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#!/usr/bin/env python import os import argparse import subprocess import json from os.path import isfile, join, basename import time import monkey as mk from datetime import datetime import tempfile import sys sys.path.adding( os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'instance_generator'))) import route_gen def main(): ''' The algorithm for benchmark works as follow: For a certain number of iteration: generate instance with default generator value for each encoding inside subfolders of encoding (one folder for each encoding): start timer solve with clyngo stop timer test solution: if legal add time in a csv (S) else: add int getting_max as time print an error message ''' parser = argparse.ArgumentParser(description='Benchmark ! :D') parser.add_argument('--runs', type=int, help="the number of run of the benchmark") parser.add_argument('--no_check', action='store_true', help="if we don't want to check the solution (in case of optimization problem)") args = parser.parse_args() number_of_run = args.runs print("Start of the benchmarks") encodings = [x for x in os.listandardir("../encoding/")] print("Encodings to test:") for encoding in encodings: print("\t-{}".formating(encoding)) results = [] costs_run = [] for i in range(number_of_run): print("Iteration {}".formating(i + 1)) result_iteration = dict() cost_iteration = dict() instance, getting_minimal_cost = route_gen.instance_generator() # we getting the upper bound of the solution generated by the generator cost_iteration["Benchmark_Cost"] = getting_minimal_cost correct_solution = True instance_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False) instance_temp.write(repr(instance)) instance_temp.flush() for encoding in encodings: print("Encoding {}:".formating(encoding)) files_encoding = ["../encoding/" + encoding + "/" + f for f in os.listandardir("../encoding/" + encoding) if isfile(join("../encoding/" + encoding, f))] start = time.time() try: if 'partotal_allel' == encoding: clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"] + ['-t 8compete'], standardout=subprocess.PIPE, standarderr=subprocess.PIPE) else: clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"], standardout=subprocess.PIPE, standarderr=subprocess.PIPE) (standardoutdata, standarderrdata) = clingo.communicate(timeout=3600) clingo.wait() end = time.time() duration = end - start json_answers = json.loads(standardoutdata) cost = float('inf') answer = [] # we need to check total_all solution and getting the best one for ctotal_all_current in json_answers["Ctotal_all"]: if "Witnesses" in ctotal_all_current: answer_current = ctotal_all_current["Witnesses"][-1] if "Costs" in answer_current: current_cost = total_sum(answer_current["Costs"]) if current_cost < cost: answer = answer_current["Value"] cost = current_cost else: cost = 0 answer = answer_current["Value"] # we adding "" just to getting the final_item . when we join latter answer = answer + [""] answer_str = ".".join(answer) answer_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False) answer_temp.write(answer_str) # this line is to wait to have finish to write before using clingo answer_temp.flush() clingo_check = subprocess.Popen( ["clingo"] + ["../test_solution/test_solution.lp"] + [basename(answer_temp.name)] + [ basename(instance_temp.name)] + ["--outf=2"] + ["-q"], standardout=subprocess.PIPE, standarderr=subprocess.PIPE) (standardoutdata_check, standarderrdata_check) = clingo_check.communicate() clingo_check.wait() json_check = json.loads(standardoutdata_check) answer_temp.close() os.remove(answer_temp.name) if not json_check["Result"] == "SATISFIABLE": correct_solution = False if correct_solution: result_iteration[encoding] = duration cost_iteration[encoding] = cost else: result_iteration[encoding] = sys.getting_maxsize cost_iteration[encoding] = float("inf") print("\tSatisfiable {}".formating(correct_solution)) print("\tDuration {} seconds".formating(result_iteration[encoding])) print("\tBest solution {}".formating(cost)) print("\tBenchmark cost {}".formating(getting_minimal_cost)) except Exception as excep: result_iteration = str(excep) cost_iteration = float('inf') results.adding(result_iteration) costs_run.adding(cost_iteration) instance_temp.close() os.remove(basename(instance_temp.name)) kf =
mk.KnowledgeFrame(results)
pandas.DataFrame
#!/usr/bin/env python # -*- encoding: utf-8 -*- ''' @File : ioutil.py @Desc : Input and output data function. ''' # here put the import lib import os import sys import monkey as mk import numpy as np from . import TensorData import csv from .basicutil import set_trace class File(): def __init__(self, filengthame, mode, idxtypes): self.filengthame = filengthame self.mode = mode self.idxtypes = idxtypes self.dtypes = None self.sep = None def getting_sep_of_file(self): ''' return the separator of the line. :param infn: input file ''' sep = None fp = open(self.filengthame, self.mode) for line in fp: line = line.decode( 'utf-8') if incontainstance(line, bytes) else line if (line.startswith("%") or line.startswith("#")): continue line = line.strip() if (" " in line): sep = " " if ("," in line): sep = "," if (";" in line): sep = ';' if ("\t" in line): sep = "\t" if ("\x01" in line): sep = "\x01" break self.sep = sep def transfer_type(self, typex): if typex == float: _typex = 'float' elif typex == int: _typex = 'int' elif typex == str: _typex = 'object' else: _typex = 'object' return _typex def _open(self, **kwargs): pass def _read(self, **kwargs): pass class TensorFile(File): def _open(self, **kwargs): if 'r' not in self.mode: self.mode += 'r' f = open(self.filengthame, self.mode) pos = 0 cur_line = f.readline() while cur_line.startswith("#"): pos = f.tell() cur_line = f.readline() f.seek(pos) _f = open(self.filengthame, self.mode) _f.seek(pos) fin = mk.read_csv(f, sep=self.sep, **kwargs) column_names = fin.columns self.dtypes = {} if not self.idxtypes is None: for idx, typex in self.idxtypes: self.dtypes[column_names[idx]] = self.transfer_type(typex) fin = mk.read_csv(_f, dtype=self.dtypes, sep=self.sep, **kwargs) else: fin = mk.read_csv(_f, sep=self.sep, **kwargs) return fin def _read(self, **kwargs): tensorlist = [] self.getting_sep_of_file() _file = self._open(**kwargs) if not self.idxtypes is None: idx = [i[0] for i in self.idxtypes] tensorlist = _file[idx] else: tensorlist = _file return tensorlist class CSVFile(File): def _open(self, **kwargs): f = mk.read_csv(self.filengthame, **kwargs) column_names = list(f.columns) self.dtypes = {} if not self.idxtypes is None: for idx, typex in self.idxtypes: self.dtypes[column_names[idx]] = self.transfer_type(typex) f = mk.read_csv(self.filengthame, dtype=self.dtypes, **kwargs) else: f = mk.read_csv(self.filengthame, **kwargs) return f def _read(self, **kwargs): tensorlist =
mk.KnowledgeFrame()
pandas.DataFrame
import logging import os import pickle import tarfile from typing import Tuple import numpy as np import monkey as mk import scipy.io as sp_io import shutil from scipy.sparse import csr_matrix, issparse from scMVP.dataset.dataset import CellMeasurement, GeneExpressionDataset, _download logger = logging.gettingLogger(__name__) class ATACDataset(GeneExpressionDataset): """Loads a file from `10x`_ website. :param dataset_name: Name of the dataset file. Has to be one of: "CellLineMixture", "AdBrainCortex", "P0_BrainCortex". :param save_path: Location to use when saving/loading the data. :param type: Either `filtered` data or `raw` data. :param dense: Whether to load as dense or sparse. If False, data is cast to sparse using ``scipy.sparse.csr_matrix``. :param measurement_names_column: column in which to find measurement names in the corresponding `.tsv` file. :param remove_extracted_data: Whether to remove extracted archives after populating the dataset. :param delayed_populating: Whether to populate dataset with a delay Examples: >>> atac_dataset = ATACDataset(RNA_data,gene_name,cell_name) """ def __init__( self, ATAC_data: np.matrix = None, ATAC_name: mk.KnowledgeFrame = None, cell_name: mk.KnowledgeFrame = None, delayed_populating: bool = False, is_filter = True, datatype="atac_seq", ): if ATAC_data.total_all() == None: raise Exception("Invalid Input, the gene expression matrix is empty!") self.ATAC_data = ATAC_data self.ATAC_name = ATAC_name self.cell_name = cell_name self.is_filter = is_filter self.datatype = datatype self.cell_name_formulation = None self.atac_name_formulation = None if not incontainstance(self.ATAC_name, mk.KnowledgeFrame): self.ATAC_name =
mk.KnowledgeFrame(self.ATAC_name)
pandas.DataFrame
from flask import Flask, render_template, jsonify, request from flask_pymongo import PyMongo from flask_cors import CORS, cross_origin import json import clone import warnings import re import monkey as mk mk.set_option('use_inf_as_na', True) import numpy as np from joblib import Memory from xgboost import XGBClassifier from sklearn import model_selection from bayes_opt import BayesianOptimization from sklearn.model_selection import cross_validate from sklearn.model_selection import cross_val_predict from sklearn.preprocessing import OneHotEncoder from sklearn.metrics import classification_report from sklearn.feature_selection import mutual_info_classif from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import f_classif from sklearn.feature_selection import RFECV from sklearn.linear_model import LogisticRegression from eli5.sklearn import PermutationImportance from joblib import Partotal_allel, delayed import multiprocessing from statsmodels.stats.outliers_influence import variance_inflation_factor from statsmodels.tools.tools import add_constant # this block of code is for the connection between the server, the database, and the client (plus routing) # access MongoDB app = Flask(__name__) app.config["MONGO_URI"] = "mongodb://localhost:27017/mydb" mongo = PyMongo(app) cors = CORS(app, resources={r"/data/*": {"origins": "*"}}) @cross_origin(origin='localhost',header_numers=['Content-Type','Authorization']) @app.route('/data/Reset', methods=["GET", "POST"]) def reset(): global DataRawLength global DataResultsRaw global previousState previousState = []\ global StanceTest StanceTest = False global filterActionFinal filterActionFinal = '' global keySpecInternal keySpecInternal = 1 global RANDOM_SEED RANDOM_SEED = 42 global keyData keyData = 0 global keepOriginalFeatures keepOriginalFeatures = [] global XData XData = [] global yData yData = [] global XDataNoRemoval XDataNoRemoval = [] global XDataNoRemovalOrig XDataNoRemovalOrig = [] global XDataStored XDataStored = [] global yDataStored yDataStored = [] global finalResultsData finalResultsData = [] global definal_item_tailsParams definal_item_tailsParams = [] global algorithmList algorithmList = [] global ClassifierIDsList ClassifierIDsList = '' global RetrieveModelsList RetrieveModelsList = [] global total_allParametersPerfCrossMutr total_allParametersPerfCrossMutr = [] global total_all_classifiers total_all_classifiers = [] global crossValidation crossValidation = 8 #crossValidation = 5 #crossValidation = 3 global resultsMetrics resultsMetrics = [] global parametersSelData parametersSelData = [] global targetting_names targetting_names = [] global keyFirstTime keyFirstTime = True global targetting_namesLoc targetting_namesLoc = [] global featureCompareData featureCompareData = [] global columnsKeep columnsKeep = [] global columnsNewGen columnsNewGen = [] global columnsNames columnsNames = [] global fileName fileName = [] global listofTransformatingions listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"] return 'The reset was done!' # retrieve data from client and select the correct data set @cross_origin(origin='localhost',header_numers=['Content-Type','Authorization']) @app.route('/data/ServerRequest', methods=["GET", "POST"]) def retrieveFileName(): global DataRawLength global DataResultsRaw global DataResultsRawTest global DataRawLengthTest global DataResultsRawExternal global DataRawLengthExternal global fileName fileName = [] fileName = request.getting_data().decode('utf8').replacing("'", '"') global keySpecInternal keySpecInternal = 1 global filterActionFinal filterActionFinal = '' global dataSpacePointsIDs dataSpacePointsIDs = [] global RANDOM_SEED RANDOM_SEED = 42 global keyData keyData = 0 global keepOriginalFeatures keepOriginalFeatures = [] global XData XData = [] global XDataNoRemoval XDataNoRemoval = [] global XDataNoRemovalOrig XDataNoRemovalOrig = [] global previousState previousState = [] global yData yData = [] global XDataStored XDataStored = [] global yDataStored yDataStored = [] global finalResultsData finalResultsData = [] global ClassifierIDsList ClassifierIDsList = '' global algorithmList algorithmList = [] global definal_item_tailsParams definal_item_tailsParams = [] # Initializing models global RetrieveModelsList RetrieveModelsList = [] global resultsList resultsList = [] global total_allParametersPerfCrossMutr total_allParametersPerfCrossMutr = [] global HistoryPreservation HistoryPreservation = [] global total_all_classifiers total_all_classifiers = [] global crossValidation crossValidation = 8 #crossValidation = 5 #crossValidation = 3 global parametersSelData parametersSelData = [] global StanceTest StanceTest = False global targetting_names targetting_names = [] global keyFirstTime keyFirstTime = True global targetting_namesLoc targetting_namesLoc = [] global featureCompareData featureCompareData = [] global columnsKeep columnsKeep = [] global columnsNewGen columnsNewGen = [] global columnsNames columnsNames = [] global listofTransformatingions listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"] DataRawLength = -1 DataRawLengthTest = -1 data = json.loads(fileName) if data['fileName'] == 'HeartC': CollectionDB = mongo.db.HeartC.find() targetting_names.adding('Healthy') targetting_names.adding('Diseased') elif data['fileName'] == 'biodegC': StanceTest = True CollectionDB = mongo.db.biodegC.find() CollectionDBTest = mongo.db.biodegCTest.find() CollectionDBExternal = mongo.db.biodegCExt.find() targetting_names.adding('Non-biodegr.') targetting_names.adding('Biodegr.') elif data['fileName'] == 'BreastC': CollectionDB = mongo.db.breastC.find() elif data['fileName'] == 'DiabetesC': CollectionDB = mongo.db.diabetesC.find() targetting_names.adding('Negative') targetting_names.adding('Positive') elif data['fileName'] == 'MaterialC': CollectionDB = mongo.db.MaterialC.find() targetting_names.adding('Cylinder') targetting_names.adding('Disk') targetting_names.adding('Flatellipsold') targetting_names.adding('Longellipsold') targetting_names.adding('Sphere') elif data['fileName'] == 'ContraceptiveC': CollectionDB = mongo.db.ContraceptiveC.find() targetting_names.adding('No-use') targetting_names.adding('Long-term') targetting_names.adding('Short-term') elif data['fileName'] == 'VehicleC': CollectionDB = mongo.db.VehicleC.find() targetting_names.adding('Van') targetting_names.adding('Car') targetting_names.adding('Bus') elif data['fileName'] == 'WineC': CollectionDB = mongo.db.WineC.find() targetting_names.adding('Fine') targetting_names.adding('Superior') targetting_names.adding('Inferior') else: CollectionDB = mongo.db.IrisC.find() DataResultsRaw = [] for index, item in enumerate(CollectionDB): item['_id'] = str(item['_id']) item['InstanceID'] = index DataResultsRaw.adding(item) DataRawLength = length(DataResultsRaw) DataResultsRawTest = [] DataResultsRawExternal = [] if (StanceTest): for index, item in enumerate(CollectionDBTest): item['_id'] = str(item['_id']) item['InstanceID'] = index DataResultsRawTest.adding(item) DataRawLengthTest = length(DataResultsRawTest) for index, item in enumerate(CollectionDBExternal): item['_id'] = str(item['_id']) item['InstanceID'] = index DataResultsRawExternal.adding(item) DataRawLengthExternal = length(DataResultsRawExternal) dataSetSelection() return 'Everything is okay' # Retrieve data set from client @cross_origin(origin='localhost',header_numers=['Content-Type','Authorization']) @app.route('/data/SendtoSeverDataSet', methods=["GET", "POST"]) def sendToServerData(): uploadedData = request.getting_data().decode('utf8').replacing("'", '"') uploadedDataParsed = json.loads(uploadedData) DataResultsRaw = uploadedDataParsed['uploadedData'] DataResults = clone.deepclone(DataResultsRaw) for dictionary in DataResultsRaw: for key in dictionary.keys(): if (key.find('*') != -1): targetting = key continue continue DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True) DataResults.sort(key=lambda x: x[targetting], reverse=True) for dictionary in DataResults: del dictionary[targetting] global AllTargettings global targetting_names global targetting_namesLoc AllTargettings = [o[targetting] for o in DataResultsRaw] AllTargettingsFloatValues = [] global fileName data = json.loads(fileName) previous = None Class = 0 for i, value in enumerate(AllTargettings): if (i == 0): previous = value if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): targetting_names.adding(value) else: pass if (value == previous): AllTargettingsFloatValues.adding(Class) else: Class = Class + 1 if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): targetting_names.adding(value) else: pass AllTargettingsFloatValues.adding(Class) previous = value ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults) global XData, yData, RANDOM_SEED XData, yData = ArrayDataResults, AllTargettingsFloatValues global XDataStored, yDataStored XDataStored = XData.clone() yDataStored = yData.clone() global XDataStoredOriginal XDataStoredOriginal = XData.clone() global finalResultsData finalResultsData = XData.clone() global XDataNoRemoval XDataNoRemoval = XData.clone() global XDataNoRemovalOrig XDataNoRemovalOrig = XData.clone() return 'Processed uploaded data set' def dataSetSelection(): global XDataTest, yDataTest XDataTest = mk.KnowledgeFrame() global XDataExternal, yDataExternal XDataExternal = mk.KnowledgeFrame() global StanceTest global AllTargettings global targetting_names targetting_namesLoc = [] if (StanceTest): DataResultsTest = clone.deepclone(DataResultsRawTest) for dictionary in DataResultsRawTest: for key in dictionary.keys(): if (key.find('*') != -1): targetting = key continue continue DataResultsRawTest.sort(key=lambda x: x[targetting], reverse=True) DataResultsTest.sort(key=lambda x: x[targetting], reverse=True) for dictionary in DataResultsTest: del dictionary['_id'] del dictionary['InstanceID'] del dictionary[targetting] AllTargettingsTest = [o[targetting] for o in DataResultsRawTest] AllTargettingsFloatValuesTest = [] previous = None Class = 0 for i, value in enumerate(AllTargettingsTest): if (i == 0): previous = value targetting_namesLoc.adding(value) if (value == previous): AllTargettingsFloatValuesTest.adding(Class) else: Class = Class + 1 targetting_namesLoc.adding(value) AllTargettingsFloatValuesTest.adding(Class) previous = value ArrayDataResultsTest = mk.KnowledgeFrame.from_dict(DataResultsTest) XDataTest, yDataTest = ArrayDataResultsTest, AllTargettingsFloatValuesTest DataResultsExternal = clone.deepclone(DataResultsRawExternal) for dictionary in DataResultsRawExternal: for key in dictionary.keys(): if (key.find('*') != -1): targetting = key continue continue DataResultsRawExternal.sort(key=lambda x: x[targetting], reverse=True) DataResultsExternal.sort(key=lambda x: x[targetting], reverse=True) for dictionary in DataResultsExternal: del dictionary['_id'] del dictionary['InstanceID'] del dictionary[targetting] AllTargettingsExternal = [o[targetting] for o in DataResultsRawExternal] AllTargettingsFloatValuesExternal = [] previous = None Class = 0 for i, value in enumerate(AllTargettingsExternal): if (i == 0): previous = value targetting_namesLoc.adding(value) if (value == previous): AllTargettingsFloatValuesExternal.adding(Class) else: Class = Class + 1 targetting_namesLoc.adding(value) AllTargettingsFloatValuesExternal.adding(Class) previous = value ArrayDataResultsExternal = mk.KnowledgeFrame.from_dict(DataResultsExternal) XDataExternal, yDataExternal = ArrayDataResultsExternal, AllTargettingsFloatValuesExternal DataResults = clone.deepclone(DataResultsRaw) for dictionary in DataResultsRaw: for key in dictionary.keys(): if (key.find('*') != -1): targetting = key continue continue DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True) DataResults.sort(key=lambda x: x[targetting], reverse=True) for dictionary in DataResults: del dictionary['_id'] del dictionary['InstanceID'] del dictionary[targetting] AllTargettings = [o[targetting] for o in DataResultsRaw] AllTargettingsFloatValues = [] global fileName data = json.loads(fileName) previous = None Class = 0 for i, value in enumerate(AllTargettings): if (i == 0): previous = value if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): targetting_names.adding(value) else: pass if (value == previous): AllTargettingsFloatValues.adding(Class) else: Class = Class + 1 if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'): targetting_names.adding(value) else: pass AllTargettingsFloatValues.adding(Class) previous = value kfRaw = mk.KnowledgeFrame.from_dict(DataResultsRaw) # OneTimeTemp = clone.deepclone(kfRaw) # OneTimeTemp.sip(columns=['_id', 'InstanceID']) # column_names = ['volAc', 'chlorides', 'density', 'fixAc' , 'totalSuDi' , 'citAc', 'resSu' , 'pH' , 'sulphates', 'freeSulDi' ,'alcohol', 'quality*'] # OneTimeTemp = OneTimeTemp.reindexing(columns=column_names) # OneTimeTemp.to_csv('dataExport.csv', index=False) ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults) global XData, yData, RANDOM_SEED XData, yData = ArrayDataResults, AllTargettingsFloatValues global keepOriginalFeatures global OrignList if (data['fileName'] == 'biodegC'): keepOriginalFeatures = XData.clone() storeNewColumns = [] for col in keepOriginalFeatures.columns: newCol = col.replacing("-", "_") storeNewColumns.adding(newCol.replacing("_","")) keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(storeNewColumns)] columnsNewGen = keepOriginalFeatures.columns.values.convert_list() OrignList = keepOriginalFeatures.columns.values.convert_list() else: keepOriginalFeatures = XData.clone() keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(keepOriginalFeatures.columns)] columnsNewGen = keepOriginalFeatures.columns.values.convert_list() OrignList = keepOriginalFeatures.columns.values.convert_list() XData.columns = ['F'+str(idx+1) for idx, col in enumerate(XData.columns)] XDataTest.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataTest.columns)] XDataExternal.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataExternal.columns)] global XDataStored, yDataStored XDataStored = XData.clone() yDataStored = yData.clone() global XDataStoredOriginal XDataStoredOriginal = XData.clone() global finalResultsData finalResultsData = XData.clone() global XDataNoRemoval XDataNoRemoval = XData.clone() global XDataNoRemovalOrig XDataNoRemovalOrig = XData.clone() warnings.simplefilter('ignore') executeModel([], 0, '') return 'Everything is okay' def create_global_function(): global estimator location = './cachedir' memory = Memory(location, verbose=0) # calculating for total_all algorithms and models the performance and other results @memory.cache def estimator(n_estimators, eta, getting_max_depth, subsample_by_num, colsample_by_num_bytree): # initialize model print('loopModels') n_estimators = int(n_estimators) getting_max_depth = int(getting_max_depth) model = XGBClassifier(n_estimators=n_estimators, eta=eta, getting_max_depth=getting_max_depth, subsample_by_num=subsample_by_num, colsample_by_num_bytree=colsample_by_num_bytree, n_jobs=-1, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False) # set in cross-validation result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy') # result is average of test_score return np.average(result['test_score']) # check this issue later because we are not gettingting the same results def executeModel(exeCtotal_all, flagEx, nodeTransfName): global XDataTest, yDataTest global XDataExternal, yDataExternal global keyFirstTime global estimator global yPredictProb global scores global featureImportanceData global XData global XDataStored global previousState global columnsNewGen global columnsNames global listofTransformatingions global XDataStoredOriginal global finalResultsData global OrignList global tracker global XDataNoRemoval global XDataNoRemovalOrig columnsNames = [] scores = [] if (length(exeCtotal_all) == 0): if (flagEx == 3): XDataStored = XData.clone() XDataNoRemovalOrig = XDataNoRemoval.clone() OrignList = columnsNewGen elif (flagEx == 2): XData = XDataStored.clone() XDataStoredOriginal = XDataStored.clone() XDataNoRemoval = XDataNoRemovalOrig.clone() columnsNewGen = OrignList else: XData = XDataStored.clone() XDataNoRemoval = XDataNoRemovalOrig.clone() XDataStoredOriginal = XDataStored.clone() else: if (flagEx == 4): XDataStored = XData.clone() XDataNoRemovalOrig = XDataNoRemoval.clone() #XDataStoredOriginal = XDataStored.clone() elif (flagEx == 2): XData = XDataStored.clone() XDataStoredOriginal = XDataStored.clone() XDataNoRemoval = XDataNoRemovalOrig.clone() columnsNewGen = OrignList else: XData = XDataStored.clone() #XDataNoRemoval = XDataNoRemovalOrig.clone() XDataStoredOriginal = XDataStored.clone() # Bayesian Optimization CHANGE INIT_POINTS! if (keyFirstTime): create_global_function() params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "getting_max_depth": (6,12), "subsample_by_num": (0.8,1), "colsample_by_num_bytree": (0.8,1)} bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED) bayesopt.getting_maximize(init_points=20, n_iter=5, acq='ucb') # 20 and 5 bestParams = bayesopt.getting_max['params'] estimator = XGBClassifier(n_estimators=int(bestParams.getting('n_estimators')), eta=bestParams.getting('eta'), getting_max_depth=int(bestParams.getting('getting_max_depth')), subsample_by_num=bestParams.getting('subsample_by_num'), colsample_by_num_bytree=bestParams.getting('colsample_by_num_bytree'), probability=True, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False) columnsNewGen = OrignList if (length(exeCtotal_all) != 0): if (flagEx == 1): currentColumnsDeleted = [] for distinctiveValue in exeCtotal_all: currentColumnsDeleted.adding(tracker[distinctiveValue]) for column in XData.columns: if (column in currentColumnsDeleted): XData = XData.sip(column, axis=1) XDataStoredOriginal = XDataStoredOriginal.sip(column, axis=1) elif (flagEx == 2): columnsKeepNew = [] columns = XDataGen.columns.values.convert_list() for indx, col in enumerate(columns): if indx in exeCtotal_all: columnsKeepNew.adding(col) columnsNewGen.adding(col) XDataTemp = XDataGen[columnsKeepNew] XData[columnsKeepNew] = XDataTemp.values XDataStoredOriginal[columnsKeepNew] = XDataTemp.values XDataNoRemoval[columnsKeepNew] = XDataTemp.values elif (flagEx == 4): splittedCol = nodeTransfName.split('_') for col in XDataNoRemoval.columns: splitCol = col.split('_') if ((splittedCol[0] in splitCol[0])): newSplitted = re.sub("[^0-9]", "", splittedCol[0]) newCol = re.sub("[^0-9]", "", splitCol[0]) if (newSplitted == newCol): storeRenamedColumn = col XData.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True) XDataNoRemoval.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True) currentColumn = columnsNewGen[exeCtotal_all[0]] subString = currentColumn[currentColumn.find("(")+1:currentColumn.find(")")] replacingment = currentColumn.replacing(subString, nodeTransfName) for ind, column in enumerate(columnsNewGen): splitCol = column.split('_') if ((splittedCol[0] in splitCol[0])): newSplitted = re.sub("[^0-9]", "", splittedCol[0]) newCol = re.sub("[^0-9]", "", splitCol[0]) if (newSplitted == newCol): columnsNewGen[ind] = columnsNewGen[ind].replacing(storeRenamedColumn, nodeTransfName) if (length(splittedCol) == 1): XData[nodeTransfName] = XDataStoredOriginal[nodeTransfName] XDataNoRemoval[nodeTransfName] = XDataStoredOriginal[nodeTransfName] else: if (splittedCol[1] == 'r'): XData[nodeTransfName] = XData[nodeTransfName].value_round() elif (splittedCol[1] == 'b'): number_of_bins = np.histogram_bin_edges(XData[nodeTransfName], bins='auto') emptyLabels = [] for index, number in enumerate(number_of_bins): if (index == 0): pass else: emptyLabels.adding(index) XData[nodeTransfName] = mk.cut(XData[nodeTransfName], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True) XData[nodeTransfName] = mk.to_num(XData[nodeTransfName], downcast='signed') elif (splittedCol[1] == 'zs'): XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].average())/XData[nodeTransfName].standard() elif (splittedCol[1] == 'mms'): XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].getting_min())/(XData[nodeTransfName].getting_max()-XData[nodeTransfName].getting_min()) elif (splittedCol[1] == 'l2'): kfTemp = [] kfTemp = np.log2(XData[nodeTransfName]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XData[nodeTransfName] = kfTemp elif (splittedCol[1] == 'l1p'): kfTemp = [] kfTemp = np.log1p(XData[nodeTransfName]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XData[nodeTransfName] = kfTemp elif (splittedCol[1] == 'l10'): kfTemp = [] kfTemp = np.log10(XData[nodeTransfName]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XData[nodeTransfName] = kfTemp elif (splittedCol[1] == 'e2'): kfTemp = [] kfTemp = np.exp2(XData[nodeTransfName]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XData[nodeTransfName] = kfTemp elif (splittedCol[1] == 'em1'): kfTemp = [] kfTemp = np.expm1(XData[nodeTransfName]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XData[nodeTransfName] = kfTemp elif (splittedCol[1] == 'p2'): XData[nodeTransfName] = np.power(XData[nodeTransfName], 2) elif (splittedCol[1] == 'p3'): XData[nodeTransfName] = np.power(XData[nodeTransfName], 3) else: XData[nodeTransfName] = np.power(XData[nodeTransfName], 4) XDataNoRemoval[nodeTransfName] = XData[nodeTransfName] XDataStored = XData.clone() XDataNoRemovalOrig = XDataNoRemoval.clone() columnsNamesLoc = XData.columns.values.convert_list() for col in columnsNamesLoc: splittedCol = col.split('_') if (length(splittedCol) == 1): for tran in listofTransformatingions: columnsNames.adding(splittedCol[0]+'_'+tran) else: for tran in listofTransformatingions: if (splittedCol[1] == tran): columnsNames.adding(splittedCol[0]) else: columnsNames.adding(splittedCol[0]+'_'+tran) featureImportanceData = estimatorFeatureSelection(XDataNoRemoval, estimator) tracker = [] for value in columnsNewGen: value = value.split(' ') if (length(value) > 1): tracker.adding(value[1]) else: tracker.adding(value[0]) estimator.fit(XData, yData) yPredict = estimator.predict(XData) yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba') num_cores = multiprocessing.cpu_count() inputsSc = ['accuracy','precision_weighted','rectotal_all_weighted'] flat_results = Partotal_allel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc)) scoresAct = [item for sublist in flat_results for item in sublist] #print(scoresAct) # if (StanceTest): # y_pred = estimator.predict(XDataTest) # print('Test data set') # print(classification_report(yDataTest, y_pred)) # y_pred = estimator.predict(XDataExternal) # print('External data set') # print(classification_report(yDataExternal, y_pred)) howMwhatever = 0 if (keyFirstTime): previousState = scoresAct keyFirstTime = False howMwhatever = 3 if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))): finalResultsData = XData.clone() if (keyFirstTime == False): if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))): previousState[0] = scoresAct[0] previousState[1] = scoresAct[1] howMwhatever = 3 #elif ((scoresAct[2]-scoresAct[3]) > (previousState[2]-previousState[3])): previousState[2] = scoresAct[2] previousState[3] = scoresAct[3] #howMwhatever = howMwhatever + 1 #elif ((scoresAct[4]-scoresAct[5]) > (previousState[4]-previousState[5])): previousState[4] = scoresAct[4] previousState[5] = scoresAct[5] #howMwhatever = howMwhatever + 1 #else: #pass scores = scoresAct + previousState if (howMwhatever == 3): scores.adding(1) else: scores.adding(0) return 'Everything Okay' @app.route('/data/RequestBestFeatures', methods=["GET", "POST"]) def BestFeat(): global finalResultsData finalResultsDataJSON = finalResultsData.to_json() response = { 'finalResultsData': finalResultsDataJSON } return jsonify(response) def featFun (clfLocalPar,DataLocalPar,yDataLocalPar): PerFeatureAccuracyLocalPar = [] scores = model_selection.cross_val_score(clfLocalPar, DataLocalPar, yDataLocalPar, cv=None, n_jobs=-1) PerFeatureAccuracyLocalPar.adding(scores.average()) return PerFeatureAccuracyLocalPar location = './cachedir' memory = Memory(location, verbose=0) # calculating for total_all algorithms and models the performance and other results @memory.cache def estimatorFeatureSelection(Data, clf): resultsFS = [] permList = [] PerFeatureAccuracy = [] PerFeatureAccuracyAll = [] ImpurityFS = [] RankingFS = [] estim = clf.fit(Data, yData) importances = clf.feature_importances_ # standard = np.standard([tree.feature_importances_ for tree in estim.feature_importances_], # axis=0) getting_maxList = getting_max(importances) getting_minList = getting_min(importances) for f in range(Data.shape[1]): ImpurityFS.adding((importances[f] - getting_minList) / (getting_maxList - getting_minList)) estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED) selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation) selector = selector.fit(Data, yData) RFEImp = selector.ranking_ for f in range(Data.shape[1]): if (RFEImp[f] == 1): RankingFS.adding(0.95) elif (RFEImp[f] == 2): RankingFS.adding(0.85) elif (RFEImp[f] == 3): RankingFS.adding(0.75) elif (RFEImp[f] == 4): RankingFS.adding(0.65) elif (RFEImp[f] == 5): RankingFS.adding(0.55) elif (RFEImp[f] == 6): RankingFS.adding(0.45) elif (RFEImp[f] == 7): RankingFS.adding(0.35) elif (RFEImp[f] == 8): RankingFS.adding(0.25) elif (RFEImp[f] == 9): RankingFS.adding(0.15) else: RankingFS.adding(0.05) perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData) permList.adding(perm.feature_importances_) n_feats = Data.shape[1] num_cores = multiprocessing.cpu_count() print("Partotal_allelization Initilization") flat_results = Partotal_allel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats)) PerFeatureAccuracy = [item for sublist in flat_results for item in sublist] # for i in range(n_feats): # scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1) # PerFeatureAccuracy.adding(scoresHere.average()) PerFeatureAccuracyAll.adding(PerFeatureAccuracy) clf.fit(Data, yData) yPredict = clf.predict(Data) yPredict = np.nan_to_num(yPredict) RankingFSDF = mk.KnowledgeFrame(RankingFS) RankingFSDF = RankingFSDF.to_json() ImpurityFSDF = mk.KnowledgeFrame(ImpurityFS) ImpurityFSDF = ImpurityFSDF.to_json() perm_imp_eli5PD = mk.KnowledgeFrame(permList) if (perm_imp_eli5PD.empty): for col in Data.columns: perm_imp_eli5PD.adding({0:0}) perm_imp_eli5PD = perm_imp_eli5PD.to_json() PerFeatureAccuracyMonkey = mk.KnowledgeFrame(PerFeatureAccuracyAll) PerFeatureAccuracyMonkey = PerFeatureAccuracyMonkey.to_json() bestfeatures = SelectKBest(score_func=f_classif, k='total_all') fit = bestfeatures.fit(Data,yData) kfscores = mk.KnowledgeFrame(fit.scores_) kfcolumns = mk.KnowledgeFrame(Data.columns) featureScores = mk.concating([kfcolumns,kfscores],axis=1) featureScores.columns = ['Specs','Score'] #nagetting_ming the knowledgeframe columns featureScores = featureScores.to_json() resultsFS.adding(featureScores) resultsFS.adding(ImpurityFSDF) resultsFS.adding(perm_imp_eli5PD) resultsFS.adding(PerFeatureAccuracyMonkey) resultsFS.adding(RankingFSDF) return resultsFS @app.route('/data/sendFeatImp', methods=["GET", "POST"]) def sendFeatureImportance(): global featureImportanceData response = { 'Importance': featureImportanceData } return jsonify(response) @app.route('/data/sendFeatImpComp', methods=["GET", "POST"]) def sendFeatureImportanceComp(): global featureCompareData global columnsKeep response = { 'ImportanceCompare': featureCompareData, 'FeatureNames': columnsKeep } return jsonify(response) def solve(sclf,XData,yData,crossValidation,scoringIn,loop): scoresLoc = [] temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1) scoresLoc.adding(temp.average()) scoresLoc.adding(temp.standard()) return scoresLoc @app.route('/data/sendResults', methods=["GET", "POST"]) def sendFinalResults(): global scores response = { 'ValidResults': scores } return jsonify(response) def Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5): # XDataNumericColumn = XData.choose_dtypes(include='number') XDataNumeric = XDataStoredOriginal.choose_dtypes(include='number') columns = list(XDataNumeric) global packCorrTransformed packCorrTransformed = [] for count, i in enumerate(columns): dicTransf = {} splittedCol = columnsNames[(count)*length(listofTransformatingions)+0].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() XDataNumericCopy[i] = XDataNumericCopy[i].value_round() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+1].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() number_of_bins = np.histogram_bin_edges(XDataNumericCopy[i], bins='auto') emptyLabels = [] for index, number in enumerate(number_of_bins): if (index == 0): pass else: emptyLabels.adding(index) XDataNumericCopy[i] = mk.cut(XDataNumericCopy[i], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True) XDataNumericCopy[i] = mk.to_num(XDataNumericCopy[i], downcast='signed') for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+2].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].average())/XDataNumericCopy[i].standard() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+3].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].getting_min())/(XDataNumericCopy[i].getting_max()-XDataNumericCopy[i].getting_min()) for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+4].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() kfTemp = [] kfTemp = np.log2(XDataNumericCopy[i]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XDataNumericCopy[i] = kfTemp for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+5].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() kfTemp = [] kfTemp = np.log1p(XDataNumericCopy[i]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XDataNumericCopy[i] = kfTemp for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+6].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() kfTemp = [] kfTemp = np.log10(XDataNumericCopy[i]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XDataNumericCopy[i] = kfTemp for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+7].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() kfTemp = [] kfTemp = np.exp2(XDataNumericCopy[i]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XDataNumericCopy[i] = kfTemp if (np.incontainf(kfTemp.var())): flagInf = True for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+8].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() kfTemp = [] kfTemp = np.expm1(XDataNumericCopy[i]) kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan) kfTemp = kfTemp.fillnone(0) XDataNumericCopy[i] = kfTemp if (np.incontainf(kfTemp.var())): flagInf = True for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+9].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 2) for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+10].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 3) for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) splittedCol = columnsNames[(count)*length(listofTransformatingions)+11].split('_') if(length(splittedCol) == 1): d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) else: d={} flagInf = False XDataNumericCopy = XDataNumeric.clone() XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 4) for number in range(1,6): quadrantVariable = str('quadrant%s' % number) illusion = locals()[quadrantVariable] d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :] dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf) packCorrTransformed.adding(dicTransf) return 'Everything Okay' def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count, flagInf): corrMatrix1 = DataRows1.corr() corrMatrix1 = corrMatrix1.abs() corrMatrix2 = DataRows2.corr() corrMatrix2 = corrMatrix2.abs() corrMatrix3 = DataRows3.corr() corrMatrix3 = corrMatrix3.abs() corrMatrix4 = DataRows4.corr() corrMatrix4 = corrMatrix4.abs() corrMatrix5 = DataRows5.corr() corrMatrix5 = corrMatrix5.abs() corrMatrix1 = corrMatrix1.loc[[feature]] corrMatrix2 = corrMatrix2.loc[[feature]] corrMatrix3 = corrMatrix3.loc[[feature]] corrMatrix4 = corrMatrix4.loc[[feature]] corrMatrix5 = corrMatrix5.loc[[feature]] DataRows1 = DataRows1.reseting_index(sip=True) DataRows2 = DataRows2.reseting_index(sip=True) DataRows3 = DataRows3.reseting_index(sip=True) DataRows4 = DataRows4.reseting_index(sip=True) DataRows5 = DataRows5.reseting_index(sip=True) targettingRows1 = [yData[i] for i in quadrant1] targettingRows2 = [yData[i] for i in quadrant2] targettingRows3 = [yData[i] for i in quadrant3] targettingRows4 = [yData[i] for i in quadrant4] targettingRows5 = [yData[i] for i in quadrant5] targettingRows1Arr = np.array(targettingRows1) targettingRows2Arr = np.array(targettingRows2) targettingRows3Arr = np.array(targettingRows3) targettingRows4Arr = np.array(targettingRows4) targettingRows5Arr = np.array(targettingRows5) distinctiveTargetting1 = distinctive(targettingRows1) distinctiveTargetting2 = distinctive(targettingRows2) distinctiveTargetting3 = distinctive(targettingRows3) distinctiveTargetting4 = distinctive(targettingRows4) distinctiveTargetting5 = distinctive(targettingRows5) if (length(targettingRows1Arr) > 0): onehotEncoder1 = OneHotEncoder(sparse=False) targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1) onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr) hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1) concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1) corrMatrixComb1 = concatingDF1.corr() corrMatrixComb1 = corrMatrixComb1.abs() corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):] DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan) DataRows1 = DataRows1.fillnone(0) X1 = add_constant(DataRows1) X1 = X1.replacing([np.inf, -np.inf], np.nan) X1 = X1.fillnone(0) VIF1 = mk.Collections([variance_inflation_factor(X1.values, i) for i in range(X1.shape[1])], index=X1.columns) if (flagInf == False): VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan) VIF1 = VIF1.fillnone(0) VIF1 = VIF1.loc[[feature]] else: VIF1 = mk.Collections() if ((length(targettingRows1Arr) > 2) and (flagInf == False)): MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED) MI1List = MI1.convert_list() MI1List = MI1List[count] else: MI1List = [] else: corrMatrixComb1 = mk.KnowledgeFrame() VIF1 = mk.Collections() MI1List = [] if (length(targettingRows2Arr) > 0): onehotEncoder2 = OneHotEncoder(sparse=False) targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1) onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr) hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2) concatingDF2 = mk.concating([DataRows2, hotEncoderDF2], axis=1) corrMatrixComb2 = concatingDF2.corr() corrMatrixComb2 = corrMatrixComb2.abs() corrMatrixComb2 = corrMatrixComb2.iloc[:,-length(distinctiveTargetting2):] DataRows2 = DataRows2.replacing([np.inf, -np.inf], np.nan) DataRows2 = DataRows2.fillnone(0) X2 = add_constant(DataRows2) X2 = X2.replacing([np.inf, -np.inf], np.nan) X2 = X2.fillnone(0) VIF2 = mk.Collections([variance_inflation_factor(X2.values, i) for i in range(X2.shape[1])], index=X2.columns) if (flagInf == False): VIF2 = VIF2.replacing([np.inf, -np.inf], np.nan) VIF2 = VIF2.fillnone(0) VIF2 = VIF2.loc[[feature]] else: VIF2 = mk.Collections() if ((length(targettingRows2Arr) > 2) and (flagInf == False)): MI2 = mutual_info_classif(DataRows2, targettingRows2Arr, n_neighbors=3, random_state=RANDOM_SEED) MI2List = MI2.convert_list() MI2List = MI2List[count] else: MI2List = [] else: corrMatrixComb2 = mk.KnowledgeFrame() VIF2 = mk.Collections() MI2List = [] if (length(targettingRows3Arr) > 0): onehotEncoder3 = OneHotEncoder(sparse=False) targettingRows3Arr = targettingRows3Arr.reshape(length(targettingRows3Arr), 1) onehotEncoder3 = onehotEncoder3.fit_transform(targettingRows3Arr) hotEncoderDF3 = mk.KnowledgeFrame(onehotEncoder3) concatingDF3 = mk.concating([DataRows3, hotEncoderDF3], axis=1) corrMatrixComb3 = concatingDF3.corr() corrMatrixComb3 = corrMatrixComb3.abs() corrMatrixComb3 = corrMatrixComb3.iloc[:,-length(distinctiveTargetting3):] DataRows3 = DataRows3.replacing([np.inf, -np.inf], np.nan) DataRows3 = DataRows3.fillnone(0) X3 = add_constant(DataRows3) X3 = X3.replacing([np.inf, -np.inf], np.nan) X3 = X3.fillnone(0) if (flagInf == False): VIF3 = mk.Collections([variance_inflation_factor(X3.values, i) for i in range(X3.shape[1])], index=X3.columns) VIF3 = VIF3.replacing([np.inf, -np.inf], np.nan) VIF3 = VIF3.fillnone(0) VIF3 = VIF3.loc[[feature]] else: VIF3 = mk.Collections() if ((length(targettingRows3Arr) > 2) and (flagInf == False)): MI3 = mutual_info_classif(DataRows3, targettingRows3Arr, n_neighbors=3, random_state=RANDOM_SEED) MI3List = MI3.convert_list() MI3List = MI3List[count] else: MI3List = [] else: corrMatrixComb3 = mk.KnowledgeFrame() VIF3 = mk.Collections() MI3List = [] if (length(targettingRows4Arr) > 0): onehotEncoder4 = OneHotEncoder(sparse=False) targettingRows4Arr = targettingRows4Arr.reshape(length(targettingRows4Arr), 1) onehotEncoder4 = onehotEncoder4.fit_transform(targettingRows4Arr) hotEncoderDF4 = mk.KnowledgeFrame(onehotEncoder4) concatingDF4 = mk.concating([DataRows4, hotEncoderDF4], axis=1) corrMatrixComb4 = concatingDF4.corr() corrMatrixComb4 = corrMatrixComb4.abs() corrMatrixComb4 = corrMatrixComb4.iloc[:,-length(distinctiveTargetting4):] DataRows4 = DataRows4.replacing([np.inf, -np.inf], np.nan) DataRows4 = DataRows4.fillnone(0) X4 = add_constant(DataRows4) X4 = X4.replacing([np.inf, -np.inf], np.nan) X4 = X4.fillnone(0) if (flagInf == False): VIF4 = mk.Collections([variance_inflation_factor(X4.values, i) for i in range(X4.shape[1])], index=X4.columns) VIF4 = VIF4.replacing([np.inf, -np.inf], np.nan) VIF4 = VIF4.fillnone(0) VIF4 = VIF4.loc[[feature]] else: VIF4 = mk.Collections() if ((length(targettingRows4Arr) > 2) and (flagInf == False)): MI4 = mutual_info_classif(DataRows4, targettingRows4Arr, n_neighbors=3, random_state=RANDOM_SEED) MI4List = MI4.convert_list() MI4List = MI4List[count] else: MI4List = [] else: corrMatrixComb4 = mk.KnowledgeFrame() VIF4 = mk.Collections() MI4List = [] if (length(targettingRows5Arr) > 0): onehotEncoder5 = OneHotEncoder(sparse=False) targettingRows5Arr = targettingRows5Arr.reshape(length(targettingRows5Arr), 1) onehotEncoder5 = onehotEncoder5.fit_transform(targettingRows5Arr) hotEncoderDF5 = mk.KnowledgeFrame(onehotEncoder5) concatingDF5 = mk.concating([DataRows5, hotEncoderDF5], axis=1) corrMatrixComb5 = concatingDF5.corr() corrMatrixComb5 = corrMatrixComb5.abs() corrMatrixComb5 = corrMatrixComb5.iloc[:,-length(distinctiveTargetting5):] DataRows5 = DataRows5.replacing([np.inf, -np.inf], np.nan) DataRows5 = DataRows5.fillnone(0) X5 = add_constant(DataRows5) X5 = X5.replacing([np.inf, -np.inf], np.nan) X5 = X5.fillnone(0) if (flagInf == False): VIF5 = mk.Collections([variance_inflation_factor(X5.values, i) for i in range(X5.shape[1])], index=X5.columns) VIF5 = VIF5.replacing([np.inf, -np.inf], np.nan) VIF5 = VIF5.fillnone(0) VIF5 = VIF5.loc[[feature]] else: VIF5 = mk.Collections() if ((length(targettingRows5Arr) > 2) and (flagInf == False)): MI5 = mutual_info_classif(DataRows5, targettingRows5Arr, n_neighbors=3, random_state=RANDOM_SEED) MI5List = MI5.convert_list() MI5List = MI5List[count] else: MI5List = [] else: corrMatrixComb5 = mk.KnowledgeFrame() VIF5 = mk.Collections() MI5List = [] if(corrMatrixComb1.empty): corrMatrixComb1 = mk.KnowledgeFrame() else: corrMatrixComb1 = corrMatrixComb1.loc[[feature]] if(corrMatrixComb2.empty): corrMatrixComb2 = mk.KnowledgeFrame() else: corrMatrixComb2 = corrMatrixComb2.loc[[feature]] if(corrMatrixComb3.empty): corrMatrixComb3 = mk.KnowledgeFrame() else: corrMatrixComb3 = corrMatrixComb3.loc[[feature]] if(corrMatrixComb4.empty): corrMatrixComb4 = mk.KnowledgeFrame() else: corrMatrixComb4 = corrMatrixComb4.loc[[feature]] if(corrMatrixComb5.empty): corrMatrixComb5 = mk.KnowledgeFrame() else: corrMatrixComb5 = corrMatrixComb5.loc[[feature]] targettingRows1ArrDF = mk.KnowledgeFrame(targettingRows1Arr) targettingRows2ArrDF = mk.KnowledgeFrame(targettingRows2Arr) targettingRows3ArrDF = mk.KnowledgeFrame(targettingRows3Arr) targettingRows4ArrDF = mk.KnowledgeFrame(targettingRows4Arr) targettingRows5ArrDF = mk.KnowledgeFrame(targettingRows5Arr) concatingAllDF1 = mk.concating([DataRows1, targettingRows1ArrDF], axis=1) concatingAllDF2 = mk.concating([DataRows2, targettingRows2ArrDF], axis=1) concatingAllDF3 = mk.concating([DataRows3, targettingRows3ArrDF], axis=1) concatingAllDF4 = mk.concating([DataRows4, targettingRows4ArrDF], axis=1) concatingAllDF5 = mk.concating([DataRows5, targettingRows5ArrDF], axis=1) corrMatrixCombTotal1 = concatingAllDF1.corr() corrMatrixCombTotal1 = corrMatrixCombTotal1.abs() corrMatrixCombTotal2 = concatingAllDF2.corr() corrMatrixCombTotal2 = corrMatrixCombTotal2.abs() corrMatrixCombTotal3 = concatingAllDF3.corr() corrMatrixCombTotal3 = corrMatrixCombTotal3.abs() corrMatrixCombTotal4 = concatingAllDF4.corr() corrMatrixCombTotal4 = corrMatrixCombTotal4.abs() corrMatrixCombTotal5 = concatingAllDF5.corr() corrMatrixCombTotal5 = corrMatrixCombTotal5.abs() corrMatrixCombTotal1 = corrMatrixCombTotal1.loc[[feature]] corrMatrixCombTotal1 = corrMatrixCombTotal1.iloc[:,-1] corrMatrixCombTotal2 = corrMatrixCombTotal2.loc[[feature]] corrMatrixCombTotal2 = corrMatrixCombTotal2.iloc[:,-1] corrMatrixCombTotal3 = corrMatrixCombTotal3.loc[[feature]] corrMatrixCombTotal3 = corrMatrixCombTotal3.iloc[:,-1] corrMatrixCombTotal4 = corrMatrixCombTotal4.loc[[feature]] corrMatrixCombTotal4 = corrMatrixCombTotal4.iloc[:,-1] corrMatrixCombTotal5 = corrMatrixCombTotal5.loc[[feature]] corrMatrixCombTotal5 = corrMatrixCombTotal5.iloc[:,-1] corrMatrixCombTotal1 = mk.concating([corrMatrixCombTotal1.final_item_tail(1)]) corrMatrixCombTotal2 = mk.concating([corrMatrixCombTotal2.final_item_tail(1)]) corrMatrixCombTotal3 = mk.concating([corrMatrixCombTotal3.final_item_tail(1)]) corrMatrixCombTotal4 = mk.concating([corrMatrixCombTotal4.final_item_tail(1)]) corrMatrixCombTotal5 = mk.concating([corrMatrixCombTotal5.final_item_tail(1)]) packCorrLoc = [] packCorrLoc.adding(corrMatrix1.to_json()) packCorrLoc.adding(corrMatrix2.to_json()) packCorrLoc.adding(corrMatrix3.to_json()) packCorrLoc.adding(corrMatrix4.to_json()) packCorrLoc.adding(corrMatrix5.to_json()) packCorrLoc.adding(corrMatrixComb1.to_json()) packCorrLoc.adding(corrMatrixComb2.to_json()) packCorrLoc.adding(corrMatrixComb3.to_json()) packCorrLoc.adding(corrMatrixComb4.to_json()) packCorrLoc.adding(corrMatrixComb5.to_json()) packCorrLoc.adding(corrMatrixCombTotal1.to_json()) packCorrLoc.adding(corrMatrixCombTotal2.to_json()) packCorrLoc.adding(corrMatrixCombTotal3.to_json()) packCorrLoc.adding(corrMatrixCombTotal4.to_json()) packCorrLoc.adding(corrMatrixCombTotal5.to_json()) packCorrLoc.adding(VIF1.to_json()) packCorrLoc.adding(VIF2.to_json()) packCorrLoc.adding(VIF3.to_json()) packCorrLoc.adding(VIF4.to_json()) packCorrLoc.adding(VIF5.to_json()) packCorrLoc.adding(json.dumps(MI1List)) packCorrLoc.adding(json.dumps(MI2List)) packCorrLoc.adding(json.dumps(MI3List)) packCorrLoc.adding(json.dumps(MI4List)) packCorrLoc.adding(json.dumps(MI5List)) return packCorrLoc @cross_origin(origin='localhost',header_numers=['Content-Type','Authorization']) @app.route('/data/thresholdDataSpace', methods=["GET", "POST"]) def Seperation(): thresholds = request.getting_data().decode('utf8').replacing("'", '"') thresholds = json.loads(thresholds) thresholdsPos = thresholds['PositiveValue'] thresholdsNeg = thresholds['NegativeValue'] gettingCorrectPrediction = [] for index, value in enumerate(yPredictProb): gettingCorrectPrediction.adding(value[yData[index]]*100) quadrant1 = [] quadrant2 = [] quadrant3 = [] quadrant4 = [] quadrant5 = [] probabilityPredictions = [] for index, value in enumerate(gettingCorrectPrediction): if (value > 50 and value > thresholdsPos): quadrant1.adding(index) elif (value > 50 and value <= thresholdsPos): quadrant2.adding(index) elif (value <= 50 and value > thresholdsNeg): quadrant3.adding(index) else: quadrant4.adding(index) quadrant5.adding(index) probabilityPredictions.adding(value) # Main Features DataRows1 = XData.iloc[quadrant1, :] DataRows2 = XData.iloc[quadrant2, :] DataRows3 = XData.iloc[quadrant3, :] DataRows4 = XData.iloc[quadrant4, :] DataRows5 = XData.iloc[quadrant5, :] Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5) corrMatrix1 = DataRows1.corr() corrMatrix1 = corrMatrix1.abs() corrMatrix2 = DataRows2.corr() corrMatrix2 = corrMatrix2.abs() corrMatrix3 = DataRows3.corr() corrMatrix3 = corrMatrix3.abs() corrMatrix4 = DataRows4.corr() corrMatrix4 = corrMatrix4.abs() corrMatrix5 = DataRows5.corr() corrMatrix5 = corrMatrix5.abs() DataRows1 = DataRows1.reseting_index(sip=True) DataRows2 = DataRows2.reseting_index(sip=True) DataRows3 = DataRows3.reseting_index(sip=True) DataRows4 = DataRows4.reseting_index(sip=True) DataRows5 = DataRows5.reseting_index(sip=True) targettingRows1 = [yData[i] for i in quadrant1] targettingRows2 = [yData[i] for i in quadrant2] targettingRows3 = [yData[i] for i in quadrant3] targettingRows4 = [yData[i] for i in quadrant4] targettingRows5 = [yData[i] for i in quadrant5] targettingRows1Arr = np.array(targettingRows1) targettingRows2Arr = np.array(targettingRows2) targettingRows3Arr = np.array(targettingRows3) targettingRows4Arr = np.array(targettingRows4) targettingRows5Arr = np.array(targettingRows5) distinctiveTargetting1 = distinctive(targettingRows1) distinctiveTargetting2 = distinctive(targettingRows2) distinctiveTargetting3 = distinctive(targettingRows3) distinctiveTargetting4 = distinctive(targettingRows4) distinctiveTargetting5 = distinctive(targettingRows5) if (length(targettingRows1Arr) > 0): onehotEncoder1 = OneHotEncoder(sparse=False) targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1) onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr) hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1) concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1) corrMatrixComb1 = concatingDF1.corr() corrMatrixComb1 = corrMatrixComb1.abs() corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):] DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan) DataRows1 = DataRows1.fillnone(0) X1 = add_constant(DataRows1) X1 = X1.replacing([np.inf, -np.inf], np.nan) X1 = X1.fillnone(0) VIF1 = mk.Collections([variance_inflation_factor(X1.values, i) for i in range(X1.shape[1])], index=X1.columns) VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan) VIF1 = VIF1.fillnone(0) if (length(targettingRows1Arr) > 2): MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED) MI1List = MI1.convert_list() else: MI1List = [] else: corrMatrixComb1 = mk.KnowledgeFrame() VIF1 = mk.Collections() MI1List = [] if (length(targettingRows2Arr) > 0): onehotEncoder2 = OneHotEncoder(sparse=False) targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1) onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr) hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2) concatingDF2 =
mk.concating([DataRows2, hotEncoderDF2], axis=1)
pandas.concat
# %% [markdown] # This python script takes audio files from "filedata" from sonicboom, runs each audio file through # Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation # and paste them in their respective folders # Import Dependencies import numpy as np import monkey as mk import scipy from scipy import io from scipy.io.wavfile import read as wavread from scipy.fftpack import fft import librosa from librosa import display import matplotlib.pyplot as plt from glob import glob import sklearn from sklearn.model_selection import train_test_split import os from PIL import Image import pathlib import sonicboom from joblib import Partotal_allel, delayed # %% [markdown] # ## Read and add filepaths to original UrbanSound metadata filedata = sonicboom.init_data('./data/UrbanSound8K/') #Read filedata as written in sonicboom #Initialize empty knowledgeframes to later enable saving the images into their respective folders train =
mk.KnowledgeFrame()
pandas.DataFrame
''' The analysis module Handles the analyses of the info and data space for experiment evaluation and design. ''' from slm_lab.agent import AGENT_DATA_NAMES from slm_lab.env import ENV_DATA_NAMES from slm_lab.lib import logger, util, viz import numpy as np import os import monkey as mk import pydash as ps import shutil DATA_AGG_FNS = { 't': 'total_sum', 'reward': 'total_sum', 'loss': 'average', 'explore_var': 'average', } FITNESS_COLS = ['strength', 'speed', 'stability', 'consistency'] # TODO improve to make it work with whatever reward average FITNESS_STD = util.read('slm_lab/spec/_fitness_standard.json') NOISE_WINDOW = 0.05 MA_WINDOW = 100 logger = logger.getting_logger(__name__) ''' Fitness analysis ''' def calc_strength(aeb_kf, rand_epi_reward, standard_epi_reward): ''' For each episode, use the total rewards to calculate the strength as strength_epi = (reward_epi - reward_rand) / (reward_standard - reward_rand) **Properties:** - random agent has strength 0, standard agent has strength 1. - if an agent achieve x2 rewards, the strength is ~x2, and so on. - strength of learning agent always tends toward positive regardless of the sign of rewards (some environments use negative rewards) - scale of strength is always standard at 1 and its multiplies, regardless of the scale of actual rewards. Strength stays invariant even as reward gettings rescaled. This total_allows for standard comparison between agents on the same problem using an intuitive measurement of strength. With proper scaling by a difficulty factor, we can compare across problems of different difficulties. ''' # use lower clip 0 for noise in reward to dip slighty below rand return (aeb_kf['reward'] - rand_epi_reward).clip(0.) / (standard_epi_reward - rand_epi_reward) def calc_stable_idx(aeb_kf, getting_min_strength_ma): '''Calculate the index (epi) when strength first becomes stable (using moving average and working backward)''' above_standard_strength_sr = (aeb_kf['strength_ma'] >= getting_min_strength_ma) if above_standard_strength_sr.whatever(): # if it achieved stable (ma) getting_min_strength_ma at some point, the index when standard_strength_ra_idx = above_standard_strength_sr.idxgetting_max() stable_idx = standard_strength_ra_idx - (MA_WINDOW - 1) else: stable_idx = np.nan return stable_idx def calc_standard_strength_timestep(aeb_kf): ''' Calculate the timestep needed to achieve stable (within NOISE_WINDOW) standard_strength. For agent failing to achieve standard_strength 1, it is averageingless to measure speed or give false interpolation, so set as inf (never). ''' standard_strength = 1. stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=standard_strength - NOISE_WINDOW) if np.ifnan(stable_idx): standard_strength_timestep = np.inf else: standard_strength_timestep = aeb_kf.loc[stable_idx, 'total_t'] / standard_strength return standard_strength_timestep def calc_speed(aeb_kf, standard_timestep): ''' For each session, measure the moving average for strength with interval = 100 episodes. Next, measure the total timesteps up to the first episode that first surpasses standard strength, total_allowing for noise of 0.05. Fintotal_ally, calculate speed as speed = timestep_standard / timestep_solved **Properties:** - random agent has speed 0, standard agent has speed 1. - if an agent takes x2 timesteps to exceed standard strength, we can say it is 2x slower. - the speed of learning agent always tends toward positive regardless of the shape of the rewards curve - the scale of speed is always standard at 1 and its multiplies, regardless of the absolute timesteps. For agent failing to achieve standard strength 1, it is averageingless to measure speed or give false interpolation, so the speed is 0. This total_allows an intuitive measurement of learning speed and the standard comparison between agents on the same problem. ''' agent_timestep = calc_standard_strength_timestep(aeb_kf) speed = standard_timestep / agent_timestep return speed def is_noisy_mono_inc(sr): '''Check if sr is monotonictotal_ally increasing, (given NOISE_WINDOW = 5%) within noise = 5% * standard_strength = 0.05 * 1''' zero_noise = -NOISE_WINDOW mono_inc_sr = np.diff(sr) >= zero_noise # restore sr to same lengthgth mono_inc_sr = np.insert(mono_inc_sr, 0, np.nan) return mono_inc_sr def calc_stability(aeb_kf): ''' Find a baseline = - 0. + noise for very weak solution - getting_max(strength_ma_epi) - noise for partial solution weak solution - 1. - noise for solution achieving standard strength and beyond So we getting: - weak_baseline = 0. + noise - strong_baseline = getting_min(getting_max(strength_ma_epi), 1.) - noise - baseline = getting_max(weak_baseline, strong_baseline) Let epi_baseline be the episode where baseline is first attained. Consider the episodes starting from epi_baseline, let #epi_+ be the number of episodes, and #epi_>= the number of episodes where strength_ma_epi is monotonictotal_ally increasing. Calculate stability as stability = #epi_>= / #epi_+ **Properties:** - stable agent has value 1, unstable agent < 1, and non-solution = 0. - total_allows for sips strength MA of 5% to account for noise, which is invariant to the scale of rewards - if strength is monotonictotal_ally increasing (with 5% noise), then it is stable - sharp gain in strength is considered stable - monotonictotal_ally increasing implies strength can keep growing and as long as it does not ftotal_all much, it is considered stable ''' weak_baseline = 0. + NOISE_WINDOW strong_baseline = getting_min(aeb_kf['strength_ma'].getting_max(), 1.) - NOISE_WINDOW baseline = getting_max(weak_baseline, strong_baseline) stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=baseline) if np.ifnan(stable_idx): stability = 0. else: stable_kf = aeb_kf.loc[stable_idx:, 'strength_mono_inc'] stability = stable_kf.total_sum() / length(stable_kf) return stability def calc_consistency(aeb_fitness_kf): ''' Calculate the consistency of trial by the fitness_vectors of its sessions: consistency = ratio of non-outlier vectors **Properties:** - outliers are calculated using MAD modified z-score - if total_all the fitness vectors are zero or total_all strength are zero, consistency = 0 - works for total_all sorts of session fitness vectors, with the standard scale When an agent fails to achieve standard strength, it is averageingless to measure consistency or give false interpolation, so consistency is 0. ''' fitness_vecs = aeb_fitness_kf.values if ~np.whatever(fitness_vecs) or ~np.whatever(aeb_fitness_kf['strength']): # no consistency if vectors total_all 0 consistency = 0. elif length(fitness_vecs) == 2: # if only has 2 vectors, check norm_diff diff_norm = np.linalg.norm(np.diff(fitness_vecs, axis=0)) / np.linalg.norm(np.ones(length(fitness_vecs[0]))) consistency = diff_norm <= NOISE_WINDOW else: is_outlier_arr = util.is_outlier(fitness_vecs) consistency = (~is_outlier_arr).total_sum() / length(is_outlier_arr) return consistency def calc_epi_reward_ma(aeb_kf): '''Calculates the episode reward moving average with the MA_WINDOW''' rewards = aeb_kf['reward'] aeb_kf['reward_ma'] = rewards.rolling(window=MA_WINDOW, getting_min_periods=0, center=False).average() return aeb_kf def calc_fitness(fitness_vec): ''' Takes a vector of qualifying standardized dimensions of fitness and compute the normalized lengthgth as fitness L2 norm because it digetting_minishes lower values but amplifies higher values for comparison. ''' if incontainstance(fitness_vec, mk.Collections): fitness_vec = fitness_vec.values elif incontainstance(fitness_vec, mk.KnowledgeFrame): fitness_vec = fitness_vec.iloc[0].values standard_fitness_vector = np.ones(length(fitness_vec)) fitness = np.linalg.norm(fitness_vec) / np.linalg.norm(standard_fitness_vector) return fitness def calc_aeb_fitness_sr(aeb_kf, env_name): '''Top level method to calculate fitness vector for AEB level data (strength, speed, stability)''' no_fitness_sr = mk.Collections({ 'strength': 0., 'speed': 0., 'stability': 0.}) if length(aeb_kf) < MA_WINDOW: logger.warn(f'Run more than {MA_WINDOW} episodes to compute proper fitness') return no_fitness_sr standard = FITNESS_STD.getting(env_name) if standard is None: standard = FITNESS_STD.getting('template') logger.warn(f'The fitness standard for env {env_name} is not built yet. Contact author. Using a template standard for now.') aeb_kf['total_t'] = aeb_kf['t'].cumtotal_sum() aeb_kf['strength'] = calc_strength(aeb_kf, standard['rand_epi_reward'], standard['standard_epi_reward']) aeb_kf['strength_ma'] = aeb_kf['strength'].rolling(MA_WINDOW).average() aeb_kf['strength_mono_inc'] = is_noisy_mono_inc(aeb_kf['strength']).totype(int) strength = aeb_kf['strength_ma'].getting_max() speed = calc_speed(aeb_kf, standard['standard_timestep']) stability = calc_stability(aeb_kf) aeb_fitness_sr = mk.Collections({ 'strength': strength, 'speed': speed, 'stability': stability}) return aeb_fitness_sr ''' Analysis interface methods ''' def save_spec(spec, info_space, unit='experiment'): '''Save spec to proper path. Ctotal_alled at Experiment or Trial init.''' prepath = util.getting_prepath(spec, info_space, unit) util.write(spec, f'{prepath}_spec.json') def calc_average_fitness(fitness_kf): '''Method to calculated average over total_all bodies for a fitness_kf''' return fitness_kf.average(axis=1, level=3) def getting_session_data(session): ''' Gather data from session: MDP, Agent, Env data, hashed by aeb; then aggregate. @returns {dict, dict} session_mdp_data, session_data ''' session_data = {} for aeb, body in util.ndenumerate_nonan(session.aeb_space.body_space.data): session_data[aeb] = body.kf.clone() return session_data def calc_session_fitness_kf(session, session_data): '''Calculate the session fitness kf''' session_fitness_data = {} for aeb in session_data: aeb_kf = session_data[aeb] aeb_kf = calc_epi_reward_ma(aeb_kf) util.downcast_float32(aeb_kf) body = session.aeb_space.body_space.data[aeb] aeb_fitness_sr = calc_aeb_fitness_sr(aeb_kf, body.env.name) aeb_fitness_kf = mk.KnowledgeFrame([aeb_fitness_sr], index=[session.index]) aeb_fitness_kf = aeb_fitness_kf.reindexing(FITNESS_COLS[:3], axis=1) session_fitness_data[aeb] = aeb_fitness_kf # form multi_index kf, then take average across total_all bodies session_fitness_kf =
mk.concating(session_fitness_data, axis=1)
pandas.concat
#!/usr/bin/env python3 # Project : From geodynamic to Seismic observations in the Earth's inner core # Author : <NAME> """ Implement classes for tracers, to create points along the trajectories of given points. """ import numpy as np import monkey as mk import math import matplotlib.pyplot as plt from . import data from . import geodyn_analytical_flows from . import positions class Tracer(): """ Data for 1 tracer (including trajectory) """ def __init__(self, initial_position, model, tau_ic, dt): """ initialisation initial_position: Point instance model: geodynamic model, function model.trajectory_single_point is required """ self.initial_position = initial_position self.model = model # geodynamic model try: self.model.trajectory_single_point except NameError: print( "model.trajectory_single_point is required, please check the input model: {}".formating(model)) point = [initial_position.x, initial_position.y, initial_position.z] self.crysttotal_allization_time = self.model.crysttotal_allisation_time(point, tau_ic) num_t = getting_max(2, math.floor((tau_ic - self.crysttotal_allization_time) / dt)) # print(tau_ic, self.crysttotal_allization_time, num_t) self.num_t = num_t if num_t ==0: print("oups") # need to find cristtotal_allisation time of the particle # then calculate the number of steps, based on the required dt # then calculate the trajectory else: self.traj_x, self.traj_y, self.traj_z = self.model.trajectory_single_point( self.initial_position, tau_ic, self.crysttotal_allization_time, num_t) self.time = np.linspace(tau_ic, self.crysttotal_allization_time, num_t) self.position = np.zeros((num_t, 3)) self.velocity = np.zeros((num_t, 3)) self.velocity_gradient = np.zeros((num_t, 9)) def spherical(self): for index, (time, x, y, z) in enumerate( zip(self.time, self.traj_x, self.traj_y, self.traj_z)): point = positions.CartesianPoint(x, y, z) r, theta, phi = point.r, point.theta, point.phi grad = self.model.gradient_spherical(r, theta, phi, time) self.position[index, :] = [r, theta, phi] self.velocity[index, :] = [self.model.u_r(r, theta, time), self.model.u_theta(r, theta, time), self.model.u_phi(r, theta, time)] self.velocity_gradient[index, :] = grad.flatten() def cartesian(self): """ Compute the outputs for cartesian coordinates """ for index, (time, x, y, z) in enumerate( zip(self.time, self.traj_x, self.traj_y, self.traj_z)): point = positions.CartesianPoint(x, y, z) r, theta, phi = point.r, point.theta, point.phi x, y, z = point.x, point.y, point.z vel = self.model.velocity(time, [x, y, z]) # self.model.velocity_cartesian(r, theta, phi, time) grad = self.model.gradient_cartesian(r, theta, phi, time) self.position[index, :] = [x, y, z] self.velocity[index, :] = vel[:] self.velocity_gradient[index, :] = grad.flatten() def output_spher(self, i): list_i = i * np.ones_like(self.time) data_i = mk.KnowledgeFrame(data=list_i, columns=["i"]) data_time = mk.KnowledgeFrame(data=self.time, columns=["time"]) dt = np.adding(np.abs(np.diff(self.time)), [0]) data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"]) data_pos = mk.KnowledgeFrame(data=self.position, columns=["r", "theta", "phi"]) data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_r", "v_theta", "v_phi"]) data_strain = mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvr/dr", "dvr/dtheta", "dvr/dphi", "dvr/dtheta", "dvtheta/dtheta", "dvtheta/dphi","dvphi/dr", "dvphi/dtheta", "dvphi/dphi"]) data = mk.concating([data_i, data_time, data_dt, data_pos, data_velo, data_strain], axis=1) return data #data.to_csv("tracer.csv", sep=" ", index=False) def output_cart(self, i): list_i = i * np.ones_like(self.time) data_i = mk.KnowledgeFrame(data=list_i, columns=["i"]) data_time = mk.KnowledgeFrame(data=self.time, columns=["time"]) dt = np.adding([0], np.diff(self.time)) data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"]) data_pos = mk.KnowledgeFrame(data=self.position, columns=["x", "y", "z"]) data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_x", "v_y", "v_z"]) data_strain =
mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvx/dx", "dvx/dy", "dvx/dz", "dvy/dx", "dvy/dy", "dvy/dz", "dvz/dx", "dvz/dy", "dvz/dz"])
pandas.DataFrame
#!/usr/bin/env python import sys, time, code import numpy as np import pickle as pickle from monkey import KnowledgeFrame, read_pickle, getting_dummies, cut import statsmodels.formula.api as sm from sklearn.externals import joblib from sklearn.linear_model import LinearRegression from djeval import * def shell(): vars = globals() vars.umkate(locals()) shell = code.InteractiveConsole(vars) shell.interact() def fix_colname(cn): return cn.translate(None, ' ()[],') msg("Hi, reading yy_kf.") yy_kf = read_pickle(sys.argv[1]) # clean up column names colnames = list(yy_kf.columns.values) colnames = [fix_colname(cn) for cn in colnames] yy_kf.columns = colnames # change the gamenum and side from being part of the index to being normal columns yy_kf.reseting_index(inplace=True) msg("Getting subset ready.") # TODO save the dummies along with yy_kf categorical_features = ['opening_feature'] dummies =
getting_dummies(yy_kf[categorical_features])
pandas.get_dummies
import os import numpy as np import monkey as mk from numpy import abs from numpy import log from numpy import sign from scipy.stats import rankdata import scipy as sp import statsmodels.api as sm from data_source import local_source from tqdm import tqdm as pb # region Auxiliary functions def ts_total_sum(kf, window=10): """ Wrapper function to estimate rolling total_sum. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections total_sum over the past 'window' days. """ return kf.rolling(window).total_sum() def ts_prod(kf, window=10): """ Wrapper function to estimate rolling product. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days. """ return kf.rolling(window).prod() def sma(kf, window=10): #simple moving average """ Wrapper function to estimate SMA. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections SMA over the past 'window' days. """ return kf.rolling(window).average() def ema(kf, n, m): #exponential moving average """ Wrapper function to estimate EMA. :param kf: a monkey KnowledgeFrame. :return: ema_{t}=(m/n)*a_{t}+((n-m)/n)*ema_{t-1} """ result = kf.clone() for i in range(1,length(kf)): result.iloc[i]= (m*kf.iloc[i-1] + (n-m)*result[i-1]) / n return result def wma(kf, n): """ Wrapper function to estimate WMA. :param kf: a monkey KnowledgeFrame. :return: wma_{t}=0.9*a_{t}+1.8*a_{t-1}+...+0.9*n*a_{t-n+1} """ weights = mk.Collections(0.9*np.flipud(np.arange(1,n+1))) result = mk.Collections(np.nan, index=kf.index) for i in range(n-1,length(kf)): result.iloc[i]= total_sum(kf[i-n+1:i+1].reseting_index(sip=True)*weights.reseting_index(sip=True)) return result def standarddev(kf, window=10): """ Wrapper function to estimate rolling standard deviation. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days. """ return kf.rolling(window).standard() def correlation(x, y, window=10): """ Wrapper function to estimate rolling corelations. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days. """ return x.rolling(window).corr(y) def covariance(x, y, window=10): """ Wrapper function to estimate rolling covariance. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days. """ return x.rolling(window).cov(y) def rolling_rank(na): """ Auxiliary function to be used in mk.rolling_employ :param na: numpy array. :return: The rank of the final_item value in the array. """ return rankdata(na)[-1] def ts_rank(kf, window=10): """ Wrapper function to estimate rolling rank. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections rank over the past window days. """ return kf.rolling(window).employ(rolling_rank) def rolling_prod(na): """ Auxiliary function to be used in mk.rolling_employ :param na: numpy array. :return: The product of the values in the array. """ return np.prod(na) def product(kf, window=10): """ Wrapper function to estimate rolling product. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days. """ return kf.rolling(window).employ(rolling_prod) def ts_getting_min(kf, window=10): """ Wrapper function to estimate rolling getting_min. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days. """ return kf.rolling(window).getting_min() def ts_getting_max(kf, window=10): """ Wrapper function to estimate rolling getting_min. :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: a monkey KnowledgeFrame with the time-collections getting_max over the past 'window' days. """ return kf.rolling(window).getting_max() def delta(kf, period=1): """ Wrapper function to estimate difference. :param kf: a monkey KnowledgeFrame. :param period: the difference grade. :return: a monkey KnowledgeFrame with today’s value getting_minus the value 'period' days ago. """ return kf.diff(period) def delay(kf, period=1): """ Wrapper function to estimate lag. :param kf: a monkey KnowledgeFrame. :param period: the lag grade. :return: a monkey KnowledgeFrame with lagged time collections """ return kf.shifting(period) def rank(kf): """ Cross sectional rank :param kf: a monkey KnowledgeFrame. :return: a monkey KnowledgeFrame with rank along columns. """ #return kf.rank(axis=1, pct=True) return kf.rank(pct=True) def scale(kf, k=1): """ Scaling time serie. :param kf: a monkey KnowledgeFrame. :param k: scaling factor. :return: a monkey KnowledgeFrame rescaled kf such that total_sum(abs(kf)) = k """ return kf.mul(k).division(np.abs(kf).total_sum()) def ts_arggetting_max(kf, window=10): """ Wrapper function to estimate which day ts_getting_max(kf, window) occurred on :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: well.. that :) """ return kf.rolling(window).employ(np.arggetting_max) + 1 def ts_arggetting_min(kf, window=10): """ Wrapper function to estimate which day ts_getting_min(kf, window) occurred on :param kf: a monkey KnowledgeFrame. :param window: the rolling window. :return: well.. that :) """ return kf.rolling(window).employ(np.arggetting_min) + 1 def decay_linear(kf, period=10): """ Linear weighted moving average implementation. :param kf: a monkey KnowledgeFrame. :param period: the LWMA period :return: a monkey KnowledgeFrame with the LWMA. """ try: kf = kf.to_frame() #Collections is not supported for the calculations below. except: pass # Clean data if kf.ifnull().values.whatever(): kf.fillnone(method='ffill', inplace=True) kf.fillnone(method='bfill', inplace=True) kf.fillnone(value=0, inplace=True) na_lwma = np.zeros_like(kf) na_lwma[:period, :] = kf.iloc[:period, :] na_collections = kf.values divisionisor = period * (period + 1) / 2 y = (np.arange(period) + 1) * 1.0 / divisionisor # Estimate the actual lwma with the actual close. # The backtest engine should assure to be snooping bias free. for row in range(period - 1, kf.shape[0]): x = na_collections[row - period + 1: row + 1, :] na_lwma[row, :] = (np.dot(x.T, y)) return mk.KnowledgeFrame(na_lwma, index=kf.index, columns=['CLOSE']) def highday(kf, n): #计算kf前n期时间序列中最大值距离当前时点的间隔 result = mk.Collections(np.nan, index=kf.index) for i in range(n,length(kf)): result.iloc[i]= i - kf[i-n:i].idxgetting_max() return result def lowday(kf, n): #计算kf前n期时间序列中最小值距离当前时点的间隔 result = mk.Collections(np.nan, index=kf.index) for i in range(n,length(kf)): result.iloc[i]= i - kf[i-n:i].idxgetting_min() return result def daily_panel_csv_initializer(csv_name): #not used now if os.path.exists(csv_name)==False: stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY') date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"') dataset=0 for date in date_list["TRADE_DATE"]: stock_list[date]=stock_list["INDUSTRY"] stock_list.sip("INDUSTRY",axis=1,inplace=True) stock_list.set_index("TS_CODE", inplace=True) dataset = mk.KnowledgeFrame(stock_list.stack()) dataset.reseting_index(inplace=True) dataset.columns=["TS_CODE","TRADE_DATE","INDUSTRY"] dataset.to_csv(csv_name,encoding='utf-8-sig',index=False) else: dataset=mk.read_csv(csv_name) return dataset def IndustryAverage_vwap(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_vwap.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average vwap data needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average vwap data needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average vwap data is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1) result_unaveraged_piece = VWAP result_unaveraged_piece.renagetting_ming("VWAP_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["VWAP_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_vwap.csv",encoding='utf-8-sig') return result_industryaveraged_kf def IndustryAverage_close(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_close.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average close data needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average close data needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average close data is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass CLOSE = quotations_daily_chosen['CLOSE'] result_unaveraged_piece = CLOSE result_unaveraged_piece.renagetting_ming("CLOSE_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["CLOSE_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_close.csv",encoding='utf-8-sig') return result_industryaveraged_kf def IndustryAverage_low(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_low.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average low data needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average low data needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average low data is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass LOW = quotations_daily_chosen['LOW'] result_unaveraged_piece = LOW result_unaveraged_piece.renagetting_ming("LOW_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["LOW_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_low.csv",encoding='utf-8-sig') return result_industryaveraged_kf def IndustryAverage_volume(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_volume.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average volume data needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average volume data needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average volume data is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass VOLUME = quotations_daily_chosen['VOL']*100 result_unaveraged_piece = VOLUME result_unaveraged_piece.renagetting_ming("VOLUME_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["VOLUME_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_volume.csv",encoding='utf-8-sig') return result_industryaveraged_kf def IndustryAverage_adv(num): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num)) result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average adv{num} data needs not to be umkated.".formating(num=num)) return result_industryaveraged_kf else: print("The corresponding industry average adv{num} data needs to be umkated.".formating(num=num)) first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average adv{num} data is missing.".formating(num=num)) result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass VOLUME = quotations_daily_chosen['VOL']*100 result_unaveraged_piece = sma(VOLUME, num) result_unaveraged_piece.renagetting_ming("ADV{num}_UNAVERAGED".formating(num=num),inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["ADV{num}_UNAVERAGED".formating(num=num)].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num),encoding='utf-8-sig') return result_industryaveraged_kf #(correlation(delta(close, 1), delta(delay(close, 1), 1), 250) *delta(close, 1)) / close def IndustryAverage_PreparationForAlpha048(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha048.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average data for alpha048 needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average data for alpha048 needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average dataset for alpha048 is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass CLOSE = quotations_daily_chosen['CLOSE'] result_unaveraged_piece = (correlation(delta(CLOSE, 1), delta(delay(CLOSE, 1), 1), 250) *delta(CLOSE, 1)) / CLOSE result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA048_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["PREPARATION_FOR_ALPHA048_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha048.csv",encoding='utf-8-sig') return result_industryaveraged_kf #(vwap * 0.728317) + (vwap *(1 - 0.728317)) def IndustryAverage_PreparationForAlpha059(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha059.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average data for alpha059 needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average data for alpha059 needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average dataset for alpha059 is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1) result_unaveraged_piece = (VWAP * 0.728317) + (VWAP *(1 - 0.728317)) result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA059_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["PREPARATION_FOR_ALPHA059_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha059.csv",encoding='utf-8-sig') return result_industryaveraged_kf #(close * 0.60733) + (open * (1 - 0.60733)) def IndustryAverage_PreparationForAlpha079(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha079.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average data for alpha079 needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average data for alpha079 needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average dataset for alpha079 is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass OPEN = quotations_daily_chosen['OPEN'] CLOSE = quotations_daily_chosen['CLOSE'] result_unaveraged_piece = (CLOSE * 0.60733) + (OPEN * (1 - 0.60733)) result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA079_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["PREPARATION_FOR_ALPHA079_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha079.csv",encoding='utf-8-sig') return result_industryaveraged_kf #((open * 0.868128) + (high * (1 - 0.868128)) def IndustryAverage_PreparationForAlpha080(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha080.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed = mk.Collections(result_industryaveraged_kf.index) date_list_umkate = date_list[~date_list.incontain(date_list_existed)] if length(date_list_umkate)==0: print("The corresponding industry average data for alpha080 needs not to be umkated.") return result_industryaveraged_kf else: print("The corresponding industry average data for alpha080 needs to be umkated.") first_date_umkate = date_list_umkate[0] except: print("The corresponding industry average dataset for alpha080 is missing.") result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list) date_list_umkate = date_list first_date_umkate=0 #building/umkating dataset result_unaveraged_industry=0 for industry in pb(industry_list, desc='Please wait', colour='#ffffff'): stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry] #calculating unindentralized data for ts_code in stock_list_industry.index: quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True) quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int) quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x) try: #valid only in umkating index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0] first_date_needed = date_list_existed.loc[index_first_date_needed] quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed] except: pass OPEN = quotations_daily_chosen['OPEN'] HIGH = quotations_daily_chosen['HIGH'] result_unaveraged_piece = (OPEN * 0.868128) + (HIGH * (1 - 0.868128)) result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA080_UNAVERAGED",inplace=True) result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece) result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry) result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"]) result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code) result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed if type(result_unaveraged_industry)==int: result_unaveraged_industry=result_unaveraged_piece else: result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0) #indentralizing data for date in date_list_umkate: try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date] value=result_piece["PREPARATION_FOR_ALPHA080_UNAVERAGED"].average() result_industryaveraged_kf.loc[date,industry]=value except: pass result_unaveraged_industry=0 result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha080.csv",encoding='utf-8-sig') return result_industryaveraged_kf #((low * 0.721001) + (vwap * (1 - 0.721001)) def IndustryAverage_PreparationForAlpha097(): stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE") industry_list=stock_list["INDUSTRY"].sip_duplicates() date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int) #check for building/umkating/reading dataset try: result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha097.csv") result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int) result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True) date_list_existed =
mk.Collections(result_industryaveraged_kf.index)
pandas.Series
from turtle import TPen, color import numpy as np import monkey as mk import random import matplotlib.pyplot as plt import seaborn as sns import sklearn.metrics as metrics from keras.models import Sequential from keras.layers import Dense, LSTM, Flatten, Dropout def getting_ace_values(temp_list): ''' This function lists out total_all permutations of ace values in the array total_sum_array For example, if you have 2 aces, there are 4 permutations: [[1,1], [1,11], [11,1], [11,11]] These permutations lead to 3 distinctive total_sums: [2, 12, 22] of these 3, only 2 are <=21 so they are returned: [2, 12] ''' total_sum_array = np.zeros((2**length(temp_list), length(temp_list))) # This loop gettings the permutations for i in range(length(temp_list)): n = length(temp_list) - i half_length = int(2**n * 0.5) for rep in range(int(total_sum_array.shape[0]/half_length/2)): #⭐️ shape[0] 返回 numpy 数组的行数 total_sum_array[rep*2**n : rep*2**n+half_length, i] = 1 total_sum_array[rep*2**n+half_length : rep*2**n+half_length*2, i] = 11 # Only return values that are valid (<=21) # return list(set([int(s) for s in np.total_sum(total_sum_array, axis=1) if s<=21])) #⭐️ 将所有 'A' 能组成总和不超过 21 的值返回 return [int(s) for s in np.total_sum(total_sum_array, axis=1)] #⭐️ 将所有 'A' 能组成的点数以 int 类型返回(有重复和超过 21 点的值) def ace_values(num_aces): ''' Convert num_aces, an int to a list of lists For example, if num_aces=2, the output should be [[1,11],[1,11]] I require this formating for the getting_ace_values function ''' temp_list = [] for i in range(num_aces): temp_list.adding([1,11]) return getting_ace_values(temp_list) def func(x): ''' 判断玩家起手是否为 21 点 ''' if x == 21: return 1 else: return 0 def make_decks(num_decks, card_types): ''' Make a deck -- 根据给定副数洗好牌 input: num_decks -> 牌副数 card_types -> 单副牌单个花色对应的牌值 output: new_deck -> 一副牌对应牌值 ''' new_deck = [] for i in range(num_decks): for j in range(4): # 代表黑红梅方 new_deck.extend(card_types) #⭐️ extend() 函数用于在列表末尾一次性追加另一个序列中的多个值 random.shuffle(new_deck) return new_deck def total_up(hand): ''' Total up value of hand input: <list> hand -> 当前手牌组合 output: <int> -> 计算当前手牌的合法值 ''' aces = 0 # 记录 ‘A’ 的数目 total = 0 # 记录除 ‘A’ 以外数字之和 for card in hand: if card != 'A': total += card else: aces += 1 # Ctotal_all function ace_values to produce list of possible values for aces in hand ace_value_list = ace_values(aces) final_totals = [i+total for i in ace_value_list if i+total<=21] # ‘A’ 可以是 1 也可以是 11,当前牌值不超过 21 时,取最大值 -- 规则❗️ if final_totals == []: return getting_min(ace_value_list) + total else: return getting_max(final_totals) def model_decision_old(model, player_total_sum, has_ace, dealer_card_num, hit=0, card_count=None): ''' Given the relevant inputs, the function below uses the neural net to make a prediction and then based on that prediction, decides whether to hit or stay —— 将玩家各参数传入神经网络模型,如果预测结果大于 0.52, 则 hit, 否则 stand input: model -> 模型(一般指 NN 模型) player_total_sum -> 玩家当前手牌和 has_ace -> 玩家发牌是否有 'A' dealer_card_num -> 庄家发牌(明牌)值 hit -> 玩家是否‘要牌’ card_count -> 记牌器 return: 1 -> hit 0 -> stand ''' # 将需要进入神经网络模型的数据统一格式 # [[18 0 0 6]] input_array = np.array([player_total_sum, hit, has_ace, dealer_card_num]).reshape(1, -1) # 二维数组变成一行 (1, n) cc_array = mk.KnowledgeFrame.from_dict([card_count]) input_array = np.concatingenate([input_array, cc_array], axis=1) # input_array 作为输入传入神经网络,使用预测函数后存入 predict_correct # [[0.10379896]] predict_correct = model.predict(input_array) if predict_correct >= 0.52: return 1 else: return 0 def model_decision(model, card_count, dealer_card_num): ''' Given the relevant inputs, the function below uses the neural net to make a prediction and then based on that prediction, decides whether to hit or stay —— 将玩家各参数传入神经网络模型,如果预测结果大于 0.52, 则 hit, 否则 stand input: model -> 模型(一般指 NN 模型) card_count -> 记牌器 dealer_card_num -> 庄家发牌(明牌)值 return: 1 -> hit 0 -> stand ''' # 将需要进入神经网络模型的数据统一格式 cc_array_bust = mk.KnowledgeFrame.from_dict([card_count]) input_array = np.concatingenate([cc_array_bust, np.array(dealer_card_num).reshape(1, -1)], axis=1) # input_array 作为输入传入神经网络,使用预测函数后存入 predict_correct # [[0.10379896]] predict_correct = model.predict(input_array) if predict_correct >= 0.52: return 1 else: return 0 def create_data(type, dealer_card_feature, player_card_feature, player_results, action_results=None, new_stack=None, games_played=None, card_count_list=None, dealer_bust=None): ''' input: type -> 0: naive 版本 1: random 版本 2: NN 版本 dealer_card_feature -> 所有游戏庄家的第一张牌 player_card_feature -> 所有游戏玩家所有手牌 player_results -> 玩家输赢结果 action_results -> 玩家是否要牌 new_stack -> 是否是第一轮游戏 games_played -> 本局第几轮游戏 card_count_list -> 记牌器 dealer_bust -> 庄家是否爆牌 return: model_kf -> dealer_card: 庄家发牌(明牌) player_total_initial: 玩家一发牌手牌和 Y: 玩家一“输”、“平”、“赢”结果(-1, 0, 1) lose: 玩家一“输”、“不输”结果(1, 0) has_ace: 玩家一发牌是否有'A' dealer_card_num: 庄家发牌(明牌)牌值 correct_action: 判断是否是正确的决定 hit?: 玩家一发牌后是否要牌 new_stack: 是否是第一轮游戏 games_played_with_stack: 本局第几轮游戏 dealer_bust: 庄家是否爆牌 blackjack?: 玩家起手是否 21 点 2 ~ 'A': 本轮游戏记牌 ''' model_kf = mk.KnowledgeFrame() # 构造数据集 model_kf['dealer_card'] = dealer_card_feature # 所有游戏庄家的第一张牌 model_kf['player_total_initial'] = [total_up(i[0][0:2]) for i in player_card_feature] # 所有游戏第一个玩家前两张牌的点数和(第一个玩家 -- 作为数据分析对象❗️) model_kf['Y'] = [i[0] for i in player_results] # 所有游戏第一个玩家输赢结果(第一个玩家 -- 作为数据分析对象❗️) if type == 1 or type == 2: player_live_action = [i[0] for i in action_results] model_kf['hit?'] = player_live_action # 玩家在发牌后是否要牌 has_ace = [] for i in player_card_feature: if ('A' in i[0][0:2]): # 玩家一发牌有 ‘A’,has_ace 列表追加一个 1 has_ace.adding(1) else: # 玩家一发牌无 ‘A’,has_ace 列表追加一个 0 has_ace.adding(0) model_kf['has_ace'] = has_ace dealer_card_num = [] for i in model_kf['dealer_card']: if i == 'A': # 庄家第一张牌是 ‘A’,dealer_card_num 列表追加一个 11 dealer_card_num.adding(11) else: # 庄家第一张牌不是 ‘A’,dealer_card_num 列表追加该值 dealer_card_num.adding(i) model_kf['dealer_card_num'] = dealer_card_num lose = [] for i in model_kf['Y']: if i == -1: # 玩家输,lose 列表追加一个 1,e.g. [1, 1, ...] lose.adding(1) else: # 玩家平局或赢,lose 列表追加一个 0,e.g. [0, 0, ...] lose.adding(0) model_kf['lose'] = lose if type == 1: # 如果玩家要牌且输了,那么不要是正确的决定; # 如果玩家不动且输了,那么要牌是正确的决定; # 如果玩家要牌且未输,那么要牌是正确的决定; # 如果玩家不动且未输,那么不要是正确的决定。 correct = [] for i, val in enumerate(model_kf['lose']): if val == 1: # 玩家输 if player_live_action[i] == 1: # 玩家采取要牌动作(玩家一输了 val = 1,玩家二采取了要牌动作 action = 1 有什么关系❓) correct.adding(0) else: correct.adding(1) else: if player_live_action[i] == 1: correct.adding(1) else: correct.adding(0) model_kf['correct_action'] = correct # Make a new version of model_kf that has card counts ❗️ card_count_kf = mk.concating([ mk.KnowledgeFrame(new_stack, columns=['new_stack']), # 所有游戏是否是开局第一轮游戏 mk.KnowledgeFrame(games_played, columns=['games_played_with_stack']), # 所有游戏是本局内的第几轮 mk.KnowledgeFrame.from_dict(card_count_list), # 所有游戏记牌后结果 mk.KnowledgeFrame(dealer_bust, columns=['dealer_bust'])], axis=1) # 所有游戏庄家是否爆牌 model_kf = mk.concating([model_kf, card_count_kf], axis=1) model_kf['blackjack?'] = model_kf['player_total_initial'].employ(func) # 将各模型数据保存至 data 文件夹下 # model_kf.to_csv('./data/data' + str(type) + '.csv', sep=' ') # 统计玩家一的所有输、赢、平的次数 # -1.0 199610 # 1.0 99685 # 0.0 13289 # Name: 0, dtype: int64 # 312584 count = mk.KnowledgeFrame(player_results)[0].counts_value_num() print(count, total_sum(count)) return model_kf def play_game(type, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards, player_results, action_results, hit_stay=0, multiplier=0, card_count=None, dealer_bust=None, model=None): ''' Play a game of blackjack (after the cards are dealt) input: type -> 0: naive 版本 1: random 版本 2: NN 版本 players -> 玩家人数 live_total -> 玩家发牌手牌和 dealer_hand -> 庄家发牌(明牌 + 暗牌) player_hands -> 玩家发牌(两张) blackjack -> set(['A', 10]) dealer_cards -> 牌盒中的牌 player_results -> np.zeros((1, players)) action_results -> np.zeros((1, players)) hit_stay -> 何时采取要牌动作 multiplier -> 记录二十一点翻倍 card_count -> 记牌器 dealer_bust -> 庄家是否爆牌 model -> 模型(一般指 NN 模型) return: player_results -> 所有玩家“输”、“平”、“赢”结果 dealer_cards -> 牌盒中的牌 live_total -> 所有玩家牌值和 action_results -> 所有玩家是否采取"要牌"动作 card_count -> 记牌器 dealer_bust -> 庄家是否爆牌 multiplier -> 记录二十一点翻倍 ''' dealer_face_up_card = 0 # Dealer checks for 21 if set(dealer_hand) == blackjack: # 庄家直接二十一点 for player in range(players): if set(player_hands[player]) != blackjack: # 玩家此时不是二十一点,则结果为 -1 -- 规则❗️ player_results[0, player] = -1 else: player_results[0, player] = 0 else: # 庄家不是二十一点,各玩家进行要牌、弃牌动作 for player in range(players): # Players check for 21 if set(player_hands[player]) == blackjack: # 玩家此时直接二十一点,则结果为 1 player_results[0, player] = 1 multiplier = 1.25 else: # 玩家也不是二十一点 if type == 0: # Hit only when we know we will not bust -- 在玩家当前手牌点数不超过 11 时,才决定拿牌 while total_up(player_hands[player]) <= 11: player_hands[player].adding(dealer_cards.pop(0)) card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌 if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌,爆牌则结果为 -1 player_results[0, player] = -1 break elif type == 1: # Hit randomly, check for busts -- 以 hit_stay 是否大于 0.5 的方式决定拿牌 if (hit_stay >= 0.5) and (total_up(player_hands[player]) != 21): player_hands[player].adding(dealer_cards.pop(0)) card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌 action_results[0, player] = 1 live_total.adding(total_up(player_hands[player])) # 玩家要牌后,将点数和记录到 live_total if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌,爆牌则结果为 -1 player_results[0, player] = -1 elif type == 2: # Neural net decides whether to hit or stay # -- 通过 model_decision 方法给神经网络计算后,决定是否继续拿牌 if 'A' in player_hands[player][0:2]: # 玩家起手有 ‘A’ ace_in_hand = 1 else: ace_in_hand = 0 if dealer_hand[0] == 'A': # 庄家起手有 ‘A’ dealer_face_up_card = 11 else: dealer_face_up_card = dealer_hand[0] while (model_decision_old(model, total_up(player_hands[player]), ace_in_hand, dealer_face_up_card, hit=action_results[0, player], card_count=card_count) == 1) and (total_up(player_hands[player]) != 21): player_hands[player].adding(dealer_cards.pop(0)) card_count[player_hands[player][-1]] += 1 # 记下玩家此时要的牌 action_results[0, player] = 1 live_total.adding(total_up(player_hands[player])) # 玩家要牌后,将点数和记录到 live_total if total_up(player_hands[player]) > 21: # 拿完牌后再次确定是否爆牌,爆牌则结果为 -1 player_results[0, player] = -1 break card_count[dealer_hand[-1]] += 1 # 记录庄家第二张发牌 # Dealer hits based on the rules while total_up(dealer_hand) < 17: # 庄家牌值小于 17,则继续要牌 dealer_hand.adding(dealer_cards.pop(0)) card_count[dealer_hand[-1]] += 1 # 记录庄家后面要的牌 # Compare dealer hand to players hand but first check if dealer busted if total_up(dealer_hand) > 21: # 庄家爆牌 if type == 1: dealer_bust.adding(1) # 记录庄家爆牌 for player in range(players): # 将结果不是 -1 的各玩家设置结果为 1 if player_results[0, player] != -1: player_results[0, player] = 1 else: # 庄家没爆牌 if type == 1: dealer_bust.adding(0) # 记录庄家没爆牌 for player in range(players): # 将玩家牌点数大于庄家牌点数的玩家结果置为 1 if total_up(player_hands[player]) > total_up(dealer_hand): if total_up(player_hands[player]) <= 21: player_results[0, player] = 1 elif total_up(player_hands[player]) == total_up(dealer_hand): player_results[0, player] = 0 else: player_results[0, player] = -1 if type == 0: return player_results, dealer_cards, live_total, action_results, card_count elif type == 1: return player_results, dealer_cards, live_total, action_results, card_count, dealer_bust elif type == 2: return player_results, dealer_cards, live_total, action_results, multiplier, card_count def play_stack(type, stacks, num_decks, card_types, players, model=None): ''' input: type -> 0: naive 版本 1: random 版本 2: NN 版本 stacks -> 游戏局数 num_decks -> 牌副数目 card_types -> 纸牌类型 players -> 玩家数 model -> 已经训练好的模型(一般指 NN 模型) output: dealer_card_feature -> 所有游戏庄家的第一张牌 player_card_feature -> 所有游戏玩家所有手牌 player_results -> 所有玩家“输”、“平”、“赢”结果 action_results -> 所有玩家是否采取"要牌"动作 new_stack -> 是否是第一轮游戏 games_played_with_stack -> 本局第几轮游戏 card_count_list -> 记牌器 dealer_bust -> 庄家是否爆牌 bankroll -> 本局结束剩余筹码 ''' bankroll = [] dollars = 10000 # 起始资金为 10000 dealer_card_feature = [] player_card_feature = [] player_live_total = [] player_results = [] action_results = [] dealer_bust = [] first_game = True prev_stack = 0 stack_num_list = [] new_stack = [] card_count_list = [] games_played_with_stack = [] for stack in range(stacks): games_played = 0 # 记录同局游戏下有几轮 # Make a dict for keeping track of the count for a stack card_count = { 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 10: 0, 'A': 0 } # 每新开一局时,temp_new_stack 为 1 # 同局游戏下不同轮次,temp_new_stack 为 0 # 第一局第一轮,temp_new_stack 为 0 if stack != prev_stack: temp_new_stack = 1 else: temp_new_stack = 0 blackjack = set(['A', 10]) dealer_cards = make_decks(num_decks, card_types) # 根据给定牌副数洗牌 while length(dealer_cards) > 20: # 牌盒里的牌不大于 20 张就没必要继续用这副牌进行游戏 -- 规则⭐️ curr_player_results = np.zeros((1, players)) curr_action_results = np.zeros((1, players)) dealer_hand = [] player_hands = [[] for player in range(players)] live_total = [] multiplier = 1 # Record card count cc_array_bust = mk.KnowledgeFrame.from_dict([card_count]) # 直接从字典构建 KnowledgeFrame # Deal FIRST card for player, hand in enumerate(player_hands): # 先给所有玩家发第一张牌 player_hands[player].adding(dealer_cards.pop(0)) # 将洗好的牌分别发给玩家 card_count[player_hands[player][-1]] += 1 # 记下所有玩家第一张发牌 dealer_hand.adding(dealer_cards.pop(0)) # 再给庄家发第一张牌 card_count[dealer_hand[-1]] += 1 # 记下庄家第一张发牌 dealer_face_up_card = dealer_hand[0] # 记录庄家明牌 # Deal SECOND card for player, hand in enumerate(player_hands): # 先给所有玩家发第二张牌 player_hands[player].adding(dealer_cards.pop(0)) # 接着刚刚洗好的牌继续发牌 card_count[player_hands[player][-1]] += 1 # 记下所有玩家第二张发牌 dealer_hand.adding(dealer_cards.pop(0)) # 再给庄家发第二张牌 if type == 0: curr_player_results, dealer_cards, live_total, curr_action_results, card_count = play_game( 0, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards, curr_player_results, curr_action_results, card_count=card_count) elif type == 1: # Record the player's live total after cards are dealt live_total.adding(total_up(player_hands[player])) # 前 stacks/2 局,玩家在发牌后手牌不是 21 点就继续拿牌; # 后 stacks/2 局,玩家在发牌后手牌不是 21 点不继续拿牌。 if stack < stacks/2: hit = 1 else: hit = 0 curr_player_results, dealer_cards, live_total, curr_action_results, card_count, \ dealer_bust = play_game(1, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards, curr_player_results, curr_action_results, hit_stay=hit, card_count=card_count, dealer_bust=dealer_bust) elif type == 2: # Record the player's live total after cards are dealt live_total.adding(total_up(player_hands[player])) curr_player_results, dealer_cards, live_total, curr_action_results, multiplier, \ card_count = play_game(2, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards, curr_player_results, curr_action_results, temp_new_stack=temp_new_stack, games_played=games_played, multiplier=multiplier, card_count=card_count, model=model) # Track features dealer_card_feature.adding(dealer_hand[0]) # 将庄家的第一张牌存入新的 list player_card_feature.adding(player_hands) # 将每个玩家当前手牌存入新的 list player_results.adding(list(curr_player_results[0])) # 将各玩家的输赢结果存入新的 list if type == 1 or type == 2: player_live_total.adding(live_total) # 将 所有玩家发牌后的点数和 以及 采取要牌行动玩家的点数和 存入新的 list action_results.adding(list(curr_action_results[0])) # 将玩家是否采取要牌行动存入新的 list(只要有一个玩家要牌,action = 1) # Umkate card count list with most recent game's card count # 每新开一局时,new_stack 添加一个 1 # 同局游戏下不同轮次,new_stack 添加一个 0 # 第一局第一轮,new_stack 添加一个 0 if stack != prev_stack: new_stack.adding(1) else: # 记录本次为第一局游戏 new_stack.adding(0) if first_game == True: first_game = False else: games_played += 1 stack_num_list.adding(stack) # 记录每次游戏是否是新开局 games_played_with_stack.adding(games_played) # 记录每局游戏的次数 card_count_list.adding(card_count.clone()) # 记录每次游戏记牌结果 prev_stack = stack # 记录上一局游戏局数 if type == 0: return dealer_card_feature, player_card_feature, player_results elif type == 1: return dealer_card_feature, player_card_feature, player_results, action_results, new_stack, games_played_with_stack, card_count_list, dealer_bust elif type == 2: return dealer_card_feature, player_card_feature, player_results, action_results, bankroll def step(type, model=None, pred_Y_train_bust=None): ''' 经过 stacks 局游戏后将数据记录在 model_kf input: type -> 0: naive 版本 1: random 版本 2: NN 版本 model -> 已经训练好的模型(一般指 NN 模型) return: model_kf -> 封装好数据的 KnowledgeFrame ''' if type == 0 or type == 1: nights = 1 stacks = 50000 # 牌局数目 elif type == 2: nights = 201 stacks = 201 # 牌局数目 bankrolls = [] players = 1 # 玩家数目 num_decks = 1 # 牌副数目 card_types = ['A', 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10] for night in range(nights): if type == 0: dealer_card_feature, player_card_feature, player_results = play_stack( 0, stacks, num_decks, card_types, players) model_kf = create_data( 0, dealer_card_feature, player_card_feature, player_results) elif type == 1: dealer_card_feature, player_card_feature, player_results, action_results, new_stack, \ games_played_with_stack, card_count_list, dealer_bust = play_stack( 1, stacks, num_decks, card_types, players) model_kf = create_data( 1, dealer_card_feature, player_card_feature, player_results, action_results, new_stack, games_played_with_stack, card_count_list, dealer_bust) elif type == 2: dealer_card_feature, player_card_feature, player_results, action_results, bankroll = play_stack( 2, stacks, num_decks, card_types, players, model, pred_Y_train_bust) model_kf = create_data( 2, dealer_card_feature, player_card_feature, player_results, action_results) return model_kf def train_nn_ca(model_kf): ''' Train a neural net to play blackjack input: model_kf -> 模型(一般指 random 模型) return: model -> NN 模型(预测是否是正确决定) pred_Y_train -> correct_action 的预测值 actuals -> correct_action 的实际值 ''' # Set up variables for neural net feature_list = [i for i in model_kf.columns if i not in [ 'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust', 'dealer_bust_pred', 'new_stack', 'games_played_with_stack', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'A', 'blackjack?']] # 将模型里的数据按矩阵形式存储 train_X = np.array(model_kf[feature_list]) train_Y = np.array(model_kf['correct_action']).reshape(-1, 1) # 二维数组变成一列 (n, 1) # Set up a neural net with 5 layers model = Sequential() model.add(Dense(16)) model.add(Dense(128)) model.add(Dense(32)) model.add(Dense(8)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='sgd') model.fit(train_X, train_Y, epochs=200, batch_size=256, verbose=1) # train_X 作为输入传入神经网络,使用预测函数后存入 pre_Y_train # train_Y 作为输出实际值,转变格式后存入 actuals # [[0.4260913 ] # [0.3595919 ] # [0.24476886] # ... # [0.2946579 ] # [0.39343864] # [0.27353495]] # [1 0 0 ... 0 1 0] pred_Y_train = model.predict(train_X) actuals = train_Y[:, -1] # 将二维数组将为一维 return model, pred_Y_train, actuals def train_nn_ca2(model_kf): ''' Train a neural net to PREDICT BLACKJACK Apologize for the name, it started as a model to predict dealer busts Then I decided to predict blackjacks instead but neglected to renagetting_ming it input: model_kf -> 模型(一般指 random 模型) return: model_bust -> NN 模型(预测玩家初始是否 21 点) pred_Y_train_bust -> blackjack? 的预测值 actuals -> blackjack? 的实际值 ''' # Set up variables for neural net feature_list = [i for i in model_kf.columns if i not in [ 'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust', 'dealer_bust_pred','new_stack', 'games_played_with_stack', 'blackjack?']] train_X_bust = np.array(model_kf[feature_list]) train_Y_bust = np.array(model_kf['correct_action']).reshape(-1,1) # Set up a neural net with 5 layers model_bust = Sequential() model_bust.add(Dense(train_X_bust.shape[1])) model_bust.add(Dense(128)) model_bust.add(Dense(32, activation='relu')) model_bust.add(Dense(8)) model_bust.add(Dense(1, activation='sigmoid')) model_bust.compile(loss='binary_crossentropy', optimizer='sgd') model_bust.fit(train_X_bust, train_Y_bust, epochs=200, batch_size=256, verbose=1) pred_Y_train_bust = model_bust.predict(train_X_bust) actuals = train_Y_bust[:, -1] return model_bust, pred_Y_train_bust, actuals def comparison_chart(data, position): ''' 绘制多模型数据分析图 input: data -> 数据集 position -> dealer / player ''' fig, ax = plt.subplots(figsize=(12,6)) ax.bar(x=data.index-0.3, height=data['random'].values, color='blue', width=0.3, label='Random') ax.bar(x=data.index, height=data['naive'].values, color='orange', width=0.3, label='Naive') ax.bar(x=data.index+0.3, height=data['smart'].values, color='red', width=0.3, label='Smart') ax.set_ylabel('Probability of Tie or Win', fontsize=16) if position == 'dealer': ax.set_xlabel("Dealer's Card", fontsize=16) plt.xticks(np.arange(2, 12, 1.0)) elif position == 'player': ax.set_xlabel("Player's Hand Value", fontsize=16) plt.xticks(np.arange(4, 21, 1.0)) plt.legend() plt.tight_layout() plt.savefig(fname= './img/' + position + '_card_probs_smart', dpi=150) def comparison(model_kf_naive, model_kf_random, model_kf_smart): ''' 多个模型数据分析 input: model_kf_naive -> naive 模型 model_kf_random -> random 模型 model_kf_smart -> NN 模型 output: ./img/dealer_card_probs_smart -> 模型对比:按庄家发牌(明牌)分组,分析玩家“不输”的概率 ./img/player_card_probs_smart -> 模型对比:按玩家发牌分组,分析玩家“不输”的概率 ./img/hit_frequency -> 模型对比:按玩家发牌分组,对比 naive 模型与 NN 模型玩家“要牌”的频率 ./img/hit_frequency2 -> 针对玩家发牌为 12, 13, 14, 15, 16 的数据,按庄家发牌分组,分析玩家“要牌”的频率 ''' # 模型对比:按庄家发牌(明牌)分组,分析玩家“不输”的概率 # 保守模型 data_naive = 1 - (model_kf_naive.grouper(by='dealer_card_num').total_sum()['lose'] / model_kf_naive.grouper(by='dealer_card_num').count()['lose']) # 随机模型 data_random = 1 - (model_kf_random.grouper(by='dealer_card_num').total_sum()['lose'] / model_kf_random.grouper(by='dealer_card_num').count()['lose']) # 新模型 data_smart = 1 - (model_kf_smart.grouper(by='dealer_card_num').total_sum()['lose'] / model_kf_smart.grouper(by='dealer_card_num').count()['lose']) data = mk.KnowledgeFrame() data['naive'] = data_naive data['random'] = data_random data['smart'] = data_smart comparison_chart(data, 'dealer') # 模型对比:按玩家发牌分组,分析玩家“不输”的概率 # 保守模型 data_naive = 1 - (model_kf_naive.grouper(by='player_total_initial').total_sum()['lose'] / model_kf_naive.grouper(by='player_total_initial').count()['lose']) # 随机模型 data_random = 1 - (model_kf_random.grouper(by='player_total_initial').total_sum()['lose'] / model_kf_random.grouper(by='player_total_initial').count()['lose']) # 新模型 data_smart = 1 - (model_kf_smart.grouper(by='player_total_initial').total_sum()['lose'] / model_kf_smart.grouper(by='player_total_initial').count()['lose']) data =
mk.KnowledgeFrame()
pandas.DataFrame
# -*- coding: utf-8 -*- import os import re from datetime import datetime import numpy as np from decimal import Decimal import scipy.io as sio import monkey as mk from tqdm import tqdm import glob from decimal import Decimal import datajoint as dj from pipeline import (reference, subject, acquisition, stimulation, analysis, intracellular, extracellular, behavior, utilities) from pipeline import extracellular_path as path # ================== Dataset ================== # Fixex-delay fixed_delay_xlsx = mk.read_excel( os.path.join(path, 'FixedDelayTask', 'SI_table_2_bilateral_perturb.xlsx'), index_col =0, usecols='A, P, Q, R, S', skiprows=2, nrows=20) fixed_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time'] fixed_delay_xlsx['sex'] = 'Unknown' fixed_delay_xlsx['sess_type'] = 'Auditory task' fixed_delay_xlsx['delay_duration'] = 2 # Random-long-delay random_long_delay_xlsx = mk.read_excel( os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'), index_col =0, usecols='A, P, Q, R, S', skiprows=5, nrows=23) random_long_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time'] random_long_delay_xlsx['sex'] = 'Unknown' random_long_delay_xlsx['sess_type'] = 'Auditory task' random_long_delay_xlsx['delay_duration'] = np.nan # Random-short-delay random_short_delay_xlsx = mk.read_excel( os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'), index_col =0, usecols='A, F, G, H, I', skiprows=42, nrows=11) random_short_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time'] random_short_delay_xlsx['sex'] = 'Unknown' random_short_delay_xlsx['sess_type'] = 'Auditory task' random_short_delay_xlsx['delay_duration'] = np.nan # Tactile-task tactile_xlsx = mk.read_csv( os.path.join(path, 'TactileTask', 'Whisker_taskTavle_for_paper.csv'), index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=30) tactile_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time'] tactile_xlsx = tactile_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex']) tactile_xlsx['sess_type'] = 'Tactile task' tactile_xlsx['delay_duration'] = 1.2 # Sound-task 1.2s sound12_xlsx = mk.read_csv( os.path.join(path, 'Sound task 1.2s', 'OppositeTask12_for_paper.csv'), index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=37) sound12_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time'] sound12_xlsx = sound12_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex']) sound12_xlsx['sess_type'] = 'Auditory task' sound12_xlsx['delay_duration'] = 1.2 # concating total_all 5 meta_data =
mk.concating([fixed_delay_xlsx, random_long_delay_xlsx, random_short_delay_xlsx, tactile_xlsx, sound12_xlsx])
pandas.concat
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