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_fit_sklearn_model_with_active_run | global | null | false | pandas_df | null | null | null | null | mlflow | def _fit_sklearn_model_with_active_run(pandas_df):
run_id = mlflow.active_run().info.run_id
_fit_sklearn(pandas_df)
return mlflow.get_run(run_id)
| ["def","_fit_sklearn_model_with_active_run","(","pandas_df",")",":","run_id","=","mlflow.active_run","(",")",".info.run_id","_fit_sklearn","(","pandas_df",")","return","mlflow.get_run","(","run_id",")"] | 32 | 35 | null | test_spark_datasource_autologging_crossframework.py | mlflow/tests/spark/autologging/datasource/test_spark_datasource_autologging_crossframework.py | import time
import numpy
import pytest
from sklearn.linear_model import LinearRegression
import mlflow
import mlflow.spark
from tests.spark.autologging.utils import _assert_spark_data_logged | 15 | null | 7 | 8 | null | null | null | Use image node_id 3 for calling a global function with example usage: _fit_sklearn_model_with_active_run(pandas_df) and returns: mlflow | 135 | node_id 3 | 1,357,874 |
test_super | TestGaussianNoise | unittest | true | self | A unittest class for testing the GaussianNoise postprocessor. | ["A","unittest","class","for","testing","the","GaussianNoise","postprocessor","."] | null | null | null | def test_super(self):
gan = GaussianNoise(scale=0.1)
self.assertTrue(gan.is_fitted)
self.assertFalse(gan._apply_fit)
self.assertTrue(gan._apply_predict)
gan.fit(preds=np.array([0.1, 0.2, 0.3]))
| ["def","test_super","(","self",")",":","gan","=","GaussianNoise","(","scale=0.1",")","self.assertTrue","(","gan.is_fitted",")","self.assertFalse","(","gan._apply_fit",")","self.assertTrue","(","gan._apply_predict",")","gan.fit","(","preds=np.array","(","[","0.1",",","0.2",",","0.3","]",")",")"] | 104 | 109 | null | test_gaussian_noise.py | adversarial-robustness-toolbox/tests/defences/test_gaussian_noise.py | import logging
import unittest
import numpy
from art.defences.postprocessor import GaussianNoise
from art.utils import load_dataset
from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 7 for calling the TestGaussianNoise obj's underlying member method code with example usage: obj.test_super() without return types | 147 | node_id 7 | 235,295 |
setUpClass | TestHighConfidence | unittest | true | cls | A unittest class for testing the HighConfidence postprocessor. | ["A","unittest","class","for","testing","the","HighConfidence","postprocessor","."] | null | null | null | def setUpClass(cls):
(x_train, y_train), (x_test, y_test), _, _ = load_dataset("mnist")
cls.mnist = (x_train, y_train), (x_test, y_test)
| ["def","setUpClass","(","cls",")",":","(","x_train",",","y_train",")",",","(","x_test",",","y_test",")",",","_",",","_","=","load_dataset","(","``","mnist","''",")","cls.mnist","=","(","x_train",",","y_train",")",",","(","x_test",",","y_test",")"] | 37 | 39 | null | test_high_confidence.py | adversarial-robustness-toolbox/tests/defences/test_high_confidence.py | import logging
import unittest
import numpy
from art.defences.postprocessor import HighConfidence
from art.utils import load_dataset
from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 1 for calling the TestHighConfidence obj's underlying member method code with example usage: obj.setUpClass(cls) without return types | 151 | node_id 1 | 235,296 |
test_ThompsonSamplerInfeasible | ThompsonSamplerTest | TestCase | true | self | null | null | null | null | null | def test_ThompsonSamplerInfeasible(self) -> None:
generator = ThompsonSampler(min_weight=0.9)
generator.fit(
# pyre-fixme[6]: For 1st param expected `List[List[List[Union[None,
# bool, float, int, str]]]]` but got `List[List[List[int]]]`.
Xs=self.Xs,
# pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
# `List[List[int]]`.
Ys=self.Ys,
# pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
# `List[List[int]]`.
Yvars=self.Yvars,
# pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
outcome_names=self.outcome_names,
)
with self.assertRaises(ModelError):
generator.gen(
n=3,
# pyre-fixme[6]: For 2nd param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
objective_weights=np.ones(1),
)
| ["def","test_ThompsonSamplerInfeasible","(","self",")","-",">","None",":","generator","=","ThompsonSampler","(","min_weight=0.9",")","generator.fit","(","#","pyre-fixme","[","6","]",":","For","1st","param","expected","`","List","[","List","[","List","[","Union","[","None",",","#","bool",",","float",",","int",",","str","]","]","]","]","`","but","got","`","List","[","List","[","List","[","int","]","]","]","`",".","Xs=self.Xs",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Ys=self.Ys",",","#","pyre-fixme","[","6","]",":","For","3rd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Yvars=self.Yvars",",","#","pyre-fixme","[","6","]",":","For","4th","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","outcome_names=self.outcome_names",",",")","with","self.assertRaises","(","ModelError",")",":","generator.gen","(","n=3",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","objective_weights=np.ones","(","1",")",",",")"] | 174 | 198 | null | test_thompson.py | Ax/ax/models/tests/test_thompson.py | import numpy
from ax.exceptions.model import ModelError
from ax.models.discrete.thompson import ThompsonSampler
from ax.utils.common.testutils import TestCase | 15 | 1 | 4 | 0 | 1 | 9 | 1 | Use image node_id 6 for calling the ThompsonSamplerTest obj's underlying member method code with example usage: obj.test_ThompsonSamplerInfeasible() without return types | 169 | node_id 6 | 9,540 |
_maybe_clause | global | null | false | clause | null | null | null | null | unknown | def _maybe_clause(clause: Optional[str]) -> Sequence[str]:
return [clause] if clause is not None else []
| ["def","_maybe_clause","(","clause",":","Optional","[","str","]",")","-",">","Sequence","[","str","]",":","return","[","clause","]","if","clause","is","not","None","else","[","]"] | 40 | 41 | null | store_ext.py | tfx/tfx/orchestration/portable/mlmd/store_ext.py | import collections
import itertools
from typing import Callable, Mapping, Optional, Sequence, Union
from tfx.dsl.compiler import compiler_utils
from tfx.dsl.compiler import constants
from tfx.orchestration.experimental.core import constants
from tfx.orchestration.portable.mlmd import event_lib
from tfx.orchestration.portable.mlmd import filter_query_builder
import ml_metadata | 15 | null | 9 | 6 | null | null | null | Use image node_id 2 for calling a global function with example usage: _maybe_clause(clause) and returns: unknown | 112 | node_id 2 | 2,198,856 |
__init__ | EventSievesConfiguration | SievesConfiguration | true | self | null | null | null | null | EventSievesConfiguration | def __init__(self):
super(EventSievesConfiguration, self).__init__()
self.run_evaluation = True
self.sieves_order = [
(RelationType.SAME_HEAD_LEMMA, 1.0),
(RelationType.EXACT_STRING, 1.0),
(RelationType.WIKIPEDIA_DISAMBIGUATION, 0.1),
(RelationType.WORD_EMBEDDING_MATCH, 0.7),
(RelationType.WIKIPEDIA_REDIRECT_LINK, 0.1),
(RelationType.FUZZY_HEAD_FIT, 0.5),
(RelationType.FUZZY_FIT, 1.0),
(RelationType.WITHIN_DOC_COREF, 1.0),
(RelationType.WIKIPEDIA_TITLE_PARENTHESIS, 0.1),
(RelationType.WIKIPEDIA_BE_COMP, 0.1),
(RelationType.WIKIPEDIA_CATEGORY, 0.1),
(RelationType.VERBOCEAN_MATCH, 0.1),
(RelationType.WORDNET_DERIVATIONALLY, 1.0),
]
| ["def","__init__","(","self",")",":","super","(","EventSievesConfiguration",",","self",")",".__init__","(",")","self.run_evaluation","=","True","self.sieves_order","=","[","(","RelationType.SAME_HEAD_LEMMA",",","1.0",")",",","(","RelationType.EXACT_STRING",",","1.0",")",",","(","RelationType.WIKIPEDIA_DISAMBIGUATION",",","0.1",")",",","(","RelationType.WORD_EMBEDDING_MATCH",",","0.7",")",",","(","RelationType.WIKIPEDIA_REDIRECT_LINK",",","0.1",")",",","(","RelationType.FUZZY_HEAD_FIT",",","0.5",")",",","(","RelationType.FUZZY_FIT",",","1.0",")",",","(","RelationType.WITHIN_DOC_COREF",",","1.0",")",",","(","RelationType.WIKIPEDIA_TITLE_PARENTHESIS",",","0.1",")",",","(","RelationType.WIKIPEDIA_BE_COMP",",","0.1",")",",","(","RelationType.WIKIPEDIA_CATEGORY",",","0.1",")",",","(","RelationType.VERBOCEAN_MATCH",",","0.1",")",",","(","RelationType.WORDNET_DERIVATIONALLY",",","1.0",")",",","]"] | 59 | 78 | null | sieves_config.py | nlp-architect/nlp_architect/models/cross_doc_coref/sieves_config.py | from typing import List, Tuple
from nlp_architect.data.cdc_resources.relations.relation_types_enums import RelationType | 15 | 3 | 2 | 0 | 3 | 1 | 1 | Use image node_id 1 to create a new EventSievesConfiguration object from inherited base classes: SievesConfiguration with example: obj = EventSievesConfiguration() | 163 | node_id 1 | 1,443,098 |
simple_segmentation_example | global | null | false | null | null | null | null | null | def simple_segmentation_example():
"Perfect results!"
parameters = legion_parameters()
parameters.eps = 0.02
parameters.alpha = 0.005
parameters.betta = 0.1
parameters.gamma = 7.0
parameters.teta = 0.9
parameters.lamda = 0.1
parameters.teta_x = -0.5
parameters.teta_p = 7.0
parameters.Wz = 0.7
parameters.mu = 0.01
parameters.fi = 3.0
parameters.teta_xz = 0.1
parameters.teta_zx = 0.1
parameters.ENABLE_POTENTIONAL = False
template_dynamic_legion(
81,
2500,
2500,
conn_type=conn_type.GRID_FOUR,
params=parameters,
stimulus=[
1,
1,
1,
0,
0,
0,
0,
0,
0,
1,
1,
1,
0,
0,
1,
1,
1,
1,
1,
1,
1,
0,
0,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
1,
1,
1,
0,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
1,
0,
0,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
0,
0,
0,
0,
0,
],
separate_repr=[
[0, 1, 2, 9, 10, 11, 18, 19, 20],
[
14,
15,
16,
17,
23,
24,
25,
26,
33,
34,
35,
42,
43,
44,
51,
52,
53,
],
[
45,
46,
47,
48,
54,
55,
56,
57,
63,
64,
65,
66,
72,
73,
74,
75,
],
],
)
| ["def","simple_segmentation_example","(",")",":","``","Perfect","results","!","''","parameters","=","legion_parameters","(",")","parameters.eps","=","0.02","parameters.alpha","=","0.005","parameters.betta","=","0.1","parameters.gamma","=","7.0","parameters.teta","=","0.9","parameters.lamda","=","0.1","parameters.teta_x","=","-0.5","parameters.teta_p","=","7.0","parameters.Wz","=","0.7","parameters.mu","=","0.01","parameters.fi","=","3.0","parameters.teta_xz","=","0.1","parameters.teta_zx","=","0.1","parameters.ENABLE_POTENTIONAL","=","False","template_dynamic_legion","(","81",",","2500",",","2500",",","conn_type=conn_type.GRID_FOUR",",","params=parameters",",","stimulus=","[","1",",","1",",","1",",","0",",","0",",","0",",","0",",","0",",","0",",","1",",","1",",","1",",","0",",","0",",","1",",","1",",","1",",","1",",","1",",","1",",","1",",","0",",","0",",","1",",","1",",","1",",","1",",","0",",","0",",","0",",","0",",","0",",","0",",","1",",","1",",","1",",","0",",","0",",","0",",","0",",","0",",","0",",","1",",","1",",","1",",","1",",","1",",","1",",","1",",","0",",","0",",","1",",","1",",","1",",","1",",","1",",","1",",","1",",","0",",","0",",","0",",","0",",","0",",","1",",","1",",","1",",","1",",","0",",","0",",","0",",","0",",","0",",","1",",","1",",","1",",","1",",","0",",","0",",","0",",","0",",","0",",","]",",","separate_repr=","[","[","0",",","1",",","2",",","9",",","10",",","11",",","18",",","19",",","20","]",",","[","14",",","15",",","16",",","17",",","23",",","24",",","25",",","26",",","33",",","34",",","35",",","42",",","43",",","44",",","51",",","52",",","53",",","]",",","[","45",",","46",",","47",",","48",",","54",",","55",",","56",",","57",",","63",",","64",",","65",",","66",",","72",",","73",",","74",",","75",",","]",",","]",",",")"] | 94 | 126 | null | legion_examples.py | pyclustering/pyclustering/nnet/examples/legion_examples.py | from pyclustering.utils import draw_dynamics
from pyclustering.nnet.legion import legion_network, legion_parameters
from pyclustering.nnet import * | 15 | null | 3 | 12 | null | null | null | Use image node_id 12 for calling a global function with example usage: simple_segmentation_example() without return types | 121 | node_id 12 | 1,634,375 |
|
forward | ConstantGate | torch.nn | true | self,inp | null | null | null | null | idx, score | def forward(self, inp):
idx = torch.zeros(
(inp.shape[0], self.top_k),
dtype=torch.int64,
device=inp.device,
)
score = (
torch.ones((inp.shape[0], 1, self.top_k), device=inp.device)
/ 2
)
return idx, score
| ["def","forward","(","self",",","inp",")",":","idx","=","torch.zeros","(","(","inp.shape","[","0","]",",","self.top_k",")",",","dtype=torch.int64",",","device=inp.device",",",")","score","=","(","torch.ones","(","(","inp.shape","[","0","]",",","1",",","self.top_k",")",",","device=inp.device",")","\/","2",")","return","idx",",","score"] | 16 | 20 | null | test_zero.py | thu-pacman-faster-moe/tests/test_zero.py | import os
import sys
import json
import torch
from fmoe.layers import _fmoe_general_global_forward
from fmoe import FMoETransformerMLP
from test_ddp import _run_distributed | 15 | 1 | 7 | 4 | 1 | 2 | 1 | Use image node_id 2 for calling the ConstantGate obj's underlying member method code with example usage: obj.forward(inp) and returns: idx, score | 146 | node_id 2 | 2,201,994 |
__init__ | ConstantGate | torch.nn | true | self,d_model,num_expert,world_size,top_k | null | null | null | null | ConstantGate | def __init__(self, d_model, num_expert, world_size, top_k=1):
super().__init__()
self.top_k = top_k
| ["def","__init__","(","self",",","d_model",",","num_expert",",","world_size",",","top_k=1",")",":","super","(",")",".__init__","(",")","self.top_k","=","top_k"] | 12 | 14 | null | test_zero.py | thu-pacman-faster-moe/tests/test_zero.py | import os
import sys
import json
import torch
from fmoe.layers import _fmoe_general_global_forward
from fmoe import FMoETransformerMLP
from test_ddp import _run_distributed | 15 | 1 | 7 | 4 | 1 | 2 | 1 | Use image node_id 1 to create a new ConstantGate object from inherited base classes: torch.nn with example: obj = ConstantGate(d_model, num_expert, world_size, top_k) | 166 | node_id 1 | 2,201,993 |
test_GetLastPoint | RandomModelTest | TestCase | true | self | null | null | null | null | null | def test_GetLastPoint(self) -> None:
generated_points = np.array([[1, 2, 3], [4, 5, 6]])
RandomModelWithPoints = RandomModel(
generated_points=generated_points
)
result = RandomModelWithPoints._get_last_point()
expected = torch.tensor([[4], [5], [6]])
comparison = result == expected
# pyre-fixme[16]: `bool` has no attribute `any`.
self.assertEqual(comparison.any(), True)
| ["def","test_GetLastPoint","(","self",")","-",">","None",":","generated_points","=","np.array","(","[","[","1",",","2",",","3","]",",","[","4",",","5",",","6","]","]",")","RandomModelWithPoints","=","RandomModel","(","generated_points=generated_points",")","result","=","RandomModelWithPoints._get_last_point","(",")","expected","=","torch.tensor","(","[","[","4","]",",","[","5","]",",","[","6","]","]",")","comparison","=","result","==","expected","#","pyre-fixme","[","16","]",":","`","bool","`","has","no","attribute","`","any","`",".","self.assertEqual","(","comparison.any","(",")",",","True",")"] | 74 | 81 | null | test_random.py | Ax/ax/models/tests/test_random.py | import numpy
import torch
from ax.models.random.base import RandomModel
from ax.utils.common.testutils import TestCase
from ax.utils.common.typeutils import not_none | 15 | 1 | 5 | 0 | 1 | 8 | 1 | Use image node_id 8 for calling the RandomModelTest obj's underlying member method code with example usage: obj.test_GetLastPoint() without return types | 152 | node_id 8 | 9,517 |
get_config | global | null | false | null | null | null | null | config, args | def get_config():
parser = argparse.ArgumentParser(
"Global Config Argument Parser", allow_abbrev=False
)
parser.add_argument(
"--config_yaml",
required=True,
type=str,
help="the configuration file for this experiment.",
)
parser.add_argument(
"--resume",
type=str,
help="a specified logging path to resume training.\
It will fall back to run from initialization if no latest checkpoint are found.",
)
parser.add_argument(
"--test", type=str, help="a specified logging path to test"
)
args, _ = parser.parse_known_args()
config = get_user_config(args.config_yaml)
add_cfg_to_argparser(config, parser)
args = parser.parse_args()
update_cfg_with_argparser(config, args)
check_config_conflicts(config)
# print(config)
return config, args
| ["def","get_config","(",")",":","parser","=","argparse.ArgumentParser","(","``","Global","Config","Argument","Parser","''",",","allow_abbrev=False",")","parser.add_argument","(","``","--","config_yaml","''",",","required=True",",","type=str",",","help=","''","the","configuration","file","for","this","experiment",".","``",",",")","parser.add_argument","(","``","--","resume","''",",","type=str",",","help=","''","a","specified","logging","path","to","resume","training.\\","It","will","fall","back","to","run","from","initialization","if","no","latest","checkpoint","are","found",".","``",",",")","parser.add_argument","(","``","--","test","''",",","type=str",",","help=","''","a","specified","logging","path","to","test","''",")","args",",","_","=","parser.parse_known_args","(",")","config","=","get_user_config","(","args.config_yaml",")","add_cfg_to_argparser","(","config",",","parser",")","args","=","parser.parse_args","(",")","update_cfg_with_argparser","(","config",",","args",")","check_config_conflicts","(","config",")","#","print","(","config",")","return","config",",","args"] | 123 | 138 | null | config.py | OpenPrompt/openprompt/config.py | import argparse
from yacs.config import CfgNode
import sys
from openprompt.utils.utils import check_config_conflicts
from .default_config import get_default_config
from openprompt.utils.logging import logger
import os | 15 | null | 7 | 8 | null | null | null | Use image node_id 8 for calling a global function with example usage: get_config() and returns: config, args | 109 | node_id 8 | 152,524 |
|
save_config_to_yaml | global | null | false | config | null | null | null | null | null | def save_config_to_yaml(config):
from contextlib import redirect_stdout
saved_yaml_path = os.path.join(config.logging.path, "config.yaml")
with open(saved_yaml_path, "w") as f:
with redirect_stdout(f):
print(config.dump())
logger.info("Config saved as {}".format(saved_yaml_path))
| ["def","save_config_to_yaml","(","config",")",":","from","contextlib","import","redirect_stdout","saved_yaml_path","=","os.path.join","(","config.logging.path",",","``","config.yaml","''",")","with","open","(","saved_yaml_path",",","``","w","''",")","as","f",":","with","redirect_stdout","(","f",")",":","print","(","config.dump","(",")",")","logger.info","(","``","Config","saved","as","{","}","''",".format","(","saved_yaml_path",")",")"] | 116 | 121 | null | config.py | OpenPrompt/openprompt/config.py | import argparse
from yacs.config import CfgNode
import sys
from openprompt.utils.utils import check_config_conflicts
from .default_config import get_default_config
from openprompt.utils.logging import logger
import os | 15 | null | 7 | 8 | null | null | null | Use image node_id 7 for calling a global function with example usage: save_config_to_yaml(config) without return types | 118 | node_id 7 | 152,523 |
update_cfg_with_argparser | global | null | false | cfg,args,prefix | null | null | null | null | null | def update_cfg_with_argparser(cfg, args, prefix=None):
r"""To support update cfg with command line"""
for key in cfg:
value = cfg[key]
full_key_name = (
prefix + "." + key if prefix is not None else key
)
if isinstance(value, CfgNode):
update_cfg_with_argparser(
value, args, prefix=full_key_name
)
else:
v = getattr(args, full_key_name)
if type(v) != type(value):
raise TypeError
if v != value:
cfg[key] = v
print(
"Update key {}, value {} -> {}".format(
full_key_name, value, v
)
)
| ["def","update_cfg_with_argparser","(","cfg",",","args",",","prefix=None",")",":","r","''","''","''","To","support","update","cfg","with","command","line","''","''","''","for","key","in","cfg",":","value","=","cfg","[","key","]","full_key_name","=","(","prefix","+","``",".","''","+","key","if","prefix","is","not","None","else","key",")","if","isinstance","(","value",",","CfgNode",")",":","update_cfg_with_argparser","(","value",",","args",",","prefix=full_key_name",")","else",":","v","=","getattr","(","args",",","full_key_name",")","if","type","(","v",")","!","=","type","(","value",")",":","raise","TypeError","if","v","!","=","value",":","cfg","[","key","]","=","v","print","(","``","Update","key","{","}",",","value","{","}","-",">","{","}","''",".format","(","full_key_name",",","value",",","v",")",")"] | 99 | 113 | null | config.py | OpenPrompt/openprompt/config.py | import argparse
from yacs.config import CfgNode
import sys
from openprompt.utils.utils import check_config_conflicts
from .default_config import get_default_config
from openprompt.utils.logging import logger
import os | 15 | null | 7 | 8 | null | null | null | Use image node_id 6 for calling a global function with example usage: update_cfg_with_argparser(cfg, args, prefix) without return types | 135 | node_id 6 | 152,522 |
test_ConvertBounds | RandomModelTest | TestCase | true | self | null | null | null | null | null | def test_ConvertBounds(self) -> None:
bounds = [(1.0, 2.0), (3.0, 4.0), (5.0, 6.0)]
bounds_result = self.random_model._convert_bounds(bounds)
bounds_expected = torch.tensor(
[[1, 3, 5], [2, 4, 6]], dtype=torch.double
)
bounds_comparison = bounds_result == bounds_expected
# pyre-fixme[16]: `bool` has no attribute `any`.
self.assertEqual(bounds_comparison.any(), True)
# pyre-fixme[6]: For 1st param expected `List[Tuple[float, float]]` but got
# `None`.
self.assertEqual(self.random_model._convert_bounds(None), None)
| ["def","test_ConvertBounds","(","self",")","-",">","None",":","bounds","=","[","(","1.0",",","2.0",")",",","(","3.0",",","4.0",")",",","(","5.0",",","6.0",")","]","bounds_result","=","self.random_model._convert_bounds","(","bounds",")","bounds_expected","=","torch.tensor","(","[","[","1",",","3",",","5","]",",","[","2",",","4",",","6","]","]",",","dtype=torch.double",")","bounds_comparison","=","bounds_result","==","bounds_expected","#","pyre-fixme","[","16","]",":","`","bool","`","has","no","attribute","`","any","`",".","self.assertEqual","(","bounds_comparison.any","(",")",",","True",")","#","pyre-fixme","[","6","]",":","For","1st","param","expected","`","List","[","Tuple","[","float",",","float","]","]","`","but","got","#","`","None","`",".","self.assertEqual","(","self.random_model._convert_bounds","(","None",")",",","None",")"] | 63 | 72 | null | test_random.py | Ax/ax/models/tests/test_random.py | import numpy
import torch
from ax.models.random.base import RandomModel
from ax.utils.common.testutils import TestCase
from ax.utils.common.typeutils import not_none | 15 | 1 | 5 | 0 | 1 | 8 | 1 | Use image node_id 7 for calling the RandomModelTest obj's underlying member method code with example usage: obj.test_ConvertBounds() without return types | 153 | node_id 7 | 9,516 |
test_ConvertInequalityConstraints | RandomModelTest | TestCase | true | self | null | null | null | null | null | def test_ConvertInequalityConstraints(self) -> None:
A = np.array([[1, 2], [3, 4]])
b = np.array([[5], [6]])
A_result, b_result = not_none(
self.random_model._convert_inequality_constraints((A, b))
)
A_expected = torch.tensor([[1, 2], [3, 4]], dtype=torch.double)
b_expected = torch.tensor([[5], [6]], dtype=torch.double)
A_comparison = A_result == A_expected
b_comparison = b_result == b_expected
self.assertEqual(A_comparison.any(), True)
self.assertEqual(b_comparison.any(), True)
self.assertEqual(
self.random_model._convert_inequality_constraints(None), None
)
| ["def","test_ConvertInequalityConstraints","(","self",")","-",">","None",":","A","=","np.array","(","[","[","1",",","2","]",",","[","3",",","4","]","]",")","b","=","np.array","(","[","[","5","]",",","[","6","]","]",")","A_result",",","b_result","=","not_none","(","self.random_model._convert_inequality_constraints","(","(","A",",","b",")",")",")","A_expected","=","torch.tensor","(","[","[","1",",","2","]",",","[","3",",","4","]","]",",","dtype=torch.double",")","b_expected","=","torch.tensor","(","[","[","5","]",",","[","6","]","]",",","dtype=torch.double",")","A_comparison","=","A_result","==","A_expected","b_comparison","=","b_result","==","b_expected","self.assertEqual","(","A_comparison.any","(",")",",","True",")","self.assertEqual","(","b_comparison.any","(",")",",","True",")","self.assertEqual","(","self.random_model._convert_inequality_constraints","(","None",")",",","None",")"] | 49 | 61 | null | test_random.py | Ax/ax/models/tests/test_random.py | import numpy
import torch
from ax.models.random.base import RandomModel
from ax.utils.common.testutils import TestCase
from ax.utils.common.typeutils import not_none | 15 | 1 | 5 | 0 | 1 | 8 | 1 | Use image node_id 6 for calling the RandomModelTest obj's underlying member method code with example usage: obj.test_ConvertInequalityConstraints() without return types | 168 | node_id 6 | 9,515 |
test_ConvertEqualityConstraints | RandomModelTest | TestCase | true | self | null | null | null | null | null | def test_ConvertEqualityConstraints(self) -> None:
fixed_features = {3: 0.7, 1: 0.5}
d = 4
C, c = not_none(
self.random_model._convert_equality_constraints(
d, fixed_features
)
)
c_expected = torch.tensor([[0.5], [0.7]], dtype=torch.double)
C_expected = torch.tensor(
[[0, 1, 0, 0], [0, 0, 0, 1]], dtype=torch.double
)
c_comparison = c == c_expected
C_comparison = C == C_expected
self.assertEqual(c_comparison.any(), True)
self.assertEqual(C_comparison.any(), True)
self.assertEqual(
self.random_model._convert_equality_constraints(d, None), None
)
| ["def","test_ConvertEqualityConstraints","(","self",")","-",">","None",":","fixed_features","=","{","3",":","0.7",",","1",":","0.5","}","d","=","4","C",",","c","=","not_none","(","self.random_model._convert_equality_constraints","(","d",",","fixed_features",")",")","c_expected","=","torch.tensor","(","[","[","0.5","]",",","[","0.7","]","]",",","dtype=torch.double",")","C_expected","=","torch.tensor","(","[","[","0",",","1",",","0",",","0","]",",","[","0",",","0",",","0",",","1","]","]",",","dtype=torch.double",")","c_comparison","=","c","==","c_expected","C_comparison","=","C","==","C_expected","self.assertEqual","(","c_comparison.any","(",")",",","True",")","self.assertEqual","(","C_comparison.any","(",")",",","True",")","self.assertEqual","(","self.random_model._convert_equality_constraints","(","d",",","None",")",",","None",")"] | 35 | 47 | null | test_random.py | Ax/ax/models/tests/test_random.py | import numpy
import torch
from ax.models.random.base import RandomModel
from ax.utils.common.testutils import TestCase
from ax.utils.common.typeutils import not_none | 15 | 1 | 5 | 0 | 1 | 8 | 1 | Use image node_id 5 for calling the RandomModelTest obj's underlying member method code with example usage: obj.test_ConvertEqualityConstraints() without return types | 166 | node_id 5 | 9,514 |
add_cfg_to_argparser | global | null | false | cfg,parser,prefix | null | null | null | null | null | def add_cfg_to_argparser(cfg, parser, prefix=None):
r"""To support argument parser style in addition to yaml style"""
for key in cfg:
value = cfg[key]
full_key_name = (
prefix + "." + key if prefix is not None else key
)
if isinstance(value, CfgNode):
add_cfg_to_argparser(
value, parser=parser, prefix=full_key_name
)
else:
if type(value) in [str, int, float]:
parser.add_argument(
"--" + full_key_name,
type=type(value),
default=value,
)
elif type(value) in [tuple, list]:
parser.add_argument(
"--" + full_key_name,
type=type(value),
default=value,
nargs="+",
)
elif type(value) == bool:
parser.add_argument(
"--" + full_key_name,
action="store_{}".format(not value).lower(),
)
elif type(value) == type(None):
parser.add_argument(
"--" + full_key_name, default=None
)
else:
raise NotImplementedError(
"The type of config value is not supported"
)
| ["def","add_cfg_to_argparser","(","cfg",",","parser",",","prefix=None",")",":","r","''","''","''","To","support","argument","parser","style","in","addition","to","yaml","style","''","''","''","for","key","in","cfg",":","value","=","cfg","[","key","]","full_key_name","=","(","prefix","+","``",".","''","+","key","if","prefix","is","not","None","else","key",")","if","isinstance","(","value",",","CfgNode",")",":","add_cfg_to_argparser","(","value",",","parser=parser",",","prefix=full_key_name",")","else",":","if","type","(","value",")","in","[","str",",","int",",","float","]",":","parser.add_argument","(","``","--","''","+","full_key_name",",","type=type","(","value",")",",","default=value",",",")","elif","type","(","value",")","in","[","tuple",",","list","]",":","parser.add_argument","(","``","--","''","+","full_key_name",",","type=type","(","value",")",",","default=value",",","nargs=","''","+","''",",",")","elif","type","(","value",")","==","bool",":","parser.add_argument","(","``","--","''","+","full_key_name",",","action=","''","store_","{","}","''",".format","(","not","value",")",".lower","(",")",",",")","elif","type","(","value",")","==","type","(","None",")",":","parser.add_argument","(","``","--","''","+","full_key_name",",","default=None",")","else",":","raise","NotImplementedError","(","``","The","type","of","config","value","is","not","supported","''",")"] | 78 | 96 | null | config.py | OpenPrompt/openprompt/config.py | import argparse
from yacs.config import CfgNode
import sys
from openprompt.utils.utils import check_config_conflicts
from .default_config import get_default_config
from openprompt.utils.logging import logger
import os | 15 | null | 7 | 8 | null | null | null | Use image node_id 5 for calling a global function with example usage: add_cfg_to_argparser(cfg, parser, prefix) without return types | 132 | node_id 5 | 152,521 |
_get_node_live_artifacts | global | null | false | store | null | null | null | null | store | def _get_node_live_artifacts(
store: mlmd.MetadataStore,
*,
pipeline_id: str,
node_id: str,
pipeline_run_id: Optional[str] = None,
) -> Sequence[mlmd.proto.Artifact]:
"""Gets all LIVE node artifacts.
Args:
store: A MetadataStore object.
pipeline_id: The pipeline ID.
node_id: The node ID.
pipeline_run_id: The pipeline run ID that the node belongs to. Only
artifacts from the specified pipeline run are returned if specified.
Returns:
A list of LIVE artifacts of the given pipeline node.
"""
artifact_state_filter_query = f"state = {mlmd.proto.Artifact.State.Name(mlmd.proto.Artifact.LIVE)}"
node_context_name = compiler_utils.node_context_name(
pipeline_id, node_id
)
node_filter_query = q.And(
[
f'contexts_0.type = "{constants.NODE_CONTEXT_TYPE_NAME}"',
f'contexts_0.name = "{node_context_name}"',
]
)
artifact_filter_query = q.And(
[
node_filter_query,
artifact_state_filter_query,
]
)
if pipeline_run_id:
artifact_filter_query.append(
q.And(
[
f'contexts_1.type = "{constants.PIPELINE_RUN_CONTEXT_TYPE_NAME}"',
f'contexts_1.name = "{pipeline_run_id}"',
]
)
)
return store.get_artifacts(
list_options=mlmd.ListOptions(
filter_query=str(artifact_filter_query)
)
)
| ["def","_get_node_live_artifacts","(","store",":","mlmd.MetadataStore",",","*",",","pipeline_id",":","str",",","node_id",":","str",",","pipeline_run_id",":","Optional","[","str","]","=","None",",",")","-",">","Sequence","[","mlmd.proto.Artifact","]",":","``","''","''","Gets","all","LIVE","node","artifacts",".","Args",":","store",":","A","MetadataStore","object",".","pipeline_id",":","The","pipeline","ID",".","node_id",":","The","node","ID",".","pipeline_run_id",":","The","pipeline","run","ID","that","the","node","belongs","to",".","Only","artifacts","from","the","specified","pipeline","run","are","returned","if","specified",".","Returns",":","A","list","of","LIVE","artifacts","of","the","given","pipeline","node.","``","''","''","artifact_state_filter_query","=","f","''","state","=","{","mlmd.proto.Artifact.State.Name","(","mlmd.proto.Artifact.LIVE",")","}","''","node_context_name","=","compiler_utils.node_context_name","(","pipeline_id",",","node_id",")","node_filter_query","=","q.And","(","[","f'contexts_0.type","=","``","{","constants.NODE_CONTEXT_TYPE_NAME","}","''","'",",","f'contexts_0.name","=","``","{","node_context_name","}","''","'",",","]",")","artifact_filter_query","=","q.And","(","[","node_filter_query",",","artifact_state_filter_query",",","]",")","if","pipeline_run_id",":","artifact_filter_query.append","(","q.And","(","[","f'contexts_1.type","=","``","{","constants.PIPELINE_RUN_CONTEXT_TYPE_NAME","}","''","'",",","f'contexts_1.name","=","``","{","pipeline_run_id","}","''","'",",","]",")",")","return","store.get_artifacts","(","list_options=mlmd.ListOptions","(","filter_query=str","(","artifact_filter_query",")",")",")"] | 44 | 87 | null | store_ext.py | tfx/tfx/orchestration/portable/mlmd/store_ext.py | import collections
import itertools
from typing import Callable, Mapping, Optional, Sequence, Union
from tfx.dsl.compiler import compiler_utils
from tfx.dsl.compiler import constants
from tfx.orchestration.experimental.core import constants
from tfx.orchestration.portable.mlmd import event_lib
from tfx.orchestration.portable.mlmd import filter_query_builder
import ml_metadata | 15 | null | 9 | 6 | null | null | null | Use image node_id 3 for calling a global function with example usage: _get_node_live_artifacts(store) and returns: store | 120 | node_id 3 | 2,198,857 |
get_node_executions | global | null | false | store | null | null | null | null | store | def get_node_executions(
store: mlmd.MetadataStore,
*,
pipeline_id: str,
node_id: str,
pipeline_run_id: Optional[str] = None,
order_by: mlmd.OrderByField = mlmd.OrderByField.ID,
is_asc: bool = True,
execution_states: Optional[
Sequence["mlmd.proto.Execution.State"]
] = None,
min_last_update_time_since_epoch: Optional[int] = None,
) -> Sequence[mlmd.proto.Execution]:
"""Gets all node executions.
Args:
store: A MetadataStore object.
pipeline_id: The pipeline ID.
node_id: The node ID.
pipeline_run_id: The pipeline run ID that the node belongs to. Only
executions from the specified pipeline run are returned if specified.
order_by: The field of execution to order results by.
is_asc: If True, the results will be returned in the ascending order. If
False, the result will be returned in the descending order.
execution_states: The MLMD execution state(s) to pull LIVE artifacts from.
If not specified or is empty, will consider all MLMD execution states.
min_last_update_time_since_epoch: The minimum update time of MLMD executions
in the format of milliseconds since the unix epoch. If not specified, will
consider all MLMD executions.
Returns:
A list of executions of the given pipeline node.
"""
node_context_name = compiler_utils.node_context_name(
pipeline_id, node_id
)
node_executions_filter_queries = []
node_executions_filter_queries.append(
q.And(
[
f'contexts_0.type = "{constants.NODE_CONTEXT_TYPE_NAME}"',
f'contexts_0.name = "{node_context_name}"',
]
)
)
if pipeline_run_id:
node_executions_filter_queries.append(
q.And(
[
f'contexts_1.type = "{constants.PIPELINE_RUN_CONTEXT_TYPE_NAME}"',
f'contexts_1.name = "{pipeline_run_id}"',
]
)
)
if execution_states:
states_str = ",".join(
[
mlmd.proto.Execution.State.Name(state)
for state in execution_states
]
)
states_filter_query = f"last_known_state IN ({states_str})"
node_executions_filter_queries.append(states_filter_query)
if min_last_update_time_since_epoch:
node_executions_filter_queries.append(
f"last_update_time_since_epoch >= {min_last_update_time_since_epoch}"
)
return store.get_executions(
list_options=mlmd.ListOptions(
filter_query=str(q.And(node_executions_filter_queries)),
order_by=order_by,
is_asc=is_asc,
)
)
| ["def","get_node_executions","(","store",":","mlmd.MetadataStore",",","*",",","pipeline_id",":","str",",","node_id",":","str",",","pipeline_run_id",":","Optional","[","str","]","=","None",",","order_by",":","mlmd.OrderByField","=","mlmd.OrderByField.ID",",","is_asc",":","bool","=","True",",","execution_states",":","Optional","[","Sequence","[","``","mlmd.proto.Execution.State","''","]","]","=","None",",","min_last_update_time_since_epoch",":","Optional","[","int","]","=","None",",",")","-",">","Sequence","[","mlmd.proto.Execution","]",":","``","''","''","Gets","all","node","executions",".","Args",":","store",":","A","MetadataStore","object",".","pipeline_id",":","The","pipeline","ID",".","node_id",":","The","node","ID",".","pipeline_run_id",":","The","pipeline","run","ID","that","the","node","belongs","to",".","Only","executions","from","the","specified","pipeline","run","are","returned","if","specified",".","order_by",":","The","field","of","execution","to","order","results","by",".","is_asc",":","If","True",",","the","results","will","be","returned","in","the","ascending","order",".","If","False",",","the","result","will","be","returned","in","the","descending","order",".","execution_states",":","The","MLMD","execution","state","(","s",")","to","pull","LIVE","artifacts","from",".","If","not","specified","or","is","empty",",","will","consider","all","MLMD","execution","states",".","min_last_update_time_since_epoch",":","The","minimum","update","time","of","MLMD","executions","in","the","format","of","milliseconds","since","the","unix","epoch",".","If","not","specified",",","will","consider","all","MLMD","executions",".","Returns",":","A","list","of","executions","of","the","given","pipeline","node.","``","''","''","node_context_name","=","compiler_utils.node_context_name","(","pipeline_id",",","node_id",")","node_executions_filter_queries","=","[","]","node_executions_filter_queries.append","(","q.And","(","[","f'contexts_0.type","=","``","{","constants.NODE_CONTEXT_TYPE_NAME","}","''","'",",","f'contexts_0.name","=","``","{","node_context_name","}","''","'",",","]",")",")","if","pipeline_run_id",":","node_executions_filter_queries.append","(","q.And","(","[","f'contexts_1.type","=","``","{","constants.PIPELINE_RUN_CONTEXT_TYPE_NAME","}","''","'",",","f'contexts_1.name","=","``","{","pipeline_run_id","}","''","'",",","]",")",")","if","execution_states",":","states_str","=","``",",","''",".join","(","[","mlmd.proto.Execution.State.Name","(","state",")","for","state","in","execution_states","]",")","states_filter_query","=","f","''","last_known_state","IN","(","{","states_str","}",")","''","node_executions_filter_queries.append","(","states_filter_query",")","if","min_last_update_time_since_epoch",":","node_executions_filter_queries.append","(","f","''","last_update_time_since_epoch",">","=","{","min_last_update_time_since_epoch","}","''",")","return","store.get_executions","(","list_options=mlmd.ListOptions","(","filter_query=str","(","q.And","(","node_executions_filter_queries",")",")",",","order_by=order_by",",","is_asc=is_asc",",",")",")"] | 90 | 154 | null | store_ext.py | tfx/tfx/orchestration/portable/mlmd/store_ext.py | import collections
import itertools
from typing import Callable, Mapping, Optional, Sequence, Union
from tfx.dsl.compiler import compiler_utils
from tfx.dsl.compiler import constants
from tfx.orchestration.experimental.core import constants
from tfx.orchestration.portable.mlmd import event_lib
from tfx.orchestration.portable.mlmd import filter_query_builder
import ml_metadata | 15 | null | 9 | 6 | null | null | null | Use image node_id 4 for calling a global function with example usage: get_node_executions(store) and returns: store | 115 | node_id 4 | 2,198,858 |
__init__ | Distribution | object | true | self,generator | Sampling distribution for mystic optimizers
| ["Sampling","distribution","for","mystic","optimizers"] | generate a sampling distribution with interface dist(size=None)
input::
- generator: a 'distribution' method from scipy.stats or numpy.random
- rng: a mystic.random_state object [default: random_state('numpy.random')]
- args: positional arguments for the distribtution object
- kwds: keyword arguments for the distribution object
note::
this method only accepts numpy.random methods with the keyword 'size',
and only accepts random_state objects built with module='numpy.random'
note::
generator may be a method object or a string of 'module.object';
similarly, rng may be a random_state object or a string of 'module'
note::
Distributions d1,d2 may be combined by adding data (i.e. d1(n) + d2(n)),
or by adding probabilitiies as Distribution(d1,d2); the former uses
the addition operator and produces a new unnormalized Distribution,
while the latter produces a new Distribution which randomly chooses from
the Distributions provided
note::
a normalization factor can be incorporated through the multiplication
or division operator, and is stored in the Distribution as 'norm'
| ["generate","a","sampling","distribution","with","interface","dist","(","size=None",")","input",":",":","-","generator",":","a","'distribution","'","method","from","scipy.stats","or","numpy.random","-","rng",":","a","mystic.random_state","object","[","default",":","random_state","(","'numpy.random","'",")","]","-","args",":","positional","arguments","for","the","distribtution","object","-","kwds",":","keyword","arguments","for","the","distribution","object","note",":",":","this","method","only","accepts","numpy.random","methods","with","the","keyword","'size","'",",","and","only","accepts","random_state","objects","built","with","module='numpy.random'","note",":",":","generator","may","be","a","method","object","or","a","string","of","'module.object","'",";","similarly",",","rng","may","be","a","random_state","object","or","a","string","of","'module'","note",":",":","Distributions","d1",",","d2","may","be","combined","by","adding","data","(","i.e",".","d1","(","n",")","+","d2","(","n",")",")",",","or","by","adding","probabilitiies","as","Distribution","(","d1",",","d2",")",";","the","former","uses","the","addition","operator","and","produces","a","new","unnormalized","Distribution",",","while","the","latter","produces","a","new","Distribution","which","randomly","chooses","from","the","Distributions","provided","note",":",":","a","normalization","factor","can","be","incorporated","through","the","multiplication","or","division","operator",",","and","is","stored","in","the","Distribution","as","'norm","'"] | Distribution | def __init__(self, generator=None, *args, **kwds):
"""
generate a sampling distribution with interface dist(size=None)
input::
- generator: a 'distribution' method from scipy.stats or numpy.random
- rng: a mystic.random_state object [default: random_state('numpy.random')]
- args: positional arguments for the distribtution object
- kwds: keyword arguments for the distribution object
note::
this method only accepts numpy.random methods with the keyword 'size',
and only accepts random_state objects built with module='numpy.random'
note::
generator may be a method object or a string of 'module.object';
similarly, rng may be a random_state object or a string of 'module'
note::
Distributions d1,d2 may be combined by adding data (i.e. d1(n) + d2(n)),
or by adding probabilitiies as Distribution(d1,d2); the former uses
the addition operator and produces a new unnormalized Distribution,
while the latter produces a new Distribution which randomly chooses from
the Distributions provided
note::
a normalization factor can be incorporated through the multiplication
or division operator, and is stored in the Distribution as 'norm'
""" # XXX: generate Distribution from list of Distributions?
self.norm = kwds.pop("norm", 1) + 0
if isinstance(generator, Distribution):
if kwds:
msg = "keyword arguments are invalid with {0} instance".format(
self.__class__.__name__
)
raise TypeError(msg)
if not args:
self._type = generator._type
self.rvs = generator.rvs
self.repr = generator.repr
self.norm *= generator.norm
return
# args can only support additional distribution instances
for arg in args:
if not isinstance(arg, Distribution): # raise TypeError
generator += arg
# use choice from multiple distributions
import numpy as np
generator = (generator,) + args
rep = (
lambda di: "{0}".format(di).split("(", 1)[-1][:-1]
if di._type == "join"
else "{0}".format(di)
)
sig = ", ".join(rep(i) for i in generator)
self.repr = lambda cls, fac: (
"{0}({1}".format(cls, sig)
+ (")" if fac == 1 else ", norm={0})".format(fac))
)
self.rvs = lambda size=None: np.choose(
np.random.choice(range(len(generator)), size=size),
tuple(d(size) for d in generator),
)
self._type = "join"
return
from mystic.tools import random_state
rng = kwds.pop("rng", random_state(module="numpy.random"))
if isinstance(rng, str):
rng = random_state(module=rng)
mod = "numpy.random"
if generator is None:
generator = rng.random
mod = rng.__name__
elif isinstance(generator, str):
from importlib import import_module
if "." in generator:
mod, generator = generator.rsplit(".", 1)
mod = import_module(mod)
else:
mod = rng
generator = getattr(mod, generator)
mod = mod.__name__
if getattr(generator, "rvs", False):
d = generator(*args, **kwds)
self.rvs = lambda size=None: d.rvs(
size=size, random_state=rng
)
name = getattr(
generator, "name", None
) # XXX: also try __name__?
mod = "scipy.stats" # XXX: assumed due to 'd.rvs'
else:
d = getattr(rng, generator.__name__)
self.rvs = lambda size=None: d(size=size, *args, **kwds)
name = generator.__name__
mod = getattr(
rng, "__name__", "numpy.random"
) # XXX: bad default?
name = "'{0}.{1}'".format(mod, name) if name else ""
sig = ", ".join(str(i) for i in args)
kwd = ", ".join("{0}={1}".format(i, j) for i, j in kwds.items())
# nrm = '' if self.norm == 1 else 'norm={0}'.format(self.norm)
# kwd = '{0}, {1}'.format(kwd, nrm) if (kwd and nrm) else (kwd or nrm)
sig = (
"{0}, {1}".format(sig, kwd) if (sig and kwd) else (sig or kwd)
)
if name and sig:
name += ", "
# sig = ", rng='{0}')".format(rng.__name__)
self.repr = lambda cls, fac: (
"{0}({1}".format(cls, name)
+ sig
+ (
""
if fac == 1
else (
(", " if (name or sig) else "")
+ "norm={0}".format(fac)
)
)
+ ")"
)
self._type = "base"
return
| 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| 55 | 144 | null | __init__.py | mystic/mystic/math/__init__.py | from .poly import polyeval, poly1d
from .grid import gridpts, samplepts, fillpts
from .approx import almostEqual, tolerance
from .approx import approx_equal
from .None import discrete
from .None import distance | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 1 to create a new Distribution object from inherited base classes: object with example: obj = Distribution(generator) | 135 | node_id 1 | 1,406,721 |
__call__ | Distribution | object | true | self,size | Sampling distribution for mystic optimizers
| ["Sampling","distribution","for","mystic","optimizers"] | generate a sample of given size (tuple) from the distribution | ["generate","a","sample","of","given","size","(","tuple",")","from","the","distribution"] | unknown | def __call__(self, size=None):
"""generate a sample of given size (tuple) from the distribution"""
return self.norm * self.rvs(size)
| ["def","__call__","(","self",",","size=None",")",":","``","''","''","generate","a","sample","of","given","size","(","tuple",")","from","the","distribution","''","''","''","return","self.norm","*","self.rvs","(","size",")"] | 145 | 147 | null | __init__.py | mystic/mystic/math/__init__.py | from .poly import polyeval, poly1d
from .grid import gridpts, samplepts, fillpts
from .approx import almostEqual, tolerance
from .approx import approx_equal
from .None import discrete
from .None import distance | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 2 for calling the Distribution obj's underlying member method code with example usage: obj.__call__(size) and returns: unknown | 144 | node_id 2 | 1,406,722 |
__repr__ | Distribution | object | true | self | Sampling distribution for mystic optimizers
| ["Sampling","distribution","for","mystic","optimizers"] | null | null | self | def __repr__(self):
return self.repr(self.__class__.__name__, self.norm)
| ["def","__repr__","(","self",")",":","return","self.repr","(","self.__class__.__name__",",","self.norm",")"] | 148 | 149 | null | __init__.py | mystic/mystic/math/__init__.py | from .poly import polyeval, poly1d
from .grid import gridpts, samplepts, fillpts
from .approx import almostEqual, tolerance
from .approx import approx_equal
from .None import discrete
from .None import distance | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 3 for calling the Distribution obj's underlying member method code with example usage: obj.__repr__() and returns: self | 137 | node_id 3 | 1,406,723 |
__add__ | Distribution | object | true | self,dist | Sampling distribution for mystic optimizers
| ["Sampling","distribution","for","mystic","optimizers"] | null | null | new | def __add__(self, dist):
if not isinstance(dist, Distribution):
msg = "unsupported operand type(s) for +: '{0}' and '{1}'".format(
self.__class__.__name__, type(dist)
)
raise TypeError(msg)
# add data from multiple distributions
new = Distribution()
first = "{0}".format(self)
second = "{0}".format(dist)
if self._type == "add":
first = first.split("(", 1)[-1][:-1]
if dist._type == "add":
second = second.split("(", 1)[-1][:-1]
new.repr = lambda cls, fac: (
"{0}({1} + {2}".format(cls, first, second)
+ (")" if fac == 1 else ", norm={0})".format(fac))
)
new.rvs = lambda size=None: (self(size) + dist(size))
new._type = "add"
new.norm = 1
return new
| ["def","__add__","(","self",",","dist",")",":","if","not","isinstance","(","dist",",","Distribution",")",":","msg","=","``","unsupported","operand","type","(","s",")","for","+",":","'","{","0","}","'","and","'","{","1","}","'","''",".format","(","self.__class__.__name__",",","type","(","dist",")",")","raise","TypeError","(","msg",")","#","add","data","from","multiple","distributions","new","=","Distribution","(",")","first","=","``","{","0","}","''",".format","(","self",")","second","=","``","{","0","}","''",".format","(","dist",")","if","self._type","==","``","add","''",":","first","=","first.split","(","``","(","``",",","1",")","[","-1","]","[",":","-1","]","if","dist._type","==","``","add","''",":","second","=","second.split","(","``","(","``",",","1",")","[","-1","]","[",":","-1","]","new.repr","=","lambda","cls",",","fac",":","(","``","{","0","}","(","{","1","}","+","{","2","}","''",".format","(","cls",",","first",",","second",")","+","(","``",")","''","if","fac","==","1","else","``",",","norm=","{","0","}",")","''",".format","(","fac",")",")",")","new.rvs","=","lambda","size=None",":","(","self","(","size",")","+","dist","(","size",")",")","new._type","=","``","add","''","new.norm","=","1","return","new"] | 150 | 164 | null | __init__.py | mystic/mystic/math/__init__.py | from .poly import polyeval, poly1d
from .grid import gridpts, samplepts, fillpts
from .approx import almostEqual, tolerance
from .approx import approx_equal
from .None import discrete
from .None import distance | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 4 for calling the Distribution obj's underlying member method code with example usage: obj.__add__(dist) and returns: new | 139 | node_id 4 | 1,406,724 |
convert_cfg_to_dict | global | null | false | cfg_node,key_list | null | null | null | null | cfg_node,cfg_dict | def convert_cfg_to_dict(cfg_node, key_list=[]):
"""Convert a config node to dictionary"""
if not isinstance(cfg_node, CfgNode):
if type(cfg_node) not in _VALID_TYPES:
print(
"Key {} with value {} is not a valid type; valid types: {}".format(
".".join(key_list), type(cfg_node), _VALID_TYPES
),
)
return cfg_node
else:
cfg_dict = dict(cfg_node)
for k, v in cfg_dict.items():
cfg_dict[k] = convert_cfg_to_dict(v, key_list + [k])
return cfg_dict
| ["def","convert_cfg_to_dict","(","cfg_node",",","key_list=","[","]",")",":","``","''","''","Convert","a","config","node","to","dictionary","''","''","''","if","not","isinstance","(","cfg_node",",","CfgNode",")",":","if","type","(","cfg_node",")","not","in","_VALID_TYPES",":","print","(","``","Key","{","}","with","value","{","}","is","not","a","valid","type",";","valid","types",":","{","}","''",".format","(","``",".","``",".join","(","key_list",")",",","type","(","cfg_node",")",",","_VALID_TYPES",")",",",")","return","cfg_node","else",":","cfg_dict","=","dict","(","cfg_node",")","for","k",",","v","in","cfg_dict.items","(",")",":","cfg_dict","[","k","]","=","convert_cfg_to_dict","(","v",",","key_list","+","[","k","]",")","return","cfg_dict"] | 65 | 76 | null | config.py | OpenPrompt/openprompt/config.py | import argparse
from yacs.config import CfgNode
import sys
from openprompt.utils.utils import check_config_conflicts
from .default_config import get_default_config
from openprompt.utils.logging import logger
import os | 15 | null | 7 | 8 | null | null | null | Use image node_id 4 for calling a global function with example usage: convert_cfg_to_dict(cfg_node, key_list) and returns: cfg_node, cfg_dict | 141 | node_id 4 | 152,520 |
get_live_output_artifacts_of_node_by_output_key | global | null | false | store | null | null | null | null | output_artifacts_by_output_key,dict,dict | def get_live_output_artifacts_of_node_by_output_key(
store: mlmd.MetadataStore,
*,
pipeline_id: str,
node_id: str,
pipeline_run_id: Optional[str] = None,
execution_states: Optional[
Sequence["mlmd.proto.Execution.State"]
] = None,
) -> Mapping[str, Sequence[Sequence[mlmd.proto.Artifact]]]:
"""Get LIVE output artifacts of the given node grouped by output key.
The LIVE output artifacts associated with an output key are represented as a
list of a list of artifacts.
1. The outer list represents artifacts generated across all executions.
2. The inner list represents artifacts generated by one execution.
3. Elements in the outer list are returned in descending order of the creation
time of the execution associated with them.
4. Elements in the inner list have no order guarantee.
5. If no LIVE output artifacts found for one execution, an empty list will be
returned.
Args:
store: A MetadataStore object.
pipeline_id: A pipeline ID.
node_id: A node ID.
pipeline_run_id: The pipeline run ID that the node belongs to. Only
artifacts from the specified pipeline run are returned if specified.
execution_states: The MLMD execution state(s) to pull LIVE artifacts from.
If not specified or is empty, will consider MLMD execution states in
[COMPLETE, CACHED].
Returns:
A mapping from output key to all output artifacts from the given node.
"""
# Step 1: Get LIVE artifacts attributed to node with `node_id`.
live_artifacts = _get_node_live_artifacts(
store,
pipeline_id=pipeline_id,
node_id=node_id,
pipeline_run_id=pipeline_run_id,
)
if not live_artifacts:
return {}
# Step 2: Get executions associated with node that created `live_artifacts`
# ordered by execution creation time in descending order.
# These executions should satisfy the constraint:
# min (execution update time) >= min (artifact create time)
min_live_artifact_create_time = min(
[a.create_time_since_epoch for a in live_artifacts], default=0
)
# Within one transaction that updates both artifacts and execution, the
# timestamp of execution is larger or equal than that of the artifacts.
# Apply time skew for the artifacts created before cl/574333630 is rolled out.
# TODO(b/275231956): Remove the following 2 lines if we are sure that there
# are no more artifacts older than the timestamp.
if (
min_live_artifact_create_time
< orchestration_constants.TIME_SKEW_DATE
):
min_live_artifact_create_time -= 24 * 3600 * 1000
executions_ordered_by_desc_creation_time = get_node_executions(
store,
pipeline_id=pipeline_id,
node_id=node_id,
pipeline_run_id=pipeline_run_id,
order_by=mlmd.OrderByField.CREATE_TIME,
is_asc=False,
execution_states=execution_states,
min_last_update_time_since_epoch=min_live_artifact_create_time,
)
if not executions_ordered_by_desc_creation_time:
return {}
# Step 3: Get output events by executions obtained in step 2.
events_by_executions = store.get_events_by_execution_ids(
_ids(executions_ordered_by_desc_creation_time)
)
output_events = [
e
for e in events_by_executions
if event_lib.is_valid_output_event(e)
]
# Step 4: Construct and return `output_artifacts_by_output_key` from events.
#
# Create a mapping from execution_id to an empty list first to make sure
# iteration orders of output_events_by_execution_id and
# output_artifacts_map_by_execution_id are both in desc order of execution's
# creation time.
#
# The desc order is guaranteed by execution_ids and dict is guaranteed to be
# iterated in the insertion order of keys.
output_events_by_execution_id = {
execution.id: []
for execution in executions_ordered_by_desc_creation_time
}
for event in output_events:
output_events_by_execution_id[event.execution_id].append(
event
)
artifact_ids_by_output_key_map_by_execution_id = {}
for exec_id, events in output_events_by_execution_id.items():
output_artifacts_map = (
event_lib.reconstruct_artifact_id_multimap(events)
)
artifact_ids_by_output_key_map_by_execution_id[
exec_id
] = output_artifacts_map
output_artifacts_by_output_key = collections.defaultdict(list)
# Keep only LIVE output artifacts when constructing the result.
live_artifacts_by_id = {a.id: a for a in live_artifacts}
for (
artifact_ids_by_output_key
) in artifact_ids_by_output_key_map_by_execution_id.values():
for (
output_key,
artifact_ids,
) in artifact_ids_by_output_key.items():
live_output_artifacts = [
live_artifacts_by_id[artifact_id]
for artifact_id in artifact_ids
if artifact_id in live_artifacts_by_id
]
output_artifacts_by_output_key[output_key].append(
live_output_artifacts
)
return output_artifacts_by_output_key
| 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| 157 | 273 | null | store_ext.py | tfx/tfx/orchestration/portable/mlmd/store_ext.py | import collections
import itertools
from typing import Callable, Mapping, Optional, Sequence, Union
from tfx.dsl.compiler import compiler_utils
from tfx.dsl.compiler import constants
from tfx.orchestration.experimental.core import constants
from tfx.orchestration.portable.mlmd import event_lib
from tfx.orchestration.portable.mlmd import filter_query_builder
import ml_metadata | 15 | null | 9 | 6 | null | null | null | Use image node_id 5 for calling a global function with example usage: get_live_output_artifacts_of_node_by_output_key(store) and returns: output_artifacts_by_output_key, dict, dict | 180 | node_id 5 | 2,198,859 |
test_zero_fwd | global | null | false | num_expert,batch_size,d_hidden,world_size | null | null | null | null | null | def test_zero_fwd(
num_expert=2, batch_size=4, d_hidden=8, world_size=1
):
_run_distributed(
"_test_zero_fwd",
1,
{
"num_expert": num_expert,
"batch_size": batch_size,
"d_hidden": d_hidden,
},
script=__file__,
)
| ["def","test_zero_fwd","(","num_expert=2",",","batch_size=4",",","d_hidden=8",",","world_size=1",")",":","_run_distributed","(","``","_test_zero_fwd","''",",","1",",","{","``","num_expert","''",":","num_expert",",","``","batch_size","''",":","batch_size",",","``","d_hidden","''",":","d_hidden",",","}",",","script=__file__",",",")"] | 23 | 32 | null | test_zero.py | thu-pacman-faster-moe/tests/test_zero.py | import os
import sys
import json
import torch
from fmoe.layers import _fmoe_general_global_forward
from fmoe import FMoETransformerMLP
from test_ddp import _run_distributed | 15 | null | 7 | 4 | null | null | null | Use image node_id 1 for calling a global function with example usage: test_zero_fwd(num_expert, batch_size, d_hidden, world_size) without return types | 150 | node_id 1 | 2,201,995 |
_test_zero_fwd | global | null | false | num_expert,batch_size,d_hidden,world_size | null | null | null | null | null | def _test_zero_fwd(
num_expert=2, batch_size=4, d_hidden=8, world_size=1
):
inp = torch.rand(batch_size, d_hidden).cuda()
gate = torch.zeros(batch_size, dtype=torch.int64).cuda()
x = _fmoe_general_global_forward(
inp, gate, lambda x, y: x, num_expert, world_size
)
| ["def","_test_zero_fwd","(","num_expert=2",",","batch_size=4",",","d_hidden=8",",","world_size=1",")",":","inp","=","torch.rand","(","batch_size",",","d_hidden",")",".cuda","(",")","gate","=","torch.zeros","(","batch_size",",","dtype=torch.int64",")",".cuda","(",")","x","=","_fmoe_general_global_forward","(","inp",",","gate",",","lambda","x",",","y",":","x",",","num_expert",",","world_size",")"] | 34 | 38 | null | test_zero.py | thu-pacman-faster-moe/tests/test_zero.py | import os
import sys
import json
import torch
from fmoe.layers import _fmoe_general_global_forward
from fmoe import FMoETransformerMLP
from test_ddp import _run_distributed | 15 | null | 7 | 4 | null | null | null | Use image node_id 2 for calling a global function with example usage: _test_zero_fwd(num_expert, batch_size, d_hidden, world_size) without return types | 151 | node_id 2 | 2,201,996 |
test_zero_transformer | global | null | false | num_expert,batch_size,d_hidden,world_size | null | null | null | null | null | def test_zero_transformer(
num_expert=2, batch_size=4, d_hidden=8, world_size=1
):
_run_distributed(
"_test_zero_transformer",
1,
{
"num_expert": num_expert,
"batch_size": batch_size,
"d_hidden": d_hidden,
},
script=__file__,
)
| ["def","test_zero_transformer","(","num_expert=2",",","batch_size=4",",","d_hidden=8",",","world_size=1",")",":","_run_distributed","(","``","_test_zero_transformer","''",",","1",",","{","``","num_expert","''",":","num_expert",",","``","batch_size","''",":","batch_size",",","``","d_hidden","''",":","d_hidden",",","}",",","script=__file__",",",")"] | 41 | 50 | null | test_zero.py | thu-pacman-faster-moe/tests/test_zero.py | import os
import sys
import json
import torch
from fmoe.layers import _fmoe_general_global_forward
from fmoe import FMoETransformerMLP
from test_ddp import _run_distributed | 15 | null | 7 | 4 | null | null | null | Use image node_id 3 for calling a global function with example usage: test_zero_transformer(num_expert, batch_size, d_hidden, world_size) without return types | 158 | node_id 3 | 2,201,997 |
_test_zero_transformer | global | null | false | num_expert,batch_size,d_hidden,world_size | null | null | null | null | null | def _test_zero_transformer(
num_expert=2, batch_size=4, d_hidden=8, world_size=1
):
inp = torch.rand(batch_size, d_hidden).cuda()
mask = torch.zeros(inp.shape[0], dtype=torch.long)
mask[1] = 1
mask_dict = {1: torch.zeros(d_hidden).cuda()}
model = FMoETransformerMLP(
num_expert,
d_hidden,
d_hidden * 4,
world_size,
gate=ConstantGate,
mask=mask,
mask_dict=mask_dict,
).cuda()
oup = model(inp)
| ["def","_test_zero_transformer","(","num_expert=2",",","batch_size=4",",","d_hidden=8",",","world_size=1",")",":","inp","=","torch.rand","(","batch_size",",","d_hidden",")",".cuda","(",")","mask","=","torch.zeros","(","inp.shape","[","0","]",",","dtype=torch.long",")","mask","[","1","]","=","1","mask_dict","=","{","1",":","torch.zeros","(","d_hidden",")",".cuda","(",")","}","model","=","FMoETransformerMLP","(","num_expert",",","d_hidden",",","d_hidden","*","4",",","world_size",",","gate=ConstantGate",",","mask=mask",",","mask_dict=mask_dict",",",")",".cuda","(",")","oup","=","model","(","inp",")"] | 52 | 61 | null | test_zero.py | thu-pacman-faster-moe/tests/test_zero.py | import os
import sys
import json
import torch
from fmoe.layers import _fmoe_general_global_forward
from fmoe import FMoETransformerMLP
from test_ddp import _run_distributed | 15 | null | 7 | 4 | null | null | null | Use image node_id 4 for calling a global function with example usage: _test_zero_transformer(num_expert, batch_size, d_hidden, world_size) without return types | 159 | node_id 4 | 2,201,998 |
setUp | TestHighConfidence | unittest | true | self | A unittest class for testing the HighConfidence postprocessor. | ["A","unittest","class","for","testing","the","HighConfidence","postprocessor","."] | null | null | null | def setUp(self):
master_seed(seed=1234)
| ["def","setUp","(","self",")",":","master_seed","(","seed=1234",")"] | 41 | 42 | null | test_high_confidence.py | adversarial-robustness-toolbox/tests/defences/test_high_confidence.py | import logging
import unittest
import numpy
from art.defences.postprocessor import HighConfidence
from art.utils import load_dataset
from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 2 for calling the TestHighConfidence obj's underlying member method code with example usage: obj.setUp() without return types | 143 | node_id 2 | 235,297 |
test_difference | TestIntervalIndex | null | true | self,closed,sort | null | null | null | null | null | def test_difference(self, closed, sort):
index = IntervalIndex.from_arrays(
[1, 0, 3, 2], [1, 2, 3, 4], closed=closed
)
result = index.difference(index[:1], sort=sort)
expected = index[1:]
if sort is None:
expected = expected.sort_values()
tm.assert_index_equal(result, expected)
# GH 19101: empty result, same dtype
result = index.difference(index, sort=sort)
expected = empty_index(dtype="int64", closed=closed)
tm.assert_index_equal(result, expected)
# GH 19101: empty result, different dtypes
other = IntervalIndex.from_arrays(
index.left.astype("float64"), index.right, closed=closed
)
result = index.difference(other, sort=sort)
tm.assert_index_equal(result, expected)
| ["def","test_difference","(","self",",","closed",",","sort",")",":","index","=","IntervalIndex.from_arrays","(","[","1",",","0",",","3",",","2","]",",","[","1",",","2",",","3",",","4","]",",","closed=closed",")","result","=","index.difference","(","index","[",":1","]",",","sort=sort",")","expected","=","index","[","1",":","]","if","sort","is","None",":","expected","=","expected.sort_values","(",")","tm.assert_index_equal","(","result",",","expected",")","#","GH","19101",":","empty","result",",","same","dtype","result","=","index.difference","(","index",",","sort=sort",")","expected","=","empty_index","(","dtype=","''","int64","''",",","closed=closed",")","tm.assert_index_equal","(","result",",","expected",")","#","GH","19101",":","empty","result",",","different","dtypes","other","=","IntervalIndex.from_arrays","(","index.left.astype","(","``","float64","''",")",",","index.right",",","closed=closed",")","result","=","index.difference","(","other",",","sort=sort",")","tm.assert_index_equal","(","result",",","expected",")"] | 131 | 149 | null | test_setops.py | pandas/pandas/tests/indexes/interval/test_setops.py | import numpy
import pytest
from pandas import Index, IntervalIndex, Timestamp, interval_range
import pandas._testing | 15 | 1 | 4 | 2 | 0 | 8 | null | Use image node_id 6 for calling the TestIntervalIndex obj's underlying member method code with example usage: obj.test_difference(closed, sort) without return types | 164 | node_id 6 | 1,514,638 |
test_ThompsonSamplerUniformWeights | ThompsonSamplerTest | TestCase | true | self | null | null | null | null | null | def test_ThompsonSamplerUniformWeights(self) -> None:
generator = ThompsonSampler(min_weight=0.0, uniform_weights=True)
generator.fit(
# pyre-fixme[6]: For 1st param expected `List[List[List[Union[None,
# bool, float, int, str]]]]` but got `List[List[List[int]]]`.
Xs=self.Xs,
# pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
# `List[List[int]]`.
Ys=self.Ys,
# pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
# `List[List[int]]`.
Yvars=self.Yvars,
# pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
outcome_names=self.outcome_names,
)
arms, weights, _ = generator.gen(
n=3,
# pyre-fixme[6]: For 2nd param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
objective_weights=np.ones(1),
)
self.assertEqual(arms, [[4, 4], [3, 3], [2, 2]])
for weight, expected_weight in zip(weights, [1.0, 1.0, 1.0]):
self.assertAlmostEqual(weight, expected_weight, 1)
| ["def","test_ThompsonSamplerUniformWeights","(","self",")","-",">","None",":","generator","=","ThompsonSampler","(","min_weight=0.0",",","uniform_weights=True",")","generator.fit","(","#","pyre-fixme","[","6","]",":","For","1st","param","expected","`","List","[","List","[","List","[","Union","[","None",",","#","bool",",","float",",","int",",","str","]","]","]","]","`","but","got","`","List","[","List","[","List","[","int","]","]","]","`",".","Xs=self.Xs",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Ys=self.Ys",",","#","pyre-fixme","[","6","]",":","For","3rd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Yvars=self.Yvars",",","#","pyre-fixme","[","6","]",":","For","4th","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","outcome_names=self.outcome_names",",",")","arms",",","weights",",","_","=","generator.gen","(","n=3",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","objective_weights=np.ones","(","1",")",",",")","self.assertEqual","(","arms",",","[","[","4",",","4","]",",","[","3",",","3","]",",","[","2",",","2","]","]",")","for","weight",",","expected_weight","in","zip","(","weights",",","[","1.0",",","1.0",",","1.0","]",")",":","self.assertAlmostEqual","(","weight",",","expected_weight",",","1",")"] | 146 | 172 | null | test_thompson.py | Ax/ax/models/tests/test_thompson.py | import numpy
from ax.exceptions.model import ModelError
from ax.models.discrete.thompson import ThompsonSampler
from ax.utils.common.testutils import TestCase | 15 | 1 | 4 | 0 | 1 | 9 | 1 | Use image node_id 5 for calling the ThompsonSamplerTest obj's underlying member method code with example usage: obj.test_ThompsonSamplerUniformWeights() without return types | 173 | node_id 5 | 9,539 |
test_ThompsonSamplerMinWeight | ThompsonSamplerTest | TestCase | true | self | null | null | null | null | null | def test_ThompsonSamplerMinWeight(self) -> None:
generator = ThompsonSampler(min_weight=0.01)
generator.fit(
# pyre-fixme[6]: For 1st param expected `List[List[List[Union[None,
# bool, float, int, str]]]]` but got `List[List[List[int]]]`.
Xs=self.Xs,
# pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
# `List[List[int]]`.
Ys=self.Ys,
# pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
# `List[List[int]]`.
Yvars=self.Yvars,
# pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
outcome_names=self.outcome_names,
)
arms, weights, _ = generator.gen(
n=5,
# pyre-fixme[6]: For 2nd param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
objective_weights=np.ones(1),
)
self.assertEqual(arms, [[4, 4], [3, 3], [2, 2]])
for weight, expected_weight in zip(
weights, [3 * i for i in [0.725, 0.225, 0.05]]
):
self.assertAlmostEqual(weight, expected_weight, 1)
| ["def","test_ThompsonSamplerMinWeight","(","self",")","-",">","None",":","generator","=","ThompsonSampler","(","min_weight=0.01",")","generator.fit","(","#","pyre-fixme","[","6","]",":","For","1st","param","expected","`","List","[","List","[","List","[","Union","[","None",",","#","bool",",","float",",","int",",","str","]","]","]","]","`","but","got","`","List","[","List","[","List","[","int","]","]","]","`",".","Xs=self.Xs",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Ys=self.Ys",",","#","pyre-fixme","[","6","]",":","For","3rd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Yvars=self.Yvars",",","#","pyre-fixme","[","6","]",":","For","4th","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","outcome_names=self.outcome_names",",",")","arms",",","weights",",","_","=","generator.gen","(","n=5",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","objective_weights=np.ones","(","1",")",",",")","self.assertEqual","(","arms",",","[","[","4",",","4","]",",","[","3",",","3","]",",","[","2",",","2","]","]",")","for","weight",",","expected_weight","in","zip","(","weights",",","[","3","*","i","for","i","in","[","0.725",",","0.225",",","0.05","]","]",")",":","self.assertAlmostEqual","(","weight",",","expected_weight",",","1",")"] | 116 | 144 | null | test_thompson.py | Ax/ax/models/tests/test_thompson.py | import numpy
from ax.exceptions.model import ModelError
from ax.models.discrete.thompson import ThompsonSampler
from ax.utils.common.testutils import TestCase | 15 | 1 | 4 | 0 | 1 | 9 | 1 | Use image node_id 4 for calling the ThompsonSamplerTest obj's underlying member method code with example usage: obj.test_ThompsonSamplerMinWeight() without return types | 168 | node_id 4 | 9,538 |
test_ThompsonSamplerValidation | ThompsonSamplerTest | TestCase | true | self | null | null | null | null | null | def test_ThompsonSamplerValidation(self) -> None:
generator = ThompsonSampler(min_weight=0.01)
# all Xs are not the same
with self.assertRaises(ValueError):
generator.fit(
Xs=[
[[1, 1], [2, 2], [3, 3], [4, 4]],
[[1, 1], [2, 2], [4, 4]],
],
# pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
# `List[List[int]]`.
Ys=self.Ys,
# pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
# `List[List[int]]`.
Yvars=self.Yvars,
# pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
outcome_names=self.outcome_names,
)
# multiple observations per parameterization
with self.assertRaises(ValueError):
generator.fit(
Xs=[[[1, 1], [2, 2], [2, 2]]],
# pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
# `List[List[int]]`.
Ys=self.Ys,
# pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
# `List[List[int]]`.
Yvars=self.Yvars,
# pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
outcome_names=self.outcome_names,
)
# these are not the same observations, so should not error
generator.fit(
Xs=[[[1, 1], [2.0, 2], [2, 2]]],
# pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
# `List[List[int]]`.
Ys=self.Ys,
# pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
# `List[List[int]]`.
Yvars=self.Yvars,
# pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
outcome_names=self.outcome_names,
)
# requires objective weights
with self.assertRaises(ValueError):
# pyre-fixme[6]: For 2nd param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
generator.gen(
5, self.parameter_values, objective_weights=None
)
| ["def","test_ThompsonSamplerValidation","(","self",")","-",">","None",":","generator","=","ThompsonSampler","(","min_weight=0.01",")","#","all","Xs","are","not","the","same","with","self.assertRaises","(","ValueError",")",":","generator.fit","(","Xs=","[","[","[","1",",","1","]",",","[","2",",","2","]",",","[","3",",","3","]",",","[","4",",","4","]","]",",","[","[","1",",","1","]",",","[","2",",","2","]",",","[","4",",","4","]","]",",","]",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Ys=self.Ys",",","#","pyre-fixme","[","6","]",":","For","3rd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Yvars=self.Yvars",",","#","pyre-fixme","[","6","]",":","For","4th","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","outcome_names=self.outcome_names",",",")","#","multiple","observations","per","parameterization","with","self.assertRaises","(","ValueError",")",":","generator.fit","(","Xs=","[","[","[","1",",","1","]",",","[","2",",","2","]",",","[","2",",","2","]","]","]",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Ys=self.Ys",",","#","pyre-fixme","[","6","]",":","For","3rd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Yvars=self.Yvars",",","#","pyre-fixme","[","6","]",":","For","4th","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","outcome_names=self.outcome_names",",",")","#","these","are","not","the","same","observations",",","so","should","not","error","generator.fit","(","Xs=","[","[","[","1",",","1","]",",","[","2.0",",","2","]",",","[","2",",","2","]","]","]",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Ys=self.Ys",",","#","pyre-fixme","[","6","]",":","For","3rd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Yvars=self.Yvars",",","#","pyre-fixme","[","6","]",":","For","4th","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","outcome_names=self.outcome_names",",",")","#","requires","objective","weights","with","self.assertRaises","(","ValueError",")",":","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","generator.gen","(","5",",","self.parameter_values",",","objective_weights=None",")"] | 60 | 114 | null | test_thompson.py | Ax/ax/models/tests/test_thompson.py | import numpy
from ax.exceptions.model import ModelError
from ax.models.discrete.thompson import ThompsonSampler
from ax.utils.common.testutils import TestCase | 15 | 1 | 4 | 0 | 1 | 9 | 1 | Use image node_id 3 for calling the ThompsonSamplerTest obj's underlying member method code with example usage: obj.test_ThompsonSamplerValidation() without return types | 169 | node_id 3 | 9,537 |
test_ThompsonSampler | ThompsonSamplerTest | TestCase | true | self | null | null | null | null | null | def test_ThompsonSampler(self) -> None:
generator = ThompsonSampler(min_weight=0.0)
generator.fit(
# pyre-fixme[6]: For 1st param expected `List[List[List[Union[None,
# bool, float, int, str]]]]` but got `List[List[List[int]]]`.
Xs=self.Xs,
# pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
# `List[List[int]]`.
Ys=self.Ys,
# pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
# `List[List[int]]`.
Yvars=self.Yvars,
# pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
outcome_names=self.outcome_names,
)
arms, weights, gen_metadata = generator.gen(
n=3,
# pyre-fixme[6]: For 2nd param expected `List[List[Union[None, bool,
# float, int, str]]]` but got `List[List[int]]`.
parameter_values=self.parameter_values,
objective_weights=np.ones(1),
)
self.assertEqual(arms, [[4, 4], [3, 3], [2, 2]])
for weight, expected_weight in zip(
weights, [3 * i for i in [0.725, 0.225, 0.05]]
):
self.assertAlmostEqual(weight, expected_weight, 1)
self.assertEqual(len(gen_metadata["arms_to_weights"]), 4)
| ["def","test_ThompsonSampler","(","self",")","-",">","None",":","generator","=","ThompsonSampler","(","min_weight=0.0",")","generator.fit","(","#","pyre-fixme","[","6","]",":","For","1st","param","expected","`","List","[","List","[","List","[","Union","[","None",",","#","bool",",","float",",","int",",","str","]","]","]","]","`","but","got","`","List","[","List","[","List","[","int","]","]","]","`",".","Xs=self.Xs",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Ys=self.Ys",",","#","pyre-fixme","[","6","]",":","For","3rd","param","expected","`","List","[","List","[","float","]","]","`","but","got","#","`","List","[","List","[","int","]","]","`",".","Yvars=self.Yvars",",","#","pyre-fixme","[","6","]",":","For","4th","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","outcome_names=self.outcome_names",",",")","arms",",","weights",",","gen_metadata","=","generator.gen","(","n=3",",","#","pyre-fixme","[","6","]",":","For","2nd","param","expected","`","List","[","List","[","Union","[","None",",","bool",",","#","float",",","int",",","str","]","]","]","`","but","got","`","List","[","List","[","int","]","]","`",".","parameter_values=self.parameter_values",",","objective_weights=np.ones","(","1",")",",",")","self.assertEqual","(","arms",",","[","[","4",",","4","]",",","[","3",",","3","]",",","[","2",",","2","]","]",")","for","weight",",","expected_weight","in","zip","(","weights",",","[","3","*","i","for","i","in","[","0.725",",","0.225",",","0.05","]","]",")",":","self.assertAlmostEqual","(","weight",",","expected_weight",",","1",")","self.assertEqual","(","len","(","gen_metadata","[","``","arms_to_weights","''","]",")",",","4",")"] | 29 | 58 | null | test_thompson.py | Ax/ax/models/tests/test_thompson.py | import numpy
from ax.exceptions.model import ModelError
from ax.models.discrete.thompson import ThompsonSampler
from ax.utils.common.testutils import TestCase | 15 | 1 | 4 | 0 | 1 | 9 | 1 | Use image node_id 2 for calling the ThompsonSamplerTest obj's underlying member method code with example usage: obj.test_ThompsonSampler() without return types | 159 | node_id 2 | 9,536 |
setUp | ThompsonSamplerTest | TestCase | true | self | null | null | null | null | null | def setUp(self) -> None:
self.Xs = [
[[1, 1], [2, 2], [3, 3], [4, 4]]
] # 4 arms, each of dimensionality 2
self.Ys = [[1, 2, 3, 4]]
self.Yvars = [[1, 1, 1, 1]]
self.parameter_values = [[1, 2, 3, 4], [1, 2, 3, 4]]
self.outcome_names = ["x", "y"] # not used for regular TS
self.multiple_metrics_Xs = [
[[1, 1], [2, 2], [3, 3], [4, 4]],
[[1, 1], [2, 2], [3, 3], [4, 4]],
] # 2 metrics, 4 arms, each of dimensionality 2
self.multiple_metrics_Ys = [[1, 2, 3, 4], [0, 0, 0, 1]]
self.multiple_metrics_Yvars = [[1, 1, 1, 1], [1, 1, 1, 1]]
| ["def","setUp","(","self",")","-",">","None",":","self.Xs","=","[","[","[","1",",","1","]",",","[","2",",","2","]",",","[","3",",","3","]",",","[","4",",","4","]","]","]","#","4","arms",",","each","of","dimensionality","2","self.Ys","=","[","[","1",",","2",",","3",",","4","]","]","self.Yvars","=","[","[","1",",","1",",","1",",","1","]","]","self.parameter_values","=","[","[","1",",","2",",","3",",","4","]",",","[","1",",","2",",","3",",","4","]","]","self.outcome_names","=","[","``","x","''",",","``","y","''","]","#","not","used","for","regular","TS","self.multiple_metrics_Xs","=","[","[","[","1",",","1","]",",","[","2",",","2","]",",","[","3",",","3","]",",","[","4",",","4","]","]",",","[","[","1",",","1","]",",","[","2",",","2","]",",","[","3",",","3","]",",","[","4",",","4","]","]",",","]","#","2","metrics",",","4","arms",",","each","of","dimensionality","2","self.multiple_metrics_Ys","=","[","[","1",",","2",",","3",",","4","]",",","[","0",",","0",",","0",",","1","]","]","self.multiple_metrics_Yvars","=","[","[","1",",","1",",","1",",","1","]",",","[","1",",","1",",","1",",","1","]","]"] | 15 | 27 | null | test_thompson.py | Ax/ax/models/tests/test_thompson.py | import numpy
from ax.exceptions.model import ModelError
from ax.models.discrete.thompson import ThompsonSampler
from ax.utils.common.testutils import TestCase | 15 | 1 | 4 | 0 | 1 | 9 | 1 | Use image node_id 1 for calling the ThompsonSamplerTest obj's underlying member method code with example usage: obj.setUp() without return types | 144 | node_id 1 | 9,535 |
test_decimals_0_1 | TestHighConfidence | unittest | true | self | A unittest class for testing the HighConfidence postprocessor. | ["A","unittest","class","for","testing","the","HighConfidence","postprocessor","."] | Test with cutoff of 0.1. | ["Test","with","cutoff","of","0.1","."] | null | def test_decimals_0_1(self):
"""
Test with cutoff of 0.1.
"""
(_, _), (x_test, _) = self.mnist
classifier = get_image_classifier_kr_tf()
preds = classifier.predict(x_test[0:1])
postprocessor = HighConfidence(cutoff=0.1)
post_preds = postprocessor(preds=preds)
classifier_prediction_expected = np.asarray(
[
[
0.12109935,
0.0498215,
0.0993958,
0.06410096,
0.11366928,
0.04645343,
0.06419807,
0.30685693,
0.07616714,
0.05823757,
]
],
dtype=np.float32,
)
post_classifier_prediction_expected = np.asarray(
[
[
0.12109935,
0.0,
0.0,
0.0,
0.11366928,
0.0,
0.0,
0.30685693,
0.0,
0.0,
]
],
dtype=np.float32,
)
np.testing.assert_array_almost_equal(
preds, classifier_prediction_expected, decimal=4
)
np.testing.assert_array_almost_equal(
post_preds, post_classifier_prediction_expected, decimal=4
)
| ["def","test_decimals_0_1","(","self",")",":","``","''","''","Test","with","cutoff","of","0.1.","``","''","''","(","_",",","_",")",",","(","x_test",",","_",")","=","self.mnist","classifier","=","get_image_classifier_kr_tf","(",")","preds","=","classifier.predict","(","x_test","[","0:1","]",")","postprocessor","=","HighConfidence","(","cutoff=0.1",")","post_preds","=","postprocessor","(","preds=preds",")","classifier_prediction_expected","=","np.asarray","(","[","[","0.12109935",",","0.0498215",",","0.0993958",",","0.06410096",",","0.11366928",",","0.04645343",",","0.06419807",",","0.30685693",",","0.07616714",",","0.05823757",",","]","]",",","dtype=np.float32",",",")","post_classifier_prediction_expected","=","np.asarray","(","[","[","0.12109935",",","0.0",",","0.0",",","0.0",",","0.11366928",",","0.0",",","0.0",",","0.30685693",",","0.0",",","0.0",",","]","]",",","dtype=np.float32",",",")","np.testing.assert_array_almost_equal","(","preds",",","classifier_prediction_expected",",","decimal=4",")","np.testing.assert_array_almost_equal","(","post_preds",",","post_classifier_prediction_expected",",","decimal=4",")"] | 44 | 76 | null | test_high_confidence.py | adversarial-robustness-toolbox/tests/defences/test_high_confidence.py | import logging
import unittest
import numpy
from art.defences.postprocessor import HighConfidence
from art.utils import load_dataset
from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 3 for calling the TestHighConfidence obj's underlying member method code with example usage: obj.test_decimals_0_1() without return types | 155 | node_id 3 | 235,298 |
test_decimals_0_2 | TestHighConfidence | unittest | true | self | A unittest class for testing the HighConfidence postprocessor. | ["A","unittest","class","for","testing","the","HighConfidence","postprocessor","."] | Test with cutoff of 0.2. | ["Test","with","cutoff","of","0.2","."] | null | def test_decimals_0_2(self):
"""
Test with cutoff of 0.2.
"""
(_, _), (x_test, _) = self.mnist
classifier = get_image_classifier_kr_tf()
preds = classifier.predict(x_test[0:1])
postprocessor = HighConfidence(cutoff=0.2)
post_preds = postprocessor(preds=preds)
classifier_prediction_expected = np.asarray(
[
[
0.12109935,
0.0498215,
0.0993958,
0.06410096,
0.11366928,
0.04645343,
0.06419807,
0.30685693,
0.07616714,
0.05823757,
]
],
dtype=np.float32,
)
post_classifier_prediction_expected = np.asarray(
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.30685693, 0.0, 0.0]],
dtype=np.float32,
)
np.testing.assert_array_almost_equal(
preds, classifier_prediction_expected, decimal=4
)
np.testing.assert_array_almost_equal(
post_preds, post_classifier_prediction_expected, decimal=4
)
| ["def","test_decimals_0_2","(","self",")",":","``","''","''","Test","with","cutoff","of","0.2.","``","''","''","(","_",",","_",")",",","(","x_test",",","_",")","=","self.mnist","classifier","=","get_image_classifier_kr_tf","(",")","preds","=","classifier.predict","(","x_test","[","0:1","]",")","postprocessor","=","HighConfidence","(","cutoff=0.2",")","post_preds","=","postprocessor","(","preds=preds",")","classifier_prediction_expected","=","np.asarray","(","[","[","0.12109935",",","0.0498215",",","0.0993958",",","0.06410096",",","0.11366928",",","0.04645343",",","0.06419807",",","0.30685693",",","0.07616714",",","0.05823757",",","]","]",",","dtype=np.float32",",",")","post_classifier_prediction_expected","=","np.asarray","(","[","[","0.0",",","0.0",",","0.0",",","0.0",",","0.0",",","0.0",",","0.0",",","0.30685693",",","0.0",",","0.0","]","]",",","dtype=np.float32",",",")","np.testing.assert_array_almost_equal","(","preds",",","classifier_prediction_expected",",","decimal=4",")","np.testing.assert_array_almost_equal","(","post_preds",",","post_classifier_prediction_expected",",","decimal=4",")"] | 78 | 110 | null | test_high_confidence.py | adversarial-robustness-toolbox/tests/defences/test_high_confidence.py | import logging
import unittest
import numpy
from art.defences.postprocessor import HighConfidence
from art.utils import load_dataset
from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 4 for calling the TestHighConfidence obj's underlying member method code with example usage: obj.test_decimals_0_2() without return types | 155 | node_id 4 | 235,299 |
test_binary_decimals_0_5 | TestHighConfidence | unittest | true | self | A unittest class for testing the HighConfidence postprocessor. | ["A","unittest","class","for","testing","the","HighConfidence","postprocessor","."] | Test with cutoff of 0.5 for binary classifier. | ["Test","with","cutoff","of","0.5","for","binary","classifier","."] | null | def test_binary_decimals_0_5(self):
"""
Test with cutoff of 0.5 for binary classifier.
"""
(_, _), (x_test, _) = self.mnist
classifier = get_image_classifier_kr_tf_binary()
preds = classifier.predict(x_test[0:1])
postprocessor = HighConfidence(cutoff=0.5)
post_preds = postprocessor(preds=preds)
classifier_prediction_expected = np.asarray(
[[0.5301345]], dtype=np.float32
)
post_classifier_prediction_expected = np.asarray(
[[0.5301345]], dtype=np.float32
)
np.testing.assert_array_almost_equal(
preds, classifier_prediction_expected, decimal=4
)
np.testing.assert_array_almost_equal(
post_preds, post_classifier_prediction_expected, decimal=4
)
| ["def","test_binary_decimals_0_5","(","self",")",":","``","''","''","Test","with","cutoff","of","0.5","for","binary","classifier.","``","''","''","(","_",",","_",")",",","(","x_test",",","_",")","=","self.mnist","classifier","=","get_image_classifier_kr_tf_binary","(",")","preds","=","classifier.predict","(","x_test","[","0:1","]",")","postprocessor","=","HighConfidence","(","cutoff=0.5",")","post_preds","=","postprocessor","(","preds=preds",")","classifier_prediction_expected","=","np.asarray","(","[","[","0.5301345","]","]",",","dtype=np.float32",")","post_classifier_prediction_expected","=","np.asarray","(","[","[","0.5301345","]","]",",","dtype=np.float32",")","np.testing.assert_array_almost_equal","(","preds",",","classifier_prediction_expected",",","decimal=4",")","np.testing.assert_array_almost_equal","(","post_preds",",","post_classifier_prediction_expected",",","decimal=4",")"] | 112 | 126 | null | test_high_confidence.py | adversarial-robustness-toolbox/tests/defences/test_high_confidence.py | import logging
import unittest
import numpy
from art.defences.postprocessor import HighConfidence
from art.utils import load_dataset
from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 5 for calling the TestHighConfidence obj's underlying member method code with example usage: obj.test_binary_decimals_0_5() without return types | 162 | node_id 5 | 235,300 |
test_RandomModelGenSamples | RandomModelTest | TestCase | true | self | null | null | null | null | null | def test_RandomModelGenSamples(self) -> None:
with self.assertRaises(NotImplementedError):
self.random_model._gen_samples(n=1, tunable_d=1)
| ["def","test_RandomModelGenSamples","(","self",")","-",">","None",":","with","self.assertRaises","(","NotImplementedError",")",":","self.random_model._gen_samples","(","n=1",",","tunable_d=1",")"] | 25 | 27 | null | test_random.py | Ax/ax/models/tests/test_random.py | import numpy
import torch
from ax.models.random.base import RandomModel
from ax.utils.common.testutils import TestCase
from ax.utils.common.typeutils import not_none | 15 | 1 | 5 | 0 | 1 | 8 | 1 | Use image node_id 3 for calling the RandomModelTest obj's underlying member method code with example usage: obj.test_RandomModelGenSamples() without return types | 161 | node_id 3 | 9,512 |
test_binary_decimals_0_6 | TestHighConfidence | unittest | true | self | A unittest class for testing the HighConfidence postprocessor. | ["A","unittest","class","for","testing","the","HighConfidence","postprocessor","."] | Test with cutoff of 0.6 for binary classifier. | ["Test","with","cutoff","of","0.6","for","binary","classifier","."] | null | def test_binary_decimals_0_6(self):
"""
Test with cutoff of 0.6 for binary classifier.
"""
(_, _), (x_test, _) = self.mnist
classifier = get_image_classifier_kr_tf_binary()
preds = classifier.predict(x_test[0:1])
postprocessor = HighConfidence(cutoff=0.6)
post_preds = postprocessor(preds=preds)
classifier_prediction_expected = np.asarray(
[[0.5301345]], dtype=np.float32
)
post_classifier_prediction_expected = np.asarray(
[[0.0]], dtype=np.float32
)
np.testing.assert_array_almost_equal(
preds, classifier_prediction_expected, decimal=4
)
np.testing.assert_array_almost_equal(
post_preds, post_classifier_prediction_expected, decimal=4
)
| ["def","test_binary_decimals_0_6","(","self",")",":","``","''","''","Test","with","cutoff","of","0.6","for","binary","classifier.","``","''","''","(","_",",","_",")",",","(","x_test",",","_",")","=","self.mnist","classifier","=","get_image_classifier_kr_tf_binary","(",")","preds","=","classifier.predict","(","x_test","[","0:1","]",")","postprocessor","=","HighConfidence","(","cutoff=0.6",")","post_preds","=","postprocessor","(","preds=preds",")","classifier_prediction_expected","=","np.asarray","(","[","[","0.5301345","]","]",",","dtype=np.float32",")","post_classifier_prediction_expected","=","np.asarray","(","[","[","0.0","]","]",",","dtype=np.float32",")","np.testing.assert_array_almost_equal","(","preds",",","classifier_prediction_expected",",","decimal=4",")","np.testing.assert_array_almost_equal","(","post_preds",",","post_classifier_prediction_expected",",","decimal=4",")"] | 128 | 142 | null | test_high_confidence.py | adversarial-robustness-toolbox/tests/defences/test_high_confidence.py | import logging
import unittest
import numpy
from art.defences.postprocessor import HighConfidence
from art.utils import load_dataset
from tests.utils import master_seed, get_image_classifier_kr_tf, get_image_classifier_kr_tf_binary | 15 | 1 | 6 | 0 | 1 | 7 | 1 | Use image node_id 6 for calling the TestHighConfidence obj's underlying member method code with example usage: obj.test_binary_decimals_0_6() without return types | 162 | node_id 6 | 235,301 |
convert | Converter | ImageConverter | true | self,_from,_to | null | null | Converts the image from SVG to PDF using chrome. | ["Converts","the","image","from","SVG","to","PDF","using","chrome","."] | True | def convert(self, _from: str, _to: str) -> bool:
"""Converts the image from SVG to PDF using chrome."""
with open(_from, "r") as f:
svg = f.read()
HTML = (
"<html><head><style>body {margin: 0; }</style><script>function init() {const element = document.querySelector('svg');const positionInfo = element.getBoundingClientRect();const height = positionInfo.height;const width = positionInfo.width;const style = document.createElement('style');style.innerHTML = `@page {margin: 0; size: ${width}px ${height}px}`;document.head.appendChild(style); }window.onload = init;</script></head><body>%s</body></html>"
% (svg)
)
temp_name = f"{_from}.html"
with open(temp_name, "w") as f:
f.write(HTML)
chromium = self.chromium_command()
code = self.command_runner(chromium, _to, temp_name)
if code != 0:
chrome = self.chrome_command()
code = self.command_runner(chrome, _to, temp_name)
if code != 0:
logger.error(
"Fail to convert svg to pdf. Make sure Chromium or Chrome is installed."
)
exit(1)
return True
| ["def","convert","(","self",",","_from",":","str",",","_to",":","str",")","-",">","bool",":","``","''","''","Converts","the","image","from","SVG","to","PDF","using","chrome",".","''","''","''","with","open","(","_from",",","``","r","''",")","as","f",":","svg","=","f.read","(",")","HTML","=","(","``","<","html",">","<","head",">","<","style",">","body","{","margin",":","0",";","}","<","\/style",">","<","script",">","function","init","(",")","{","const","element","=","document.querySelector","(","'svg","'",")",";","const","positionInfo","=","element.getBoundingClientRect","(",")",";","const","height","=","positionInfo.height",";","const","width","=","positionInfo.width",";","const","style","=","document.createElement","(","'style","'",")",";","style.innerHTML","=","`","@","page","{","margin",":","0",";","size",":","$","{","width","}","px","$","{","height","}","px","}","`",";","document.head.appendChild","(","style",")",";","}","window.onload","=","init",";","<","\/script",">","<","\/head",">","<","body",">","%","s","<","\/body",">","<","\/html",">","''","%","(","svg",")",")","temp_name","=","f","''","{","_from","}",".html","''","with","open","(","temp_name",",","``","w","''",")","as","f",":","f.write","(","HTML",")","chromium","=","self.chromium_command","(",")","code","=","self.command_runner","(","chromium",",","_to",",","temp_name",")","if","code","!","=","0",":","chrome","=","self.chrome_command","(",")","code","=","self.command_runner","(","chrome",",","_to",",","temp_name",")","if","code","!","=","0",":","logger.error","(","``","Fail","to","convert","svg","to","pdf",".","Make","sure","Chromium","or","Chrome","is","installed",".","''",")","exit","(","1",")","return","True"] | 71 | 89 | null | convert-svg-to-pdf.py | sympy/doc/ext/convert-svg-to-pdf.py | from __future__ import annotations
from sphinx.transforms.post_transforms.images import ImageConverter
from sphinx.util import logging
import os
import platform
from typing import Any
from sphinx.application import Sphinx | 15 | 1 | 7 | 1 | 1 | 5 | 1 | Use image node_id 5 for calling the Converter obj's underlying member method code with example usage: obj.convert(_from, _to) and returns: True | 143 | node_id 5 | 2,029,278 |
_setup | RunnerTrafficMetricsMiddleware | null | true | self,metrics_client | null | null | null | null | null | def _setup(
self,
metrics_client: "PrometheusClient" = Provide[
BentoMLContainer.metrics_client
],
):
self.metrics_client = metrics_client
self.metrics_request_duration = metrics_client.Histogram(
namespace=self.namespace,
name="request_duration_seconds",
documentation="runner RPC duration in seconds",
labelnames=[
"endpoint",
"service_name",
"service_version",
"http_response_code",
"runner_name",
],
)
self.metrics_request_total = metrics_client.Counter(
namespace=self.namespace,
name="request_total",
documentation="Total number of runner RPC",
labelnames=[
"endpoint",
"service_name",
"service_version",
"http_response_code",
"runner_name",
],
)
self.metrics_request_in_progress = metrics_client.Gauge(
namespace=self.namespace,
name="request_in_progress",
documentation="Total number of runner RPC in progress now",
labelnames=[
"endpoint",
"service_name",
"service_version",
"runner_name",
],
multiprocess_mode="livesum",
)
self._is_setup = True
| ["def","_setup","(","self",",","metrics_client",":","``","PrometheusClient","''","=","Provide","[","BentoMLContainer.metrics_client","]",",",")",":","self.metrics_client","=","metrics_client","self.metrics_request_duration","=","metrics_client.Histogram","(","namespace=self.namespace",",","name=","''","request_duration_seconds","''",",","documentation=","''","runner","RPC","duration","in","seconds","''",",","labelnames=","[","``","endpoint","''",",","``","service_name","''",",","``","service_version","''",",","``","http_response_code","''",",","``","runner_name","''",",","]",",",")","self.metrics_request_total","=","metrics_client.Counter","(","namespace=self.namespace",",","name=","''","request_total","''",",","documentation=","''","Total","number","of","runner","RPC","''",",","labelnames=","[","``","endpoint","''",",","``","service_name","''",",","``","service_version","''",",","``","http_response_code","''",",","``","runner_name","''",",","]",",",")","self.metrics_request_in_progress","=","metrics_client.Gauge","(","namespace=self.namespace",",","name=","''","request_in_progress","''",",","documentation=","''","Total","number","of","runner","RPC","in","progress","now","''",",","labelnames=","[","``","endpoint","''",",","``","service_name","''",",","``","service_version","''",",","``","runner_name","''",",","]",",","multiprocess_mode=","''","livesum","''",",",")","self._is_setup","=","True"] | 150 | 187 | null | instruments.py | BentoML/src/bentoml/_internal/server/http/instruments.py | from __future__ import annotations
import contextvars
import logging
from timeit import default_timer
from typing import TYPE_CHECKING
from simple_di import Provide
from simple_di import inject
from ...configuration.containers import BentoMLContainer
from ...context import component_context | 15 | 2 | 9 | 0 | 0 | 2 | null | Use image node_id 2 for calling the RunnerTrafficMetricsMiddleware obj's underlying member method code with example usage: obj._setup(metrics_client) without return types | 170 | node_id 2 | 14,515 |
command_runner | Converter | ImageConverter | true | self,chrome,_to,temp_name | null | null | null | null | os,int | def command_runner(
self, chrome: str | None, _to: str, temp_name: str
) -> int:
if not chrome:
return 1
command = f"{chrome} --headless --disable-gpu --disable-software-rasterizer --print-to-pdf={_to} {temp_name}"
logger.error(command)
return os.system(command)
| ["def","command_runner","(","self",",","chrome",":","str","|","None",",","_to",":","str",",","temp_name",":","str",")","-",">","int",":","if","not","chrome",":","return","1","command","=","f","''","{","chrome","}","--","headless","--","disable-gpu","--","disable-software-rasterizer","--","print-to-pdf=","{","_to","}","{","temp_name","}","''","logger.error","(","command",")","return","os.system","(","command",")"] | 64 | 69 | null | convert-svg-to-pdf.py | sympy/doc/ext/convert-svg-to-pdf.py | from __future__ import annotations
from sphinx.transforms.post_transforms.images import ImageConverter
from sphinx.util import logging
import os
import platform
from typing import Any
from sphinx.application import Sphinx | 15 | 1 | 7 | 1 | 1 | 5 | 1 | Use image node_id 4 for calling the Converter obj's underlying member method code with example usage: obj.command_runner(chrome, _to, temp_name) and returns: os, int | 165 | node_id 4 | 2,029,277 |
chromium_command | Converter | ImageConverter | true | self | null | null | null | null | None,None,str,str,str+path+str,path,str | def chromium_command(self) -> str | None:
if platform.win32_ver()[0]:
if os.system("where chromium") == 0:
return "chromium"
path = os.path.join(
os.environ["PROGRAMW6432"],
"Chromium\\Application\\chrome.exe",
)
if os.path.exists(path):
return f'"{path}"'
return None
if os.system("chromium --version") == 0:
return "chromium"
if platform.mac_ver()[0]:
path = "/Applications/Chromium.app/Contents/MacOS/Chromium"
if os.path.exists(path):
return path
elif platform.libc_ver()[0]:
if os.system("chromium-browser --version") == 0:
return "chromium-browser"
return None
| ["def","chromium_command","(","self",")","-",">","str","|","None",":","if","platform.win32_ver","(",")","[","0","]",":","if","os.system","(","``","where","chromium","''",")","==","0",":","return","``","chromium","''","path","=","os.path.join","(","os.environ","[","``","PROGRAMW6432","''","]",",","``","Chromium\\\\Application\\\\chrome.exe","''",",",")","if","os.path.exists","(","path",")",":","return","f","'","''","{","path","}","''","'","return","None","if","os.system","(","``","chromium","--","version","''",")","==","0",":","return","``","chromium","''","if","platform.mac_ver","(",")","[","0","]",":","path","=","``","\/Applications\/Chromium.app\/Contents\/MacOS\/Chromium","''","if","os.path.exists","(","path",")",":","return","path","elif","platform.libc_ver","(",")","[","0","]",":","if","os.system","(","``","chromium-browser","--","version","''",")","==","0",":","return","``","chromium-browser","''","return","None"] | 44 | 61 | null | convert-svg-to-pdf.py | sympy/doc/ext/convert-svg-to-pdf.py | from __future__ import annotations
from sphinx.transforms.post_transforms.images import ImageConverter
from sphinx.util import logging
import os
import platform
from typing import Any
from sphinx.application import Sphinx | 15 | 1 | 7 | 1 | 1 | 5 | 1 | Use image node_id 3 for calling the Converter obj's underlying member method code with example usage: obj.chromium_command() and returns: None, None, str, str, str, path, str, path, str | 185 | node_id 3 | 2,029,276 |
_test_texture_as_input | TestShapeShifter | TestBase | true | self,sign_gradients,use_spectral,soft_clip | null | null | null | null | background, image_frame, y_,current_image | def _test_texture_as_input(
self, sign_gradients, use_spectral, soft_clip
):
# We must start a new graph
tf.reset_default_graph()
# Only import if object detection module is available
from art.estimators.object_detection.tensorflow_faster_rcnn import (
TensorFlowFasterRCNN,
)
from art.attacks.evasion.shapeshifter import ShapeShifter
# Define object detector
images = tf.Variable(
initial_value=np.zeros([1, 28, 28, 1]), dtype=tf.float32
)
obj_dec = TensorFlowFasterRCNN(images=images)
# Create labels
result = obj_dec.predict(self.x_test_mnist[:1].astype(np.float32))
groundtruth_boxes_list = [result[i]["boxes"] for i in range(1)]
groundtruth_classes_list = [result[i]["labels"] for i in range(1)]
groundtruth_weights_list = [
np.ones_like(r) for r in groundtruth_classes_list
]
y = {
"groundtruth_boxes_list": groundtruth_boxes_list,
"groundtruth_classes_list": groundtruth_classes_list,
"groundtruth_weights_list": groundtruth_weights_list,
}
# Define random transform
def random_transform(x):
background = np.random.rand(*x.shape)
image_frame = np.random.rand(*(list(x.shape[:-1]) + [4]))
y_ = y.copy()
y_["groundtruth_boxes_list"][0] = (
y_["groundtruth_boxes_list"][0] + np.random.rand()
)
y_["groundtruth_weights_list"][0] = (
y_["groundtruth_weights_list"][0] + np.random.rand()
)
return background, image_frame, y_
# Define attack
attack = ShapeShifter(
estimator=obj_dec,
random_transform=random_transform,
box_classifier_weight=1.0,
box_localizer_weight=1.0,
rpn_classifier_weight=1.0,
rpn_localizer_weight=1.0,
box_iou_threshold=0.3,
box_victim_weight=1.0,
box_target_weight=1.0,
box_victim_cw_weight=1.0,
box_victim_cw_confidence=1.0,
box_target_cw_weight=1.0,
box_target_cw_confidence=1.0,
rpn_iou_threshold=0.3,
rpn_background_weight=1.0,
rpn_foreground_weight=1.0,
rpn_cw_weight=1.0,
rpn_cw_confidence=1.0,
similarity_weight=1.0,
learning_rate=0.1,
optimizer="MomentumOptimizer",
momentum=0.01,
decay=0.01,
sign_gradients=sign_gradients,
random_size=2,
max_iter=2,
texture_as_input=True,
use_spectral=use_spectral,
soft_clip=soft_clip,
)
# Define rendering function
def rendering_function(
background_phd, image_frame_phd, current_texture
):
current_image = background_phd + current_texture
current_image = tf.clip_by_value(current_image, 0, 1)
return current_image
# Targeted attack
adv_x = attack.generate(
x=self.x_test_mnist[:1].astype(np.float32),
label=y,
target_class=2,
victim_class=5,
rendering_function=rendering_function,
)
self.assertTrue(adv_x.shape == (1, 28, 28, 1))
# Untargeted attack
adv_x = attack.generate(
x=self.x_test_mnist[:1].astype(np.float32),
label=y,
target_class=8,
victim_class=8,
rendering_function=rendering_function,
)
self.assertTrue(adv_x.shape == (1, 28, 28, 1))
| ["def","_test_texture_as_input","(","self",",","sign_gradients",",","use_spectral",",","soft_clip",")",":","#","We","must","start","a","new","graph","tf.reset_default_graph","(",")","#","Only","import","if","object","detection","module","is","available","from","art.estimators.object_detection.tensorflow_faster_rcnn","import","(","TensorFlowFasterRCNN",",",")","from","art.attacks.evasion.shapeshifter","import","ShapeShifter","#","Define","object","detector","images","=","tf.Variable","(","initial_value=np.zeros","(","[","1",",","28",",","28",",","1","]",")",",","dtype=tf.float32",")","obj_dec","=","TensorFlowFasterRCNN","(","images=images",")","#","Create","labels","result","=","obj_dec.predict","(","self.x_test_mnist","[",":1","]",".astype","(","np.float32",")",")","groundtruth_boxes_list","=","[","result","[","i","]","[","``","boxes","''","]","for","i","in","range","(","1",")","]","groundtruth_classes_list","=","[","result","[","i","]","[","``","labels","''","]","for","i","in","range","(","1",")","]","groundtruth_weights_list","=","[","np.ones_like","(","r",")","for","r","in","groundtruth_classes_list","]","y","=","{","``","groundtruth_boxes_list","''",":","groundtruth_boxes_list",",","``","groundtruth_classes_list","''",":","groundtruth_classes_list",",","``","groundtruth_weights_list","''",":","groundtruth_weights_list",",","}","#","Define","random","transform","def","random_transform","(","x",")",":","background","=","np.random.rand","(","*","x.shape",")","image_frame","=","np.random.rand","(","*","(","list","(","x.shape","[",":","-1","]",")","+","[","4","]",")",")","y_","=","y.copy","(",")","y_","[","``","groundtruth_boxes_list","''","]","[","0","]","=","(","y_","[","``","groundtruth_boxes_list","''","]","[","0","]","+","np.random.rand","(",")",")","y_","[","``","groundtruth_weights_list","''","]","[","0","]","=","(","y_","[","``","groundtruth_weights_list","''","]","[","0","]","+","np.random.rand","(",")",")","return","background",",","image_frame",",","y_","#","Define","attack","attack","=","ShapeShifter","(","estimator=obj_dec",",","random_transform=random_transform",",","box_classifier_weight=1.0",",","box_localizer_weight=1.0",",","rpn_classifier_weight=1.0",",","rpn_localizer_weight=1.0",",","box_iou_threshold=0.3",",","box_victim_weight=1.0",",","box_target_weight=1.0",",","box_victim_cw_weight=1.0",",","box_victim_cw_confidence=1.0",",","box_target_cw_weight=1.0",",","box_target_cw_confidence=1.0",",","rpn_iou_threshold=0.3",",","rpn_background_weight=1.0",",","rpn_foreground_weight=1.0",",","rpn_cw_weight=1.0",",","rpn_cw_confidence=1.0",",","similarity_weight=1.0",",","learning_rate=0.1",",","optimizer=","''","MomentumOptimizer","''",",","momentum=0.01",",","decay=0.01",",","sign_gradients=sign_gradients",",","random_size=2",",","max_iter=2",",","texture_as_input=True",",","use_spectral=use_spectral",",","soft_clip=soft_clip",",",")","#","Define","rendering","function","def","rendering_function","(","background_phd",",","image_frame_phd",",","current_texture",")",":","current_image","=","background_phd","+","current_texture","current_image","=","tf.clip_by_value","(","current_image",",","0",",","1",")","return","current_image","#","Targeted","attack","adv_x","=","attack.generate","(","x=self.x_test_mnist","[",":1","]",".astype","(","np.float32",")",",","label=y",",","target_class=2",",","victim_class=5",",","rendering_function=rendering_function",",",")","self.assertTrue","(","adv_x.shape","==","(","1",",","28",",","28",",","1",")",")","#","Untargeted","attack","adv_x","=","attack.generate","(","x=self.x_test_mnist","[",":1","]",".astype","(","np.float32",")",",","label=y",",","target_class=8",",","victim_class=8",",","rendering_function=rendering_function",",",")","self.assertTrue","(","adv_x.shape","==","(","1",",","28",",","28",",","1",")",")"] | 139 | 235 | null | test_shapeshifter.py | adversarial-robustness-toolbox/tests/attacks/test_shapeshifter.py | from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import unittest
import importlib
import tensorflow
import numpy
from tests.utils import TestBase, master_seed | 15 | 1 | 7 | 0 | 1 | 6 | 1 | Use image node_id 5 for calling the TestShapeShifter obj's underlying member method code with example usage: obj._test_texture_as_input(sign_gradients, use_spectral, soft_clip) and returns: background, image_frame, y_, current_image | 234 | node_id 5 | 234,965 |
test_check_params | TestShapeShifter | TestBase | true | self | null | null | null | null | null | def test_check_params(self):
from art.estimators.object_detection import TensorFlowFasterRCNN
from art.attacks.evasion import ShapeShifter
images = tf.Variable(
initial_value=np.zeros([1, 28, 28, 1]), dtype=tf.float32
)
obj_dec = TensorFlowFasterRCNN(images=images)
with self.assertRaises(ValueError):
_ = ShapeShifter(obj_dec, random_transform="1")
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_classifier_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_classifier_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_localizer_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_localizer_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_classifier_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_classifier_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_localizer_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_localizer_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_iou_threshold=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_iou_threshold=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_victim_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_victim_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_target_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_target_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_victim_cw_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_victim_cw_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_victim_cw_confidence=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_victim_cw_confidence=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_target_cw_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_target_cw_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_target_cw_confidence=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
box_target_cw_confidence=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_iou_threshold=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_iou_threshold=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_background_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_background_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_foreground_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_foreground_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_cw_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_cw_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_cw_confidence=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
rpn_cw_confidence=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
similarity_weight=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
similarity_weight=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
learning_rate=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
learning_rate=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
optimizer="test",
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
optimizer="MomentumOptimizer",
momentum=1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
optimizer="MomentumOptimizer",
momentum=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
optimizer="RMSPropOptimizer",
momentum=0.5,
decay="1",
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
optimizer="RMSPropOptimizer",
momentum=0.5,
decay=-1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
optimizer="RMSPropOptimizer",
momentum=0.5,
decay=2.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
sign_gradients="true",
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
random_size=1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
random_size=-1,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
max_iter=1.0,
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec, random_transform=lambda x: x + 1e-10, max_iter=-1
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
texture_as_input="true",
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
use_spectral="true",
)
with self.assertRaises(ValueError):
_ = ShapeShifter(
obj_dec,
random_transform=lambda x: x + 1e-10,
soft_clip="true",
)
| 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| 237 | 380 | null | test_shapeshifter.py | adversarial-robustness-toolbox/tests/attacks/test_shapeshifter.py | from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import unittest
import importlib
import tensorflow
import numpy
from tests.utils import TestBase, master_seed | 15 | 1 | 7 | 0 | 1 | 6 | 1 | Use image node_id 6 for calling the TestShapeShifter obj's underlying member method code with example usage: obj.test_check_params() without return types | 153 | node_id 6 | 234,966 |
do_measure | global | null | false | opts | null | null | null | null | null | def do_measure(opts):
name = opts["-d"]
_log.info("reading data %s", name)
test = pd.read_parquet(f"data/{name}-test.parquet")
recs = pd.read_parquet(f"data/{name}-recs.parquet")
_log.info("setting up analysis")
rla = RecListAnalysis()
rla.add_metric(ndcg)
rla.add_metric(recip_rank)
timer = Stopwatch()
results = rla.compute(recs, test, include_missing=True)
_log.info("analyzed in %s", timer)
results = results.fillna(0)
a_res = results.groupby("Algorithm").mean()
a_res["count"] = results.groupby("Algorithm")["nrecs"].count()
_log.info("finished")
print(a_res)
print(results.groupby("Algorithm")["recip_rank"].describe())
| ["def","do_measure","(","opts",")",":","name","=","opts","[","``","-d","''","]","_log.info","(","``","reading","data","%","s","''",",","name",")","test","=","pd.read_parquet","(","f","''","data\/","{","name","}","-test.parquet","''",")","recs","=","pd.read_parquet","(","f","''","data\/","{","name","}","-recs.parquet","''",")","_log.info","(","``","setting","up","analysis","''",")","rla","=","RecListAnalysis","(",")","rla.add_metric","(","ndcg",")","rla.add_metric","(","recip_rank",")","timer","=","Stopwatch","(",")","results","=","rla.compute","(","recs",",","test",",","include_missing=True",")","_log.info","(","``","analyzed","in","%","s","''",",","timer",")","results","=","results.fillna","(","0",")","a_res","=","results.groupby","(","``","Algorithm","''",")",".mean","(",")","a_res","[","``","count","''","]","=","results.groupby","(","``","Algorithm","''",")","[","``","nrecs","''","]",".count","(",")","_log.info","(","``","finished","''",")","print","(","a_res",")","print","(","results.groupby","(","``","Algorithm","''",")","[","``","recip_rank","''","]",".describe","(",")",")"] | 65 | 86 | null | rla-perf.py | lkpy/utils/rla-perf.py | import sys
import logging
import tqdm
from docopt import docopt
import pandas
from lenskit.datasets import MovieLens
from lenskit.util import Stopwatch
from lenskit.batch import recommend
from lenskit.crossfold import sample_users, SampleN
from lenskit.algorithms.basic import Popular
from lenskit.algorithms import Recommender
from lenskit.algorithms.als import ImplicitMF
from lenskit.topn import RecListAnalysis, ndcg, recip_rank | 15 | null | 13 | 2 | null | null | null | Use image node_id 2 for calling a global function with example usage: do_measure(opts) without return types | 107 | node_id 2 | 1,270,200 |
__init__ | EntitySievesConfiguration | SievesConfiguration | true | self | null | null | null | null | EntitySievesConfiguration | def __init__(self):
super(EntitySievesConfiguration, self).__init__()
self.run_evaluation = True
self.sieves_order = [
(RelationType.SAME_HEAD_LEMMA, 1.0),
(RelationType.EXACT_STRING, 1.0),
(RelationType.FUZZY_FIT, 1.0),
(RelationType.WIKIPEDIA_REDIRECT_LINK, 0.1),
(RelationType.WIKIPEDIA_DISAMBIGUATION, 0.1),
(RelationType.WORD_EMBEDDING_MATCH, 0.7),
(RelationType.WORDNET_PARTIAL_SYNSET_MATCH, 0.1),
(RelationType.FUZZY_HEAD_FIT, 0.5),
(RelationType.WIKIPEDIA_CATEGORY, 0.1),
(RelationType.WITHIN_DOC_COREF, 1.0),
(RelationType.WIKIPEDIA_BE_COMP, 0.1),
(RelationType.WIKIPEDIA_TITLE_PARENTHESIS, 0.1),
(RelationType.WORDNET_SAME_SYNSET, 1.0),
(RelationType.REFERENT_DICT, 0.5),
]
| ["def","__init__","(","self",")",":","super","(","EntitySievesConfiguration",",","self",")",".__init__","(",")","self.run_evaluation","=","True","self.sieves_order","=","[","(","RelationType.SAME_HEAD_LEMMA",",","1.0",")",",","(","RelationType.EXACT_STRING",",","1.0",")",",","(","RelationType.FUZZY_FIT",",","1.0",")",",","(","RelationType.WIKIPEDIA_REDIRECT_LINK",",","0.1",")",",","(","RelationType.WIKIPEDIA_DISAMBIGUATION",",","0.1",")",",","(","RelationType.WORD_EMBEDDING_MATCH",",","0.7",")",",","(","RelationType.WORDNET_PARTIAL_SYNSET_MATCH",",","0.1",")",",","(","RelationType.FUZZY_HEAD_FIT",",","0.5",")",",","(","RelationType.WIKIPEDIA_CATEGORY",",","0.1",")",",","(","RelationType.WITHIN_DOC_COREF",",","1.0",")",",","(","RelationType.WIKIPEDIA_BE_COMP",",","0.1",")",",","(","RelationType.WIKIPEDIA_TITLE_PARENTHESIS",",","0.1",")",",","(","RelationType.WORDNET_SAME_SYNSET",",","1.0",")",",","(","RelationType.REFERENT_DICT",",","0.5",")",",","]"] | 82 | 102 | null | sieves_config.py | nlp-architect/nlp_architect/models/cross_doc_coref/sieves_config.py | from typing import List, Tuple
from nlp_architect.data.cdc_resources.relations.relation_types_enums import RelationType | 15 | 3 | 2 | 0 | 3 | 1 | 1 | Use image node_id 1 to create a new EntitySievesConfiguration object from inherited base classes: SievesConfiguration with example: obj = EntitySievesConfiguration() | 165 | node_id 1 | 1,443,099 |
get_boutiques_output_from_inp | global | null | false | inputs,inp_spec,inp_name | null | null | null | null | output | def get_boutiques_output_from_inp(inputs, inp_spec, inp_name):
"""
Takes a Nipype input representing an output file and generates a
Boutiques output for it
"""
output = {}
output["name"] = inp_name.replace("_", " ").capitalize()
output["id"] = inp_name
output["optional"] = True
output["description"] = get_description_from_spec(
inputs, inp_name, inp_spec
)
if not (hasattr(inp_spec, "mandatory") and inp_spec.mandatory):
output["optional"] = True
else:
output["optional"] = False
if inp_spec.usedefault:
output["default-value"] = inp_spec.default_value()[1]
if isinstance(inp_spec.name_source, list):
source = inp_spec.name_source[0]
else:
source = inp_spec.name_source
output["path-template"] = inp_spec.name_template.replace(
"%s", "[" + source.upper() + "]"
)
output["value-key"] = "[" + inp_name.upper() + "]"
flag, flag_sep = get_command_line_flag(inp_spec)
if flag is not None:
output["command-line-flag"] = flag
if flag_sep is not None:
output["command-line-flag-separator"] = flag_sep
return output
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import sys
import simplejson
from ..scripts.instance import import_module | 15 | null | 4 | 11 | null | null | null | Use image node_id 11 for calling a global function with example usage: get_boutiques_output_from_inp(inputs, inp_spec, inp_name) and returns: output | 148 | node_id 11 | 1,442,053 |
_ids | global | null | false | values | null | null | null | null | unknown | def _ids(values: Sequence[_Metadata]) -> Sequence[int]:
return [v.id for v in values]
| ["def","_ids","(","values",":","Sequence","[","_Metadata","]",")","-",">","Sequence","[","int","]",":","return","[","v.id","for","v","in","values","]"] | 36 | 37 | null | store_ext.py | tfx/tfx/orchestration/portable/mlmd/store_ext.py | import collections
import itertools
from typing import Callable, Mapping, Optional, Sequence, Union
from tfx.dsl.compiler import compiler_utils
from tfx.dsl.compiler import constants
from tfx.orchestration.experimental.core import constants
from tfx.orchestration.portable.mlmd import event_lib
from tfx.orchestration.portable.mlmd import filter_query_builder
import ml_metadata | 15 | null | 9 | 6 | null | null | null | Use image node_id 1 for calling a global function with example usage: _ids(values) and returns: unknown | 103 | node_id 1 | 2,198,855 |
setup | global | null | false | app | null | null | null | null | dict | def setup(app: Sphinx) -> dict[str, Any]:
app.add_post_transform(Converter)
return {
"version": "builtin",
"parallel_read_safe": True,
"parallel_write_safe": True,
}
| ["def","setup","(","app",":","Sphinx",")","-",">","dict","[","str",",","Any","]",":","app.add_post_transform","(","Converter",")","return","{","``","version","''",":","``","builtin","''",",","``","parallel_read_safe","''",":","True",",","``","parallel_write_safe","''",":","True",",","}"] | 92 | 99 | null | convert-svg-to-pdf.py | sympy/doc/ext/convert-svg-to-pdf.py | from __future__ import annotations
from sphinx.transforms.post_transforms.images import ImageConverter
from sphinx.util import logging
import os
import platform
from typing import Any
from sphinx.application import Sphinx | 15 | null | 7 | 1 | null | null | null | Use image node_id 1 for calling a global function with example usage: setup(app) and returns: dict | 98 | node_id 1 | 2,029,279 |
__init__ | SearchSpaceToChoice | Transform | true | self,search_space,observations,modelbridge,config | Replaces the search space with a single choice parameter, whose values
are the signatures of the arms observed in the data.
This transform is meant to be used with ThompsonSampler.
Choice parameter will be unordered unless config["use_ordered"] specifies
otherwise.
Transform is done in-place. | ["Replaces","the","search","space","with","a","single","choice","parameter",",","whose","values","are","the","signatures","of","the","arms","observed","in","the","data",".","This","transform","is","meant","to","be","used","with","ThompsonSampler",".","Choice","parameter","will","be","unordered","unless","config","[","``","use_ordered","''","]","specifies","otherwise",".","Transform","is","done","in-place","."] | null | null | SearchSpaceToChoice | def __init__(
self,
search_space: Optional[SearchSpace] = None,
observations: Optional[List[Observation]] = None,
modelbridge: Optional[
"modelbridge_module.base.ModelBridge"
] = None,
config: Optional[TConfig] = None,
) -> None:
assert (
search_space is not None
), "SearchSpaceToChoice requires search space"
assert (
observations is not None
), "SeachSpaceToChoice requires observations"
super().__init__(
search_space=search_space,
observations=observations,
config=config,
)
if any(p.is_fidelity for p in search_space.parameters.values()):
raise ValueError(
"Cannot perform SearchSpaceToChoice conversion if fidelity "
"parameters are present"
)
if isinstance(search_space, RobustSearchSpace):
raise UnsupportedError(
"SearchSpaceToChoice transform is not supported for RobustSearchSpace."
)
self.parameter_name = "arms"
# pyre-fixme[4]: Attribute must be annotated.
self.signature_to_parameterization = {
Arm(
parameters=obs.features.parameters
).signature: obs.features.parameters
for obs in observations
}
| ["def","__init__","(","self",",","search_space",":","Optional","[","SearchSpace","]","=","None",",","observations",":","Optional","[","List","[","Observation","]","]","=","None",",","modelbridge",":","Optional","[","``","modelbridge_module.base.ModelBridge","''","]","=","None",",","config",":","Optional","[","TConfig","]","=","None",",",")","-",">","None",":","assert","(","search_space","is","not","None",")",",","``","SearchSpaceToChoice","requires","search","space","''","assert","(","observations","is","not","None",")",",","``","SeachSpaceToChoice","requires","observations","''","super","(",")",".__init__","(","search_space=search_space",",","observations=observations",",","config=config",",",")","if","any","(","p.is_fidelity","for","p","in","search_space.parameters.values","(",")",")",":","raise","ValueError","(","``","Can","not","perform","SearchSpaceToChoice","conversion","if","fidelity","``","``","parameters","are","present","''",")","if","isinstance","(","search_space",",","RobustSearchSpace",")",":","raise","UnsupportedError","(","``","SearchSpaceToChoice","transform","is","not","supported","for","RobustSearchSpace",".","''",")","self.parameter_name","=","``","arms","''","#","pyre-fixme","[","4","]",":","Attribute","must","be","annotated",".","self.signature_to_parameterization","=","{","Arm","(","parameters=obs.features.parameters",")",".signature",":","obs.features.parameters","for","obs","in","observations","}"] | 35 | 63 | null | search_space_to_choice.py | Ax/ax/modelbridge/transforms/search_space_to_choice.py | from typing import List, Optional, TYPE_CHECKING
from ax.core.arm import Arm
from ax.core.observation import Observation, ObservationFeatures
from ax.core.parameter import ChoiceParameter, FixedParameter, ParameterType
from ax.core.search_space import RobustSearchSpace, SearchSpace
from ax.exceptions.core import UnsupportedError
from ax.modelbridge.transforms.base import Transform
from ax.models.types import TConfig
from ax.utils.common.typeutils import checked_cast | 15 | 1 | 9 | 0 | 1 | 4 | 1 | Use image node_id 1 to create a new SearchSpaceToChoice object from inherited base classes: Transform with example: obj = SearchSpaceToChoice(search_space, observations, modelbridge, config) | 190 | node_id 1 | 9,096 |
cs_diff | global | null | false | x,a,b,period,_cache | null | null | null | null | convolve,unknown,int,unknown | def cs_diff(x, a, b, period=None, _cache=_cache):
"""
Return (a,b)-cosh/sinh pseudo-derivative of a periodic sequence.
If ``x_j`` and ``y_j`` are Fourier coefficients of periodic functions x
and y, respectively, then::
y_j = -sqrt(-1)*cosh(j*a*2*pi/period)/sinh(j*b*2*pi/period) * x_j
y_0 = 0
Parameters
----------
x : array_like
The array to take the pseudo-derivative from.
a, b : float
Defines the parameters of the cosh/sinh pseudo-differential
operator.
period : float, optional
The period of the sequence. Default period is ``2*pi``.
Returns
-------
cs_diff : ndarray
Pseudo-derivative of periodic sequence `x`.
Notes
-----
For even len(`x`), the Nyquist mode of `x` is taken as zero.
"""
tmp = asarray(x)
if iscomplexobj(tmp):
return cs_diff(tmp.real, a, b, period) + 1j * cs_diff(
tmp.imag, a, b, period
)
if period is not None:
a = a * 2 * pi / period
b = b * 2 * pi / period
n = len(x)
omega = _cache.get((n, a, b))
if omega is None:
if len(_cache) > 20:
while _cache:
_cache.popitem()
def kernel(k, a=a, b=b):
if k:
return -cosh(a * k) / sinh(b * k)
return 0
omega = convolve.init_convolution_kernel(n, kernel, d=1)
_cache[(n, a, b)] = omega
overwrite_x = _datacopied(tmp, x)
return convolve.convolve(
tmp, omega, swap_real_imag=1, overwrite_x=overwrite_x
)
| ["def","cs_diff","(","x",",","a",",","b",",","period=None",",","_cache=_cache",")",":","``","''","''","Return","(","a",",","b",")","-cosh\/sinh","pseudo-derivative","of","a","periodic","sequence",".","If","``","x_j","``","and","``","y_j","``","are","Fourier","coefficients","of","periodic","functions","x","and","y",",","respectively",",","then",":",":","y_j","=","-sqrt","(","-1",")","*","cosh","(","j","*","a","*","2","*","pi\/period",")","\/sinh","(","j","*","b","*","2","*","pi\/period",")","*","x_j","y_0","=","0","Parameters","--","--","--","--","--","x",":","array_like","The","array","to","take","the","pseudo-derivative","from",".","a",",","b",":","float","Defines","the","parameters","of","the","cosh\/sinh","pseudo-differential","operator",".","period",":","float",",","optional","The","period","of","the","sequence",".","Default","period","is","``","2","*","pi","``",".","Returns","--","--","--","-","cs_diff",":","ndarray","Pseudo-derivative","of","periodic","sequence","`","x","`",".","Notes","--","--","-","For","even","len","(","`","x","`",")",",","the","Nyquist","mode","of","`","x","`","is","taken","as","zero.","``","''","''","tmp","=","asarray","(","x",")","if","iscomplexobj","(","tmp",")",":","return","cs_diff","(","tmp.real",",","a",",","b",",","period",")","+","1j","*","cs_diff","(","tmp.imag",",","a",",","b",",","period",")","if","period","is","not","None",":","a","=","a","*","2","*","pi","\/","period","b","=","b","*","2","*","pi","\/","period","n","=","len","(","x",")","omega","=","_cache.get","(","(","n",",","a",",","b",")",")","if","omega","is","None",":","if","len","(","_cache",")",">","20",":","while","_cache",":","_cache.popitem","(",")","def","kernel","(","k",",","a=a",",","b=b",")",":","if","k",":","return","-cosh","(","a","*","k",")","\/","sinh","(","b","*","k",")","return","0","omega","=","convolve.init_convolution_kernel","(","n",",","kernel",",","d=1",")","_cache","[","(","n",",","a",",","b",")","]","=","omega","overwrite_x","=","_datacopied","(","tmp",",","x",")","return","convolve.convolve","(","tmp",",","omega",",","swap_real_imag=1",",","overwrite_x=overwrite_x",")"] | 282 | 333 | null | pseudo_diffs.py | catboost/contrib/python/scipy/py2/scipy/fftpack/pseudo_diffs.py | from __future__ import division, print_function, absolute_import
from numpy import pi, asarray, sin, cos, sinh, cosh, tanh, iscomplexobj
from .None import convolve
from scipy.fftpack.basic import _datacopied
import atexit | 15 | null | 5 | 10 | null | null | null | Use image node_id 6 for calling a global function with example usage: cs_diff(x, a, b, period, _cache) and returns: convolve, unknown, int, unknown | 147 | node_id 6 | 523,402 |
hilbert | global | null | false | x,_cache | null | null | null | null | convolve,unknown,int,int,unknown | def hilbert(x, _cache=_cache):
"""
Return Hilbert transform of a periodic sequence x.
If x_j and y_j are Fourier coefficients of periodic functions x
and y, respectively, then::
y_j = sqrt(-1)*sign(j) * x_j
y_0 = 0
Parameters
----------
x : array_like
The input array, should be periodic.
_cache : dict, optional
Dictionary that contains the kernel used to do a convolution with.
Returns
-------
y : ndarray
The transformed input.
See Also
--------
scipy.signal.hilbert : Compute the analytic signal, using the Hilbert
transform.
Notes
-----
If ``sum(x, axis=0) == 0`` then ``hilbert(ihilbert(x)) == x``.
For even len(x), the Nyquist mode of x is taken zero.
The sign of the returned transform does not have a factor -1 that is more
often than not found in the definition of the Hilbert transform. Note also
that `scipy.signal.hilbert` does have an extra -1 factor compared to this
function.
"""
tmp = asarray(x)
if iscomplexobj(tmp):
return hilbert(tmp.real) + 1j * hilbert(tmp.imag)
n = len(x)
omega = _cache.get(n)
if omega is None:
if len(_cache) > 20:
while _cache:
_cache.popitem()
def kernel(k):
if k > 0:
return 1.0
elif k < 0:
return -1.0
return 0.0
omega = convolve.init_convolution_kernel(n, kernel, d=1)
_cache[n] = omega
overwrite_x = _datacopied(tmp, x)
return convolve.convolve(
tmp, omega, swap_real_imag=1, overwrite_x=overwrite_x
)
| ["def","hilbert","(","x",",","_cache=_cache",")",":","``","''","''","Return","Hilbert","transform","of","a","periodic","sequence","x",".","If","x_j","and","y_j","are","Fourier","coefficients","of","periodic","functions","x","and","y",",","respectively",",","then",":",":","y_j","=","sqrt","(","-1",")","*","sign","(","j",")","*","x_j","y_0","=","0","Parameters","--","--","--","--","--","x",":","array_like","The","input","array",",","should","be","periodic",".","_cache",":","dict",",","optional","Dictionary","that","contains","the","kernel","used","to","do","a","convolution","with",".","Returns","--","--","--","-","y",":","ndarray","The","transformed","input",".","See","Also","--","--","--","--","scipy.signal.hilbert",":","Compute","the","analytic","signal",",","using","the","Hilbert","transform",".","Notes","--","--","-","If","``","sum","(","x",",","axis=0",")","==","0","``","then","``","hilbert","(","ihilbert","(","x",")",")","==","x","``",".","For","even","len","(","x",")",",","the","Nyquist","mode","of","x","is","taken","zero",".","The","sign","of","the","returned","transform","does","not","have","a","factor","-1","that","is","more","often","than","not","found","in","the","definition","of","the","Hilbert","transform",".","Note","also","that","`","scipy.signal.hilbert","`","does","have","an","extra","-1","factor","compared","to","this","function.","``","''","''","tmp","=","asarray","(","x",")","if","iscomplexobj","(","tmp",")",":","return","hilbert","(","tmp.real",")","+","1j","*","hilbert","(","tmp.imag",")","n","=","len","(","x",")","omega","=","_cache.get","(","n",")","if","omega","is","None",":","if","len","(","_cache",")",">","20",":","while","_cache",":","_cache.popitem","(",")","def","kernel","(","k",")",":","if","k",">","0",":","return","1.0","elif","k","<","0",":","return","-1.0","return","0.0","omega","=","convolve.init_convolution_kernel","(","n",",","kernel",",","d=1",")","_cache","[","n","]","=","omega","overwrite_x","=","_datacopied","(","tmp",",","x",")","return","convolve.convolve","(","tmp",",","omega",",","swap_real_imag=1",",","overwrite_x=overwrite_x",")"] | 201 | 259 | null | pseudo_diffs.py | catboost/contrib/python/scipy/py2/scipy/fftpack/pseudo_diffs.py | from __future__ import division, print_function, absolute_import
from numpy import pi, asarray, sin, cos, sinh, cosh, tanh, iscomplexobj
from .None import convolve
from scipy.fftpack.basic import _datacopied
import atexit | 15 | null | 5 | 10 | null | null | null | Use image node_id 4 for calling a global function with example usage: hilbert(x, _cache) and returns: convolve, unknown, int, int, unknown | 138 | node_id 4 | 523,400 |
price | InterestRateSwap | null | true | self,valuation_date,market,model,pricing_context,name | Represents a batch of Interest Rate Swaps (IRS).
An Interest rate swap (IRS) is a contract between two counterparties for an
exchange of a series of payments over a period of time. The payments are made
periodically (for example quarterly or semi-annually) where the last payment
is made at the maturity (or termination) of the contract. In the case of
fixed-for-floating IRS, one counterparty pays a fixed rate while the other
counterparty's payments are linked to a floating index, most commonly the
LIBOR rate. On the other hand, in the case of interest rate basis swap, the
payments of both counterparties are linked to a floating index. Typically, the
floating rate is observed (or fixed) at the beginning of each period while the
payments are made at the end of each period [1].
For example, consider a vanilla swap with the starting date T_0 and maturity
date T_n and equally spaced coupon payment dates T_1, T_2, ..., T_n such that
T_0 < T_1 < T_2 < ... < T_n and dt_i = T_(i+1) - T_i (A)
The floating rate is fixed on T_0, T_1, ..., T_(n-1) and both the fixed and
floating payments are made on T_1, T_2, ..., T_n (payment dates).
The InterestRateSwap class can be used to create and price multiple IRS
simultaneously. The class supports vanilla fixed-for-floating swaps as
well as basis swaps. However all IRS within an IRS object must be priced using
a common reference and discount curve.
#### Example (non batch):
The following example illustrates the construction of an IRS instrument and
calculating its price.
```python
import numpy as np
import tensorflow as tf
import tf_quant_finance as tff
dates = tff.datetime
instruments = tff.experimental.instruments
dtype = np.float64
start_date = dates.convert_to_date_tensor([(2020, 2, 8)])
maturity_date = dates.convert_to_date_tensor([(2022, 2, 8)])
valuation_date = dates.convert_to_date_tensor([(2020, 2, 8)])
period_3m = dates.periods.months(3)
period_6m = dates.periods.months(6)
fix_spec = instruments.FixedCouponSpecs(
coupon_frequency=period_6m, currency='usd',
notional=1., coupon_rate=0.03134,
daycount_convention=instruments.DayCountConvention.ACTUAL_365,
businessday_rule=dates.BusinessDayConvention.NONE)
flt_spec = instruments.FloatCouponSpecs(
coupon_frequency=period_3m, reference_rate_term=period_3m,
reset_frequency=period_3m, currency='usd', notional=1.,
businessday_rule=dates.BusinessDayConvention.NONE,
coupon_basis=0., coupon_multiplier=1.,
daycount_convention=instruments.DayCountConvention.ACTUAL_365)
swap = instruments.InterestRateSwap([(2020,2,2)], [(2023,2,2)], [fix_spec],
[flt_spec], dtype=np.float64)
curve_dates = valuation_date + dates.periods.years([1, 2, 3, 5, 7, 10, 30])
reference_curve = instruments.RateCurve(
curve_dates,
np.array([
0.02834814, 0.03077457, 0.03113739, 0.03130794, 0.03160892,
0.03213901, 0.03257991
], dtype=dtype),
valuation_date=valuation_date,
dtype=dtype)
market = instruments.InterestRateMarket(
reference_curve=reference_curve, discount_curve=reference_curve)
price = swap.price(valuation_date, market)
# Expected result: 1e-7
```
#### Example (batch):
The following example illustrates the construction and pricing of IRS using
batches.
```python
import numpy as np
import tensorflow as tf
import tf_quant_finance as tff
dates = tff.datetime
instruments = tff.experimental.instruments
dtype = np.float64
notional = 1.0
maturity_date = dates.convert_to_date_tensor([(2023, 2, 8), (2027, 2, 8)])
start_date = dates.convert_to_date_tensor([(2020, 2, 8), (2020, 2, 8)])
valuation_date = dates.convert_to_date_tensor([(2020, 2, 8)])
period3m = dates.periods.months([3, 3])
period6m = dates.periods.months([6, 6])
fix_spec = instruments.FixedCouponSpecs(
coupon_frequency=period6m, currency='usd',
notional=notional,
coupon_rate=[0.03134, 0.03181],
daycount_convention=instruments.DayCountConvention.ACTUAL_365,
businessday_rule=dates.BusinessDayConvention.NONE)
flt_spec = instruments.FloatCouponSpecs(
coupon_frequency=period3m, reference_rate_term=period3m,
reset_frequency=period3m, currency='usd',
notional=notional,
businessday_rule=dates.BusinessDayConvention.NONE,
coupon_basis=0.0, coupon_multiplier=1.0,
daycount_convention=instruments.DayCountConvention.ACTUAL_365)
swap = instruments.InterestRateSwap(start_date, maturity_date,
fix_spec, flt_spec,
dtype=dtype)
curve_dates = valuation_date + dates.periods.years([1, 2, 3, 5, 7, 10, 30])
reference_curve = instruments.RateCurve(
curve_dates,
np.array([
0.02834814, 0.03077457, 0.03113739, 0.03130794, 0.03160892,
0.03213901, 0.03257991
], dtype=dtype),
valuation_date=valuation_date,
dtype=dtype)
market = instruments.InterestRateMarket(
reference_curve=reference_curve, discount_curve=reference_curve)
price = swap.price(valuation_date, market)
# Expected result: [1.0e-7, 1.0e-7]
```
#### References:
[1]: Leif B.G. Andersen and Vladimir V. Piterbarg. Interest Rate Modeling,
Volume I: Foundations and Vanilla Models. Chapter 5. 2010. | ["Represents","a","batch","of","Interest","Rate","Swaps","(","IRS",")",".","An","Interest","rate","swap","(","IRS",")","is","a","contract","between","two","counterparties","for","an","exchange","of","a","series","of","payments","over","a","period","of","time",".","The","payments","are","made","periodically","(","for","example","quarterly","or","semi-annually",")","where","the","last","payment","is","made","at","the","maturity","(","or","termination",")","of","the","contract",".","In","the","case","of","fixed-for-floating","IRS",",","one","counterparty","pays","a","fixed","rate","while","the","other","counterparty","'s","payments","are","linked","to","a","floating","index",",","most","commonly","the","LIBOR","rate",".","On","the","other","hand",",","in","the","case","of","interest","rate","basis","swap",",","the","payments","of","both","counterparties","are","linked","to","a","floating","index",".","Typically",",","the","floating","rate","is","observed","(","or","fixed",")","at","the","beginning","of","each","period","while","the","payments","are","made","at","the","end","of","each","period","[","1","]",".","For","example",",","consider","a","vanilla","swap","with","the","starting","date","T_0","and","maturity","date","T_n","and","equally","spaced","coupon","payment","dates","T_1",",","T_2",",","...",",","T_n","such","that","T_0","<","T_1","<","T_2","<","...","<","T_n","and","dt_i","=","T_","(","i+1",")","-","T_i","(","A",")","The","floating","rate","is","fixed","on","T_0",",","T_1",",","...",",","T_","(","n-1",")","and","both","the","fixed","and","floating","payments","are","made","on","T_1",",","T_2",",","...",",","T_n","(","payment","dates",")",".","The","InterestRateSwap","class","can","be","used","to","create","and","price","multiple","IRS","simultaneously",".","The","class","supports","vanilla","fixed-for-floating","swaps","as","well","as","basis","swaps",".","However","all","IRS","within","an","IRS","object","must","be","priced","using","a","common","reference","and","discount","curve",".","#","#","#","#","Example","(","non","batch",")",":","The","following","example","illustrates","the","construction","of","an","IRS","instrument","and","calculating","its","price",".","``","`","python","import","numpy","as","np","import","tensorflow","as","tf","import","tf_quant_finance","as","tff","dates","=","tff.datetime","instruments","=","tff.experimental.instruments","dtype","=","np.float64","start_date","=","dates.convert_to_date_tensor","(","[","(","2020",",","2",",","8",")","]",")","maturity_date","=","dates.convert_to_date_tensor","(","[","(","2022",",","2",",","8",")","]",")","valuation_date","=","dates.convert_to_date_tensor","(","[","(","2020",",","2",",","8",")","]",")","period_3m","=","dates.periods.months","(","3",")","period_6m","=","dates.periods.months","(","6",")","fix_spec","=","instruments.FixedCouponSpecs","(","coupon_frequency=period_6m",",","currency='usd","'",",","notional=1.",",","coupon_rate=0.03134",",","daycount_convention=instruments.DayCountConvention.ACTUAL_365",",","businessday_rule=dates.BusinessDayConvention.NONE",")","flt_spec","=","instruments.FloatCouponSpecs","(","coupon_frequency=period_3m",",","reference_rate_term=period_3m",",","reset_frequency=period_3m",",","currency='usd","'",",","notional=1.",",","businessday_rule=dates.BusinessDayConvention.NONE",",","coupon_basis=0.",",","coupon_multiplier=1.",",","daycount_convention=instruments.DayCountConvention.ACTUAL_365",")","swap","=","instruments.InterestRateSwap","(","[","(","2020,2,2",")","]",",","[","(","2023,2,2",")","]",",","[","fix_spec","]",",","[","flt_spec","]",",","dtype=np.float64",")","curve_dates","=","valuation_date","+","dates.periods.years","(","[","1",",","2",",","3",",","5",",","7",",","10",",","30","]",")","reference_curve","=","instruments.RateCurve","(","curve_dates",",","np.array","(","[","0.02834814",",","0.03077457",",","0.03113739",",","0.03130794",",","0.03160892",",","0.03213901",",","0.03257991","]",",","dtype=dtype",")",",","valuation_date=valuation_date",",","dtype=dtype",")","market","=","instruments.InterestRateMarket","(","reference_curve=reference_curve",",","discount_curve=reference_curve",")","price","=","swap.price","(","valuation_date",",","market",")","#","Expected","result",":","1e-7","``","`","#","#","#","#","Example","(","batch",")",":","The","following","example","illustrates","the","construction","and","pricing","of","IRS","using","batches",".","``","`","python","import","numpy","as","np","import","tensorflow","as","tf","import","tf_quant_finance","as","tff","dates","=","tff.datetime","instruments","=","tff.experimental.instruments","dtype","=","np.float64","notional","=","1.0","maturity_date","=","dates.convert_to_date_tensor","(","[","(","2023",",","2",",","8",")",",","(","2027",",","2",",","8",")","]",")","start_date","=","dates.convert_to_date_tensor","(","[","(","2020",",","2",",","8",")",",","(","2020",",","2",",","8",")","]",")","valuation_date","=","dates.convert_to_date_tensor","(","[","(","2020",",","2",",","8",")","]",")","period3m","=","dates.periods.months","(","[","3",",","3","]",")","period6m","=","dates.periods.months","(","[","6",",","6","]",")","fix_spec","=","instruments.FixedCouponSpecs","(","coupon_frequency=period6m",",","currency='usd","'",",","notional=notional",",","coupon_rate=","[","0.03134",",","0.03181","]",",","daycount_convention=instruments.DayCountConvention.ACTUAL_365",",","businessday_rule=dates.BusinessDayConvention.NONE",")","flt_spec","=","instruments.FloatCouponSpecs","(","coupon_frequency=period3m",",","reference_rate_term=period3m",",","reset_frequency=period3m",",","currency='usd","'",",","notional=notional",",","businessday_rule=dates.BusinessDayConvention.NONE",",","coupon_basis=0.0",",","coupon_multiplier=1.0",",","daycount_convention=instruments.DayCountConvention.ACTUAL_365",")","swap","=","instruments.InterestRateSwap","(","start_date",",","maturity_date",",","fix_spec",",","flt_spec",",","dtype=dtype",")","curve_dates","=","valuation_date","+","dates.periods.years","(","[","1",",","2",",","3",",","5",",","7",",","10",",","30","]",")","reference_curve","=","instruments.RateCurve","(","curve_dates",",","np.array","(","[","0.02834814",",","0.03077457",",","0.03113739",",","0.03130794",",","0.03160892",",","0.03213901",",","0.03257991","]",",","dtype=dtype",")",",","valuation_date=valuation_date",",","dtype=dtype",")","market","=","instruments.InterestRateMarket","(","reference_curve=reference_curve",",","discount_curve=reference_curve",")","price","=","swap.price","(","valuation_date",",","market",")","#","Expected","result",":","[","1.0e-7",",","1.0e-7","]","``","`","#","#","#","#","References",":","[","1","]",":","Leif","B.G",".","Andersen","and","Vladimir","V.","Piterbarg",".","Interest","Rate","Modeling",",","Volume","I",":","Foundations","and","Vanilla","Models",".","Chapter","5",".","2010","."] | Returns the present value of the instrument on the valuation date.
Args:
valuation_date: A scalar `DateTensor` specifying the date on which
valuation is being desired.
market: A namedtuple of type `InterestRateMarket` which contains the
necessary information for pricing the interest rate swap.
model: Reserved for future use.
pricing_context: Additional context relevant for pricing.
name: Python str. The name to give to the ops created by this function.
Default value: `None` which maps to 'price'.
Returns:
A Rank 1 `Tensor` of real type containing the modeled price of each IRS
contract based on the input market data. | ["Returns","the","present","value","of","the","instrument","on","the","valuation","date",".","Args",":","valuation_date",":","A","scalar","`","DateTensor","`","specifying","the","date","on","which","valuation","is","being","desired",".","market",":","A","namedtuple","of","type","`","InterestRateMarket","`","which","contains","the","necessary","information","for","pricing","the","interest","rate","swap",".","model",":","Reserved","for","future","use",".","pricing_context",":","Additional","context","relevant","for","pricing",".","name",":","Python","str",".","The","name","to","give","to","the","ops","created","by","this","function",".","Default","value",":","`","None","`","which","maps","to","'price","'",".","Returns",":","A","Rank","1","`","Tensor","`","of","real","type","containing","the","modeled","price","of","each","IRS","contract","based","on","the","input","market","data","."] | unknown | def price(
self,
valuation_date,
market,
model=None,
pricing_context=None,
name=None,
):
"""Returns the present value of the instrument on the valuation date.
Args:
valuation_date: A scalar `DateTensor` specifying the date on which
valuation is being desired.
market: A namedtuple of type `InterestRateMarket` which contains the
necessary information for pricing the interest rate swap.
model: Reserved for future use.
pricing_context: Additional context relevant for pricing.
name: Python str. The name to give to the ops created by this function.
Default value: `None` which maps to 'price'.
Returns:
A Rank 1 `Tensor` of real type containing the modeled price of each IRS
contract based on the input market data.
"""
name = name or (self._name + "_price")
with tf.name_scope(name):
valuation_date = dates.convert_to_date_tensor(valuation_date)
pay_cf = self._pay_leg.price(
valuation_date, market, model, pricing_context
)
receive_cf = self._receive_leg.price(
valuation_date, market, model, pricing_context
)
return receive_cf - pay_cf
| ["def","price","(","self",",","valuation_date",",","market",",","model=None",",","pricing_context=None",",","name=None",",",")",":","``","''","''","Returns","the","present","value","of","the","instrument","on","the","valuation","date",".","Args",":","valuation_date",":","A","scalar","`","DateTensor","`","specifying","the","date","on","which","valuation","is","being","desired",".","market",":","A","namedtuple","of","type","`","InterestRateMarket","`","which","contains","the","necessary","information","for","pricing","the","interest","rate","swap",".","model",":","Reserved","for","future","use",".","pricing_context",":","Additional","context","relevant","for","pricing",".","name",":","Python","str",".","The","name","to","give","to","the","ops","created","by","this","function",".","Default","value",":","`","None","`","which","maps","to","'price","'",".","Returns",":","A","Rank","1","`","Tensor","`","of","real","type","containing","the","modeled","price","of","each","IRS","contract","based","on","the","input","market","data.","``","''","''","name","=","name","or","(","self._name","+","``","_price","''",")","with","tf.name_scope","(","name",")",":","valuation_date","=","dates.convert_to_date_tensor","(","valuation_date",")","pay_cf","=","self._pay_leg.price","(","valuation_date",",","market",",","model",",","pricing_context",")","receive_cf","=","self._receive_leg.price","(","valuation_date",",","market",",","model",",","pricing_context",")","return","receive_cf","-","pay_cf"] | 213 | 239 | null | interest_rate_swap.py | tf-quant-finance/tf_quant_finance/experimental/instruments/interest_rate_swap.py | import tensorflow.compat.v2
from tf_quant_finance import datetime
from tf_quant_finance.experimental.instruments import cashflow_stream
from tf_quant_finance.experimental.instruments import rates_common | 15 | 1 | 4 | 0 | 0 | 10 | null | Use image node_id 2 for calling the InterestRateSwap obj's underlying member method code with example usage: obj.price(valuation_date, market, model, pricing_context, name) and returns: unknown | 193 | node_id 2 | 2,191,435 |
create_attn | global | null | false | attn_type,channels | null | null | null | null | None,module_cls | def create_attn(attn_type, channels, **kwargs):
module_cls = get_attn(attn_type)
if module_cls is not None:
# NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
return module_cls(channels, **kwargs)
return None
| ["def","create_attn","(","attn_type",",","channels",",","*","*","kwargs",")",":","module_cls","=","get_attn","(","attn_type",")","if","module_cls","is","not","None",":","#","NOTE",":","it","'s","expected","the","first","(","positional",")","argument","of","all","attention","layers","is","the","#","input","channels","return","module_cls","(","channels",",","*","*","kwargs",")","return","None"] | 84 | 89 | null | create_attn.py | pytorch-image-models/timm/layers/create_attn.py | import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule | 15 | null | 13 | 2 | null | null | null | Use image node_id 2 for calling a global function with example usage: create_attn(attn_type, channels) and returns: None, module_cls | 132 | node_id 2 | 1,692,280 |
test_unstructured | TestRetrieveUtils | null | true | self | null | null | null | null | null | def test_unstructured(self):
pdf_file_path = os.path.join(test_dir, "example.pdf")
txt_file_path = os.path.join(test_dir, "example.txt")
word_file_path = os.path.join(test_dir, "example.docx")
chunks = split_files_to_chunks(
[pdf_file_path, txt_file_path, word_file_path]
)
assert all(
isinstance(chunk, str)
and "AutoGen is an advanced tool designed to assist developers"
in chunk.strip()
for chunk in chunks
)
| ["def","test_unstructured","(","self",")",":","pdf_file_path","=","os.path.join","(","test_dir",",","``","example.pdf","''",")","txt_file_path","=","os.path.join","(","test_dir",",","``","example.txt","''",")","word_file_path","=","os.path.join","(","test_dir",",","``","example.docx","''",")","chunks","=","split_files_to_chunks","(","[","pdf_file_path",",","txt_file_path",",","word_file_path","]",")","assert","all","(","isinstance","(","chunk",",","str",")","and","``","AutoGen","is","an","advanced","tool","designed","to","assist","developers","''","in","chunk.strip","(",")","for","chunk","in","chunks",")"] | 226 | 234 | null | test_retrieve_utils.py | autogen/test/test_retrieve_utils.py | import pytest
import os | 15 | 1 | 2 | 0 | 0 | 12 | null | Use image node_id 12 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_unstructured() without return types | 155 | node_id 12 | 319,457 |
on | TextLogger | NullLogger | true | self | null | null | null | null | True | def on(self):
return True
| ["def","on","(","self",")",":","return","True"] | 45 | 46 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 4 | 1 | Use image node_id 4 for calling the TextLogger obj's underlying member method code with example usage: obj.on() and returns: True | 129 | node_id 4 | 2,276,657 |
normalize | ImageGPTFeatureExtractor | FeatureExtractionMixin,ImageFeatureExtractionMixin | true | self,image | Constructs an ImageGPT feature extractor. This feature extractor can be used to resize images to a smaller
resolution (such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel
values" (color clusters).
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.
Args:
clusters (`np.ndarray`):
The color clusters to use, as a `np.ndarray` of shape `(n_clusters, 3)`.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input to a certain `size`.
size (`int` or `Tuple(int)`, *optional*, defaults to 32):
Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an
integer is provided, then the input will be resized to (size, size). Only has an effect if `do_resize` is
set to `True`.
resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`):
An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
`PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
`PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input to the range between -1 and +1. | ["Constructs","an","ImageGPT","feature","extractor",".","This","feature","extractor","can","be","used","to","resize","images","to","a","smaller","resolution","(","such","as","32x32","or","64x64",")",",","normalize","them","and","finally","color","quantize","them","to","obtain","sequences","of","``","pixel","values","''","(","color","clusters",")",".","This","feature","extractor","inherits","from","[","`","FeatureExtractionMixin","`","]","which","contains","most","of","the","main","methods",".","Users","should","refer","to","this","superclass","for","more","information","regarding","those","methods",".","Args",":","clusters","(","`","np.ndarray","`",")",":","The","color","clusters","to","use",",","as","a","`","np.ndarray","`","of","shape","`","(","n_clusters",",","3",")","`",".","do_resize","(","`","bool","`",",","*","optional","*",",","defaults","to","`","True","`",")",":","Whether","to","resize","the","input","to","a","certain","`","size","`",".","size","(","`","int","`","or","`","Tuple","(","int",")","`",",","*","optional","*",",","defaults","to","32",")",":","Resize","the","input","to","the","given","size",".","If","a","tuple","is","provided",",","it","should","be","(","width",",","height",")",".","If","only","an","integer","is","provided",",","then","the","input","will","be","resized","to","(","size",",","size",")",".","Only","has","an","effect","if","`","do_resize","`","is","set","to","`","True","`",".","resample","(","`","int","`",",","*","optional","*",",","defaults","to","`","PIL.Image.Resampling.BILINEAR","`",")",":","An","optional","resampling","filter",".","This","can","be","one","of","`","PIL.Image.Resampling.NEAREST","`",",","`","PIL.Image.Resampling.BOX","`",",","`","PIL.Image.Resampling.BILINEAR","`",",","`","PIL.Image.Resampling.HAMMING","`",",","`","PIL.Image.Resampling.BICUBIC","`","or","`","PIL.Image.Resampling.LANCZOS","`",".","Only","has","an","effect","if","`","do_resize","`","is","set","to","`","True","`",".","do_normalize","(","`","bool","`",",","*","optional","*",",","defaults","to","`","True","`",")",":","Whether","or","not","to","normalize","the","input","to","the","range","between","-1","and","+1","."] | Normalizes `image` into the range -1 to +1.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to normalize.
Returns:
`np.ndarray`: The normalized image. | ["Normalizes","`","image","`","into","the","range","-1","to","+1",".","Args",":","image","(","`","PIL.Image.Image","`","or","`","np.ndarray","`","or","`","torch.Tensor","`",")",":","The","image","to","normalize",".","Returns",":","`","np.ndarray","`",":","The","normalized","image","."] | unknown | def normalize(self, image):
"""
Normalizes `image` into the range -1 to +1.
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to normalize.
Returns:
`np.ndarray`: The normalized image.
"""
image = self.to_numpy_array(
image, rescale=False, channel_first=False
)
return image / 127.5 - 1
| ["def","normalize","(","self",",","image",")",":","``","''","''","Normalizes","`","image","`","into","the","range","-1","to","+1",".","Args",":","image","(","`","PIL.Image.Image","`","or","`","np.ndarray","`","or","`","torch.Tensor","`",")",":","The","image","to","normalize",".","Returns",":","`","np.ndarray","`",":","The","normalized","image.","``","''","''","image","=","self.to_numpy_array","(","image",",","rescale=False",",","channel_first=False",")","return","image","\/","127.5","-","1"] | 86 | 99 | null | feature_extraction_imagegpt.py | H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/imagegpt/feature_extraction_imagegpt.py | from typing import List, Optional, Union
import numpy
from PIL import Image
from transformers.image_utils import PILImageResampling
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, logging | 15 | 1 | 7 | 2 | 2 | 3 | 2 | Use image node_id 2 for calling the ImageGPTFeatureExtractor obj's underlying member method code with example usage: obj.normalize(image) and returns: unknown | 158 | node_id 2 | 95,363 |
test_retrieve_utils | TestRetrieveUtils | null | true | self | null | null | null | null | null | def test_retrieve_utils(self):
client = chromadb.PersistentClient(path="/tmp/chromadb")
create_vector_db_from_dir(
dir_path="./website/docs",
client=client,
collection_name="autogen-docs",
custom_text_types=["txt", "md", "rtf", "rst"],
get_or_create=True,
)
results = query_vector_db(
query_texts=[
"How can I use AutoGen UserProxyAgent and AssistantAgent to do code generation?",
],
n_results=4,
client=client,
collection_name="autogen-docs",
search_string="AutoGen",
)
print(results["ids"][0])
assert len(results["ids"][0]) == 4
| ["def","test_retrieve_utils","(","self",")",":","client","=","chromadb.PersistentClient","(","path=","''","\/tmp\/chromadb","''",")","create_vector_db_from_dir","(","dir_path=","''",".\/website\/docs","''",",","client=client",",","collection_name=","''","autogen-docs","''",",","custom_text_types=","[","``","txt","''",",","``","md","''",",","``","rtf","''",",","``","rst","''","]",",","get_or_create=True",",",")","results","=","query_vector_db","(","query_texts=","[","``","How","can","I","use","AutoGen","UserProxyAgent","and","AssistantAgent","to","do","code","generation","?","``",",","]",",","n_results=4",",","client=client",",","collection_name=","''","autogen-docs","''",",","search_string=","''","AutoGen","''",",",")","print","(","results","[","``","ids","''","]","[","0","]",")","assert","len","(","results","[","``","ids","''","]","[","0","]",")","==","4"] | 201 | 220 | null | test_retrieve_utils.py | autogen/test/test_retrieve_utils.py | import pytest
import os | 15 | 1 | 2 | 0 | 0 | 12 | null | Use image node_id 11 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_retrieve_utils() without return types | 157 | node_id 11 | 319,456 |
_maximum_given_samples | global | null | false | f,pts,map | null | null | null | null | max | def _maximum_given_samples(f, pts, map=None):
"""
use given sample pts to calculate maximum for function f
Inputs:
f -- a function that returns a single value, given a list of inputs
pts -- a list of sample points
map -- the mapping function [Default is builtins.map]"""
if map is None:
from builtins import map
from numpy import transpose, atleast_2d
return max(list(map(f, atleast_2d(transpose(pts)).tolist())))
| ["def","_maximum_given_samples","(","f",",","pts",",","map=None",")",":","``","''","''","use","given","sample","pts","to","calculate","maximum","for","function","f","Inputs",":","f","--","a","function","that","returns","a","single","value",",","given","a","list","of","inputs","pts","--","a","list","of","sample","points","map","--","the","mapping","function","[","Default","is","builtins.map","]","''","''","''","if","map","is","None",":","from","builtins","import","map","from","numpy","import","transpose",",","atleast_2d","return","max","(","list","(","map","(","f",",","atleast_2d","(","transpose","(","pts",")",")",".tolist","(",")",")",")",")"] | 226 | 238 | null | samples.py | mystic/mystic/math/samples.py | 15 | null | 0 | 15 | null | null | null | Use image node_id 11 for calling a global function with example usage: _maximum_given_samples(f, pts, map) and returns: max | 123 | node_id 11 | 1,407,031 |
|
test_custom_text_split_function | TestRetrieveUtils | null | true | self | null | null | null | null | list | def test_custom_text_split_function(self):
def custom_text_split_function(text):
return [text[: len(text) // 2], text[len(text) // 2 :]]
db_path = "/tmp/test_retrieve_utils_chromadb.db"
client = chromadb.PersistentClient(path=db_path)
create_vector_db_from_dir(
os.path.join(test_dir, "example.txt"),
client=client,
collection_name="mytestcollection",
custom_text_split_function=custom_text_split_function,
get_or_create=True,
recursive=False,
)
results = query_vector_db(
["autogen"],
client=client,
collection_name="mytestcollection",
n_results=1,
)
assert (
"AutoGen is an advanced tool designed to assist developers in harnessing the capabilities"
in results.get("documents")[0][0]
)
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import os | 15 | 1 | 2 | 0 | 0 | 12 | null | Use image node_id 10 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_custom_text_split_function() and returns: list | 166 | node_id 10 | 319,455 |
_ptp_given_samples | global | null | false | f,pts,map | null | null | null | null | ptp | def _ptp_given_samples(f, pts, map=None):
"""
use given sample pts to calculate spread for function f
Inputs:
f -- a function that returns a single value, given a list of inputs
pts -- a list of sample points
map -- the mapping function [Default is builtins.map]"""
if map is None:
from builtins import map
from numpy import transpose, ptp, atleast_2d
return ptp(list(map(f, atleast_2d(transpose(pts)).tolist())))
| ["def","_ptp_given_samples","(","f",",","pts",",","map=None",")",":","``","''","''","use","given","sample","pts","to","calculate","spread","for","function","f","Inputs",":","f","--","a","function","that","returns","a","single","value",",","given","a","list","of","inputs","pts","--","a","list","of","sample","points","map","--","the","mapping","function","[","Default","is","builtins.map","]","''","''","''","if","map","is","None",":","from","builtins","import","map","from","numpy","import","transpose",",","ptp",",","atleast_2d","return","ptp","(","list","(","map","(","f",",","atleast_2d","(","transpose","(","pts",")",")",".tolist","(",")",")",")",")"] | 241 | 253 | null | samples.py | mystic/mystic/math/samples.py | 15 | null | 0 | 15 | null | null | null | Use image node_id 12 for calling a global function with example usage: _ptp_given_samples(f, pts, map) and returns: ptp | 119 | node_id 12 | 1,407,032 |
|
test_custom_vector_db | TestRetrieveUtils | null | true | self | null | null | null | null | dict | def test_custom_vector_db(self):
try:
import lancedb
except ImportError:
return
from autogen.agentchat.contrib.retrieve_user_proxy_agent import (
RetrieveUserProxyAgent,
)
db_path = "/tmp/lancedb"
def create_lancedb():
db = lancedb.connect(db_path)
data = [
{
"vector": [1.1, 1.2],
"id": 1,
"documents": "This is a test document spark",
},
{
"vector": [0.2, 1.8],
"id": 2,
"documents": "This is another test document",
},
{
"vector": [0.1, 0.3],
"id": 3,
"documents": "This is a third test document spark",
},
{
"vector": [0.5, 0.7],
"id": 4,
"documents": "This is a fourth test document",
},
{
"vector": [2.1, 1.3],
"id": 5,
"documents": "This is a fifth test document spark",
},
{
"vector": [5.1, 8.3],
"id": 6,
"documents": "This is a sixth test document",
},
]
try:
db.create_table("my_table", data)
except OSError:
pass
class MyRetrieveUserProxyAgent(RetrieveUserProxyAgent):
def query_vector_db(
self,
query_texts,
n_results=10,
search_string="",
):
if query_texts:
vector = [0.1, 0.3]
db = lancedb.connect(db_path)
table = db.open_table("my_table")
query = (
table.search(vector)
.where(f"documents LIKE '%{search_string}%'")
.limit(n_results)
.to_df()
)
return {
"ids": [query["id"].tolist()],
"documents": [query["documents"].tolist()],
}
def retrieve_docs(
self,
problem: str,
n_results: int = 20,
search_string: str = "",
):
results = self.query_vector_db(
query_texts=[problem],
n_results=n_results,
search_string=search_string,
)
self._results = results
print("doc_ids: ", results["ids"])
ragragproxyagent = MyRetrieveUserProxyAgent(
name="ragproxyagent",
human_input_mode="NEVER",
max_consecutive_auto_reply=2,
retrieve_config={
"task": "qa",
"chunk_token_size": 2000,
"client": "__",
"embedding_model": "all-mpnet-base-v2",
},
)
create_lancedb()
ragragproxyagent.retrieve_docs(
"This is a test document spark",
n_results=10,
search_string="spark",
)
assert ragragproxyagent._results["ids"] == [[3, 1, 5]]
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import os | 15 | 1 | 2 | 0 | 0 | 12 | null | Use image node_id 9 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_custom_vector_db() and returns: dict | 155 | node_id 9 | 319,454 |
test_query_vector_db | TestRetrieveUtils | null | true | self | null | null | null | null | null | def test_query_vector_db(self):
db_path = "/tmp/test_retrieve_utils_chromadb.db"
if os.path.exists(db_path):
client = chromadb.PersistentClient(path=db_path)
else: # If the database does not exist, create it first
client = chromadb.PersistentClient(path=db_path)
create_vector_db_from_dir(test_dir, client=client)
results = query_vector_db(["autogen"], client=client)
assert isinstance(results, dict) and any(
"autogen" in res[0].lower()
for res in results.get("documents", [])
)
| ["def","test_query_vector_db","(","self",")",":","db_path","=","``","\/tmp\/test_retrieve_utils_chromadb.db","''","if","os.path.exists","(","db_path",")",":","client","=","chromadb.PersistentClient","(","path=db_path",")","else",":","#","If","the","database","does","not","exist",",","create","it","first","client","=","chromadb.PersistentClient","(","path=db_path",")","create_vector_db_from_dir","(","test_dir",",","client=client",")","results","=","query_vector_db","(","[","``","autogen","''","]",",","client=client",")","assert","isinstance","(","results",",","dict",")","and","any","(","``","autogen","''","in","res","[","0","]",".lower","(",")","for","res","in","results.get","(","``","documents","''",",","[","]",")",")"] | 106 | 115 | null | test_retrieve_utils.py | autogen/test/test_retrieve_utils.py | import pytest
import os | 15 | 1 | 2 | 0 | 0 | 12 | null | Use image node_id 8 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_query_vector_db() without return types | 157 | node_id 8 | 319,453 |
get_boxes | global | null | false | model,val_set,num_images,image_dir,score_threshold | null | null | null | null | all_boxes, all_gt_boxes | def get_boxes(
model, val_set, num_images=0, image_dir=None, score_threshold=0.6
):
n = 0
all_boxes = []
all_gt_boxes = []
with tqdm(total=val_set.ndata) as pbar: # progress bar
for img, (
gt_boxes,
gt_classes,
num_gt_boxes,
difficult,
im_shape,
) in val_set:
outputs = model.fprop(img, inference=True)
for k, boxes in enumerate(outputs):
pbar.update(1)
all_boxes.append(boxes)
ngt = num_gt_boxes[0, k]
gtb = gt_boxes[:, k].reshape((-1, 4))
# retrieve gt boxes
# we add a extra column to track detections during the AP calculation
detected = np.array([False] * ngt)
gtb = np.hstack(
[
gtb[:ngt],
gt_classes[:ngt, k][:, np.newaxis],
difficult[:ngt, k][:, np.newaxis],
detected[:, np.newaxis],
]
)
all_gt_boxes.append(gtb)
# plot images if needed
if n < num_images:
gt_boxes = np.copy(
gt_boxes.reshape((-1, 4, val_set.be.bsz))
)
boxes = np.copy(boxes)
ngt = num_gt_boxes[0, k]
img = plot_image(
img=img[:, k].get(),
im_shape=im_shape[:, k],
gt_boxes=gt_boxes[:ngt, :, k],
boxes=boxes,
score_threshold=score_threshold,
)
file_name = os.path.join(
image_dir, "image_{}.jpg".format(n)
)
img.save(file_name)
n = n + 1
return (all_boxes, all_gt_boxes)
| ["def","get_boxes","(","model",",","val_set",",","num_images=0",",","image_dir=None",",","score_threshold=0.6",")",":","n","=","0","all_boxes","=","[","]","all_gt_boxes","=","[","]","with","tqdm","(","total=val_set.ndata",")","as","pbar",":","#","progress","bar","for","img",",","(","gt_boxes",",","gt_classes",",","num_gt_boxes",",","difficult",",","im_shape",",",")","in","val_set",":","outputs","=","model.fprop","(","img",",","inference=True",")","for","k",",","boxes","in","enumerate","(","outputs",")",":","pbar.update","(","1",")","all_boxes.append","(","boxes",")","ngt","=","num_gt_boxes","[","0",",","k","]","gtb","=","gt_boxes","[",":",",","k","]",".reshape","(","(","-1",",","4",")",")","#","retrieve","gt","boxes","#","we","add","a","extra","column","to","track","detections","during","the","AP","calculation","detected","=","np.array","(","[","False","]","*","ngt",")","gtb","=","np.hstack","(","[","gtb","[",":","ngt","]",",","gt_classes","[",":","ngt",",","k","]","[",":",",","np.newaxis","]",",","difficult","[",":","ngt",",","k","]","[",":",",","np.newaxis","]",",","detected","[",":",",","np.newaxis","]",",","]",")","all_gt_boxes.append","(","gtb",")","#","plot","images","if","needed","if","n","<","num_images",":","gt_boxes","=","np.copy","(","gt_boxes.reshape","(","(","-1",",","4",",","val_set.be.bsz",")",")",")","boxes","=","np.copy","(","boxes",")","ngt","=","num_gt_boxes","[","0",",","k","]","img","=","plot_image","(","img=img","[",":",",","k","]",".get","(",")",",","im_shape=im_shape","[",":",",","k","]",",","gt_boxes=gt_boxes","[",":","ngt",",",":",",","k","]",",","boxes=boxes",",","score_threshold=score_threshold",",",")","file_name","=","os.path.join","(","image_dir",",","``","image_","{","}",".jpg","''",".format","(","n",")",")","img.save","(","file_name",")","n","=","n","+","1","return","(","all_boxes",",","all_gt_boxes",")"] | 41 | 83 | null | inference.py | neon/examples/ssd/inference.py | from neon.backends import gen_backend
from neon.util.argparser import NeonArgparser
from neon.models.model import Model
import numpy
from ssd_container import SSD
from util.voc_eval import voc_eval
from tqdm import tqdm
import json
from ssd_dataloader import build_dataloader
import pickle
import os
from sys import exit
from util.util import plot_image
from collections import OrderedDict | 15 | null | 14 | 1 | null | null | null | Use image node_id 1 for calling a global function with example usage: get_boxes(model, val_set, num_images, image_dir, score_threshold) and returns: all_boxes, all_gt_boxes | 173 | node_id 1 | 1,411,035 |
test_create_vector_db_from_dir | TestRetrieveUtils | null | true | self | null | null | null | null | null | def test_create_vector_db_from_dir(self):
db_path = "/tmp/test_retrieve_utils_chromadb.db"
if os.path.exists(db_path):
client = chromadb.PersistentClient(path=db_path)
else:
client = chromadb.PersistentClient(path=db_path)
create_vector_db_from_dir(test_dir, client=client)
assert client.get_collection("all-my-documents")
| ["def","test_create_vector_db_from_dir","(","self",")",":","db_path","=","``","\/tmp\/test_retrieve_utils_chromadb.db","''","if","os.path.exists","(","db_path",")",":","client","=","chromadb.PersistentClient","(","path=db_path",")","else",":","client","=","chromadb.PersistentClient","(","path=db_path",")","create_vector_db_from_dir","(","test_dir",",","client=client",")","assert","client.get_collection","(","``","all-my-documents","''",")"] | 96 | 104 | null | test_retrieve_utils.py | autogen/test/test_retrieve_utils.py | import pytest
import os | 15 | 1 | 2 | 0 | 0 | 12 | null | Use image node_id 7 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_create_vector_db_from_dir() without return types | 167 | node_id 7 | 319,452 |
test_is_url | TestRetrieveUtils | null | true | self | null | null | null | null | null | def test_is_url(self):
assert is_url("https://www.example.com")
assert not is_url("not_a_url")
| ["def","test_is_url","(","self",")",":","assert","is_url","(","``","https",":","\/\/www.example.com","''",")","assert","not","is_url","(","``","not_a_url","''",")"] | 92 | 94 | null | test_retrieve_utils.py | autogen/test/test_retrieve_utils.py | import pytest
import os | 15 | 1 | 2 | 0 | 0 | 12 | null | Use image node_id 6 for calling the TestRetrieveUtils obj's underlying member method code with example usage: obj.test_is_url() without return types | 148 | node_id 6 | 319,451 |
test_download_all | TestDatasets | null | true | self | null | null | null | null | null | def test_download_all(self):
# This fixture requires INTERNET CONNECTION
# test_setup phase
download_all()
yield
| ["def","test_download_all","(","self",")",":","#","This","fixture","requires","INTERNET","CONNECTION","#","test_setup","phase","download_all","(",")","yield"] | 30 | 36 | null | test_data.py | scipy/scipy/datasets/tests/test_data.py | from scipy.datasets._registry import registry
from scipy.datasets._fetchers import data_fetcher
from scipy.datasets._utils import _clear_cache
from scipy.datasets import ascent, face, electrocardiogram, download_all
from numpy.testing import assert_equal, assert_almost_equal
import os
import pytest | 15 | 1 | 7 | 2 | 0 | 5 | null | Use image node_id 1 for calling the TestDatasets obj's underlying member method code with example usage: obj.test_download_all() without return types | 149 | node_id 1 | 1,884,887 |
difference | global | null | false | ctx,s,n | null | null | null | null | d | def difference(ctx, s, n):
r"""
Given a sequence `(s_k)` containing at least `n+1` items, returns the
`n`-th forward difference,
.. math ::
\Delta^n = \sum_{k=0}^{\infty} (-1)^{k+n} {n \choose k} s_k.
"""
n = int(n)
d = ctx.zero
b = (-1) ** (n & 1)
for k in xrange(n + 1):
d += b * s[k]
b = (b * (k - n)) // (k + 1)
return d
| ["def","difference","(","ctx",",","s",",","n",")",":","r","''","''","''","Given","a","sequence","`","(","s_k",")","`","containing","at","least","`","n+1","`","items",",","returns","the","`","n","`","-th","forward","difference",",","..","math",":",":","\\Delta^n","=","\\sum_","{","k=0","}","^","{","\\infty","}","(","-1",")","^","{","k+n","}","{","n","\\choose","k","}","s_k.","``","''","''","n","=","int","(","n",")","d","=","ctx.zero","b","=","(","-1",")","*","*","(","n","&","1",")","for","k","in","xrange","(","n","+","1",")",":","d","+=","b","*","s","[","k","]","b","=","(","b","*","(","k","-","n",")",")","\/\/","(","k","+","1",")","return","d"] | 14 | 29 | null | differentiation.py | catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py | from ..libmp.backend import xrange
from .calculus import defun | 15 | null | 2 | 13 | null | null | null | Use image node_id 1 for calling a global function with example usage: difference(ctx, s, n) and returns: d | 106 | node_id 1 | 407,212 |
test_existence_all | TestDatasets | null | true | self | null | null | null | null | null | def test_existence_all(self):
assert len(os.listdir(data_dir)) >= len(registry)
| ["def","test_existence_all","(","self",")",":","assert","len","(","os.listdir","(","data_dir",")",")",">","=","len","(","registry",")"] | 38 | 39 | null | test_data.py | scipy/scipy/datasets/tests/test_data.py | from scipy.datasets._registry import registry
from scipy.datasets._fetchers import data_fetcher
from scipy.datasets._utils import _clear_cache
from scipy.datasets import ascent, face, electrocardiogram, download_all
from numpy.testing import assert_equal, assert_almost_equal
import os
import pytest | 15 | 1 | 7 | 2 | 0 | 5 | null | Use image node_id 2 for calling the TestDatasets obj's underlying member method code with example usage: obj.test_existence_all() without return types | 150 | node_id 2 | 1,884,888 |
hsteps | global | null | false | ctx,f,x,n,prec | null | null | null | null | values, norm, workprec | def hsteps(ctx, f, x, n, prec, **options):
singular = options.get("singular")
addprec = options.get("addprec", 10)
direction = options.get("direction", 0)
workprec = (prec + 2 * addprec) * (n + 1)
orig = ctx.prec
try:
ctx.prec = workprec
h = options.get("h")
if h is None:
if options.get("relative"):
hextramag = int(ctx.mag(x))
else:
hextramag = 0
h = ctx.ldexp(1, -prec - addprec - hextramag)
else:
h = ctx.convert(h)
# Directed: steps x, x+h, ... x+n*h
direction = options.get("direction", 0)
if direction:
h *= ctx.sign(direction)
steps = xrange(n + 1)
norm = h
# Central: steps x-n*h, x-(n-2)*h ..., x, ..., x+(n-2)*h, x+n*h
else:
steps = xrange(-n, n + 1, 2)
norm = 2 * h
# Perturb
if singular:
x += 0.5 * h
values = [f(x + k * h) for k in steps]
return values, norm, workprec
finally:
ctx.prec = orig
| ["def","hsteps","(","ctx",",","f",",","x",",","n",",","prec",",","*","*","options",")",":","singular","=","options.get","(","``","singular","''",")","addprec","=","options.get","(","``","addprec","''",",","10",")","direction","=","options.get","(","``","direction","''",",","0",")","workprec","=","(","prec","+","2","*","addprec",")","*","(","n","+","1",")","orig","=","ctx.prec","try",":","ctx.prec","=","workprec","h","=","options.get","(","``","h","''",")","if","h","is","None",":","if","options.get","(","``","relative","''",")",":","hextramag","=","int","(","ctx.mag","(","x",")",")","else",":","hextramag","=","0","h","=","ctx.ldexp","(","1",",","-prec","-","addprec","-","hextramag",")","else",":","h","=","ctx.convert","(","h",")","#","Directed",":","steps","x",",","x+h",",","...","x+n","*","h","direction","=","options.get","(","``","direction","''",",","0",")","if","direction",":","h","*","=","ctx.sign","(","direction",")","steps","=","xrange","(","n","+","1",")","norm","=","h","#","Central",":","steps","x-n","*","h",",","x-","(","n-2",")","*","h","...",",","x",",","...",",","x+","(","n-2",")","*","h",",","x+n","*","h","else",":","steps","=","xrange","(","-n",",","n","+","1",",","2",")","norm","=","2","*","h","#","Perturb","if","singular",":","x","+=","0.5","*","h","values","=","[","f","(","x","+","k","*","h",")","for","k","in","steps","]","return","values",",","norm",",","workprec","finally",":","ctx.prec","=","orig"] | 31 | 64 | null | differentiation.py | catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py | from ..libmp.backend import xrange
from .calculus import defun | 15 | null | 2 | 13 | null | null | null | Use image node_id 2 for calling a global function with example usage: hsteps(ctx, f, x, n, prec) and returns: values, norm, workprec | 134 | node_id 2 | 407,213 |
diff | global | null | false | ctx,f,x,n | null | null | null | null | unknown,_partial_diff,f,unknown | def diff(ctx, f, x, n=1, **options):
r"""
Numerically computes the derivative of `f`, `f'(x)`, or generally for
an integer `n \ge 0`, the `n`-th derivative `f^{(n)}(x)`.
A few basic examples are::
>>> from mpmath import *
>>> mp.dps = 15; mp.pretty = True
>>> diff(lambda x: x**2 + x, 1.0)
3.0
>>> diff(lambda x: x**2 + x, 1.0, 2)
2.0
>>> diff(lambda x: x**2 + x, 1.0, 3)
0.0
>>> nprint([diff(exp, 3, n) for n in range(5)]) # exp'(x) = exp(x)
[20.0855, 20.0855, 20.0855, 20.0855, 20.0855]
Even more generally, given a tuple of arguments `(x_1, \ldots, x_k)`
and order `(n_1, \ldots, n_k)`, the partial derivative
`f^{(n_1,\ldots,n_k)}(x_1,\ldots,x_k)` is evaluated. For example::
>>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (0,1))
2.75
>>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (1,1))
3.0
**Options**
The following optional keyword arguments are recognized:
``method``
Supported methods are ``'step'`` or ``'quad'``: derivatives may be
computed using either a finite difference with a small step
size `h` (default), or numerical quadrature.
``direction``
Direction of finite difference: can be -1 for a left
difference, 0 for a central difference (default), or +1
for a right difference; more generally can be any complex number.
``addprec``
Extra precision for `h` used to account for the function's
sensitivity to perturbations (default = 10).
``relative``
Choose `h` relative to the magnitude of `x`, rather than an
absolute value; useful for large or tiny `x` (default = False).
``h``
As an alternative to ``addprec`` and ``relative``, manually
select the step size `h`.
``singular``
If True, evaluation exactly at the point `x` is avoided; this is
useful for differentiating functions with removable singularities.
Default = False.
``radius``
Radius of integration contour (with ``method = 'quad'``).
Default = 0.25. A larger radius typically is faster and more
accurate, but it must be chosen so that `f` has no
singularities within the radius from the evaluation point.
A finite difference requires `n+1` function evaluations and must be
performed at `(n+1)` times the target precision. Accordingly, `f` must
support fast evaluation at high precision.
With integration, a larger number of function evaluations is
required, but not much extra precision is required. For high order
derivatives, this method may thus be faster if f is very expensive to
evaluate at high precision.
**Further examples**
The direction option is useful for computing left- or right-sided
derivatives of nonsmooth functions::
>>> diff(abs, 0, direction=0)
0.0
>>> diff(abs, 0, direction=1)
1.0
>>> diff(abs, 0, direction=-1)
-1.0
More generally, if the direction is nonzero, a right difference
is computed where the step size is multiplied by sign(direction).
For example, with direction=+j, the derivative from the positive
imaginary direction will be computed::
>>> diff(abs, 0, direction=j)
(0.0 - 1.0j)
With integration, the result may have a small imaginary part
even even if the result is purely real::
>>> diff(sqrt, 1, method='quad') # doctest:+ELLIPSIS
(0.5 - 4.59...e-26j)
>>> chop(_)
0.5
Adding precision to obtain an accurate value::
>>> diff(cos, 1e-30)
0.0
>>> diff(cos, 1e-30, h=0.0001)
-9.99999998328279e-31
>>> diff(cos, 1e-30, addprec=100)
-1.0e-30
"""
partial = False
try:
orders = list(n)
x = list(x)
partial = True
except TypeError:
pass
if partial:
x = [ctx.convert(_) for _ in x]
return _partial_diff(ctx, f, x, orders, options)
method = options.get("method", "step")
if n == 0 and method != "quad" and not options.get("singular"):
return f(ctx.convert(x))
prec = ctx.prec
try:
if method == "step":
values, norm, workprec = hsteps(
ctx, f, x, n, prec, **options
)
ctx.prec = workprec
v = ctx.difference(values, n) / norm**n
elif method == "quad":
ctx.prec += 10
radius = ctx.convert(options.get("radius", 0.25))
def g(t):
rei = radius * ctx.expj(t)
z = x + rei
return f(z) / rei**n
d = ctx.quadts(g, [0, 2 * ctx.pi])
v = d * ctx.factorial(n) / (2 * ctx.pi)
else:
raise ValueError("unknown method: %r" % method)
finally:
ctx.prec = prec
return +v
| 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| 68 | 204 | null | differentiation.py | catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py | from ..libmp.backend import xrange
from .calculus import defun | 15 | null | 2 | 13 | null | null | null | Use image node_id 3 for calling a global function with example usage: diff(ctx, f, x, n) and returns: unknown, _partial_diff, f, unknown | 136 | node_id 3 | 407,214 |
test_extra_tags_paddle_autolog | global | null | false | null | null | null | null | null | def test_extra_tags_paddle_autolog():
mlflow.paddle.autolog(extra_tags={"test_tag": "paddle_autolog"})
train_model()
run = mlflow.last_active_run()
assert run.data.tags["test_tag"] == "paddle_autolog"
assert (
run.data.tags[mlflow.utils.mlflow_tags.MLFLOW_AUTOLOGGING]
== "paddle"
)
| ["def","test_extra_tags_paddle_autolog","(",")",":","mlflow.paddle.autolog","(","extra_tags=","{","``","test_tag","''",":","``","paddle_autolog","''","}",")","train_model","(",")","run","=","mlflow.last_active_run","(",")","assert","run.data.tags","[","``","test_tag","''","]","==","``","paddle_autolog","''","assert","(","run.data.tags","[","mlflow.utils.mlflow_tags.MLFLOW_AUTOLOGGING","]","==","``","paddle","''",")"] | 107 | 113 | null | test_paddle_autolog.py | mlflow/tests/paddle/test_paddle_autolog.py | import paddle
import pytest
import mlflow
from mlflow import MlflowClient | 15 | null | 4 | 7 | null | null | null | Use image node_id 7 for calling a global function with example usage: test_extra_tags_paddle_autolog() without return types | 123 | node_id 7 | 1,356,432 |
|
_partial_diff | global | null | false | ctx,f,xs,orders,options | null | null | null | null | _partial_diff,f,f,ctx,f | def _partial_diff(ctx, f, xs, orders, options):
if not orders:
return f()
if not sum(orders):
return f(*xs)
i = 0
for i in range(len(orders)):
if orders[i]:
break
order = orders[i]
def fdiff_inner(*f_args):
def inner(t):
return f(*(f_args[:i] + (t,) + f_args[i + 1 :]))
return ctx.diff(inner, f_args[i], order, **options)
orders[i] = 0
return _partial_diff(ctx, fdiff_inner, xs, orders, options)
| ["def","_partial_diff","(","ctx",",","f",",","xs",",","orders",",","options",")",":","if","not","orders",":","return","f","(",")","if","not","sum","(","orders",")",":","return","f","(","*","xs",")","i","=","0","for","i","in","range","(","len","(","orders",")",")",":","if","orders","[","i","]",":","break","order","=","orders","[","i","]","def","fdiff_inner","(","*","f_args",")",":","def","inner","(","t",")",":","return","f","(","*","(","f_args","[",":","i","]","+","(","t",",",")","+","f_args","[","i","+","1",":","]",")",")","return","ctx.diff","(","inner",",","f_args","[","i","]",",","order",",","*","*","options",")","orders","[","i","]","=","0","return","_partial_diff","(","ctx",",","fdiff_inner",",","xs",",","orders",",","options",")"] | 206 | 221 | null | differentiation.py | catboost/contrib/python/mpmath/py3/mpmath/calculus/differentiation.py | from ..libmp.backend import xrange
from .calculus import defun | 15 | null | 2 | 13 | null | null | null | Use image node_id 4 for calling a global function with example usage: _partial_diff(ctx, f, xs, orders, options) and returns: _partial_diff, f, f, ctx, f | 153 | node_id 4 | 407,215 |
__init__ | AudioInput | null | true | self,channels_first,channels,sample_rate,batch_size | Create audio batch. | ["Create","audio","batch","."] | null | null | AudioInput | def __init__(
self, channels_first, channels, sample_rate=44100, batch_size=2
):
self.channels_first = channels_first
self.channels = channels
self.sample_rate = sample_rate
self.batch_size = batch_size
| ["def","__init__","(","self",",","channels_first",",","channels",",","sample_rate=44100",",","batch_size=2",")",":","self.channels_first","=","channels_first","self.channels","=","channels","self.sample_rate","=","sample_rate","self.batch_size","=","batch_size"] | 38 | 42 | null | test_mp3_compression_pytorch.py | adversarial-robustness-toolbox/tests/defences/preprocessor/test_mp3_compression_pytorch.py | from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy
import pytest
from numpy.testing import assert_array_equal
from art.defences.preprocessor import Mp3CompressionPyTorch
from tests.utils import ARTTestException | 15 | 1 | 7 | 5 | 0 | 2 | null | Use image node_id 1 to create a new AudioInput object with example: obj = AudioInput(channels_first, channels, sample_rate, batch_size) | 136 | node_id 1 | 235,203 |
test_autolog_registering_model | global | null | false | null | null | null | null | null | def test_autolog_registering_model():
registered_model_name = "test_autolog_registered_model"
mlflow.paddle.autolog(registered_model_name=registered_model_name)
with mlflow.start_run():
train_model()
registered_model = MlflowClient().get_registered_model(
registered_model_name
)
assert registered_model.name == registered_model_name
| ["def","test_autolog_registering_model","(",")",":","registered_model_name","=","``","test_autolog_registered_model","''","mlflow.paddle.autolog","(","registered_model_name=registered_model_name",")","with","mlflow.start_run","(",")",":","train_model","(",")","registered_model","=","MlflowClient","(",")",".get_registered_model","(","registered_model_name",")","assert","registered_model.name","==","registered_model_name"] | 96 | 104 | null | test_paddle_autolog.py | mlflow/tests/paddle/test_paddle_autolog.py | import paddle
import pytest
import mlflow
from mlflow import MlflowClient | 15 | null | 4 | 7 | null | null | null | Use image node_id 6 for calling a global function with example usage: test_autolog_registering_model() without return types | 123 | node_id 6 | 1,356,431 |
|
get_data | AudioInput | null | true | self | Create audio batch. | ["Create","audio","batch","."] | null | null | np | def get_data(self):
if self.channels_first:
shape = (self.batch_size, self.channels, self.sample_rate)
else:
shape = (self.batch_size, self.sample_rate, self.channels)
return np.zeros(shape, dtype=np.int16)
| ["def","get_data","(","self",")",":","if","self.channels_first",":","shape","=","(","self.batch_size",",","self.channels",",","self.sample_rate",")","else",":","shape","=","(","self.batch_size",",","self.sample_rate",",","self.channels",")","return","np.zeros","(","shape",",","dtype=np.int16",")"] | 44 | 49 | null | test_mp3_compression_pytorch.py | adversarial-robustness-toolbox/tests/defences/preprocessor/test_mp3_compression_pytorch.py | from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy
import pytest
from numpy.testing import assert_array_equal
from art.defences.preprocessor import Mp3CompressionPyTorch
from tests.utils import ARTTestException | 15 | 1 | 7 | 5 | 0 | 2 | null | Use image node_id 2 for calling the AudioInput obj's underlying member method code with example usage: obj.get_data() and returns: np | 133 | node_id 2 | 235,204 |
audio_batch | global | null | false | request,channels_first | null | null | null | null | test_input, test_output, audio_input | def audio_batch(request, channels_first):
"""
Audio fixtures of shape `(batch_size, channels, samples)` or `(batch_size, samples, channels)`.
"""
channels = request.param
audio_input = AudioInput(channels_first, channels)
test_input = audio_input.get_data()
test_output = test_input.copy()
return test_input, test_output, audio_input.sample_rate
| ["def","audio_batch","(","request",",","channels_first",")",":","``","''","''","Audio","fixtures","of","shape","`","(","batch_size",",","channels",",","samples",")","`","or","`","(","batch_size",",","samples",",","channels",")","`",".","``","''","''","channels","=","request.param","audio_input","=","AudioInput","(","channels_first",",","channels",")","test_input","=","audio_input.get_data","(",")","test_output","=","test_input.copy","(",")","return","test_input",",","test_output",",","audio_input.sample_rate"] | 53 | 61 | null | test_mp3_compression_pytorch.py | adversarial-robustness-toolbox/tests/defences/preprocessor/test_mp3_compression_pytorch.py | from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy
import pytest
from numpy.testing import assert_array_equal
from art.defences.preprocessor import Mp3CompressionPyTorch
from tests.utils import ARTTestException | 15 | null | 7 | 5 | null | null | null | Use image node_id 1 for calling a global function with example usage: audio_batch(request, channels_first) and returns: test_input, test_output, audio_input | 158 | node_id 1 | 235,205 |
log | NullLogger | null | true | self,source_name | null | null | null | null | null | def log(self, source_name, *args):
if self.on() and self.interesting(source_name):
self.do_log(self.indent_)
for i in args:
self.do_log(i)
self.do_log("\n")
| ["def","log","(","self",",","source_name",",","*","args",")",":","if","self.on","(",")","and","self.interesting","(","source_name",")",":","self.do_log","(","self.indent_",")","for","i","in","args",":","self.do_log","(","i",")","self.do_log","(","``","\\n","''",")"] | 11 | 16 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 7 | null | Use image node_id 2 for calling the NullLogger obj's underlying member method code with example usage: obj.log(source_name) without return types | 144 | node_id 2 | 2,276,648 |
increase_indent | NullLogger | null | true | self | null | null | null | null | null | def increase_indent(self):
if self.on():
self.indent_ += " "
| ["def","increase_indent","(","self",")",":","if","self.on","(",")",":","self.indent_","+=","``","``"] | 18 | 20 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 7 | null | Use image node_id 3 for calling the NullLogger obj's underlying member method code with example usage: obj.increase_indent() without return types | 145 | node_id 3 | 2,276,649 |
_pof_given_samples | global | null | false | f,pts,map | null | null | null | null | pof | def _pof_given_samples(f, pts, map=None):
"""
use given sample pts to calculate probability of failure for function f
Inputs:
f -- a function that returns True for 'success' and False for 'failure'
pts -- a list of sample points
map -- the mapping function [Default is builtins.map]"""
if map is None:
from builtins import map
from numpy import transpose, atleast_2d
results = list(map(f, atleast_2d(transpose(pts)).tolist()))
pof = float(results.count(False)) / float(len(results))
return pof
| ["def","_pof_given_samples","(","f",",","pts",",","map=None",")",":","``","''","''","use","given","sample","pts","to","calculate","probability","of","failure","for","function","f","Inputs",":","f","--","a","function","that","returns","True","for","'success","'","and","False","for","'failure'","pts","--","a","list","of","sample","points","map","--","the","mapping","function","[","Default","is","builtins.map","]","''","''","''","if","map","is","None",":","from","builtins","import","map","from","numpy","import","transpose",",","atleast_2d","results","=","list","(","map","(","f",",","atleast_2d","(","transpose","(","pts",")",")",".tolist","(",")",")",")","pof","=","float","(","results.count","(","False",")",")","\/","float","(","len","(","results",")",")","return","pof"] | 164 | 178 | null | samples.py | mystic/mystic/math/samples.py | 15 | null | 0 | 15 | null | null | null | Use image node_id 7 for calling a global function with example usage: _pof_given_samples(f, pts, map) and returns: pof | 118 | node_id 7 | 1,407,027 |
|
_minimum_given_samples | global | null | false | f,pts,map | null | null | null | null | min | def _minimum_given_samples(f, pts, map=None):
"""
use given sample pts to calculate minimum for function f
Inputs:
f -- a function that returns a single value, given a list of inputs
pts -- a list of sample points
map -- the mapping function [Default is builtins.map]"""
if map is None:
from builtins import map
from numpy import transpose, atleast_2d
return min(list(map(f, atleast_2d(transpose(pts)).tolist())))
| ["def","_minimum_given_samples","(","f",",","pts",",","map=None",")",":","``","''","''","use","given","sample","pts","to","calculate","minimum","for","function","f","Inputs",":","f","--","a","function","that","returns","a","single","value",",","given","a","list","of","inputs","pts","--","a","list","of","sample","points","map","--","the","mapping","function","[","Default","is","builtins.map","]","''","''","''","if","map","is","None",":","from","builtins","import","map","from","numpy","import","transpose",",","atleast_2d","return","min","(","list","(","map","(","f",",","atleast_2d","(","transpose","(","pts",")",")",".tolist","(",")",")",")",")"] | 181 | 193 | null | samples.py | mystic/mystic/math/samples.py | 15 | null | 0 | 15 | null | null | null | Use image node_id 8 for calling a global function with example usage: _minimum_given_samples(f, pts, map) and returns: min | 122 | node_id 8 | 1,407,028 |
|
to_json_file | Config | null | true | self,json_file | Hold some config params to control generation and report procedure. | ["Hold","some","config","params","to","control","generation","and","report","procedure","."] | Serializes this instance to a JSON file. | ["Serializes","this","instance","to","a","JSON","file","."] | null | def to_json_file(self, json_file):
r"""
Serializes this instance to a JSON file.
"""
with open(json_file, "w+", encoding="utf-8") as writer:
json.dump(
self.to_dict(), writer, indent=2, ensure_ascii=False
)
| ["def","to_json_file","(","self",",","json_file",")",":","r","''","''","''","Serializes","this","instance","to","a","JSON","file.","``","''","''","with","open","(","json_file",",","``","w+","''",",","encoding=","''","utf-8","''",")","as","writer",":","json.dump","(","self.to_dict","(",")",",","writer",",","indent=2",",","ensure_ascii=False",")"] | 233 | 244 | null | config.py | textflint/textflint/input/config/config.py | import os
import six
import json
import copy
from ...common.utils.logger import logger
from ...common.settings import NLP_TASK_MAP, ALLOWED_TRANSFORMATIONS, TRANSFORM_FIELDS, ALLOWED_SUBPOPULATIONS, ALLOWED_VALIDATORS, ALLOWED_cn_TRANSFORMATIONS | 15 | 1 | 6 | 0 | 0 | 8 | null | Use image node_id 8 for calling the Config obj's underlying member method code with example usage: obj.to_json_file(json_file) without return types | 147 | node_id 8 | 2,188,355 |
_expectation_given_samples | global | null | false | f,pts,map | null | null | null | null | mean | def _expectation_given_samples(f, pts, map=None):
"""
use given sample pts to calculate expected value for function f
Inputs:
f -- a function that returns a single value, given a list of inputs
pts -- a list of sample points
map -- the mapping function [Default is builtins.map]"""
if map is None:
from builtins import map
from numpy import transpose, mean, atleast_2d
return mean(list(map(f, atleast_2d(transpose(pts)).tolist())))
| ["def","_expectation_given_samples","(","f",",","pts",",","map=None",")",":","``","''","''","use","given","sample","pts","to","calculate","expected","value","for","function","f","Inputs",":","f","--","a","function","that","returns","a","single","value",",","given","a","list","of","inputs","pts","--","a","list","of","sample","points","map","--","the","mapping","function","[","Default","is","builtins.map","]","''","''","''","if","map","is","None",":","from","builtins","import","map","from","numpy","import","transpose",",","mean",",","atleast_2d","return","mean","(","list","(","map","(","f",",","atleast_2d","(","transpose","(","pts",")",")",".tolist","(",")",")",")",")"] | 196 | 208 | null | samples.py | mystic/mystic/math/samples.py | 15 | null | 0 | 15 | null | null | null | Use image node_id 9 for calling a global function with example usage: _expectation_given_samples(f, pts, map) and returns: mean | 127 | node_id 9 | 1,407,029 |
|
test_import_vispy | global | null | false | null | null | null | null | null | def test_import_vispy():
"""Importing vispy should only pull in other vispy.util submodule."""
modnames = loaded_vispy_modules("vispy", 2)
assert_equal(modnames, set(_min_modules))
| ["def","test_import_vispy","(",")",":","``","''","''","Importing","vispy","should","only","pull","in","other","vispy.util","submodule",".","''","''","''","modnames","=","loaded_vispy_modules","(","``","vispy","''",",","2",")","assert_equal","(","modnames",",","set","(","_min_modules",")",")"] | 58 | 61 | null | test_import.py | vispy/vispy/util/tests/test_import.py | import sys
import os
from vispy.testing import assert_in, assert_not_in, requires_pyopengl, run_tests_if_main, assert_equal
from vispy.util import run_subprocess
import vispy | 15 | null | 5 | 10 | null | null | null | Use image node_id 3 for calling a global function with example usage: test_import_vispy() without return types | 110 | node_id 3 | 2,320,261 |
|
test_import_vispy_util | global | null | false | null | null | null | null | null | def test_import_vispy_util():
"""Importing vispy.util should not pull in other vispy submodules."""
modnames = loaded_vispy_modules("vispy.util", 2)
assert_equal(modnames, set(_min_modules))
| ["def","test_import_vispy_util","(",")",":","``","''","''","Importing","vispy.util","should","not","pull","in","other","vispy","submodules",".","''","''","''","modnames","=","loaded_vispy_modules","(","``","vispy.util","''",",","2",")","assert_equal","(","modnames",",","set","(","_min_modules",")",")"] | 64 | 67 | null | test_import.py | vispy/vispy/util/tests/test_import.py | import sys
import os
from vispy.testing import assert_in, assert_not_in, requires_pyopengl, run_tests_if_main, assert_equal
from vispy.util import run_subprocess
import vispy | 15 | null | 5 | 10 | null | null | null | Use image node_id 4 for calling a global function with example usage: test_import_vispy_util() without return types | 115 | node_id 4 | 2,320,262 |
|
_variance_given_samples | global | null | false | f,pts,map | null | null | null | null | var | def _variance_given_samples(f, pts, map=None):
"""
use given sample pts to calculate expected variance for function f
Inputs:
f -- a function that returns a single value, given a list of inputs
pts -- a list of sample points
map -- the mapping function [Default is builtins.map]"""
if map is None:
from builtins import map
from numpy import transpose, var, atleast_2d
return var(list(map(f, atleast_2d(transpose(pts)).tolist())))
| ["def","_variance_given_samples","(","f",",","pts",",","map=None",")",":","``","''","''","use","given","sample","pts","to","calculate","expected","variance","for","function","f","Inputs",":","f","--","a","function","that","returns","a","single","value",",","given","a","list","of","inputs","pts","--","a","list","of","sample","points","map","--","the","mapping","function","[","Default","is","builtins.map","]","''","''","''","if","map","is","None",":","from","builtins","import","map","from","numpy","import","transpose",",","var",",","atleast_2d","return","var","(","list","(","map","(","f",",","atleast_2d","(","transpose","(","pts",")",")",".tolist","(",")",")",")",")"] | 211 | 223 | null | samples.py | mystic/mystic/math/samples.py | 15 | null | 0 | 15 | null | null | null | Use image node_id 10 for calling a global function with example usage: _variance_given_samples(f, pts, map) and returns: var | 124 | node_id 10 | 1,407,030 |
|
__init__ | InterestRateSwap | null | true | self,start_date,maturity_date,pay_leg,receive_leg,holiday_calendar,dtype,name | Represents a batch of Interest Rate Swaps (IRS).
An Interest rate swap (IRS) is a contract between two counterparties for an
exchange of a series of payments over a period of time. The payments are made
periodically (for example quarterly or semi-annually) where the last payment
is made at the maturity (or termination) of the contract. In the case of
fixed-for-floating IRS, one counterparty pays a fixed rate while the other
counterparty's payments are linked to a floating index, most commonly the
LIBOR rate. On the other hand, in the case of interest rate basis swap, the
payments of both counterparties are linked to a floating index. Typically, the
floating rate is observed (or fixed) at the beginning of each period while the
payments are made at the end of each period [1].
For example, consider a vanilla swap with the starting date T_0 and maturity
date T_n and equally spaced coupon payment dates T_1, T_2, ..., T_n such that
T_0 < T_1 < T_2 < ... < T_n and dt_i = T_(i+1) - T_i (A)
The floating rate is fixed on T_0, T_1, ..., T_(n-1) and both the fixed and
floating payments are made on T_1, T_2, ..., T_n (payment dates).
The InterestRateSwap class can be used to create and price multiple IRS
simultaneously. The class supports vanilla fixed-for-floating swaps as
well as basis swaps. However all IRS within an IRS object must be priced using
a common reference and discount curve.
#### Example (non batch):
The following example illustrates the construction of an IRS instrument and
calculating its price.
```python
import numpy as np
import tensorflow as tf
import tf_quant_finance as tff
dates = tff.datetime
instruments = tff.experimental.instruments
dtype = np.float64
start_date = dates.convert_to_date_tensor([(2020, 2, 8)])
maturity_date = dates.convert_to_date_tensor([(2022, 2, 8)])
valuation_date = dates.convert_to_date_tensor([(2020, 2, 8)])
period_3m = dates.periods.months(3)
period_6m = dates.periods.months(6)
fix_spec = instruments.FixedCouponSpecs(
coupon_frequency=period_6m, currency='usd',
notional=1., coupon_rate=0.03134,
daycount_convention=instruments.DayCountConvention.ACTUAL_365,
businessday_rule=dates.BusinessDayConvention.NONE)
flt_spec = instruments.FloatCouponSpecs(
coupon_frequency=period_3m, reference_rate_term=period_3m,
reset_frequency=period_3m, currency='usd', notional=1.,
businessday_rule=dates.BusinessDayConvention.NONE,
coupon_basis=0., coupon_multiplier=1.,
daycount_convention=instruments.DayCountConvention.ACTUAL_365)
swap = instruments.InterestRateSwap([(2020,2,2)], [(2023,2,2)], [fix_spec],
[flt_spec], dtype=np.float64)
curve_dates = valuation_date + dates.periods.years([1, 2, 3, 5, 7, 10, 30])
reference_curve = instruments.RateCurve(
curve_dates,
np.array([
0.02834814, 0.03077457, 0.03113739, 0.03130794, 0.03160892,
0.03213901, 0.03257991
], dtype=dtype),
valuation_date=valuation_date,
dtype=dtype)
market = instruments.InterestRateMarket(
reference_curve=reference_curve, discount_curve=reference_curve)
price = swap.price(valuation_date, market)
# Expected result: 1e-7
```
#### Example (batch):
The following example illustrates the construction and pricing of IRS using
batches.
```python
import numpy as np
import tensorflow as tf
import tf_quant_finance as tff
dates = tff.datetime
instruments = tff.experimental.instruments
dtype = np.float64
notional = 1.0
maturity_date = dates.convert_to_date_tensor([(2023, 2, 8), (2027, 2, 8)])
start_date = dates.convert_to_date_tensor([(2020, 2, 8), (2020, 2, 8)])
valuation_date = dates.convert_to_date_tensor([(2020, 2, 8)])
period3m = dates.periods.months([3, 3])
period6m = dates.periods.months([6, 6])
fix_spec = instruments.FixedCouponSpecs(
coupon_frequency=period6m, currency='usd',
notional=notional,
coupon_rate=[0.03134, 0.03181],
daycount_convention=instruments.DayCountConvention.ACTUAL_365,
businessday_rule=dates.BusinessDayConvention.NONE)
flt_spec = instruments.FloatCouponSpecs(
coupon_frequency=period3m, reference_rate_term=period3m,
reset_frequency=period3m, currency='usd',
notional=notional,
businessday_rule=dates.BusinessDayConvention.NONE,
coupon_basis=0.0, coupon_multiplier=1.0,
daycount_convention=instruments.DayCountConvention.ACTUAL_365)
swap = instruments.InterestRateSwap(start_date, maturity_date,
fix_spec, flt_spec,
dtype=dtype)
curve_dates = valuation_date + dates.periods.years([1, 2, 3, 5, 7, 10, 30])
reference_curve = instruments.RateCurve(
curve_dates,
np.array([
0.02834814, 0.03077457, 0.03113739, 0.03130794, 0.03160892,
0.03213901, 0.03257991
], dtype=dtype),
valuation_date=valuation_date,
dtype=dtype)
market = instruments.InterestRateMarket(
reference_curve=reference_curve, discount_curve=reference_curve)
price = swap.price(valuation_date, market)
# Expected result: [1.0e-7, 1.0e-7]
```
#### References:
[1]: Leif B.G. Andersen and Vladimir V. Piterbarg. Interest Rate Modeling,
Volume I: Foundations and Vanilla Models. Chapter 5. 2010. | 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| Initialize a batch of IRS contracts.
Args:
start_date: A rank 1 `DateTensor` specifying the dates for the inception
(start of the accrual) of the swap contracts. The shape of the input
correspond to the number of instruments being created.
maturity_date: A rank 1 `DateTensor` specifying the maturity dates for
each contract. The shape of the input should be the same as that of
`start_date`.
pay_leg: A scalar or a list of either `FixedCouponSpecs` or
`FloatCouponSpecs` specifying the coupon payments for the payment leg
of the swap. If specified as a list then the length of the list should
be the same as the number of instruments being created. If specified as
a scalar, then the elements of the namedtuple must be of the same shape
as (or compatible to) the shape of `start_date`.
receive_leg: A scalar or a list of either `FixedCouponSpecs` or
`FloatCouponSpecs` specifying the coupon payments for the receiving leg
of the swap. If specified as a list then the length of the list should
be the same as the number of instruments being created. If specified as
a scalar, then the elements of the namedtuple must be of the same shape
as (or compatible with) the shape of `start_date`.
holiday_calendar: An instance of `dates.HolidayCalendar` to specify
weekends and holidays.
Default value: None in which case a holiday calendar would be created
with Saturday and Sunday being the holidays.
dtype: `tf.Dtype`. If supplied the dtype for the real variables or ops
either supplied to the IRS object or created by the IRS object.
Default value: None which maps to the default dtype inferred by
TensorFlow.
name: Python str. The name to give to the ops created by this class.
Default value: `None` which maps to 'interest_rate_swap'. | ["Initialize","a","batch","of","IRS","contracts",".","Args",":","start_date",":","A","rank","1","`","DateTensor","`","specifying","the","dates","for","the","inception","(","start","of","the","accrual",")","of","the","swap","contracts",".","The","shape","of","the","input","correspond","to","the","number","of","instruments","being","created",".","maturity_date",":","A","rank","1","`","DateTensor","`","specifying","the","maturity","dates","for","each","contract",".","The","shape","of","the","input","should","be","the","same","as","that","of","`","start_date","`",".","pay_leg",":","A","scalar","or","a","list","of","either","`","FixedCouponSpecs","`","or","`","FloatCouponSpecs","`","specifying","the","coupon","payments","for","the","payment","leg","of","the","swap",".","If","specified","as","a","list","then","the","length","of","the","list","should","be","the","same","as","the","number","of","instruments","being","created",".","If","specified","as","a","scalar",",","then","the","elements","of","the","namedtuple","must","be","of","the","same","shape","as","(","or","compatible","to",")","the","shape","of","`","start_date","`",".","receive_leg",":","A","scalar","or","a","list","of","either","`","FixedCouponSpecs","`","or","`","FloatCouponSpecs","`","specifying","the","coupon","payments","for","the","receiving","leg","of","the","swap",".","If","specified","as","a","list","then","the","length","of","the","list","should","be","the","same","as","the","number","of","instruments","being","created",".","If","specified","as","a","scalar",",","then","the","elements","of","the","namedtuple","must","be","of","the","same","shape","as","(","or","compatible","with",")","the","shape","of","`","start_date","`",".","holiday_calendar",":","An","instance","of","`","dates.HolidayCalendar","`","to","specify","weekends","and","holidays",".","Default","value",":","None","in","which","case","a","holiday","calendar","would","be","created","with","Saturday","and","Sunday","being","the","holidays",".","dtype",":","`","tf.Dtype","`",".","If","supplied","the","dtype","for","the","real","variables","or","ops","either","supplied","to","the","IRS","object","or","created","by","the","IRS","object",".","Default","value",":","None","which","maps","to","the","default","dtype","inferred","by","TensorFlow",".","name",":","Python","str",".","The","name","to","give","to","the","ops","created","by","this","class",".","Default","value",":","`","None","`","which","maps","to","'interest_rate_swap","'","."] | InterestRateSwap | def __init__(
self,
start_date,
maturity_date,
pay_leg,
receive_leg,
holiday_calendar=None,
dtype=None,
name=None,
):
"""Initialize a batch of IRS contracts.
Args:
start_date: A rank 1 `DateTensor` specifying the dates for the inception
(start of the accrual) of the swap contracts. The shape of the input
correspond to the number of instruments being created.
maturity_date: A rank 1 `DateTensor` specifying the maturity dates for
each contract. The shape of the input should be the same as that of
`start_date`.
pay_leg: A scalar or a list of either `FixedCouponSpecs` or
`FloatCouponSpecs` specifying the coupon payments for the payment leg
of the swap. If specified as a list then the length of the list should
be the same as the number of instruments being created. If specified as
a scalar, then the elements of the namedtuple must be of the same shape
as (or compatible to) the shape of `start_date`.
receive_leg: A scalar or a list of either `FixedCouponSpecs` or
`FloatCouponSpecs` specifying the coupon payments for the receiving leg
of the swap. If specified as a list then the length of the list should
be the same as the number of instruments being created. If specified as
a scalar, then the elements of the namedtuple must be of the same shape
as (or compatible with) the shape of `start_date`.
holiday_calendar: An instance of `dates.HolidayCalendar` to specify
weekends and holidays.
Default value: None in which case a holiday calendar would be created
with Saturday and Sunday being the holidays.
dtype: `tf.Dtype`. If supplied the dtype for the real variables or ops
either supplied to the IRS object or created by the IRS object.
Default value: None which maps to the default dtype inferred by
TensorFlow.
name: Python str. The name to give to the ops created by this class.
Default value: `None` which maps to 'interest_rate_swap'.
"""
self._name = name or "interest_rate_swap"
if holiday_calendar is None:
holiday_calendar = dates.create_holiday_calendar(
weekend_mask=dates.WeekendMask.SATURDAY_SUNDAY
)
with tf.name_scope(self._name):
self._dtype = dtype
self._start_date = dates.convert_to_date_tensor(start_date)
self._maturity_date = dates.convert_to_date_tensor(
maturity_date
)
self._holiday_calendar = holiday_calendar
self._floating_leg = None
self._fixed_leg = None
self._pay_leg = self._setup_leg(pay_leg)
self._receive_leg = self._setup_leg(receive_leg)
self._is_payer = isinstance(
self._pay_leg, cs.FixedCashflowStream
)
| ["def","__init__","(","self",",","start_date",",","maturity_date",",","pay_leg",",","receive_leg",",","holiday_calendar=None",",","dtype=None",",","name=None",",",")",":","``","''","''","Initialize","a","batch","of","IRS","contracts",".","Args",":","start_date",":","A","rank","1","`","DateTensor","`","specifying","the","dates","for","the","inception","(","start","of","the","accrual",")","of","the","swap","contracts",".","The","shape","of","the","input","correspond","to","the","number","of","instruments","being","created",".","maturity_date",":","A","rank","1","`","DateTensor","`","specifying","the","maturity","dates","for","each","contract",".","The","shape","of","the","input","should","be","the","same","as","that","of","`","start_date","`",".","pay_leg",":","A","scalar","or","a","list","of","either","`","FixedCouponSpecs","`","or","`","FloatCouponSpecs","`","specifying","the","coupon","payments","for","the","payment","leg","of","the","swap",".","If","specified","as","a","list","then","the","length","of","the","list","should","be","the","same","as","the","number","of","instruments","being","created",".","If","specified","as","a","scalar",",","then","the","elements","of","the","namedtuple","must","be","of","the","same","shape","as","(","or","compatible","to",")","the","shape","of","`","start_date","`",".","receive_leg",":","A","scalar","or","a","list","of","either","`","FixedCouponSpecs","`","or","`","FloatCouponSpecs","`","specifying","the","coupon","payments","for","the","receiving","leg","of","the","swap",".","If","specified","as","a","list","then","the","length","of","the","list","should","be","the","same","as","the","number","of","instruments","being","created",".","If","specified","as","a","scalar",",","then","the","elements","of","the","namedtuple","must","be","of","the","same","shape","as","(","or","compatible","with",")","the","shape","of","`","start_date","`",".","holiday_calendar",":","An","instance","of","`","dates.HolidayCalendar","`","to","specify","weekends","and","holidays",".","Default","value",":","None","in","which","case","a","holiday","calendar","would","be","created","with","Saturday","and","Sunday","being","the","holidays",".","dtype",":","`","tf.Dtype","`",".","If","supplied","the","dtype","for","the","real","variables","or","ops","either","supplied","to","the","IRS","object","or","created","by","the","IRS","object",".","Default","value",":","None","which","maps","to","the","default","dtype","inferred","by","TensorFlow",".","name",":","Python","str",".","The","name","to","give","to","the","ops","created","by","this","class",".","Default","value",":","`","None","`","which","maps","to","'interest_rate_swap'.","``","''","''","self._name","=","name","or","``","interest_rate_swap","''","if","holiday_calendar","is","None",":","holiday_calendar","=","dates.create_holiday_calendar","(","weekend_mask=dates.WeekendMask.SATURDAY_SUNDAY",")","with","tf.name_scope","(","self._name",")",":","self._dtype","=","dtype","self._start_date","=","dates.convert_to_date_tensor","(","start_date",")","self._maturity_date","=","dates.convert_to_date_tensor","(","maturity_date",")","self._holiday_calendar","=","holiday_calendar","self._floating_leg","=","None","self._fixed_leg","=","None","self._pay_leg","=","self._setup_leg","(","pay_leg",")","self._receive_leg","=","self._setup_leg","(","receive_leg",")","self._is_payer","=","isinstance","(","self._pay_leg",",","cs.FixedCashflowStream",")"] | 156 | 211 | null | interest_rate_swap.py | tf-quant-finance/tf_quant_finance/experimental/instruments/interest_rate_swap.py | import tensorflow.compat.v2
from tf_quant_finance import datetime
from tf_quant_finance.experimental.instruments import cashflow_stream
from tf_quant_finance.experimental.instruments import rates_common | 15 | 1 | 4 | 0 | 0 | 10 | null | Use image node_id 1 to create a new InterestRateSwap object with example: obj = InterestRateSwap(start_date, maturity_date, pay_leg, receive_leg, holiday_calendar, dtype, name) | 177 | node_id 1 | 2,191,434 |
__call__ | ImageGPTFeatureExtractor | FeatureExtractionMixin,ImageFeatureExtractionMixin | true | self,images,return_tensors | Constructs an ImageGPT feature extractor. This feature extractor can be used to resize images to a smaller
resolution (such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel
values" (color clusters).
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.
Args:
clusters (`np.ndarray`):
The color clusters to use, as a `np.ndarray` of shape `(n_clusters, 3)`.
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the input to a certain `size`.
size (`int` or `Tuple(int)`, *optional*, defaults to 32):
Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an
integer is provided, then the input will be resized to (size, size). Only has an effect if `do_resize` is
set to `True`.
resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`):
An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
`PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
`PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether or not to normalize the input to the range between -1 and +1. | ["Constructs","an","ImageGPT","feature","extractor",".","This","feature","extractor","can","be","used","to","resize","images","to","a","smaller","resolution","(","such","as","32x32","or","64x64",")",",","normalize","them","and","finally","color","quantize","them","to","obtain","sequences","of","``","pixel","values","''","(","color","clusters",")",".","This","feature","extractor","inherits","from","[","`","FeatureExtractionMixin","`","]","which","contains","most","of","the","main","methods",".","Users","should","refer","to","this","superclass","for","more","information","regarding","those","methods",".","Args",":","clusters","(","`","np.ndarray","`",")",":","The","color","clusters","to","use",",","as","a","`","np.ndarray","`","of","shape","`","(","n_clusters",",","3",")","`",".","do_resize","(","`","bool","`",",","*","optional","*",",","defaults","to","`","True","`",")",":","Whether","to","resize","the","input","to","a","certain","`","size","`",".","size","(","`","int","`","or","`","Tuple","(","int",")","`",",","*","optional","*",",","defaults","to","32",")",":","Resize","the","input","to","the","given","size",".","If","a","tuple","is","provided",",","it","should","be","(","width",",","height",")",".","If","only","an","integer","is","provided",",","then","the","input","will","be","resized","to","(","size",",","size",")",".","Only","has","an","effect","if","`","do_resize","`","is","set","to","`","True","`",".","resample","(","`","int","`",",","*","optional","*",",","defaults","to","`","PIL.Image.Resampling.BILINEAR","`",")",":","An","optional","resampling","filter",".","This","can","be","one","of","`","PIL.Image.Resampling.NEAREST","`",",","`","PIL.Image.Resampling.BOX","`",",","`","PIL.Image.Resampling.BILINEAR","`",",","`","PIL.Image.Resampling.HAMMING","`",",","`","PIL.Image.Resampling.BICUBIC","`","or","`","PIL.Image.Resampling.LANCZOS","`",".","Only","has","an","effect","if","`","do_resize","`","is","set","to","`","True","`",".","do_normalize","(","`","bool","`",",","*","optional","*",",","defaults","to","`","True","`",")",":","Whether","or","not","to","normalize","the","input","to","the","range","between","-1","and","+1","."] | Main method to prepare for the model one or several image(s).
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- Input IDs to be fed to a model, of shape `(batch_size, height * width)`. | ["Main","method","to","prepare","for","the","model","one","or","several","image","(","s",")",".","<","Tip","warning=","{","true","}",">","NumPy","arrays","and","PyTorch","tensors","are","converted","to","PIL","images","when","resizing",",","so","the","most","efficient","is","to","pass","PIL","images",".","<","\/Tip",">","Args",":","images","(","`","PIL.Image.Image","`",",","`","np.ndarray","`",",","`","torch.Tensor","`",",","`","List","[","PIL.Image.Image","]","`",",","`","List","[","np.ndarray","]","`",",","`","List","[","torch.Tensor","]","`",")",":","The","image","or","batch","of","images","to","be","prepared",".","Each","image","can","be","a","PIL","image",",","NumPy","array","or","PyTorch","tensor",".","In","case","of","a","NumPy","array\/PyTorch","tensor",",","each","image","should","be","of","shape","(","C",",","H",",","W",")",",","where","C","is","a","number","of","channels",",","H","and","W","are","image","height","and","width",".","return_tensors","(","`","str","`","or","[","`","~utils.TensorType","`","]",",","*","optional","*",",","defaults","to","`","'np","'","`",")",":","If","set",",","will","return","tensors","of","a","particular","framework",".","Acceptable","values","are",":","-","`","'tf","'","`",":","Return","TensorFlow","`","tf.constant","`","objects",".","-","`","'pt","'","`",":","Return","PyTorch","`","torch.Tensor","`","objects",".","-","`","'np","'","`",":","Return","NumPy","`","np.ndarray","`","objects",".","-","`","'jax","'","`",":","Return","JAX","`","jnp.ndarray","`","objects",".","Returns",":","[","`","BatchFeature","`","]",":","A","[","`","BatchFeature","`","]","with","the","following","fields",":","-","*","*","input_ids","*","*","--","Input","IDs","to","be","fed","to","a","model",",","of","shape","`","(","batch_size",",","height","*","width",")","`","."] | encoded_inputs | def __call__(
self,
images: Union[
Image.Image,
np.ndarray,
"torch.Tensor",
List[Image.Image],
List[np.ndarray],
List["torch.Tensor"], # noqa
],
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs,
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s).
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- Input IDs to be fed to a model, of shape `(batch_size, height * width)`.
"""
# Input type checking for clearer error
valid_images = False
# Check that images has a valid type
if isinstance(
images, (Image.Image, np.ndarray)
) or is_torch_tensor(images):
valid_images = True
elif isinstance(images, (list, tuple)):
if (
len(images) == 0
or isinstance(images[0], (Image.Image, np.ndarray))
or is_torch_tensor(images[0])
):
valid_images = True
if not valid_images:
raise ValueError(
"Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
"`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
)
is_batched = bool(
isinstance(images, (list, tuple))
and (
isinstance(images[0], (Image.Image, np.ndarray))
or is_torch_tensor(images[0])
)
)
if not is_batched:
images = [images]
# transformations (resizing + normalization)
if self.do_resize and self.size is not None:
images = [
self.resize(image, size=self.size, resample=self.resample)
for image in images
]
if self.do_normalize:
images = [self.normalize(image) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
images = np.array(images)
images = color_quantize(images, self.clusters).reshape(
images.shape[:-1]
)
# flatten to (batch_size, height*width)
batch_size = images.shape[0]
images = images.reshape(batch_size, -1)
# return as BatchFeature
data = {"input_ids": images}
encoded_inputs = BatchFeature(
data=data, tensor_type=return_tensors
)
return encoded_inputs
| ["def","__call__","(","self",",","images",":","Union","[","Image.Image",",","np.ndarray",",","``","torch.Tensor","''",",","List","[","Image.Image","]",",","List","[","np.ndarray","]",",","List","[","``","torch.Tensor","''","]",",","#","noqa","]",",","return_tensors",":","Optional","[","Union","[","str",",","TensorType","]","]","=","None",",","*","*","kwargs",",",")","-",">","BatchFeature",":","``","''","''","Main","method","to","prepare","for","the","model","one","or","several","image","(","s",")",".","<","Tip","warning=","{","true","}",">","NumPy","arrays","and","PyTorch","tensors","are","converted","to","PIL","images","when","resizing",",","so","the","most","efficient","is","to","pass","PIL","images",".","<","\/Tip",">","Args",":","images","(","`","PIL.Image.Image","`",",","`","np.ndarray","`",",","`","torch.Tensor","`",",","`","List","[","PIL.Image.Image","]","`",",","`","List","[","np.ndarray","]","`",",","`","List","[","torch.Tensor","]","`",")",":","The","image","or","batch","of","images","to","be","prepared",".","Each","image","can","be","a","PIL","image",",","NumPy","array","or","PyTorch","tensor",".","In","case","of","a","NumPy","array\/PyTorch","tensor",",","each","image","should","be","of","shape","(","C",",","H",",","W",")",",","where","C","is","a","number","of","channels",",","H","and","W","are","image","height","and","width",".","return_tensors","(","`","str","`","or","[","`","~utils.TensorType","`","]",",","*","optional","*",",","defaults","to","`","'np","'","`",")",":","If","set",",","will","return","tensors","of","a","particular","framework",".","Acceptable","values","are",":","-","`","'tf","'","`",":","Return","TensorFlow","`","tf.constant","`","objects",".","-","`","'pt","'","`",":","Return","PyTorch","`","torch.Tensor","`","objects",".","-","`","'np","'","`",":","Return","NumPy","`","np.ndarray","`","objects",".","-","`","'jax","'","`",":","Return","JAX","`","jnp.ndarray","`","objects",".","Returns",":","[","`","BatchFeature","`","]",":","A","[","`","BatchFeature","`","]","with","the","following","fields",":","-","*","*","input_ids","*","*","--","Input","IDs","to","be","fed","to","a","model",",","of","shape","`","(","batch_size",",","height","*","width",")","`",".","``","''","''","#","Input","type","checking","for","clearer","error","valid_images","=","False","#","Check","that","images","has","a","valid","type","if","isinstance","(","images",",","(","Image.Image",",","np.ndarray",")",")","or","is_torch_tensor","(","images",")",":","valid_images","=","True","elif","isinstance","(","images",",","(","list",",","tuple",")",")",":","if","(","len","(","images",")","==","0","or","isinstance","(","images","[","0","]",",","(","Image.Image",",","np.ndarray",")",")","or","is_torch_tensor","(","images","[","0","]",")",")",":","valid_images","=","True","if","not","valid_images",":","raise","ValueError","(","``","Images","must","of","type","`","PIL.Image.Image","`",",","`","np.ndarray","`","or","`","torch.Tensor","`","(","single","example",")",",","``","``","`","List","[","PIL.Image.Image","]","`",",","`","List","[","np.ndarray","]","`","or","`","List","[","torch.Tensor","]","`","(","batch","of","examples",")",".","''",")","is_batched","=","bool","(","isinstance","(","images",",","(","list",",","tuple",")",")","and","(","isinstance","(","images","[","0","]",",","(","Image.Image",",","np.ndarray",")",")","or","is_torch_tensor","(","images","[","0","]",")",")",")","if","not","is_batched",":","images","=","[","images","]","#","transformations","(","resizing","+","normalization",")","if","self.do_resize","and","self.size","is","not","None",":","images","=","[","self.resize","(","image",",","size=self.size",",","resample=self.resample",")","for","image","in","images","]","if","self.do_normalize",":","images","=","[","self.normalize","(","image",")","for","image","in","images","]","#","color","quantize","from","(","batch_size",",","height",",","width",",","3",")","to","(","batch_size",",","height",",","width",")","images","=","np.array","(","images",")","images","=","color_quantize","(","images",",","self.clusters",")",".reshape","(","images.shape","[",":","-1","]",")","#","flatten","to","(","batch_size",",","height","*","width",")","batch_size","=","images.shape","[","0","]","images","=","images.reshape","(","batch_size",",","-1",")","#","return","as","BatchFeature","data","=","{","``","input_ids","''",":","images","}","encoded_inputs","=","BatchFeature","(","data=data",",","tensor_type=return_tensors",")","return","encoded_inputs"] | 101 | 181 | null | feature_extraction_imagegpt.py | H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/imagegpt/feature_extraction_imagegpt.py | from typing import List, Optional, Union
import numpy
from PIL import Image
from transformers.image_utils import PILImageResampling
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, logging | 15 | 1 | 7 | 2 | 2 | 3 | 2 | Use image node_id 3 for calling the ImageGPTFeatureExtractor obj's underlying member method code with example usage: obj.__call__(images, return_tensors) and returns: encoded_inputs | 181 | node_id 3 | 95,364 |
test_sample_rate_error | global | null | false | art_warning | null | null | null | null | null | def test_sample_rate_error(art_warning):
try:
exc_msg = "Sample rate be must a positive integer."
with pytest.raises(ValueError, match=exc_msg):
Mp3CompressionPyTorch(sample_rate=0)
except ARTTestException as e:
art_warning(e)
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import logging
import numpy
import pytest
from numpy.testing import assert_array_equal
from art.defences.preprocessor import Mp3CompressionPyTorch
from tests.utils import ARTTestException | 15 | null | 7 | 5 | null | null | null | Use image node_id 2 for calling a global function with example usage: test_sample_rate_error(art_warning) without return types | 126 | node_id 2 | 235,206 |
_mean_square_error | global | null | false | y,y_pred,w | null | null | null | null | np | def _mean_square_error(y, y_pred, w):
"""Calculate the mean square error."""
return np.average(((y_pred - y) ** 2), weights=w)
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import numpy
from joblib import wrap_non_picklable_objects
from scipy.stats import rankdata | 15 | null | 4 | 7 | null | null | null | Use image node_id 5 for calling a global function with example usage: _mean_square_error(y, y_pred, w) and returns: np | 118 | node_id 5 | 1,106,034 |
_root_mean_square_error | global | null | false | y,y_pred,w | null | null | null | null | np | def _root_mean_square_error(y, y_pred, w):
"""Calculate the root mean square error."""
return np.sqrt(np.average(((y_pred - y) ** 2), weights=w))
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import numpy
from joblib import wrap_non_picklable_objects
from scipy.stats import rankdata | 15 | null | 4 | 7 | null | null | null | Use image node_id 6 for calling a global function with example usage: _root_mean_square_error(y, y_pred, w) and returns: np | 123 | node_id 6 | 1,106,035 |
decrease_indent | NullLogger | null | true | self | null | null | null | null | null | def decrease_indent(self):
if self.on() and len(self.indent_) > 4:
self.indent_ = self.indent_[-4:]
| ["def","decrease_indent","(","self",")",":","if","self.on","(",")","and","len","(","self.indent_",")",">","4",":","self.indent_","=","self.indent_","[","-4",":","]"] | 22 | 24 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 7 | null | Use image node_id 4 for calling the NullLogger obj's underlying member method code with example usage: obj.decrease_indent() without return types | 145 | node_id 4 | 2,276,650 |
do_log | NullLogger | null | true | self | null | null | null | null | null | def do_log(self, *args):
pass
| ["def","do_log","(","self",",","*","args",")",":","pass"] | 26 | 27 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 7 | null | Use image node_id 5 for calling the NullLogger obj's underlying member method code with example usage: obj.do_log() without return types | 136 | node_id 5 | 2,276,651 |
_log_loss | global | null | false | y,y_pred,w | null | null | null | null | np | def _log_loss(y, y_pred, w):
"""Calculate the log loss."""
eps = 1e-15
inv_y_pred = np.clip(1 - y_pred, eps, 1 - eps)
y_pred = np.clip(y_pred, eps, 1 - eps)
score = y * np.log(y_pred) + (1 - y) * np.log(inv_y_pred)
return np.average(-score, weights=w)
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import numpy
from joblib import wrap_non_picklable_objects
from scipy.stats import rankdata | 15 | null | 4 | 7 | null | null | null | Use image node_id 7 for calling a global function with example usage: _log_loss(y, y_pred, w) and returns: np | 109 | node_id 7 | 1,106,036 |
interesting | NullLogger | null | true | self,source_name | null | null | null | null | False | def interesting(self, source_name):
return False
| ["def","interesting","(","self",",","source_name",")",":","return","False"] | 29 | 30 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 7 | null | Use image node_id 6 for calling the NullLogger obj's underlying member method code with example usage: obj.interesting(source_name) and returns: False | 150 | node_id 6 | 2,276,652 |
on | NullLogger | null | true | self | null | null | null | null | True | def on(self):
return True
| ["def","on","(","self",")",":","return","True"] | 32 | 33 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 7 | null | Use image node_id 7 for calling the NullLogger obj's underlying member method code with example usage: obj.on() and returns: True | 129 | node_id 7 | 2,276,653 |
__init__ | TextLogger | NullLogger | true | self | null | null | null | null | TextLogger | def __init__(self):
NullLogger.__init__(self)
| ["def","__init__","(","self",")",":","NullLogger.__init__","(","self",")"] | 36 | 37 | null | logger.py | turicreate/src/external/boost/boost_1_68_0/tools/build/src/util/logger.py | import sys | 15 | 2 | 1 | 0 | 1 | 4 | 1 | Use image node_id 1 to create a new TextLogger object from inherited base classes: NullLogger with example: obj = TextLogger() | 126 | node_id 1 | 2,276,654 |