Shape: {}'.format(sample_image.shape)) print('Label - Label Id: {} Name: {}'.format(sample_label, label_names[sample_label])) plt.axis('off') plt.imshow(sample_image) def _preprocess_and_save(normalize, one_hot_encode, features, labels, filename): """ Preprocess data and save it to file """ features = normalize(features) labels = one_hot_encode(labels) pickle.dump((features, labels), open(filename, 'wb')) def preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode): """ Preprocess Training and Validation Data """ n_batches = 5 valid_features = [] valid_labels = [] for batch_i in range(1, n_batches + 1): features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i) validation_count = int(len(features) * 0.1) # Prprocess and save a batch of training data _preprocess_and_save( normalize, one_hot_encode, features[:-validation_count], labels[:-validation_count], 'preprocess_batch_' + str(batch_i) + '.p') # Use a portion of training batch for validation valid_features.extend(features[-validation_count:]) valid_labels.extend(labels[-validation_count:]) # Preprocess and Save all validation data _preprocess_and_save( normalize, one_hot_encode, np.array(valid_features), np.array(valid_labels), 'preprocess_validation.p') with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file: batch = pickle.load(file, encoding='latin1') # load the test data test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1) test_labels = batch['labels'] # Preprocess and Save all test data _preprocess_and_save( normalize, one_hot_encode, np.array(test_features), np.array(test_labels), 'preprocess_test.p') def batch_features_labels(features, labels, batch_size): """ Split features and labels into batches """ for start in range(0, len(features), batch_size): end = min(start + batch_size, len(features)) yield features[start:end], labels[start:end] def load_preprocess_training_batch(batch_id, batch_size): """ Load the Preprocessed Training data and return them in batches of <batch_size> or less """ filename = 'preprocess_batch_' + str(batch_id) + '.p' features, labels = pickle.load(open(filename, mode='rb')) # Return the training data in batches of size <batch_size> or less return batch_features_labels(features, labels, batch_size) def display_image_predictions(features, labels, predictions): n_classes = 10 label_names = _load_label_names() label_binarizer = LabelBinarizer() label_binarizer.fit(range(n_classes)) label_ids = label_binarizer.inverse_transform(np.array(labels)) fig, axies = plt.subplots(nrows=4, ncols=2) fig.tight_layout() fig.suptitle('Softmax Predictions', fontsize=20, y=1.1) n_predictions = 3 margin = 0.05 ind = np.arange(n_predictions) width = (1. - 2. * margin) / n_predictions for image_i, (feature, label_id, pred_indicies, pred_values) in enumerate(zip(features, label_ids, predictions.indices, predictions.values)): pred_names = [label_names[pred_i] for pred_i in pred_indicies] correct_name = label_names[label_id] axies[image_i][0].imshow(feature) axies[image_i][0].set_title(correct_name) axies[image_i][0].set_axis_off() axies[image_i][1].barh(ind + margin, pred_values[::-1], width) axies[image_i][1].set_yticks(ind + margin) axies[image_i][1].set_yticklabels(pred_names[::-1]) axies[image_i][1].set_xticks([0, 0.5, 1.0])

mit

benoitsteiner/tensorflow-xsmm

tensorflow/contrib/boosted_trees/examples/mnist.py

61

5840

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== r"""Demonstrates multiclass MNIST TF Boosted trees example. This example demonstrates how to run experiments with TF Boosted Trees on a MNIST dataset. We are using layer by layer boosting with diagonal hessian strategy for multiclass handling, and cross entropy loss. Example Usage: python tensorflow/contrib/boosted_trees/examples/mnist.py \ --output_dir="/tmp/mnist" --depth=4 --learning_rate=0.3 --batch_size=60000 \ --examples_per_layer=60000 --eval_batch_size=10000 --num_eval_steps=1 \ --num_trees=10 --l2=1 --vmodule=training_ops=1 When training is done, accuracy on eval data is reported. Point tensorboard to the directory for the run to see how the training progresses: tensorboard --logdir=/tmp/mnist """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import numpy as np import tensorflow as tf from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier from tensorflow.contrib.boosted_trees.proto import learner_pb2 from tensorflow.contrib.learn import learn_runner def get_input_fn(dataset_split, batch_size, capacity=10000, min_after_dequeue=3000): """Input function over MNIST data.""" def _input_fn(): """Prepare features and labels.""" images_batch, labels_batch = tf.train.shuffle_batch( tensors=[dataset_split.images, dataset_split.labels.astype(np.int32)], batch_size=batch_size, capacity=capacity, min_after_dequeue=min_after_dequeue, enqueue_many=True, num_threads=4) features_map = {"images": images_batch} return features_map, labels_batch return _input_fn # Main config - creates a TF Boosted Trees Estimator based on flags. def _get_tfbt(output_dir): """Configures TF Boosted Trees estimator based on flags.""" learner_config = learner_pb2.LearnerConfig() num_classes = 10 learner_config.learning_rate_tuner.fixed.learning_rate = FLAGS.learning_rate learner_config.num_classes = num_classes learner_config.regularization.l1 = 0.0 learner_config.regularization.l2 = FLAGS.l2 / FLAGS.examples_per_layer learner_config.constraints.max_tree_depth = FLAGS.depth growing_mode = learner_pb2.LearnerConfig.LAYER_BY_LAYER learner_config.growing_mode = growing_mode run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300) learner_config.multi_class_strategy = ( learner_pb2.LearnerConfig.DIAGONAL_HESSIAN) # Create a TF Boosted trees estimator that can take in custom loss. estimator = GradientBoostedDecisionTreeClassifier( learner_config=learner_config, n_classes=num_classes, examples_per_layer=FLAGS.examples_per_layer, model_dir=output_dir, num_trees=FLAGS.num_trees, center_bias=False, config=run_config) return estimator def _make_experiment_fn(output_dir): """Creates experiment for gradient boosted decision trees.""" data = tf.contrib.learn.datasets.mnist.load_mnist() train_input_fn = get_input_fn(data.train, FLAGS.batch_size) eval_input_fn = get_input_fn(data.validation, FLAGS.eval_batch_size) return tf.contrib.learn.Experiment( estimator=_get_tfbt(output_dir), train_input_fn=train_input_fn, eval_input_fn=eval_input_fn, train_steps=None, eval_steps=FLAGS.num_eval_steps, eval_metrics=None) def main(unused_argv): learn_runner.run( experiment_fn=_make_experiment_fn, output_dir=FLAGS.output_dir, schedule="train_and_evaluate") if __name__ == "__main__": tf.logging.set_verbosity(tf.logging.INFO) parser = argparse.ArgumentParser() # Define the list of flags that users can change. parser.add_argument( "--output_dir", type=str, required=True, help="Choose the dir for the output.") parser.add_argument( "--batch_size", type=int, default=1000, help="The batch size for reading data.") parser.add_argument( "--eval_batch_size", type=int, default=1000, help="Size of the batch for eval.") parser.add_argument( "--num_eval_steps", type=int, default=1, help="The number of steps to run evaluation for.") # Flags for gradient boosted trees config. parser.add_argument( "--depth", type=int, default=4, help="Maximum depth of weak learners.") parser.add_argument( "--l2", type=float, default=1.0, help="l2 regularization per batch.") parser.add_argument( "--learning_rate", type=float, default=0.1, help="Learning rate (shrinkage weight) with which each new tree is added." ) parser.add_argument( "--examples_per_layer", type=int, default=1000, help="Number of examples to accumulate stats for per layer.") parser.add_argument( "--num_trees", type=int, default=None, required=True, help="Number of trees to grow before stopping.") FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

apache-2.0

pansapiens/mytardis

tardis/tardis_portal/south_migrations/0020_remove_old_datafile_fields.py

3

21156

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Deleting field 'Dataset_File.stay_remote' db.delete_column('tardis_portal_dataset_file', 'stay_remote') # Deleting field 'Dataset_File.protocol' db.delete_column('tardis_portal_dataset_file', 'protocol') # Deleting field 'Dataset_File.verified' db.delete_column('tardis_portal_dataset_file', 'verified') # Deleting field 'Dataset_File.url' db.delete_column('tardis_portal_dataset_file', 'url') def backwards(self, orm): # Adding field 'Dataset_File.stay_remote' db.add_column('tardis_portal_dataset_file', 'stay_remote', self.gf('django.db.models.fields.BooleanField')(default=False), keep_default=False) # Adding field 'Dataset_File.protocol' db.add_column('tardis_portal_dataset_file', 'protocol', self.gf('django.db.models.fields.CharField')(default='', max_length=10, blank=True), keep_default=False) # Adding field 'Dataset_File.verified' db.add_column('tardis_portal_dataset_file', 'verified', self.gf('django.db.models.fields.BooleanField')(default=False), keep_default=False) # User chose to not deal with backwards NULL issues for 'Dataset_File.url' raise RuntimeError("Cannot reverse this migration. 'Dataset_File.url' and its values cannot be restored.") models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, 'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, 'tardis_portal.author_experiment': { 'Meta': {'ordering': "['order']", 'unique_together': "(('experiment', 'author'),)", 'object_name': 'Author_Experiment'}, 'author': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'experiment': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Experiment']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'order': ('django.db.models.fields.PositiveIntegerField', [], {}), 'url': ('django.db.models.fields.URLField', [], {'max_length': '2000', 'blank': 'True'}) }, 'tardis_portal.datafileparameter': { 'Meta': {'ordering': "['name']", 'object_name': 'DatafileParameter'}, 'datetime_value': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.ParameterName']"}), 'numerical_value': ('django.db.models.fields.FloatField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'parameterset': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.DatafileParameterSet']"}), 'string_value': ('django.db.models.fields.TextField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}) }, 'tardis_portal.datafileparameterset': { 'Meta': {'ordering': "['id']", 'object_name': 'DatafileParameterSet'}, 'dataset_file': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Dataset_File']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'schema': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Schema']"}) }, 'tardis_portal.dataset': { 'Meta': {'object_name': 'Dataset'}, 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'experiments': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "'datasets'", 'symmetrical': 'False', 'to': "orm['tardis_portal.Experiment']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'immutable': ('django.db.models.fields.BooleanField', [], {'default': 'False'}) }, 'tardis_portal.dataset_file': { 'Meta': {'object_name': 'Dataset_File'}, 'created_time': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'dataset': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Dataset']"}), 'filename': ('django.db.models.fields.CharField', [], {'max_length': '400'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'md5sum': ('django.db.models.fields.CharField', [], {'max_length': '32', 'blank': 'True'}), 'mimetype': ('django.db.models.fields.CharField', [], {'max_length': '80', 'blank': 'True'}), 'modification_time': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'sha512sum': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}), 'size': ('django.db.models.fields.CharField', [], {'max_length': '400', 'blank': 'True'}) }, 'tardis_portal.datasetparameter': { 'Meta': {'ordering': "['name']", 'object_name': 'DatasetParameter'}, 'datetime_value': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.ParameterName']"}), 'numerical_value': ('django.db.models.fields.FloatField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'parameterset': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.DatasetParameterSet']"}), 'string_value': ('django.db.models.fields.TextField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}) }, 'tardis_portal.datasetparameterset': { 'Meta': {'ordering': "['id']", 'object_name': 'DatasetParameterSet'}, 'dataset': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Dataset']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'schema': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Schema']"}) }, 'tardis_portal.experiment': { 'Meta': {'object_name': 'Experiment'}, 'approved': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'created_by': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}), 'created_time': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'end_time': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'handle': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'institution_name': ('django.db.models.fields.CharField', [], {'default': "'The University of Queensland'", 'max_length': '400'}), 'license': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.License']", 'null': 'True', 'blank': 'True'}), 'locked': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'public_access': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '1'}), 'start_time': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '400'}), 'update_time': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}), 'url': ('django.db.models.fields.URLField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}) }, 'tardis_portal.experimentacl': { 'Meta': {'ordering': "['experiment__id']", 'object_name': 'ExperimentACL'}, 'aclOwnershipType': ('django.db.models.fields.IntegerField', [], {'default': '1'}), 'canDelete': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'canRead': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'canWrite': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'effectiveDate': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), 'entityId': ('django.db.models.fields.CharField', [], {'max_length': '320'}), 'experiment': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Experiment']"}), 'expiryDate': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'isOwner': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'pluginId': ('django.db.models.fields.CharField', [], {'max_length': '30'}) }, 'tardis_portal.experimentparameter': { 'Meta': {'ordering': "['name']", 'object_name': 'ExperimentParameter'}, 'datetime_value': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.ParameterName']"}), 'numerical_value': ('django.db.models.fields.FloatField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'parameterset': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.ExperimentParameterSet']"}), 'string_value': ('django.db.models.fields.TextField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}) }, 'tardis_portal.experimentparameterset': { 'Meta': {'ordering': "['id']", 'object_name': 'ExperimentParameterSet'}, 'experiment': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Experiment']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'schema': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Schema']"}) }, 'tardis_portal.freetextsearchfield': { 'Meta': {'object_name': 'FreeTextSearchField'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'parameter_name': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.ParameterName']"}) }, 'tardis_portal.groupadmin': { 'Meta': {'object_name': 'GroupAdmin'}, 'group': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.Group']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'tardis_portal.license': { 'Meta': {'object_name': 'License'}, 'allows_distribution': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image_url': ('django.db.models.fields.URLField', [], {'max_length': '2000', 'blank': 'True'}), 'internal_description': ('django.db.models.fields.TextField', [], {}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '400'}), 'url': ('django.db.models.fields.URLField', [], {'unique': 'True', 'max_length': '2000'}) }, 'tardis_portal.location': { 'Meta': {'object_name': 'Location'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_available': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '10'}), 'priority': ('django.db.models.fields.IntegerField', [], {}), 'transfer_provider': ('django.db.models.fields.CharField', [], {'default': "'local'", 'max_length': '10'}), 'type': ('django.db.models.fields.CharField', [], {'max_length': '10'}), 'url': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '400'}) }, 'tardis_portal.parametername': { 'Meta': {'ordering': "('order', 'name')", 'unique_together': "(('schema', 'name'),)", 'object_name': 'ParameterName'}, 'choices': ('django.db.models.fields.CharField', [], {'max_length': '500', 'blank': 'True'}), 'comparison_type': ('django.db.models.fields.IntegerField', [], {'default': '1'}), 'data_type': ('django.db.models.fields.IntegerField', [], {'default': '2'}), 'full_name': ('django.db.models.fields.CharField', [], {'max_length': '60'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'immutable': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_searchable': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '60'}), 'order': ('django.db.models.fields.PositiveIntegerField', [], {'default': '9999', 'null': 'True', 'blank': 'True'}), 'schema': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Schema']"}), 'units': ('django.db.models.fields.CharField', [], {'max_length': '60', 'blank': 'True'}) }, 'tardis_portal.providerparameter': { 'Meta': {'unique_together': "(('location', 'name'),)", 'object_name': 'ProviderParameter'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'location': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Location']"}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '10'}), 'value': ('django.db.models.fields.CharField', [], {'max_length': '80', 'blank': 'True'}) }, 'tardis_portal.replica': { 'Meta': {'unique_together': "(('datafile', 'location'),)", 'object_name': 'Replica'}, 'datafile': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Dataset_File']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'location': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Location']"}), 'protocol': ('django.db.models.fields.CharField', [], {'max_length': '10', 'blank': 'True'}), 'stay_remote': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'url': ('django.db.models.fields.CharField', [], {'max_length': '400'}), 'verified': ('django.db.models.fields.BooleanField', [], {'default': 'False'}) }, 'tardis_portal.schema': { 'Meta': {'object_name': 'Schema'}, 'hidden': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'immutable': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}), 'namespace': ('django.db.models.fields.URLField', [], {'unique': 'True', 'max_length': '255'}), 'subtype': ('django.db.models.fields.CharField', [], {'max_length': '30', 'null': 'True', 'blank': 'True'}), 'type': ('django.db.models.fields.IntegerField', [], {'default': '1'}) }, 'tardis_portal.token': { 'Meta': {'object_name': 'Token'}, 'experiment': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.Experiment']"}), 'expiry_date': ('django.db.models.fields.DateField', [], {'default': 'datetime.datetime(2013, 4, 7, 0, 0)'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'token': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}) }, 'tardis_portal.userauthentication': { 'Meta': {'object_name': 'UserAuthentication'}, 'authenticationMethod': ('django.db.models.fields.CharField', [], {'max_length': '30'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'userProfile': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tardis_portal.UserProfile']"}), 'username': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'tardis_portal.userprofile': { 'Meta': {'object_name': 'UserProfile'}, 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'isDjangoAccount': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']", 'unique': 'True'}) } } complete_apps = ['tardis_portal']

bsd-3-clause

openaid-IATI/OIPA

OIPA/OIPA/settings.py

1

13127

# Django settings for OIPA project. import os import sys from ast import literal_eval from os import environ as env from celery.schedules import crontab # from tzlocal import get_localzone BASE_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) DEBUG = literal_eval(env.get('OIPA_DEBUG', 'True')) FTS_ENABLED = literal_eval(env.get('OIPA_FTS_ENABLED', 'True')) LOGIN_REDIRECT_URL = '/admin/' LOGOUT_URL = '/logout' # LOGOUT_REDIRECT_URL = '/admin/logout' DATA_UPLOAD_MAX_NUMBER_FIELDS = 3000 SECRET_KEY = env.get('OIPA_SECRET_KEY', 'PXwlMOpfNJTgIdQeH5zk39jKfUMZPOUK') DATABASES = { 'default': { 'ENGINE': env.get( 'OIPA_DB_ENGINE', 'django.contrib.gis.db.backends.postgis' ), 'HOST': os.getenv('OIPA_DB_HOST', 'localhost'), 'PORT': os.getenv('OIPA_DB_PORT', 5432), 'NAME': os.getenv('OIPA_DB_NAME', 'oipa'), 'USER': os.getenv('OIPA_DB_USER', 'oipa'), 'PASSWORD': os.getenv('OIPA_DB_PASSWORD', 'oipa'), 'CONN_MAX_AGE': int(os.getenv('OIPA_DB_CONN_MAX_AGE', 500)) }, } TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': (os.path.join(BASE_DIR, 'templates'),), 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ # Insert your TEMPLATE_CONTEXT_PROCESSORS here or use this # list if you haven't customized them: 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.request', 'django.template.context_processors.tz', 'django.contrib.messages.context_processors.messages', ], # 'loaders': [ # ('django.template.loaders.cached.Loader', [ # 'django.template.loaders.filesystem.Loader', # 'django.template.loaders.app_directories.Loader', # ]), # ], }, }, ] def rel(*x): return os.path.join(os.path.abspath(os.path.dirname(__file__)), *x) sys.path.insert(0, rel('..', 'lib')) # Hosts/domain names that are valid for this site; required if DEBUG is False # See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts ALLOWED_HOSTS = env.get('OIPA_ALLOWED_HOSTS', '*').split() # Local time zone for this installation. Choices can be found here: # http://en.wikipedia.org/wiki/List_of_tz_zones_by_name # although not all choices may be available on all operating systems. # In a Windows environment this must be set to your system time zone. # Celery is needed UTC # TIME_ZONE = get_localzone().zone TIME_ZONE = 'UTC' # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = 'en-us' APPEND_SLASH = True SITE_ID = 1 # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True # If you set this to False, Django will not format dates, numbers and # calendars according to the current locale. USE_L10N = True # If you set this to False, Django will not use timezone-aware datetimes. USE_TZ = False # URL for static files STATIC_URL = '/static/' STATIC_ROOT = os.environ.get( 'OIPA_STATIC_ROOT', os.path.join( os.path.dirname(BASE_DIR), 'public/static')) MEDIA_URL = '/media/' MEDIA_ROOT = os.environ.get( 'OIPA_MEDIA_ROOT', os.path.join( os.path.dirname(BASE_DIR), 'public/media')) # Additional locations of static files STATICFILES_DIRS = ( os.path.join(BASE_DIR, 'static/'), ) # Python dotted path to the WSGI application used by Django's runserver. WSGI_APPLICATION = 'OIPA.wsgi.application' # List of finder classes that know how to find static files in # various locations. STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # 'django.contrib.staticfiles.finders.DefaultStorageFinder', ) MIDDLEWARE = [ 'corsheaders.middleware.CorsMiddleware', 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'api.middleware.FileExportMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django_otp.middleware.OTPMiddleware', ] ROOT_URLCONF = 'OIPA.urls' INSTALLED_APPS = [ # 'two-factor 'django_otp', 'django_otp.plugins.otp_static', 'django_otp.plugins.otp_totp', # 'two_factor', # 'otp_yubikey', # 'django_rq', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.messages', 'django.contrib.staticfiles', 'allauth', 'allauth.account', 'allauth.socialaccount', # 'grappelli', 'django.contrib.admin', 'django.contrib.admindocs', 'django.contrib.gis', 'corsheaders', 'common', 'iati.apps.IatiConfig', 'iati_organisation.apps.IatiOrganisationConfig', 'iati_synchroniser.apps.IatiSynchroniserConfig', 'geodata.apps.GeodataConfig', 'currency_convert.apps.CurrencyConvertConfig', 'traceability.apps.TraceabilityConfig', 'api', 'task_queue', 'djsupervisor', 'rest_framework', 'rest_framework_csv', 'django_extensions', 'iati_vocabulary.apps.IatiVocabularyConfig', 'iati_codelists.apps.IatiCodelistsConfig', 'test_without_migrations', 'rest_framework.authtoken', 'iati.permissions', 'rest_auth', 'rest_auth.registration', 'django_filters', 'markdownify', 'solr', 'django_celery_beat', 'django_celery_results' ] RQ_SHOW_ADMIN_LINK = True TEST_RUNNER = 'django.test.runner.DiscoverRunner' REST_FRAMEWORK = { 'DEFAULT_PAGINATION_CLASS': 'api.pagination.CustomPagination', 'PAGE_SIZE': 10, 'DEFAULT_FILTER_BACKENDS': ( 'django_filters.rest_framework.DjangoFilterBackend', ), 'DEFAULT_PARSER_CLASSES': ( 'rest_framework.parsers.JSONParser', ), 'DEFAULT_RENDERER_CLASSES': ( 'rest_framework.renderers.JSONRenderer', 'api.renderers.PaginatedCSVRenderer', 'api.renderers.XlsRenderer', 'api.renderers.IATIXMLRenderer', ), 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework.authentication.BasicAuthentication', 'rest_framework.authentication.SessionAuthentication', 'rest_framework.authentication.TokenAuthentication', ) } RQ_REDIS_URL = env.get('OIPA_RQ_REDIS_URL', 'redis://localhost:6379/0') RQ_QUEUES = { 'default': { 'URL': RQ_REDIS_URL, 'DEFAULT_TIMEOUT': 10800, # 3 hours }, 'parser': { 'URL': RQ_REDIS_URL, 'DEFAULT_TIMEOUT': 5400, }, 'export': { 'URL': RQ_REDIS_URL, 'DEFAULT_TIMEOUT': 5400, }, 'document_collector': { 'URL': RQ_REDIS_URL, 'DEFAULT_TIMEOUT': 5400, }, 'solr': { 'URL': RQ_REDIS_URL, 'DEFAULT_TIMEOUT': 10800, } } # TWO_FACTOR_FORCE_OTP_ADMIN = True # LOGIN_URL = 'two_factor:login' # LOGIN_REDIRECT_URL = '/admin' # Redirect admin dashboard GRAPPELLI_ADMIN_TITLE = 'OIPA admin' ADMINFILES_UPLOAD_TO = 'csv_files' CORS_ORIGIN_ALLOW_ALL = True CORS_URLS_REGEX = r'^/api/.*$' CORS_ALLOW_METHODS = ('GET',) IATI_PARSER_DISABLED = False CONVERT_CURRENCIES = True ROOT_ORGANISATIONS = [] ERROR_LOGS_ENABLED = literal_eval(env.get('OIPA_ERROR_LOGS_ENABLED', 'True')) DEFAULT_LANG = 'en' # django-all-auth ACCOUNT_EMAIL_VERIFICATION = 'none' # django-rest-auth REST_AUTH_SERIALIZERS = { 'USER_DETAILS_SERIALIZER': 'api.permissions.serializers.UserSerializer', } REST_AUTH_REGISTER_SERIALIZERS = { 'REGISTER_SERIALIZER': 'api.permissions.serializers.RegistrationSerializer' } # EXPORT_COMMENT = 'Published with tools developed by Zimmerman & Zimmerman' FIXTURE_DIRS = ( os.path.join(BASE_DIR, '../fixtures/'), ) CKAN_URL = env.get('OIPA_CKAN_URL', 'https://iati-staging.ckan.io') API_CACHE_SECONDS = int(env.get('OIPA_API_CACHE_SECONDS', 0)) CACHES = { 'default': { 'BACKEND': env.get( 'OIPA_CACHES_DEFAULT_BACKEND', 'redis_cache.RedisCache' ), 'LOCATION': env.get('OIPA_CACHES_DEFAULT_LOCATION', 'localhost:6379'), }, 'api': { 'BACKEND': env.get( 'OIPA_CACHES_DEFAULT_BACKEND', 'redis_cache.RedisCache' ), 'LOCATION': env.get('OIPA_CACHES_DEFAULT_LOCATION', 'localhost:6379'), } } OIPA_LOG_LEVEL = env.get('OIPA_LOG_LEVEL', 'ERROR') # These settings are overriden in development_settings and # produduction_settings modules: LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'handlers': { # Useful for local development: 'console': { 'class': 'logging.StreamHandler', }, }, 'loggers': { # All other errors: '': { 'handlers': ['console'], 'level': OIPA_LOG_LEVEL, 'propagate': False, }, # IATI Parser related errors: 'iati.parser': { 'handlers': ['console'], 'level': OIPA_LOG_LEVEL, 'propagate': False, }, # Django-related errors: 'django': { 'handlers': ['console'], 'level': OIPA_LOG_LEVEL, 'propagate': False, }, }, } REST_FRAMEWORK_EXTENSIONS = { 'DEFAULT_USE_CACHE': 'api', # reset cache every x seconds: 'DEFAULT_CACHE_RESPONSE_TIMEOUT': 1 * 60 * 60 * 24 * 7, # 1 week } # DATA PLUGINS is a dict with data which is not related to the IATI data. # For example, for M49 Regions import, add such code block it in the # local_settings.py: BASE_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) DATA_PLUGINS = { 'codelist': { 'm49_region_file': '{base_dir}/plugins/data/{filename}'.format( base_dir=BASE_DIR, filename='regions.json') } } # DATA_PLUGINS = {} # A setting indicating whether to save XML datasets (files) to local machine or # not: DOWNLOAD_DATASETS = False # CELERY CONFIG CELERY_ACKS_LATE = True CELERY_WORKER_PREFETCH_MULTIPLIER = 1 # limiting the number of reserved tasks. CELERY_TIMEZONE = 'UTC' CELERY_ENABLE_UTC = True CELERY_TASK_ROUTES = {'task_queue.tasks.revoke_all_tasks': {'queue': 'revoke_queue'}, 'task_queue.tasks.continuous_parse_all_existing_sources_task': {'queue': 'revoke_queue'}} # NOQA: E501 CELERY_BROKER_URL = 'amqp://localhost' CELERY_RESULT_BACKEND = 'django-db' # 'rpc://localhost' # 'db+postgresql://oipa:oipa@localhost/oipa' CELERY_ALWAYS_EAGER = True CELERY_BROKER_POOL_LIMIT = None CELERY_EAGER_PROPAGATES_EXCEPTIONS = True CELERY_IMPORTS = 'iati.PostmanJsonImport.tasks' CELERY_BEAT_SCHEDULE = { 'getting_postman-api': { 'task': 'iati.PostmanJsonImport.tasks.get_postman_api', 'schedule': crontab(minute=0, hour=0), }, 'Update the exchange rates': { 'task': 'task_queue.tasks.update_exchange_rates', 'schedule': crontab(minute=0, hour=0), }, } SOLR = { 'indexing': False, 'url': 'http://localhost:8983/solr', 'cores': { 'activity': 'activity', 'activity-sector': 'activity-sector', 'budget': 'budget', 'codelist': { 'country': 'codelist-country', 'region': 'codelist-region' }, 'dataset': 'dataset', 'datasetnote': 'datasetnote', 'organisation': 'organisation', 'publisher': 'publisher', 'result': 'result', 'transaction': 'transaction', 'transaction-sector': 'transaction-sector' } } VALIDATION = { 'host': 'http://iativalidator.iatistandard.org/', 'api': { 'root': 'api', 'version': '/v1', 'urls': { 'post_file': '/iati-testfiles/file/source', 'start_validation': '/iati-testdatasets/{validation_id}', 'get_json_file': '/iati-files/file/json/{json_file}', 'get_json_file_ad_hoc': '/iati-testfiles/file/json/{json_file}', }, 'max_loop_process': 50, 'sleep_second_process': 5, 'valid_status': 'success', 'retry': { 'max_retries': 5, } } } # To be overwritten by local_settings.py in any case for security purposes. POSTMAN_API_KEY = 'OverwriteFromLocalSettings' try: from .local_settings import * # noqa: F401, F403 except ImportError: pass

agpl-3.0

dvorka/endurance-training-log

src/import_concept2_to_etl_csv.py

1

5751

#!/usr/bin/env python # # Endurance Training Log # # Copyright (C) 2020 Martin Dvorak <martin.dvorak@mindforger.com> # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. import datetime import datatable as dt from src.import_strava_torben_to_etl_csv import EtlDataset C2_YEARS = [2009, 2010, 2011, 2012, 2013, 2015, 2016, 2017, 2019, 2020] FILE_SRC_CSV = ( "/home/dvorka/p/endurance-training-log/github/endurance-training-log/datasets/" "concept2.com/concept2-season-" ) FILE_DST_CSV_FILE = ( "/home/dvorka/p/endurance-training-log/github/endurance-training-log/test/datasets/" "concept2-training-log-import-" ) # TODO fix rank # TODO quote description to avoid problems class Concept2Dataset: """concept2.com dataset exported from training log web. Columns description: "ID", [source] concept2:<ID> "Date", [year, month, day, when] "Description", [description] "Work Time (Formatted)", "Work Time (Seconds)", [time] int(1194.3) "Rest Time (Formatted)", "Rest Time (Seconds)", "Work Distance", [distance] "Rest Distance", "Stroke Rate/Cadence", [description] if not None then "@24" "Stroke Count", "Pace", [description] if not None then "1:59"/500m "Avg Watts", "Cal/Hour", "Total Cal", [kcal] "Avg Heart Rate", "Drag Factor", [description] if not None then DF"122" "Age", "Weight", "Type", "Ranked", [intensity] if not None then "rank" "Comments" [description] if not None then ("...") """ URL_CONCEPT2_ACTIVITY = "https://log.concept2.com/profile/737678/log/" def __init__(self, dataset_path: str): self.dataset_path = dataset_path self.frame: dt.Frame = dt.fread(dataset_path) def __str__(self) -> str: result: str = f"Dataset path: {self.dataset_path}\n" result = f"{result}Columns:\n" for name in self.frame.names: result = f"{result} {name}\n" return result def to_etl_dataset(self) -> dt.Frame: etl_frame: dt.Frame = EtlDataset.get_empty_frame() for row in range(self.frame.shape[0]): print(f"{row}: {self.frame[row,'ID']}") new_row: dict = EtlDataset.get_empty_frame_dict() # "2020-03-04 11:34:00" date_time_obj = datetime.datetime.strptime( self.frame[row, "Date"], "%Y-%m-%d %H:%M:%S" ) new_row["year"] = [date_time_obj.year] new_row["month"] = [date_time_obj.month] new_row["day"] = [date_time_obj.day] new_row["when"] = [ f"{date_time_obj.hour:02}:{date_time_obj.minute:02}" f":{date_time_obj.second:02}" ] new_row["activity"] = ["rowing"] description: str = "" cadence = self.frame[row, "Stroke Rate/Cadence"] description = f"{description} @{cadence}" if cadence else f"{description}" pace = self.frame[row, "Pace"] description = f"{description} {pace}/500m" if pace else f"{description}" drag = self.frame[row, "Drag Factor"] description = f"{description} DF{drag}" if drag else f"{description}" comment = self.frame[row, "Comments"] description = f"{description} ({comment})" if comment else f"{description}" new_row["description"] = [description] new_row["distance_meters"] = [int(self.frame[row, "Work Distance"])] new_row["time_seconds"] = [int(self.frame[row, "Work Time (Seconds)"])] new_row["total_distance_meters"] = new_row["distance_meters"] new_row["total_time_seconds"] = new_row["time_seconds"] speed: float = float(new_row["distance_meters"][0]) / float( new_row["time_seconds"][0] ) * 3.6 new_row["avg_speed"] = [speed] new_row["max_speed"] = new_row["avg_speed"] new_row["elevation_gain"] = [0] avg_watts = self.frame[row, "Avg Watts"] new_row["avg_watts"] = [int(avg_watts) if avg_watts else 0] new_row["kcal"] = [self.frame[row, "Total Cal"]] new_row["commute"] = [False] intensity = self.frame[row, "Ranked"] new_row["intensity"] = ["rank" if intensity else "fartlek"] new_row["gear"] = ["my_concept2_e"] new_row["url"] = [ f"{Concept2Dataset.URL_CONCEPT2_ACTIVITY}{self.frame[row, 'ID']}" ] new_row["source"] = [f"concept2:{self.frame[row, 'ID']}"] etl_frame.rbind(dt.Frame(new_row)) print(f"Imported frame:\n{etl_frame}") return etl_frame # # main # if __name__ == "__main__": for year in C2_YEARS: concept2_dataset = Concept2Dataset( f"{FILE_SRC_CSV}{year}.csv" ) print(concept2_dataset) etl_frame = concept2_dataset.to_etl_dataset() etl_frame.to_csv( f"{FILE_DST_CSV_FILE}{year}.csv" )

apache-2.0

yonglehou/scikit-learn

sklearn/__check_build/__init__.py

342

1671

""" Module to give helpful messages to the user that did not compile the scikit properly. """ import os INPLACE_MSG = """ It appears that you are importing a local scikit-learn source tree. For this, you need to have an inplace install. Maybe you are in the source directory and you need to try from another location.""" STANDARD_MSG = """ If you have used an installer, please check that it is suited for your Python version, your operating system and your platform.""" def raise_build_error(e): # Raise a comprehensible error and list the contents of the # directory to help debugging on the mailing list. local_dir = os.path.split(__file__)[0] msg = STANDARD_MSG if local_dir == "sklearn/__check_build": # Picking up the local install: this will work only if the # install is an 'inplace build' msg = INPLACE_MSG dir_content = list() for i, filename in enumerate(os.listdir(local_dir)): if ((i + 1) % 3): dir_content.append(filename.ljust(26)) else: dir_content.append(filename + '\n') raise ImportError("""%s ___________________________________________________________________________ Contents of %s: %s ___________________________________________________________________________ It seems that scikit-learn has not been built correctly. If you have installed scikit-learn from source, please do not forget to build the package before using it: run `python setup.py install` or `make` in the source directory. %s""" % (e, local_dir, ''.join(dir_content).strip(), msg)) try: from ._check_build import check_build except ImportError as e: raise_build_error(e)

bsd-3-clause

nickgentoo/scikit-learn-graph

scripts/ODDSTKernel_example_calculate_matrix.py

1

1663

# -*- coding: utf-8 -*- """ Created on Fri Mar 13 13:02:41 2015 Copyright 2015 Nicolo' Navarin This file is part of scikit-learn-graph. scikit-learn-graph is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. scikit-learn-graph is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with scikit-learn-graph. If not, see <http://www.gnu.org/licenses/>. """ import sys from skgraph.kernel.ODDSTGraphKernel import ODDSTGraphKernel from skgraph.datasets import load_graph_datasets if __name__=='__main__': if len(sys.argv)<1: sys.exit("python ODDKernel_example.py filename") max_radius=3 la=1 #hashs=int(sys.argv[3]) njobs=1 name=str(sys.argv[1]) g_it=load_graph_datasets.load_graphs_bursi() ODDkernel=ODDSTGraphKernel(r=max_radius,l=la) GM=ODDkernel.computeKernelMatrixTrain([g_it.graphs[i] for i in range(21)]) #Parallel ,njobs GMsvm=[] for i in range(len(GM)): GMsvm.append([]) GMsvm[i]=[i+1] GMsvm[i].extend(GM[i]) from sklearn import datasets print "Saving Gram matrix" #datasets.dump_svmlight_file(GMsvm,g_it.target, name+".svmlight") datasets.dump_svmlight_file(GMsvm,[g_it.target[i] for i in range(21)], name+".svmlight") #print GM

gpl-3.0

nickgentoo/scikit-learn-graph

scripts/cross_validation_ICML16_norm_10fold.py

1

5398

import sys, os sys.path.append(os.path.join(os.path.dirname(__file__), '..', '','')) import numpy as np #from skgraph import datasets from sklearn import svm #from skgraph.ioskgraph import * from math import sqrt from copy import copy import sys #"sys.path.append('..\\..\\Multiple Kernel Learning\\Framework')" if len(sys.argv)<4: sys.exit("python cross_validation_from_matrix_norm.py inputMatrix.libsvm C outfile") c=float(sys.argv[2]) ##TODO read from libsvm format from sklearn.datasets import load_svmlight_file #TODO metodo + veloce per caricar ele amtrici (anche per fare dump) #from svmlight_loader import load_svmlight_file # riga 22 non serve km, target_array = load_svmlight_file(sys.argv[1]) #print type(target_array) #print target_array #Controlla se target array ha +1 e -1! se ha 0, sostituisco gli 0 ai -1 if not -1 in target_array: print "WARNING: no -1 in target array! Changing 0s to -1s" target_array = np.array([-1 if x == 0 else x for x in target_array]) #print km #tolgo indice ##############kmgood=km[:,1:].todense() gram=km[:,1:].todense() kmgood=copy(gram) #NORMALIZATION for i in xrange(len(target_array)): for j in xrange(0,len(target_array)): #print i,j,kmgood[i,j],kmgood[i,i],kmgood[j,j] if kmgood[i,i]*kmgood[j,j]==0: print "WARNING: avoided divizion by zero" gram[i,j]=0 else: gram[i,j]=kmgood[i,j]/sqrt(kmgood[i,i]*kmgood[j,j]) #----------------------------------- print "matrix normalization completed" #from sklearn.metrics import make_scorer # (16) in the paper def my_custom_loss_func(ground_truth, predictions): total_loss=0.0 for gt,p in zip(ground_truth, predictions): #print gt, p diff = (1.0 - (gt * p)) / 2.0 if diff<0: diff=0.0 if diff > 1.0: diff=1.0 total_loss+=diff return total_loss / len(predictions) from sklearn import cross_validation import time start = time.time() for rs in range(42,43): #for rs in range(42,53): f=open(str(sys.argv[3]+".seed"+str(rs)+".c"+str(c)),'w') kf = cross_validation.StratifiedKFold(target_array, n_folds=10, shuffle=True,random_state=rs) #print kf #remove column zero because #first entry of each line is the index #gram=km[:,1:].todense() f.write("Total examples "+str(len(gram))+"\n") f.write("|W| \t train_loss \t test_loss\n") #print gram # normalization from math import sqrt #for i in range(len(gram)): # for j in range(len(gram)): # gram[i,j]=gram[i,j]/sqrt(gram[i,i]+gram[j,j]) sc=[] for train_index, test_index in kf: #print("TRAIN:", train_index, "TEST:", test_index) #generated train and test lists, incuding indices of the examples in training/test #for the specific fold. Indices starts from 0 now clf = svm.SVC(C=c, kernel='precomputed') train_gram = [] #[[] for x in xrange(0,len(train))] test_gram = []# [[] for x in xrange(0,len(test))] #compute training and test sub-matrices index=-1 for row in gram: index+=1 if index in train_index: train_gram.append([gram[index,i] for i in train_index]) else: test_gram.append([gram[index,i] for i in train_index]) #print gram X_train, X_test, y_train, y_test = np.array(train_gram), np.array(test_gram), target_array[train_index], target_array[test_index] clf.fit(X_train, y_train) #print |W|^2= alpha Q alpha, where Q_ij= y_i y_j K(x_i,x_j) alpha = clf.dual_coef_ yw=target_array[clf.support_] Kw=gram[clf.support_,:][:,clf.support_] #print yw.shape, Kw.shape, gram.shape yw.shape=(yw.shape[0],1) YM=np.ones(yw.shape[0])*yw.T Q= np.multiply(np.multiply(YM,Kw),YM.T) #print Q.shape #print alpha.shape #alpha.shape=(alpha.shape[1],1) W2=alpha*Q*alpha.T print "|W|" , sqrt(W2), f.write(str(sqrt(W2))+"\t") #loss = make_scorer(my_custom_loss_func, greater_is_better=False) from sklearn.metrics import accuracy_score #predictions on training set y_train_predicted=clf.decision_function(X_train) #print type( my_custom_loss_func(y_train, y_train_predicted)) print " training loss ",(str( my_custom_loss_func(y_train, y_train_predicted))), f.write(str(my_custom_loss_func(y_train, y_train_predicted))+"\t") # predict on test examples y_test_predicted=clf.decision_function(X_test) #print y_test.shape, y_test_predicted.shape print " test loss ",(str( my_custom_loss_func(y_test, y_test_predicted))) y_test_predicted_binary=clf.predict(X_test) #print y_test #print y_test_predicted_binary #print "Accuracy: ", accuracy_score(y_test, y_test_predicted_binary) #y_test_sign=map(np.sign, y_test_predicted) #print "Accuracy_decision: ", accuracy_score(y_test, y_test_sign) sc.append(my_custom_loss_func(y_test, y_test_predicted)) f.write(str( my_custom_loss_func(y_test, y_test_predicted))+"\n") f.close() end = time.time() print "Total time:", end-start scores=np.array(sc) print "Accuracy: %0.4f (+/- %0.4f)" % (scores.mean(), scores.std() / 2)

gpl-3.0

rui-castro/Sick-Beard

lib/guessit/transfo/split_path_components.py

18

1697

#!/usr/bin/env python # -*- coding: utf-8 -*- # # GuessIt - A library for guessing information from filenames # Copyright (c) 2013 Nicolas Wack <wackou@gmail.com> # # GuessIt is free software; you can redistribute it and/or modify it under # the terms of the Lesser GNU General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # GuessIt is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # Lesser GNU General Public License for more details. # # You should have received a copy of the Lesser GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # from __future__ import absolute_import, division, print_function, unicode_literals from guessit.plugins.transformers import Transformer from guessit import fileutils from os.path import splitext class SplitPathComponents(Transformer): def __init__(self): Transformer.__init__(self, 255) def process(self, mtree, options=None): """first split our path into dirs + basename + ext :return: the filename split into [ dir*, basename, ext ] """ if not options.get('name_only'): components = fileutils.split_path(mtree.value) basename = components.pop(-1) components += list(splitext(basename)) components[-1] = components[-1][1:] # remove the '.' from the extension mtree.split_on_components(components) else: mtree.split_on_components([mtree.value, ''])

gpl-3.0

rishizsinha/project-beta

code/scenes_pred.py

4

7072

""" The following script will analyze the scenes data. Specifically, it will: * Try to find patterns between neural responses and scenes * Use SVM and KNN to link these together * Predict scenes based on BOLD activity """ #Import Standard Libraries from __future__ import print_function, division import numpy as np import pandas as pd import nibabel as nib import matplotlib.pyplot as plt import itertools from pylab import * #Local Modules import utils.data_loading as dl import utils.save_files as sv import utils.scenes as sn #Clustering Libraries from sklearn import preprocessing as pp from sklearn.decomposition import PCA from sklearn import svm from sklearn.neighbors import KNeighborsClassifier from sklearn.cluster import KMeans from sklearn.metrics import accuracy_score from sklearn.grid_search import GridSearchCV from sklearn.metrics import classification_report from sklearn.svm import SVC #Load in filtered data and normalize masked_path = "../data/filtered_data.npy" combined_runs = pp.normalize(np.transpose(np.load("../data/filtered_data.npy"))) #Too many predictors (55k) - filter to around 1500 predictors xvar = np.var(combined_runs, axis=0) varmask = np.where(xvar > .0000000015)[0] combined_runs = combined_runs.T[varmask] #1584 voxels #Load in scenes data scenes_path = '../data/scene_times_nums.csv' scenes = pd.read_csv(scenes_path, header = None) scenes = scenes.values #Now just a numpy array TR = 2 NUM_VOLUMES = combined_runs.shape[-1] #3543 ONSET_TIMES = scenes[:,0] ONSET_TIMES_NORMED = ONSET_TIMES - 17 #First recorded scene occurs at t = 17 sec DURATION = scenes[:,1] LABELS = scenes[:,3] SCAN_TIMES = np.arange(start=0, stop=2*NUM_VOLUMES, step=2) #Creates a list that tells us scene id at given scan time factor_grid = [] for scan_time in SCAN_TIMES: index_list = np.where(ONSET_TIMES_NORMED < scan_time)[0] if scan_time == 0: label_index = 0 else: label_index = index_list[-1] factor_id = LABELS[label_index] factor_grid.append(factor_id) factor_grid = np.array(factor_grid) #Convert to np array for future analysis #Grouped Factors Ids GUMP_SCENES_IDS = [38, 40, 41, 42] #factor ids of Gump scenes MILITARY_IDS = [52, 62, 77, 78, 80, 81, 82, 83] SCHOOL = [22,43, 67, 61, 69] SAVANNA = [66] POLITICAL = [86, 85, 2, 87, 84] OUTSIDE = [27, 73, 58, 53, 59] CHURCH = [20] DEATH = [16, 48] ############ SVM and KNN Analysis ################################# #Comparison between Military and Gump Scenes #Set up training and testing samples and data all_ids_1 = GUMP_SCENES_IDS + MILITARY_IDS sample1, missing_facts1 = sn.gen_sample_by_factors(all_ids_1, factor_grid, True, prop=.9) train_samp1 = sn.get_training_samples(sample1) test_samp1 = sn.get_tst_samples(sample1) train1_labs, train1_times = sn.make_label_by_time(train_samp1) test1_labs, test1_times = sn.make_label_by_time(test_samp1) on_off1_train = sn.on_off_course(GUMP_SCENES_IDS, train1_labs) on_off1_test = sn.on_off_course(GUMP_SCENES_IDS, test1_labs) subarr1_train = combined_runs[:,train1_times].T #rows correspond to images, colums to voxels subarr1_test = combined_runs[:,test1_times].T #data we feed into our classifier clf = svm.SVC(C=100, kernel='linear') #Paramters obtained through cross-validation clf.fit(subarr1_train, on_off1_train) pred_svm1 = clf.predict(subarr1_test) accuracy_score(on_off1_test, pred_svm1) #52% knn = KNeighborsClassifier() knn.fit(subarr1_train, on_off1_train) pred_knn1 = knn.predict(subarr1_test) accuracy_score(on_off1_test, pred_knn1) #69% #Compare more scenes all_ids_2 = GUMP_SCENES_IDS + SCHOOL + MILITARY_IDS + SAVANNA + POLITICAL + OUTSIDE + DEATH + CHURCH sample2, missing_facts2 = sn.gen_sample_by_factors(all_ids_2, factor_grid, True, prop=.9) train_samp2 = sn.get_training_samples(sample2) test_samp2 = sn.get_tst_samples(sample2) train2_labs, train2_times = sn.make_label_by_time(train_samp2) test2_labs, test2_times = sn.make_label_by_time(test_samp2) #Set up ids for each category labels2_train = [] for val in train2_labs: if val in GUMP_SCENES_IDS: labels2_train.append(0) elif val in SCHOOL: labels2_train.append(1) elif val in MILITARY_IDS: labels2_train.append(2) elif val in SAVANNA: labels2_train.append(3) elif val in POLITICAL: labels2_train.append(4) elif val in OUTSIDE: labels2_train.append(5) elif val in DEATH: labels2_train.append(6) else: labels2_train.append(7) labels2_train = np.array(labels2_train) labels2_test = [] for val in test2_labs: if val in GUMP_SCENES_IDS: labels2_test.append(0) elif val in SCHOOL: labels2_test.append(1) elif val in MILITARY_IDS: labels2_test.append(2) elif val in SAVANNA: labels2_test.append(3) elif val in POLITICAL: labels2_test.append(4) elif val in OUTSIDE: labels2_test.append(5) elif val in DEATH: labels2_test.append(6) else: labels2_test.append(7) labels2_test = np.array(labels2_test) subarr2_train = combined_runs[:,train2_times].T subarr2_test = combined_runs[:,test2_times].T clf = svm.SVC(C=100, kernel='linear') #Paramters obtained through cross-validation clf.fit(subarr2_train, labels2_train) pred_svm2 = clf.predict(subarr2_test) accuracy_score(labels2_test, pred_svm2) #27.7% knn = KNeighborsClassifier() knn.fit(subarr2_train, labels2_train) pred_knn2 = knn.predict(subarr2_test) accuracy_score(labels2_test, pred_knn2) #34% #Knn looks better - let's see how it performs by cateogry #Check performance over the 6 categories gump_indcs = np.where(labels2_test == 0)[0] school_inds = np.where(labels2_test == 1)[0] milit_incs = np.where(labels2_test == 2)[0] savan_indcs = np.where(labels2_test == 3)[0] political_indcs = np.where(labels2_test == 4)[0] outside_indcs = np.where(labels2_test == 5)[0] death_indcs = np.where(labels2_test == 6)[0] church_inds = np.where(labels2_test == 7)[0] by_cat = [gump_indcs, school_inds, milit_incs, savan_indcs, political_indcs, outside_indcs, death_indcs, church_inds] perform_by_cat = [] actual_count = [] pred_count = [] for scence_ind in by_cat: acc = accuracy_score(labels2_test[scence_ind], pred_knn2[scence_ind]) weight = scence_ind.shape[0] perform_by_cat.append(acc) actual_count.append(weight) #Plot this actual_count = np.array(actual_count) relative_weights = actual_count / sum(actual_count) #create labels for pie chart categories = ['gump', 'school', 'military', 'savanna', 'political', 'outside', 'death', 'church'] categories_per = [] for index, name in enumerate(categories): name2 = name + ': ' + '' + str(round(perform_by_cat[index], 3) * 100) + '%' categories_per.append(name2) fig = plt.figure() ax = fig.gca() ax.pie(relative_weights, labels=categories_per,autopct='%1.1f%%') plt.title('Category Weight and Performance by Category') plt.savefig('../figure/scenes_pie_chart.png') plt.close()

bsd-3-clause

ResidentMario/geoplot

examples/plot_obesity.py

1

1380

""" Cartogram of US states by obesity rate ====================================== This example ``cartogram`` showcases regional trends for obesity in the United States. Rugged mountain states are the healthiest; the deep South, the unhealthiest. This example inspired by the `"Non-Contiguous Cartogram" <https://bl.ocks.org/mbostock/4055908>`_ example in the D3.JS example gallery. """ import pandas as pd import geopandas as gpd import geoplot as gplt import geoplot.crs as gcrs import matplotlib.pyplot as plt import mapclassify as mc # load the data obesity_by_state = pd.read_csv(gplt.datasets.get_path('obesity_by_state'), sep='\t') contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa')) contiguous_usa['Obesity Rate'] = contiguous_usa['state'].map( lambda state: obesity_by_state.query("State == @state").iloc[0]['Percent'] ) scheme = mc.Quantiles(contiguous_usa['Obesity Rate'], k=5) ax = gplt.cartogram( contiguous_usa, scale='Obesity Rate', limits=(0.75, 1), projection=gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5), hue='Obesity Rate', cmap='Reds', scheme=scheme, linewidth=0.5, legend=True, legend_kwargs={'loc': 'lower right'}, legend_var='hue', figsize=(12, 7) ) gplt.polyplot(contiguous_usa, facecolor='lightgray', edgecolor='None', ax=ax) plt.title("Adult Obesity Rate by State, 2013")

mit

longubu/datumio

examples/keras/__init__.py

1

1674

# COPYRIGHT # --------- # All contributions by Long Van Ho: # Copyright (c) 2015 Long Van Ho # All rights reserved. # # All contributions by Sander Dielman: # Copyright (c) 2015 Sander Dieleman # All rights reserved. # # All other contributions: # Copyright (c) 2015, the respective contributors. # All rights reserved. # # LICENSE # --------- # The MIT License (MIT) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN # ============================================================================== """Imports mnist tutorial libraries used by tutorial examples.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from keras.datasets import cifar10 from keras.datasets import mnist

mit

matthias-k/pysaliency

pysaliency/external_datasets/figrim.py

1

7300

from __future__ import absolute_import, print_function, division import zipfile import os import glob import numpy as np from scipy.io import loadmat from natsort import natsorted from boltons.fileutils import mkdir_p from ..datasets import FixationTrains from ..utils import ( TemporaryDirectory, download_and_check, atomic_directory_setup, ) from .utils import create_stimuli, _load def _load_FIGRIM_data(filename, stimuli_indices, stimulus_type): data = loadmat(filename)['allImages'].flatten() xs = [] ys = [] ts = [] ns = [] train_subjects = [] which_times = [] which_time_names = ['enc', 'rec', 'rec2'] stimulus_types = [] responses = [] for stimulus_data in data: n = stimuli_indices[stimulus_data['filename'][0]] # category = stimulus_data['category'][0] # TODO: use for subject, subject_data in enumerate(stimulus_data['userdata'].flatten()): if not subject_data['trial']: # No data for this subject and this stimulus continue for which_time in which_time_names: fixations = subject_data['fixations'][0, 0][which_time] if not len(fixations): continue # if len(fixations) and which_time != 'enc': # print("Problem:", n, subject_name, which_time) subject_response = subject_data['SDT'][0][which_time_names.index(which_time)] xs.append(fixations[:, 0]) ys.append(fixations[:, 1]) ts.append(np.arange(len(xs[-1]))) ns.append(n) train_subjects.append(subject) which_times.append(which_time_names.index(which_time)) stimulus_types.append(stimulus_type) responses.append(subject_response) return xs, ys, ts, ns, train_subjects, which_times, stimulus_types, responses def get_FIGRIM(location=None): """ Loads or downloads and caches the FIGRIM dataset. The dataset consists of >2700 scenes of sizes 1000x1000px and the fixations of subjects while doing a repetition recognition task with 3 seconds presentation time. subject responses etc are included. @type location: string, defaults to `None` @param location: If and where to cache the dataset. The dataset will be stored in the subdirectory `toronto` of location and read from there, if already present. @return: Stimuli, FixationTrains .. note:: This dataset comes with additional annotations: - stimulus_type: 0=filler, 1=target - which_time: 0=encoding, 1=first recognition, 2=second recognition - response: 1=hit, 2=false alarm, 3=miss, 4=correct rejection .. seealso:: Bylinskii, Zoya and Isola, Phillip and Bainbridge, Constance and Torralba, Antonio and Oliva, Aude. Intrinsic and Extrinsic Effects on Image Memorability [Vision research 2015] http://figrim.mit.edu/index_eyetracking.html """ if location: location = os.path.join(location, 'FIGRIM') if os.path.exists(location): stimuli = _load(os.path.join(location, 'stimuli.hdf5')) fixations = _load(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations os.makedirs(location) with atomic_directory_setup(location): with TemporaryDirectory(cleanup=True) as temp_dir: download_and_check('http://figrim.mit.edu/Fillers.zip', os.path.join(temp_dir, 'Fillers.zip'), 'dc0bc9561b5bc90e158ec32074dd1060') download_and_check('http://figrim.mit.edu/Targets.zip', os.path.join(temp_dir, 'Targets.zip'), '2ad3a42ebc377efe4b39064405568201') download_and_check('https://github.com/cvzoya/figrim/blob/master/targetData/allImages_release.mat?raw=True', os.path.join(temp_dir, 'allImages_release.mat'), 'c72843b05e95ab27594c1d11c849c897') download_and_check('https://github.com/cvzoya/figrim/blob/master/fillerData/allImages_fillers.mat?raw=True', os.path.join(temp_dir, 'allImages_fillers.mat'), 'ce4f8b4961005d62f7a21191a64cab5e') # Stimuli mkdir_p(os.path.join(temp_dir, 'stimuli')) print('Creating stimuli') f = zipfile.ZipFile(os.path.join(temp_dir, 'Fillers.zip')) f.extractall(os.path.join(temp_dir, 'stimuli')) f = zipfile.ZipFile(os.path.join(temp_dir, 'Targets.zip')) f.extractall(os.path.join(temp_dir, 'stimuli')) stimuli_src_location = os.path.join(temp_dir, 'stimuli') stimuli_target_location = os.path.join(location, 'Stimuli') if location else None images = glob.glob(os.path.join(stimuli_src_location, '**', '**', '*.jpg')) images = [os.path.relpath(img, start=stimuli_src_location) for img in images] stimuli_filenames = natsorted(images) stimuli = create_stimuli(stimuli_src_location, stimuli_filenames, stimuli_target_location) stimuli_basenames = [os.path.basename(filename) for filename in stimuli_filenames] stimulus_indices = {s: stimuli_basenames.index(s) for s in stimuli_basenames} # FixationTrains print('Creating fixations') print('Fillers...') (xs_filler, ys_filler, ts_filler, ns_filler, train_subjects_filler, which_times_filler, stimulus_types_filler, responses_filler) = _load_FIGRIM_data(os.path.join(temp_dir, 'allImages_fillers.mat'), stimulus_indices, stimulus_type=0) print("Targets...") (xs_target, ys_target, ts_target, ns_target, train_subjects_target, which_times_target, stimulus_types_target, responses_target) = _load_FIGRIM_data(os.path.join(temp_dir, 'allImages_release.mat'), stimulus_indices, stimulus_type=0) print("Finalizing...") xs = xs_filler + xs_target ys = ys_filler + ys_target ts = ts_filler + ts_target ns = ns_filler + ns_target train_subjects = train_subjects_filler + train_subjects_target which_times = which_times_filler + which_times_target stimulus_types = stimulus_types_filler + stimulus_types_target responses = responses_filler + responses_target fixations = FixationTrains.from_fixation_trains( xs, ys, ts, ns, train_subjects, scanpath_attributes={ 'which_time': which_times, 'stimulus_type': stimulus_types, 'response': responses }) if location: stimuli.to_hdf5(os.path.join(location, 'stimuli.hdf5')) fixations.to_hdf5(os.path.join(location, 'fixations.hdf5')) return stimuli, fixations

mit

yonglehou/scikit-learn

examples/decomposition/plot_sparse_coding.py

246

3846

""" =========================================== Sparse coding with a precomputed dictionary =========================================== Transform a signal as a sparse combination of Ricker wavelets. This example visually compares different sparse coding methods using the :class:`sklearn.decomposition.SparseCoder` estimator. The Ricker (also known as Mexican hat or the second derivative of a Gaussian) is not a particularly good kernel to represent piecewise constant signals like this one. It can therefore be seen how much adding different widths of atoms matters and it therefore motivates learning the dictionary to best fit your type of signals. The richer dictionary on the right is not larger in size, heavier subsampling is performed in order to stay on the same order of magnitude. """ print(__doc__) import numpy as np import matplotlib.pylab as pl from sklearn.decomposition import SparseCoder def ricker_function(resolution, center, width): """Discrete sub-sampled Ricker (Mexican hat) wavelet""" x = np.linspace(0, resolution - 1, resolution) x = ((2 / ((np.sqrt(3 * width) * np.pi ** 1 / 4))) * (1 - ((x - center) ** 2 / width ** 2)) * np.exp((-(x - center) ** 2) / (2 * width ** 2))) return x def ricker_matrix(width, resolution, n_components): """Dictionary of Ricker (Mexican hat) wavelets""" centers = np.linspace(0, resolution - 1, n_components) D = np.empty((n_components, resolution)) for i, center in enumerate(centers): D[i] = ricker_function(resolution, center, width) D /= np.sqrt(np.sum(D ** 2, axis=1))[:, np.newaxis] return D resolution = 1024 subsampling = 3 # subsampling factor width = 100 n_components = resolution / subsampling # Compute a wavelet dictionary D_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components) D_multi = np.r_[tuple(ricker_matrix(width=w, resolution=resolution, n_components=np.floor(n_components / 5)) for w in (10, 50, 100, 500, 1000))] # Generate a signal y = np.linspace(0, resolution - 1, resolution) first_quarter = y < resolution / 4 y[first_quarter] = 3. y[np.logical_not(first_quarter)] = -1. # List the different sparse coding methods in the following format: # (title, transform_algorithm, transform_alpha, transform_n_nozero_coefs) estimators = [('OMP', 'omp', None, 15), ('Lasso', 'lasso_cd', 2, None), ] pl.figure(figsize=(13, 6)) for subplot, (D, title) in enumerate(zip((D_fixed, D_multi), ('fixed width', 'multiple widths'))): pl.subplot(1, 2, subplot + 1) pl.title('Sparse coding against %s dictionary' % title) pl.plot(y, ls='dotted', label='Original signal') # Do a wavelet approximation for title, algo, alpha, n_nonzero in estimators: coder = SparseCoder(dictionary=D, transform_n_nonzero_coefs=n_nonzero, transform_alpha=alpha, transform_algorithm=algo) x = coder.transform(y) density = len(np.flatnonzero(x)) x = np.ravel(np.dot(x, D)) squared_error = np.sum((y - x) ** 2) pl.plot(x, label='%s: %s nonzero coefs,\n%.2f error' % (title, density, squared_error)) # Soft thresholding debiasing coder = SparseCoder(dictionary=D, transform_algorithm='threshold', transform_alpha=20) x = coder.transform(y) _, idx = np.where(x != 0) x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y) x = np.ravel(np.dot(x, D)) squared_error = np.sum((y - x) ** 2) pl.plot(x, label='Thresholding w/ debiasing:\n%d nonzero coefs, %.2f error' % (len(idx), squared_error)) pl.axis('tight') pl.legend() pl.subplots_adjust(.04, .07, .97, .90, .09, .2) pl.show()

bsd-3-clause

pkruskal/scikit-learn

examples/decomposition/plot_sparse_coding.py

246

3846

""" =========================================== Sparse coding with a precomputed dictionary =========================================== Transform a signal as a sparse combination of Ricker wavelets. This example visually compares different sparse coding methods using the :class:`sklearn.decomposition.SparseCoder` estimator. The Ricker (also known as Mexican hat or the second derivative of a Gaussian) is not a particularly good kernel to represent piecewise constant signals like this one. It can therefore be seen how much adding different widths of atoms matters and it therefore motivates learning the dictionary to best fit your type of signals. The richer dictionary on the right is not larger in size, heavier subsampling is performed in order to stay on the same order of magnitude. """ print(__doc__) import numpy as np import matplotlib.pylab as pl from sklearn.decomposition import SparseCoder def ricker_function(resolution, center, width): """Discrete sub-sampled Ricker (Mexican hat) wavelet""" x = np.linspace(0, resolution - 1, resolution) x = ((2 / ((np.sqrt(3 * width) * np.pi ** 1 / 4))) * (1 - ((x - center) ** 2 / width ** 2)) * np.exp((-(x - center) ** 2) / (2 * width ** 2))) return x def ricker_matrix(width, resolution, n_components): """Dictionary of Ricker (Mexican hat) wavelets""" centers = np.linspace(0, resolution - 1, n_components) D = np.empty((n_components, resolution)) for i, center in enumerate(centers): D[i] = ricker_function(resolution, center, width) D /= np.sqrt(np.sum(D ** 2, axis=1))[:, np.newaxis] return D resolution = 1024 subsampling = 3 # subsampling factor width = 100 n_components = resolution / subsampling # Compute a wavelet dictionary D_fixed = ricker_matrix(width=width, resolution=resolution, n_components=n_components) D_multi = np.r_[tuple(ricker_matrix(width=w, resolution=resolution, n_components=np.floor(n_components / 5)) for w in (10, 50, 100, 500, 1000))] # Generate a signal y = np.linspace(0, resolution - 1, resolution) first_quarter = y < resolution / 4 y[first_quarter] = 3. y[np.logical_not(first_quarter)] = -1. # List the different sparse coding methods in the following format: # (title, transform_algorithm, transform_alpha, transform_n_nozero_coefs) estimators = [('OMP', 'omp', None, 15), ('Lasso', 'lasso_cd', 2, None), ] pl.figure(figsize=(13, 6)) for subplot, (D, title) in enumerate(zip((D_fixed, D_multi), ('fixed width', 'multiple widths'))): pl.subplot(1, 2, subplot + 1) pl.title('Sparse coding against %s dictionary' % title) pl.plot(y, ls='dotted', label='Original signal') # Do a wavelet approximation for title, algo, alpha, n_nonzero in estimators: coder = SparseCoder(dictionary=D, transform_n_nonzero_coefs=n_nonzero, transform_alpha=alpha, transform_algorithm=algo) x = coder.transform(y) density = len(np.flatnonzero(x)) x = np.ravel(np.dot(x, D)) squared_error = np.sum((y - x) ** 2) pl.plot(x, label='%s: %s nonzero coefs,\n%.2f error' % (title, density, squared_error)) # Soft thresholding debiasing coder = SparseCoder(dictionary=D, transform_algorithm='threshold', transform_alpha=20) x = coder.transform(y) _, idx = np.where(x != 0) x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y) x = np.ravel(np.dot(x, D)) squared_error = np.sum((y - x) ** 2) pl.plot(x, label='Thresholding w/ debiasing:\n%d nonzero coefs, %.2f error' % (len(idx), squared_error)) pl.axis('tight') pl.legend() pl.subplots_adjust(.04, .07, .97, .90, .09, .2) pl.show()

bsd-3-clause

nickgentoo/scikit-learn-graph

scripts/Online_PassiveAggressive_countmeansketch_unbiased_median.py

1

10081

# -*- coding: utf-8 -*- """ python -m scripts/Online_PassiveAggressive_countmeansketch LMdata 3 1 a ODDST 0.01 Created on Fri Mar 13 13:02:41 2015 Copyright 2015 Nicolo' Navarin This file is part of scikit-learn-graph. scikit-learn-graph is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. scikit-learn-graph is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with scikit-learn-graph. If not, see <http://www.gnu.org/licenses/>. """ import sys from skgraph.feature_extraction.graph.ODDSTVectorizer import ODDSTVectorizer from skgraph.feature_extraction.graph.WLVectorizer import WLVectorizer from sklearn.linear_model import PassiveAggressiveClassifier as PAC from skgraph.datasets import load_graph_datasets import numpy as np from scipy.sparse import csc_matrix from sklearn.utils import compute_class_weight from scipy.sparse import csr_matrix from countminsketch_unbiased_Numpy_median import CountMinSketch from itertools import izip if __name__=='__main__': if len(sys.argv)<1: sys.exit("python ODDKernel_example.py dataset r l filename kernel C m d") dataset=sys.argv[1] max_radius=int(sys.argv[2]) la=float(sys.argv[3]) #hashs=int(sys.argv[3]) njobs=1 name=str(sys.argv[4]) kernel=sys.argv[5] C=float(sys.argv[6]) m=int(sys.argv[7]) d=int(sys.argv[8]) #lr=float(sys.argv[7]) #FIXED PARAMETERS normalization=False #working with Chemical g_it=load_graph_datasets.dispatch(dataset) f=open(name,'w') #At this point, one_hot_encoding contains the encoding for each symbol in the alphabet if kernel=="WL": print "Lambda ignored" print "Using WL fast subtree kernel" Vectorizer=WLVectorizer(r=max_radius,normalization=normalization) elif kernel=="ODDST": print "Using ST kernel" Vectorizer=ODDSTVectorizer(r=max_radius,l=la,normalization=normalization) elif kernel=="NSPDK": print "Using NSPDK kernel, lambda parameter interpreted as d" Vectorizer=NSPDKVectorizer(r=max_radius,d=int(la),normalization=normalization) else: print "Unrecognized kernel" #TODO the C parameter should probably be optimized #print zip(_letters, _one_hot) #exit() features=Vectorizer.transform(g_it.graphs) #Parallel ,njobs print "examples, features", features.shape errors=0 tp=0 fp=0 tn=0 fn=0 predictions=[0]*50 correct=[0]*50 #print ESN #netDataSet=[] #netTargetSet=[] #netKeyList=[] BERtotal=[] bintargets=[1,-1] #print features #print list_for_deep.keys() tp = 0 fp = 0 fn = 0 tn = 0 part_plus=0 part_minus=0 WCMS=CountMinSketch(m,d) for i in xrange(features.shape[0]): exCMS=CountMinSketch(m,d) ex=features[i][0] #W=csr_matrix(ex) rows,cols = ex.nonzero() dot=0.0 module=0.0 for row,col in izip(rows,cols): #((row,col), ex[row,col]) value=ex[row,col] module+=value**2 #print col, ex[row,col] #dot+=WCMS[col]*ex[row,col] exCMS.add(col,value) #print dot #TODO aggiungere bias dot=WCMS.dot(exCMS) #print "dot:", dot, "dotCMS:",dot1 if (np.sign(dot) != g_it.target[i] ): #print "error on example",i, "predicted:", dot, "correct:", g_it.target[i] errors+=1 if g_it.target[i]==1: fn+=1 else: fp+=1 else: #print "correct classification", g_it.target[i] if g_it.target[i]==1: tp+=1 else: tn+=1 if(g_it.target[i]==1): coef=(part_minus+1.0)/(part_plus+part_minus+1.0) part_plus+=1 else: coef=(part_plus+1.0)/(part_plus+part_minus+1.0) part_minus+=1 tao = min (C, max (0.0,( (1.0 - g_it.target[i]*dot )*coef) / module ) ); if (tao > 0.0): exCMS*=(tao*g_it.target[i]) WCMS+=(exCMS) # for row,col in zip(rows,cols): # ((row,col), ex[row,col]) # #print col, ex[row,col] # WCMS.add(col,g_it.target[i]*tao*ex[row,col]) #print "Correct prediction example",i, "pred", score, "target",g_it.target[i] if i%50==0 and i!=0: #output performance statistics every 50 examples if (tn+fp) > 0: pos_part= float(fp) / (tn+fp) else: pos_part=0 if (tp+fn) > 0: neg_part=float(fn) / (tp+fn) else: neg_part=0 BER = 0.5 * ( pos_part + neg_part) print "1-BER Window esempio ",i, (1.0 - BER) f.write("1-BER Window esempio "+str(i)+" "+str(1.0 - BER)+"\n") #print>>f,"1-BER Window esempio "+str(i)+" "+str(1.0 - BER) BERtotal.append(1.0 - BER) tp = 0 fp = 0 fn = 0 tn = 0 part_plus=0 part_minus=0 print "BER AVG", str(np.average(BERtotal)),"std", np.std(BERtotal) f.write("BER AVG "+ str(np.average(BERtotal))+" std "+str(np.std(BERtotal))+"\n") f.close() #print "N_features", ex.shape #generate explicit W from CountMeanSketch #print W #raw_input("W (output)") #============================================================================== # # tao = /*(double)labels->get_label(idx_a) **/ min (C, max (0.0,(1.0 - (((double)labels->get_label(idx_a))*(classe_mod) )) * c_plus ) / modulo_test); # # #W=W_old #dump line # # # #set the weights of PA to the predicted values # PassiveAggressive.coef_=W # pred=PassiveAggressive.predict(ex) # # score=PassiveAggressive.decision_function(ex) # # bintargets.append(g_it.target[i]) # if pred!=g_it.target[i]: # errors+=1 # print "Error",errors," on example",i, "pred", score, "target",g_it.target[i] # if g_it.target[i]==1: # fn+=1 # else: # fp+=1 # # else: # if g_it.target[i]==1: # tp+=1 # else: # tn+=1 # #print "Correct prediction example",i, "pred", score, "target",g_it.target[i] # # else: # #first example is always an error! # pred=0 # score=0 # errors+=1 # print "Error",errors," on example",i # if g_it.target[i]==1: # fn+=1 # else: # fp+=1 # #print i # if i%50==0 and i!=0: # #output performance statistics every 50 examples # if (tn+fp) > 0: # pos_part= float(fp) / (tn+fp) # else: # pos_part=0 # if (tp+fn) > 0: # neg_part=float(fn) / (tp+fn) # else: # neg_part=0 # BER = 0.5 * ( pos_part + neg_part) # print "1-BER Window esempio ",i, (1.0 - BER) # print>>f,"1-BER Window esempio "+str(i)+" "+str(1.0 - BER) # BERtotal.append(1.0 - BER) # tp = 0 # fp = 0 # fn = 0 # tn = 0 # bintargets=[1,-1] # #print features[0][i] # #print features[0][i].shape # #f=features[0][i,:] # #print f.shape # #print f.shape # #print g_it.target[i] # #third parameter is compulsory just for the first call # print "prediction", pred, score # #print "intecept",PassiveAggressive.intercept_ # #raw_input() # if abs(score)<1.0 or pred!=g_it.target[i]: # # ClassWeight=compute_class_weight('auto',np.asarray([1,-1]),bintargets) # #print "class weights", {1:ClassWeight[0],-1:ClassWeight[1]} # PassiveAggressive.class_weight={1:ClassWeight[0],-1:ClassWeight[1]} # # PassiveAggressive.partial_fit(ex,np.array([g_it.target[i]]),np.unique(g_it.target)) # #PassiveAggressive.partial_fit(ex,np.array([g_it.target[i]]),np.unique(g_it.target)) # W_old=PassiveAggressive.coef_ # # # #ESN target---# # netTargetSet=[] # for key,rowDict in list_for_deep[i].iteritems(): # # # target=np.asarray( [np.asarray([W_old[0,key]])]*len(rowDict)) # # # netTargetSet.append(target) # # # # # #------------ESN TargetSetset--------------------# # # ESN Training # # #for ftDataset,ftTargetSet in zip(netDataSet,netTargetSet): # #print "Input" # #print netDataSet # #raw_input("Output") # #print netTargetSet # #raw_input("Target") # model.OnlineTrain(netDataSet,netTargetSet,lr) # #raw_input("TR") # #calcolo statistiche # # print "BER AVG", sum(BERtotal) / float(len(BERtotal)) # print>>f,"BER AVG "+str(sum(BERtotal) / float(len(BERtotal))) # f.close() #==============================================================================

gpl-3.0

hammerlab/immuno_research

Jan30_exclude_hla_a2.py

1

5343

import numpy as np import sklearn import sklearn.cross_validation import sklearn.ensemble import sklearn.linear_model from epitopes import iedb, amino_acid, features, reduced_alphabet import eval_dataset """ Do results from a restrict HLA sample (only A2) generalize to all the other HLA types? """ A2 = 'A2$|A\*02' print print "---" print "Human MHC1 (keep)" X_human_mhc1, Y_human_mhc1 = iedb.load_tcell_ngrams( noisy_labels = 'keep', human = True, mhc_class = 1) eval_dataset.eval_cv(X_human_mhc1, Y_human_mhc1) print print "---" print "Human MHC1 (drop)" X_human_mhc1_filter, Y_human_mhc1_filter = iedb.load_tcell_ngrams( noisy_labels = 'drop', human = True, mhc_class = 1) eval_dataset.eval_cv(X_human_mhc1_filter, Y_human_mhc1_filter) print print "---" print "Human MHC1 noisy = positive" X_human_mhc1_positive, Y_human_mhc1_positive = iedb.load_tcell_ngrams( noisy_labels = 'positive', human = True, mhc_class = 1) eval_dataset.eval_cv(X_human_mhc1_positive, Y_human_mhc1_positive) print print "---" print "Human MHC1 noisy = negative" X_human_mhc1_negative, Y_human_mhc1_negative = iedb.load_tcell_ngrams( noisy_labels = 'negative', human = True, mhc_class = 1) eval_dataset.eval_cv(X_human_mhc1_positive, Y_human_mhc1_positive) print print "---" print "No HLA-A2" X_no_hla_a2, Y_no_hla_a2 = iedb.load_tcell_ngrams( noisy_labels = 'keep', human = True, mhc_class = 1, exclude_hla_type = A2) eval_dataset.eval_cv(X_no_hla_a2, Y_no_hla_a2) print print "---" print "No HLA-A2 filtered" X_no_hla_a2_filter, Y_no_hla_a2_filter = iedb.load_tcell_ngrams( noisy_labels = 'drop', human = True, mhc_class = 1, exclude_hla_type = A2) eval_dataset.eval_cv(X_no_hla_a2_filter, Y_no_hla_a2_filter) print print "---" print "No HLA-A2 noisy = positive" X_no_hla_a2_positive, Y_no_hla_a2_positive = iedb.load_tcell_ngrams( noisy_labels = 'positive', human = True, mhc_class = 1, exclude_hla_type = A2) eval_dataset.eval_cv(X_no_hla_a2_positive, Y_no_hla_a2_positive) print print "---" print "No HLA-A2 noisy = negative" X_no_hla_a2_negtive, Y_no_hla_a2_negative = iedb.load_tcell_ngrams( noisy_labels = 'negative', human = True, mhc_class = 1, exclude_hla_type = A2) eval_dataset.eval_cv(X_no_hla_a2_positive, Y_no_hla_a2_positive) print print "---" print "Cross-accuracy for HLA-A2 data" X_hla_a2, Y_hla_a2 = iedb.load_tcell_ngrams( noisy_labels = 'keep', human = True, mhc_class = 1, hla_type = A2) eval_dataset.eval_split(X_no_hla_a2, Y_no_hla_a2, X_hla_a2, Y_hla_a2) print print "---" print "Cross-accuracy for HLA-A2 data filtered" X_hla_a2_filtered, Y_hla_a2_filtered = iedb.load_tcell_ngrams( noisy_labels = 'drop', human = True, mhc_class = 1, hla_type = A2) eval_dataset.eval_split(X_no_hla_a2_filter, Y_no_hla_a2_filter, X_hla_a2_filtered, Y_hla_a2_filtered) print print "---" print "Cross-accuracy for HLA-A2 data noisy = positive" X_hla_a2_positive, Y_hla_a2_positive = iedb.load_tcell_ngrams( noisy_labels = 'positive', human = True, mhc_class = 1, hla_type = A2) eval_dataset.eval_split(X_no_hla_a2_positive, Y_no_hla_a2_positive, X_hla_a2_positive, Y_hla_a2_positive) print print "---" print "Cross-accuracy for HLA-A2 data filtered (assay_group = cytotoxity)" X_no_hla_a2_cytotoxicity, Y_no_hla_a2_cytotoxicity = iedb.load_tcell_ngrams( noisy_labels = 'drop', assay_group = 'cytotoxicity', human = True, mhc_class = 1, exclude_hla_type = A2) X_hla_a2_cytotoxicity, Y_hla_a2_cytotoxicity = iedb.load_tcell_ngrams( noisy_labels = 'drop', assay_group = 'cytotoxicity', human = True, mhc_class = 1, hla_type = A2) eval_dataset.eval_split(X_no_hla_a2_cytotoxicity, Y_no_hla_a2_cytotoxicity, X_hla_a2_cytotoxicity, Y_hla_a2_cytotoxicity) print print "---" print "Cross-accuracy for HLA-A2 data (noisy = positive, assay_group = cytotoxity)" X_no_hla_a2_positive_cytotoxicity, Y_no_hla_a2_positive_cytotoxicity = iedb.load_tcell_ngrams( noisy_labels = 'positive', assay_group = 'cytotoxicity', human = True, mhc_class = 1, exclude_hla_type = A2) X_hla_a2_positive_cytotoxicity, Y_hla_a2_positive_cytotoxicity = iedb.load_tcell_ngrams( noisy_labels = 'positive', assay_group = 'cytotoxicity', human = True, mhc_class = 1, hla_type = A2) eval_dataset.eval_split(X_no_hla_a2_positive_cytotoxicity, Y_no_hla_a2_positive_cytotoxicity, X_hla_a2_positive_cytotoxicity, Y_hla_a2_positive_cytotoxicity)

gpl-2.0

tclose/python-neo

neo/test/iotest/common_io_test.py

7

22757

# -*- coding: utf-8 -*- ''' Common tests for IOs: * check presence of all necessary attr * check types * write/read consistency See BaseTestIO. The public URL is in url_for_tests. The private url for writing is ssh://gate.g-node.org/groups/neo/io_test_files/ ''' # needed for python 3 compatibility from __future__ import absolute_import __test__ = False url_for_tests = "https://portal.g-node.org/neo/" import os try: import unittest2 as unittest except ImportError: import unittest from neo.core import Block, Segment from neo.test.tools import (assert_same_sub_schema, assert_neo_object_is_compliant, assert_sub_schema_is_lazy_loaded, assert_lazy_sub_schema_can_be_loaded, assert_children_empty) from neo.test.iotest.tools import (can_use_network, cleanup_test_file, close_object_safe, create_generic_io_object, create_generic_reader, create_generic_writer, create_local_temp_dir, download_test_file, iter_generic_io_objects, iter_generic_readers, iter_read_objects, make_all_directories, read_generic, write_generic) from neo.test.generate_datasets import generate_from_supported_objects class BaseTestIO(object): ''' This class make common tests for all IOs. Several startegies: * for IO able to read write : test_write_then_read * for IO able to read write with hash conservation (optional): test_read_then_write * for all IOs : test_assert_readed_neo_object_is_compliant 2 cases: * files are at G-node and downloaded: download_test_files_if_not_present * files are generated by MyIO.write() ''' #~ __test__ = False # all IO test need to modify this: ioclass = None # the IOclass to be tested files_to_test = [] # list of files to test compliances files_to_download = [] # when files are at G-Node # when reading then writing produces files with identical hashes hash_conserved_when_write_read = False # when writing then reading creates an identical neo object read_and_write_is_bijective = True # allow environment to tell avoid using network use_network = can_use_network() local_test_dir = None def setUp(self): ''' Set up the test fixture. This is run for every test ''' self.higher = self.ioclass.supported_objects[0] self.shortname = self.ioclass.__name__.lower().strip('io') # these objects can both be written and read self.io_readandwrite = list(set(self.ioclass.readable_objects) & set(self.ioclass.writeable_objects)) # these objects can be either written or read self.io_readorwrite = list(set(self.ioclass.readable_objects) | set(self.ioclass.writeable_objects)) self.create_local_dir_if_not_exists() self.download_test_files_if_not_present() self.files_generated = [] self.generate_files_for_io_able_to_write() self.files_to_test.extend(self.files_generated) self.cascade_modes = [True] if hasattr(self.ioclass, 'load_lazy_cascade'): self.cascade_modes.append('lazy') def create_local_dir_if_not_exists(self): ''' Create a local directory to store testing files and return it. The directory path is also written to self.local_test_dir ''' self.local_test_dir = create_local_temp_dir(self.shortname) return self.local_test_dir def download_test_files_if_not_present(self): ''' Download %s file at G-node for testing url_for_tests is global at beginning of this file. ''' % self.ioclass.__name__ if not self.use_network: raise unittest.SkipTest("Requires download of data from the web") url = url_for_tests+self.shortname try: make_all_directories(self.files_to_download, self.local_test_dir) download_test_file(self.files_to_download, self.local_test_dir, url) except IOError as exc: raise unittest.SkipTest(exc) download_test_files_if_not_present.__test__ = False def cleanup_file(self, path): ''' Remove test files or directories safely. ''' cleanup_test_file(self.ioclass, path, directory=self.local_test_dir) def able_to_write_or_read(self, writeread=False, readwrite=False): ''' Return True if generalized writing or reading is possible. If writeread=True, return True if writing then reading is possible and produces identical neo objects. If readwrite=True, return True if reading then writing is possible and produces files with identical hashes. ''' # Find the highest object that is supported by the IO # Test only if it is a Block or Segment, and if it can both read # and write this object. if self.higher not in self.io_readandwrite: return False if self.higher not in [Block, Segment]: return False # when io need external knowldge for writting or read such as # sampling_rate (RawBinaryIO...) the test is too much complex to design # genericaly. if (self.higher in self.ioclass.read_params and len(self.ioclass.read_params[self.higher]) != 0): return False # handle cases where the test should write then read if writeread and not self.read_and_write_is_bijective: return False # handle cases where the test should read then write if readwrite and not self.hash_conserved_when_write_read: return False return True def get_filename_path(self, filename): ''' Get the path to a filename in the current temporary file directory ''' return os.path.join(self.local_test_dir, filename) def generic_io_object(self, filename=None, return_path=False, clean=False): ''' Create an io object in a generic way that can work with both file-based and directory-based io objects. If filename is None, create a filename (default). If return_path is True, return the full path of the file along with the io object. return ioobj, path. Default is False. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. ''' return create_generic_io_object(ioclass=self.ioclass, filename=filename, directory=self.local_test_dir, return_path=return_path, clean=clean) def create_file_reader(self, filename=None, return_path=False, clean=False, target=None, readall=False): ''' Create a function that can read from the specified filename. If filename is None, create a filename (default). If return_path is True, return the full path of the file along with the reader function. return reader, path. Default is False. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. If target is None, use the first supported_objects from ioobj If target is False, use the 'read' method. If target is the Block or Segment class, use read_block or read_segment, respectively. If target is a string, use 'read_'+target. If readall is True, use the read_all_ method instead of the read_ method. Default is False. ''' ioobj, path = self.generic_io_object(filename=filename, return_path=True, clean=clean) res = create_generic_reader(ioobj, target=target, readall=readall) if return_path: return res, path return res def create_file_writer(self, filename=None, return_path=False, clean=False, target=None): ''' Create a function that can write from the specified filename. If filename is None, create a filename (default). If return_path is True, return the full path of the file along with the writer function. return writer, path. Default is False. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. If target is None, use the first supported_objects from ioobj If target is False, use the 'write' method. If target is the Block or Segment class, use write_block or write_segment, respectively. If target is a string, use 'write_'+target. ''' ioobj, path = self.generic_io_object(filename=filename, return_path=True, clean=clean) res = create_generic_writer(ioobj, target=target) if return_path: return res, path return res def read_file(self, filename=None, return_path=False, clean=False, target=None, readall=False, cascade=True, lazy=False): ''' Read from the specified filename. If filename is None, create a filename (default). If return_path is True, return the full path of the file along with the object. return obj, path. Default is False. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. If target is None, use the first supported_objects from ioobj If target is False, use the 'read' method. If target is the Block or Segment class, use read_block or read_segment, respectively. If target is a string, use 'read_'+target. The cascade and lazy parameters are passed to the reader. Defaults are True and False, respectively. If readall is True, use the read_all_ method instead of the read_ method. Default is False. ''' ioobj, path = self.generic_io_object(filename=filename, return_path=True, clean=clean) obj = read_generic(ioobj, target=target, cascade=cascade, lazy=lazy, readall=readall, return_reader=False) if return_path: return obj, path return obj def write_file(self, obj=None, filename=None, return_path=False, clean=False, target=None): ''' Write the target object to a file using the given neo io object ioobj. If filename is None, create a filename (default). If return_path is True, return the full path of the file along with the object. return obj, path. Default is False. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. If target is None, use the first supported_objects from ioobj If target is False, use the 'read' method. If target is the Block or Segment class, use read_block or read_segment, respectively. If target is a string, use 'read_'+target. obj is the object to write. If obj is None, an object is created automatically for the io class. ''' ioobj, path = self.generic_io_object(filename=filename, return_path=True, clean=clean) obj = write_generic(ioobj, target=target, return_reader=False) if return_path: return obj, path return obj def iter_io_objects(self, return_path=False, clean=False): ''' Return an iterable over the io objects created from files_to_test If return_path is True, yield the full path of the file along with the io object. yield ioobj, path Default is False. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. ''' return iter_generic_io_objects(ioclass=self.ioclass, filenames=self.files_to_test, directory=self.local_test_dir, return_path=return_path, clean=clean) def iter_readers(self, target=None, readall=False, return_path=False, return_ioobj=False, clean=False): ''' Return an iterable over readers created from files_to_test. If return_path is True, return the full path of the file along with the reader object. return reader, path. If return_ioobj is True, return the io object as well as the reader. return reader, ioobj. Default is False. If both return_path and return_ioobj is True, return reader, path, ioobj. Default is False. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. If readall is True, use the read_all_ method instead of the read_ method. Default is False. ''' return iter_generic_readers(ioclass=self.ioclass, filenames=self.files_to_test, directory=self.local_test_dir, return_path=return_path, return_ioobj=return_ioobj, target=target, clean=clean, readall=readall) def iter_objects(self, target=None, return_path=False, return_ioobj=False, return_reader=False, clean=False, readall=False, cascade=True, lazy=False): ''' Iterate over objects read from the list of filenames in files_to_test. If target is None, use the first supported_objects from ioobj If target is False, use the 'read' method. If target is the Block or Segment class, use read_block or read_segment, respectively. If target is a string, use 'read_'+target. If return_path is True, yield the full path of the file along with the object. yield obj, path. If return_ioobj is True, yield the io object as well as the object. yield obj, ioobj. Default is False. If return_reader is True, yield the io reader function as well as the object. yield obj, reader. Default is False. If some combination of return_path, return_ioobj, and return_reader is True, they are yielded in the order: obj, path, ioobj, reader. If clean is True, try to delete existing versions of the file before creating the io object. Default is False. The cascade and lazy parameters are passed to the reader. Defaults are True and False, respectively. If readall is True, use the read_all_ method instead of the read_ method. Default is False. ''' return iter_read_objects(ioclass=self.ioclass, filenames=self.files_to_test, directory=self.local_test_dir, target=target, return_path=return_path, return_ioobj=return_ioobj, return_reader=return_reader, clean=clean, readall=readall, cascade=cascade, lazy=lazy) def generate_files_for_io_able_to_write(self): ''' Write files for use in testing. ''' self.files_generated = [] if not self.able_to_write_or_read(): return generate_from_supported_objects(self.ioclass.supported_objects) ioobj, path = self.generic_io_object(return_path=True, clean=True) if ioobj is None: return self.files_generated.append(path) write_generic(ioobj, target=self.higher) close_object_safe(ioobj) def test_write_then_read(self): ''' Test for IO that are able to write and read - here %s: 1 - Generate a full schema with supported objects. 2 - Write to a file 3 - Read from the file 4 - Check the hierachy 5 - Check data Work only for IO for Block and Segment for the highest object (main cases). ''' % self.ioclass.__name__ if not self.able_to_write_or_read(writeread=True): return for cascade in self.cascade_modes: ioobj1 = self.generic_io_object(clean=True) if ioobj1 is None: return ob1 = write_generic(ioobj1, target=self.higher) close_object_safe(ioobj1) ioobj2 = self.generic_io_object() # Read the highest supported object from the file obj_reader = create_generic_reader(ioobj2, target=False) ob2 = obj_reader(cascade=cascade)[0] if self.higher == Segment: ob2 = ob2.segments[0] # some formats (e.g. elphy) do not support double floating # point spiketrains try: assert_same_sub_schema(ob1, ob2, True, 1e-8) assert_neo_object_is_compliant(ob1) assert_neo_object_is_compliant(ob2) # intercept exceptions and add more information except BaseException as exc: exc.args += ('with cascade=%s ' % cascade,) raise close_object_safe(ioobj2) def test_read_then_write(self): ''' Test for IO that are able to read and write, here %s: 1 - Read a file 2 Write object set in another file 3 Compare the 2 files hash NOTE: TODO: Not implemented yet ''' % self.ioclass.__name__ if not self.able_to_write_or_read(readwrite=True): return #assert_file_contents_equal(a, b) def test_assert_readed_neo_object_is_compliant(self): ''' Reading %s files in `files_to_test` produces compliant objects. Compliance test: neo.test.tools.assert_neo_object_is_compliant for all cascade and lazy modes ''' % self.ioclass.__name__ # This is for files presents at G-Node or generated for cascade in self.cascade_modes: for lazy in [True, False]: for obj, path in self.iter_objects(cascade=cascade, lazy=lazy, return_path=True): try: # Check compliance of the block assert_neo_object_is_compliant(obj) # intercept exceptions and add more information except BaseException as exc: exc.args += ('from %s with cascade=%s and lazy=%s' % (os.path.basename(path), cascade, lazy),) raise def test_readed_with_cascade_is_compliant(self): ''' Reading %s files in `files_to_test` with `cascade` is compliant. A reader with cascade = False should return empty children. ''' % self.ioclass.__name__ # This is for files presents at G-Node or generated for obj, path in self.iter_objects(cascade=False, lazy=False, return_path=True): try: # Check compliance of the block or segment assert_neo_object_is_compliant(obj) assert_children_empty(obj, self.ioclass) # intercept exceptions and add more information except BaseException as exc: exc.args += ('from %s ' % os.path.basename(path),) raise def test_readed_with_lazy_is_compliant(self): ''' Reading %s files in `files_to_test` with `lazy` is compliant. Test the reader with lazy = True. All objects derived from ndarray or Quantity should have a size of 0. Also, AnalogSignal, AnalogSignalArray, SpikeTrain, Epocharray, and EventArray should contain the lazy_shape attribute. ''' % self.ioclass.__name__ # This is for files presents at G-Node or generated for cascade in self.cascade_modes: for obj, path in self.iter_objects(cascade=cascade, lazy=True, return_path=True): try: assert_sub_schema_is_lazy_loaded(obj) # intercept exceptions and add more information except BaseException as exc: exc.args += ('from %s with cascade=%s ' % (os.path.basename(path), cascade),) raise def test_load_lazy_objects(self): ''' Reading %s files in `files_to_test` with `lazy` works. Test the reader with lazy = True. All objects derived from ndarray or Quantity should have a size of 0. Also, AnalogSignal, AnalogSignalArray, SpikeTrain, Epocharray, and EventArray should contain the lazy_shape attribute. ''' % self.ioclass.__name__ if not hasattr(self.ioclass, 'load_lazy_object'): return # This is for files presents at G-Node or generated for cascade in self.cascade_modes: for obj, path, ioobj in self.iter_objects(cascade=cascade, lazy=True, return_ioobj=True, return_path=True): try: assert_lazy_sub_schema_can_be_loaded(obj, ioobj) # intercept exceptions and add more information except BaseException as exc: exc.args += ('from %s with cascade=%s ' % (os.path.basename(path), cascade),) raise

bsd-3-clause

ageron/tensorflow

tensorflow/contrib/eager/python/tfe.py

1

5748

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """TensorFlow Eager execution prototype. EXPERIMENTAL: APIs here are unstable and likely to change without notice. To use, at program startup, call `tf.enable_eager_execution()`. @@metrics @@list_devices @@num_gpus @@py_func @@defun @@function @@make_template @@implicit_gradients @@implicit_value_and_gradients @@gradients_function @@value_and_gradients_function @@GradientTape @@run @@enable_eager_execution @@enable_remote_eager_execution @@custom_gradient @@add_execution_callback @@clear_execution_callbacks @@errstate @@ExecutionCallback @@inf_callback @@inf_nan_callback @@nan_callback @@seterr @@Iterator @@Saver @@restore_variables_on_create @@Variable @@get_optimizer_variables @@EagerVariableStore @@Network @@Sequential @@save_network_checkpoint @@restore_network_checkpoint @@Checkpoint @@Checkpointable @@executing_eagerly @@in_eager_mode @@set_execution_mode @@execution_mode @@async_wait @@async_clear_error @@set_server_def @@run_test_in_graph_and_eager_modes @@run_all_tests_in_graph_and_eager_modes @@TensorSpec @@connect_to_remote_host @@DEVICE_PLACEMENT_EXPLICIT @@DEVICE_PLACEMENT_WARN @@DEVICE_PLACEMENT_SILENT @@SYNC @@ASYNC """ from __future__ import absolute_import from __future__ import division from __future__ import print_function # pylint:disable=g-bad-import-order,g-import-not-at-top,unused-import # from tensorflow.contrib.eager.python import metrics from tensorflow.contrib.eager.python.datasets import Iterator from tensorflow.contrib.eager.python.network import Network from tensorflow.contrib.eager.python.network import Sequential from tensorflow.contrib.eager.python.network import save_network_checkpoint from tensorflow.contrib.eager.python.network import restore_network_checkpoint from tensorflow.contrib.eager.python.saver import get_optimizer_variables from tensorflow.contrib.eager.python.saver import restore_variables_on_create from tensorflow.contrib.eager.python.saver import Saver from tensorflow.python.eager import backprop from tensorflow.python.eager import function as _function_lib from tensorflow.python.eager.context import DEVICE_PLACEMENT_EXPLICIT from tensorflow.python.eager.context import DEVICE_PLACEMENT_WARN from tensorflow.python.eager.context import DEVICE_PLACEMENT_SILENT from tensorflow.python.eager.context import executing_eagerly from tensorflow.python.eager.context import list_devices from tensorflow.python.eager.context import set_execution_mode from tensorflow.python.eager.context import execution_mode from tensorflow.python.eager.context import async_wait from tensorflow.python.eager.context import async_clear_error from tensorflow.python.eager.context import SYNC from tensorflow.python.eager.context import ASYNC from tensorflow.python.eager.context import num_gpus from tensorflow.python.eager.context import set_server_def from tensorflow.python.eager.def_function import function from tensorflow.python.eager.execution_callbacks import add_execution_callback from tensorflow.python.eager.execution_callbacks import clear_execution_callbacks from tensorflow.python.eager.execution_callbacks import errstate from tensorflow.python.eager.execution_callbacks import ExecutionCallback from tensorflow.python.eager.execution_callbacks import inf_callback from tensorflow.python.eager.execution_callbacks import inf_nan_callback from tensorflow.python.eager.execution_callbacks import nan_callback from tensorflow.python.eager.execution_callbacks import seterr from tensorflow.python.eager.remote import connect_to_remote_host from tensorflow.python.framework.tensor_spec import TensorSpec from tensorflow.python.framework.ops import enable_eager_execution from tensorflow.python.framework.ops import enable_eager_execution_internal as enable_remote_eager_execution from tensorflow.python.framework.ops import eager_run as run from tensorflow.python.framework.test_util import run_in_graph_and_eager_modes as run_test_in_graph_and_eager_modes from tensorflow.python.framework.test_util import run_all_in_graph_and_eager_modes as run_all_tests_in_graph_and_eager_modes from tensorflow.python.ops.custom_gradient import custom_gradient from tensorflow.python.ops.resource_variable_ops import ResourceVariable as Variable from tensorflow.python.ops.variable_scope import EagerVariableStore from tensorflow.python.ops import script_ops from tensorflow.python.ops import template from tensorflow.python.training.tracking.tracking import AutoTrackable as Checkpointable from tensorflow.python.training.tracking.util import Checkpoint from tensorflow.python.util.all_util import remove_undocumented py_func = script_ops.eager_py_func defun = _function_lib.defun make_template = template.make_template_internal implicit_gradients = backprop.implicit_grad implicit_value_and_gradients = backprop.implicit_val_and_grad gradients_function = backprop.gradients_function value_and_gradients_function = backprop.val_and_grad_function GradientTape = backprop.GradientTape # pylint: disable=invalid-name in_eager_mode = executing_eagerly remove_undocumented(__name__)

apache-2.0

benoitsteiner/tensorflow-xsmm

tensorflow/contrib/slim/python/slim/nets/resnet_utils.py

66

10979

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Contains building blocks for various versions of Residual Networks. Residual networks (ResNets) were proposed in: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015 More variants were introduced in: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Identity Mappings in Deep Residual Networks. arXiv: 1603.05027, 2016 We can obtain different ResNet variants by changing the network depth, width, and form of residual unit. This module implements the infrastructure for building them. Concrete ResNet units and full ResNet networks are implemented in the accompanying resnet_v1.py and resnet_v2.py modules. Compared to https://github.com/KaimingHe/deep-residual-networks, in the current implementation we subsample the output activations in the last residual unit of each block, instead of subsampling the input activations in the first residual unit of each block. The two implementations give identical results but our implementation is more memory efficient. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections from tensorflow.contrib import layers as layers_lib from tensorflow.contrib.framework.python.ops import add_arg_scope from tensorflow.contrib.framework.python.ops import arg_scope from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import layers from tensorflow.contrib.layers.python.layers import regularizers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import variable_scope class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])): """A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which takes as input a `Tensor` and returns another `Tensor` with the output of the ResNet unit. args: A list of length equal to the number of units in the `Block`. The list contains one (depth, depth_bottleneck, stride) tuple for each unit in the block to serve as argument to unit_fn. """ def subsample(inputs, factor, scope=None): """Subsamples the input along the spatial dimensions. Args: inputs: A `Tensor` of size [batch, height_in, width_in, channels]. factor: The subsampling factor. scope: Optional variable_scope. Returns: output: A `Tensor` of size [batch, height_out, width_out, channels] with the input, either intact (if factor == 1) or subsampled (if factor > 1). """ if factor == 1: return inputs else: return layers.max_pool2d(inputs, [1, 1], stride=factor, scope=scope) def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None): """Strided 2-D convolution with 'SAME' padding. When stride > 1, then we do explicit zero-padding, followed by conv2d with 'VALID' padding. Note that net = conv2d_same(inputs, num_outputs, 3, stride=stride) is equivalent to net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME') net = subsample(net, factor=stride) whereas net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME') is different when the input's height or width is even, which is why we add the current function. For more details, see ResnetUtilsTest.testConv2DSameEven(). Args: inputs: A 4-D tensor of size [batch, height_in, width_in, channels]. num_outputs: An integer, the number of output filters. kernel_size: An int with the kernel_size of the filters. stride: An integer, the output stride. rate: An integer, rate for atrous convolution. scope: Scope. Returns: output: A 4-D tensor of size [batch, height_out, width_out, channels] with the convolution output. """ if stride == 1: return layers_lib.conv2d( inputs, num_outputs, kernel_size, stride=1, rate=rate, padding='SAME', scope=scope) else: kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) pad_total = kernel_size_effective - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg inputs = array_ops.pad( inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) return layers_lib.conv2d( inputs, num_outputs, kernel_size, stride=stride, rate=rate, padding='VALID', scope=scope) @add_arg_scope def stack_blocks_dense(net, blocks, output_stride=None, outputs_collections=None): """Stacks ResNet `Blocks` and controls output feature density. First, this function creates scopes for the ResNet in the form of 'block_name/unit_1', 'block_name/unit_2', etc. Second, this function allows the user to explicitly control the ResNet output_stride, which is the ratio of the input to output spatial resolution. This is useful for dense prediction tasks such as semantic segmentation or object detection. Most ResNets consist of 4 ResNet blocks and subsample the activations by a factor of 2 when transitioning between consecutive ResNet blocks. This results to a nominal ResNet output_stride equal to 8. If we set the output_stride to half the nominal network stride (e.g., output_stride=4), then we compute responses twice. Control of the output feature density is implemented by atrous convolution. Args: net: A `Tensor` of size [batch, height, width, channels]. blocks: A list of length equal to the number of ResNet `Blocks`. Each element is a ResNet `Block` object describing the units in the `Block`. output_stride: If `None`, then the output will be computed at the nominal network stride. If output_stride is not `None`, it specifies the requested ratio of input to output spatial resolution, which needs to be equal to the product of unit strides from the start up to some level of the ResNet. For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1, then valid values for the output_stride are 1, 2, 6, 24 or None (which is equivalent to output_stride=24). outputs_collections: Collection to add the ResNet block outputs. Returns: net: Output tensor with stride equal to the specified output_stride. Raises: ValueError: If the target output_stride is not valid. """ # The current_stride variable keeps track of the effective stride of the # activations. This allows us to invoke atrous convolution whenever applying # the next residual unit would result in the activations having stride larger # than the target output_stride. current_stride = 1 # The atrous convolution rate parameter. rate = 1 for block in blocks: with variable_scope.variable_scope(block.scope, 'block', [net]) as sc: for i, unit in enumerate(block.args): if output_stride is not None and current_stride > output_stride: raise ValueError('The target output_stride cannot be reached.') with variable_scope.variable_scope('unit_%d' % (i + 1), values=[net]): # If we have reached the target output_stride, then we need to employ # atrous convolution with stride=1 and multiply the atrous rate by the # current unit's stride for use in subsequent layers. if output_stride is not None and current_stride == output_stride: net = block.unit_fn(net, rate=rate, **dict(unit, stride=1)) rate *= unit.get('stride', 1) else: net = block.unit_fn(net, rate=1, **unit) current_stride *= unit.get('stride', 1) net = utils.collect_named_outputs(outputs_collections, sc.name, net) if output_stride is not None and current_stride != output_stride: raise ValueError('The target output_stride cannot be reached.') return net def resnet_arg_scope(weight_decay=0.0001, batch_norm_decay=0.997, batch_norm_epsilon=1e-5, batch_norm_scale=True): """Defines the default ResNet arg scope. TODO(gpapan): The batch-normalization related default values above are appropriate for use in conjunction with the reference ResNet models released at https://github.com/KaimingHe/deep-residual-networks. When training ResNets from scratch, they might need to be tuned. Args: weight_decay: The weight decay to use for regularizing the model. batch_norm_decay: The moving average decay when estimating layer activation statistics in batch normalization. batch_norm_epsilon: Small constant to prevent division by zero when normalizing activations by their variance in batch normalization. batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the activations in the batch normalization layer. Returns: An `arg_scope` to use for the resnet models. """ batch_norm_params = { 'decay': batch_norm_decay, 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, 'updates_collections': ops.GraphKeys.UPDATE_OPS, } with arg_scope( [layers_lib.conv2d], weights_regularizer=regularizers.l2_regularizer(weight_decay), weights_initializer=initializers.variance_scaling_initializer(), activation_fn=nn_ops.relu, normalizer_fn=layers.batch_norm, normalizer_params=batch_norm_params): with arg_scope([layers.batch_norm], **batch_norm_params): # The following implies padding='SAME' for pool1, which makes feature # alignment easier for dense prediction tasks. This is also used in # https://github.com/facebook/fb.resnet.torch. However the accompanying # code of 'Deep Residual Learning for Image Recognition' uses # padding='VALID' for pool1. You can switch to that choice by setting # tf.contrib.framework.arg_scope([tf.contrib.layers.max_pool2d], padding='VALID'). with arg_scope([layers.max_pool2d], padding='SAME') as arg_sc: return arg_sc

apache-2.0

ageron/tensorflow

tensorflow/contrib/slim/python/slim/nets/resnet_utils.py

66

10979

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Contains building blocks for various versions of Residual Networks. Residual networks (ResNets) were proposed in: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015 More variants were introduced in: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Identity Mappings in Deep Residual Networks. arXiv: 1603.05027, 2016 We can obtain different ResNet variants by changing the network depth, width, and form of residual unit. This module implements the infrastructure for building them. Concrete ResNet units and full ResNet networks are implemented in the accompanying resnet_v1.py and resnet_v2.py modules. Compared to https://github.com/KaimingHe/deep-residual-networks, in the current implementation we subsample the output activations in the last residual unit of each block, instead of subsampling the input activations in the first residual unit of each block. The two implementations give identical results but our implementation is more memory efficient. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections from tensorflow.contrib import layers as layers_lib from tensorflow.contrib.framework.python.ops import add_arg_scope from tensorflow.contrib.framework.python.ops import arg_scope from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import layers from tensorflow.contrib.layers.python.layers import regularizers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import variable_scope class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])): """A named tuple describing a ResNet block. Its parts are: scope: The scope of the `Block`. unit_fn: The ResNet unit function which takes as input a `Tensor` and returns another `Tensor` with the output of the ResNet unit. args: A list of length equal to the number of units in the `Block`. The list contains one (depth, depth_bottleneck, stride) tuple for each unit in the block to serve as argument to unit_fn. """ def subsample(inputs, factor, scope=None): """Subsamples the input along the spatial dimensions. Args: inputs: A `Tensor` of size [batch, height_in, width_in, channels]. factor: The subsampling factor. scope: Optional variable_scope. Returns: output: A `Tensor` of size [batch, height_out, width_out, channels] with the input, either intact (if factor == 1) or subsampled (if factor > 1). """ if factor == 1: return inputs else: return layers.max_pool2d(inputs, [1, 1], stride=factor, scope=scope) def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None): """Strided 2-D convolution with 'SAME' padding. When stride > 1, then we do explicit zero-padding, followed by conv2d with 'VALID' padding. Note that net = conv2d_same(inputs, num_outputs, 3, stride=stride) is equivalent to net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME') net = subsample(net, factor=stride) whereas net = tf.contrib.layers.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME') is different when the input's height or width is even, which is why we add the current function. For more details, see ResnetUtilsTest.testConv2DSameEven(). Args: inputs: A 4-D tensor of size [batch, height_in, width_in, channels]. num_outputs: An integer, the number of output filters. kernel_size: An int with the kernel_size of the filters. stride: An integer, the output stride. rate: An integer, rate for atrous convolution. scope: Scope. Returns: output: A 4-D tensor of size [batch, height_out, width_out, channels] with the convolution output. """ if stride == 1: return layers_lib.conv2d( inputs, num_outputs, kernel_size, stride=1, rate=rate, padding='SAME', scope=scope) else: kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) pad_total = kernel_size_effective - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg inputs = array_ops.pad( inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) return layers_lib.conv2d( inputs, num_outputs, kernel_size, stride=stride, rate=rate, padding='VALID', scope=scope) @add_arg_scope def stack_blocks_dense(net, blocks, output_stride=None, outputs_collections=None): """Stacks ResNet `Blocks` and controls output feature density. First, this function creates scopes for the ResNet in the form of 'block_name/unit_1', 'block_name/unit_2', etc. Second, this function allows the user to explicitly control the ResNet output_stride, which is the ratio of the input to output spatial resolution. This is useful for dense prediction tasks such as semantic segmentation or object detection. Most ResNets consist of 4 ResNet blocks and subsample the activations by a factor of 2 when transitioning between consecutive ResNet blocks. This results to a nominal ResNet output_stride equal to 8. If we set the output_stride to half the nominal network stride (e.g., output_stride=4), then we compute responses twice. Control of the output feature density is implemented by atrous convolution. Args: net: A `Tensor` of size [batch, height, width, channels]. blocks: A list of length equal to the number of ResNet `Blocks`. Each element is a ResNet `Block` object describing the units in the `Block`. output_stride: If `None`, then the output will be computed at the nominal network stride. If output_stride is not `None`, it specifies the requested ratio of input to output spatial resolution, which needs to be equal to the product of unit strides from the start up to some level of the ResNet. For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1, then valid values for the output_stride are 1, 2, 6, 24 or None (which is equivalent to output_stride=24). outputs_collections: Collection to add the ResNet block outputs. Returns: net: Output tensor with stride equal to the specified output_stride. Raises: ValueError: If the target output_stride is not valid. """ # The current_stride variable keeps track of the effective stride of the # activations. This allows us to invoke atrous convolution whenever applying # the next residual unit would result in the activations having stride larger # than the target output_stride. current_stride = 1 # The atrous convolution rate parameter. rate = 1 for block in blocks: with variable_scope.variable_scope(block.scope, 'block', [net]) as sc: for i, unit in enumerate(block.args): if output_stride is not None and current_stride > output_stride: raise ValueError('The target output_stride cannot be reached.') with variable_scope.variable_scope('unit_%d' % (i + 1), values=[net]): # If we have reached the target output_stride, then we need to employ # atrous convolution with stride=1 and multiply the atrous rate by the # current unit's stride for use in subsequent layers. if output_stride is not None and current_stride == output_stride: net = block.unit_fn(net, rate=rate, **dict(unit, stride=1)) rate *= unit.get('stride', 1) else: net = block.unit_fn(net, rate=1, **unit) current_stride *= unit.get('stride', 1) net = utils.collect_named_outputs(outputs_collections, sc.name, net) if output_stride is not None and current_stride != output_stride: raise ValueError('The target output_stride cannot be reached.') return net def resnet_arg_scope(weight_decay=0.0001, batch_norm_decay=0.997, batch_norm_epsilon=1e-5, batch_norm_scale=True): """Defines the default ResNet arg scope. TODO(gpapan): The batch-normalization related default values above are appropriate for use in conjunction with the reference ResNet models released at https://github.com/KaimingHe/deep-residual-networks. When training ResNets from scratch, they might need to be tuned. Args: weight_decay: The weight decay to use for regularizing the model. batch_norm_decay: The moving average decay when estimating layer activation statistics in batch normalization. batch_norm_epsilon: Small constant to prevent division by zero when normalizing activations by their variance in batch normalization. batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the activations in the batch normalization layer. Returns: An `arg_scope` to use for the resnet models. """ batch_norm_params = { 'decay': batch_norm_decay, 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, 'updates_collections': ops.GraphKeys.UPDATE_OPS, } with arg_scope( [layers_lib.conv2d], weights_regularizer=regularizers.l2_regularizer(weight_decay), weights_initializer=initializers.variance_scaling_initializer(), activation_fn=nn_ops.relu, normalizer_fn=layers.batch_norm, normalizer_params=batch_norm_params): with arg_scope([layers.batch_norm], **batch_norm_params): # The following implies padding='SAME' for pool1, which makes feature # alignment easier for dense prediction tasks. This is also used in # https://github.com/facebook/fb.resnet.torch. However the accompanying # code of 'Deep Residual Learning for Image Recognition' uses # padding='VALID' for pool1. You can switch to that choice by setting # tf.contrib.framework.arg_scope([tf.contrib.layers.max_pool2d], padding='VALID'). with arg_scope([layers.max_pool2d], padding='SAME') as arg_sc: return arg_sc

apache-2.0

yonglehou/scikit-learn

sklearn/tests/test_metaestimators.py

225

4954

"""Common tests for metaestimators""" import functools import numpy as np from sklearn.base import BaseEstimator from sklearn.externals.six import iterkeys from sklearn.datasets import make_classification from sklearn.utils.testing import assert_true, assert_false, assert_raises from sklearn.pipeline import Pipeline from sklearn.grid_search import GridSearchCV, RandomizedSearchCV from sklearn.feature_selection import RFE, RFECV from sklearn.ensemble import BaggingClassifier class DelegatorData(object): def __init__(self, name, construct, skip_methods=(), fit_args=make_classification()): self.name = name self.construct = construct self.fit_args = fit_args self.skip_methods = skip_methods DELEGATING_METAESTIMATORS = [ DelegatorData('Pipeline', lambda est: Pipeline([('est', est)])), DelegatorData('GridSearchCV', lambda est: GridSearchCV( est, param_grid={'param': [5]}, cv=2), skip_methods=['score']), DelegatorData('RandomizedSearchCV', lambda est: RandomizedSearchCV( est, param_distributions={'param': [5]}, cv=2, n_iter=1), skip_methods=['score']), DelegatorData('RFE', RFE, skip_methods=['transform', 'inverse_transform', 'score']), DelegatorData('RFECV', RFECV, skip_methods=['transform', 'inverse_transform', 'score']), DelegatorData('BaggingClassifier', BaggingClassifier, skip_methods=['transform', 'inverse_transform', 'score', 'predict_proba', 'predict_log_proba', 'predict']) ] def test_metaestimator_delegation(): # Ensures specified metaestimators have methods iff subestimator does def hides(method): @property def wrapper(obj): if obj.hidden_method == method.__name__: raise AttributeError('%r is hidden' % obj.hidden_method) return functools.partial(method, obj) return wrapper class SubEstimator(BaseEstimator): def __init__(self, param=1, hidden_method=None): self.param = param self.hidden_method = hidden_method def fit(self, X, y=None, *args, **kwargs): self.coef_ = np.arange(X.shape[1]) return True def _check_fit(self): if not hasattr(self, 'coef_'): raise RuntimeError('Estimator is not fit') @hides def inverse_transform(self, X, *args, **kwargs): self._check_fit() return X @hides def transform(self, X, *args, **kwargs): self._check_fit() return X @hides def predict(self, X, *args, **kwargs): self._check_fit() return np.ones(X.shape[0]) @hides def predict_proba(self, X, *args, **kwargs): self._check_fit() return np.ones(X.shape[0]) @hides def predict_log_proba(self, X, *args, **kwargs): self._check_fit() return np.ones(X.shape[0]) @hides def decision_function(self, X, *args, **kwargs): self._check_fit() return np.ones(X.shape[0]) @hides def score(self, X, *args, **kwargs): self._check_fit() return 1.0 methods = [k for k in iterkeys(SubEstimator.__dict__) if not k.startswith('_') and not k.startswith('fit')] methods.sort() for delegator_data in DELEGATING_METAESTIMATORS: delegate = SubEstimator() delegator = delegator_data.construct(delegate) for method in methods: if method in delegator_data.skip_methods: continue assert_true(hasattr(delegate, method)) assert_true(hasattr(delegator, method), msg="%s does not have method %r when its delegate does" % (delegator_data.name, method)) # delegation before fit raises an exception assert_raises(Exception, getattr(delegator, method), delegator_data.fit_args[0]) delegator.fit(*delegator_data.fit_args) for method in methods: if method in delegator_data.skip_methods: continue # smoke test delegation getattr(delegator, method)(delegator_data.fit_args[0]) for method in methods: if method in delegator_data.skip_methods: continue delegate = SubEstimator(hidden_method=method) delegator = delegator_data.construct(delegate) assert_false(hasattr(delegate, method)) assert_false(hasattr(delegator, method), msg="%s has method %r when its delegate does not" % (delegator_data.name, method))

bsd-3-clause

thesandlord/gcloud-python

gcloud/datastore/connection.py

5

16975

# Copyright 2014 Google Inc. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Connections to gcloud datastore API servers.""" import os from gcloud import connection from gcloud.environment_vars import GCD_HOST from gcloud.exceptions import make_exception from gcloud.datastore import _datastore_v1_pb2 as datastore_pb SCOPE = ('https://www.googleapis.com/auth/datastore', 'https://www.googleapis.com/auth/userinfo.email') """The scopes required for authenticating as a Cloud Datastore consumer.""" class Connection(connection.Connection): """A connection to the Google Cloud Datastore via the Protobuf API. This class should understand only the basic types (and protobufs) in method arguments, however should be capable of returning advanced types. :type credentials: :class:`oauth2client.client.OAuth2Credentials` :param credentials: The OAuth2 Credentials to use for this connection. :type http: :class:`httplib2.Http` or class that defines ``request()``. :param http: An optional HTTP object to make requests. :type api_base_url: string :param api_base_url: The base of the API call URL. Defaults to the value from :mod:`gcloud.connection`. """ API_VERSION = 'v1beta2' """The version of the API, used in building the API call's URL.""" API_URL_TEMPLATE = ('{api_base}/datastore/{api_version}' '/datasets/{dataset_id}/{method}') """A template for the URL of a particular API call.""" def __init__(self, credentials=None, http=None, api_base_url=None): credentials = self._create_scoped_credentials(credentials, SCOPE) super(Connection, self).__init__(credentials=credentials, http=http) if api_base_url is None: api_base_url = os.getenv(GCD_HOST, connection.API_BASE_URL) self.api_base_url = api_base_url def _request(self, dataset_id, method, data): """Make a request over the Http transport to the Cloud Datastore API. :type dataset_id: string :param dataset_id: The ID of the dataset of which to make the request. :type method: string :param method: The API call method name (ie, ``runQuery``, ``lookup``, etc) :type data: string :param data: The data to send with the API call. Typically this is a serialized Protobuf string. :rtype: string :returns: The string response content from the API call. :raises: :class:`gcloud.exceptions.GCloudError` if the response code is not 200 OK. """ headers = { 'Content-Type': 'application/x-protobuf', 'Content-Length': str(len(data)), 'User-Agent': self.USER_AGENT, } headers, content = self.http.request( uri=self.build_api_url(dataset_id=dataset_id, method=method), method='POST', headers=headers, body=data) status = headers['status'] if status != '200': raise make_exception(headers, content, use_json=False) return content def _rpc(self, dataset_id, method, request_pb, response_pb_cls): """Make a protobuf RPC request. :type dataset_id: string :param dataset_id: The ID of the dataset to connect to. This is usually your project name in the cloud console. :type method: string :param method: The name of the method to invoke. :type request_pb: :class:`google.protobuf.message.Message` instance :param request_pb: the protobuf instance representing the request. :type response_pb_cls: A :class:`google.protobuf.message.Message' subclass. :param response_pb_cls: The class used to unmarshall the response protobuf. """ response = self._request(dataset_id=dataset_id, method=method, data=request_pb.SerializeToString()) return response_pb_cls.FromString(response) def build_api_url(self, dataset_id, method, base_url=None, api_version=None): """Construct the URL for a particular API call. This method is used internally to come up with the URL to use when making RPCs to the Cloud Datastore API. :type dataset_id: string :param dataset_id: The ID of the dataset to connect to. This is usually your project name in the cloud console. :type method: string :param method: The API method to call (ie, runQuery, lookup, ...). :type base_url: string :param base_url: The base URL where the API lives. You shouldn't have to provide this. :type api_version: string :param api_version: The version of the API to connect to. You shouldn't have to provide this. """ return self.API_URL_TEMPLATE.format( api_base=(base_url or self.api_base_url), api_version=(api_version or self.API_VERSION), dataset_id=dataset_id, method=method) def lookup(self, dataset_id, key_pbs, eventual=False, transaction_id=None): """Lookup keys from a dataset in the Cloud Datastore. Maps the ``DatastoreService.Lookup`` protobuf RPC. This method deals only with protobufs (:class:`gcloud.datastore._datastore_v1_pb2.Key` and :class:`gcloud.datastore._datastore_v1_pb2.Entity`) and is used under the hood in :func:`gcloud.datastore.get`: >>> from gcloud import datastore >>> key = datastore.Key('MyKind', 1234, dataset_id='dataset-id') >>> datastore.get(key) [<Entity object>] Using the ``connection`` class directly: >>> connection.lookup('dataset-id', [key.to_protobuf()]) [<Entity protobuf>] :type dataset_id: string :param dataset_id: The ID of the dataset to look up the keys. :type key_pbs: list of :class:`gcloud.datastore._datastore_v1_pb2.Key` :param key_pbs: The keys to retrieve from the datastore. :type eventual: boolean :param eventual: If False (the default), request ``STRONG`` read consistency. If True, request ``EVENTUAL`` read consistency. :type transaction_id: string :param transaction_id: If passed, make the request in the scope of the given transaction. Incompatible with ``eventual==True``. :rtype: tuple :returns: A triple of (``results``, ``missing``, ``deferred``) where both ``results`` and ``missing`` are lists of :class:`gcloud.datastore._datastore_v1_pb2.Entity` and ``deferred`` is a list of :class:`gcloud.datastore._datastore_v1_pb2.Key`. """ lookup_request = datastore_pb.LookupRequest() _set_read_options(lookup_request, eventual, transaction_id) _add_keys_to_request(lookup_request.key, key_pbs) lookup_response = self._rpc(dataset_id, 'lookup', lookup_request, datastore_pb.LookupResponse) results = [result.entity for result in lookup_response.found] missing = [result.entity for result in lookup_response.missing] return results, missing, list(lookup_response.deferred) def run_query(self, dataset_id, query_pb, namespace=None, eventual=False, transaction_id=None): """Run a query on the Cloud Datastore. Maps the ``DatastoreService.RunQuery`` protobuf RPC. Given a Query protobuf, sends a ``runQuery`` request to the Cloud Datastore API and returns a list of entity protobufs matching the query. You typically wouldn't use this method directly, in favor of the :meth:`gcloud.datastore.query.Query.fetch` method. Under the hood, the :class:`gcloud.datastore.query.Query` class uses this method to fetch data: >>> from gcloud import datastore >>> query = datastore.Query(kind='MyKind') >>> query.add_filter('property', '=', 'val') Using the query's ``fetch_page`` method... >>> entities, cursor, more_results = query.fetch_page() >>> entities [<list of Entity unmarshalled from protobuf>] >>> cursor <string containing cursor where fetch stopped> >>> more_results <boolean of more results> Under the hood this is doing... >>> connection.run_query('dataset-id', query.to_protobuf()) [<list of Entity Protobufs>], cursor, more_results, skipped_results :type dataset_id: string :param dataset_id: The ID of the dataset over which to run the query. :type query_pb: :class:`gcloud.datastore._datastore_v1_pb2.Query` :param query_pb: The Protobuf representing the query to run. :type namespace: string :param namespace: The namespace over which to run the query. :type eventual: boolean :param eventual: If False (the default), request ``STRONG`` read consistency. If True, request ``EVENTUAL`` read consistency. :type transaction_id: string :param transaction_id: If passed, make the request in the scope of the given transaction. Incompatible with ``eventual==True``. """ request = datastore_pb.RunQueryRequest() _set_read_options(request, eventual, transaction_id) if namespace: request.partition_id.namespace = namespace request.query.CopyFrom(query_pb) response = self._rpc(dataset_id, 'runQuery', request, datastore_pb.RunQueryResponse) return ( [e.entity for e in response.batch.entity_result], response.batch.end_cursor, # Assume response always has cursor. response.batch.more_results, response.batch.skipped_results, ) def begin_transaction(self, dataset_id, serializable=False): """Begin a transaction. Maps the ``DatastoreService.BeginTransaction`` protobuf RPC. :type dataset_id: string :param dataset_id: The ID dataset to which the transaction applies. :type serializable: boolean :param serializable: Boolean indicating if the isolation level of the transaction should be SERIALIZABLE (True) or SNAPSHOT (False). :rtype: :class:`._datastore_v1_pb2.BeginTransactionResponse` :returns': the result protobuf for the begin transaction request. """ request = datastore_pb.BeginTransactionRequest() if serializable: request.isolation_level = ( datastore_pb.BeginTransactionRequest.SERIALIZABLE) else: request.isolation_level = ( datastore_pb.BeginTransactionRequest.SNAPSHOT) response = self._rpc(dataset_id, 'beginTransaction', request, datastore_pb.BeginTransactionResponse) return response.transaction def commit(self, dataset_id, mutation_pb, transaction_id): """Commit dataset mutations in context of current transation (if any). Maps the ``DatastoreService.Commit`` protobuf RPC. :type dataset_id: string :param dataset_id: The ID dataset to which the transaction applies. :type mutation_pb: :class:`datastore_pb.Mutation`. :param mutation_pb: The protobuf for the mutations being saved. :type transaction_id: string or None :param transaction_id: The transaction ID returned from :meth:`begin_transaction`. Non-transactional batches must pass ``None``. :rtype: :class:`gcloud.datastore._datastore_v1_pb2.MutationResult`. :returns': the result protobuf for the mutation. """ request = datastore_pb.CommitRequest() if transaction_id: request.mode = datastore_pb.CommitRequest.TRANSACTIONAL request.transaction = transaction_id else: request.mode = datastore_pb.CommitRequest.NON_TRANSACTIONAL request.mutation.CopyFrom(mutation_pb) response = self._rpc(dataset_id, 'commit', request, datastore_pb.CommitResponse) return response.mutation_result def rollback(self, dataset_id, transaction_id): """Rollback the connection's existing transaction. Maps the ``DatastoreService.Rollback`` protobuf RPC. :type dataset_id: string :param dataset_id: The ID of the dataset to which the transaction belongs. :type transaction_id: string :param transaction_id: The transaction ID returned from :meth:`begin_transaction`. """ request = datastore_pb.RollbackRequest() request.transaction = transaction_id # Nothing to do with this response, so just execute the method. self._rpc(dataset_id, 'rollback', request, datastore_pb.RollbackResponse) def allocate_ids(self, dataset_id, key_pbs): """Obtain backend-generated IDs for a set of keys. Maps the ``DatastoreService.AllocateIds`` protobuf RPC. :type dataset_id: string :param dataset_id: The ID of the dataset to which the transaction belongs. :type key_pbs: list of :class:`gcloud.datastore._datastore_v1_pb2.Key` :param key_pbs: The keys for which the backend should allocate IDs. :rtype: list of :class:`gcloud.datastore._datastore_v1_pb2.Key` :returns: An equal number of keys, with IDs filled in by the backend. """ request = datastore_pb.AllocateIdsRequest() _add_keys_to_request(request.key, key_pbs) # Nothing to do with this response, so just execute the method. response = self._rpc(dataset_id, 'allocateIds', request, datastore_pb.AllocateIdsResponse) return list(response.key) def _set_read_options(request, eventual, transaction_id): """Validate rules for read options, and assign to the request. Helper method for ``lookup()`` and ``run_query``. :raises: :class:`ValueError` if ``eventual`` is ``True`` and the ``transaction_id`` is not ``None``. """ if eventual and (transaction_id is not None): raise ValueError('eventual must be False when in a transaction') opts = request.read_options if eventual: opts.read_consistency = datastore_pb.ReadOptions.EVENTUAL elif transaction_id: opts.transaction = transaction_id def _prepare_key_for_request(key_pb): # pragma: NO COVER copied from helpers """Add protobuf keys to a request object. .. note:: This is copied from `helpers` to avoid a cycle: _implicit_environ -> connection -> helpers -> key -> _implicit_environ :type key_pb: :class:`gcloud.datastore._datastore_v1_pb2.Key` :param key_pb: A key to be added to a request. :rtype: :class:`gcloud.datastore._datastore_v1_pb2.Key` :returns: A key which will be added to a request. It will be the original if nothing needs to be changed. """ if key_pb.partition_id.HasField('dataset_id'): new_key_pb = datastore_pb.Key() new_key_pb.CopyFrom(key_pb) new_key_pb.partition_id.ClearField('dataset_id') key_pb = new_key_pb return key_pb def _add_keys_to_request(request_field_pb, key_pbs): """Add protobuf keys to a request object. :type request_field_pb: `RepeatedCompositeFieldContainer` :param request_field_pb: A repeated proto field that contains keys. :type key_pbs: list of :class:`gcloud.datastore._datastore_v1_pb2.Key` :param key_pbs: The keys to add to a request. """ for key_pb in key_pbs: key_pb = _prepare_key_for_request(key_pb) request_field_pb.add().CopyFrom(key_pb)

apache-2.0

davidam/python-examples

matplotlib/stackplot_demo.py

1

2255

#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright (C) 2018 David Arroyo Menéndez # Author: David Arroyo Menéndez <davidam@gnu.org> # Maintainer: David Arroyo Menéndez <davidam@gnu.org> # This file is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 3, or (at your option) # any later version. # This file is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with GNU Emacs; see the file COPYING. If not, write to # the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, # Boston, MA 02110-1301 USA, """ ============== Stackplot Demo ============== How to create stackplots with Matplotlib. Stackplots are generated by plotting different datasets vertically on top of one another rather than overlapping with one another. Below we show some examples to accomplish this with Matplotlib. """ import numpy as np import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y1 = [1, 1, 2, 3, 5] y2 = [0, 4, 2, 6, 8] y3 = [1, 3, 5, 7, 9] y = np.vstack([y1, y2, y3]) labels = ["Fibonacci ", "Evens", "Odds"] fig, ax = plt.subplots() ax.stackplot(x, y1, y2, y3, labels=labels) ax.legend(loc=2) plt.show() fig, ax = plt.subplots() ax.stackplot(x, y) plt.show() ############################################################################### # Here we show an example of making a streamgraph using stackplot def layers(n, m): """ Return *n* random Gaussian mixtures, each of length *m*. """ def bump(a): x = 1 / (.1 + np.random.random()) y = 2 * np.random.random() - .5 z = 10 / (.1 + np.random.random()) for i in range(m): w = (i / m - y) * z a[i] += x * np.exp(-w * w) a = np.zeros((m, n)) for i in range(n): for j in range(5): bump(a[:, i]) return a d = layers(3, 100) fig, ax = plt.subplots() ax.stackplot(range(100), d.T, baseline='wiggle') plt.show()

gpl-3.0

ageron/tensorflow

tensorflow/examples/get_started/regression/imports85.py

39

6589

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """A dataset loader for imports85.data.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import numpy as np import tensorflow as tf try: import pandas as pd # pylint: disable=g-import-not-at-top except ImportError: pass URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data" # Order is important for the csv-readers, so we use an OrderedDict here. defaults = collections.OrderedDict([ ("symboling", [0]), ("normalized-losses", [0.0]), ("make", [""]), ("fuel-type", [""]), ("aspiration", [""]), ("num-of-doors", [""]), ("body-style", [""]), ("drive-wheels", [""]), ("engine-location", [""]), ("wheel-base", [0.0]), ("length", [0.0]), ("width", [0.0]), ("height", [0.0]), ("curb-weight", [0.0]), ("engine-type", [""]), ("num-of-cylinders", [""]), ("engine-size", [0.0]), ("fuel-system", [""]), ("bore", [0.0]), ("stroke", [0.0]), ("compression-ratio", [0.0]), ("horsepower", [0.0]), ("peak-rpm", [0.0]), ("city-mpg", [0.0]), ("highway-mpg", [0.0]), ("price", [0.0]) ]) # pyformat: disable types = collections.OrderedDict((key, type(value[0])) for key, value in defaults.items()) def _get_imports85(): path = tf.contrib.keras.utils.get_file(URL.split("/")[-1], URL) return path def dataset(y_name="price", train_fraction=0.7): """Load the imports85 data as a (train,test) pair of `Dataset`. Each dataset generates (features_dict, label) pairs. Args: y_name: The name of the column to use as the label. train_fraction: A float, the fraction of data to use for training. The remainder will be used for evaluation. Returns: A (train,test) pair of `Datasets` """ # Download and cache the data path = _get_imports85() # Define how the lines of the file should be parsed def decode_line(line): """Convert a csv line into a (features_dict,label) pair.""" # Decode the line to a tuple of items based on the types of # csv_header.values(). items = tf.decode_csv(line, list(defaults.values())) # Convert the keys and items to a dict. pairs = zip(defaults.keys(), items) features_dict = dict(pairs) # Remove the label from the features_dict label = features_dict.pop(y_name) return features_dict, label def has_no_question_marks(line): """Returns True if the line of text has no question marks.""" # split the line into an array of characters chars = tf.string_split(line[tf.newaxis], "").values # for each character check if it is a question mark is_question = tf.equal(chars, "?") any_question = tf.reduce_any(is_question) no_question = ~any_question return no_question def in_training_set(line): """Returns a boolean tensor, true if the line is in the training set.""" # If you randomly split the dataset you won't get the same split in both # sessions if you stop and restart training later. Also a simple # random split won't work with a dataset that's too big to `.cache()` as # we are doing here. num_buckets = 1000000 bucket_id = tf.string_to_hash_bucket_fast(line, num_buckets) # Use the hash bucket id as a random number that's deterministic per example return bucket_id < int(train_fraction * num_buckets) def in_test_set(line): """Returns a boolean tensor, true if the line is in the training set.""" # Items not in the training set are in the test set. # This line must use `~` instead of `not` because `not` only works on python # booleans but we are dealing with symbolic tensors. return ~in_training_set(line) base_dataset = ( tf.data # Get the lines from the file. .TextLineDataset(path) # drop lines with question marks. .filter(has_no_question_marks)) train = (base_dataset # Take only the training-set lines. .filter(in_training_set) # Decode each line into a (features_dict, label) pair. .map(decode_line) # Cache data so you only decode the file once. .cache()) # Do the same for the test-set. test = (base_dataset.filter(in_test_set).cache().map(decode_line)) return train, test def raw_dataframe(): """Load the imports85 data as a pd.DataFrame.""" # Download and cache the data path = _get_imports85() # Load it into a pandas dataframe df = pd.read_csv(path, names=types.keys(), dtype=types, na_values="?") return df def load_data(y_name="price", train_fraction=0.7, seed=None): """Get the imports85 data set. A description of the data is available at: https://archive.ics.uci.edu/ml/datasets/automobile The data itself can be found at: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data Args: y_name: the column to return as the label. train_fraction: the fraction of the dataset to use for training. seed: The random seed to use when shuffling the data. `None` generates a unique shuffle every run. Returns: a pair of pairs where the first pair is the training data, and the second is the test data: `(x_train, y_train), (x_test, y_test) = get_imports85_dataset(...)` `x` contains a pandas DataFrame of features, while `y` contains the label array. """ # Load the raw data columns. data = raw_dataframe() # Delete rows with unknowns data = data.dropna() # Shuffle the data np.random.seed(seed) # Split the data into train/test subsets. x_train = data.sample(frac=train_fraction, random_state=seed) x_test = data.drop(x_train.index) # Extract the label from the features dataframe. y_train = x_train.pop(y_name) y_test = x_test.pop(y_name) return (x_train, y_train), (x_test, y_test)

apache-2.0

pkruskal/scikit-learn

sklearn/neural_network/rbm.py

205

12292

"""Restricted Boltzmann Machine """ # Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca> # Vlad Niculae # Gabriel Synnaeve # Lars Buitinck # License: BSD 3 clause import time import numpy as np import scipy.sparse as sp from ..base import BaseEstimator from ..base import TransformerMixin from ..externals.six.moves import xrange from ..utils import check_array from ..utils import check_random_state from ..utils import gen_even_slices from ..utils import issparse from ..utils.extmath import safe_sparse_dot from ..utils.extmath import log_logistic from ..utils.fixes import expit # logistic function from ..utils.validation import check_is_fitted class BernoulliRBM(BaseEstimator, TransformerMixin): """Bernoulli Restricted Boltzmann Machine (RBM). A Restricted Boltzmann Machine with binary visible units and binary hiddens. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2]. The time complexity of this implementation is ``O(d ** 2)`` assuming d ~ n_features ~ n_components. Read more in the :ref:`User Guide <rbm>`. Parameters ---------- n_components : int, optional Number of binary hidden units. learning_rate : float, optional The learning rate for weight updates. It is *highly* recommended to tune this hyper-parameter. Reasonable values are in the 10**[0., -3.] range. batch_size : int, optional Number of examples per minibatch. n_iter : int, optional Number of iterations/sweeps over the training dataset to perform during training. verbose : int, optional The verbosity level. The default, zero, means silent mode. random_state : integer or numpy.RandomState, optional A random number generator instance to define the state of the random permutations generator. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator. Attributes ---------- intercept_hidden_ : array-like, shape (n_components,) Biases of the hidden units. intercept_visible_ : array-like, shape (n_features,) Biases of the visible units. components_ : array-like, shape (n_components, n_features) Weight matrix, where n_features in the number of visible units and n_components is the number of hidden units. Examples -------- >>> import numpy as np >>> from sklearn.neural_network import BernoulliRBM >>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) >>> model = BernoulliRBM(n_components=2) >>> model.fit(X) BernoulliRBM(batch_size=10, learning_rate=0.1, n_components=2, n_iter=10, random_state=None, verbose=0) References ---------- [1] Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for deep belief nets. Neural Computation 18, pp 1527-1554. http://www.cs.toronto.edu/~hinton/absps/fastnc.pdf [2] Tieleman, T. Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient. International Conference on Machine Learning (ICML) 2008 """ def __init__(self, n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None): self.n_components = n_components self.learning_rate = learning_rate self.batch_size = batch_size self.n_iter = n_iter self.verbose = verbose self.random_state = random_state def transform(self, X): """Compute the hidden layer activation probabilities, P(h=1|v=X). Parameters ---------- X : {array-like, sparse matrix} shape (n_samples, n_features) The data to be transformed. Returns ------- h : array, shape (n_samples, n_components) Latent representations of the data. """ check_is_fitted(self, "components_") X = check_array(X, accept_sparse='csr', dtype=np.float) return self._mean_hiddens(X) def _mean_hiddens(self, v): """Computes the probabilities P(h=1|v). Parameters ---------- v : array-like, shape (n_samples, n_features) Values of the visible layer. Returns ------- h : array-like, shape (n_samples, n_components) Corresponding mean field values for the hidden layer. """ p = safe_sparse_dot(v, self.components_.T) p += self.intercept_hidden_ return expit(p, out=p) def _sample_hiddens(self, v, rng): """Sample from the distribution P(h|v). Parameters ---------- v : array-like, shape (n_samples, n_features) Values of the visible layer to sample from. rng : RandomState Random number generator to use. Returns ------- h : array-like, shape (n_samples, n_components) Values of the hidden layer. """ p = self._mean_hiddens(v) return (rng.random_sample(size=p.shape) < p) def _sample_visibles(self, h, rng): """Sample from the distribution P(v|h). Parameters ---------- h : array-like, shape (n_samples, n_components) Values of the hidden layer to sample from. rng : RandomState Random number generator to use. Returns ------- v : array-like, shape (n_samples, n_features) Values of the visible layer. """ p = np.dot(h, self.components_) p += self.intercept_visible_ expit(p, out=p) return (rng.random_sample(size=p.shape) < p) def _free_energy(self, v): """Computes the free energy F(v) = - log sum_h exp(-E(v,h)). Parameters ---------- v : array-like, shape (n_samples, n_features) Values of the visible layer. Returns ------- free_energy : array-like, shape (n_samples,) The value of the free energy. """ return (- safe_sparse_dot(v, self.intercept_visible_) - np.logaddexp(0, safe_sparse_dot(v, self.components_.T) + self.intercept_hidden_).sum(axis=1)) def gibbs(self, v): """Perform one Gibbs sampling step. Parameters ---------- v : array-like, shape (n_samples, n_features) Values of the visible layer to start from. Returns ------- v_new : array-like, shape (n_samples, n_features) Values of the visible layer after one Gibbs step. """ check_is_fitted(self, "components_") if not hasattr(self, "random_state_"): self.random_state_ = check_random_state(self.random_state) h_ = self._sample_hiddens(v, self.random_state_) v_ = self._sample_visibles(h_, self.random_state_) return v_ def partial_fit(self, X, y=None): """Fit the model to the data X which should contain a partial segment of the data. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data. Returns ------- self : BernoulliRBM The fitted model. """ X = check_array(X, accept_sparse='csr', dtype=np.float) if not hasattr(self, 'random_state_'): self.random_state_ = check_random_state(self.random_state) if not hasattr(self, 'components_'): self.components_ = np.asarray( self.random_state_.normal( 0, 0.01, (self.n_components, X.shape[1]) ), order='fortran') if not hasattr(self, 'intercept_hidden_'): self.intercept_hidden_ = np.zeros(self.n_components, ) if not hasattr(self, 'intercept_visible_'): self.intercept_visible_ = np.zeros(X.shape[1], ) if not hasattr(self, 'h_samples_'): self.h_samples_ = np.zeros((self.batch_size, self.n_components)) self._fit(X, self.random_state_) def _fit(self, v_pos, rng): """Inner fit for one mini-batch. Adjust the parameters to maximize the likelihood of v using Stochastic Maximum Likelihood (SML). Parameters ---------- v_pos : array-like, shape (n_samples, n_features) The data to use for training. rng : RandomState Random number generator to use for sampling. """ h_pos = self._mean_hiddens(v_pos) v_neg = self._sample_visibles(self.h_samples_, rng) h_neg = self._mean_hiddens(v_neg) lr = float(self.learning_rate) / v_pos.shape[0] update = safe_sparse_dot(v_pos.T, h_pos, dense_output=True).T update -= np.dot(h_neg.T, v_neg) self.components_ += lr * update self.intercept_hidden_ += lr * (h_pos.sum(axis=0) - h_neg.sum(axis=0)) self.intercept_visible_ += lr * (np.asarray( v_pos.sum(axis=0)).squeeze() - v_neg.sum(axis=0)) h_neg[rng.uniform(size=h_neg.shape) < h_neg] = 1.0 # sample binomial self.h_samples_ = np.floor(h_neg, h_neg) def score_samples(self, X): """Compute the pseudo-likelihood of X. Parameters ---------- X : {array-like, sparse matrix} shape (n_samples, n_features) Values of the visible layer. Must be all-boolean (not checked). Returns ------- pseudo_likelihood : array-like, shape (n_samples,) Value of the pseudo-likelihood (proxy for likelihood). Notes ----- This method is not deterministic: it computes a quantity called the free energy on X, then on a randomly corrupted version of X, and returns the log of the logistic function of the difference. """ check_is_fitted(self, "components_") v = check_array(X, accept_sparse='csr') rng = check_random_state(self.random_state) # Randomly corrupt one feature in each sample in v. ind = (np.arange(v.shape[0]), rng.randint(0, v.shape[1], v.shape[0])) if issparse(v): data = -2 * v[ind] + 1 v_ = v + sp.csr_matrix((data.A.ravel(), ind), shape=v.shape) else: v_ = v.copy() v_[ind] = 1 - v_[ind] fe = self._free_energy(v) fe_ = self._free_energy(v_) return v.shape[1] * log_logistic(fe_ - fe) def fit(self, X, y=None): """Fit the model to the data X. Parameters ---------- X : {array-like, sparse matrix} shape (n_samples, n_features) Training data. Returns ------- self : BernoulliRBM The fitted model. """ X = check_array(X, accept_sparse='csr', dtype=np.float) n_samples = X.shape[0] rng = check_random_state(self.random_state) self.components_ = np.asarray( rng.normal(0, 0.01, (self.n_components, X.shape[1])), order='fortran') self.intercept_hidden_ = np.zeros(self.n_components, ) self.intercept_visible_ = np.zeros(X.shape[1], ) self.h_samples_ = np.zeros((self.batch_size, self.n_components)) n_batches = int(np.ceil(float(n_samples) / self.batch_size)) batch_slices = list(gen_even_slices(n_batches * self.batch_size, n_batches, n_samples)) verbose = self.verbose begin = time.time() for iteration in xrange(1, self.n_iter + 1): for batch_slice in batch_slices: self._fit(X[batch_slice], rng) if verbose: end = time.time() print("[%s] Iteration %d, pseudo-likelihood = %.2f," " time = %.2fs" % (type(self).__name__, iteration, self.score_samples(X).mean(), end - begin)) begin = end return self

bsd-3-clause

tclose/python-neo

neo/test/coretest/test_block.py

7

34814

# -*- coding: utf-8 -*- """ Tests of the neo.core.block.Block class """ # needed for python 3 compatibility from __future__ import absolute_import, division, print_function from datetime import datetime try: import unittest2 as unittest except ImportError: import unittest import numpy as np try: from IPython.lib.pretty import pretty except ImportError as err: HAVE_IPYTHON = False else: HAVE_IPYTHON = True from neo.core.block import Block from neo.core.container import filterdata from neo.core import SpikeTrain, Unit from neo.test.tools import (assert_neo_object_is_compliant, assert_same_sub_schema) from neo.test.generate_datasets import (get_fake_value, get_fake_values, fake_neo, clone_object, get_annotations, TEST_ANNOTATIONS) class Test__generate_datasets(unittest.TestCase): def setUp(self): np.random.seed(0) self.annotations = dict([(str(x), TEST_ANNOTATIONS[x]) for x in range(len(TEST_ANNOTATIONS))]) def test__get_fake_values(self): self.annotations['seed'] = 0 file_datetime = get_fake_value('file_datetime', datetime, seed=0) rec_datetime = get_fake_value('rec_datetime', datetime, seed=1) index = get_fake_value('index', int, seed=2) name = get_fake_value('name', str, seed=3, obj=Block) description = get_fake_value('description', str, seed=4, obj='Block') file_origin = get_fake_value('file_origin', str) attrs1 = {'file_datetime': file_datetime, 'rec_datetime': rec_datetime, 'index': index, 'name': name, 'description': description, 'file_origin': file_origin} attrs2 = attrs1.copy() attrs2.update(self.annotations) res11 = get_fake_values(Block, annotate=False, seed=0) res12 = get_fake_values('Block', annotate=False, seed=0) res21 = get_fake_values(Block, annotate=True, seed=0) res22 = get_fake_values('Block', annotate=True, seed=0) self.assertEqual(res11, attrs1) self.assertEqual(res12, attrs1) self.assertEqual(res21, attrs2) self.assertEqual(res22, attrs2) def test__fake_neo__cascade(self): self.annotations['seed'] = None obj_type = 'Block' cascade = True res = fake_neo(obj_type=obj_type, cascade=cascade) for child in res.children_recur: del child.annotations['i'] del child.annotations['j'] self.assertTrue(isinstance(res, Block)) assert_neo_object_is_compliant(res) self.assertEqual(res.annotations, self.annotations) self.assertEqual(len(res.segments), 1) seg = res.segments[0] self.assertEqual(seg.annotations, self.annotations) self.assertEqual(len(res.recordingchannelgroups), 1) rcg = res.recordingchannelgroups[0] self.assertEqual(rcg.annotations, self.annotations) self.assertEqual(len(seg.analogsignalarrays), 1) self.assertEqual(len(seg.analogsignals), 1) self.assertEqual(len(seg.irregularlysampledsignals), 1) self.assertEqual(len(seg.spiketrains), 1) self.assertEqual(len(seg.spikes), 1) self.assertEqual(len(seg.events), 1) self.assertEqual(len(seg.epochs), 1) self.assertEqual(len(seg.eventarrays), 1) self.assertEqual(len(seg.epocharrays), 1) self.assertEqual(seg.analogsignalarrays[0].annotations, self.annotations) self.assertEqual(seg.analogsignals[0].annotations, self.annotations) self.assertEqual(seg.irregularlysampledsignals[0].annotations, self.annotations) self.assertEqual(seg.spiketrains[0].annotations, self.annotations) self.assertEqual(seg.spikes[0].annotations, self.annotations) self.assertEqual(seg.events[0].annotations, self.annotations) self.assertEqual(seg.epochs[0].annotations, self.annotations) self.assertEqual(seg.eventarrays[0].annotations, self.annotations) self.assertEqual(seg.epocharrays[0].annotations, self.annotations) self.assertEqual(len(rcg.recordingchannels), 1) rchan = rcg.recordingchannels[0] self.assertEqual(rchan.annotations, self.annotations) self.assertEqual(len(rcg.units), 1) unit = rcg.units[0] self.assertEqual(unit.annotations, self.annotations) self.assertEqual(len(rcg.analogsignalarrays), 1) self.assertEqual(rcg.analogsignalarrays[0].annotations, self.annotations) self.assertEqual(len(rchan.analogsignals), 1) self.assertEqual(len(rchan.irregularlysampledsignals), 1) self.assertEqual(rchan.analogsignals[0].annotations, self.annotations) self.assertEqual(rchan.irregularlysampledsignals[0].annotations, self.annotations) self.assertEqual(len(unit.spiketrains), 1) self.assertEqual(len(unit.spikes), 1) self.assertEqual(unit.spiketrains[0].annotations, self.annotations) self.assertEqual(unit.spikes[0].annotations, self.annotations) def test__fake_neo__nocascade(self): self.annotations['seed'] = None obj_type = Block cascade = False res = fake_neo(obj_type=obj_type, cascade=cascade) self.assertTrue(isinstance(res, Block)) assert_neo_object_is_compliant(res) self.assertEqual(res.annotations, self.annotations) self.assertEqual(len(res.segments), 0) self.assertEqual(len(res.recordingchannelgroups), 0) class TestBlock(unittest.TestCase): def setUp(self): self.nchildren = 2 self.seed1 = 0 self.seed2 = 10000 self.blk1 = fake_neo(Block, seed=self.seed1, n=self.nchildren) self.blk2 = fake_neo(Block, seed=self.seed2, n=self.nchildren) self.targobj = self.blk1 self.segs1 = self.blk1.segments self.segs2 = self.blk2.segments self.rcgs1 = self.blk1.recordingchannelgroups self.rcgs2 = self.blk2.recordingchannelgroups self.units1 = [[unit for unit in rcg.units] for rcg in self.rcgs1] self.units2 = [[unit for unit in rcg.units] for rcg in self.rcgs2] self.rchans1 = [[rchan for rchan in rcg.recordingchannels] for rcg in self.rcgs1] self.rchans2 = [[rchan for rchan in rcg.recordingchannels] for rcg in self.rcgs2] self.units1 = sum(self.units1, []) self.units2 = sum(self.units2, []) self.rchans1 = sum(self.rchans1, []) self.rchans2 = sum(self.rchans2, []) self.sigarrs1 = [[sigarr for sigarr in rcg.analogsignalarrays] for rcg in self.rcgs1] self.sigarrs2 = [[sigarr for sigarr in rcg.analogsignalarrays] for rcg in self.rcgs2] self.spikes1 = [[spike for spike in unit.spikes] for unit in self.units1] self.spikes2 = [[spike for spike in unit.spikes] for unit in self.units2] self.trains1 = [[train for train in unit.spiketrains] for unit in self.units1] self.trains2 = [[train for train in unit.spiketrains] for unit in self.units2] self.sigs1 = [[sig for sig in rchan.analogsignals] for rchan in self.rchans1] self.sigs2 = [[sig for sig in rchan.analogsignals] for rchan in self.rchans2] self.irsigs1 = [[irsig for irsig in rchan.irregularlysampledsignals] for rchan in self.rchans1] self.irsigs2 = [[irsig for irsig in rchan.irregularlysampledsignals] for rchan in self.rchans2] self.epcs1 = [[epc for epc in seg.epochs] for seg in self.segs1] self.epcs2 = [[epc for epc in seg.epochs] for seg in self.segs2] self.epcas1 = [[epca for epca in seg.epocharrays] for seg in self.segs1] self.epcas2 = [[epca for epca in seg.epocharrays] for seg in self.segs2] self.evts1 = [[evt for evt in seg.events] for seg in self.segs1] self.evts2 = [[evt for evt in seg.events] for seg in self.segs2] self.evtas1 = [[evta for evta in seg.eventarrays] for seg in self.segs1] self.evtas2 = [[evta for evta in seg.eventarrays] for seg in self.segs2] self.sigarrs1 = sum(self.sigarrs1, []) self.sigarrs2 = sum(self.sigarrs2, []) self.spikes1 = sum(self.spikes1, []) self.spikes2 = sum(self.spikes2, []) self.trains1 = sum(self.trains1, []) self.trains2 = sum(self.trains2, []) self.sigs1 = sum(self.sigs1, []) self.sigs2 = sum(self.sigs2, []) self.irsigs1 = sum(self.irsigs1, []) self.irsigs2 = sum(self.irsigs2, []) self.epcs1 = sum(self.epcs1, []) self.epcs2 = sum(self.epcs2, []) self.epcas1 = sum(self.epcas1, []) self.epcas2 = sum(self.epcas2, []) self.evts1 = sum(self.evts1, []) self.evts2 = sum(self.evts2, []) self.evtas1 = sum(self.evtas1, []) self.evtas2 = sum(self.evtas2, []) def test_block_init(self): blk = Block(name='a block') assert_neo_object_is_compliant(blk) self.assertEqual(blk.name, 'a block') self.assertEqual(blk.file_origin, None) def check_creation(self, blk): assert_neo_object_is_compliant(blk) seed = blk.annotations['seed'] targ0 = get_fake_value('file_datetime', datetime, seed=seed+0) self.assertEqual(blk.file_datetime, targ0) targ1 = get_fake_value('rec_datetime', datetime, seed=seed+1) self.assertEqual(blk.rec_datetime, targ1) targ2 = get_fake_value('index', int, seed=seed+2, obj=Block) self.assertEqual(blk.index, targ2) targ3 = get_fake_value('name', str, seed=seed+3, obj=Block) self.assertEqual(blk.name, targ3) targ4 = get_fake_value('description', str, seed=seed+4, obj=Block) self.assertEqual(blk.description, targ4) targ5 = get_fake_value('file_origin', str) self.assertEqual(blk.file_origin, targ5) targ6 = get_annotations() targ6['seed'] = seed self.assertEqual(blk.annotations, targ6) self.assertTrue(hasattr(blk, 'recordingchannelgroups')) self.assertTrue(hasattr(blk, 'segments')) self.assertEqual(len(blk.recordingchannelgroups), self.nchildren) self.assertEqual(len(blk.segments), self.nchildren) def test__creation(self): self.check_creation(self.blk1) self.check_creation(self.blk2) def test__merge(self): blk1a = fake_neo(Block, seed=self.seed1, n=self.nchildren) assert_same_sub_schema(self.blk1, blk1a) blk1a.annotate(seed=self.seed2) blk1a.segments.append(self.segs2[0]) blk1a.merge(self.blk2) segs1a = clone_object(self.blk1).segments rcgs1a = clone_object(self.rcgs1) assert_same_sub_schema(rcgs1a + self.rcgs2, blk1a.recordingchannelgroups) assert_same_sub_schema(segs1a + self.segs2, blk1a.segments) def test__children(self): segs1a = clone_object(self.blk1).segments rcgs1a = clone_object(self.rcgs1) self.assertEqual(self.blk1._container_child_objects, ('Segment', 'RecordingChannelGroup')) self.assertEqual(self.blk1._data_child_objects, ()) self.assertEqual(self.blk1._single_parent_objects, ()) self.assertEqual(self.blk1._multi_child_objects, ()) self.assertEqual(self.blk1._multi_parent_objects, ()) self.assertEqual(self.blk1._child_properties, ('Unit', 'RecordingChannel')) self.assertEqual(self.blk1._single_child_objects, ('Segment', 'RecordingChannelGroup')) self.assertEqual(self.blk1._container_child_containers, ('segments', 'recordingchannelgroups')) self.assertEqual(self.blk1._data_child_containers, ()) self.assertEqual(self.blk1._single_child_containers, ('segments', 'recordingchannelgroups')) self.assertEqual(self.blk1._single_parent_containers, ()) self.assertEqual(self.blk1._multi_child_containers, ()) self.assertEqual(self.blk1._multi_parent_containers, ()) self.assertEqual(self.blk1._child_objects, ('Segment', 'RecordingChannelGroup')) self.assertEqual(self.blk1._child_containers, ('segments', 'recordingchannelgroups')) self.assertEqual(self.blk1._parent_objects, ()) self.assertEqual(self.blk1._parent_containers, ()) self.assertEqual(len(self.blk1._single_children), 2*self.nchildren) self.assertEqual(len(self.blk1._multi_children), 0) self.assertEqual(len(self.blk1.data_children), 0) self.assertEqual(len(self.blk1.data_children_recur), 4*self.nchildren**3 + 5*self.nchildren**2) self.assertEqual(len(self.blk1.container_children), 2*self.nchildren) self.assertEqual(len(self.blk1.container_children_recur), 2*self.nchildren + 2*self.nchildren**2) self.assertEqual(len(self.blk1.children), 2*self.nchildren) self.assertEqual(len(self.blk1.children_recur), 2*self.nchildren + 2*self.nchildren**2 + 4*self.nchildren**3 + 5*self.nchildren**2) self.assertEqual(self.blk1._multi_children, ()) assert_same_sub_schema(list(self.blk1._single_children), self.segs1 + self.rcgs1) assert_same_sub_schema(list(self.blk1.container_children), self.segs1 + self.rcgs1) assert_same_sub_schema(list(self.blk1.container_children_recur), self.segs1 + self.rcgs1 + self.units1[:2] + self.rchans1[:2] + self.units1[2:] + self.rchans1[2:]) assert_same_sub_schema(list(self.blk1.data_children_recur), self.sigs1[::2] + self.sigarrs1[::2] + self.epcs1[:2] + self.epcas1[:2] + self.evts1[:2] + self.evtas1[:2] + self.irsigs1[::2] + self.spikes1[::2] + self.trains1[::2] + self.sigs1[1::2] + self.sigarrs1[1::2] + self.epcs1[2:] + self.epcas1[2:] + self.evts1[2:] + self.evtas1[2:] + self.irsigs1[1::2] + self.spikes1[1::2] + self.trains1[1::2], exclude=['channel_index']) assert_same_sub_schema(list(self.blk1.children), segs1a + rcgs1a) assert_same_sub_schema(list(self.blk1.children_recur), self.sigs1[::2] + self.sigarrs1[::2] + self.epcs1[:2] + self.epcas1[:2] + self.evts1[:2] + self.evtas1[:2] + self.irsigs1[::2] + self.spikes1[::2] + self.trains1[::2] + self.sigs1[1::2] + self.sigarrs1[1::2] + self.epcs1[2:] + self.epcas1[2:] + self.evts1[2:] + self.evtas1[2:] + self.irsigs1[1::2] + self.spikes1[1::2] + self.trains1[1::2] + self.segs1 + self.rcgs1 + self.units1[:2] + self.rchans1[:2] + self.units1[2:] + self.rchans1[2:], exclude=['channel_index']) def test__size(self): targ = {'segments': self.nchildren, 'recordingchannelgroups': self.nchildren} self.assertEqual(self.targobj.size, targ) def test__filter_none(self): targ = [] res1 = self.targobj.filter() res2 = self.targobj.filter({}) res3 = self.targobj.filter([]) res4 = self.targobj.filter([{}]) res5 = self.targobj.filter([{}, {}]) res6 = self.targobj.filter([{}, {}]) res7 = self.targobj.filter(targdict={}) res8 = self.targobj.filter(targdict=[]) res9 = self.targobj.filter(targdict=[{}]) res10 = self.targobj.filter(targdict=[{}, {}]) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) assert_same_sub_schema(res3, targ) assert_same_sub_schema(res4, targ) assert_same_sub_schema(res5, targ) assert_same_sub_schema(res6, targ) assert_same_sub_schema(res7, targ) assert_same_sub_schema(res8, targ) assert_same_sub_schema(res9, targ) assert_same_sub_schema(res10, targ) def test__filter_annotation_single(self): targ = ([self.epcs1[1], self.epcas1[1], self.evts1[1], self.evtas1[1]] + self.sigs1[1::2] + self.sigarrs1[1::2] + [self.epcs1[3], self.epcas1[3], self.evts1[3], self.evtas1[3]] + self.irsigs1[1::2] + self.spikes1[1::2] + self.trains1[1::2]) res0 = self.targobj.filter(j=1) res1 = self.targobj.filter({'j': 1}) res2 = self.targobj.filter(targdict={'j': 1}) res3 = self.targobj.filter([{'j': 1}]) res4 = self.targobj.filter(targdict=[{'j': 1}]) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) assert_same_sub_schema(res3, targ) assert_same_sub_schema(res4, targ) def test__filter_single_annotation_nores(self): targ = [] res0 = self.targobj.filter(j=5) res1 = self.targobj.filter({'j': 5}) res2 = self.targobj.filter(targdict={'j': 5}) res3 = self.targobj.filter([{'j': 5}]) res4 = self.targobj.filter(targdict=[{'j': 5}]) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) assert_same_sub_schema(res3, targ) assert_same_sub_schema(res4, targ) def test__filter_attribute_single(self): targ = [self.spikes1[0]] name = self.spikes1[0].name res0 = self.targobj.filter(name=name) res1 = self.targobj.filter({'name': name}) res2 = self.targobj.filter(targdict={'name': name}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) def test__filter_attribute_single_nores(self): targ = [] name = self.spikes2[0].name res0 = self.targobj.filter(name=name) res1 = self.targobj.filter({'name': name}) res2 = self.targobj.filter(targdict={'name': name}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) def test__filter_multi(self): targ = ([self.epcs1[1], self.epcas1[1], self.evts1[1], self.evtas1[1]] + self.sigs1[1::2] + self.sigarrs1[1::2] + [self.epcs1[3], self.epcas1[3], self.evts1[3], self.evtas1[3]] + self.irsigs1[1::2] + self.spikes1[1::2] + self.trains1[1::2] + [self.spikes1[0]]) name = self.spikes1[0].name res0 = self.targobj.filter(name=name, j=1) res1 = self.targobj.filter({'name': name, 'j': 1}) res2 = self.targobj.filter(targdict={'name': name, 'j': 1}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) def test__filter_multi_nores(self): targ = [] name0 = self.sigarrs2[0].name res0 = self.targobj.filter([{'j': 5}, {}]) res1 = self.targobj.filter({}, j=0) res2 = self.targobj.filter([{}], i=0) res3 = self.targobj.filter({'name': name0}, j=1) res4 = self.targobj.filter(targdict={'name': name0}, j=1) res5 = self.targobj.filter(name=name0, targdict={'j': 1}) res6 = self.targobj.filter(name=name0, j=5) res7 = self.targobj.filter({'name': name0, 'j': 5}) res8 = self.targobj.filter(targdict={'name': name0, 'j': 5}) res9 = self.targobj.filter({'name': name0}, j=5) res10 = self.targobj.filter(targdict={'name': name0}, j=5) res11 = self.targobj.filter(name=name0, targdict={'j': 5}) res12 = self.targobj.filter({'name': name0}, j=5) res13 = self.targobj.filter(targdict={'name': name0}, j=5) res14 = self.targobj.filter(name=name0, targdict={'j': 5}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) assert_same_sub_schema(res3, targ) assert_same_sub_schema(res4, targ) assert_same_sub_schema(res5, targ) assert_same_sub_schema(res6, targ) assert_same_sub_schema(res7, targ) assert_same_sub_schema(res8, targ) assert_same_sub_schema(res9, targ) assert_same_sub_schema(res10, targ) assert_same_sub_schema(res11, targ) assert_same_sub_schema(res12, targ) assert_same_sub_schema(res13, targ) assert_same_sub_schema(res14, targ) def test__filter_multi_partres_annotation_attribute(self): targ = [self.spikes1[0]] name = self.spikes1[0].name res0 = self.targobj.filter(name=name, j=90) res1 = self.targobj.filter({'name': name, 'j': 90}) res2 = self.targobj.filter(targdict={'name': name, 'j': 90}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) def test__filter_multi_partres_annotation_annotation(self): targ = self.sigs1[::2] + self.spikes1[::2] res0 = self.targobj.filter([{'j': 0}, {'i': 0}]) res1 = self.targobj.filter({'j': 0}, i=0) res2 = self.targobj.filter([{'j': 0}], i=0) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) def test__filter_single_annotation_obj_single(self): targ = self.trains1[1::2] res0 = self.targobj.filter(j=1, objects='SpikeTrain') res1 = self.targobj.filter(j=1, objects=SpikeTrain) res2 = self.targobj.filter(j=1, objects=['SpikeTrain']) res3 = self.targobj.filter(j=1, objects=[SpikeTrain]) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) assert_same_sub_schema(res3, targ) def test__filter_single_annotation_obj_multi(self): targ = self.spikes1[1::2] + self.trains1[1::2] res0 = self.targobj.filter(j=1, objects=['Spike', SpikeTrain]) assert_same_sub_schema(res0, targ) def test__filter_single_annotation_norecur(self): targ = [] res0 = self.targobj.filter(j=1, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_norecur(self): targ = [] res0 = self.targobj.filter(name=self.sigarrs1[0].name, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_annotation_nodata(self): targ = [] res0 = self.targobj.filter(j=1, data=False) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_nodata(self): targ = [] res0 = self.targobj.filter(name=self.sigarrs1[0].name, data=False) assert_same_sub_schema(res0, targ) def test__filter_single_annotation_nodata_norecur(self): targ = [] res0 = self.targobj.filter(j=1, data=False, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_nodata_norecur(self): targ = [] res0 = self.targobj.filter(name=self.sigarrs1[0].name, data=False, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_annotation_container(self): targ = ([self.epcs1[1], self.epcas1[1], self.evts1[1], self.evtas1[1]] + self.sigs1[1::2] + self.sigarrs1[1::2] + [self.epcs1[3], self.epcas1[3], self.evts1[3], self.evtas1[3]] + self.irsigs1[1::2] + self.spikes1[1::2] + self.trains1[1::2] + [self.segs1[1], self.rcgs1[1], self.units1[1], self.rchans1[1], self.units1[3], self.rchans1[3]]) res0 = self.targobj.filter(j=1, container=True) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_container_data(self): targ = [self.spikes1[0]] res0 = self.targobj.filter(name=self.spikes1[0].name, container=True) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_container_container(self): targ = [self.rchans1[0]] res0 = self.targobj.filter(name=self.rchans1[0].name, container=True) assert_same_sub_schema(res0, targ) def test__filter_single_annotation_container_norecur(self): targ = [self.segs1[1], self.rcgs1[1]] res0 = self.targobj.filter(j=1, container=True, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_container_norecur(self): targ = [self.segs1[0]] res0 = self.targobj.filter(name=self.segs1[0].name, container=True, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_container_norecur_nores(self): targ = [] res0 = self.targobj.filter(name=self.spikes1[0].name, container=True, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_annotation_nodata_container(self): targ = [self.segs1[1], self.rcgs1[1], self.units1[1], self.rchans1[1], self.units1[3], self.rchans1[3]] res0 = self.targobj.filter(j=1, data=False, container=True) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_nodata_container(self): targ = [self.rchans1[0]] res0 = self.targobj.filter(name=self.rchans1[0].name, data=False, container=True) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_nodata_container_nores(self): targ = [] res0 = self.targobj.filter(name=self.spikes1[0].name, data=False, container=True) assert_same_sub_schema(res0, targ) def test__filter_single_annotation_nodata_container_norecur(self): targ = [self.segs1[1], self.rcgs1[1]] res0 = self.targobj.filter(j=1, data=False, container=True, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_nodata_container_norecur(self): targ = [self.segs1[0]] res0 = self.targobj.filter(name=self.segs1[0].name, data=False, container=True, recursive=False) assert_same_sub_schema(res0, targ) def test__filter_single_attribute_nodata_container_norecur_nores(self): targ = [] res0 = self.targobj.filter(name=self.spikes1[0].name, data=False, container=True, recursive=False) assert_same_sub_schema(res0, targ) def test__filterdata_multi(self): data = self.targobj.children_recur targ = ([self.epcs1[1], self.epcas1[1], self.evts1[1], self.evtas1[1]] + self.sigs1[1::2] + self.sigarrs1[1::2] + [self.epcs1[3], self.epcas1[3], self.evts1[3], self.evtas1[3]] + self.irsigs1[1::2] + self.spikes1[1::2] + self.trains1[1::2] + [self.segs1[1], self.rcgs1[1], self.units1[1], self.rchans1[1], self.units1[3], self.rchans1[3], self.spikes1[0]]) name = self.spikes1[0].name res0 = filterdata(data, name=name, j=1) res1 = filterdata(data, {'name': name, 'j': 1}) res2 = filterdata(data, targdict={'name': name, 'j': 1}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) def test__filterdata_multi_nores(self): data = self.targobj.children_recur targ = [] name1 = self.sigarrs1[0].name name2 = self.sigarrs2[0].name res0 = filterdata(data, [{'j': 0}, {}]) res1 = filterdata(data, {}, i=0) res2 = filterdata(data, [{}], i=0) res3 = filterdata(data, name=name1, targdict={'j': 1}) res4 = filterdata(data, {'name': name1}, j=1) res5 = filterdata(data, targdict={'name': name1}, j=1) res6 = filterdata(data, name=name2, j=5) res7 = filterdata(data, {'name': name2, 'j': 5}) res8 = filterdata(data, targdict={'name': name2, 'j': 5}) res9 = filterdata(data, {'name': name2}, j=5) res10 = filterdata(data, targdict={'name': name2}, j=5) res11 = filterdata(data, name=name2, targdict={'j': 5}) res12 = filterdata(data, {'name': name1}, j=5) res13 = filterdata(data, targdict={'name': name1}, j=5) res14 = filterdata(data, name=name1, targdict={'j': 5}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) assert_same_sub_schema(res3, targ) assert_same_sub_schema(res4, targ) assert_same_sub_schema(res5, targ) assert_same_sub_schema(res6, targ) assert_same_sub_schema(res7, targ) assert_same_sub_schema(res8, targ) assert_same_sub_schema(res9, targ) assert_same_sub_schema(res10, targ) assert_same_sub_schema(res11, targ) assert_same_sub_schema(res12, targ) assert_same_sub_schema(res13, targ) assert_same_sub_schema(res14, targ) def test__filterdata_multi_partres_annotation_attribute(self): data = self.targobj.children_recur targ = [self.spikes1[0]] name = self.spikes1[0].name res0 = filterdata(data, name=name, j=90) res1 = filterdata(data, {'name': name, 'j': 90}) res2 = filterdata(data, targdict={'name': name, 'j': 90}) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) def test__filterdata_multi_partres_annotation_annotation(self): data = self.targobj.children_recur targ = (self.sigs1[::2] + self.spikes1[::2] + self.segs1[:1] + self.units1[::2]) res0 = filterdata(data, [{'j': 0}, {'i': 0}]) res1 = filterdata(data, {'j': 0}, i=0) res2 = filterdata(data, [{'j': 0}], i=0) assert_same_sub_schema(res0, targ) assert_same_sub_schema(res1, targ) assert_same_sub_schema(res2, targ) @unittest.skipUnless(HAVE_IPYTHON, "requires IPython") def test__pretty(self): res = pretty(self.blk1) ann = get_annotations() ann['seed'] = self.seed1 ann = pretty(ann).replace('\n ', '\n ') seg0 = pretty(self.segs1[0]) seg1 = pretty(self.segs1[1]) seg0 = seg0.replace('\n', '\n ') seg1 = seg1.replace('\n', '\n ') targ = ("Block with " + ("%s segments, %s recordingchannelgroups\n" % (len(self.segs1), len(self.rcgs1))) + ("name: '%s'\ndescription: '%s'\n" % (self.blk1.name, self.blk1.description)) + ("annotations: %s\n" % ann) + ("file_origin: '%s'\n" % self.blk1.file_origin) + ("file_datetime: %s\n" % repr(self.blk1.file_datetime)) + ("rec_datetime: %s\n" % repr(self.blk1.rec_datetime)) + ("index: %s\n" % self.blk1.index) + ("# segments (N=%s)\n" % len(self.segs1)) + ('%s: %s\n' % (0, seg0)) + ('%s: %s' % (1, seg1))) self.assertEqual(res, targ) def test_block_list_units(self): assert_same_sub_schema(self.units1, self.blk1.list_units) assert_same_sub_schema(self.units2, self.blk2.list_units) assert_same_sub_schema(self.units1, self.blk1.list_children_by_class(Unit)) assert_same_sub_schema(self.units2, self.blk2.list_children_by_class(Unit)) assert_same_sub_schema(self.units1, self.blk1.list_children_by_class('Unit')) assert_same_sub_schema(self.units2, self.blk2.list_children_by_class('Unit')) assert_same_sub_schema(self.units1, self.blk1.list_children_by_class('units')) assert_same_sub_schema(self.units2, self.blk2.list_children_by_class('units')) def test_block_list_recordingchannels(self): assert_same_sub_schema(self.rchans1, self.blk1.list_recordingchannels) assert_same_sub_schema(self.rchans2, self.blk2.list_recordingchannels) if __name__ == "__main__": unittest.main()

bsd-3-clause

GoogleCloudPlatform/public-datasets-pipelines

datasets/nhtsa_traffic_fatalities/pipelines/nhtsa_traffic_fatalities/nhtsa_traffic_fatalities_dag.py

1

319461

# Copyright 2022 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from airflow import DAG from airflow.providers.cncf.kubernetes.operators import kubernetes_pod from airflow.providers.google.cloud.operators import kubernetes_engine default_args = { "owner": "Google", "depends_on_past": False, "start_date": "2022-03-01", } with DAG( dag_id="nhtsa_traffic_fatalities.nhtsa_traffic_fatalities", default_args=default_args, max_active_runs=1, schedule_interval="@daily", catchup=False, default_view="graph", ) as dag: create_cluster = kubernetes_engine.GKECreateClusterOperator( task_id="create_cluster", project_id="{{ var.value.gcp_project }}", location="us-central1-c", body={ "name": "nhtsa-traffic-fatalities", "initial_node_count": 2, "network": "{{ var.value.vpc_network }}", "node_config": { "machine_type": "e2-standard-16", "oauth_scopes": [ "https://www.googleapis.com/auth/devstorage.read_write", "https://www.googleapis.com/auth/cloud-platform", ], }, }, ) # Run CSV transform within kubernetes pod for accident pipeline accident_2015_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="accident_2015_transform_csv", startup_timeout_seconds=600, name="accident_2015", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "accident.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.accident_2015.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "number_of_vehicle_forms_submitted_all",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_parked_working_vehicles",\n "number_of_forms_submitted_for_persons_not_in_motor_vehicles",\n "number_of_forms_submitted_for_persons_in_motor_vehicles",\n "number_of_persons_in_motor_vehicles_in_transport_mvit",\n "number_of_persons_not_in_motor_vehicles_in_transport_mvit",\n "county",\n "city",\n "day_of_crash",\n "day_name",\n "month_of_crash",\n "month_of_crash_name",\n "year_of_crash",\n "day_of_week",\n "day_of_week_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "national_highway_system",\n "national_highway_system_name",\n "route_signing",\n "route_signing_name",\n "trafficway_identifier",\n "trafficway_identifier_2",\n "land_use",\n "land_use_name",\n "functional_system",\n "functional_system_name",\n "ownership",\n "ownership_name",\n "milepoint",\n "milepoint_name",\n "latitude",\n "latitude_name",\n "longitude",\n "longitude_name",\n "special_jurisdiction",\n "special_jurisdiction_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "relation_to_junction_within_interchange_area",\n "relation_to_junction_within_interchange_area_name",\n "relation_to_junction_specific_location",\n "relation_to_junction_specific_location_name",\n "type_of_intersection",\n "type_of_intersection_name",\n "work_zone",\n "work_zone_name",\n "relation_to_trafficway",\n "relation_to_trafficway_name",\n "light_condition",\n "light_condition_name",\n "atmospheric_conditions_1",\n "atmospheric_conditions_1_name",\n "atmospheric_conditions_2",\n "atmospheric_conditions_2_name",\n "atmospheric_conditions",\n "atmospheric_conditions_name",\n "school_bus_related",\n "school_bus_related_name",\n "rail_grade_crossing_identifier",\n "rail_grade_crossing_identifier_name",\n "hour_of_notification",\n "hour_of_notification_name",\n "minute_of_notification",\n "minute_of_notification_name",\n "hour_of_arrival_at_scene",\n "hour_of_arrival_at_scene_name",\n "minute_of_arrival_at_scene",\n "minute_of_arrival_at_scene_name",\n "hour_of_ems_arrival_at_hospital",\n "hour_of_ems_arrival_at_hospital_name",\n "minute_of_ems_arrival_at_hospital",\n "minute_of_ems_arrival_at_hospital_name",\n "related_factors_crash_level_1",\n "related_factors_crash_level_1_name",\n "related_factors_crash_level_2",\n "related_factors_crash_level_2_name",\n "related_factors_crash_level_3",\n "related_factors_crash_level_3_name",\n "number_of_fatalities",\n "number_of_drunk_drivers"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "number_of_vehicle_forms_submitted_all": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_parked_working_vehicles": "str",\n "number_of_forms_submitted_for_persons_not_in_motor_vehicles": "str",\n "number_of_forms_submitted_for_persons_in_motor_vehicles": "str",\n "number_of_persons_in_motor_vehicles_in_transport_mvit": "str",\n "number_of_persons_not_in_motor_vehicles_in_transport_mvit": "str",\n "county": "str",\n "city": "str",\n "day_of_crash": "str",\n "day_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "year_of_crash": "str",\n "day_of_week": "str",\n "day_of_week_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "national_highway_system": "str",\n "national_highway_system_name": "str",\n "route_signing": "str",\n "route_signing_name": "str",\n "trafficway_identifier": "str",\n "trafficway_identifier_2": "str",\n "land_use": "str",\n "land_use_name": "str",\n "functional_system": "str",\n "functional_system_name": "str",\n "ownership": "str",\n "ownership_name": "str",\n "milepoint": "str",\n "milepoint_name": "str",\n "latitude": "str",\n "latitude_name": "str",\n "longitude": "str",\n "longitude_name": "str",\n "special_jurisdiction": "str",\n "special_jurisdiction_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "relation_to_junction_within_interchange_area": "str",\n "relation_to_junction_within_interchange_area_name": "str",\n "relation_to_junction_specific_location": "str",\n "relation_to_junction_specific_location_name": "str",\n "type_of_intersection": "str",\n "type_of_intersection_name": "str",\n "work_zone": "str",\n "work_zone_name": "str",\n "relation_to_trafficway": "str",\n "relation_to_trafficway_name": "str",\n "light_condition": "str",\n "light_condition_name": "str",\n "atmospheric_conditions_1": "str",\n "atmospheric_conditions_1_name": "str",\n "atmospheric_conditions_2": "str",\n "atmospheric_conditions_2_name": "str",\n "atmospheric_conditions": "str",\n "atmospheric_conditions_name": "str",\n "school_bus_related": "str",\n "school_bus_related_name": "str",\n "rail_grade_crossing_identifier": "str",\n "rail_grade_crossing_identifier_name": "str",\n "hour_of_notification": "str",\n "hour_of_notification_name": "str",\n "minute_of_notification": "str",\n "minute_of_notification_name": "str",\n "hour_of_arrival_at_scene": "str",\n "hour_of_arrival_at_scene_name": "str",\n "minute_of_arrival_at_scene": "str",\n "minute_of_arrival_at_scene_name": "str",\n "hour_of_ems_arrival_at_hospital": "str",\n "hour_of_ems_arrival_at_hospital_name": "str",\n "minute_of_ems_arrival_at_hospital": "str",\n "minute_of_ems_arrival_at_hospital_name": "str",\n "related_factors_crash_level_1": "str",\n "related_factors_crash_level_1_name": "str",\n "related_factors_crash_level_2": "str",\n "related_factors_crash_level_2_name": "str",\n "related_factors_crash_level_3": "str",\n "related_factors_crash_level_3_name": "str",\n "number_of_fatalities": "str",\n "number_of_drunk_drivers": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VE_TOTAL": "number_of_vehicle_forms_submitted_all",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PVH_INVL": "number_of_parked_working_vehicles",\n "PEDS": "number_of_forms_submitted_for_persons_not_in_motor_vehicles",\n "PERSONS": "number_of_forms_submitted_for_persons_in_motor_vehicles",\n "PERMVIT": "number_of_persons_in_motor_vehicles_in_transport_mvit",\n "PERNOTMVIT": "number_of_persons_not_in_motor_vehicles_in_transport_mvit",\n "COUNTY": "county",\n "CITY": "city",\n "DAY": "day_of_crash",\n "DAYNAME": "day_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "YEAR": "year_of_crash",\n "DAY_WEEK": "day_of_week",\n "DAY_WEEKNAME": "day_of_week_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "NHS": "national_highway_system",\n "NHSNAME": "national_highway_system_name",\n "ROUTE": "route_signing",\n "ROUTENAME": "route_signing_name",\n "TWAY_ID": "trafficway_identifier",\n "TWAY_ID2": "trafficway_identifier_2",\n "RUR_URB": "land_use",\n "RUR_URBNAME": "land_use_name",\n "FUNC_SYS": "functional_system",\n "FUNC_SYSNAME": "functional_system_name",\n "RD_OWNER": "ownership",\n "RD_OWNERNAME": "ownership_name",\n "MILEPT": "milepoint",\n "MILEPTNAME": "milepoint_name",\n "LATITUDE": "latitude",\n "LATITUDENAME": "latitude_name",\n "LONGITUD": "longitude",\n "LONGITUDNAME": "longitude_name",\n "SP_JUR": "special_jurisdiction",\n "SP_JURNAME": "special_jurisdiction_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "RELJCT1": "relation_to_junction_within_interchange_area",\n "RELJCT1NAME": "relation_to_junction_within_interchange_area_name",\n "RELJCT2": "relation_to_junction_specific_location",\n "RELJCT2NAME": "relation_to_junction_specific_location_name",\n "TYP_INT" : "type_of_intersection",\n "TYP_INTNAME": "type_of_intersection_name",\n "WRK_ZONE": "work_zone",\n "WRK_ZONENAME": "work_zone_name",\n "REL_ROAD": "relation_to_trafficway",\n "REL_ROADNAME": "relation_to_trafficway_name",\n "LGT_COND": "light_condition",\n "LGT_CONDNAME": "light_condition_name",\n "WEATHER1": "atmospheric_conditions_1",\n "WEATHER1NAME": "atmospheric_conditions_1_name",\n "WEATHER2": "atmospheric_conditions_2",\n "WEATHER2NAME": "atmospheric_conditions_2_name",\n "WEATHER": "atmospheric_conditions",\n "WEATHERNAME": "atmospheric_conditions_name",\n "SCH_BUS": "school_bus_related",\n "SCH_BUSNAME": "school_bus_related_name",\n "RAIL": "rail_grade_crossing_identifier",\n "RAILNAME": "rail_grade_crossing_identifier_name",\n "NOT_HOUR": "hour_of_notification",\n "NOT_HOURNAME": "hour_of_notification_name",\n "NOT_MIN": "minute_of_notification",\n "NOT_MINNAME": "minute_of_notification_name",\n "ARR_HOUR": "hour_of_arrival_at_scene",\n "ARR_HOURNAME": "hour_of_arrival_at_scene_name",\n "ARR_MIN": "minute_of_arrival_at_scene",\n "ARR_MINNAME": "minute_of_arrival_at_scene_name",\n "HOSP_HR": "hour_of_ems_arrival_at_hospital",\n "HOSP_HRNAME": "hour_of_ems_arrival_at_hospital_name",\n "HOSP_MN": "minute_of_ems_arrival_at_hospital",\n "HOSP_MNNAME": "minute_of_ems_arrival_at_hospital_name",\n "CF1": "related_factors_crash_level_1",\n "CF1NAME": "related_factors_crash_level_1_name",\n "CF2": "related_factors_crash_level_2",\n "CF2NAME": "related_factors_crash_level_2_name",\n "CF3": "related_factors_crash_level_3",\n "CF3NAME": "related_factors_crash_level_3_name",\n "FATALS": "number_of_fatalities",\n "DRUNK_DR": "number_of_drunk_drivers"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for accident pipeline accident_2016_2019_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="accident_2016_2019_transform_csv", startup_timeout_seconds=600, name="accident_2016_2019", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "accident.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.accident_2016_2019.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "number_of_vehicle_forms_submitted_all",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_parked_working_vehicles",\n "number_of_forms_submitted_for_persons_not_in_motor_vehicles",\n "number_of_forms_submitted_for_persons_in_motor_vehicles",\n "number_of_persons_in_motor_vehicles_in_transport_mvit",\n "number_of_persons_not_in_motor_vehicles_in_transport_mvit",\n "county",\n "county_name",\n "city",\n "city_name",\n "day_of_crash",\n "day_name",\n "month_of_crash",\n "month_of_crash_name",\n "year_of_crash",\n "day_of_week",\n "day_of_week_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "national_highway_system",\n "national_highway_system_name",\n "route_signing",\n "route_signing_name",\n "trafficway_identifier",\n "trafficway_identifier_2",\n "land_use",\n "land_use_name",\n "functional_system",\n "functional_system_name",\n "ownership",\n "ownership_name",\n "milepoint",\n "milepoint_name",\n "latitude",\n "latitude_name",\n "longitude",\n "longitude_name",\n "special_jurisdiction",\n "special_jurisdiction_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "relation_to_junction_within_interchange_area",\n "relation_to_junction_within_interchange_area_name",\n "relation_to_junction_specific_location",\n "relation_to_junction_specific_location_name",\n "type_of_intersection",\n "type_of_intersection_name",\n "work_zone",\n "work_zone_name",\n "relation_to_trafficway",\n "relation_to_trafficway_name",\n "light_condition",\n "light_condition_name",\n "atmospheric_conditions_1",\n "atmospheric_conditions_1_name",\n "atmospheric_conditions_2",\n "atmospheric_conditions_2_name",\n "atmospheric_conditions",\n "atmospheric_conditions_name",\n "school_bus_related",\n "school_bus_related_name",\n "rail_grade_crossing_identifier",\n "rail_grade_crossing_identifier_name",\n "hour_of_notification",\n "hour_of_notification_name",\n "minute_of_notification",\n "minute_of_notification_name",\n "hour_of_arrival_at_scene",\n "hour_of_arrival_at_scene_name",\n "minute_of_arrival_at_scene",\n "minute_of_arrival_at_scene_name",\n "hour_of_ems_arrival_at_hospital",\n "hour_of_ems_arrival_at_hospital_name",\n "minute_of_ems_arrival_at_hospital",\n "minute_of_ems_arrival_at_hospital_name",\n "related_factors_crash_level_1",\n "related_factors_crash_level_1_name",\n "related_factors_crash_level_2",\n "related_factors_crash_level_2_name",\n "related_factors_crash_level_3",\n "related_factors_crash_level_3_name",\n "number_of_fatalities",\n "number_of_drunk_drivers"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "number_of_vehicle_forms_submitted_all": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_parked_working_vehicles": "str",\n "number_of_forms_submitted_for_persons_not_in_motor_vehicles": "str",\n "number_of_forms_submitted_for_persons_in_motor_vehicles": "str",\n "number_of_persons_in_motor_vehicles_in_transport_mvit": "str",\n "number_of_persons_not_in_motor_vehicles_in_transport_mvit": "str",\n "county": "str",\n "county_name": "str",\n "city": "str",\n "city_name": "str",\n "day_of_crash": "str",\n "day_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "year_of_crash": "str",\n "day_of_week": "str",\n "day_of_week_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "national_highway_system": "str",\n "national_highway_system_name": "str",\n "route_signing": "str",\n "route_signing_name": "str",\n "trafficway_identifier": "str",\n "trafficway_identifier_2": "str",\n "land_use": "str",\n "land_use_name": "str",\n "functional_system": "str",\n "functional_system_name": "str",\n "ownership": "str",\n "ownership_name": "str",\n "milepoint": "str",\n "milepoint_name": "str",\n "latitude": "str",\n "latitude_name": "str",\n "longitude": "str",\n "longitude_name": "str",\n "special_jurisdiction": "str",\n "special_jurisdiction_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "relation_to_junction_within_interchange_area": "str",\n "relation_to_junction_within_interchange_area_name": "str",\n "relation_to_junction_specific_location": "str",\n "relation_to_junction_specific_location_name": "str",\n "type_of_intersection": "str",\n "type_of_intersection_name": "str",\n "work_zone": "str",\n "work_zone_name": "str",\n "relation_to_trafficway": "str",\n "relation_to_trafficway_name": "str",\n "light_condition": "str",\n "light_condition_name": "str",\n "atmospheric_conditions_1": "str",\n "atmospheric_conditions_1_name": "str",\n "atmospheric_conditions_2": "str",\n "atmospheric_conditions_2_name": "str",\n "atmospheric_conditions": "str",\n "atmospheric_conditions_name": "str",\n "school_bus_related": "str",\n "school_bus_related_name": "str",\n "rail_grade_crossing_identifier": "str",\n "rail_grade_crossing_identifier_name": "str",\n "hour_of_notification": "str",\n "hour_of_notification_name": "str",\n "minute_of_notification": "str",\n "minute_of_notification_name": "str",\n "hour_of_arrival_at_scene": "str",\n "hour_of_arrival_at_scene_name": "str",\n "minute_of_arrival_at_scene": "str",\n "minute_of_arrival_at_scene_name": "str",\n "hour_of_ems_arrival_at_hospital": "str",\n "hour_of_ems_arrival_at_hospital_name": "str",\n "minute_of_ems_arrival_at_hospital": "str",\n "minute_of_ems_arrival_at_hospital_name": "str",\n "related_factors_crash_level_1": "str",\n "related_factors_crash_level_1_name": "str",\n "related_factors_crash_level_2": "str",\n "related_factors_crash_level_2_name": "str",\n "related_factors_crash_level_3": "str",\n "related_factors_crash_level_3_name": "str",\n "number_of_fatalities": "str",\n "number_of_drunk_drivers": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VE_TOTAL": "number_of_vehicle_forms_submitted_all",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PVH_INVL": "number_of_parked_working_vehicles",\n "PEDS": "number_of_forms_submitted_for_persons_not_in_motor_vehicles",\n "PERSONS": "number_of_forms_submitted_for_persons_in_motor_vehicles",\n "PERMVIT": "number_of_persons_in_motor_vehicles_in_transport_mvit",\n "PERNOTMVIT": "number_of_persons_not_in_motor_vehicles_in_transport_mvit",\n "COUNTY": "county",\n "COUNTYNAME": "county_name",\n "CITY": "city",\n "CITYNAME": "city_name",\n "DAY": "day_of_crash",\n "DAYNAME": "day_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "YEAR": "year_of_crash",\n "DAY_WEEK": "day_of_week",\n "DAY_WEEKNAME": "day_of_week_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "NHS": "national_highway_system",\n "NHSNAME": "national_highway_system_name",\n "ROUTE": "route_signing",\n "ROUTENAME": "route_signing_name",\n "TWAY_ID": "trafficway_identifier",\n "TWAY_ID2": "trafficway_identifier_2",\n "RUR_URB": "land_use",\n "RUR_URBNAME": "land_use_name",\n "FUNC_SYS": "functional_system",\n "FUNC_SYSNAME": "functional_system_name",\n "RD_OWNER": "ownership",\n "RD_OWNERNAME": "ownership_name",\n "MILEPT": "milepoint",\n "MILEPTNAME": "milepoint_name",\n "LATITUDE": "latitude",\n "LATITUDENAME": "latitude_name",\n "LONGITUD": "longitude",\n "LONGITUDNAME": "longitude_name",\n "SP_JUR": "special_jurisdiction",\n "SP_JURNAME": "special_jurisdiction_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "RELJCT1": "relation_to_junction_within_interchange_area",\n "RELJCT1NAME": "relation_to_junction_within_interchange_area_name",\n "RELJCT2": "relation_to_junction_specific_location",\n "RELJCT2NAME": "relation_to_junction_specific_location_name",\n "TYP_INT" : "type_of_intersection",\n "TYP_INTNAME": "type_of_intersection_name",\n "WRK_ZONE": "work_zone",\n "WRK_ZONENAME": "work_zone_name",\n "REL_ROAD": "relation_to_trafficway",\n "REL_ROADNAME": "relation_to_trafficway_name",\n "LGT_COND": "light_condition",\n "LGT_CONDNAME": "light_condition_name",\n "WEATHER1": "atmospheric_conditions_1",\n "WEATHER1NAME": "atmospheric_conditions_1_name",\n "WEATHER2": "atmospheric_conditions_2",\n "WEATHER2NAME": "atmospheric_conditions_2_name",\n "WEATHER": "atmospheric_conditions",\n "WEATHERNAME": "atmospheric_conditions_name",\n "SCH_BUS": "school_bus_related",\n "SCH_BUSNAME": "school_bus_related_name",\n "RAIL": "rail_grade_crossing_identifier",\n "RAILNAME": "rail_grade_crossing_identifier_name",\n "NOT_HOUR": "hour_of_notification",\n "NOT_HOURNAME": "hour_of_notification_name",\n "NOT_MIN": "minute_of_notification",\n "NOT_MINNAME": "minute_of_notification_name",\n "ARR_HOUR": "hour_of_arrival_at_scene",\n "ARR_HOURNAME": "hour_of_arrival_at_scene_name",\n "ARR_MIN": "minute_of_arrival_at_scene",\n "ARR_MINNAME": "minute_of_arrival_at_scene_name",\n "HOSP_HR": "hour_of_ems_arrival_at_hospital",\n "HOSP_HRNAME": "hour_of_ems_arrival_at_hospital_name",\n "HOSP_MN": "minute_of_ems_arrival_at_hospital",\n "HOSP_MNNAME": "minute_of_ems_arrival_at_hospital_name",\n "CF1": "related_factors_crash_level_1",\n "CF1NAME": "related_factors_crash_level_1_name",\n "CF2": "related_factors_crash_level_2",\n "CF2NAME": "related_factors_crash_level_2_name",\n "CF3": "related_factors_crash_level_3",\n "CF3NAME": "related_factors_crash_level_3_name",\n "FATALS": "number_of_fatalities",\n "DRUNK_DR": "number_of_drunk_drivers"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for accident pipeline accident_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="accident_2020_transform_csv", startup_timeout_seconds=600, name="accident_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "accident.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.accident_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "number_of_vehicle_forms_submitted_all",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_parked_working_vehicles",\n "number_of_forms_submitted_for_persons_not_in_motor_vehicles",\n "number_of_forms_submitted_for_persons_in_motor_vehicles",\n "number_of_persons_in_motor_vehicles_in_transport_mvit",\n "number_of_persons_not_in_motor_vehicles_in_transport_mvit",\n "county",\n "county_name",\n "city",\n "city_name",\n "day_of_crash",\n "day_name",\n "month_of_crash",\n "month_of_crash_name",\n "year_of_crash",\n "day_of_week",\n "day_of_week_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "national_highway_system",\n "national_highway_system_name",\n "route_signing",\n "route_signing_name",\n "trafficway_identifier",\n "trafficway_identifier_2",\n "land_use",\n "land_use_name",\n "functional_system",\n "functional_system_name",\n "ownership",\n "ownership_name",\n "milepoint",\n "milepoint_name",\n "latitude",\n "latitude_name",\n "longitude",\n "longitude_name",\n "special_jurisdiction",\n "special_jurisdiction_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "relation_to_junction_within_interchange_area",\n "relation_to_junction_within_interchange_area_name",\n "relation_to_junction_specific_location",\n "relation_to_junction_specific_location_name",\n "type_of_intersection",\n "type_of_intersection_name",\n "work_zone",\n "work_zone_name",\n "relation_to_trafficway",\n "relation_to_trafficway_name",\n "light_condition",\n "light_condition_name",\n "atmospheric_conditions_1",\n "atmospheric_conditions_1_name",\n "atmospheric_conditions_2",\n "atmospheric_conditions_2_name",\n "rail_grade_crossing_identifier",\n "rail_grade_crossing_identifier_name",\n "hour_of_notification",\n "hour_of_notification_name",\n "minute_of_notification",\n "minute_of_notification_name",\n "hour_of_arrival_at_scene",\n "hour_of_arrival_at_scene_name",\n "minute_of_arrival_at_scene",\n "minute_of_arrival_at_scene_name",\n "hour_of_ems_arrival_at_hospital",\n "hour_of_ems_arrival_at_hospital_name",\n "minute_of_ems_arrival_at_hospital",\n "minute_of_ems_arrival_at_hospital_name",\n "number_of_fatalities",\n "number_of_drunk_drivers"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "number_of_vehicle_forms_submitted_all": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_parked_working_vehicles": "str",\n "number_of_forms_submitted_for_persons_not_in_motor_vehicles": "str",\n "number_of_forms_submitted_for_persons_in_motor_vehicles": "str",\n "number_of_persons_in_motor_vehicles_in_transport_mvit": "str",\n "number_of_persons_not_in_motor_vehicles_in_transport_mvit": "str",\n "county": "str",\n "county_name": "str",\n "city": "str",\n "city_name": "str",\n "day_of_crash": "str",\n "day_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "year_of_crash": "str",\n "day_of_week": "str",\n "day_of_week_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "national_highway_system": "str",\n "national_highway_system_name": "str",\n "route_signing": "str",\n "route_signing_name": "str",\n "trafficway_identifier": "str",\n "trafficway_identifier_2": "str",\n "land_use": "str",\n "land_use_name": "str",\n "functional_system": "str",\n "functional_system_name": "str",\n "ownership": "str",\n "ownership_name": "str",\n "milepoint": "str",\n "milepoint_name": "str",\n "latitude": "str",\n "latitude_name": "str",\n "longitude": "str",\n "longitude_name": "str",\n "special_jurisdiction": "str",\n "special_jurisdiction_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "relation_to_junction_within_interchange_area": "str",\n "relation_to_junction_within_interchange_area_name": "str",\n "relation_to_junction_specific_location": "str",\n "relation_to_junction_specific_location_name": "str",\n "type_of_intersection": "str",\n "type_of_intersection_name": "str",\n "work_zone": "str",\n "work_zone_name": "str",\n "relation_to_trafficway": "str",\n "relation_to_trafficway_name": "str",\n "light_condition": "str",\n "light_condition_name": "str",\n "atmospheric_conditions_1": "str",\n "atmospheric_conditions_1_name": "str",\n "atmospheric_conditions_2": "str",\n "atmospheric_conditions_2_name": "str",\n "rail_grade_crossing_identifier": "str",\n "rail_grade_crossing_identifier_name": "str",\n "hour_of_notification": "str",\n "hour_of_notification_name": "str",\n "minute_of_notification": "str",\n "minute_of_notification_name": "str",\n "hour_of_arrival_at_scene": "str",\n "hour_of_arrival_at_scene_name": "str",\n "minute_of_arrival_at_scene": "str",\n "minute_of_arrival_at_scene_name": "str",\n "hour_of_ems_arrival_at_hospital": "str",\n "hour_of_ems_arrival_at_hospital_name": "str",\n "minute_of_ems_arrival_at_hospital": "str",\n "minute_of_ems_arrival_at_hospital_name": "str",\n "number_of_fatalities": "str",\n "number_of_drunk_drivers": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VE_TOTAL": "number_of_vehicle_forms_submitted_all",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PVH_INVL": "number_of_parked_working_vehicles",\n "PEDS": "number_of_forms_submitted_for_persons_not_in_motor_vehicles",\n "PERSONS": "number_of_forms_submitted_for_persons_in_motor_vehicles",\n "PERMVIT": "number_of_persons_in_motor_vehicles_in_transport_mvit",\n "PERNOTMVIT": "number_of_persons_not_in_motor_vehicles_in_transport_mvit",\n "COUNTY": "county",\n "COUNTYNAME": "county_name",\n "CITY": "city",\n "CITYNAME": "city_name",\n "DAY": "day_of_crash",\n "DAYNAME": "day_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "YEAR": "year_of_crash",\n "DAY_WEEK": "day_of_week",\n "DAY_WEEKNAME": "day_of_week_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "NHS": "national_highway_system",\n "NHSNAME": "national_highway_system_name",\n "ROUTE": "route_signing",\n "ROUTENAME": "route_signing_name",\n "TWAY_ID": "trafficway_identifier",\n "TWAY_ID2": "trafficway_identifier_2",\n "RUR_URB": "land_use",\n "RUR_URBNAME": "land_use_name",\n "FUNC_SYS": "functional_system",\n "FUNC_SYSNAME": "functional_system_name",\n "RD_OWNER": "ownership",\n "RD_OWNERNAME": "ownership_name",\n "MILEPT": "milepoint",\n "MILEPTNAME": "milepoint_name",\n "LATITUDE": "latitude",\n "LATITUDENAME": "latitude_name",\n "LONGITUD": "longitude",\n "LONGITUDNAME": "longitude_name",\n "SP_JUR": "special_jurisdiction",\n "SP_JURNAME": "special_jurisdiction_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "RELJCT1": "relation_to_junction_within_interchange_area",\n "RELJCT1NAME": "relation_to_junction_within_interchange_area_name",\n "RELJCT2": "relation_to_junction_specific_location",\n "RELJCT2NAME": "relation_to_junction_specific_location_name",\n "TYP_INT" : "type_of_intersection",\n "TYP_INTNAME": "type_of_intersection_name",\n "WRK_ZONE": "work_zone",\n "WRK_ZONENAME": "work_zone_name",\n "REL_ROAD": "relation_to_trafficway",\n "REL_ROADNAME": "relation_to_trafficway_name",\n "LGT_COND": "light_condition",\n "LGT_CONDNAME": "light_condition_name",\n "WEATHER1": "atmospheric_conditions_1",\n "WEATHER1NAME": "atmospheric_conditions_1_name",\n "WEATHER2": "atmospheric_conditions_2",\n "WEATHER2NAME": "atmospheric_conditions_2_name",\n "RAIL": "rail_grade_crossing_identifier",\n "RAILNAME": "rail_grade_crossing_identifier_name",\n "NOT_HOUR": "hour_of_notification",\n "NOT_HOURNAME": "hour_of_notification_name",\n "NOT_MIN": "minute_of_notification",\n "NOT_MINNAME": "minute_of_notification_name",\n "ARR_HOUR": "hour_of_arrival_at_scene",\n "ARR_HOURNAME": "hour_of_arrival_at_scene_name",\n "ARR_MIN": "minute_of_arrival_at_scene",\n "ARR_MINNAME": "minute_of_arrival_at_scene_name",\n "HOSP_HR": "hour_of_ems_arrival_at_hospital",\n "HOSP_HRNAME": "hour_of_ems_arrival_at_hospital_name",\n "HOSP_MN": "minute_of_ems_arrival_at_hospital",\n "HOSP_MNNAME": "minute_of_ems_arrival_at_hospital_name",\n "FATALS": "number_of_fatalities",\n "DRUNK_DR": "number_of_drunk_drivers"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for cevent pipeline cevent_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="cevent_2015_2020_transform_csv", startup_timeout_seconds=600, name="cevent_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "cevent.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.cevent_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "event_number",\n "vehicle_number_this_vehicle",\n "area_of_impact_this_vehicle",\n "area_of_impact_this_vehicle_name",\n "sequence_of_events",\n "sequence_of_events_name",\n "vehicle_number_other_vehicle",\n "vehicle_number_other_vehicle_name",\n "area_of_impact_other_vehicle",\n "area_of_impact_other_vehicle_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "event_number": "str",\n "vehicle_number_this_vehicle": "str",\n "area_of_impact_this_vehicle": "str",\n "area_of_impact_this_vehicle_name": "str",\n "sequence_of_events": "str",\n "sequence_of_events_name": "str",\n "vehicle_number_other_vehicle": "str",\n "vehicle_number_other_vehicle_name": "str",\n "area_of_impact_other_vehicle": "str",\n "area_of_impact_other_vehicle_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "EVENTNUM": "event_number",\n "VNUMBER1": "vehicle_number_this_vehicle",\n "AOI1": "area_of_impact_this_vehicle",\n "AOI1NAME": "area_of_impact_this_vehicle_name",\n "SOE": "sequence_of_events",\n "SOENAME": "sequence_of_events_name",\n "VNUMBER2": "vehicle_number_other_vehicle",\n "VNUMBER2NAME": "vehicle_number_other_vehicle_name",\n "AOI2": "area_of_impact_other_vehicle",\n "AOI2NAME": "area_of_impact_other_vehicle_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for damage pipeline damage_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="damage_2015_2020_transform_csv", startup_timeout_seconds=600, name="damage_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "damage.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.damage_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "damaged_areas",\n "damaged_areas_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "damaged_areas": "str",\n "damaged_areas_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "MDAREAS": "damaged_areas",\n "MDAREASNAME": "area_of_impact_this_vehicle"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for distract pipeline distract_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="distract_2015_2020_transform_csv", startup_timeout_seconds=600, name="distract_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "distract.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.distract_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "driver_distracted_by",\n "driver_distracted_by_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "driver_distracted_by": "str",\n "driver_distracted_by_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "MDRDSTRD": "driver_distracted_by",\n "MDRDSTRDNAME": "driver_distracted_by_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for drimpair pipeline drimpair_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="drimpair_2015_2020_transform_csv", startup_timeout_seconds=600, name="drimpair_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "drimpair.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.drimpair_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "condition_impairment_at_time_of_crash_driver",\n "condition_impairment_at_time_of_crash_driver_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "condition_impairment_at_time_of_crash_driver": "str",\n "condition_impairment_at_time_of_crash_driver_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "DRIMPAIR": "condition_impairment_at_time_of_crash_driver",\n "DRIMPAIRNAME": "condition_impairment_at_time_of_crash_driver_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for factor pipeline factor_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="factor_2015_2020_transform_csv", startup_timeout_seconds=600, name="factor_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "factor.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.factor_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "contributing_circumstances_motor_vehicle",\n "contributing_circumstances_motor_vehicle_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "contributing_circumstances_motor_vehicle": "str",\n "contributing_circumstances_motor_vehicle_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "MFACTOR": "contributing_circumstances_motor_vehicle",\n "MFACTORNAME": "contributing_circumstances_motor_vehicle_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for maneuver pipeline maneuver_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="maneuver_2015_2020_transform_csv", startup_timeout_seconds=600, name="maneuver_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "maneuver.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.maneuver_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "driver_maneuvered_to_avoid",\n "driver_maneuvered_to_avoid_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "driver_maneuvered_to_avoid": "str",\n "driver_maneuvered_to_avoid_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "MDRMANAV": "driver_maneuvered_to_avoid",\n "MDRMANAVNAME": "driver_maneuvered_to_avoid_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for nmcrash pipeline nmcrash_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="nmcrash_2015_2020_transform_csv", startup_timeout_seconds=600, name="nmcrash_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "nmcrash.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.nmcrash_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "person_number",\n "non_motorist_contributing_circumstances",\n "non_motorist_contributing_circumstances_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "non_motorist_contributing_circumstances": "str",\n "non_motorist_contributing_circumstances_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "MTM_CRSH": "non_motorist_contributing_circumstances",\n "MTM_CRSHNAME": "non_motorist_contributing_circumstances_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for nmimpair pipeline nmimpair_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="nmimpair_2015_2020_transform_csv", startup_timeout_seconds=600, name="nmimpair_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "nmimpair.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.nmimpair_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "person_number",\n "condition_impairment_at_time_of_crash_non_motorist",\n "condition_impairment_at_time_of_crash_non_motorist_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "condition_impairment_at_time_of_crash_non_motorist": "str",\n "condition_impairment_at_time_of_crash_non_motorist_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "NMIMPAIR": "condition_impairment_at_time_of_crash_non_motorist",\n "NMIMPAIRNAME": "condition_impairment_at_time_of_crash_non_motorist_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for nmprior pipeline nmprior_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="nmprior_2015_2020_transform_csv", startup_timeout_seconds=600, name="nmprior_2015_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "nmprior.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.nmprior_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "person_number",\n "non_motorist_action_circumstances",\n "non_motorist_action_circumstances_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "non_motorist_action_circumstances": "str",\n "non_motorist_action_circumstances_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "NMIMPAIR": "non_motorist_action_circumstances",\n "NMIMPAIRNAME": "non_motorist_action_circumstances_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for parkwork_2015 pipeline parkwork_2015_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="parkwork_2015_transform_csv", startup_timeout_seconds=600, name="parkwork_2015", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "parkwork.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2015.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number",\n "motor_carrier_identification_number_name",\n "gross_vehicle_weight_rating",\n "gross_vehicle_weight_rating_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "underride_override",\n "underride_override_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "related_factors_vehicle_level1",\n "related_factors_vehicle_level1_name",\n "related_factors_vehicle_level2",\n "related_factors_vehicle_level2_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "fatalities_in_vehicle"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number": "str",\n "motor_carrier_identification_number_name": "str",\n "gross_vehicle_weight_rating": "str",\n "gross_vehicle_weight_rating_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "related_factors_vehicle_level1": "str",\n "related_factors_vehicle_level1_name": "str",\n "related_factors_vehicle_level2": "str",\n "related_factors_vehicle_level2_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "fatalities_in_vehicle": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PVE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PNUMOCCS": "number_of_occupants",\n "PNUMOCCSNAME": "number_of_occupants_name",\n "PDAY": "day_of_crash",\n "PDAYNAME": "day_of_crash_name",\n "PMONTH": "month_of_crash",\n "PMONTHNAME": "month_of_crash_name",\n "PHOUR": "hour_of_crash",\n "PHOURNAME": "hour_of_crash_name",\n "PMINUTE": "minute_of_crash",\n "PMINUTENAME": "minute_of_crash_name",\n "PHARM_EV": "first_harmful_event",\n "PHARM_EVNAME": "first_harmful_event_name",\n "PMAN_COLL": "manner_of_collision",\n "PMAN_COLLNAME": "manner_of_collision_name",\n "PTYPE": "unit_type",\n "PTYPENAME": "unit_type_name",\n "PHIT_RUN": "hit_and_run",\n "PHIT_RUNNAME": "hit_and_run_name",\n "PREG_STAT": "registration_state",\n "PREG_STATNAME": "registration_state_name",\n "POWNER": "registered_vehicle_owner",\n "POWNERNAME": "registered_vehicle_owner_name",\n "PMAKE": "vehicle_make",\n "PMAKENAME": "vehicle_make_name",\n "PMODEL": "vehicle_model",\n "PMAK_MOD": "make_model_combined",\n "PBODYTYP": "body_type",\n "PBODYTYPNAME": "body_type_name",\n "PMODYEAR": "vehicle_model_year",\n "PMODYEARNAME": "vehicle_model_year_name",\n "PVIN": "vehicle_identification_number_vin",\n "PVINNAME": "vehicle_identification_number_vin_name",\n "PVIN_1": "vin_character_1",\n "PVIN_2": "vin_character_2",\n "PVIN_3": "vin_character_3",\n "PVIN_4": "vin_character_4",\n "PVIN_5": "vin_character_5",\n "PVIN_6": "vin_character_6",\n "PVIN_7": "vin_character_7",\n "PVIN_8": "vin_character_8",\n "PVIN_9": "vin_character_9",\n "PVIN_10": "vin_character_10",\n "PVIN_11": "vin_character_11",\n "PVIN_12": "vin_character_12",\n "PTRAILER": "vehicle_trailing",\n "PTRAILERNAME": "vehicle_trailing_name",\n "PMCARR_I1": "mcid_issuing_authority",\n "PMCARR_I1NAME": "mcid_issuing_authority_name",\n "PMCARR_I2": "mcid_identification_number",\n "PMCARR_I2NAME": "mcid_identification_number_name",\n "PMCARR_ID": "motor_carrier_identification_number",\n "PMCARR_IDNAME": "motor_carrier_identification_number_name",\n "PGVWR": "gross_vehicle_weight_rating",\n "PGVWRNAME": "gross_vehicle_weight_rating_name",\n "PV_CONFIG": "vehicle_configuration",\n "PV_CONFIGNAME": "vehicle_configuration_name",\n "PCARGTYP": "cargo_body_type",\n "PCARGTYPNAME": "cargo_body_type_name",\n "PHAZ_INV": "hazardous_material_involvement",\n "PHAZ_INVNAME": "hazardous_material_involvement_name",\n "PHAZPLAC": "hazardous_material_placard",\n "PHAZPLACNAME": "hazardous_material_placard_name",\n "PHAZ_ID": "hazardous_material_identification_number",\n "PHAZ_IDNAME": "hazardous_material_identification_number_name",\n "PHAZ_CNO": "hazardous_material_class_number",\n "PHAZ_CNONAME": "hazardous_material_class_number_name",\n "PHAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "PHAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "PBUS_USE": "bus_use",\n "PBUS_USENAME": "bus_use_name",\n "PSP_USE": "special_use",\n "PSP_USENAME": "special_use_name",\n "PEM_USE": "emergency_motor_vehicle_use",\n "PEM_USENAME": "emergency_motor_vehicle_use_name",\n "PUNDERIDE": "underride_override",\n "PUNDERIDENAME": "underride_override_name",\n "PIMPACT1": "initial_contact_point",\n "PIMPACT1NAME": "initial_contact_point_name",\n "PVEH_SEV": "extent_of_damage",\n "PVEH_SEVNAME": "extent_of_damage_name",\n "PTOWED": "vehicle_removal",\n "PTOWEDNAME": "vehicle_removal_name",\n "PM_HARM": "most_harmful_event",\n "PM_HARMNAME": "most_harmful_event_name",\n "PVEH_SC1": "related_factors_vehicle_level1",\n "PVEH_SC1NAME": "related_factors_vehicle_level1_name",\n "PVEH_SC2": "related_factors_vehicle_level2",\n "PVEH_SC2NAME": "related_factors_vehicle_level2_name",\n "PFIRE": "fire_occurrence",\n "PFIRENAME": "fire_occurrence_name",\n "PDEATHS": "fatalities_in_vehicle"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for parkwork_2016_2017 pipelines parkwork_2016_2017_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="parkwork_2016_2017_transform_csv", startup_timeout_seconds=600, name="parkwork_2016_2017", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "parkwork.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2016_2017.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number",\n "motor_carrier_identification_number_name",\n "gross_vehicle_weight_rating",\n "gross_vehicle_weight_rating_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "underride_override",\n "underride_override_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "related_factors_vehicle_level1",\n "related_factors_vehicle_level1_name",\n "related_factors_vehicle_level2",\n "related_factors_vehicle_level2_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "fatalities_in_vehicle",\n "ptrlr1vin",\n "ptrlr1vinname",\n "ptrlr2vin",\n "ptrlr2vinname",\n "ptrlr3vin",\n "ptrlr3vinname"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number": "str",\n "motor_carrier_identification_number_name": "str",\n "gross_vehicle_weight_rating": "str",\n "gross_vehicle_weight_rating_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "related_factors_vehicle_level1": "str",\n "related_factors_vehicle_level1_name": "str",\n "related_factors_vehicle_level2": "str",\n "related_factors_vehicle_level2_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "fatalities_in_vehicle": "str",\n "ptrlr1vin": "str",\n "ptrlr1vinname": "str",\n "ptrlr2vin": "str",\n "ptrlr2vinname": "str",\n "ptrlr3vin": "str",\n "ptrlr3vinname": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PVE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PNUMOCCS": "number_of_occupants",\n "PNUMOCCSNAME": "number_of_occupants_name",\n "PDAY": "day_of_crash",\n "PDAYNAME": "day_of_crash_name",\n "PMONTH": "month_of_crash",\n "PMONTHNAME": "month_of_crash_name",\n "PHOUR": "hour_of_crash",\n "PHOURNAME": "hour_of_crash_name",\n "PMINUTE": "minute_of_crash",\n "PMINUTENAME": "minute_of_crash_name",\n "PHARM_EV": "first_harmful_event",\n "PHARM_EVNAME": "first_harmful_event_name",\n "PMAN_COLL": "manner_of_collision",\n "PMAN_COLLNAME": "manner_of_collision_name",\n "PTYPE": "unit_type",\n "PTYPENAME": "unit_type_name",\n "PHIT_RUN": "hit_and_run",\n "PHIT_RUNNAME": "hit_and_run_name",\n "PREG_STAT": "registration_state",\n "PREG_STATNAME": "registration_state_name",\n "POWNER": "registered_vehicle_owner",\n "POWNERNAME": "registered_vehicle_owner_name",\n "PMAKE": "vehicle_make",\n "PMAKENAME": "vehicle_make_name",\n "PMODEL": "vehicle_model",\n "PMAK_MOD": "make_model_combined",\n "PBODYTYP": "body_type",\n "PBODYTYPNAME": "body_type_name",\n "PMODYEAR": "vehicle_model_year",\n "PMODYEARNAME": "vehicle_model_year_name",\n "PVIN": "vehicle_identification_number_vin",\n "PVINNAME": "vehicle_identification_number_vin_name",\n "PVIN_1": "vin_character_1",\n "PVIN_2": "vin_character_2",\n "PVIN_3": "vin_character_3",\n "PVIN_4": "vin_character_4",\n "PVIN_5": "vin_character_5",\n "PVIN_6": "vin_character_6",\n "PVIN_7": "vin_character_7",\n "PVIN_8": "vin_character_8",\n "PVIN_9": "vin_character_9",\n "PVIN_10": "vin_character_10",\n "PVIN_11": "vin_character_11",\n "PVIN_12": "vin_character_12",\n "PTRAILER": "vehicle_trailing",\n "PTRAILERNAME": "vehicle_trailing_name",\n "PMCARR_I1": "mcid_issuing_authority",\n "PMCARR_I1NAME": "mcid_issuing_authority_name",\n "PMCARR_I2": "mcid_identification_number",\n "PMCARR_I2NAME": "mcid_identification_number_name",\n "PMCARR_ID": "motor_carrier_identification_number",\n "PMCARR_IDNAME": "motor_carrier_identification_number_name",\n "PGVWR": "gross_vehicle_weight_rating",\n "PGVWRNAME": "gross_vehicle_weight_rating_name",\n "PV_CONFIG": "vehicle_configuration",\n "PV_CONFIGNAME": "vehicle_configuration_name",\n "PCARGTYP": "cargo_body_type",\n "PCARGTYPNAME": "cargo_body_type_name",\n "PHAZ_INV": "hazardous_material_involvement",\n "PHAZ_INVNAME": "hazardous_material_involvement_name",\n "PHAZPLAC": "hazardous_material_placard",\n "PHAZPLACNAME": "hazardous_material_placard_name",\n "PHAZ_ID": "hazardous_material_identification_number",\n "PHAZ_IDNAME": "hazardous_material_identification_number_name",\n "PHAZ_CNO": "hazardous_material_class_number",\n "PHAZ_CNONAME": "hazardous_material_class_number_name",\n "PHAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "PHAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "PBUS_USE": "bus_use",\n "PBUS_USENAME": "bus_use_name",\n "PSP_USE": "special_use",\n "PSP_USENAME": "special_use_name",\n "PEM_USE": "emergency_motor_vehicle_use",\n "PEM_USENAME": "emergency_motor_vehicle_use_name",\n "PUNDERIDE": "underride_override",\n "PUNDERIDENAME": "underride_override_name",\n "PIMPACT1": "initial_contact_point",\n "PIMPACT1NAME": "initial_contact_point_name",\n "PVEH_SEV": "extent_of_damage",\n "PVEH_SEVNAME": "extent_of_damage_name",\n "PTOWED": "vehicle_removal",\n "PTOWEDNAME": "vehicle_removal_name",\n "PM_HARM": "most_harmful_event",\n "PM_HARMNAME": "most_harmful_event_name",\n "PVEH_SC1": "related_factors_vehicle_level1",\n "PVEH_SC1NAME": "related_factors_vehicle_level1_name",\n "PVEH_SC2": "related_factors_vehicle_level2",\n "PVEH_SC2NAME": "related_factors_vehicle_level2_name",\n "PFIRE": "fire_occurrence",\n "PFIRENAME": "fire_occurrence_name",\n "PDEATHS": "fatalities_in_vehicle",\n "PTRLR1VIN": "ptrlr1vin",\n "PTRLR1VINNAME": "ptrlr1vinname",\n "PTRLR2VIN": "ptrlr2vin",\n "PTRLR2VINNAME": "ptrlr2vinname",\n "PTRLR3VIN": "ptrlr3vin",\n "PTRLR3VINNAME": "ptrlr3vinname"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for parkwork_2018 pipelines parkwork_2018_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="parkwork_2018_transform_csv", startup_timeout_seconds=600, name="parkwork_2018", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "parkwork.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2018.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "make_model_combined_name",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number",\n "motor_carrier_identification_number_name",\n "gross_vehicle_weight_rating",\n "gross_vehicle_weight_rating_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "underride_override",\n "underride_override_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "related_factors_vehicle_level1",\n "related_factors_vehicle_level1_name",\n "related_factors_vehicle_level2",\n "related_factors_vehicle_level2_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "fatalities_in_vehicle",\n "ptrlr1vin",\n "ptrlr1vinname",\n "ptrlr2vin",\n "ptrlr2vinname",\n "ptrlr3vin",\n "ptrlr3vinname"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "make_model_combined_name": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number": "str",\n "motor_carrier_identification_number_name": "str",\n "gross_vehicle_weight_rating": "str",\n "gross_vehicle_weight_rating_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "related_factors_vehicle_level1": "str",\n "related_factors_vehicle_level1_name": "str",\n "related_factors_vehicle_level2": "str",\n "related_factors_vehicle_level2_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "fatalities_in_vehicle": "str",\n "ptrlr1vin": "str",\n "ptrlr1vinname": "str",\n "ptrlr2vin": "str",\n "ptrlr2vinname": "str",\n "ptrlr3vin": "str",\n "ptrlr3vinname": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PVE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PNUMOCCS": "number_of_occupants",\n "PNUMOCCSNAME": "number_of_occupants_name",\n "PDAY": "day_of_crash",\n "PDAYNAME": "day_of_crash_name",\n "PMONTH": "month_of_crash",\n "PMONTHNAME": "month_of_crash_name",\n "PHOUR": "hour_of_crash",\n "PHOURNAME": "hour_of_crash_name",\n "PMINUTE": "minute_of_crash",\n "PMINUTENAME": "minute_of_crash_name",\n "PHARM_EV": "first_harmful_event",\n "PHARM_EVNAME": "first_harmful_event_name",\n "PMAN_COLL": "manner_of_collision",\n "PMAN_COLLNAME": "manner_of_collision_name",\n "PTYPE": "unit_type",\n "PTYPENAME": "unit_type_name",\n "PHIT_RUN": "hit_and_run",\n "PHIT_RUNNAME": "hit_and_run_name",\n "PREG_STAT": "registration_state",\n "PREG_STATNAME": "registration_state_name",\n "POWNER": "registered_vehicle_owner",\n "POWNERNAME": "registered_vehicle_owner_name",\n "PMAKE": "vehicle_make",\n "PMAKENAME": "vehicle_make_name",\n "PMODEL": "vehicle_model",\n "PMAK_MOD": "make_model_combined",\n "PMAK_MODNAME": "make_model_combined_name",\n "PBODYTYP": "body_type",\n "PBODYTYPNAME": "body_type_name",\n "PMODYEAR": "vehicle_model_year",\n "PMODYEARNAME": "vehicle_model_year_name",\n "PVIN": "vehicle_identification_number_vin",\n "PVINNAME": "vehicle_identification_number_vin_name",\n "PVIN_1": "vin_character_1",\n "PVIN_2": "vin_character_2",\n "PVIN_3": "vin_character_3",\n "PVIN_4": "vin_character_4",\n "PVIN_5": "vin_character_5",\n "PVIN_6": "vin_character_6",\n "PVIN_7": "vin_character_7",\n "PVIN_8": "vin_character_8",\n "PVIN_9": "vin_character_9",\n "PVIN_10": "vin_character_10",\n "PVIN_11": "vin_character_11",\n "PVIN_12": "vin_character_12",\n "PTRAILER": "vehicle_trailing",\n "PTRAILERNAME": "vehicle_trailing_name",\n "PMCARR_I1": "mcid_issuing_authority",\n "PMCARR_I1NAME": "mcid_issuing_authority_name",\n "PMCARR_I2": "mcid_identification_number",\n "PMCARR_I2NAME": "mcid_identification_number_name",\n "PMCARR_ID": "motor_carrier_identification_number",\n "PMCARR_IDNAME": "motor_carrier_identification_number_name",\n "PGVWR": "gross_vehicle_weight_rating",\n "PGVWRNAME": "gross_vehicle_weight_rating_name",\n "PV_CONFIG": "vehicle_configuration",\n "PV_CONFIGNAME": "vehicle_configuration_name",\n "PCARGTYP": "cargo_body_type",\n "PCARGTYPNAME": "cargo_body_type_name",\n "PHAZ_INV": "hazardous_material_involvement",\n "PHAZ_INVNAME": "hazardous_material_involvement_name",\n "PHAZPLAC": "hazardous_material_placard",\n "PHAZPLACNAME": "hazardous_material_placard_name",\n "PHAZ_ID": "hazardous_material_identification_number",\n "PHAZ_IDNAME": "hazardous_material_identification_number_name",\n "PHAZ_CNO": "hazardous_material_class_number",\n "PHAZ_CNONAME": "hazardous_material_class_number_name",\n "PHAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "PHAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "PBUS_USE": "bus_use",\n "PBUS_USENAME": "bus_use_name",\n "PSP_USE": "special_use",\n "PSP_USENAME": "special_use_name",\n "PEM_USE": "emergency_motor_vehicle_use",\n "PEM_USENAME": "emergency_motor_vehicle_use_name",\n "PUNDERIDE": "underride_override",\n "PUNDERIDENAME": "underride_override_name",\n "PIMPACT1": "initial_contact_point",\n "PIMPACT1NAME": "initial_contact_point_name",\n "PVEH_SEV": "extent_of_damage",\n "PVEH_SEVNAME": "extent_of_damage_name",\n "PTOWED": "vehicle_removal",\n "PTOWEDNAME": "vehicle_removal_name",\n "PM_HARM": "most_harmful_event",\n "PM_HARMNAME": "most_harmful_event_name",\n "PVEH_SC1": "related_factors_vehicle_level1",\n "PVEH_SC1NAME": "related_factors_vehicle_level1_name",\n "PVEH_SC2": "related_factors_vehicle_level2",\n "PVEH_SC2NAME": "related_factors_vehicle_level2_name",\n "PFIRE": "fire_occurrence",\n "PFIRENAME": "fire_occurrence_name",\n "PDEATHS": "fatalities_in_vehicle",\n "PTRLR1VIN": "ptrlr1vin",\n "PTRLR1VINNAME": "ptrlr1vinname",\n "PTRLR2VIN": "ptrlr2vin",\n "PTRLR2VINNAME": "ptrlr2vinname",\n "PTRLR3VIN": "ptrlr3vin",\n "PTRLR3VINNAME": "ptrlr3vinname"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for parkwork_2019 pipelines parkwork_2019_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="parkwork_2019_transform_csv", startup_timeout_seconds=600, name="parkwork_2019", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "parkwork.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2019.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number",\n "motor_carrier_identification_number_name",\n "gross_vehicle_weight_rating",\n "gross_vehicle_weight_rating_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "underride_override",\n "underride_override_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "related_factors_vehicle_level1",\n "related_factors_vehicle_level1_name",\n "related_factors_vehicle_level2",\n "related_factors_vehicle_level2_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "fatalities_in_vehicle",\n "ptrlr1vin",\n "ptrlr1vinname",\n "ptrlr2vin",\n "ptrlr2vinname",\n "ptrlr3vin",\n "ptrlr3vinname"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number": "str",\n "motor_carrier_identification_number_name": "str",\n "gross_vehicle_weight_rating": "str",\n "gross_vehicle_weight_rating_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "related_factors_vehicle_level1": "str",\n "related_factors_vehicle_level1_name": "str",\n "related_factors_vehicle_level2": "str",\n "related_factors_vehicle_level2_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "fatalities_in_vehicle": "str",\n "ptrlr1vin": "str",\n "ptrlr1vinname": "str",\n "ptrlr2vin": "str",\n "ptrlr2vinname": "str",\n "ptrlr3vin": "str",\n "ptrlr3vinname": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PVE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PNUMOCCS": "number_of_occupants",\n "PNUMOCCSNAME": "number_of_occupants_name",\n "PDAY": "day_of_crash",\n "PDAYNAME": "day_of_crash_name",\n "PMONTH": "month_of_crash",\n "PMONTHNAME": "month_of_crash_name",\n "PHOUR": "hour_of_crash",\n "PHOURNAME": "hour_of_crash_name",\n "PMINUTE": "minute_of_crash",\n "PMINUTENAME": "minute_of_crash_name",\n "PHARM_EV": "first_harmful_event",\n "PHARM_EVNAME": "first_harmful_event_name",\n "PMAN_COLL": "manner_of_collision",\n "PMAN_COLLNAME": "manner_of_collision_name",\n "PTYPE": "unit_type",\n "PTYPENAME": "unit_type_name",\n "PHIT_RUN": "hit_and_run",\n "PHIT_RUNNAME": "hit_and_run_name",\n "PREG_STAT": "registration_state",\n "PREG_STATNAME": "registration_state_name",\n "POWNER": "registered_vehicle_owner",\n "POWNERNAME": "registered_vehicle_owner_name",\n "PMAKE": "vehicle_make",\n "PMAKENAME": "vehicle_make_name",\n "PMODEL": "vehicle_model",\n "PMAK_MOD": "make_model_combined",\n "PBODYTYP": "body_type",\n "PBODYTYPNAME": "body_type_name",\n "PMODYEAR": "vehicle_model_year",\n "PMODYEARNAME": "vehicle_model_year_name",\n "PVIN": "vehicle_identification_number_vin",\n "PVINNAME": "vehicle_identification_number_vin_name",\n "PVIN_1": "vin_character_1",\n "PVIN_2": "vin_character_2",\n "PVIN_3": "vin_character_3",\n "PVIN_4": "vin_character_4",\n "PVIN_5": "vin_character_5",\n "PVIN_6": "vin_character_6",\n "PVIN_7": "vin_character_7",\n "PVIN_8": "vin_character_8",\n "PVIN_9": "vin_character_9",\n "PVIN_10": "vin_character_10",\n "PVIN_11": "vin_character_11",\n "PVIN_12": "vin_character_12",\n "PTRAILER": "vehicle_trailing",\n "PTRAILERNAME": "vehicle_trailing_name",\n "PMCARR_I1": "mcid_issuing_authority",\n "PMCARR_I1NAME": "mcid_issuing_authority_name",\n "PMCARR_I2": "mcid_identification_number",\n "PMCARR_I2NAME": "mcid_identification_number_name",\n "PMCARR_ID": "motor_carrier_identification_number",\n "PMCARR_IDNAME": "motor_carrier_identification_number_name",\n "PGVWR": "gross_vehicle_weight_rating",\n "PGVWRNAME": "gross_vehicle_weight_rating_name",\n "PV_CONFIG": "vehicle_configuration",\n "PV_CONFIGNAME": "vehicle_configuration_name",\n "PCARGTYP": "cargo_body_type",\n "PCARGTYPNAME": "cargo_body_type_name",\n "PHAZ_INV": "hazardous_material_involvement",\n "PHAZ_INVNAME": "hazardous_material_involvement_name",\n "PHAZPLAC": "hazardous_material_placard",\n "PHAZPLACNAME": "hazardous_material_placard_name",\n "PHAZ_ID": "hazardous_material_identification_number",\n "PHAZ_IDNAME": "hazardous_material_identification_number_name",\n "PHAZ_CNO": "hazardous_material_class_number",\n "PHAZ_CNONAME": "hazardous_material_class_number_name",\n "PHAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "PHAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "PBUS_USE": "bus_use",\n "PBUS_USENAME": "bus_use_name",\n "PSP_USE": "special_use",\n "PSP_USENAME": "special_use_name",\n "PEM_USE": "emergency_motor_vehicle_use",\n "PEM_USENAME": "emergency_motor_vehicle_use_name",\n "PUNDERIDE": "underride_override",\n "PUNDERIDENAME": "underride_override_name",\n "PIMPACT1": "initial_contact_point",\n "PIMPACT1NAME": "initial_contact_point_name",\n "PVEH_SEV": "extent_of_damage",\n "PVEH_SEVNAME": "extent_of_damage_name",\n "PTOWED": "vehicle_removal",\n "PTOWEDNAME": "vehicle_removal_name",\n "PM_HARM": "most_harmful_event",\n "PM_HARMNAME": "most_harmful_event_name",\n "PVEH_SC1": "related_factors_vehicle_level1",\n "PVEH_SC1NAME": "related_factors_vehicle_level1_name",\n "PVEH_SC2": "related_factors_vehicle_level2",\n "PVEH_SC2NAME": "related_factors_vehicle_level2_name",\n "PFIRE": "fire_occurrence",\n "PFIRENAME": "fire_occurrence_name",\n "PDEATHS": "fatalities_in_vehicle",\n "PTRLR1VIN": "ptrlr1vin",\n "PTRLR1VINNAME": "ptrlr1vinname",\n "PTRLR2VIN": "ptrlr2vin",\n "PTRLR2VINNAME": "ptrlr2vinname",\n "PTRLR3VIN": "ptrlr3vin",\n "PTRLR3VINNAME": "ptrlr3vinname"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for parkwork_2020 pipelines parkwork_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="parkwork_2020_transform_csv", startup_timeout_seconds=600, name="parkwork_2020", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "parkwork.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.parkwork_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number",\n "motor_carrier_identification_number_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "underride_override",\n "underride_override_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "fatalities_in_vehicle",\n "ptrlr1vin",\n "ptrlr1vinname",\n "ptrlr2vin",\n "ptrlr2vinname",\n "ptrlr3vin",\n "ptrlr3vinname",\n "pvpicmake",\n "pvpicmakename",\n "pvpicmodel",\n "pvpicmodelname",\n "pvpicbodyclass",\n "pvpicbodyclassname",\n "picfinalbody",\n "picfinalbodyname",\n "pgvwr_from",\n "pgvwr_fromname",\n "pgvwr_to",\n "pgvwr_toname",\n "ptrlr1gvwr",\n "ptrlr1gvwrname",\n "ptrlr2gvwr",\n "ptrlr2gvwrname",\n "ptrlr3gvwr",\n "ptrlr3gvwrname"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number": "str",\n "motor_carrier_identification_number_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "fatalities_in_vehicle": "str",\n "ptrlr1vin": "str",\n "ptrlr1vinname": "str",\n "ptrlr2vin": "str",\n "ptrlr2vinname": "str",\n "ptrlr3vin": "str",\n "ptrlr3vinname": "str",\n "pvpicmake": "str",\n "pvpicmakename": "str",\n "pvpicmodel": "str",\n "pvpicmodelname": "str",\n "pvpicbodyclass": "str",\n "pvpicbodyclassname": "str",\n "picfinalbody": "str",\n "picfinalbodyname": "str",\n "pgvwr_from": "str",\n "pgvwr_fromname": "str",\n "pgvwr_to": "str",\n "pgvwr_toname": "str",\n "ptrlr1gvwr": "str",\n "ptrlr1gvwrname": "str",\n "ptrlr2gvwr": "str",\n "ptrlr2gvwrname": "str",\n "ptrlr3gvwr": "str",\n "ptrlr3gvwrname": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PVE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "PNUMOCCS": "number_of_occupants",\n "PNUMOCCSNAME": "number_of_occupants_name",\n "PDAY": "day_of_crash",\n "PDAYNAME": "day_of_crash_name",\n "PMONTH": "month_of_crash",\n "PMONTHNAME": "month_of_crash_name",\n "PHOUR": "hour_of_crash",\n "PHOURNAME": "hour_of_crash_name",\n "PMINUTE": "minute_of_crash",\n "PMINUTENAME": "minute_of_crash_name",\n "PHARM_EV": "first_harmful_event",\n "PHARM_EVNAME": "first_harmful_event_name",\n "PMAN_COLL": "manner_of_collision",\n "PMAN_COLLNAME": "manner_of_collision_name",\n "PTYPE": "unit_type",\n "PTYPENAME": "unit_type_name",\n "PHIT_RUN": "hit_and_run",\n "PHIT_RUNNAME": "hit_and_run_name",\n "PREG_STAT": "registration_state",\n "PREG_STATNAME": "registration_state_name",\n "POWNER": "registered_vehicle_owner",\n "POWNERNAME": "registered_vehicle_owner_name",\n "PMAKE": "vehicle_make",\n "PMAKENAME": "vehicle_make_name",\n "PMODEL": "vehicle_model",\n "PMAK_MOD": "make_model_combined",\n "PBODYTYP": "body_type",\n "PBODYTYPNAME": "body_type_name",\n "PMODYEAR": "vehicle_model_year",\n "PMODYEARNAME": "vehicle_model_year_name",\n "PVIN": "vehicle_identification_number_vin",\n "PVINNAME": "vehicle_identification_number_vin_name",\n "PVIN_1": "vin_character_1",\n "PVIN_2": "vin_character_2",\n "PVIN_3": "vin_character_3",\n "PVIN_4": "vin_character_4",\n "PVIN_5": "vin_character_5",\n "PVIN_6": "vin_character_6",\n "PVIN_7": "vin_character_7",\n "PVIN_8": "vin_character_8",\n "PVIN_9": "vin_character_9",\n "PVIN_10": "vin_character_10",\n "PVIN_11": "vin_character_11",\n "PVIN_12": "vin_character_12",\n "PTRAILER": "vehicle_trailing",\n "PTRAILERNAME": "vehicle_trailing_name",\n "PMCARR_I1": "mcid_issuing_authority",\n "PMCARR_I1NAME": "mcid_issuing_authority_name",\n "PMCARR_I2": "mcid_identification_number",\n "PMCARR_I2NAME": "mcid_identification_number_name",\n "PMCARR_ID": "motor_carrier_identification_number",\n "PMCARR_IDNAME": "motor_carrier_identification_number_name",\n "PV_CONFIG": "vehicle_configuration",\n "PV_CONFIGNAME": "vehicle_configuration_name",\n "PCARGTYP": "cargo_body_type",\n "PCARGTYPNAME": "cargo_body_type_name",\n "PHAZ_INV": "hazardous_material_involvement",\n "PHAZ_INVNAME": "hazardous_material_involvement_name",\n "PHAZPLAC": "hazardous_material_placard",\n "PHAZPLACNAME": "hazardous_material_placard_name",\n "PHAZ_ID": "hazardous_material_identification_number",\n "PHAZ_IDNAME": "hazardous_material_identification_number_name",\n "PHAZ_CNO": "hazardous_material_class_number",\n "PHAZ_CNONAME": "hazardous_material_class_number_name",\n "PHAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "PHAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "PBUS_USE": "bus_use",\n "PBUS_USENAME": "bus_use_name",\n "PSP_USE": "special_use",\n "PSP_USENAME": "special_use_name",\n "PEM_USE": "emergency_motor_vehicle_use",\n "PEM_USENAME": "emergency_motor_vehicle_use_name",\n "PUNDERIDE": "underride_override",\n "PUNDERIDENAME": "underride_override_name",\n "PIMPACT1": "initial_contact_point",\n "PIMPACT1NAME": "initial_contact_point_name",\n "PVEH_SEV": "extent_of_damage",\n "PVEH_SEVNAME": "extent_of_damage_name",\n "PTOWED": "vehicle_removal",\n "PTOWEDNAME": "vehicle_removal_name",\n "PM_HARM": "most_harmful_event",\n "PM_HARMNAME": "most_harmful_event_name",\n "PFIRE": "fire_occurrence",\n "PFIRENAME": "fire_occurrence_name",\n "PDEATHS": "fatalities_in_vehicle",\n "PTRLR1VIN": "ptrlr1vin",\n "PTRLR1VINNAME": "ptrlr1vinname",\n "PTRLR2VIN": "ptrlr2vin",\n "PTRLR2VINNAME": "ptrlr2vinname",\n "PTRLR3VIN": "ptrlr3vin",\n "PTRLR3VINNAME": "ptrlr3vinname",\n "PVPICMAKE": "pvpicmake",\n "PVPICMAKENAME": "pvpicmakename",\n "PVPICMODEL": "pvpicmodel",\n "PVPICMODELNAME": "pvpicmodelname",\n "PVPICBODYCLASS": "pvpicbodyclass",\n "PVPICBODYCLASSNAME": "pvpicbodyclassname",\n "PICFINALBODY": "picfinalbody",\n "PICFINALBODYNAME": "picfinalbodyname",\n "PGVWR_FROM": "pgvwr_from",\n "PGVWR_FROMNAME": "pgvwr_fromname",\n "PGVWR_TO": "pgvwr_to",\n "PGVWR_TONAME": "pgvwr_toname",\n "PTRLR1GVWR": "ptrlr1gvwr",\n "PTRLR1GVWRNAME": "ptrlr1gvwrname",\n "PTRLR2GVWR": "ptrlr2gvwr",\n "PTRLR2GVWRNAME": "ptrlr2gvwrname",\n "PTRLR3GVWR": "ptrlr3gvwr",\n "PTRLR3GVWRNAME": "ptrlr3gvwrname"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for pbtype pipelines pbtype_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="pbtype_transform_csv", startup_timeout_seconds=600, name="pbtype", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "pbtype.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.pbtype_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "person_number",\n "person_type",\n "person_type_name",\n "age",\n "age_name",\n "sex",\n "sex_name",\n "marked_crosswalk_present",\n "marked_crosswalk_present_name",\n "sidewalk_present",\n "sidewalk_present_name",\n "school_zone",\n "school_zone_name",\n "crash_type_pedestrian",\n "crash_type_pedestrian_name",\n "crash_type_bicycle",\n "crash_type_bicycle_name",\n "crash_location_pedestrian",\n "crash_location_pedestrian_name",\n "crash_location_bicycle",\n "crash_location_bicycle_name",\n "pedestrian_position",\n "pedestrian_position_name",\n "bicyclist_position",\n "bicyclist_position_name",\n "pedestrian_initial_direction_of_travel",\n "pedestrian_initial_direction_of_travel_name",\n "bicyclist_initial_direction_of_travel",\n "bicyclist_initial_direction_of_travel_name",\n "motorist_initial_direction_of_travel",\n "motorist_initial_direction_of_travel_name",\n "motorist_maneuver",\n "motorist_maneuver_name",\n "intersection_leg",\n "intersection_leg_name",\n "pedestrian_scenario",\n "pedestrian_scenario_name",\n "crash_group_pedestrian",\n "crash_group_pedestrian_name",\n "crash_group_bicycle",\n "crash_group_bicycle_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "person_type": "str",\n "person_type_name": "str",\n "age": "str",\n "age_name": "str",\n "sex": "str",\n "sex_name": "str",\n "marked_crosswalk_present": "str",\n "marked_crosswalk_present_name": "str",\n "sidewalk_present": "str",\n "sidewalk_present_name": "str",\n "school_zone": "str",\n "school_zone_name": "str",\n "crash_type_pedestrian": "str",\n "crash_type_pedestrian_name": "str",\n "crash_type_bicycle": "str",\n "crash_type_bicycle_name": "str",\n "crash_location_pedestrian": "str",\n "crash_location_pedestrian_name": "str",\n "crash_location_bicycle": "str",\n "crash_location_bicycle_name": "str",\n "pedestrian_position": "str",\n "pedestrian_position_name": "str",\n "bicyclist_position": "str",\n "bicyclist_position_name": "str",\n "pedestrian_initial_direction_of_travel": "str",\n "pedestrian_initial_direction_of_travel_name": "str",\n "bicyclist_initial_direction_of_travel": "str",\n "bicyclist_initial_direction_of_travel_name": "str",\n "motorist_initial_direction_of_travel": "str",\n "motorist_initial_direction_of_travel_name": "str",\n "motorist_maneuver": "str",\n "motorist_maneuver_name": "str",\n "intersection_leg": "str",\n "intersection_leg_name": "str",\n "pedestrian_scenario": "str",\n "pedestrian_scenario_name": "str",\n "crash_group_pedestrian": "str",\n "crash_group_pedestrian_name": "str",\n "crash_group_bicycle": "str",\n "crash_group_bicycle_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "PBPTYPE": "person_type",\n "PBPTYPENAME": "person_type_name",\n "PBAGE": "age",\n "PBAGENAME": "age_name",\n "PBSEX": "sex",\n "PBSEXNAME": "sex_name",\n "PBCWALK": "marked_crosswalk_present",\n "PBCWALKNAME": "marked_crosswalk_present_name",\n "PBSWALK": "sidewalk_present",\n "PBSWALKNAME": "sidewalk_present_name",\n "PBSZONE": "school_zone",\n "PBSZONENAME": "school_zone_name",\n "PEDCTYPE": "crash_type_pedestrian",\n "PEDCTYPENAME": "crash_type_pedestrian_name",\n "BIKECTYPE": "crash_type_bicycle",\n "BIKECTYPENAME": "crash_type_bicycle_name",\n "PEDLOC": "crash_location_pedestrian",\n "PEDLOCNAME": "crash_location_pedestrian_name",\n "BIKELOC": "crash_location_bicycle",\n "BIKELOCNAME": "crash_location_bicycle_name",\n "PEDPOS": "pedestrian_position",\n "PEDPOSNAME": "pedestrian_position_name",\n "BIKEPOS": "bicyclist_position",\n "BIKEPOSNAME": "bicyclist_position_name",\n "PEDDIR": "pedestrian_initial_direction_of_travel",\n "PEDDIRNAME": "pedestrian_initial_direction_of_travel_name",\n "BIKEDIR": "bicyclist_initial_direction_of_travel",\n "BIKEDIRNAME": "bicyclist_initial_direction_of_travel_name",\n "MOTDIR": "motorist_initial_direction_of_travel",\n "MOTDIRNAME": "motorist_initial_direction_of_travel_name",\n "MOTMAN": "motorist_maneuver",\n "MOTMANNAME": "motorist_maneuver_name",\n "PEDLEG": "intersection_leg",\n "PEDLEGNAME": "intersection_leg_name",\n "PEDSNR": "pedestrian_scenario",\n "PEDSNRNAME": "pedestrian_scenario_name",\n "PEDCGP": "crash_group_pedestrian",\n "PEDCGPNAME": "crash_group_pedestrian_name",\n "BIKECGP": "crash_group_bicycle",\n "BIKECGPNAME": "crash_group_bicycle_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for person pipelines person_2015_2017_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="person_2015_2017_transform_csv", startup_timeout_seconds=600, name="person", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "person.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2015_2017.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "vehicle_number",\n "person_number",\n "number_of_motor_vehicle_striking_non_motorist",\n "county",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "land_use",\n "land_use_name",\n "functional_system",\n "functional_system_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "school_bus_related",\n "school_bus_related_name",\n "vehicle_make",\n "vehicle_make_name",\n "make_model_combined",\n "body_type",\n "body_type_name",\n "model_year",\n "model_year_name",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "rollover",\n "rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "age",\n "age_name",\n "sex",\n "sex_name",\n "person_type",\n "person_type_name",\n "injury_severity",\n "injury_severity_name",\n "seating_position",\n "seating_position_name",\n "restraint_system_helmet_use",\n "restraint_system_helmet_use_name",\n "indication_of_misuse_of_restraint_system_helmet",\n "indication_of_misuse_of_restraint_system_helmet_name",\n "air_bag_deployed",\n "air_bag_deployed_name",\n "ejection",\n "ejection_name",\n "ejection_path",\n "ejection_path_name",\n "extrication",\n "extrication_name",\n "police_reported_alcohol_involvement",\n "police_reported_alcohol_involvement_name",\n "method_of_alcohol_determination_by_police",\n "method_of_alcohol_determination_by_police_name",\n "alcohol_test_status",\n "alcohol_test_status_name",\n "alcohol_test_type",\n "alcohol_test_type_name",\n "alcohol_result",\n "alcohol_result_name",\n "police_reported_drug_involvement",\n "police_reported_drug_involvement_name",\n "method_of_drug_determination_by_police",\n "method_of_drug_determination_by_police_name",\n "drug_test_status",\n "drug_test_status_name",\n "drug_test_type1",\n "drug_test_type1_name",\n "drug_test_type2",\n "drug_test_type2_name",\n "drug_test_type3",\n "drug_test_type3_name",\n "drug_result1",\n "drug_result1_name",\n "drug_result2",\n "drug_result2_name",\n "drug_result3",\n "drug_result3_name",\n "hospital",\n "hospital_name",\n "died_at_scene_en_route",\n "died_at_scene_en_route_name",\n "day_of_death",\n "day_of_death_name",\n "month_of_death",\n "month_of_death_name",\n "year_of_death",\n "year_of_death_name",\n "hour_of_death",\n "hour_of_death_name",\n "minute_of_death",\n "minute_of_death_name",\n "death_time",\n "death_time_name",\n "lag_hours",\n "lag_hours_name",\n "lag_minutes",\n "lag_minutes_name",\n "related_factors_person_level1",\n "related_factors_person_level1_name",\n "related_factors_person_level2",\n "related_factors_person_level2_name",\n "related_factors_person_level3",\n "related_factors_person_level3_name",\n "fatal_injury_at_work",\n "fatal_injury_at_work_name",\n "hispanic_origin",\n "hispanic_origin_name",\n "race",\n "race_name",\n "non_motorist_location_at_time_of_crash",\n "non_motorist_location_at_time_of_crash_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "number_of_motor_vehicle_striking_non_motorist": "str",\n "county": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "land_use": "str",\n "land_use_name": "str",\n "functional_system": "str",\n "functional_system_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "school_bus_related": "str",\n "school_bus_related_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "make_model_combined": "str",\n "body_type": "str",\n "body_type_name": "str",\n "model_year": "str",\n "model_year_name": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "age": "str",\n "age_name": "str",\n "sex": "str",\n "sex_name": "str",\n "person_type": "str",\n "person_type_name": "str",\n "injury_severity": "str",\n "injury_severity_name": "str",\n "seating_position": "str",\n "seating_position_name": "str",\n "restraint_system_helmet_use": "str",\n "restraint_system_helmet_use_name": "str",\n "indication_of_misuse_of_restraint_system_helmet": "str",\n "indication_of_misuse_of_restraint_system_helmet_name": "str",\n "air_bag_deployed": "str",\n "air_bag_deployed_name": "str",\n "ejection": "str",\n "ejection_name": "str",\n "ejection_path": "str",\n "ejection_path_name": "str",\n "extrication": "str",\n "extrication_name": "str",\n "police_reported_alcohol_involvement": "str",\n "police_reported_alcohol_involvement_name": "str",\n "method_of_alcohol_determination_by_police": "str",\n "method_of_alcohol_determination_by_police_name": "str",\n "alcohol_test_status": "str",\n "alcohol_test_status_name": "str",\n "alcohol_test_type": "str",\n "alcohol_test_type_name": "str",\n "alcohol_result": "str",\n "alcohol_result_name": "str",\n "police_reported_drug_involvement": "str",\n "police_reported_drug_involvement_name": "str",\n "method_of_drug_determination_by_police": "str",\n "method_of_drug_determination_by_police_name": "str",\n "drug_test_status": "str",\n "drug_test_status_name": "str",\n "drug_test_type1": "str",\n "drug_test_type1_name": "str",\n "drug_test_type2": "str",\n "drug_test_type2_name": "str",\n "drug_test_type3": "str",\n "drug_test_type3_name": "str",\n "drug_result1": "str",\n "drug_result1_name": "str",\n "drug_result2": "str",\n "drug_result2_name": "str",\n "drug_result3": "str",\n "drug_result3_name": "str",\n "hospital": "str",\n "hospital_name": "str",\n "died_at_scene_en_route": "str",\n "died_at_scene_en_route_name": "str",\n "day_of_death": "str",\n "day_of_death_name": "str",\n "month_of_death": "str",\n "month_of_death_name": "str",\n "year_of_death": "str",\n "year_of_death_name": "str",\n "hour_of_death": "str",\n "hour_of_death_name": "str",\n "minute_of_death": "str",\n "minute_of_death_name": "str",\n "death_time": "str",\n "death_time_name": "str",\n "lag_hours": "str",\n "lag_hours_name": "str",\n "lag_minutes": "str",\n "lag_minutes_name": "str",\n "related_factors_person_level1": "str",\n "related_factors_person_level1_name": "str",\n "related_factors_person_level2": "str",\n "related_factors_person_level2_name": "str",\n "related_factors_person_level3": "str",\n "related_factors_person_level3_name": "str",\n "fatal_injury_at_work": "str",\n "fatal_injury_at_work_name": "str",\n "hispanic_origin": "str",\n "hispanic_origin_name": "str",\n "race": "str",\n "race_name": "str",\n "non_motorist_location_at_time_of_crash": "str",\n "non_motorist_location_at_time_of_crash_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "STR_VEH": "number_of_motor_vehicle_striking_non_motorist",\n "COUNTY": "county",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "RUR_URB": "land_use",\n "RUR_URBNAME": "land_use_name",\n "FUNC_SYS": "functional_system",\n "FUNC_SYSNAME": "functional_system_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "SCH_BUS": "school_bus_related",\n "SCH_BUSNAME": "school_bus_related_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MAK_MOD": "make_model_combined",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "model_year",\n "MOD_YEARNAME": "model_year_name",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "AGE": "age",\n "AGENAME": "age_name",\n "SEX": "sex",\n "SEXNAME": "sex_name",\n "PER_TYP": "person_type",\n "PER_TYPNAME": "person_type_name",\n "INJ_SEV": "injury_severity",\n "INJ_SEVNAME": "injury_severity_name",\n "SEAT_POS": "seating_position",\n "SEAT_POSNAME": "seating_position_name",\n "REST_USE": "restraint_system_helmet_use",\n "REST_USENAME": "restraint_system_helmet_use_name",\n "REST_MIS": "indication_of_misuse_of_restraint_system_helmet",\n "REST_MISNAME": "indication_of_misuse_of_restraint_system_helmet_name",\n "AIR_BAG": "air_bag_deployed",\n "AIR_BAGNAME": "air_bag_deployed_name",\n "EJECTION": "ejection",\n "EJECTIONNAME": "ejection_name",\n "EJ_PATH": "ejection_path",\n "EJ_PATHNAME": "ejection_path_name",\n "EXTRICAT": "extrication",\n "EXTRICATNAME": "extrication_name",\n "DRINKING": "police_reported_alcohol_involvement",\n "DRINKINGNAME": "police_reported_alcohol_involvement_name",\n "ALC_DET": "method_of_alcohol_determination_by_police",\n "ALC_DETNAME": "method_of_alcohol_determination_by_police_name",\n "ALC_STATUS": "alcohol_test_status",\n "ALC_STATUSNAME": "alcohol_test_status_name",\n "ATST_TYP": "alcohol_test_type",\n "ATST_TYPNAME": "alcohol_test_type_name",\n "ALC_RES": "alcohol_result",\n "ALC_RESNAME": "alcohol_result_name",\n "DRUGS": "police_reported_drug_involvement",\n "DRUGSNAME": "police_reported_drug_involvement_name",\n "DRUG_DET": "method_of_drug_determination_by_police",\n "DRUG_DETNAME": "method_of_drug_determination_by_police_name",\n "DSTATUS": "drug_test_status",\n "DSTATUSNAME": "drug_test_status_name",\n "DRUGTST1": "drug_test_type1",\n "DRUGTST1NAME": "drug_test_type1_name",\n "DRUGTST2": "drug_test_type2",\n "DRUGTST2NAME": "drug_test_type2_name",\n "DRUGTST3": "drug_test_type3",\n "DRUGTST3NAME": "drug_test_type3_name",\n "DRUGRES1": "drug_result1",\n "DRUGRES1NAME": "drug_result1_name",\n "DRUGRES2": "drug_result2",\n "DRUGRES2NAME": "drug_result2_name",\n "DRUGRES3": "drug_result3",\n "DRUGRES3NAME": "drug_result3_name",\n "HOSPITAL": "hospital",\n "HOSPITALNAME": "hospital_name",\n "DOA": "died_at_scene_en_route",\n "DOANAME": "died_at_scene_en_route_name",\n "DEATH_DA": "day_of_death",\n "DEATH_DANAME": "day_of_death_name",\n "DEATH_MO": "month_of_death",\n "DEATH_MONAME": "month_of_death_name",\n "DEATH_YR": "year_of_death",\n "DEATH_YRNAME": "year_of_death_name",\n "DEATH_HR": "hour_of_death",\n "DEATH_HRNAME": "hour_of_death_name",\n "DEATH_MN": "minute_of_death",\n "DEATH_MNNAME": "minute_of_death_name",\n "DEATH_TM": "death_time",\n "DEATH_TMNAME": "death_time_name",\n "LAG_HRS": "lag_hours",\n "LAG_HRSNAME": "lag_hours_name",\n "LAG_MINS": "lag_minutes",\n "LAG_MINSNAME": "lag_minutes_name",\n "P_SF1": "related_factors_person_level1",\n "P_SF1NAME": "related_factors_person_level1_name",\n "P_SF2": "related_factors_person_level2",\n "P_SF2NAME": "related_factors_person_level2_name",\n "P_SF3": "related_factors_person_level3",\n "P_SF3NAME": "related_factors_person_level3_name",\n "WORK_INJ": "fatal_injury_at_work",\n "WORK_INJNAME": "fatal_injury_at_work_name",\n "HISPANIC": "hispanic_origin",\n "HISPANICNAME": "hispanic_origin_name",\n "RACE": "race",\n "RACENAME": "race_name",\n "LOCATION": "non_motorist_location_at_time_of_crash",\n "LOCATIONNAME": "non_motorist_location_at_time_of_crash_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for person pipelines person_2018_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="person_2018_transform_csv", startup_timeout_seconds=600, name="person", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.person_2018.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.person_2018.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.person_2018.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "person.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.person_2018.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2018.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2018.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2018.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2018.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.person_2018.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2018.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2018.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "vehicle_number",\n "person_number",\n "number_of_motor_vehicle_striking_non_motorist",\n "county",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "land_use",\n "land_use_name",\n "functional_system",\n "functional_system_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "school_bus_related",\n "school_bus_related_name",\n "vehicle_make",\n "vehicle_make_name",\n "make_model_combined",\n "make_model_combined_name",\n "body_type",\n "body_type_name",\n "model_year",\n "model_year_name",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "rollover",\n "rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "age",\n "age_name",\n "sex",\n "sex_name",\n "person_type",\n "person_type_name",\n "injury_severity",\n "injury_severity_name",\n "seating_position",\n "seating_position_name",\n "restraint_system_helmet_use",\n "restraint_system_helmet_use_name",\n "indication_of_misuse_of_restraint_system_helmet",\n "indication_of_misuse_of_restraint_system_helmet_name",\n "air_bag_deployed",\n "air_bag_deployed_name",\n "ejection",\n "ejection_name",\n "ejection_path",\n "ejection_path_name",\n "extrication",\n "extrication_name",\n "police_reported_alcohol_involvement",\n "police_reported_alcohol_involvement_name",\n "method_of_alcohol_determination_by_police",\n "method_of_alcohol_determination_by_police_name",\n "alcohol_test_status",\n "alcohol_test_status_name",\n "alcohol_test_type",\n "alcohol_test_type_name",\n "alcohol_result",\n "alcohol_result_name",\n "police_reported_drug_involvement",\n "police_reported_drug_involvement_name",\n "method_of_drug_determination_by_police",\n "method_of_drug_determination_by_police_name",\n "drug_test_status",\n "drug_test_status_name",\n "hospital",\n "hospital_name",\n "died_at_scene_en_route",\n "died_at_scene_en_route_name",\n "day_of_death",\n "day_of_death_name",\n "month_of_death",\n "month_of_death_name",\n "year_of_death",\n "year_of_death_name",\n "hour_of_death",\n "hour_of_death_name",\n "minute_of_death",\n "minute_of_death_name",\n "death_time",\n "death_time_name",\n "lag_hours",\n "lag_hours_name",\n "lag_minutes",\n "lag_minutes_name",\n "related_factors_person_level1",\n "related_factors_person_level1_name",\n "related_factors_person_level2",\n "related_factors_person_level2_name",\n "related_factors_person_level3",\n "related_factors_person_level3_name",\n "fatal_injury_at_work",\n "fatal_injury_at_work_name",\n "hispanic_origin",\n "hispanic_origin_name",\n "race",\n "race_name",\n "non_motorist_location_at_time_of_crash",\n "non_motorist_location_at_time_of_crash_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "number_of_motor_vehicle_striking_non_motorist": "str",\n "county": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "land_use": "str",\n "land_use_name": "str",\n "functional_system": "str",\n "functional_system_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "school_bus_related": "str",\n "school_bus_related_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "make_model_combined": "str",\n "make_model_combined_name": "str",\n "body_type": "str",\n "body_type_name": "str",\n "model_year": "str",\n "model_year_name": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "age": "str",\n "age_name": "str",\n "sex": "str",\n "sex_name": "str",\n "person_type": "str",\n "person_type_name": "str",\n "injury_severity": "str",\n "injury_severity_name": "str",\n "seating_position": "str",\n "seating_position_name": "str",\n "restraint_system_helmet_use": "str",\n "restraint_system_helmet_use_name": "str",\n "indication_of_misuse_of_restraint_system_helmet": "str",\n "indication_of_misuse_of_restraint_system_helmet_name": "str",\n "air_bag_deployed": "str",\n "air_bag_deployed_name": "str",\n "ejection": "str",\n "ejection_name": "str",\n "ejection_path": "str",\n "ejection_path_name": "str",\n "extrication": "str",\n "extrication_name": "str",\n "police_reported_alcohol_involvement": "str",\n "police_reported_alcohol_involvement_name": "str",\n "method_of_alcohol_determination_by_police": "str",\n "method_of_alcohol_determination_by_police_name": "str",\n "alcohol_test_status": "str",\n "alcohol_test_status_name": "str",\n "alcohol_test_type": "str",\n "alcohol_test_type_name": "str",\n "alcohol_result": "str",\n "alcohol_result_name": "str",\n "police_reported_drug_involvement": "str",\n "police_reported_drug_involvement_name": "str",\n "method_of_drug_determination_by_police": "str",\n "method_of_drug_determination_by_police_name": "str",\n "drug_test_status": "str",\n "drug_test_status_name": "str",\n "hospital": "str",\n "hospital_name": "str",\n "died_at_scene_en_route": "str",\n "died_at_scene_en_route_name": "str",\n "day_of_death": "str",\n "day_of_death_name": "str",\n "month_of_death": "str",\n "month_of_death_name": "str",\n "year_of_death": "str",\n "year_of_death_name": "str",\n "hour_of_death": "str",\n "hour_of_death_name": "str",\n "minute_of_death": "str",\n "minute_of_death_name": "str",\n "death_time": "str",\n "death_time_name": "str",\n "lag_hours": "str",\n "lag_hours_name": "str",\n "lag_minutes": "str",\n "lag_minutes_name": "str",\n "related_factors_person_level1": "str",\n "related_factors_person_level1_name": "str",\n "related_factors_person_level2": "str",\n "related_factors_person_level2_name": "str",\n "related_factors_person_level3": "str",\n "related_factors_person_level3_name": "str",\n "fatal_injury_at_work": "str",\n "fatal_injury_at_work_name": "str",\n "hispanic_origin": "str",\n "hispanic_origin_name": "str",\n "race": "str",\n "race_name": "str",\n "non_motorist_location_at_time_of_crash": "str",\n "non_motorist_location_at_time_of_crash_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "STR_VEH": "number_of_motor_vehicle_striking_non_motorist",\n "COUNTY": "county",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "RUR_URB": "land_use",\n "RUR_URBNAME": "land_use_name",\n "FUNC_SYS": "functional_system",\n "FUNC_SYSNAME": "functional_system_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "SCH_BUS": "school_bus_related",\n "SCH_BUSNAME": "school_bus_related_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MAK_MOD": "make_model_combined",\n "MAK_MODNAME": "make_model_combined_name",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "model_year",\n "MOD_YEARNAME": "model_year_name",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "AGE": "age",\n "AGENAME": "age_name",\n "SEX": "sex",\n "SEXNAME": "sex_name",\n "PER_TYP": "person_type",\n "PER_TYPNAME": "person_type_name",\n "INJ_SEV": "injury_severity",\n "INJ_SEVNAME": "injury_severity_name",\n "SEAT_POS": "seating_position",\n "SEAT_POSNAME": "seating_position_name",\n "REST_USE": "restraint_system_helmet_use",\n "REST_USENAME": "restraint_system_helmet_use_name",\n "REST_MIS": "indication_of_misuse_of_restraint_system_helmet",\n "REST_MISNAME": "indication_of_misuse_of_restraint_system_helmet_name",\n "AIR_BAG": "air_bag_deployed",\n "AIR_BAGNAME": "air_bag_deployed_name",\n "EJECTION": "ejection",\n "EJECTIONNAME": "ejection_name",\n "EJ_PATH": "ejection_path",\n "EJ_PATHNAME": "ejection_path_name",\n "EXTRICAT": "extrication",\n "EXTRICATNAME": "extrication_name",\n "DRINKING": "police_reported_alcohol_involvement",\n "DRINKINGNAME": "police_reported_alcohol_involvement_name",\n "ALC_DET": "method_of_alcohol_determination_by_police",\n "ALC_DETNAME": "method_of_alcohol_determination_by_police_name",\n "ALC_STATUS": "alcohol_test_status",\n "ALC_STATUSNAME": "alcohol_test_status_name",\n "ATST_TYP": "alcohol_test_type",\n "ATST_TYPNAME": "alcohol_test_type_name",\n "ALC_RES": "alcohol_result",\n "ALC_RESNAME": "alcohol_result_name",\n "DRUGS": "police_reported_drug_involvement",\n "DRUGSNAME": "police_reported_drug_involvement_name",\n "DRUG_DET": "method_of_drug_determination_by_police",\n "DRUG_DETNAME": "method_of_drug_determination_by_police_name",\n "DSTATUS": "drug_test_status",\n "DSTATUSNAME": "drug_test_status_name",\n "HOSPITAL": "hospital",\n "HOSPITALNAME": "hospital_name",\n "DOA": "died_at_scene_en_route",\n "DOANAME": "died_at_scene_en_route_name",\n "DEATH_DA": "day_of_death",\n "DEATH_DANAME": "day_of_death_name",\n "DEATH_MO": "month_of_death",\n "DEATH_MONAME": "month_of_death_name",\n "DEATH_YR": "year_of_death",\n "DEATH_YRNAME": "year_of_death_name",\n "DEATH_HR": "hour_of_death",\n "DEATH_HRNAME": "hour_of_death_name",\n "DEATH_MN": "minute_of_death",\n "DEATH_MNNAME": "minute_of_death_name",\n "DEATH_TM": "death_time",\n "DEATH_TMNAME": "death_time_name",\n "LAG_HRS": "lag_hours",\n "LAG_HRSNAME": "lag_hours_name",\n "LAG_MINS": "lag_minutes",\n "LAG_MINSNAME": "lag_minutes_name",\n "P_SF1": "related_factors_person_level1",\n "P_SF1NAME": "related_factors_person_level1_name",\n "P_SF2": "related_factors_person_level2",\n "P_SF2NAME": "related_factors_person_level2_name",\n "P_SF3": "related_factors_person_level3",\n "P_SF3NAME": "related_factors_person_level3_name",\n "WORK_INJ": "fatal_injury_at_work",\n "WORK_INJNAME": "fatal_injury_at_work_name",\n "HISPANIC": "hispanic_origin",\n "HISPANICNAME": "hispanic_origin_name",\n "RACE": "race",\n "RACENAME": "race_name",\n "LOCATION": "non_motorist_location_at_time_of_crash",\n "LOCATIONNAME": "non_motorist_location_at_time_of_crash_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for person pipelines person_2019_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="person_2019_transform_csv", startup_timeout_seconds=600, name="person", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.person_2019.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.person_2019.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.person_2019.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "person.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.person_2019.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2019.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2019.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2019.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2019.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.person_2019.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2019.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2019.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "vehicle_number",\n "person_number",\n "number_of_motor_vehicle_striking_non_motorist",\n "county",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "land_use",\n "land_use_name",\n "functional_system",\n "functional_system_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "school_bus_related",\n "school_bus_related_name",\n "vehicle_make",\n "vehicle_make_name",\n "make_model_combined",\n "body_type",\n "body_type_name",\n "model_year",\n "model_year_name",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "rollover",\n "rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "age",\n "age_name",\n "sex",\n "sex_name",\n "person_type",\n "person_type_name",\n "injury_severity",\n "injury_severity_name",\n "seating_position",\n "seating_position_name",\n "restraint_system_helmet_use",\n "restraint_system_helmet_use_name",\n "indication_of_misuse_of_restraint_system_helmet",\n "indication_of_misuse_of_restraint_system_helmet_name",\n "air_bag_deployed",\n "air_bag_deployed_name",\n "ejection",\n "ejection_name",\n "ejection_path",\n "ejection_path_name",\n "extrication",\n "extrication_name",\n "police_reported_alcohol_involvement",\n "police_reported_alcohol_involvement_name",\n "method_of_alcohol_determination_by_police",\n "method_of_alcohol_determination_by_police_name",\n "alcohol_test_status",\n "alcohol_test_status_name",\n "alcohol_test_type",\n "alcohol_test_type_name",\n "alcohol_result",\n "alcohol_result_name",\n "police_reported_drug_involvement",\n "police_reported_drug_involvement_name",\n "method_of_drug_determination_by_police",\n "method_of_drug_determination_by_police_name",\n "drug_test_status",\n "drug_test_status_name",\n "hospital",\n "hospital_name",\n "died_at_scene_en_route",\n "died_at_scene_en_route_name",\n "day_of_death",\n "day_of_death_name",\n "month_of_death",\n "month_of_death_name",\n "year_of_death",\n "year_of_death_name",\n "hour_of_death",\n "hour_of_death_name",\n "minute_of_death",\n "minute_of_death_name",\n "death_time",\n "death_time_name",\n "lag_hours",\n "lag_hours_name",\n "lag_minutes",\n "lag_minutes_name",\n "related_factors_person_level1",\n "related_factors_person_level1_name",\n "related_factors_person_level2",\n "related_factors_person_level2_name",\n "related_factors_person_level3",\n "related_factors_person_level3_name",\n "fatal_injury_at_work",\n "fatal_injury_at_work_name",\n "hispanic_origin",\n "hispanic_origin_name",\n "non_motorist_location_at_time_of_crash",\n "non_motorist_location_at_time_of_crash_name",\n "helm_use",\n "helm_usename",\n "helm_mis",\n "helm_misname"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "number_of_motor_vehicle_striking_non_motorist": "str",\n "county": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "land_use": "str",\n "land_use_name": "str",\n "functional_system": "str",\n "functional_system_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "school_bus_related": "str",\n "school_bus_related_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "make_model_combined": "str",\n "body_type": "str",\n "body_type_name": "str",\n "model_year": "str",\n "model_year_name": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "age": "str",\n "age_name": "str",\n "sex": "str",\n "sex_name": "str",\n "person_type": "str",\n "person_type_name": "str",\n "injury_severity": "str",\n "injury_severity_name": "str",\n "seating_position": "str",\n "seating_position_name": "str",\n "restraint_system_helmet_use": "str",\n "restraint_system_helmet_use_name": "str",\n "indication_of_misuse_of_restraint_system_helmet": "str",\n "indication_of_misuse_of_restraint_system_helmet_name": "str",\n "air_bag_deployed": "str",\n "air_bag_deployed_name": "str",\n "ejection": "str",\n "ejection_name": "str",\n "ejection_path": "str",\n "ejection_path_name": "str",\n "extrication": "str",\n "extrication_name": "str",\n "police_reported_alcohol_involvement": "str",\n "police_reported_alcohol_involvement_name": "str",\n "method_of_alcohol_determination_by_police": "str",\n "method_of_alcohol_determination_by_police_name": "str",\n "alcohol_test_status": "str",\n "alcohol_test_status_name": "str",\n "alcohol_test_type": "str",\n "alcohol_test_type_name": "str",\n "alcohol_result": "str",\n "alcohol_result_name": "str",\n "police_reported_drug_involvement": "str",\n "police_reported_drug_involvement_name": "str",\n "method_of_drug_determination_by_police": "str",\n "method_of_drug_determination_by_police_name": "str",\n "drug_test_status": "str",\n "drug_test_status_name": "str",\n "hospital": "str",\n "hospital_name": "str",\n "died_at_scene_en_route": "str",\n "died_at_scene_en_route_name": "str",\n "day_of_death": "str",\n "day_of_death_name": "str",\n "month_of_death": "str",\n "month_of_death_name": "str",\n "year_of_death": "str",\n "year_of_death_name": "str",\n "hour_of_death": "str",\n "hour_of_death_name": "str",\n "minute_of_death": "str",\n "minute_of_death_name": "str",\n "death_time": "str",\n "death_time_name": "str",\n "lag_hours": "str",\n "lag_hours_name": "str",\n "lag_minutes": "str",\n "lag_minutes_name": "str",\n "related_factors_person_level1": "str",\n "related_factors_person_level1_name": "str",\n "related_factors_person_level2": "str",\n "related_factors_person_level2_name": "str",\n "related_factors_person_level3": "str",\n "related_factors_person_level3_name": "str",\n "fatal_injury_at_work": "str",\n "fatal_injury_at_work_name": "str",\n "hispanic_origin": "str",\n "hispanic_origin_name": "str",\n "non_motorist_location_at_time_of_crash": "str",\n "non_motorist_location_at_time_of_crash_name": "str",\n "helm_use": "str",\n "helm_usename": "str",\n "helm_mis": "str",\n "helm_misname": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "STR_VEH": "number_of_motor_vehicle_striking_non_motorist",\n "COUNTY": "county",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "RUR_URB": "land_use",\n "RUR_URBNAME": "land_use_name",\n "FUNC_SYS": "functional_system",\n "FUNC_SYSNAME": "functional_system_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "SCH_BUS": "school_bus_related",\n "SCH_BUSNAME": "school_bus_related_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MAK_MOD": "make_model_combined",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "model_year",\n "MOD_YEARNAME": "model_year_name",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "AGE": "age",\n "AGENAME": "age_name",\n "SEX": "sex",\n "SEXNAME": "sex_name",\n "PER_TYP": "person_type",\n "PER_TYPNAME": "person_type_name",\n "INJ_SEV": "injury_severity",\n "INJ_SEVNAME": "injury_severity_name",\n "SEAT_POS": "seating_position",\n "SEAT_POSNAME": "seating_position_name",\n "REST_USE": "restraint_system_helmet_use",\n "REST_USENAME": "restraint_system_helmet_use_name",\n "REST_MIS": "indication_of_misuse_of_restraint_system_helmet",\n "REST_MISNAME": "indication_of_misuse_of_restraint_system_helmet_name",\n "AIR_BAG": "air_bag_deployed",\n "AIR_BAGNAME": "air_bag_deployed_name",\n "EJECTION": "ejection",\n "EJECTIONNAME": "ejection_name",\n "EJ_PATH": "ejection_path",\n "EJ_PATHNAME": "ejection_path_name",\n "EXTRICAT": "extrication",\n "EXTRICATNAME": "extrication_name",\n "DRINKING": "police_reported_alcohol_involvement",\n "DRINKINGNAME": "police_reported_alcohol_involvement_name",\n "ALC_DET": "method_of_alcohol_determination_by_police",\n "ALC_DETNAME": "method_of_alcohol_determination_by_police_name",\n "ALC_STATUS": "alcohol_test_status",\n "ALC_STATUSNAME": "alcohol_test_status_name",\n "ATST_TYP": "alcohol_test_type",\n "ATST_TYPNAME": "alcohol_test_type_name",\n "ALC_RES": "alcohol_result",\n "ALC_RESNAME": "alcohol_result_name",\n "DRUGS": "police_reported_drug_involvement",\n "DRUGSNAME": "police_reported_drug_involvement_name",\n "DRUG_DET": "method_of_drug_determination_by_police",\n "DRUG_DETNAME": "method_of_drug_determination_by_police_name",\n "DSTATUS": "drug_test_status",\n "DSTATUSNAME": "drug_test_status_name",\n "HOSPITAL": "hospital",\n "HOSPITALNAME": "hospital_name",\n "DOA": "died_at_scene_en_route",\n "DOANAME": "died_at_scene_en_route_name",\n "DEATH_DA": "day_of_death",\n "DEATH_DANAME": "day_of_death_name",\n "DEATH_MO": "month_of_death",\n "DEATH_MONAME": "month_of_death_name",\n "DEATH_YR": "year_of_death",\n "DEATH_YRNAME": "year_of_death_name",\n "DEATH_HR": "hour_of_death",\n "DEATH_HRNAME": "hour_of_death_name",\n "DEATH_MN": "minute_of_death",\n "DEATH_MNNAME": "minute_of_death_name",\n "DEATH_TM": "death_time",\n "DEATH_TMNAME": "death_time_name",\n "LAG_HRS": "lag_hours",\n "LAG_HRSNAME": "lag_hours_name",\n "LAG_MINS": "lag_minutes",\n "LAG_MINSNAME": "lag_minutes_name",\n "P_SF1": "related_factors_person_level1",\n "P_SF1NAME": "related_factors_person_level1_name",\n "P_SF2": "related_factors_person_level2",\n "P_SF2NAME": "related_factors_person_level2_name",\n "P_SF3": "related_factors_person_level3",\n "P_SF3NAME": "related_factors_person_level3_name",\n "WORK_INJ": "fatal_injury_at_work",\n "WORK_INJNAME": "fatal_injury_at_work_name",\n "HISPANIC": "hispanic_origin",\n "HISPANICNAME": "hispanic_origin_name",\n "LOCATION": "non_motorist_location_at_time_of_crash",\n "LOCATIONNAME": "non_motorist_location_at_time_of_crash_name",\n "HELM_USE": "helm_use",\n "HELM_USENAME": "helm_usename",\n "HELM_MIS": "helm_mis",\n "HELM_MISNAME": "helm_misname"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for person pipelines person_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="person_2020_transform_csv", startup_timeout_seconds=600, name="person", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.person_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.person_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.person_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "person.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.person_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.person_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.person_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.person_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.person_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "vehicle_number",\n "person_number",\n "number_of_motor_vehicle_striking_non_motorist",\n "county",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "land_use",\n "land_use_name",\n "functional_system",\n "functional_system_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "school_bus_related",\n "school_bus_related_name",\n "vehicle_make",\n "vehicle_make_name",\n "make_model_combined",\n "body_type",\n "body_type_name",\n "model_year",\n "model_year_name",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "rollover",\n "rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "age",\n "age_name",\n "sex",\n "sex_name",\n "person_type",\n "person_type_name",\n "injury_severity",\n "injury_severity_name",\n "seating_position",\n "seating_position_name",\n "restraint_system_helmet_use",\n "restraint_system_helmet_use_name",\n "indication_of_misuse_of_restraint_system_helmet",\n "indication_of_misuse_of_restraint_system_helmet_name",\n "air_bag_deployed",\n "air_bag_deployed_name",\n "ejection",\n "ejection_name",\n "ejection_path",\n "ejection_path_name",\n "extrication",\n "extrication_name",\n "police_reported_alcohol_involvement",\n "police_reported_alcohol_involvement_name",\n "method_of_alcohol_determination_by_police",\n "method_of_alcohol_determination_by_police_name",\n "alcohol_test_status",\n "alcohol_test_status_name",\n "alcohol_test_type",\n "alcohol_test_type_name",\n "alcohol_result",\n "alcohol_result_name",\n "police_reported_drug_involvement",\n "police_reported_drug_involvement_name",\n "method_of_drug_determination_by_police",\n "method_of_drug_determination_by_police_name",\n "drug_test_status",\n "drug_test_status_name",\n "hospital",\n "hospital_name",\n "died_at_scene_en_route",\n "died_at_scene_en_route_name",\n "day_of_death",\n "day_of_death_name",\n "month_of_death",\n "month_of_death_name",\n "year_of_death",\n "year_of_death_name",\n "hour_of_death",\n "hour_of_death_name",\n "minute_of_death",\n "minute_of_death_name",\n "death_time",\n "death_time_name",\n "lag_hours",\n "lag_hours_name",\n "lag_minutes",\n "lag_minutes_name",\n "fatal_injury_at_work",\n "fatal_injury_at_work_name",\n "hispanic_origin",\n "hispanic_origin_name",\n "non_motorist_location_at_time_of_crash",\n "non_motorist_location_at_time_of_crash_name",\n "helm_use",\n "helm_usename",\n "helm_mis",\n "helm_misname",\n "vpic_make",\n "vpic_make_name",\n "vpic_model",\n "vpic_model_name",\n "vpic_body_class",\n "vpic_body_class_name",\n "icfinal_body",\n "icfinalbody_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "number_of_motor_vehicle_striking_non_motorist": "str",\n "county": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "land_use": "str",\n "land_use_name": "str",\n "functional_system": "str",\n "functional_system_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "school_bus_related": "str",\n "school_bus_related_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "make_model_combined": "str",\n "body_type": "str",\n "body_type_name": "str",\n "model_year": "str",\n "model_year_name": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "age": "str",\n "age_name": "str",\n "sex": "str",\n "sex_name": "str",\n "person_type": "str",\n "person_type_name": "str",\n "injury_severity": "str",\n "injury_severity_name": "str",\n "seating_position": "str",\n "seating_position_name": "str",\n "restraint_system_helmet_use": "str",\n "restraint_system_helmet_use_name": "str",\n "indication_of_misuse_of_restraint_system_helmet": "str",\n "indication_of_misuse_of_restraint_system_helmet_name": "str",\n "air_bag_deployed": "str",\n "air_bag_deployed_name": "str",\n "ejection": "str",\n "ejection_name": "str",\n "ejection_path": "str",\n "ejection_path_name": "str",\n "extrication": "str",\n "extrication_name": "str",\n "police_reported_alcohol_involvement": "str",\n "police_reported_alcohol_involvement_name": "str",\n "method_of_alcohol_determination_by_police": "str",\n "method_of_alcohol_determination_by_police_name": "str",\n "alcohol_test_status": "str",\n "alcohol_test_status_name": "str",\n "alcohol_test_type": "str",\n "alcohol_test_type_name": "str",\n "alcohol_result": "str",\n "alcohol_result_name": "str",\n "police_reported_drug_involvement": "str",\n "police_reported_drug_involvement_name": "str",\n "method_of_drug_determination_by_police": "str",\n "method_of_drug_determination_by_police_name": "str",\n "drug_test_status": "str",\n "drug_test_status_name": "str",\n "hospital": "str",\n "hospital_name": "str",\n "died_at_scene_en_route": "str",\n "died_at_scene_en_route_name": "str",\n "day_of_death": "str",\n "day_of_death_name": "str",\n "month_of_death": "str",\n "month_of_death_name": "str",\n "year_of_death": "str",\n "year_of_death_name": "str",\n "hour_of_death": "str",\n "hour_of_death_name": "str",\n "minute_of_death": "str",\n "minute_of_death_name": "str",\n "death_time": "str",\n "death_time_name": "str",\n "lag_hours": "str",\n "lag_hours_name": "str",\n "lag_minutes": "str",\n "lag_minutes_name": "str",\n "fatal_injury_at_work": "str",\n "fatal_injury_at_work_name": "str",\n "hispanic_origin": "str",\n "hispanic_origin_name": "str",\n "non_motorist_location_at_time_of_crash": "str",\n "non_motorist_location_at_time_of_crash_name": "str",\n "helm_use": "str",\n "helm_usename": "str",\n "helm_mis": "str",\n "helm_misname": "str",\n "vpic_make": "str",\n "vpic_make_name": "str",\n "vpic_model": "str",\n "vpic_model_name": "str",\n "vpic_body_class": "str",\n "vpic_body_class_name": "str",\n "icfinal_body": "str",\n "icfinalbody_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "STR_VEH": "number_of_motor_vehicle_striking_non_motorist",\n "COUNTY": "county",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "RUR_URB": "land_use",\n "RUR_URBNAME": "land_use_name",\n "FUNC_SYS": "functional_system",\n "FUNC_SYSNAME": "functional_system_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "SCH_BUS": "school_bus_related",\n "SCH_BUSNAME": "school_bus_related_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MAK_MOD": "make_model_combined",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "model_year",\n "MOD_YEARNAME": "model_year_name",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "AGE": "age",\n "AGENAME": "age_name",\n "SEX": "sex",\n "SEXNAME": "sex_name",\n "PER_TYP": "person_type",\n "PER_TYPNAME": "person_type_name",\n "INJ_SEV": "injury_severity",\n "INJ_SEVNAME": "injury_severity_name",\n "SEAT_POS": "seating_position",\n "SEAT_POSNAME": "seating_position_name",\n "REST_USE": "restraint_system_helmet_use",\n "REST_USENAME": "restraint_system_helmet_use_name",\n "REST_MIS": "indication_of_misuse_of_restraint_system_helmet",\n "REST_MISNAME": "indication_of_misuse_of_restraint_system_helmet_name",\n "AIR_BAG": "air_bag_deployed",\n "AIR_BAGNAME": "air_bag_deployed_name",\n "EJECTION": "ejection",\n "EJECTIONNAME": "ejection_name",\n "EJ_PATH": "ejection_path",\n "EJ_PATHNAME": "ejection_path_name",\n "EXTRICAT": "extrication",\n "EXTRICATNAME": "extrication_name",\n "DRINKING": "police_reported_alcohol_involvement",\n "DRINKINGNAME": "police_reported_alcohol_involvement_name",\n "ALC_DET": "method_of_alcohol_determination_by_police",\n "ALC_DETNAME": "method_of_alcohol_determination_by_police_name",\n "ALC_STATUS": "alcohol_test_status",\n "ALC_STATUSNAME": "alcohol_test_status_name",\n "ATST_TYP": "alcohol_test_type",\n "ATST_TYPNAME": "alcohol_test_type_name",\n "ALC_RES": "alcohol_result",\n "ALC_RESNAME": "alcohol_result_name",\n "DRUGS": "police_reported_drug_involvement",\n "DRUGSNAME": "police_reported_drug_involvement_name",\n "DRUG_DET": "method_of_drug_determination_by_police",\n "DRUG_DETNAME": "method_of_drug_determination_by_police_name",\n "DSTATUS": "drug_test_status",\n "DSTATUSNAME": "drug_test_status_name",\n "HOSPITAL": "hospital",\n "HOSPITALNAME": "hospital_name",\n "DOA": "died_at_scene_en_route",\n "DOANAME": "died_at_scene_en_route_name",\n "DEATH_DA": "day_of_death",\n "DEATH_DANAME": "day_of_death_name",\n "DEATH_MO": "month_of_death",\n "DEATH_MONAME": "month_of_death_name",\n "DEATH_YR": "year_of_death",\n "DEATH_YRNAME": "year_of_death_name",\n "DEATH_HR": "hour_of_death",\n "DEATH_HRNAME": "hour_of_death_name",\n "DEATH_MN": "minute_of_death",\n "DEATH_MNNAME": "minute_of_death_name",\n "DEATH_TM": "death_time",\n "DEATH_TMNAME": "death_time_name",\n "LAG_HRS": "lag_hours",\n "LAG_HRSNAME": "lag_hours_name",\n "LAG_MINS": "lag_minutes",\n "LAG_MINSNAME": "lag_minutes_name",\n "WORK_INJ": "fatal_injury_at_work",\n "WORK_INJNAME": "fatal_injury_at_work_name",\n "HISPANIC": "hispanic_origin",\n "HISPANICNAME": "hispanic_origin_name",\n "LOCATION": "non_motorist_location_at_time_of_crash",\n "LOCATIONNAME": "non_motorist_location_at_time_of_crash_name",\n "HELM_USE": "helm_use",\n "HELM_USENAME": "helm_usename",\n "HELM_MIS": "helm_mis",\n "HELM_MISNAME": "helm_misname",\n "VPICMAKE": "vpic_make",\n "VPICMAKENAME": "vpic_make_name",\n "VPICMODEL": "vpic_model",\n "VPICMODELNAME": "vpic_model_name",\n "VPICBODYCLASS": "vpic_body_class",\n "VPICBODYCLASSNAME": "vpic_body_class_name",\n "ICFINALBODY": "icfinal_body",\n "ICFINALBODYNAME": "icfinalbody_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for safetyeq pipelines safetyeq_2015_2016_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="safetyeq_2015_2016_transform_csv", startup_timeout_seconds=600, name="safetyeq", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "safetyeq.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2015_2016.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "person_number",\n "non_motorist_safety_equipment_use",\n "non_motorist_safety_equipment_use_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "non_motorist_safety_equipment_use": "str",\n "non_motorist_safety_equipment_use_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "MSAFEQMT": "non_motorist_safety_equipment_use",\n "MSAFEQMTNAME": "non_motorist_safety_equipment_use_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for safetyeq pipelines safetyeq_2017_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="safetyeq_2017_2020_transform_csv", startup_timeout_seconds=600, name="safetyeq", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "safetyeq.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.safetyeq_2017_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "person_number",\n "nm_helmet",\n "nm_helmet_name",\n "nm_propad",\n "nm_propad_name",\n "nm_othpro",\n "nm_othpro_name",\n "nm_refclo",\n "nm_refclo_name",\n "nm_light",\n "nm_light_name",\n "nm_othpre",\n "nm_othpre_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "person_number": "str",\n "nm_helmet": "str",\n "nm_helmet_name": "str",\n "nm_propad": "str",\n "nm_propad_name": "str",\n "nm_othpro": "str",\n "nm_othpro_name": "str",\n "nm_refclo": "str",\n "nm_refclo_name": "str",\n "nm_light": "str",\n "nm_light_name": "str",\n "nm_othpre": "str",\n "nm_othpre_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "PER_NO": "person_number",\n "NMHELMET": "nm_helmet",\n "NMHELMETNAME": "nm_helmet_name",\n "NMPROPAD": "nm_propad",\n "NMPROPADNAME": "nm_propad_name",\n "NMOTHPRO": "nm_othpro",\n "NMOTHPRONAME": "nm_othpro_name",\n "NMREFCLO": "nm_refclo",\n "NMREFCLONAME": "nm_refclo_name",\n "NMLIGHT": "nm_light",\n "NMLIGHTNAME": "nm_light_name",\n "NMOTHPRE": "nm_othpre",\n "NMOTHPRENAME": "nm_othpre_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vehicle pipelines vehicle_2015_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vehicle_2015_transform_csv", startup_timeout_seconds=600, name="vehicle", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vehicle.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2015.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "make_model_combined_name",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "jackknife",\n "jackknife_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number_mcid",\n "motor_carrier_identification_number_mcid_name",\n "gross_vehicle_weight_rating",\n "gross_vehicle_weight_rating_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "travel_speed",\n "travel_speed_name",\n "underride_override",\n "underride_override_name",\n "rollover",\n "rollover_name",\n "location_of_rollover",\n "location_of_rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "related_factors_vehicle_level_1",\n "related_factors_vehicle_level_1_name",\n "related_factors_vehicle_level_2",\n "related_factors_vehicle_level_2_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "driver_presence",\n "driver_presence_name",\n "drivers_license_state",\n "drivers_license_state_name",\n "drivers_zip_code",\n "drivers_zip_code_name",\n "non_cdl_license_status",\n "non_cdl_license_status_name",\n "non_cdl_license_type",\n "non_cdl_license_type_name",\n "commercial_motor_vehicle_license_status",\n "commercial_motor_vehicle_license_status_name",\n "compliance_with_cdl_endorsements",\n "compliance_with_cdl_endorsements_name",\n "license_compliance_with_class_of_vehicle",\n "license_compliance_with_class_of_vehicle_name",\n "compliance_with_license_restrictions",\n "compliance_with_license_restrictions_name",\n "driver_height",\n "driver_height_name",\n "driver_weight",\n "driver_weight_name",\n "previous_recorded_crashes",\n "previous_recorded_crashes_name",\n "previous_recorded_suspensions_and_revocations",\n "previous_recorded_suspensions_and_revocations_name",\n "previous_dwi_convictions",\n "previous_dwi_convictions_name",\n "previous_speeding_convictions",\n "previous_speeding_convictions_name",\n "previous_other_moving_violation_convictions",\n "previous_other_moving_violation_convictions_name",\n "month_of_first_crash_suspension_or_conviction",\n "month_of_first_crash_suspension_or_conviction_name",\n "year_of_first_crash_suspension_or_conviction",\n "year_of_first_crash_suspension_or_conviction_name",\n "month_of_last_crash_suspension_or_conviction",\n "month_of_last_crash_suspension_or_conviction_name",\n "year_of_last_crash_suspension_or_conviction",\n "year_of_last_crash_suspension_or_conviction_name",\n "speeding_related",\n "speeding_related_name",\n "related_factors_driver_level_1",\n "related_factors_driver_level_1_name",\n "related_factors_driver_level_2",\n "related_factors_driver_level_2_name",\n "related_factors_driver_level_3",\n "related_factors_driver_level_3_name",\n "related_factors_driver_level_4",\n "related_factors_driver_level_4_name",\n "trafficway_description",\n "trafficway_description_name",\n "total_lanes_in_roadway",\n "total_lanes_in_roadway_name",\n "speed_limit",\n "speed_limit_name",\n "roadway_alignment",\n "roadway_alignment_name",\n "roadway_grade",\n "roadway_grade_name",\n "roadway_surface_type",\n "roadway_surface_type_name",\n "roadway_surface_condition",\n "roadway_surface_condition_name",\n "traffic_control_device",\n "traffic_control_device_name",\n "traffic_control_device_functioning",\n "traffic_control_device_functioning_name",\n "pre_event_movement_prior_to_recognition_of_critical_event",\n "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "critical_event_precrash",\n "critical_event_precrash_name",\n "attempted_avoidance_maneuver",\n "attempted_avoidance_maneuver_name",\n "pre_impact_stability",\n "pre_impact_stability_name",\n "pre_impact_location",\n "pre_impact_location_name",\n "crash_type",\n "crash_type_name",\n "fatalities_in_vehicle",\n "driver_drinking",\n "driver_drinking_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "make_model_combined_name": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "jackknife": "str",\n "jackknife_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number_mcid": "str",\n "motor_carrier_identification_number_mcid_name": "str",\n "gross_vehicle_weight_rating": "str",\n "gross_vehicle_weight_rating_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "travel_speed": "str",\n "travel_speed_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "location_of_rollover": "str",\n "location_of_rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "related_factors_vehicle_level_1": "str",\n "related_factors_vehicle_level_1_name": "str",\n "related_factors_vehicle_level_2": "str",\n "related_factors_vehicle_level_2_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "driver_presence": "str",\n "driver_presence_name": "str",\n "drivers_license_state": "str",\n "drivers_license_state_name": "str",\n "drivers_zip_code": "str",\n "drivers_zip_code_name": "str",\n "non_cdl_license_status": "str",\n "non_cdl_license_status_name": "str",\n "non_cdl_license_type": "str",\n "non_cdl_license_type_name": "str",\n "commercial_motor_vehicle_license_status": "str",\n "commercial_motor_vehicle_license_status_name": "str",\n "compliance_with_cdl_endorsements": "str",\n "compliance_with_cdl_endorsements_name": "str",\n "license_compliance_with_class_of_vehicle": "str",\n "license_compliance_with_class_of_vehicle_name": "str",\n "compliance_with_license_restrictions": "str",\n "compliance_with_license_restrictions_name": "str",\n "driver_height": "str",\n "driver_height_name": "str",\n "driver_weight": "str",\n "driver_weight_name": "str",\n "previous_recorded_crashes": "str",\n "previous_recorded_crashes_name": "str",\n "previous_recorded_suspensions_and_revocations": "str",\n "previous_recorded_suspensions_and_revocations_name": "str",\n "previous_dwi_convictions": "str",\n "previous_dwi_convictions_name": "str",\n "previous_speeding_convictions": "str",\n "previous_speeding_convictions_name": "str",\n "previous_other_moving_violation_convictions": "str",\n "previous_other_moving_violation_convictions_name": "str",\n "month_of_first_crash_suspension_or_conviction": "str",\n "month_of_first_crash_suspension_or_conviction_name": "str",\n "year_of_first_crash_suspension_or_conviction": "str",\n "year_of_first_crash_suspension_or_conviction_name": "str",\n "month_of_last_crash_suspension_or_conviction": "str",\n "month_of_last_crash_suspension_or_conviction_name": "str",\n "year_of_last_crash_suspension_or_conviction": "str",\n "year_of_last_crash_suspension_or_conviction_name": "str",\n "speeding_related": "str",\n "speeding_related_name": "str",\n "related_factors_driver_level_1": "str",\n "related_factors_driver_level_1_name": "str",\n "related_factors_driver_level_2": "str",\n "related_factors_driver_level_2_name": "str",\n "related_factors_driver_level_3": "str",\n "related_factors_driver_level_3_name": "str",\n "related_factors_driver_level_4": "str",\n "related_factors_driver_level_4_name": "str",\n "trafficway_description": "str",\n "trafficway_description_name": "str",\n "total_lanes_in_roadway": "str",\n "total_lanes_in_roadway_name": "str",\n "speed_limit": "str",\n "speed_limit_name": "str",\n "roadway_alignment": "str",\n "roadway_alignment_name": "str",\n "roadway_grade": "str",\n "roadway_grade_name": "str",\n "roadway_surface_type": "str",\n "roadway_surface_type_name": "str",\n "roadway_surface_condition": "str",\n "roadway_surface_condition_name": "str",\n "traffic_control_device": "str",\n "traffic_control_device_name": "str",\n "traffic_control_device_functioning": "str",\n "traffic_control_device_functioning_name": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event_name": "str",\n "critical_event_precrash": "str",\n "critical_event_precrash_name": "str",\n "attempted_avoidance_maneuver": "str",\n "attempted_avoidance_maneuver_name": "str",\n "pre_impact_stability": "str",\n "pre_impact_stability_name": "str",\n "pre_impact_location": "str",\n "pre_impact_location_name": "str",\n "crash_type": "str",\n "crash_type_name": "str",\n "fatalities_in_vehicle": "str",\n "driver_drinking": "str",\n "driver_drinking_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "NUMOCCS": "number_of_occupants",\n "NUMOCCSNAME": "number_of_occupants_name",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "UNITTYPE": "unit_type",\n "UNITTYPENAME": "unit_type_name",\n "HIT_RUN": "hit_and_run",\n "HIT_RUNNAME": "hit_and_run_name",\n "REG_STAT": "registration_state",\n "REG_STATNAME": "registration_state_name",\n "OWNER": "registered_vehicle_owner",\n "OWNERNAME": "registered_vehicle_owner_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MODEL": "vehicle_model",\n "MAK_MOD": "make_model_combined",\n "MAK_MODNAME": "make_model_combined_name",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "vehicle_model_year",\n "MOD_YEARNAME": "vehicle_model_year_name",\n "VIN": "vehicle_identification_number_vin",\n "VINNAME": "vehicle_identification_number_vin_name",\n "VIN_1": "vin_character_1",\n "VIN_2": "vin_character_2",\n "VIN_3": "vin_character_3",\n "VIN_4": "vin_character_4",\n "VIN_5": "vin_character_5",\n "VIN_6": "vin_character_6",\n "VIN_7": "vin_character_7",\n "VIN_8": "vin_character_8",\n "VIN_9": "vin_character_9",\n "VIN_10": "vin_character_10",\n "VIN_11": "vin_character_11",\n "VIN_12": "vin_character_12",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "J_KNIFE": "jackknife",\n "J_KNIFENAME": "jackknife_name",\n "MCARR_I1": "mcid_issuing_authority",\n "MCARR_I1NAME": "mcid_issuing_authority_name",\n "MCARR_I2": "mcid_identification_number",\n "MCARR_I2NAME": "mcid_identification_number_name",\n "MCARR_ID": "motor_carrier_identification_number_mcid",\n "MCARR_IDNAME": "motor_carrier_identification_number_mcid_name",\n "GVWR": "gross_vehicle_weight_rating",\n "GVWRNAME": "gross_vehicle_weight_rating_name",\n "V_CONFIG": "vehicle_configuration",\n "V_CONFIGNAME": "vehicle_configuration_name",\n "CARGO_BT": "cargo_body_type",\n "CARGO_BTNAME": "cargo_body_type_name",\n "HAZ_INV": "hazardous_material_involvement",\n "HAZ_INVNAME": "hazardous_material_involvement_name",\n "HAZ_PLAC": "hazardous_material_placard",\n "HAZ_PLACNAME": "hazardous_material_placard_name",\n "HAZ_ID": "hazardous_material_identification_number",\n "HAZ_IDNAME": "hazardous_material_identification_number_name",\n "HAZ_CNO": "hazardous_material_class_number",\n "HAZ_CNONAME": "hazardous_material_class_number_name",\n "HAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "HAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "BUS_USE": "bus_use",\n "BUS_USENAME": "bus_use_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "TRAV_SP": "travel_speed",\n "TRAV_SPNAME": "travel_speed_name",\n "UNDERIDE": "underride_override",\n "UNDERIDENAME": "underride_override_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "ROLINLOC": "location_of_rollover",\n "ROLINLOCNAME": "location_of_rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "DEFORMED": "extent_of_damage",\n "DEFORMEDNAME": "extent_of_damage_name",\n "TOWED": "vehicle_removal",\n "TOWEDNAME": "vehicle_removal_name",\n "M_HARM": "most_harmful_event",\n "M_HARMNAME": "most_harmful_event_name",\n "VEH_SC1": "related_factors_vehicle_level_1",\n "VEH_SC1NAME": "related_factors_vehicle_level_1_name",\n "VEH_SC2": "related_factors_vehicle_level_2",\n "VEH_SC2NAME": "related_factors_vehicle_level_2_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "DR_PRES": "driver_presence",\n "DR_PRESNAME": "driver_presence_name",\n "L_STATE": "drivers_license_state",\n "L_STATENAME": "drivers_license_state_name",\n "DR_ZIP": "drivers_zip_code",\n "DR_ZIPNAME": "drivers_zip_code_name",\n "L_STATUS": "non_cdl_license_status",\n "L_STATUSNAME": "non_cdl_license_status_name",\n "L_TYPE": "non_cdl_license_type",\n "L_TYPENAME": "non_cdl_license_type_name",\n "CDL_STAT": "commercial_motor_vehicle_license_status",\n "CDL_STATNAME": "commercial_motor_vehicle_license_status_name",\n "L_ENDORS": "compliance_with_cdl_endorsements",\n "L_ENDORSNAME": "compliance_with_cdl_endorsements_name",\n "L_COMPL": "license_compliance_with_class_of_vehicle",\n "L_COMPLNAME": "license_compliance_with_class_of_vehicle_name",\n "L_RESTRI": "compliance_with_license_restrictions",\n "L_RESTRINAME": "compliance_with_license_restrictions_name",\n "DR_HGT": "driver_height",\n "DR_HGTNAME": "driver_height_name",\n "DR_WGT": "driver_weight",\n "DR_WGTNAME": "driver_weight_name",\n "PREV_ACC": "previous_recorded_crashes",\n "PREV_ACCNAME": "previous_recorded_crashes_name",\n "PREV_SUS": "previous_recorded_suspensions_and_revocations",\n "PREV_SUSNAME": "previous_recorded_suspensions_and_revocations_name",\n "PREV_DWI": "previous_dwi_convictions",\n "PREV_DWINAME": "previous_dwi_convictions_name",\n "PREV_SPD": "previous_speeding_convictions",\n "PREV_SPDNAME": "previous_speeding_convictions_name",\n "PREV_OTH": "previous_other_moving_violation_convictions",\n "PREV_OTHNAME": "previous_other_moving_violation_convictions_name",\n "FIRST_MO": "month_of_first_crash_suspension_or_conviction",\n "FIRST_MONAME": "month_of_first_crash_suspension_or_conviction_name",\n "FIRST_YR": "year_of_first_crash_suspension_or_conviction",\n "FIRST_YRNAME": "year_of_first_crash_suspension_or_conviction_name",\n "LAST_MO": "month_of_last_crash_suspension_or_conviction",\n "LAST_MONAME": "month_of_last_crash_suspension_or_conviction_name",\n "LAST_YR": "year_of_last_crash_suspension_or_conviction",\n "LAST_YRNAME": "year_of_last_crash_suspension_or_conviction_name",\n "SPEEDREL": "speeding_related",\n "SPEEDRELNAME": "speeding_related_name",\n "DR_SF1": "related_factors_driver_level_1",\n "DR_SF1NAME": "related_factors_driver_level_1_name",\n "DR_SF2": "related_factors_driver_level_2",\n "DR_SF2NAME": "related_factors_driver_level_2_name",\n "DR_SF3": "related_factors_driver_level_3",\n "DR_SF3NAME": "related_factors_driver_level_3_name",\n "DR_SF4": "related_factors_driver_level_4",\n "DR_SF4NAME": "related_factors_driver_level_4_name",\n "VTRAFWAY": "trafficway_description",\n "VTRAFWAYNAME": "trafficway_description_name",\n "VNUM_LAN": "total_lanes_in_roadway",\n "VNUM_LANNAME": "total_lanes_in_roadway_name",\n "VSPD_LIM": "speed_limit",\n "VSPD_LIMNAME": "speed_limit_name",\n "VALIGN": "roadway_alignment",\n "VALIGNNAME": "roadway_alignment_name",\n "VPROFILE": "roadway_grade",\n "VPROFILENAME": "roadway_grade_name",\n "VPAVETYP": "roadway_surface_type",\n "VPAVETYPNAME": "roadway_surface_type_name",\n "VSURCOND": "roadway_surface_condition",\n "VSURCONDNAME": "roadway_surface_condition_name",\n "VTRAFCON": "traffic_control_device",\n "VTRAFCONNAME": "traffic_control_device_name",\n "VTCONT_F": "traffic_control_device_functioning",\n "VTCONT_FNAME": "traffic_control_device_functioning_name",\n "P_CRASH1": "pre_event_movement_prior_to_recognition_of_critical_event",\n "P_CRASH1NAME": "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "P_CRASH2": "critical_event_precrash",\n "P_CRASH2NAME": "critical_event_precrash_name",\n "P_CRASH3": "attempted_avoidance_maneuver",\n "P_CRASH3NAME": "attempted_avoidance_maneuver_name",\n "PCRASH4": "pre_impact_stability",\n "PCRASH4NAME": "pre_impact_stability_name",\n "PCRASH5": "pre_impact_location",\n "PCRASH5NAME": "pre_impact_location_name",\n "ACC_TYPE": "crash_type",\n "ACC_TYPENAME": "crash_type_name",\n "DEATHS": "fatalities_in_vehicle",\n "DR_DRINK": "driver_drinking",\n "DR_DRINKNAME": "driver_drinking_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vehicle pipelines vehicle_2016_2017_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vehicle_2016_2017_transform_csv", startup_timeout_seconds=600, name="vehicle", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vehicle.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2016_2017.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "make_model_combined_name",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "jackknife",\n "jackknife_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number_mcid",\n "motor_carrier_identification_number_mcid_name",\n "gross_vehicle_weight_rating",\n "gross_vehicle_weight_rating_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "travel_speed",\n "travel_speed_name",\n "underride_override",\n "underride_override_name",\n "rollover",\n "rollover_name",\n "location_of_rollover",\n "location_of_rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "related_factors_vehicle_level_1",\n "related_factors_vehicle_level_1_name",\n "related_factors_vehicle_level_2",\n "related_factors_vehicle_level_2_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "driver_presence",\n "driver_presence_name",\n "drivers_license_state",\n "drivers_license_state_name",\n "drivers_zip_code",\n "drivers_zip_code_name",\n "non_cdl_license_status",\n "non_cdl_license_status_name",\n "non_cdl_license_type",\n "non_cdl_license_type_name",\n "commercial_motor_vehicle_license_status",\n "commercial_motor_vehicle_license_status_name",\n "compliance_with_cdl_endorsements",\n "compliance_with_cdl_endorsements_name",\n "license_compliance_with_class_of_vehicle",\n "license_compliance_with_class_of_vehicle_name",\n "compliance_with_license_restrictions",\n "compliance_with_license_restrictions_name",\n "driver_height",\n "driver_height_name",\n "driver_weight",\n "driver_weight_name",\n "previous_recorded_crashes",\n "previous_recorded_crashes_name",\n "previous_recorded_suspensions_and_revocations",\n "previous_recorded_suspensions_and_revocations_name",\n "previous_dwi_convictions",\n "previous_dwi_convictions_name",\n "previous_speeding_convictions",\n "previous_speeding_convictions_name",\n "previous_other_moving_violation_convictions",\n "previous_other_moving_violation_convictions_name",\n "month_of_first_crash_suspension_or_conviction",\n "month_of_first_crash_suspension_or_conviction_name",\n "year_of_first_crash_suspension_or_conviction",\n "year_of_first_crash_suspension_or_conviction_name",\n "month_of_last_crash_suspension_or_conviction",\n "month_of_last_crash_suspension_or_conviction_name",\n "year_of_last_crash_suspension_or_conviction",\n "year_of_last_crash_suspension_or_conviction_name",\n "speeding_related",\n "speeding_related_name",\n "related_factors_driver_level_1",\n "related_factors_driver_level_1_name",\n "related_factors_driver_level_2",\n "related_factors_driver_level_2_name",\n "related_factors_driver_level_3",\n "related_factors_driver_level_3_name",\n "related_factors_driver_level_4",\n "related_factors_driver_level_4_name",\n "trafficway_description",\n "trafficway_description_name",\n "total_lanes_in_roadway",\n "total_lanes_in_roadway_name",\n "speed_limit",\n "speed_limit_name",\n "roadway_alignment",\n "roadway_alignment_name",\n "roadway_grade",\n "roadway_grade_name",\n "roadway_surface_type",\n "roadway_surface_type_name",\n "roadway_surface_condition",\n "roadway_surface_condition_name",\n "traffic_control_device",\n "traffic_control_device_name",\n "traffic_control_device_functioning",\n "traffic_control_device_functioning_name",\n "pre_event_movement_prior_to_recognition_of_critical_event",\n "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "critical_event_precrash",\n "critical_event_precrash_name",\n "attempted_avoidance_maneuver",\n "attempted_avoidance_maneuver_name",\n "pre_impact_stability",\n "pre_impact_stability_name",\n "pre_impact_location",\n "pre_impact_location_name",\n "crash_type",\n "crash_type_name",\n "fatalities_in_vehicle",\n "driver_drinking",\n "driver_drinking_name",\n "trailer_vehicle_identification_number_1",\n "trailer_vehicle_identification_number_1_name",\n "trailer_vehicle_identification_number_2",\n "trailer_vehicle_identification_number_2_name",\n "trailer_vehicle_identification_number_3",\n "trailer_vehicle_identification_number_3_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "make_model_combined_name": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "jackknife": "str",\n "jackknife_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number_mcid": "str",\n "motor_carrier_identification_number_mcid_name": "str",\n "gross_vehicle_weight_rating": "str",\n "gross_vehicle_weight_rating_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "travel_speed": "str",\n "travel_speed_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "location_of_rollover": "str",\n "location_of_rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "related_factors_vehicle_level_1": "str",\n "related_factors_vehicle_level_1_name": "str",\n "related_factors_vehicle_level_2": "str",\n "related_factors_vehicle_level_2_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "driver_presence": "str",\n "driver_presence_name": "str",\n "drivers_license_state": "str",\n "drivers_license_state_name": "str",\n "drivers_zip_code": "str",\n "drivers_zip_code_name": "str",\n "non_cdl_license_status": "str",\n "non_cdl_license_status_name": "str",\n "non_cdl_license_type": "str",\n "non_cdl_license_type_name": "str",\n "commercial_motor_vehicle_license_status": "str",\n "commercial_motor_vehicle_license_status_name": "str",\n "compliance_with_cdl_endorsements": "str",\n "compliance_with_cdl_endorsements_name": "str",\n "license_compliance_with_class_of_vehicle": "str",\n "license_compliance_with_class_of_vehicle_name": "str",\n "compliance_with_license_restrictions": "str",\n "compliance_with_license_restrictions_name": "str",\n "driver_height": "str",\n "driver_height_name": "str",\n "driver_weight": "str",\n "driver_weight_name": "str",\n "previous_recorded_crashes": "str",\n "previous_recorded_crashes_name": "str",\n "previous_recorded_suspensions_and_revocations": "str",\n "previous_recorded_suspensions_and_revocations_name": "str",\n "previous_dwi_convictions": "str",\n "previous_dwi_convictions_name": "str",\n "previous_speeding_convictions": "str",\n "previous_speeding_convictions_name": "str",\n "previous_other_moving_violation_convictions": "str",\n "previous_other_moving_violation_convictions_name": "str",\n "month_of_first_crash_suspension_or_conviction": "str",\n "month_of_first_crash_suspension_or_conviction_name": "str",\n "year_of_first_crash_suspension_or_conviction": "str",\n "year_of_first_crash_suspension_or_conviction_name": "str",\n "month_of_last_crash_suspension_or_conviction": "str",\n "month_of_last_crash_suspension_or_conviction_name": "str",\n "year_of_last_crash_suspension_or_conviction": "str",\n "year_of_last_crash_suspension_or_conviction_name": "str",\n "speeding_related": "str",\n "speeding_related_name": "str",\n "related_factors_driver_level_1": "str",\n "related_factors_driver_level_1_name": "str",\n "related_factors_driver_level_2": "str",\n "related_factors_driver_level_2_name": "str",\n "related_factors_driver_level_3": "str",\n "related_factors_driver_level_3_name": "str",\n "related_factors_driver_level_4": "str",\n "related_factors_driver_level_4_name": "str",\n "trafficway_description": "str",\n "trafficway_description_name": "str",\n "total_lanes_in_roadway": "str",\n "total_lanes_in_roadway_name": "str",\n "speed_limit": "str",\n "speed_limit_name": "str",\n "roadway_alignment": "str",\n "roadway_alignment_name": "str",\n "roadway_grade": "str",\n "roadway_grade_name": "str",\n "roadway_surface_type": "str",\n "roadway_surface_type_name": "str",\n "roadway_surface_condition": "str",\n "roadway_surface_condition_name": "str",\n "traffic_control_device": "str",\n "traffic_control_device_name": "str",\n "traffic_control_device_functioning": "str",\n "traffic_control_device_functioning_name": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event_name": "str",\n "critical_event_precrash": "str",\n "critical_event_precrash_name": "str",\n "attempted_avoidance_maneuver": "str",\n "attempted_avoidance_maneuver_name": "str",\n "pre_impact_stability": "str",\n "pre_impact_stability_name": "str",\n "pre_impact_location": "str",\n "pre_impact_location_name": "str",\n "crash_type": "str",\n "crash_type_name": "str",\n "fatalities_in_vehicle": "str",\n "driver_drinking": "str",\n "driver_drinking_name": "str",\n "trailer_vehicle_identification_number_1": "str",\n "trailer_vehicle_identification_number_1_name": "str",\n "trailer_vehicle_identification_number_2": "str",\n "trailer_vehicle_identification_number_2_name": "str",\n "trailer_vehicle_identification_number_3": "str",\n "trailer_vehicle_identification_number_3_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "NUMOCCS": "number_of_occupants",\n "NUMOCCSNAME": "number_of_occupants_name",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "UNITTYPE": "unit_type",\n "UNITTYPENAME": "unit_type_name",\n "HIT_RUN": "hit_and_run",\n "HIT_RUNNAME": "hit_and_run_name",\n "REG_STAT": "registration_state",\n "REG_STATNAME": "registration_state_name",\n "OWNER": "registered_vehicle_owner",\n "OWNERNAME": "registered_vehicle_owner_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MODEL": "vehicle_model",\n "MAK_MOD": "make_model_combined",\n "MAK_MODNAME": "make_model_combined_name",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "vehicle_model_year",\n "MOD_YEARNAME": "vehicle_model_year_name",\n "VIN": "vehicle_identification_number_vin",\n "VINNAME": "vehicle_identification_number_vin_name",\n "VIN_1": "vin_character_1",\n "VIN_2": "vin_character_2",\n "VIN_3": "vin_character_3",\n "VIN_4": "vin_character_4",\n "VIN_5": "vin_character_5",\n "VIN_6": "vin_character_6",\n "VIN_7": "vin_character_7",\n "VIN_8": "vin_character_8",\n "VIN_9": "vin_character_9",\n "VIN_10": "vin_character_10",\n "VIN_11": "vin_character_11",\n "VIN_12": "vin_character_12",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "J_KNIFE": "jackknife",\n "J_KNIFENAME": "jackknife_name",\n "MCARR_I1": "mcid_issuing_authority",\n "MCARR_I1NAME": "mcid_issuing_authority_name",\n "MCARR_I2": "mcid_identification_number",\n "MCARR_I2NAME": "mcid_identification_number_name",\n "MCARR_ID": "motor_carrier_identification_number_mcid",\n "MCARR_IDNAME": "motor_carrier_identification_number_mcid_name",\n "GVWR": "gross_vehicle_weight_rating",\n "GVWRNAME": "gross_vehicle_weight_rating_name",\n "V_CONFIG": "vehicle_configuration",\n "V_CONFIGNAME": "vehicle_configuration_name",\n "CARGO_BT": "cargo_body_type",\n "CARGO_BTNAME": "cargo_body_type_name",\n "HAZ_INV": "hazardous_material_involvement",\n "HAZ_INVNAME": "hazardous_material_involvement_name",\n "HAZ_PLAC": "hazardous_material_placard",\n "HAZ_PLACNAME": "hazardous_material_placard_name",\n "HAZ_ID": "hazardous_material_identification_number",\n "HAZ_IDNAME": "hazardous_material_identification_number_name",\n "HAZ_CNO": "hazardous_material_class_number",\n "HAZ_CNONAME": "hazardous_material_class_number_name",\n "HAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "HAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "BUS_USE": "bus_use",\n "BUS_USENAME": "bus_use_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "TRAV_SP": "travel_speed",\n "TRAV_SPNAME": "travel_speed_name",\n "UNDERIDE": "underride_override",\n "UNDERIDENAME": "underride_override_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "ROLINLOC": "location_of_rollover",\n "ROLINLOCNAME": "location_of_rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "DEFORMED": "extent_of_damage",\n "DEFORMEDNAME": "extent_of_damage_name",\n "TOWED": "vehicle_removal",\n "TOWEDNAME": "vehicle_removal_name",\n "M_HARM": "most_harmful_event",\n "M_HARMNAME": "most_harmful_event_name",\n "VEH_SC1": "related_factors_vehicle_level_1",\n "VEH_SC1NAME": "related_factors_vehicle_level_1_name",\n "VEH_SC2": "related_factors_vehicle_level_2",\n "VEH_SC2NAME": "related_factors_vehicle_level_2_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "DR_PRES": "driver_presence",\n "DR_PRESNAME": "driver_presence_name",\n "L_STATE": "drivers_license_state",\n "L_STATENAME": "drivers_license_state_name",\n "DR_ZIP": "drivers_zip_code",\n "DR_ZIPNAME": "drivers_zip_code_name",\n "L_STATUS": "non_cdl_license_status",\n "L_STATUSNAME": "non_cdl_license_status_name",\n "L_TYPE": "non_cdl_license_type",\n "L_TYPENAME": "non_cdl_license_type_name",\n "CDL_STAT": "commercial_motor_vehicle_license_status",\n "CDL_STATNAME": "commercial_motor_vehicle_license_status_name",\n "L_ENDORS": "compliance_with_cdl_endorsements",\n "L_ENDORSNAME": "compliance_with_cdl_endorsements_name",\n "L_COMPL": "license_compliance_with_class_of_vehicle",\n "L_COMPLNAME": "license_compliance_with_class_of_vehicle_name",\n "L_RESTRI": "compliance_with_license_restrictions",\n "L_RESTRINAME": "compliance_with_license_restrictions_name",\n "DR_HGT": "driver_height",\n "DR_HGTNAME": "driver_height_name",\n "DR_WGT": "driver_weight",\n "DR_WGTNAME": "driver_weight_name",\n "PREV_ACC": "previous_recorded_crashes",\n "PREV_ACCNAME": "previous_recorded_crashes_name",\n "PREV_SUS": "previous_recorded_suspensions_and_revocations",\n "PREV_SUSNAME": "previous_recorded_suspensions_and_revocations_name",\n "PREV_DWI": "previous_dwi_convictions",\n "PREV_DWINAME": "previous_dwi_convictions_name",\n "PREV_SPD": "previous_speeding_convictions",\n "PREV_SPDNAME": "previous_speeding_convictions_name",\n "PREV_OTH": "previous_other_moving_violation_convictions",\n "PREV_OTHNAME": "previous_other_moving_violation_convictions_name",\n "FIRST_MO": "month_of_first_crash_suspension_or_conviction",\n "FIRST_MONAME": "month_of_first_crash_suspension_or_conviction_name",\n "FIRST_YR": "year_of_first_crash_suspension_or_conviction",\n "FIRST_YRNAME": "year_of_first_crash_suspension_or_conviction_name",\n "LAST_MO": "month_of_last_crash_suspension_or_conviction",\n "LAST_MONAME": "month_of_last_crash_suspension_or_conviction_name",\n "LAST_YR": "year_of_last_crash_suspension_or_conviction",\n "LAST_YRNAME": "year_of_last_crash_suspension_or_conviction_name",\n "SPEEDREL": "speeding_related",\n "SPEEDRELNAME": "speeding_related_name",\n "DR_SF1": "related_factors_driver_level_1",\n "DR_SF1NAME": "related_factors_driver_level_1_name",\n "DR_SF2": "related_factors_driver_level_2",\n "DR_SF2NAME": "related_factors_driver_level_2_name",\n "DR_SF3": "related_factors_driver_level_3",\n "DR_SF3NAME": "related_factors_driver_level_3_name",\n "DR_SF4": "related_factors_driver_level_4",\n "DR_SF4NAME": "related_factors_driver_level_4_name",\n "VTRAFWAY": "trafficway_description",\n "VTRAFWAYNAME": "trafficway_description_name",\n "VNUM_LAN": "total_lanes_in_roadway",\n "VNUM_LANNAME": "total_lanes_in_roadway_name",\n "VSPD_LIM": "speed_limit",\n "VSPD_LIMNAME": "speed_limit_name",\n "VALIGN": "roadway_alignment",\n "VALIGNNAME": "roadway_alignment_name",\n "VPROFILE": "roadway_grade",\n "VPROFILENAME": "roadway_grade_name",\n "VPAVETYP": "roadway_surface_type",\n "VPAVETYPNAME": "roadway_surface_type_name",\n "VSURCOND": "roadway_surface_condition",\n "VSURCONDNAME": "roadway_surface_condition_name",\n "VTRAFCON": "traffic_control_device",\n "VTRAFCONNAME": "traffic_control_device_name",\n "VTCONT_F": "traffic_control_device_functioning",\n "VTCONT_FNAME": "traffic_control_device_functioning_name",\n "P_CRASH1": "pre_event_movement_prior_to_recognition_of_critical_event",\n "P_CRASH1NAME": "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "P_CRASH2": "critical_event_precrash",\n "P_CRASH2NAME": "critical_event_precrash_name",\n "P_CRASH3": "attempted_avoidance_maneuver",\n "P_CRASH3NAME": "attempted_avoidance_maneuver_name",\n "PCRASH4": "pre_impact_stability",\n "PCRASH4NAME": "pre_impact_stability_name",\n "PCRASH5": "pre_impact_location",\n "PCRASH5NAME": "pre_impact_location_name",\n "ACC_TYPE": "crash_type",\n "ACC_TYPENAME": "crash_type_name",\n "DEATHS": "fatalities_in_vehicle",\n "DR_DRINK": "driver_drinking",\n "DR_DRINKNAME": "driver_drinking_name",\n "TRLR1VIN": "trailer_vehicle_identification_number_1",\n "TRLR1VINNAME": "trailer_vehicle_identification_number_1_name",\n "TRLR2VIN": "trailer_vehicle_identification_number_2",\n "TRLR2VINNAME": "trailer_vehicle_identification_number_2_name",\n "TRLR3VIN": "trailer_vehicle_identification_number_3",\n "TRLR3VINNAME": "trailer_vehicle_identification_number_3_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vehicle pipelines vehicle_2018_2019_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vehicle_2018_2019_transform_csv", startup_timeout_seconds=600, name="vehicle", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vehicle.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2018_2019.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "make_model_combined_name",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "jackknife",\n "jackknife_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number_mcid",\n "motor_carrier_identification_number_mcid_name",\n "gross_vehicle_weight_rating",\n "gross_vehicle_weight_rating_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "travel_speed",\n "travel_speed_name",\n "underride_override",\n "underride_override_name",\n "rollover",\n "rollover_name",\n "location_of_rollover",\n "location_of_rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "related_factors_vehicle_level_1",\n "related_factors_vehicle_level_1_name",\n "related_factors_vehicle_level_2",\n "related_factors_vehicle_level_2_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "driver_presence",\n "driver_presence_name",\n "drivers_license_state",\n "drivers_license_state_name",\n "drivers_zip_code",\n "drivers_zip_code_name",\n "non_cdl_license_status",\n "non_cdl_license_status_name",\n "non_cdl_license_type",\n "non_cdl_license_type_name",\n "commercial_motor_vehicle_license_status",\n "commercial_motor_vehicle_license_status_name",\n "compliance_with_cdl_endorsements",\n "compliance_with_cdl_endorsements_name",\n "license_compliance_with_class_of_vehicle",\n "license_compliance_with_class_of_vehicle_name",\n "compliance_with_license_restrictions",\n "compliance_with_license_restrictions_name",\n "driver_height",\n "driver_height_name",\n "driver_weight",\n "driver_weight_name",\n "previous_recorded_crashes",\n "previous_recorded_crashes_name",\n "previous_recorded_suspensions_and_revocations1",\n "previous_recorded_suspensions_and_revocations1_name",\n "previous_recorded_suspensions_and_revocations2",\n "previous_recorded_suspensions_and_revocations2_name",\n "previous_recorded_suspensions_and_revocations3",\n "previous_recorded_suspensions_and_revocations3_name",\n "previous_dwi_convictions",\n "previous_dwi_convictions_name",\n "previous_speeding_convictions",\n "previous_speeding_convictions_name",\n "previous_other_moving_violation_convictions",\n "previous_other_moving_violation_convictions_name",\n "month_of_first_crash_suspension_or_conviction",\n "month_of_first_crash_suspension_or_conviction_name",\n "year_of_first_crash_suspension_or_conviction",\n "year_of_first_crash_suspension_or_conviction_name",\n "month_of_last_crash_suspension_or_conviction",\n "month_of_last_crash_suspension_or_conviction_name",\n "year_of_last_crash_suspension_or_conviction",\n "year_of_last_crash_suspension_or_conviction_name",\n "speeding_related",\n "speeding_related_name",\n "related_factors_driver_level_1",\n "related_factors_driver_level_1_name",\n "related_factors_driver_level_2",\n "related_factors_driver_level_2_name",\n "related_factors_driver_level_3",\n "related_factors_driver_level_3_name",\n "related_factors_driver_level_4",\n "related_factors_driver_level_4_name",\n "trafficway_description",\n "trafficway_description_name",\n "total_lanes_in_roadway",\n "total_lanes_in_roadway_name",\n "speed_limit",\n "speed_limit_name",\n "roadway_alignment",\n "roadway_alignment_name",\n "roadway_grade",\n "roadway_grade_name",\n "roadway_surface_type",\n "roadway_surface_type_name",\n "roadway_surface_condition",\n "roadway_surface_condition_name",\n "traffic_control_device",\n "traffic_control_device_name",\n "traffic_control_device_functioning",\n "traffic_control_device_functioning_name",\n "pre_event_movement_prior_to_recognition_of_critical_event",\n "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "critical_event_precrash",\n "critical_event_precrash_name",\n "attempted_avoidance_maneuver",\n "attempted_avoidance_maneuver_name",\n "pre_impact_stability",\n "pre_impact_stability_name",\n "pre_impact_location",\n "pre_impact_location_name",\n "crash_type",\n "crash_type_name",\n "fatalities_in_vehicle",\n "driver_drinking",\n "driver_drinking_name",\n "trailer_vehicle_identification_number_1",\n "trailer_vehicle_identification_number_1_name",\n "trailer_vehicle_identification_number_2",\n "trailer_vehicle_identification_number_2_name",\n "trailer_vehicle_identification_number_3",\n "trailer_vehicle_identification_number_3_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "make_model_combined_name": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "jackknife": "str",\n "jackknife_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number_mcid": "str",\n "motor_carrier_identification_number_mcid_name": "str",\n "gross_vehicle_weight_rating": "str",\n "gross_vehicle_weight_rating_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "travel_speed": "str",\n "travel_speed_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "location_of_rollover": "str",\n "location_of_rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "related_factors_vehicle_level_1": "str",\n "related_factors_vehicle_level_1_name": "str",\n "related_factors_vehicle_level_2": "str",\n "related_factors_vehicle_level_2_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "driver_presence": "str",\n "driver_presence_name": "str",\n "drivers_license_state": "str",\n "drivers_license_state_name": "str",\n "drivers_zip_code": "str",\n "drivers_zip_code_name": "str",\n "non_cdl_license_status": "str",\n "non_cdl_license_status_name": "str",\n "non_cdl_license_type": "str",\n "non_cdl_license_type_name": "str",\n "commercial_motor_vehicle_license_status": "str",\n "commercial_motor_vehicle_license_status_name": "str",\n "compliance_with_cdl_endorsements": "str",\n "compliance_with_cdl_endorsements_name": "str",\n "license_compliance_with_class_of_vehicle": "str",\n "license_compliance_with_class_of_vehicle_name": "str",\n "compliance_with_license_restrictions": "str",\n "compliance_with_license_restrictions_name": "str",\n "driver_height": "str",\n "driver_height_name": "str",\n "driver_weight": "str",\n "driver_weight_name": "str",\n "previous_recorded_crashes": "str",\n "previous_recorded_crashes_name": "str",\n "previous_recorded_suspensions_and_revocations1": "str",\n "previous_recorded_suspensions_and_revocations1_name": "str",\n "previous_recorded_suspensions_and_revocations2": "str",\n "previous_recorded_suspensions_and_revocations2_name": "str",\n "previous_recorded_suspensions_and_revocations3": "str",\n "previous_recorded_suspensions_and_revocations3_name": "str",\n "previous_dwi_convictions": "str",\n "previous_dwi_convictions_name": "str",\n "previous_speeding_convictions": "str",\n "previous_speeding_convictions_name": "str",\n "previous_other_moving_violation_convictions": "str",\n "previous_other_moving_violation_convictions_name": "str",\n "month_of_first_crash_suspension_or_conviction": "str",\n "month_of_first_crash_suspension_or_conviction_name": "str",\n "year_of_first_crash_suspension_or_conviction": "str",\n "year_of_first_crash_suspension_or_conviction_name": "str",\n "month_of_last_crash_suspension_or_conviction": "str",\n "month_of_last_crash_suspension_or_conviction_name": "str",\n "year_of_last_crash_suspension_or_conviction": "str",\n "year_of_last_crash_suspension_or_conviction_name": "str",\n "speeding_related": "str",\n "speeding_related_name": "str",\n "related_factors_driver_level_1": "str",\n "related_factors_driver_level_1_name": "str",\n "related_factors_driver_level_2": "str",\n "related_factors_driver_level_2_name": "str",\n "related_factors_driver_level_3": "str",\n "related_factors_driver_level_3_name": "str",\n "related_factors_driver_level_4": "str",\n "related_factors_driver_level_4_name": "str",\n "trafficway_description": "str",\n "trafficway_description_name": "str",\n "total_lanes_in_roadway": "str",\n "total_lanes_in_roadway_name": "str",\n "speed_limit": "str",\n "speed_limit_name": "str",\n "roadway_alignment": "str",\n "roadway_alignment_name": "str",\n "roadway_grade": "str",\n "roadway_grade_name": "str",\n "roadway_surface_type": "str",\n "roadway_surface_type_name": "str",\n "roadway_surface_condition": "str",\n "roadway_surface_condition_name": "str",\n "traffic_control_device": "str",\n "traffic_control_device_name": "str",\n "traffic_control_device_functioning": "str",\n "traffic_control_device_functioning_name": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event_name": "str",\n "critical_event_precrash": "str",\n "critical_event_precrash_name": "str",\n "attempted_avoidance_maneuver": "str",\n "attempted_avoidance_maneuver_name": "str",\n "pre_impact_stability": "str",\n "pre_impact_stability_name": "str",\n "pre_impact_location": "str",\n "pre_impact_location_name": "str",\n "crash_type": "str",\n "crash_type_name": "str",\n "fatalities_in_vehicle": "str",\n "driver_drinking": "str",\n "driver_drinking_name": "str",\n "trailer_vehicle_identification_number_1": "str",\n "trailer_vehicle_identification_number_1_name": "str",\n "trailer_vehicle_identification_number_2": "str",\n "trailer_vehicle_identification_number_2_name": "str",\n "trailer_vehicle_identification_number_3": "str",\n "trailer_vehicle_identification_number_3_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "NUMOCCS": "number_of_occupants",\n "NUMOCCSNAME": "number_of_occupants_name",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "UNITTYPE": "unit_type",\n "UNITTYPENAME": "unit_type_name",\n "HIT_RUN": "hit_and_run",\n "HIT_RUNNAME": "hit_and_run_name",\n "REG_STAT": "registration_state",\n "REG_STATNAME": "registration_state_name",\n "OWNER": "registered_vehicle_owner",\n "OWNERNAME": "registered_vehicle_owner_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MODEL": "vehicle_model",\n "MAK_MOD": "make_model_combined",\n "MAK_MODNAME": "make_model_combined_name",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "vehicle_model_year",\n "MOD_YEARNAME": "vehicle_model_year_name",\n "VIN": "vehicle_identification_number_vin",\n "VINNAME": "vehicle_identification_number_vin_name",\n "VIN_1": "vin_character_1",\n "VIN_2": "vin_character_2",\n "VIN_3": "vin_character_3",\n "VIN_4": "vin_character_4",\n "VIN_5": "vin_character_5",\n "VIN_6": "vin_character_6",\n "VIN_7": "vin_character_7",\n "VIN_8": "vin_character_8",\n "VIN_9": "vin_character_9",\n "VIN_10": "vin_character_10",\n "VIN_11": "vin_character_11",\n "VIN_12": "vin_character_12",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "J_KNIFE": "jackknife",\n "J_KNIFENAME": "jackknife_name",\n "MCARR_I1": "mcid_issuing_authority",\n "MCARR_I1NAME": "mcid_issuing_authority_name",\n "MCARR_I2": "mcid_identification_number",\n "MCARR_I2NAME": "mcid_identification_number_name",\n "MCARR_ID": "motor_carrier_identification_number_mcid",\n "MCARR_IDNAME": "motor_carrier_identification_number_mcid_name",\n "GVWR": "gross_vehicle_weight_rating",\n "GVWRNAME": "gross_vehicle_weight_rating_name",\n "V_CONFIG": "vehicle_configuration",\n "V_CONFIGNAME": "vehicle_configuration_name",\n "CARGO_BT": "cargo_body_type",\n "CARGO_BTNAME": "cargo_body_type_name",\n "HAZ_INV": "hazardous_material_involvement",\n "HAZ_INVNAME": "hazardous_material_involvement_name",\n "HAZ_PLAC": "hazardous_material_placard",\n "HAZ_PLACNAME": "hazardous_material_placard_name",\n "HAZ_ID": "hazardous_material_identification_number",\n "HAZ_IDNAME": "hazardous_material_identification_number_name",\n "HAZ_CNO": "hazardous_material_class_number",\n "HAZ_CNONAME": "hazardous_material_class_number_name",\n "HAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "HAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "BUS_USE": "bus_use",\n "BUS_USENAME": "bus_use_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "TRAV_SP": "travel_speed",\n "TRAV_SPNAME": "travel_speed_name",\n "UNDERIDE": "underride_override",\n "UNDERIDENAME": "underride_override_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "ROLINLOC": "location_of_rollover",\n "ROLINLOCNAME": "location_of_rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "DEFORMED": "extent_of_damage",\n "DEFORMEDNAME": "extent_of_damage_name",\n "TOWED": "vehicle_removal",\n "TOWEDNAME": "vehicle_removal_name",\n "M_HARM": "most_harmful_event",\n "M_HARMNAME": "most_harmful_event_name",\n "VEH_SC1": "related_factors_vehicle_level_1",\n "VEH_SC1NAME": "related_factors_vehicle_level_1_name",\n "VEH_SC2": "related_factors_vehicle_level_2",\n "VEH_SC2NAME": "related_factors_vehicle_level_2_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "DR_PRES": "driver_presence",\n "DR_PRESNAME": "driver_presence_name",\n "L_STATE": "drivers_license_state",\n "L_STATENAME": "drivers_license_state_name",\n "DR_ZIP": "drivers_zip_code",\n "DR_ZIPNAME": "drivers_zip_code_name",\n "L_STATUS": "non_cdl_license_status",\n "L_STATUSNAME": "non_cdl_license_status_name",\n "L_TYPE": "non_cdl_license_type",\n "L_TYPENAME": "non_cdl_license_type_name",\n "CDL_STAT": "commercial_motor_vehicle_license_status",\n "CDL_STATNAME": "commercial_motor_vehicle_license_status_name",\n "L_ENDORS": "compliance_with_cdl_endorsements",\n "L_ENDORSNAME": "compliance_with_cdl_endorsements_name",\n "L_COMPL": "license_compliance_with_class_of_vehicle",\n "L_COMPLNAME": "license_compliance_with_class_of_vehicle_name",\n "L_RESTRI": "compliance_with_license_restrictions",\n "L_RESTRINAME": "compliance_with_license_restrictions_name",\n "DR_HGT": "driver_height",\n "DR_HGTNAME": "driver_height_name",\n "DR_WGT": "driver_weight",\n "DR_WGTNAME": "driver_weight_name",\n "PREV_ACC": "previous_recorded_crashes",\n "PREV_ACCNAME": "previous_recorded_crashes_name",\n "PREV_SUS1": "previous_recorded_suspensions_and_revocations1",\n "PREV_SUS1NAME": "previous_recorded_suspensions_and_revocations1_name",\n "PREV_SUS2": "previous_recorded_suspensions_and_revocations2",\n "PREV_SUS2NAME": "previous_recorded_suspensions_and_revocations2_name",\n "PREV_SUS3": "previous_recorded_suspensions_and_revocations3",\n "PREV_SUS3NAME": "previous_recorded_suspensions_and_revocations3_name",\n "PREV_DWI": "previous_dwi_convictions",\n "PREV_DWINAME": "previous_dwi_convictions_name",\n "PREV_SPD": "previous_speeding_convictions",\n "PREV_SPDNAME": "previous_speeding_convictions_name",\n "PREV_OTH": "previous_other_moving_violation_convictions",\n "PREV_OTHNAME": "previous_other_moving_violation_convictions_name",\n "FIRST_MO": "month_of_first_crash_suspension_or_conviction",\n "FIRST_MONAME": "month_of_first_crash_suspension_or_conviction_name",\n "FIRST_YR": "year_of_first_crash_suspension_or_conviction",\n "FIRST_YRNAME": "year_of_first_crash_suspension_or_conviction_name",\n "LAST_MO": "month_of_last_crash_suspension_or_conviction",\n "LAST_MONAME": "month_of_last_crash_suspension_or_conviction_name",\n "LAST_YR": "year_of_last_crash_suspension_or_conviction",\n "LAST_YRNAME": "year_of_last_crash_suspension_or_conviction_name",\n "SPEEDREL": "speeding_related",\n "SPEEDRELNAME": "speeding_related_name",\n "DR_SF1": "related_factors_driver_level_1",\n "DR_SF1NAME": "related_factors_driver_level_1_name",\n "DR_SF2": "related_factors_driver_level_2",\n "DR_SF2NAME": "related_factors_driver_level_2_name",\n "DR_SF3": "related_factors_driver_level_3",\n "DR_SF3NAME": "related_factors_driver_level_3_name",\n "DR_SF4": "related_factors_driver_level_4",\n "DR_SF4NAME": "related_factors_driver_level_4_name",\n "VTRAFWAY": "trafficway_description",\n "VTRAFWAYNAME": "trafficway_description_name",\n "VNUM_LAN": "total_lanes_in_roadway",\n "VNUM_LANNAME": "total_lanes_in_roadway_name",\n "VSPD_LIM": "speed_limit",\n "VSPD_LIMNAME": "speed_limit_name",\n "VALIGN": "roadway_alignment",\n "VALIGNNAME": "roadway_alignment_name",\n "VPROFILE": "roadway_grade",\n "VPROFILENAME": "roadway_grade_name",\n "VPAVETYP": "roadway_surface_type",\n "VPAVETYPNAME": "roadway_surface_type_name",\n "VSURCOND": "roadway_surface_condition",\n "VSURCONDNAME": "roadway_surface_condition_name",\n "VTRAFCON": "traffic_control_device",\n "VTRAFCONNAME": "traffic_control_device_name",\n "VTCONT_F": "traffic_control_device_functioning",\n "VTCONT_FNAME": "traffic_control_device_functioning_name",\n "P_CRASH1": "pre_event_movement_prior_to_recognition_of_critical_event",\n "P_CRASH1NAME": "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "P_CRASH2": "critical_event_precrash",\n "P_CRASH2NAME": "critical_event_precrash_name",\n "P_CRASH3": "attempted_avoidance_maneuver",\n "P_CRASH3NAME": "attempted_avoidance_maneuver_name",\n "PCRASH4": "pre_impact_stability",\n "PCRASH4NAME": "pre_impact_stability_name",\n "PCRASH5": "pre_impact_location",\n "PCRASH5NAME": "pre_impact_location_name",\n "ACC_TYPE": "crash_type",\n "ACC_TYPENAME": "crash_type_name",\n "DEATHS": "fatalities_in_vehicle",\n "DR_DRINK": "driver_drinking",\n "DR_DRINKNAME": "driver_drinking_name",\n "TRLR1VIN": "trailer_vehicle_identification_number_1",\n "TRLR1VINNAME": "trailer_vehicle_identification_number_1_name",\n "TRLR2VIN": "trailer_vehicle_identification_number_2",\n "TRLR2VINNAME": "trailer_vehicle_identification_number_2_name",\n "TRLR3VIN": "trailer_vehicle_identification_number_3",\n "TRLR3VINNAME": "trailer_vehicle_identification_number_3_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vehicle pipelines vehicle_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vehicle_2020_transform_csv", startup_timeout_seconds=600, name="vehicle", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vehicle.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vehicle_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "number_of_motor_vehicles_in_transport_mvit",\n "number_of_occupants",\n "number_of_occupants_name",\n "day_of_crash",\n "day_of_crash_name",\n "month_of_crash",\n "month_of_crash_name",\n "hour_of_crash",\n "hour_of_crash_name",\n "minute_of_crash",\n "minute_of_crash_name",\n "first_harmful_event",\n "first_harmful_event_name",\n "manner_of_collision",\n "manner_of_collision_name",\n "unit_type",\n "unit_type_name",\n "hit_and_run",\n "hit_and_run_name",\n "registration_state",\n "registration_state_name",\n "registered_vehicle_owner",\n "registered_vehicle_owner_name",\n "vehicle_make",\n "vehicle_make_name",\n "vehicle_model",\n "make_model_combined",\n "make_model_combined_name",\n "body_type",\n "body_type_name",\n "vehicle_model_year",\n "vehicle_model_year_name",\n "vehicle_identification_number_vin",\n "vehicle_identification_number_vin_name",\n "vin_character_1",\n "vin_character_2",\n "vin_character_3",\n "vin_character_4",\n "vin_character_5",\n "vin_character_6",\n "vin_character_7",\n "vin_character_8",\n "vin_character_9",\n "vin_character_10",\n "vin_character_11",\n "vin_character_12",\n "vehicle_trailing",\n "vehicle_trailing_name",\n "jackknife",\n "jackknife_name",\n "mcid_issuing_authority",\n "mcid_issuing_authority_name",\n "mcid_identification_number",\n "mcid_identification_number_name",\n "motor_carrier_identification_number_mcid",\n "motor_carrier_identification_number_mcid_name",\n "vehicle_configuration",\n "vehicle_configuration_name",\n "cargo_body_type",\n "cargo_body_type_name",\n "hazardous_material_involvement",\n "hazardous_material_involvement_name",\n "hazardous_material_placard",\n "hazardous_material_placard_name",\n "hazardous_material_identification_number",\n "hazardous_material_identification_number_name",\n "hazardous_material_class_number",\n "hazardous_material_class_number_name",\n "release_of_hazardous_material_from_the_cargo_compartment",\n "release_of_hazardous_material_from_the_cargo_compartment_name",\n "bus_use",\n "bus_use_name",\n "special_use",\n "special_use_name",\n "emergency_motor_vehicle_use",\n "emergency_motor_vehicle_use_name",\n "travel_speed",\n "travel_speed_name",\n "underride_override",\n "underride_override_name",\n "rollover",\n "rollover_name",\n "location_of_rollover",\n "location_of_rollover_name",\n "initial_contact_point",\n "initial_contact_point_name",\n "extent_of_damage",\n "extent_of_damage_name",\n "vehicle_removal",\n "vehicle_removal_name",\n "most_harmful_event",\n "most_harmful_event_name",\n "fire_occurrence",\n "fire_occurrence_name",\n "driver_presence",\n "driver_presence_name",\n "drivers_license_state",\n "drivers_license_state_name",\n "drivers_zip_code",\n "drivers_zip_code_name",\n "non_cdl_license_status",\n "non_cdl_license_status_name",\n "non_cdl_license_type",\n "non_cdl_license_type_name",\n "commercial_motor_vehicle_license_status",\n "commercial_motor_vehicle_license_status_name",\n "compliance_with_cdl_endorsements",\n "compliance_with_cdl_endorsements_name",\n "license_compliance_with_class_of_vehicle",\n "license_compliance_with_class_of_vehicle_name",\n "compliance_with_license_restrictions",\n "compliance_with_license_restrictions_name",\n "driver_height",\n "driver_height_name",\n "driver_weight",\n "driver_weight_name",\n "previous_recorded_crashes",\n "previous_recorded_crashes_name",\n "previous_recorded_suspensions_and_revocations1",\n "previous_recorded_suspensions_and_revocations1_name",\n "previous_recorded_suspensions_and_revocations2",\n "previous_recorded_suspensions_and_revocations2_name",\n "previous_recorded_suspensions_and_revocations3",\n "previous_recorded_suspensions_and_revocations3_name",\n "previous_dwi_convictions",\n "previous_dwi_convictions_name",\n "previous_speeding_convictions",\n "previous_speeding_convictions_name",\n "previous_other_moving_violation_convictions",\n "previous_other_moving_violation_convictions_name",\n "month_of_first_crash_suspension_or_conviction",\n "month_of_first_crash_suspension_or_conviction_name",\n "year_of_first_crash_suspension_or_conviction",\n "year_of_first_crash_suspension_or_conviction_name",\n "month_of_last_crash_suspension_or_conviction",\n "month_of_last_crash_suspension_or_conviction_name",\n "year_of_last_crash_suspension_or_conviction",\n "year_of_last_crash_suspension_or_conviction_name",\n "speeding_related",\n "speeding_related_name",\n "trafficway_description",\n "trafficway_description_name",\n "total_lanes_in_roadway",\n "total_lanes_in_roadway_name",\n "speed_limit",\n "speed_limit_name",\n "roadway_alignment",\n "roadway_alignment_name",\n "roadway_grade",\n "roadway_grade_name",\n "roadway_surface_type",\n "roadway_surface_type_name",\n "roadway_surface_condition",\n "roadway_surface_condition_name",\n "traffic_control_device",\n "traffic_control_device_name",\n "traffic_control_device_functioning",\n "traffic_control_device_functioning_name",\n "pre_event_movement_prior_to_recognition_of_critical_event",\n "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "critical_event_precrash",\n "critical_event_precrash_name",\n "attempted_avoidance_maneuver",\n "attempted_avoidance_maneuver_name",\n "pre_impact_stability",\n "pre_impact_stability_name",\n "pre_impact_location",\n "pre_impact_location_name",\n "crash_type",\n "crash_type_name",\n "fatalities_in_vehicle",\n "driver_drinking",\n "driver_drinking_name",\n "trailer_vehicle_identification_number_1",\n "trailer_vehicle_identification_number_1_name",\n "trailer_vehicle_identification_number_2",\n "trailer_vehicle_identification_number_2_name",\n "trailer_vehicle_identification_number_3",\n "trailer_vehicle_identification_number_3_name",\n "vpic_make",\n "vpic_make_name",\n "vpic_model",\n "vpic_model_name",\n "vpic_body_class",\n "vpic_body_class_name",\n "final_stage_body_class",\n "final_stage_body_class_name",\n "power_unit_gross_vehicle_weight_rating_from",\n "power_unit_gross_vehicle_weight_rating_from_name",\n "power_unit_gross_vehicle_weight_rating_to",\n "power_unit_gross_vehicle_weight_rating_to_name",\n "trailer_gross_vehicle_weight_rating_1",\n "trailer_gross_vehicle_weight_rating_1_name",\n "trailer_gross_vehicle_weight_rating_2",\n "trailer_gross_vehicle_weight_rating_2_name",\n "trailer_gross_vehicle_weight_rating_3",\n "trailer_gross_vehicle_weight_rating_3_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "number_of_motor_vehicles_in_transport_mvit": "str",\n "number_of_occupants": "str",\n "number_of_occupants_name": "str",\n "day_of_crash": "str",\n "day_of_crash_name": "str",\n "month_of_crash": "str",\n "month_of_crash_name": "str",\n "hour_of_crash": "str",\n "hour_of_crash_name": "str",\n "minute_of_crash": "str",\n "minute_of_crash_name": "str",\n "first_harmful_event": "str",\n "first_harmful_event_name": "str",\n "manner_of_collision": "str",\n "manner_of_collision_name": "str",\n "unit_type": "str",\n "unit_type_name": "str",\n "hit_and_run": "str",\n "hit_and_run_name": "str",\n "registration_state": "str",\n "registration_state_name": "str",\n "registered_vehicle_owner": "str",\n "registered_vehicle_owner_name": "str",\n "vehicle_make": "str",\n "vehicle_make_name": "str",\n "vehicle_model": "str",\n "make_model_combined": "str",\n "make_model_combined_name": "str",\n "body_type": "str",\n "body_type_name": "str",\n "vehicle_model_year": "str",\n "vehicle_model_year_name": "str",\n "vehicle_identification_number_vin": "str",\n "vehicle_identification_number_vin_name": "str",\n "vin_character_1": "str",\n "vin_character_2": "str",\n "vin_character_3": "str",\n "vin_character_4": "str",\n "vin_character_5": "str",\n "vin_character_6": "str",\n "vin_character_7": "str",\n "vin_character_8": "str",\n "vin_character_9": "str",\n "vin_character_10": "str",\n "vin_character_11": "str",\n "vin_character_12": "str",\n "vehicle_trailing": "str",\n "vehicle_trailing_name": "str",\n "jackknife": "str",\n "jackknife_name": "str",\n "mcid_issuing_authority": "str",\n "mcid_issuing_authority_name": "str",\n "mcid_identification_number": "str",\n "mcid_identification_number_name": "str",\n "motor_carrier_identification_number_mcid": "str",\n "motor_carrier_identification_number_mcid_name": "str",\n "vehicle_configuration": "str",\n "vehicle_configuration_name": "str",\n "cargo_body_type": "str",\n "cargo_body_type_name": "str",\n "hazardous_material_involvement": "str",\n "hazardous_material_involvement_name": "str",\n "hazardous_material_placard": "str",\n "hazardous_material_placard_name": "str",\n "hazardous_material_identification_number": "str",\n "hazardous_material_identification_number_name": "str",\n "hazardous_material_class_number": "str",\n "hazardous_material_class_number_name": "str",\n "release_of_hazardous_material_from_the_cargo_compartment": "str",\n "release_of_hazardous_material_from_the_cargo_compartment_name": "str",\n "bus_use": "str",\n "bus_use_name": "str",\n "special_use": "str",\n "special_use_name": "str",\n "emergency_motor_vehicle_use": "str",\n "emergency_motor_vehicle_use_name": "str",\n "travel_speed": "str",\n "travel_speed_name": "str",\n "underride_override": "str",\n "underride_override_name": "str",\n "rollover": "str",\n "rollover_name": "str",\n "location_of_rollover": "str",\n "location_of_rollover_name": "str",\n "initial_contact_point": "str",\n "initial_contact_point_name": "str",\n "extent_of_damage": "str",\n "extent_of_damage_name": "str",\n "vehicle_removal": "str",\n "vehicle_removal_name": "str",\n "most_harmful_event": "str",\n "most_harmful_event_name": "str",\n "fire_occurrence": "str",\n "fire_occurrence_name": "str",\n "driver_presence": "str",\n "driver_presence_name": "str",\n "drivers_license_state": "str",\n "drivers_license_state_name": "str",\n "drivers_zip_code": "str",\n "drivers_zip_code_name": "str",\n "non_cdl_license_status": "str",\n "non_cdl_license_status_name": "str",\n "non_cdl_license_type": "str",\n "non_cdl_license_type_name": "str",\n "commercial_motor_vehicle_license_status": "str",\n "commercial_motor_vehicle_license_status_name": "str",\n "compliance_with_cdl_endorsements": "str",\n "compliance_with_cdl_endorsements_name": "str",\n "license_compliance_with_class_of_vehicle": "str",\n "license_compliance_with_class_of_vehicle_name": "str",\n "compliance_with_license_restrictions": "str",\n "compliance_with_license_restrictions_name": "str",\n "driver_height": "str",\n "driver_height_name": "str",\n "driver_weight": "str",\n "driver_weight_name": "str",\n "previous_recorded_crashes": "str",\n "previous_recorded_crashes_name": "str",\n "previous_recorded_suspensions_and_revocations1": "str",\n "previous_recorded_suspensions_and_revocations1_name": "str",\n "previous_recorded_suspensions_and_revocations2": "str",\n "previous_recorded_suspensions_and_revocations2_name": "str",\n "previous_recorded_suspensions_and_revocations3": "str",\n "previous_recorded_suspensions_and_revocations3_name": "str",\n "previous_dwi_convictions": "str",\n "previous_dwi_convictions_name": "str",\n "previous_speeding_convictions": "str",\n "previous_speeding_convictions_name": "str",\n "previous_other_moving_violation_convictions": "str",\n "previous_other_moving_violation_convictions_name": "str",\n "month_of_first_crash_suspension_or_conviction": "str",\n "month_of_first_crash_suspension_or_conviction_name": "str",\n "year_of_first_crash_suspension_or_conviction": "str",\n "year_of_first_crash_suspension_or_conviction_name": "str",\n "month_of_last_crash_suspension_or_conviction": "str",\n "month_of_last_crash_suspension_or_conviction_name": "str",\n "year_of_last_crash_suspension_or_conviction": "str",\n "year_of_last_crash_suspension_or_conviction_name": "str",\n "speeding_related": "str",\n "speeding_related_name": "str",\n "trafficway_description": "str",\n "trafficway_description_name": "str",\n "total_lanes_in_roadway": "str",\n "total_lanes_in_roadway_name": "str",\n "speed_limit": "str",\n "speed_limit_name": "str",\n "roadway_alignment": "str",\n "roadway_alignment_name": "str",\n "roadway_grade": "str",\n "roadway_grade_name": "str",\n "roadway_surface_type": "str",\n "roadway_surface_type_name": "str",\n "roadway_surface_condition": "str",\n "roadway_surface_condition_name": "str",\n "traffic_control_device": "str",\n "traffic_control_device_name": "str",\n "traffic_control_device_functioning": "str",\n "traffic_control_device_functioning_name": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event": "str",\n "pre_event_movement_prior_to_recognition_of_critical_event_name": "str",\n "critical_event_precrash": "str",\n "critical_event_precrash_name": "str",\n "attempted_avoidance_maneuver": "str",\n "attempted_avoidance_maneuver_name": "str",\n "pre_impact_stability": "str",\n "pre_impact_stability_name": "str",\n "pre_impact_location": "str",\n "pre_impact_location_name": "str",\n "crash_type": "str",\n "crash_type_name": "str",\n "fatalities_in_vehicle": "str",\n "driver_drinking": "str",\n "driver_drinking_name": "str",\n "trailer_vehicle_identification_number_1": "str",\n "trailer_vehicle_identification_number_1_name": "str",\n "trailer_vehicle_identification_number_2": "str",\n "trailer_vehicle_identification_number_2_name": "str",\n "trailer_vehicle_identification_number_3": "str",\n "trailer_vehicle_identification_number_3_name": "str",\n "vpic_make": "str",\n "vpic_make_name": "str",\n "vpic_model": "str",\n "vpic_model_name": "str",\n "vpic_body_class": "str",\n "vpic_body_class_name": "str",\n "final_stage_body_class": "str",\n "final_stage_body_class_name": "str",\n "power_unit_gross_vehicle_weight_rating_from": "str",\n "power_unit_gross_vehicle_weight_rating_from_name": "str",\n "power_unit_gross_vehicle_weight_rating_to": "str",\n "power_unit_gross_vehicle_weight_rating_to_name": "str",\n "trailer_gross_vehicle_weight_rating_1": "str",\n "trailer_gross_vehicle_weight_rating_1_name": "str",\n "trailer_gross_vehicle_weight_rating_2": "str",\n "trailer_gross_vehicle_weight_rating_2_name": "str",\n "trailer_gross_vehicle_weight_rating_3": "str",\n "trailer_gross_vehicle_weight_rating_3_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "VE_FORMS": "number_of_motor_vehicles_in_transport_mvit",\n "NUMOCCS": "number_of_occupants",\n "NUMOCCSNAME": "number_of_occupants_name",\n "DAY": "day_of_crash",\n "DAYNAME": "day_of_crash_name",\n "MONTH": "month_of_crash",\n "MONTHNAME": "month_of_crash_name",\n "HOUR": "hour_of_crash",\n "HOURNAME": "hour_of_crash_name",\n "MINUTE": "minute_of_crash",\n "MINUTENAME": "minute_of_crash_name",\n "HARM_EV": "first_harmful_event",\n "HARM_EVNAME": "first_harmful_event_name",\n "MAN_COLL": "manner_of_collision",\n "MAN_COLLNAME": "manner_of_collision_name",\n "UNITTYPE": "unit_type",\n "UNITTYPENAME": "unit_type_name",\n "HIT_RUN": "hit_and_run",\n "HIT_RUNNAME": "hit_and_run_name",\n "REG_STAT": "registration_state",\n "REG_STATNAME": "registration_state_name",\n "OWNER": "registered_vehicle_owner",\n "OWNERNAME": "registered_vehicle_owner_name",\n "MAKE": "vehicle_make",\n "MAKENAME": "vehicle_make_name",\n "MODEL": "vehicle_model",\n "MAK_MOD": "make_model_combined",\n "MAK_MODNAME": "make_model_combined_name",\n "BODY_TYP": "body_type",\n "BODY_TYPNAME": "body_type_name",\n "MOD_YEAR": "vehicle_model_year",\n "MOD_YEARNAME": "vehicle_model_year_name",\n "VIN": "vehicle_identification_number_vin",\n "VINNAME": "vehicle_identification_number_vin_name",\n "VIN_1": "vin_character_1",\n "VIN_2": "vin_character_2",\n "VIN_3": "vin_character_3",\n "VIN_4": "vin_character_4",\n "VIN_5": "vin_character_5",\n "VIN_6": "vin_character_6",\n "VIN_7": "vin_character_7",\n "VIN_8": "vin_character_8",\n "VIN_9": "vin_character_9",\n "VIN_10": "vin_character_10",\n "VIN_11": "vin_character_11",\n "VIN_12": "vin_character_12",\n "TOW_VEH": "vehicle_trailing",\n "TOW_VEHNAME": "vehicle_trailing_name",\n "J_KNIFE": "jackknife",\n "J_KNIFENAME": "jackknife_name",\n "MCARR_I1": "mcid_issuing_authority",\n "MCARR_I1NAME": "mcid_issuing_authority_name",\n "MCARR_I2": "mcid_identification_number",\n "MCARR_I2NAME": "mcid_identification_number_name",\n "MCARR_ID": "motor_carrier_identification_number_mcid",\n "MCARR_IDNAME": "motor_carrier_identification_number_mcid_name",\n "V_CONFIG": "vehicle_configuration",\n "V_CONFIGNAME": "vehicle_configuration_name",\n "CARGO_BT": "cargo_body_type",\n "CARGO_BTNAME": "cargo_body_type_name",\n "HAZ_INV": "hazardous_material_involvement",\n "HAZ_INVNAME": "hazardous_material_involvement_name",\n "HAZ_PLAC": "hazardous_material_placard",\n "HAZ_PLACNAME": "hazardous_material_placard_name",\n "HAZ_ID": "hazardous_material_identification_number",\n "HAZ_IDNAME": "hazardous_material_identification_number_name",\n "HAZ_CNO": "hazardous_material_class_number",\n "HAZ_CNONAME": "hazardous_material_class_number_name",\n "HAZ_REL": "release_of_hazardous_material_from_the_cargo_compartment",\n "HAZ_RELNAME": "release_of_hazardous_material_from_the_cargo_compartment_name",\n "BUS_USE": "bus_use",\n "BUS_USENAME": "bus_use_name",\n "SPEC_USE": "special_use",\n "SPEC_USENAME": "special_use_name",\n "EMER_USE": "emergency_motor_vehicle_use",\n "EMER_USENAME": "emergency_motor_vehicle_use_name",\n "TRAV_SP": "travel_speed",\n "TRAV_SPNAME": "travel_speed_name",\n "UNDERIDE": "underride_override",\n "UNDERIDENAME": "underride_override_name",\n "ROLLOVER": "rollover",\n "ROLLOVERNAME": "rollover_name",\n "ROLINLOC": "location_of_rollover",\n "ROLINLOCNAME": "location_of_rollover_name",\n "IMPACT1": "initial_contact_point",\n "IMPACT1NAME": "initial_contact_point_name",\n "DEFORMED": "extent_of_damage",\n "DEFORMEDNAME": "extent_of_damage_name",\n "TOWED": "vehicle_removal",\n "TOWEDNAME": "vehicle_removal_name",\n "M_HARM": "most_harmful_event",\n "M_HARMNAME": "most_harmful_event_name",\n "FIRE_EXP": "fire_occurrence",\n "FIRE_EXPNAME": "fire_occurrence_name",\n "DR_PRES": "driver_presence",\n "DR_PRESNAME": "driver_presence_name",\n "L_STATE": "drivers_license_state",\n "L_STATENAME": "drivers_license_state_name",\n "DR_ZIP": "drivers_zip_code",\n "DR_ZIPNAME": "drivers_zip_code_name",\n "L_STATUS": "non_cdl_license_status",\n "L_STATUSNAME": "non_cdl_license_status_name",\n "L_TYPE": "non_cdl_license_type",\n "L_TYPENAME": "non_cdl_license_type_name",\n "CDL_STAT": "commercial_motor_vehicle_license_status",\n "CDL_STATNAME": "commercial_motor_vehicle_license_status_name",\n "L_ENDORS": "compliance_with_cdl_endorsements",\n "L_ENDORSNAME": "compliance_with_cdl_endorsements_name",\n "L_COMPL": "license_compliance_with_class_of_vehicle",\n "L_COMPLNAME": "license_compliance_with_class_of_vehicle_name",\n "L_RESTRI": "compliance_with_license_restrictions",\n "L_RESTRINAME": "compliance_with_license_restrictions_name",\n "DR_HGT": "driver_height",\n "DR_HGTNAME": "driver_height_name",\n "DR_WGT": "driver_weight",\n "DR_WGTNAME": "driver_weight_name",\n "PREV_ACC": "previous_recorded_crashes",\n "PREV_ACCNAME": "previous_recorded_crashes_name",\n "PREV_SUS1": "previous_recorded_suspensions_and_revocations1",\n "PREV_SUS1NAME": "previous_recorded_suspensions_and_revocations1_name",\n "PREV_SUS2": "previous_recorded_suspensions_and_revocations2",\n "PREV_SUS2NAME": "previous_recorded_suspensions_and_revocations2_name",\n "PREV_SUS3": "previous_recorded_suspensions_and_revocations3",\n "PREV_SUS3NAME": "previous_recorded_suspensions_and_revocations3_name",\n "PREV_DWI": "previous_dwi_convictions",\n "PREV_DWINAME": "previous_dwi_convictions_name",\n "PREV_SPD": "previous_speeding_convictions",\n "PREV_SPDNAME": "previous_speeding_convictions_name",\n "PREV_OTH": "previous_other_moving_violation_convictions",\n "PREV_OTHNAME": "previous_other_moving_violation_convictions_name",\n "FIRST_MO": "month_of_first_crash_suspension_or_conviction",\n "FIRST_MONAME": "month_of_first_crash_suspension_or_conviction_name",\n "FIRST_YR": "year_of_first_crash_suspension_or_conviction",\n "FIRST_YRNAME": "year_of_first_crash_suspension_or_conviction_name",\n "LAST_MO": "month_of_last_crash_suspension_or_conviction",\n "LAST_MONAME": "month_of_last_crash_suspension_or_conviction_name",\n "LAST_YR": "year_of_last_crash_suspension_or_conviction",\n "LAST_YRNAME": "year_of_last_crash_suspension_or_conviction_name",\n "SPEEDREL": "speeding_related",\n "SPEEDRELNAME": "speeding_related_name",\n "VTRAFWAY": "trafficway_description",\n "VTRAFWAYNAME": "trafficway_description_name",\n "VNUM_LAN": "total_lanes_in_roadway",\n "VNUM_LANNAME": "total_lanes_in_roadway_name",\n "VSPD_LIM": "speed_limit",\n "VSPD_LIMNAME": "speed_limit_name",\n "VALIGN": "roadway_alignment",\n "VALIGNNAME": "roadway_alignment_name",\n "VPROFILE": "roadway_grade",\n "VPROFILENAME": "roadway_grade_name",\n "VPAVETYP": "roadway_surface_type",\n "VPAVETYPNAME": "roadway_surface_type_name",\n "VSURCOND": "roadway_surface_condition",\n "VSURCONDNAME": "roadway_surface_condition_name",\n "VTRAFCON": "traffic_control_device",\n "VTRAFCONNAME": "traffic_control_device_name",\n "VTCONT_F": "traffic_control_device_functioning",\n "VTCONT_FNAME": "traffic_control_device_functioning_name",\n "P_CRASH1": "pre_event_movement_prior_to_recognition_of_critical_event",\n "P_CRASH1NAME": "pre_event_movement_prior_to_recognition_of_critical_event_name",\n "P_CRASH2": "critical_event_precrash",\n "P_CRASH2NAME": "critical_event_precrash_name",\n "P_CRASH3": "attempted_avoidance_maneuver",\n "P_CRASH3NAME": "attempted_avoidance_maneuver_name",\n "PCRASH4": "pre_impact_stability",\n "PCRASH4NAME": "pre_impact_stability_name",\n "PCRASH5": "pre_impact_location",\n "PCRASH5NAME": "pre_impact_location_name",\n "ACC_TYPE": "crash_type",\n "ACC_TYPENAME": "crash_type_name",\n "DEATHS": "fatalities_in_vehicle",\n "DR_DRINK": "driver_drinking",\n "DR_DRINKNAME": "driver_drinking_name",\n "TRLR1VIN": "trailer_vehicle_identification_number_1",\n "TRLR1VINNAME": "trailer_vehicle_identification_number_1_name",\n "TRLR2VIN": "trailer_vehicle_identification_number_2",\n "TRLR2VINNAME": "trailer_vehicle_identification_number_2_name",\n "TRLR3VIN": "trailer_vehicle_identification_number_3",\n "TRLR3VINNAME": "trailer_vehicle_identification_number_3_name",\n "VPICMAKE": "vpic_make",\n "VPICMAKENAME": "vpic_make_name",\n "VPICMODEL": "vpic_model",\n "VPICMODELNAME": "vpic_model_name",\n "VPICBODYCLASS": "vpic_body_class",\n "VPICBODYCLASSNAME": "vpic_body_class_name",\n "ICFINALBODY": "final_stage_body_class",\n "ICFINALBODYNAME": "final_stage_body_class_name",\n "GVWR_FROM": "power_unit_gross_vehicle_weight_rating_from",\n "GVWR_FROMNAME": "power_unit_gross_vehicle_weight_rating_from_name",\n "GVWR_TO": "power_unit_gross_vehicle_weight_rating_to",\n "GVWR_TONAME": "power_unit_gross_vehicle_weight_rating_to_name",\n "TRLR1GVWR": "trailer_gross_vehicle_weight_rating_1",\n "TRLR1GVWRNAME": "trailer_gross_vehicle_weight_rating_1_name",\n "TRLR2GVWR": "trailer_gross_vehicle_weight_rating_2",\n "TRLR2GVWRNAME": "trailer_gross_vehicle_weight_rating_2_name",\n "TRLR3GVWR": "trailer_gross_vehicle_weight_rating_3",\n "TRLR3GVWRNAME": "trailer_gross_vehicle_weight_rating_3_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vevent pipelines vevent_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vevent_2015_2020_transform_csv", startup_timeout_seconds=600, name="vevent", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vevent.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vevent_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "event_number",\n "vehicle_number",\n "vehicle_event_number",\n "vehicle_number_this_vehicle",\n "area_of_impact_this_vehicle",\n "area_of_impact_this_vehicle_name",\n "sequence_of_events",\n "sequence_of_events_name",\n "vehicle_number_other_vehicle",\n "vehicle_number_other_vehicle_name",\n "area_of_impact_other_vehicle",\n "area_of_impact_other_vehicle_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "event_number": "str",\n "vehicle_number": "str",\n "vehicle_event_number": "str",\n "vehicle_number_this_vehicle": "str",\n "area_of_impact_this_vehicle": "str",\n "area_of_impact_this_vehicle_name": "str",\n "sequence_of_events": "str",\n "sequence_of_events_name": "str",\n "vehicle_number_other_vehicle": "str",\n "vehicle_number_other_vehicle_name": "str",\n "area_of_impact_other_vehicle": "str",\n "area_of_impact_other_vehicle_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "EVENTNUM": "event_number",\n "VEH_NO": "vehicle_number",\n "VEVENTNUM": "vehicle_event_number",\n "VNUMBER1": "vehicle_number_this_vehicle",\n "AOI1": "area_of_impact_this_vehicle",\n "AOI1NAME": "area_of_impact_this_vehicle_name",\n "SOE": "sequence_of_events",\n "SOENAME": "sequence_of_events_name",\n "VNUMBER2": "vehicle_number_other_vehicle",\n "VNUMBER2NAME": "vehicle_number_other_vehicle_name",\n "AOI2": "area_of_impact_other_vehicle",\n "AOI2NAME": "area_of_impact_other_vehicle_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vindecode pipelines vindecode_2015_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vindecode_2015_transform_csv", startup_timeout_seconds=600, name="vindecode", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vindecode.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vindecode_2015.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "vehicle_make",\n "marketing_year",\n "vehicle_type_code",\n "vehicle_type",\n "make_name",\n "model_code",\n "vehicle_trim",\n "vehicle_trim_1",\n "vehicle_trim_2",\n "vehicle_trim_3",\n "vehicle_trim_4",\n "body_style_code",\n "body_style",\n "num_of_doors",\n "number_of_wheels",\n "num_of_wheels_by_power_train",\n "vehicle_manufacturer_code",\n "vehicle_manufacturer_name",\n "displacement_cid",\n "displacement_cc",\n "cylinder_count_code",\n "cycle_count",\n "fuel_code",\n "fuel",\n "type_of_fuel_code",\n "type_of_fuel",\n "carburetion_types_code",\n "carburetion_types",\n "num_of_barrels",\n "gross_vehicle_weights_range_code",\n "gross_vehicle_weights_range",\n "distance_between_axles_for_base_model",\n "distance_between_axles_for_particular_series",\n "front_tire",\n "front_tire_pressure",\n "front_tire_size_code",\n "front_tire_size",\n "rear_tire",\n "rear_tire_pressure",\n "rear_tire_size_code",\n "rear_tire_size",\n "tonnage_rating",\n "shipping_weight",\n "base_price",\n "drive_type_1",\n "drive_type_2",\n "country_sold_code",\n "country_sold",\n "brakes_abs_code",\n "brakes_abs_description",\n "security_type_code",\n "security_type",\n "daytime_running_lights_1",\n "daytime_running_lights_2",\n "restraint_type_code",\n "restraint_type",\n "cab_configuration_code",\n "cab_configuration",\n "axle_type_front_axle_code",\n "axle_type_front_axle",\n "axle_type_rear_axle_code",\n "axle_type_rear_axle",\n "brake_type_code",\n "brake_type",\n "engine_manufacture_code",\n "engine_manufacture",\n "engine_model",\n "duty_type_code",\n "duty_type",\n "bed_length_code",\n "bed_length",\n "standard_segmentation_code",\n "standard_segmentation",\n "plant_code",\n "plant_country",\n "plant_city",\n "plant_country_code",\n "plant_state_code",\n "plant_state",\n "origin_code",\n "origin",\n "displacement_liters",\n "block_type",\n "head_configuration_1",\n "head_configuration_2",\n "valves_per_cylinder",\n "valves_total",\n "engine_code",\n "is_incomplete",\n "battery_type_code",\n "battery_type",\n "total_battery_power",\n "battery_voltage",\n "supercharge_flag",\n "supercharge_flag_description",\n "turbocharger_flag",\n "turbocharger_flag_description",\n "variable_valve_timing_flag",\n "motorcycles_body_style_code",\n "motorcycles_body_style"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "vehicle_make": "str",\n "marketing_year": "str",\n "vehicle_type_code": "str",\n "vehicle_type": "str",\n "make_name": "str",\n "model_code": "str",\n "vehicle_trim": "str",\n "vehicle_trim_1": "str",\n "vehicle_trim_2": "str",\n "vehicle_trim_3": "str",\n "vehicle_trim_4": "str",\n "body_style_code": "str",\n "body_style": "str",\n "num_of_doors": "str",\n "number_of_wheels": "str",\n "num_of_wheels_by_power_train": "str",\n "vehicle_manufacturer_code": "str",\n "vehicle_manufacturer_name": "str",\n "displacement_cid": "str",\n "displacement_cc": "str",\n "cylinder_count_code": "str",\n "cycle_count": "str",\n "fuel_code": "str",\n "fuel": "str",\n "type_of_fuel_code": "str",\n "type_of_fuel": "str",\n "carburetion_types_code": "str",\n "carburetion_types": "str",\n "num_of_barrels": "str",\n "gross_vehicle_weights_range_code": "str",\n "gross_vehicle_weights_range": "str",\n "distance_between_axles_for_base_model": "str",\n "distance_between_axles_for_particular_series": "str",\n "front_tire": "str",\n "front_tire_pressure": "str",\n "front_tire_size_code": "str",\n "front_tire_size": "str",\n "rear_tire": "str",\n "rear_tire_pressure": "str",\n "rear_tire_size_code": "str",\n "rear_tire_size": "str",\n "tonnage_rating": "str",\n "shipping_weight": "str",\n "base_price": "str",\n "drive_type_1": "str",\n "drive_type_2": "str",\n "country_sold_code": "str",\n "country_sold": "str",\n "brakes_abs_code": "str",\n "brakes_abs_description": "str",\n "security_type_code": "str",\n "security_type": "str",\n "daytime_running_lights_1": "str",\n "daytime_running_lights_2": "str",\n "restraint_type_code": "str",\n "restraint_type": "str",\n "cab_configuration_code": "str",\n "cab_configuration": "str",\n "axle_type_front_axle_code": "str",\n "axle_type_front_axle": "str",\n "axle_type_rear_axle_code": "str",\n "axle_type_rear_axle": "str",\n "brake_type_code": "str",\n "brake_type": "str",\n "engine_manufacture_code": "str",\n "engine_manufacture": "str",\n "engine_model": "str",\n "duty_type_code": "str",\n "duty_type": "str",\n "bed_length_code": "str",\n "bed_length": "str",\n "standard_segmentation_code": "str",\n "standard_segmentation": "str",\n "plant_code": "str",\n "plant_country": "str",\n "plant_city": "str",\n "plant_country_code": "str",\n "plant_state_code": "str",\n "plant_state": "str",\n "origin_code": "str",\n "origin": "str",\n "displacement_liters": "str",\n "block_type": "str",\n "head_configuration_1": "str",\n "head_configuration_2": "str",\n "valves_per_cylinder": "str",\n "valves_total": "str",\n "engine_code": "str",\n "is_incomplete": "str",\n "battery_type_code": "str",\n "battery_type": "str",\n "total_battery_power": "str",\n "battery_voltage": "str",\n "supercharge_flag": "str",\n "supercharge_flag_description": "str",\n "turbocharger_flag": "str",\n "turbocharger_flag_description": "str",\n "variable_valve_timing_flag": "str",\n "motorcycles_body_style_code": "str",\n "motorcycles_body_style": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "NCICMAKE": "vehicle_make",\n "VINYEAR": "marketing_year",\n "VEHTYPE": "vehicle_type_code",\n "VEHTYPE_T": "vehicle_type",\n "VINMAKE_T": "make_name",\n "VINMODEL_T": "model_code",\n "VINTRIM_T": "vehicle_trim",\n "VINTRIM1_T": "vehicle_trim_1",\n "VINTRIM2_T": "vehicle_trim_2",\n "VINTRIM3_T": "vehicle_trim_3",\n "VINTRIM4_T": "vehicle_trim_4",\n "BODYSTYL": "body_style_code",\n "BODYSTYL_T": "body_style",\n "DOORS": "num_of_doors",\n "WHEELS": "number_of_wheels",\n "DRIVWHLS": "num_of_wheels_by_power_train",\n "MFG": "vehicle_manufacturer_code",\n "MFG_T": "vehicle_manufacturer_name",\n "DISPLCI": "displacement_cid",\n "DISPLCC": "displacement_cc",\n "CYLNDRS": "cylinder_count_code",\n "CYCLES": "cycle_count",\n "FUEL": "fuel_code",\n "FUEL_T": "fuel",\n "FUELINJ": "type_of_fuel_code",\n "FUELINJ_T": "type_of_fuel",\n "CARBTYPE": "carburetion_types_code",\n "CARBTYPE_T": "carburetion_types",\n "CARBBRLS": "num_of_barrels",\n "GVWRANGE": "gross_vehicle_weights_range_code",\n "GVWRANGE_T": "gross_vehicle_weights_range",\n "WHLBSH": "distance_between_axles_for_base_model",\n "WHLBLG": "distance_between_axles_for_particular_series",\n "TIREDESC_F": "front_tire",\n "PSI_F": "front_tire_pressure",\n "TIRESZ_F": "front_tire_size_code",\n "TIRESZ_F_T": "front_tire_size",\n "TIREDESC_R": "rear_tire",\n "PSI_R": "rear_tire_pressure",\n "REARSIZE": "rear_tire_size_code",\n "REARSIZE_T": "rear_tire_size",\n "TONRATING": "tonnage_rating",\n "SHIPWEIGHT": "shipping_weight",\n "MSRP": "base_price",\n "DRIVETYP": "drive_type_1",\n "DRIVETYP_T": "drive_type_2",\n "SALECTRY": "country_sold_code",\n "SALECTRY_T": "country_sold",\n "ABS": "brakes_abs_code",\n "ABS_T": "brakes_abs_description",\n "SECURITY": "security_type_code",\n "SECURITY_T": "security_type",\n "DRL": "daytime_running_lights_1",\n "DRL_T": "daytime_running_lights_2",\n "RSTRNT": "restraint_type_code",\n "RSTRNT_T": "restraint_type",\n "TKCAB": "cab_configuration_code",\n "TKCAB_T": "cab_configuration",\n "TKAXLEF": "axle_type_front_axle_code",\n "TKAXLEF_T": "axle_type_front_axle",\n "TKAXLER": "axle_type_rear_axle_code",\n "TKAXLER_T": "axle_type_rear_axle",\n "TKBRAK": "brake_type_code",\n "TKBRAK_T": "brake_type",\n "ENGMFG": "engine_manufacture_code",\n "ENGMFG_T": "engine_manufacture",\n "ENGMODEL": "engine_model",\n "TKDUTY": "duty_type_code",\n "TKDUTY_T": "duty_type",\n "TKBEDL": "bed_length_code",\n "TKBEDL_T": "bed_length",\n "SEGMNT": "standard_segmentation_code",\n "SEGMNT_T": "standard_segmentation",\n "PLANT": "plant_code",\n "PLNTCTRY_T": "plant_country",\n "PLNTCITY": "plant_city",\n "PLNTCTRY": "plant_country_code",\n "PLNTSTAT": "plant_state_code",\n "PLNTSTAT_T": "plant_state",\n "ORIGIN": "origin_code",\n "ORIGIN_T": "origin",\n "DISPCLMT": "displacement_liters",\n "BLOCKTYPE": "block_type",\n "ENGHEAD": "head_configuration_1",\n "ENGHEAD_T": "head_configuration_2",\n "VLVCLNDR": "valves_per_cylinder",\n "VLVTOTAL": "valves_total",\n "ENGVINCD": "engine_code",\n "INCOMPLT": "is_incomplete",\n "BATTYP": "battery_type_code",\n "BATTYP_T": "battery_type",\n "BATKWRTG": "total_battery_power",\n "BATVOLT": "battery_voltage",\n "SUPCHRGR": "supercharge_flag",\n "SUPCHRGR_T": "supercharge_flag_description",\n "TURBO": "turbocharger_flag",\n "TURBO_T": "turbocharger_flag_description",\n "ENGVVT": "variable_valve_timing_flag",\n "MCYUSAGE": "motorcycles_body_style_code",\n "MCYUSAGE_T": "motorcycles_body_style"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for violatn pipelines violatn_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="violatn_2015_2020_transform_csv", startup_timeout_seconds=600, name="violatn", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "violatn.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.violatn_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "violations_charged",\n "violations_charged_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "violations_charged": "str",\n "violations_charged_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "MVIOLATN": "violations_charged",\n "MVIOLATNNAME": "violations_charged_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vision pipelines vision_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vision_2015_2020_transform_csv", startup_timeout_seconds=600, name="vision", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vision.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vision_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "drivers_vision_obscured_by",\n "drivers_vision_obscured_by_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "drivers_vision_obscured_by": "str",\n "drivers_vision_obscured_by_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "MVISOBSC": "drivers_vision_obscured_by",\n "MVISOBSCNAME": "drivers_vision_obscured_by_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) # Run CSV transform within kubernetes pod for vsoe pipelines vsoe_2015_2020_transform_csv = kubernetes_pod.KubernetesPodOperator( task_id="vsoe_2015_2020_transform_csv", startup_timeout_seconds=600, name="vsoe", namespace="composer", service_account_name="datasets", image_pull_policy="Always", image="{{ var.json.nhtsa_traffic_fatalities.container_registry.run_csv_transform_kub }}", env_vars={ "PIPELINE_NAME": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.pipeline_name }}", "SOURCE_URL": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.source_url }}", "CHUNKSIZE": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.chunksize }}", "SOURCE_ZIPFILE_EXTRACTED": "vsoe.csv", "SOURCE_FILE": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.source_file }}", "PROJECT_ID": "{{ var.value.gcp_project }}", "DATASET_ID": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.dataset_id }}", "TABLE_ID": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.destination_table }}", "START_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.start_year }}", "END_YEAR": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.end_year }}", "DROP_DEST_TABLE": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.drop_dest_table }}", "TARGET_GCS_BUCKET": "{{ var.value.composer_bucket }}", "TARGET_GCS_PATH": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.target_gcs_path }}", "SCHEMA_PATH": "{{ var.json.nhtsa_traffic_fatalities.vsoe_2015_2020.schema_path }}", "INPUT_CSV_HEADERS": '[\n "state_number",\n "state_name",\n "consecutive_number",\n "vehicle_number",\n "vehicle_event_number",\n "sequence_of_events",\n "sequence_of_events_name",\n "area_of_Impact_associated_with_the_event",\n "area_of_Impact_associated_with_the_event_name"\n]', "INPUT_DTYPES": '{\n "state_number": "str",\n "state_name": "str",\n "consecutive_number": "str",\n "vehicle_number": "str",\n "vehicle_event_number": "str",\n "sequence_of_events": "str",\n "sequence_of_events_name": "str",\n "area_of_Impact_associated_with_the_event": "str",\n "area_of_Impact_associated_with_the_event_name": "str"\n}', "RENAME_MAPPINGS_LIST": '{\n "STATE": "state_number",\n "STATENAME": "state_name",\n "ST_CASE": "consecutive_number",\n "VEH_NO": "vehicle_number",\n "VEVENTNUM": "vehicle_event_number",\n "SOE": "sequence_of_events",\n "SOENAME": "sequence_of_events_name",\n "AOI": "area_of_Impact_associated_with_the_event",\n "AOINAME": "area_of_Impact_associated_with_the_event_name"\n}', }, resources={ "request_ephemeral_storage": "4G", "request_cpu": "1", "request_memory": "4G", }, ) delete_cluster = kubernetes_engine.GKEDeleteClusterOperator( task_id="delete_cluster", project_id="{{ var.value.gcp_project }}", location="us-central1-c", name="nhtsa-traffic-fatalities", ) ( create_cluster >> [ accident_2015_transform_csv, accident_2016_2019_transform_csv, accident_2020_transform_csv, cevent_2015_2020_transform_csv, damage_2015_2020_transform_csv, distract_2015_2020_transform_csv, drimpair_2015_2020_transform_csv, factor_2015_2020_transform_csv, maneuver_2015_2020_transform_csv, nmcrash_2015_2020_transform_csv, nmimpair_2015_2020_transform_csv, nmprior_2015_2020_transform_csv, parkwork_2015_transform_csv, parkwork_2016_2017_transform_csv, parkwork_2018_transform_csv, parkwork_2019_transform_csv, parkwork_2020_transform_csv, pbtype_transform_csv, person_2015_2017_transform_csv, person_2018_transform_csv, person_2019_transform_csv, person_2020_transform_csv, safetyeq_2015_2016_transform_csv, safetyeq_2017_2020_transform_csv, vehicle_2015_transform_csv, vehicle_2016_2017_transform_csv, vehicle_2018_2019_transform_csv, vehicle_2020_transform_csv, vevent_2015_2020_transform_csv, vindecode_2015_transform_csv, violatn_2015_2020_transform_csv, vision_2015_2020_transform_csv, vsoe_2015_2020_transform_csv, ] >> delete_cluster )

apache-2.0

uber/pyro

pyro/distributions/improper_uniform.py

1

2448

# Copyright Contributors to the Pyro project. # SPDX-License-Identifier: Apache-2.0 import torch from torch.distributions import constraints from .torch_distribution import TorchDistribution from .util import broadcast_shape class ImproperUniform(TorchDistribution): """ Improper distribution with zero :meth:`log_prob` and undefined :meth:`sample`. This is useful for transforming a model from generative dag form to factor graph form for use in HMC. For example the following are equal in distribution:: # Version 1. a generative dag x = pyro.sample("x", Normal(0, 1)) y = pyro.sample("y", Normal(x, 1)) z = pyro.sample("z", Normal(y, 1)) # Version 2. a factor graph xyz = pyro.sample("xyz", ImproperUniform(constraints.real, (), (3,))) x, y, z = xyz.unbind(-1) pyro.sample("x", Normal(0, 1), obs=x) pyro.sample("y", Normal(x, 1), obs=y) pyro.sample("z", Normal(y, 1), obs=z) Note this distribution errors when :meth:`sample` is called. To create a similar distribution that instead samples from a specified distribution consider using ``.mask(False)`` as in:: xyz = dist.Normal(0, 1).expand([3]).to_event(1).mask(False) :param support: The support of the distribution. :type support: ~torch.distributions.constraints.Constraint :param torch.Size batch_shape: The batch shape. :param torch.Size event_shape: The event shape. """ arg_constraints = {} def __init__(self, support, batch_shape, event_shape): assert isinstance(support, constraints.Constraint) self._support = support super().__init__(batch_shape, event_shape) @constraints.dependent_property def support(self): return self._support def expand(self, batch_shape, _instance=None): batch_shape = torch.Size(batch_shape) new = self._get_checked_instance(ImproperUniform, _instance) new._support = self._support super(ImproperUniform, new).__init__(batch_shape, self.event_shape) return new def log_prob(self, value): batch_shape = value.shape[: value.dim() - self.event_dim] batch_shape = broadcast_shape(batch_shape, self.batch_shape) return torch.zeros(()).expand(batch_shape) def sample(self, sample_shape=torch.Size()): raise NotImplementedError("ImproperUniform does not support sampling")

apache-2.0