xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

23680bb0d69ffb530187aa59bc1f2f42c9fc6239

27

py

Python

Yue_tools/generate_groundTruth_dataset.py

YueXiNPU/SSH_DensityMap

c52d214e5ccb819dfb13aad6a729f0fcb5f89283

[ "MIT" ]

null

Yue_tools/generate_groundTruth_dataset.py

YueXiNPU/SSH_DensityMap

c52d214e5ccb819dfb13aad6a729f0fcb5f89283

[ "MIT" ]

null

Yue_tools/generate_groundTruth_dataset.py

YueXiNPU/SSH_DensityMap

c52d214e5ccb819dfb13aad6a729f0fcb5f89283

[ "MIT" ]

null

import test_utils mytest()

9

17

0.814815

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null

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5

237cfca4fde45969ecf3dfc49bfb2a5f05d88c35

77

py

Python

bmtrain/benchmark/__init__.py

Achazwl/BMTrain

776c10b21886f12137641c56b12ebf8d601aa9e0

[ "Apache-2.0" ]

19

2022-03-14T12:30:23.000Z

2022-03-31T11:52:29.000Z

bmtrain/benchmark/__init__.py

Achazwl/BMTrain

776c10b21886f12137641c56b12ebf8d601aa9e0

[ "Apache-2.0" ]

1

2022-03-24T02:11:32.000Z

2022-03-24T02:14:17.000Z

bmtrain/benchmark/__init__.py

Achazwl/BMTrain

776c10b21886f12137641c56b12ebf8d601aa9e0

[ "Apache-2.0" ]

5

2022-03-18T02:03:02.000Z

2022-03-29T13:19:09.000Z

from .all_gather import all_gather from .reduce_scatter import reduce_scatter

38.5

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0.883117

12

77

5.333333

0.5

0.28125

0

0.090909

77

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42

38.5

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null

0

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5

88c3a3d200c50f5c08e5d53332906c551deb3d2e

45

py

Python

python/utils.py

urigoren/elm-flask-quickstart

c0a639fecc59011abadf19331bf1af22932d380e

[ "MIT" ]

1

2019-10-16T09:15:08.000Z

python/utils.py

urigoren/elm-flask-quickstart

c0a639fecc59011abadf19331bf1af22932d380e

[ "MIT" ]

null

python/utils.py

urigoren/elm-flask-quickstart

c0a639fecc59011abadf19331bf1af22932d380e

[ "MIT" ]

null

# empty file, your logic goes in this folder

22.5

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0.944444

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1

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1

0

null

0

1

0

5

88c7c40fc013a6da02b2ce5b7dea6c6aea4906cb

217

py

Python

hms_tz/hms_tz/doctype/patient_care_type/test_patient_care_type.py

av-dev2/hms_tz

a36dbe8bfacf6a770913b1bfa000d43edd2cd87a

[ "MIT" ]

5

2021-04-20T06:11:25.000Z

2021-11-18T15:37:25.000Z

hms_tz/hms_tz/doctype/patient_care_type/test_patient_care_type.py

av-dev2/hms_tz

a36dbe8bfacf6a770913b1bfa000d43edd2cd87a

[ "MIT" ]

90

2021-04-05T13:36:34.000Z

2022-03-31T07:26:25.000Z

hms_tz/hms_tz/doctype/patient_care_type/test_patient_care_type.py

av-dev2/hms_tz

a36dbe8bfacf6a770913b1bfa000d43edd2cd87a

[ "MIT" ]

10

2021-03-26T06:43:20.000Z

2022-02-18T06:36:58.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2018, earthians and Contributors # See license.txt from __future__ import unicode_literals # import frappe import unittest class TestPatientCareType(unittest.TestCase): pass

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217

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1

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5

88df0fda8f8478699a28936f6c874a615fd027c6

3,157

py

Python

_unittests/ut_loghelper/test_process_script.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

18

2015-11-10T08:09:23.000Z

2022-02-16T11:46:45.000Z

_unittests/ut_loghelper/test_process_script.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

321

2015-06-14T21:34:28.000Z

2021-11-28T17:10:03.000Z

_unittests/ut_loghelper/test_process_script.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

10

2015-06-20T01:35:00.000Z

2022-01-19T15:54:32.000Z

""" @brief test log(time=2s) """ import sys import unittest import textwrap from pyquickhelper.pycode import ( ExtTestCase, get_temp_folder, skipif_appveyor, skipif_azure_macosx) from pyquickhelper.loghelper.process_script import ( execute_script, execute_script_get_local_variables, dictionary_as_class) class TestRunScript(ExtTestCase): @skipif_appveyor("job stuck") @skipif_azure_macosx('issue with popen') def test_run_script(self): code = textwrap.dedent(""" import os res = dict(a = os.getcwd()) """) exe = execute_script(code) self.assertIsInstance(exe, dict) self.assertIn('res', exe) @skipif_appveyor("job stuck") @skipif_azure_macosx('issue with popen') def test_run_script_error(self): code = textwrap.dedent(""" import os res = dict('a' = os.getcwd()) """) exe = execute_script(code) self.assertIsInstance(exe, dict) self.assertIn('ERROR', exe) @skipif_appveyor("job stuck") @skipif_azure_macosx('issue with popen') def test_run_script_error2(self): code = textwrap.dedent(""" import os res = dict(a = os.getcwd() + 3) """) exe = execute_script(code) self.assertIsInstance(exe, dict) self.assertIn('ERROR', exe) @skipif_appveyor("job stuck") @skipif_azure_macosx('issue with popen') def test_run_script_process(self): code = textwrap.dedent(""" import os res = dict(a = os.getcwd()) """) exe = execute_script_get_local_variables(code) self.assertIsInstance(exe, dict) self.assertIn('res', exe) @skipif_appveyor("job stuck") @skipif_azure_macosx('issue with popen') def test_run_script_process_check(self): code = textwrap.dedent(""" import os res = dict(a = os.getcwd()) import sys sys.path.append("-azerty-") """) exe = execute_script_get_local_variables(code) self.assertIsInstance(exe, dict) self.assertIn('res', exe) self.assertNotIn("-azerty-", sys.path) du = dictionary_as_class(exe) def test_dummy_class(self): cl = dictionary_as_class(dict(d1="e", r=4)) st = str(cl) self.assertEqual(st, "{'d1': 'e', 'r': 4}") def test_dummy_class_drop(self): cl = dictionary_as_class(dict(d1="e", r=4)) st = str(cl) self.assertEqual(st, "{'d1': 'e', 'r': 4}") cl = cl.drop("d1") st = str(cl) self.assertEqual(st, "{'r': 4}") @skipif_appveyor("job stuck") @skipif_azure_macosx('issue with popen') def test_run_script_popen(self): temp = get_temp_folder(__file__, "temp_run_script_popen") code = textwrap.dedent(""" import os res = dict(a = os.getcwd()) """) exe = execute_script(code, folder=temp) self.assertIsInstance(exe, dict) self.assertIn('res', exe) self.assertIn('__file__', exe) if __name__ == "__main__": unittest.main()

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null

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null

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5

0007c3c5bd401a927122b6e585f5fe4accb0e664

505

py

Python

TODO.py

amcumber/mtgCardDatabase

9648d4b6a94a46782c61731f357228dfcc40184a

[ "MIT" ]

null

TODO.py

amcumber/mtgCardDatabase

9648d4b6a94a46782c61731f357228dfcc40184a

[ "MIT" ]

null

TODO.py

amcumber/mtgCardDatabase

9648d4b6a94a46782c61731f357228dfcc40184a

[ "MIT" ]

null

#! /usr/bin/python # TODO move me to an investment excel helper def raw_investment_return(time_year, initial_investment, rate): return initial_investment * (1 + rate) ** time_year def yearly_installment(time_year, initial_investment, rate): total = 0 while t > 0: total += raw_investment_return(time_year, initial_investment, rate) t -= 1 return total def return_on_investment(time_year, initial_investment, rate): return yearly_installment(t, inv, r) - (t * inv)

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0.009804

0.192079

505

17

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1

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5

cc3d5a6a5550a0be35266dd4f7b9a1aef9257f8e

172

py

Python

robustpointclouds/__init__.py

ACIS2021/robust-point-clouds

127860149d2f4bd2db6ae015af0be132c156dd34

[ "Unlicense" ]

null

robustpointclouds/__init__.py

ACIS2021/robust-point-clouds

127860149d2f4bd2db6ae015af0be132c156dd34

[ "Unlicense" ]

null

robustpointclouds/__init__.py

ACIS2021/robust-point-clouds

127860149d2f4bd2db6ae015af0be132c156dd34

[ "Unlicense" ]

null

from .datamodule import mmdetection3dDataModule from .lightningmodule import mmdetection3dLightningModule __all__ = [mmdetection3dDataModule, mmdetection3dLightningModule]

43

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null

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1

0

5

cc43b28174ce7f8f667d13a99713e16d34164f93

181

py

Python

samples/LuceneInAction/LockTest.py

romanchyla/pylucene-trunk

990079ff0c76b972ce5ef2bac9b85334a0a1f27a

[ "Apache-2.0" ]

15

2015-05-21T09:28:01.000Z

2022-03-18T23:41:49.000Z

samples/LuceneInAction/LockTest.py

fnp/pylucene

fb16ac375de5479dec3919a5559cda02c899e387

[ "Apache-2.0" ]

1

2021-09-30T03:59:43.000Z

samples/LuceneInAction/LockTest.py

romanchyla/pylucene-trunk

990079ff0c76b972ce5ef2bac9b85334a0a1f27a

[ "Apache-2.0" ]

13

2015-04-18T23:05:11.000Z

2021-11-29T21:23:26.000Z

import os, sys, unittest, lucene lucene.initVM() sys.path.append(os.path.dirname(os.path.abspath(sys.argv[0]))) import lia.indexing.LockTest unittest.main(lia.indexing.LockTest)

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181

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1

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null

0

1

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1

0

5

cc4f5a3efef2037a84972b5edfbdfa3b85405278

82

py

Python

natoki/chat/__init__.py

gamikun/natoki

40ccd7084103bd25214078675abe181497cd4092

[ "MIT" ]

null

natoki/chat/__init__.py

gamikun/natoki

40ccd7084103bd25214078675abe181497cd4092

[ "MIT" ]

null

natoki/chat/__init__.py

gamikun/natoki

40ccd7084103bd25214078675abe181497cd4092

[ "MIT" ]

null

from __future__ import absolute_import from natoki.chat.server import start_server

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82

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1

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1

0

5

cc790dad4019e7376e44de3c0270cda4b3fe47bc

12,595

py

Python

defences/CIFAR10/adversarial_training.py

calinbiberea/imperial-individual-project

86f224f183b8348d21b4c7a4aed408cd1ca41df1

[ "MIT" ]

null

defences/CIFAR10/adversarial_training.py

calinbiberea/imperial-individual-project

86f224f183b8348d21b4c7a4aed408cd1ca41df1

[ "MIT" ]

null

defences/CIFAR10/adversarial_training.py

calinbiberea/imperial-individual-project

86f224f183b8348d21b4c7a4aed408cd1ca41df1

[ "MIT" ]

null

# Unlike the other datasets, CIFAR-10 uses ResNet and suffers from # a variety of problems, including exploding gradients import torch import torch.nn as nn from tqdm.notebook import tnrange, tqdm # For loading model sanely import os.path import sys import torchattacks # This here actually adds the path sys.path.append("../../") import defences.utils.iat as iat import models.resnet as resnet import utils.clean_test as clean_test # Define the `device` PyTorch will be running on, please hope it is CUDA device = "cuda" if torch.cuda.is_available() else "cpu" print("Notebook will use PyTorch Device: " + device.upper()) # Helps adjust learning rate for better results def adjust_learning_rate(optimizer, epoch, learning_rate, long_training): actual_learning_rate = learning_rate if long_training: first_update_threshold = 100 second_update_threshold = 150 else: first_update_threshold = 20 second_update_threshold = 25 if epoch >= first_update_threshold: actual_learning_rate = 0.01 if epoch >= second_update_threshold: actual_learning_rate = 0.001 for param_group in optimizer.param_groups: param_group["lr"] = actual_learning_rate def adjust_learning_rate_alternative(optimizer, epoch, learning_rate, long_training): actual_learning_rate = learning_rate if long_training: first_update_threshold = 75 second_update_threshold = 150 else: first_update_threshold = 20 second_update_threshold = 25 if epoch >= first_update_threshold: actual_learning_rate = 0.05 if epoch >= second_update_threshold: actual_learning_rate = 0.01 for param_group in optimizer.param_groups: param_group["lr"] = actual_learning_rate # Adversarial examples should be typically generated when model parameters are not # changing i.e. model parameters are frozen. This step may not be required for very # simple linear models, but is a must for models using components such as dropout # or batch normalization. def adversarial_training( trainSetLoader, attack_name, attack_function, long_training=True, load_if_available=False, load_path="../models_data/CIFAR10/cifar10_adversarial", **kwargs ): # Number of epochs is decided by training length if long_training: epochs = 200 else: epochs = 30 learning_rate = 0.1 # Network parameters loss_function = nn.CrossEntropyLoss() model = resnet.ResNet18() model = model.to(device) model = nn.DataParallel(model) model.train() # Consider using ADAM here as another gradient descent algorithm optimizer = torch.optim.SGD( model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.0002 ) # If a trained model already exists, give up the training part if load_if_available and os.path.isfile(load_path): print("Found already trained model...") model = torch.load(load_path) print("... loaded!") else: print("Training the model...") # Check if using epsilon if "epsilon" in kwargs: epsilon = kwargs["epsilon"] else: epsilon = None # Check if using alpha if "alpha" in kwargs: alpha = kwargs["alpha"] else: alpha = None # Get iterations if "iterations" in kwargs: iterations = kwargs["iterations"] else: iterations = None # Use a pretty progress bar to show updates for epoch in tnrange(epochs, desc="Adversarial Training Progress"): # Adjust the learning rate adjust_learning_rate(optimizer, epoch, learning_rate, long_training) for _, (images, labels) in enumerate(tqdm(trainSetLoader, desc="Batches")): # Cast to proper tensors images, labels = images.to(device), labels.to(device) # Run the attack model.eval() perturbed_images = attack_function( images, labels, model, loss_function, epsilon=epsilon, alpha=alpha, scale=True, iterations=iterations, ) model.train() # Predict and optimise optimizer.zero_grad() logits = model(perturbed_images) loss = loss_function(logits, labels) # Gradient descent loss.backward() # Also clip the gradients (ReLU leads to vanishing or # exploding gradients) torch.nn.utils.clip_grad_norm_(model.parameters(), 10) optimizer.step() print("... done!") # Make sure the model is in eval mode before returning model.eval() return model def cw_adversarial_training( trainSetLoader, long_training=True, load_if_available=False, load_path="../models_data/CIFAR10/cifar10_cw", **kwargs ): # Number of epochs is decided by training length if long_training: epochs = 200 else: epochs = 30 learning_rate = 0.1 # Network parameters loss_function = nn.CrossEntropyLoss() model = resnet.ResNet18() model = model.to(device) model = nn.DataParallel(model) model.train() # Consider using ADAM here as another gradient descent algorithm optimizer = torch.optim.SGD( model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.0002 ) # If a trained model already exists, give up the training part if load_if_available and os.path.isfile(load_path): print("Found already trained model...") model = torch.load(load_path) print("... loaded!") else: print("Training the model...") # Check if more epochs suplied if "steps" in kwargs: steps = kwargs["steps"] else: steps = 1000 # Check if more epochs suplied if "c" in kwargs: c = kwargs["c"] else: c = 1000 # Check if more epochs suplied if "epochs" in kwargs: epochs = kwargs["epochs"] # Define the attack attack_function = torchattacks.CW(model, c=c, steps=steps) # Use a pretty progress bar to show updates for epoch in tnrange(epochs, desc="Adversarial Training Progress"): # Adjust the learning rate adjust_learning_rate(optimizer, epoch, learning_rate, long_training) for _, (images, labels) in enumerate(tqdm(trainSetLoader, desc="Batches")): # Cast to proper tensors images, labels = images.to(device), labels.to(device) # Run the attack model.eval() perturbed_images = attack_function( images, labels, ) model.train() # Predict and optimise optimizer.zero_grad() logits = model(perturbed_images) loss = loss_function(logits, labels) # Gradient descent loss.backward() # Also clip the gradients (ReLU leads to vanishing or # exploding gradients) torch.nn.utils.clip_grad_norm_(model.parameters(), 10) optimizer.step() print("... done!") # Make sure the model is in eval mode before returning model.eval() return model def interpolated_adversarial_training( trainSetLoader, attack_name, attack_function, long_training=True, load_if_available=False, clip=True, verbose=False, test=False, load_path="../models_data/CIFAR10/cifar10_interpolated_adversarial", **kwargs ): # Number of epochs is decided by training length if long_training: epochs = 200 else: epochs = 30 learning_rate = 0.1 # Network parameters loss_function = nn.CrossEntropyLoss() model = resnet.ResNet18() model = model.to(device) model = nn.DataParallel(model) model.train() # Consider using ADAM here as another gradient descent algorithm optimizer = torch.optim.SGD( model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.0004 ) # If a trained model already exists, give up the training part if load_if_available and os.path.isfile(load_path): print("Found already trained model...") model = torch.load(load_path) print("... loaded!") else: print("Training the model...") # Check if using epsilon if "epsilon" in kwargs: epsilon = kwargs["epsilon"] else: epsilon = None # Check if using alpha if "alpha" in kwargs: alpha = kwargs["alpha"] else: alpha = None # Get iterations if "iterations" in kwargs: iterations = kwargs["iterations"] else: iterations = None # Get testSetLoader if testing required if test and "testSetLoader" in kwargs: testSetLoader = kwargs["testSetLoader"] # Use a pretty progress bar to show updates for epoch in tnrange(epochs, desc="Adversarial Training Progress"): # Calculate loss: total_loss = 0 # Adjust the learning rate adjust_learning_rate_alternative(optimizer, epoch, learning_rate, long_training) for _, (images, labels) in enumerate(tqdm(trainSetLoader, desc="Batches")): # Cast to proper tensors images, labels = images.to(device), labels.to(device) # Make sure previous step gradients are not used optimizer.zero_grad() # Use manifold mixup to modify the data ( benign_mix_images, benign_mix_labels_a, benign_mix_labels_b, benign_mix_lamda, ) = iat.mix_inputs(1, images, labels) # Predict and calculate benign loss benign_logits = model(benign_mix_images) benign_loss = iat.mixup_loss_function( loss_function, benign_mix_lamda, benign_logits, benign_mix_labels_a, benign_mix_labels_b, ) # Run the adversarial attack model.eval() perturbed_images = attack_function( images, labels, model, loss_function, epsilon=epsilon, alpha=alpha, scale=True, iterations=iterations, ) model.train() # Use manifold mixup on the adversarial data ( adversarial_mix_images, adversarial_mix_labels_a, adversarial_mix_labels_b, adversarial_mix_lamda, ) = iat.mix_inputs(1, perturbed_images, labels) # Predict and calculate adversarial loss adversarial_logits = model(adversarial_mix_images) adversarial_loss = iat.mixup_loss_function( loss_function, adversarial_mix_lamda, adversarial_logits, adversarial_mix_labels_a, adversarial_mix_labels_b, ) # Take average of the two losses loss = (benign_loss + adversarial_loss) / 2 # Gather loss total_loss += loss.item() # Gradient descent loss.backward() # Also clip the gradients (ReLU leads to vanishing or # exploding gradients) if clip: torch.nn.utils.clip_grad_norm_(model.parameters(), 10) optimizer.step() if test: clean_test.test_trained_model(model, testSetLoader) if verbose: print("Epoch {} loss is {}".format(epoch, total_loss)) print("... done!") # Make sure the model is in eval mode before returning model.eval() return model

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cc8b0c22c128fed0a7f9247bcd0d84ef2294bb37

93

py

Python

terrascript/tls/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

4

2022-02-07T21:08:14.000Z

2022-03-03T04:41:28.000Z

terrascript/tls/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

null

terrascript/tls/__init__.py

hugovk/python-terrascript

08fe185904a70246822f5cfbdc9e64e9769ec494

[ "BSD-2-Clause" ]

2

2022-02-06T01:49:42.000Z

2022-02-08T14:15:00.000Z

# terrascript/tls/__init__.py import terrascript class tls(terrascript.Provider): pass

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1

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0

1

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5

cc968f8bfec328ffb3fb6106ddc5ce1d6577a534

489

py

Python

bank_customer.py

rizuchaa/atm-machine

272882c336e43249d5910c3832be4c605da504a9

[ "Apache-2.0" ]

null

bank_customer.py

rizuchaa/atm-machine

272882c336e43249d5910c3832be4c605da504a9

[ "Apache-2.0" ]

null

bank_customer.py

rizuchaa/atm-machine

272882c336e43249d5910c3832be4c605da504a9

[ "Apache-2.0" ]

null

# import bank_card as bc class customer: def __init__(self, id, pin=1234, balance=10000): self.cust_id = id self.cust_pin = pin self.cust_bal = balance def check_id_card(self): return self.cust_id def check_pin(self): return self.cust_pin def check_bal(self): return self.cust_bal def get_debit(self, nominal): self.cust_bal -= nominal def last_balance(self, nominal): self.cust_bal += nominal

20.375

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0.627812

70

489

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0.328571

0.222222

0.152778

0.1875

0.201389

0

0.025788

0.286299

489

23

53

21.26087

0.799427

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1

0.4

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0.2

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null

1

0

1

0

null

0

1

0

1

0

5

aeb901a33b8113eb5535c79ce90c9eec1596ce69

6,230

py

Python

tests/test_seating.py

lionel-panhaleux/krcg

40238e2dababb23cdb1895221c58e81a0bf8c21d

[ "MIT" ]

6

2020-05-05T18:59:20.000Z

2021-10-11T12:19:45.000Z

tests/test_seating.py

lionel-panhaleux/krcg

40238e2dababb23cdb1895221c58e81a0bf8c21d

[ "MIT" ]

340

2020-04-15T08:19:29.000Z

2022-03-31T09:59:19.000Z

tests/test_seating.py

lionel-panhaleux/krcg

40238e2dababb23cdb1895221c58e81a0bf8c21d

[ "MIT" ]

8

2020-05-05T16:10:50.000Z

2021-07-21T00:16:11.000Z

from krcg import seating def test_rounds(): len(seating.get_rounds(5, 2)) == 2 len(seating.get_rounds(6, 2)) == 3 len(seating.get_rounds(7, 2)) == 3 len(seating.get_rounds(8, 2)) == 2 len(seating.get_rounds(9, 2)) == 2 len(seating.get_rounds(10, 2)) == 2 len(seating.get_rounds(11, 2)) == 3 len(seating.get_rounds(12, 2)) == 2 len(seating.get_rounds(6, 3)) == 4 len(seating.get_rounds(7, 3)) == 5 len(seating.get_rounds(11, 3)) == 4 len(seating.get_rounds(7, 4)) == 6 len(seating.get_rounds(7, 5)) == 7 len(seating.get_rounds(7, 6)) == 9 len(seating.get_rounds(6, 6)) == 7 len(seating.get_rounds(6, 7)) == 9 def test_round(): assert seating.Round.from_players([1, 2, 3, 4]) == [[1, 2, 3, 4]] assert seating.Round.from_players([1, 2, 3, 4, 5]) == [[1, 2, 3, 4, 5]] assert seating.Round.from_players([1, 2, 3, 4, 5, 6, 7, 8]) == [ [1, 2, 3, 4], [5, 6, 7, 8], ] assert seating.Round.from_players([1, 2, 3, 4, 5, 6, 7, 8, 9]) == [ [1, 2, 3, 4, 5], [6, 7, 8, 9], ] def test_measure(): M = seating.measure(4, seating.Round.from_players([1, 2, 3, 4])) assert M.position.tolist() == [ [1, 4, 1, 1, 0, 0, 0, 0], [1, 4, 2, 0, 1, 0, 0, 0], [1, 4, 3, 0, 0, 1, 0, 0], [1, 4, 4, 0, 0, 0, 1, 0], ] assert M.opponents.tolist() == [ [ [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0], [1, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 1, 0, 1, 0], ], [ [1, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0], [1, 0, 0, 0, 0, 1, 0, 1], ], [ [1, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0], ], [ [1, 1, 0, 0, 0, 0, 1, 0], [1, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], ], ] MM = sum((M, M)) assert MM.position.tolist() == [ [2, 8, 2, 2, 0, 0, 0, 0], [2, 8, 4, 0, 2, 0, 0, 0], [2, 8, 6, 0, 0, 2, 0, 0], [2, 8, 8, 0, 0, 0, 2, 0], ] assert MM.opponents.tolist() == [ [ [0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 0, 0, 0, 0, 2, 0], [2, 0, 0, 0, 0, 2, 0, 2], [2, 0, 0, 0, 2, 0, 2, 0], ], [ [2, 0, 0, 0, 2, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 0, 0, 0, 0, 2, 0], [2, 0, 0, 0, 0, 2, 0, 2], ], [ [2, 0, 0, 0, 0, 2, 0, 2], [2, 0, 0, 0, 2, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 0, 0, 0, 0, 2, 0], ], [ [2, 2, 0, 0, 0, 0, 2, 0], [2, 0, 0, 0, 0, 2, 0, 2], [2, 0, 0, 0, 2, 0, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0], ], ] M = seating.measure(5, seating.Round.from_players([1, 2, 3, 4, 5])) assert M.position.tolist() == [ [1, 5, 1, 1, 0, 0, 0, 0], [1, 5, 2, 0, 1, 0, 0, 0], [1, 5, 3, 0, 0, 1, 0, 0], [1, 5, 4, 0, 0, 0, 1, 0], [1, 5, 4, 0, 0, 0, 0, 1], ] assert M.opponents.tolist() == [ [ [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0], [1, 0, 1, 0, 0, 0, 0, 1], [1, 0, 0, 1, 0, 0, 0, 1], [1, 0, 0, 0, 1, 0, 1, 0], ], [ [1, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0], [1, 0, 1, 0, 0, 0, 0, 1], [1, 0, 0, 1, 0, 0, 0, 1], ], [ [1, 0, 0, 1, 0, 0, 0, 1], [1, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0], [1, 0, 1, 0, 0, 0, 0, 1], ], [ [1, 0, 1, 0, 0, 0, 0, 1], [1, 0, 0, 1, 0, 0, 0, 1], [1, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0], ], [ [1, 1, 0, 0, 0, 0, 1, 0], [1, 0, 1, 0, 0, 0, 0, 1], [1, 0, 0, 1, 0, 0, 0, 1], [1, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0], ], ] def test_score(): permutations = [[1, 2, 3, 4, 5], [2, 5, 3, 1, 4], [2, 1, 5, 4, 3]] rounds = [seating.Round.from_players(p) for p in permutations] score = seating.Score(rounds) assert score.R1 == [] assert score.R2 == [ (1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5), (4, 5), ] assert score.R3 == 0.0 assert score.R4 == [ (1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (3, 5), (4, 5), ] assert score.R7 == [(2, 1), (3, 3), (4, 4)] assert score.R5 == [] assert score.R6 == [] assert score.R8 == 0.9092121131323905 assert score.R9 == [ (1, 2, 1), (1, 3, 2), (1, 4, 2), (1, 5, 1), (2, 3, 1), (2, 4, 2), (2, 5, 2), (3, 4, 1), (3, 5, 2), (4, 5, 1), ] assert score.mean_vps == 5.0 assert score.mean_transfers == 2.8 assert score.vps == [] assert score.transfers == [ (1, 2 + 1 / 3), (2, 1 + 1 / 3), (3, 3 + 1 / 3), (4, 4.0), ] assert score.rules == [0, 10, 0, 10, 0, 0, 3, 0.9092121131323905, 10] assert score.total == 10010003100.921211 def test_best_seating(): # mainly check the function executes, results are not stable rounds, score = seating.optimise(seating.get_rounds(13, 3), iterations=1000) assert len(rounds) == 3 # mean values don't change assert round(score.mean_vps, 5) == 4.38462 assert round(score.mean_transfers, 5) == 2.61538 # these rules are never satisfied for 13 players assert score.R3 > 0 assert score.R4 != [] assert score.R8 > 0 assert score.R9 != []

27.8125

80

0.356019

1,072

6,230

2.037313

0.071828

0.230769

0.236264

0.192308

0.57967

0.499084

0.430403

0.362179

0.346154

0.313645

0

0.246256

0.421188

6,230

223

81

27.93722

0.359401

0.020867

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0.454106

0

0.154589

1

0.024155

false

0

0.004831

0

0.028986

0

null

1

0

1

0

1

0

null

0

5

aefdd6daa205cb6495ef9621ce2f80779ff3390a

101

py

Python

sota/parser/__init__.py

sota/lang

cce40333f1b7bdcb5fed3471c5db0105d0263328

[ "MIT" ]

2

2015-03-10T20:31:18.000Z

2015-11-08T06:29:56.000Z

sota/parser/__init__.py

sota/lang

cce40333f1b7bdcb5fed3471c5db0105d0263328

[ "MIT" ]

null

sota/parser/__init__.py

sota/lang

cce40333f1b7bdcb5fed3471c5db0105d0263328

[ "MIT" ]

null

#!/usr/bin/env python2.7 # -*- coding: utf-8 -*- ''' sota.parser ''' from .parser import SotaParser

12.625

30

0.623762

14

101

4.5

0.928571

0

0.034884

0.148515

101

7

31

14.428571

0.697674

0.564356

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

9d6491a07e00e6b803b8a8748a7caa98d2924c8c

223

py

Python

misc/apps.py

810Teams/clubs-and-events-backend

cf429f43251ad7e77c0d9bc9fe91bb030ca8bae8

[ "MIT" ]

1

2021-06-25T17:16:13.000Z

misc/apps.py

810Teams/clubs-and-events-backend

cf429f43251ad7e77c0d9bc9fe91bb030ca8bae8

[ "MIT" ]

null

misc/apps.py

810Teams/clubs-and-events-backend

cf429f43251ad7e77c0d9bc9fe91bb030ca8bae8

[ "MIT" ]

null

''' Miscellaneous Application Configuration misc/apps.py @auto_created ''' from django.apps import AppConfig class MiscConfig(AppConfig): ''' Miscellaneous Application Configuration ''' name = 'misc'

17.153846

51

0.704036

21

223

7.428571

0.714286

0.307692

0.474359

0

0.197309

223

12

52

18.583333

0.871508

0.479821

0

0.044444

0

1

0

false

0

0.333333

0

1

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1

0

null

1

0

null

0

1

0

1

0

5

9d6552757ae8def8d57cadbfafc876fce957a95c

30

py

Python

src/contracts/test/__init__.py

xellDart/oken_nft_ip

66b9e9b99eca606b4dcf27c3c27c5b8338d8b91d

[ "MIT" ]

9

2021-02-03T09:15:20.000Z

2022-01-20T18:43:05.000Z

src/contracts/test/__init__.py

xellDart/oken_nft_ip

66b9e9b99eca606b4dcf27c3c27c5b8338d8b91d

[ "MIT" ]

2

2021-11-17T15:42:00.000Z

2021-12-19T18:39:36.000Z

src/contracts/test/__init__.py

xellDart/oken_nft_ip

66b9e9b99eca606b4dcf27c3c27c5b8338d8b91d

[ "MIT" ]

11

2021-05-17T16:42:20.000Z

2022-02-08T09:17:45.000Z

from test.tests_utils import *

30

0.833333

5

30

4.8

1

0

0.1

30

1

30

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9db37d3a3a02cd6fea6579e6e07bbc764748dec2

43

py

Python

test_print_range.py

diamondjaxx/PyNet_Test1

144d796403cd2e8806c820b8d7280c5fe152a845

[ "Apache-2.0" ]

null

test_print_range.py

diamondjaxx/PyNet_Test1

144d796403cd2e8806c820b8d7280c5fe152a845

[ "Apache-2.0" ]

null

test_print_range.py

diamondjaxx/PyNet_Test1

144d796403cd2e8806c820b8d7280c5fe152a845

[ "Apache-2.0" ]

null

print range(10) print "Adding test branch"

14.333333

26

0.767442

7

43

4.714286

0.857143

0

0.054054

0.139535

43

2

27

21.5

0.837838

0

0.418605

0

null

0

null

1

0

null

0

1

0

null

0

1

0

1

0

5

9ded72206511f1619399b58497311c1dde69c316

225

py

Python

migrations/015-initial_activity_data.py

mozilla/FlightDeck

61d66783252ac1318c990e342877a26c64f59062

[ "BSD-3-Clause" ]

6

2015-04-24T03:10:44.000Z

2020-12-27T19:46:33.000Z

migrations/015-initial_activity_data.py

fox2mike/FlightDeck

3a2fc78c13dd968041b349c4f9343e6c8b22dd25

[ "BSD-3-Clause" ]

null

migrations/015-initial_activity_data.py

fox2mike/FlightDeck

3a2fc78c13dd968041b349c4f9343e6c8b22dd25

[ "BSD-3-Clause" ]

5

2015-09-18T19:58:31.000Z

2020-01-28T05:46:55.000Z

""" Collect all the revisions for each package, distinct by day, in the past year and determine the year of activity. """ from jetpack.cron import fill_package_activity def run(*args, **kwargs): fill_package_activity()

22.5

77

0.755556

34

225

4.882353

0.764706

0.13253

0.228916

0

0.16

225

9

78

25

0.878307

0.502222

0

1

0.333333

true

0

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null

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1

0

1

0

null

0

1

0

1

0

5

d1cd6ca38b899679f66ee0ce5c35555eb32dfaa5

33

py

Python

karateclub/utils/__init__.py

Laeyoung/ainized-karateclub

26d8e10d9cb15a7ae6bf43db6ec338a6ae4f9aa0

[ "MIT" ]

null

karateclub/utils/__init__.py

Laeyoung/ainized-karateclub

26d8e10d9cb15a7ae6bf43db6ec338a6ae4f9aa0

[ "MIT" ]

null

karateclub/utils/__init__.py

Laeyoung/ainized-karateclub

26d8e10d9cb15a7ae6bf43db6ec338a6ae4f9aa0

[ "MIT" ]

1

2020-01-08T07:38:37.000Z

from .walker import RandomWalker

16.5

32

0.848485

4

33

7

1

0

0.121212

33

1

33

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

d1e036edd819bee61bfcc543f37351c3295fe52c

343

py

Python

app/serializers/__init__.py

uwhvz/uwhvz

72805d0e55740c3d90251dd4b4e40bf5c9e296d1

[ "MIT" ]

2

2019-12-15T06:30:37.000Z

2020-01-26T23:12:27.000Z

app/serializers/__init__.py

uwhvz/uwhvz

72805d0e55740c3d90251dd4b4e40bf5c9e296d1

[ "MIT" ]

37

2020-01-22T02:36:32.000Z

2020-10-06T15:05:37.000Z

app/serializers/__init__.py

uwhvz/uwhvz

72805d0e55740c3d90251dd4b4e40bf5c9e296d1

[ "MIT" ]

2

2020-06-24T03:07:36.000Z

2020-06-24T03:10:46.000Z

from .faction_serializer import FactionSerializer from .game_serializer import GameSerializer from .modifier_serializer import ModifierSerializer from .tag_serializer import TagSerializer from .player_serializer import PlayerSerializer, SimplePlayerSerializer from .non_player_serializers import SimpleModeratorSerializer, SpectatorSerializer

49

82

0.900875

33

343

9.151515

0.545455

0.264901

0

0.075802

343

6

83

57.166667

0.952681

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

d1e048fb3e5a6e45a5312554b5cedbb19fc75b18

100

py

Python

endless_bot/emitters/abstractemitter.py

aisouard/endless-bot

f480a9ad9ad9dc744b9f91c4e80d9bf7ca47c5aa

[ "MIT" ]

null

endless_bot/emitters/abstractemitter.py

aisouard/endless-bot

f480a9ad9ad9dc744b9f91c4e80d9bf7ca47c5aa

[ "MIT" ]

null

endless_bot/emitters/abstractemitter.py

aisouard/endless-bot

f480a9ad9ad9dc744b9f91c4e80d9bf7ca47c5aa

[ "MIT" ]

null

# -*- coding: utf-8 -*- from abc import ABC, abstractmethod class AbstractEmitter(ABC): pass

12.5

35

0.67

12

100

5.583333

0.833333

0

0.0125

0.2

100

7

36

14.285714

0.825

0.21

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

5

d1ebc4f5f8af80bcc958730b50a579d09dbebe6f

77

py

Python

src/main.py

TomMarti/LIL-CRAWLER

bbfcd85c943c1b5e4ae8f10b024e9ebd15387b1b

[ "MIT" ]

null

src/main.py

TomMarti/LIL-CRAWLER

bbfcd85c943c1b5e4ae8f10b024e9ebd15387b1b

[ "MIT" ]

null

src/main.py

TomMarti/LIL-CRAWLER

bbfcd85c943c1b5e4ae8f10b024e9ebd15387b1b

[ "MIT" ]

null

from src.crawler.crawler import Crawler crawler = Crawler() crawler.crawl()

15.4

39

0.779221

10

77

6

0.5

0.933333

0.7

0

0.116883

77

5

40

15.4

0.882353

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

d1f48a9626430ba411db724977968304797b4e3d

108

py

Python

Contests/Projects/practice/a.py

aqfaridi/Competitve-Programming-Codes

d055de2f42d3d6bc36e03e67804a1dd6b212241f

[ "MIT" ]

null

Contests/Projects/practice/a.py

aqfaridi/Competitve-Programming-Codes

d055de2f42d3d6bc36e03e67804a1dd6b212241f

[ "MIT" ]

null

Contests/Projects/practice/a.py

aqfaridi/Competitve-Programming-Codes

d055de2f42d3d6bc36e03e67804a1dd6b212241f

[ "MIT" ]

null

import random print 1 print 1000000 for i in range(1,1000001): print random.randint(-2000000 , 2000000)

18

44

0.75

17

108

4.764706

0.705882

0

0.333333

0.166667

108

5

45

21.6

0.566667

0

null

0

0.2

null

0.6

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

ae08c15d4d74265767c7b0acc445baa16b899ed6

21

py

Python

dtp/__init__.py

mayoralade/cloud-devops-tools

2e2edd40fcce5ceff9126f0b7f94cc1a0e456f54

[ "MIT" ]

1

2018-03-07T23:24:07.000Z

dtp/__init__.py

mayoralade/cloud-devops-tools

2e2edd40fcce5ceff9126f0b7f94cc1a0e456f54

[ "MIT" ]

null

dtp/__init__.py

mayoralade/cloud-devops-tools

2e2edd40fcce5ceff9126f0b7f94cc1a0e456f54

[ "MIT" ]

null

from .dtp import main

21

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ae6811b4ee482ea9fc97eea76f8db07af2e0770e

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py

Python

tests/hardware_usage_notifier/cli/config/notifiers_test_instances/well_defined_notifier.py

ovidiupw/HardwareUsageNotifier

b5f600fa66c1ede1a2337c4a39fc6ec8a209dcf5

[ "MIT" ]

null

tests/hardware_usage_notifier/cli/config/notifiers_test_instances/well_defined_notifier.py

ovidiupw/HardwareUsageNotifier

b5f600fa66c1ede1a2337c4a39fc6ec8a209dcf5

[ "MIT" ]

null

tests/hardware_usage_notifier/cli/config/notifiers_test_instances/well_defined_notifier.py

ovidiupw/HardwareUsageNotifier

b5f600fa66c1ede1a2337c4a39fc6ec8a209dcf5

[ "MIT" ]

null

from hardware_usage_notifier.notifiers.notifier import Notifier class NoopNotifier(Notifier): def __init__(self, configuration): super().__init__(configuration) def notify(self): pass

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ae87708d623525c657a82a7d0894795242d02f88

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py

Python

skaben/actions/main.py

skaben/server_core

46ba0551459790dda75abc9cf0ff147fae6d62e8

[ "MIT" ]

null

skaben/actions/main.py

skaben/server_core

46ba0551459790dda75abc9cf0ff147fae6d62e8

[ "MIT" ]

12

2020-08-14T12:43:04.000Z

2021-09-01T00:22:26.000Z

skaben/actions/main.py

skaben/server_core

46ba0551459790dda75abc9cf0ff147fae6d62e8

[ "MIT" ]

null

from actions.scenario.default import Scenario as EventManager # noqa

69

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1

0

1

0

1

0

5

88adeddbf125b3dacb4bab9a0c44cb3db78e5e18

98

py

Python

mutations/util.py

omarish/mutations

f667fbdf4b82af9822ff479bbeaf9748e2397360

[ "MIT" ]

53

2018-02-06T18:38:58.000Z

2020-10-14T19:20:19.000Z

mutations/util.py

omarish/mutations

f667fbdf4b82af9822ff479bbeaf9748e2397360

[ "MIT" ]

4

2018-05-26T22:03:51.000Z

2021-06-01T22:33:21.000Z

mutations/util.py

omarish/mutations

f667fbdf4b82af9822ff479bbeaf9748e2397360

[ "MIT" ]

8

2018-05-29T15:32:22.000Z

2019-11-12T01:22:23.000Z

def wrap(item): if not isinstance(item, (list, tuple)): return [item] return item

19.6

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null

0

1

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1

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5

31f5c673b634c57fc6453b799efa862f174553a2

84

py

Python

mytool/subtool/__init__.py

alxrsngrtn/beam-cli-example

e4c37b45f31a04fd291bb935c9a2d33517fd97e6

[ "MIT" ]

1

2021-11-18T06:50:31.000Z

mytool/subtool/__init__.py

alxrsngrtn/beam-cli-example

e4c37b45f31a04fd291bb935c9a2d33517fd97e6

[ "MIT" ]

null

mytool/subtool/__init__.py

alxrsngrtn/beam-cli-example

e4c37b45f31a04fd291bb935c9a2d33517fd97e6

[ "MIT" ]

null

from .main import run def cli(extra=[]): import sys run(sys.argv + extra)

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25

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null

0

1

0

1

0

1

0

5

ee31fb6de3072a3803bb92febb3c8043246fc805

100

py

Python

main.py

sesamechicken/py-toy

5abdcdef7a764bb298689d87446d59aafdddaac9

[ "MIT" ]

null

main.py

sesamechicken/py-toy

5abdcdef7a764bb298689d87446d59aafdddaac9

[ "MIT" ]

null

main.py

sesamechicken/py-toy

5abdcdef7a764bb298689d87446d59aafdddaac9

[ "MIT" ]

null

def thing(a=int, b=int): # It's obvious, but thing returns the sum of its args return a + b

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null

0

1

0

null

0

1

0

1

0

5

ee4329840a27aa727105edfd6c78c266866b95a2

1,305

py

Python

traffic/genetic_algorithm/temp2.py

pangpifuta/react-ui-project

dea2ad1b114718f6d034e3afb94c75780d328198

[ "MIT" ]

null

traffic/genetic_algorithm/temp2.py

pangpifuta/react-ui-project

dea2ad1b114718f6d034e3afb94c75780d328198

[ "MIT" ]

6

2020-06-05T20:28:09.000Z

2022-01-13T01:10:19.000Z

traffic/genetic_algorithm/temp2.py

pangpifuta/react-ui-project

dea2ad1b114718f6d034e3afb94c75780d328198

[ "MIT" ]

null

import requests import click import json import subprocess from joblib import Parallel, delayed import time import numpy as np import os import shutil import paramiko import time import random import csv # def getPositions(timings): # previousSave2 = 0 # request = [r'.\ForAlok'+'\SingleSimulation.exe'] # positionLocation = r'.\ForAlok\temp3_' # for timing in timings: # request.append(str(timing)) # positionLocation+=str(timing)+"_" # positionLocation+="saved_state_2_"+str(previousSave2)+"_txt_" # positionLocation+="saved_state_2_"+str((previousSave2+1)%2)+"_txt" # positionLocation+="_temp3" # request.append("saved_state_2_"+str(previousSave2)+".txt") # request.append("saved_state_2_"+str((previousSave2+1)%2)+".txt") # previousSave2 = (previousSave2 + 1)%2 # request.append("temp3") # result = subprocess.Popen(request, stdout=subprocess.PIPE).communicate()[0] # positions = {} # with open(positionLocation+"//cars119.csv", 'r') as csvFile: # reader = csv.reader(csvFile) # for row in reader: # positions[row[0]] = [row[2], row[3]] # shutil.rmtree(positionLocation) # return positions # getPositions([10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10])

33.461538

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1

0

1

0

1

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5

ee515b526c512c97f13e82e9a463120f610231f2

145

py

Python

ebc/__init__.py

yangfl/python-easy-bytecode

264cbde75dbd3369001604e12ae7a8c2ed04a039

[ "Unlicense" ]

1

2020-01-10T18:46:39.000Z

ebc/__init__.py

yangfl/python-easy-bytecode

264cbde75dbd3369001604e12ae7a8c2ed04a039

[ "Unlicense" ]

null

ebc/__init__.py

yangfl/python-easy-bytecode

264cbde75dbd3369001604e12ae7a8c2ed04a039

[ "Unlicense" ]

null

from . import mdis as dis from .disassemble import disassemble, print_disassemble, iter_disassemble from .assemble import assemble, use_assemble

36.25

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1

0

1

0

5

c9dfd3121bfb697d2d99e56e550a4b43b8b2a9db

35

py

Python

Lib/parallel/http/constants.py

pyparallel/pyparallel

11e8c6072d48c8f13641925d17b147bf36ee0ba3

[ "PSF-2.0" ]

652

2015-07-26T00:00:17.000Z

2022-02-24T18:30:04.000Z

Lib/parallel/http/constants.py

tpn/pyparallel

11e8c6072d48c8f13641925d17b147bf36ee0ba3

[ "PSF-2.0" ]

8

2015-09-07T03:38:19.000Z

2021-05-23T03:18:51.000Z

Lib/parallel/http/constants.py

tpn/pyparallel

11e8c6072d48c8f13641925d17b147bf36ee0ba3

[ "PSF-2.0" ]

40

2015-07-24T19:45:08.000Z

2021-11-01T14:54:56.000Z

from async.http.constants import *

17.5

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0.8

5

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5.6

1

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1

35

0.903226

0

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1

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null

0

1

0

1

0

5

c9f29e9079956739057f874e7fdfdf3e8e9a2eac

934

py

Python

matrix_multiplication_tests.py

Avery2/BackpropFromScratch

25d866586129f1b95c55479c34619047a6ea9a5b

[ "MIT" ]

null

matrix_multiplication_tests.py

Avery2/BackpropFromScratch

25d866586129f1b95c55479c34619047a6ea9a5b

[ "MIT" ]

null

matrix_multiplication_tests.py

Avery2/BackpropFromScratch

25d866586129f1b95c55479c34619047a6ea9a5b

[ "MIT" ]

null

import matrix_multiplication as mm # unit tests here # test matrix multiplication: # [[1,2,3,4,5],[1,2,3,4,5],[1,2,3,4,5],[1,2,3,4,5],[5,4,3,2,1]] by # [[1,1,1,1,1],[2,2,2,2,2],[3,3,3,3,3],[4,4,4,4,4],[5,5,5,5,5]] # results in [[55,55,55,55,55],[55,55,55,55,55],[55,55,55,55,55],[55,55,55,55,55],[35,35,35,35,35]] def test_matmult(): a = [[1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [5, 4, 3, 2, 1]] b = [[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5]] expected_answer = [[55, 55, 55, 55, 55], [55, 55, 55, 55, 55], [55, 55, 55, 55, 55], [55, 55, 55, 55, 55], [35, 35, 35, 35, 35]] answer = mm.matmul(mm.Matrix(values=a), mm.Matrix(values=b)) if answer == expected_answer: return True else: print(expected_answer) print(answer) return False print("Starting tests..") print(test_matmult())

35.923077

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a00db69bb9cf0650ef4a2d328a6d56b0afcac5ca

317

py

Python

api/serializers.py

shiyanshirani/Mozio-Backend-Task

f7a8e5c051b84f3680c3fb0673b3fd8e56b63e30

[ "MIT" ]

null

api/serializers.py

shiyanshirani/Mozio-Backend-Task

f7a8e5c051b84f3680c3fb0673b3fd8e56b63e30

[ "MIT" ]

null

api/serializers.py

shiyanshirani/Mozio-Backend-Task

f7a8e5c051b84f3680c3fb0673b3fd8e56b63e30

[ "MIT" ]

null

from .models import * from rest_framework import serializers class ProviderSerializer(serializers.ModelSerializer): class Meta: model = Provider fields = "__all__" class PolygonAreaSerializer(serializers.ModelSerializer): class Meta: model = PolygonArea fields = "__all__"

21.133333

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0

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317

14

58

22.642857

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0.4

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false

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1

0

null

0

1

0

5

a01479acae31e7e97e95814f9d75c211b7d51d1e

457

py

Python

tools/leetcode.119.Pascal's Triangle II/leetcode.119.Pascal's Triangle II.submission8.py

tedye/leetcode

975d7e3b8cb9b6be9e80e07febf4bcf6414acd46

[ "MIT" ]

4

2015-10-10T00:30:55.000Z

2020-07-27T19:45:54.000Z

tools/leetcode.119.Pascal's Triangle II/leetcode.119.Pascal's Triangle II.submission8.py

tedye/leetcode

975d7e3b8cb9b6be9e80e07febf4bcf6414acd46

[ "MIT" ]

null

tools/leetcode.119.Pascal's Triangle II/leetcode.119.Pascal's Triangle II.submission8.py

tedye/leetcode

975d7e3b8cb9b6be9e80e07febf4bcf6414acd46

[ "MIT" ]

null

class Solution: # @return a list of integers def getRow(self, rowIndex): temp = [] for i in range(0,rowIndex+1): temp.append(self.combination(rowIndex,i)) return temp def combination(self,n,k): return self.factorials(n)/(self.factorials(n-k)*self.factorials(k)) def factorials(self,n): if n == 0 or n == 1: return 1 return n * self.factorials(n-1)

457

0.551422

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457

4.131148

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0

0.019544

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0

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5

4e79da579b52f8cd832e68eac3b2a88b7f2b5b2e

155

py

Python

Pygame Test.py

matthew-e-brown/Grade-11-Pygame

def26d52d4b2e1af687e2d9727d95fc958372bd5

[ "MIT" ]

null

Pygame Test.py

matthew-e-brown/Grade-11-Pygame

def26d52d4b2e1af687e2d9727d95fc958372bd5

[ "MIT" ]

null

Pygame Test.py

matthew-e-brown/Grade-11-Pygame

def26d52d4b2e1af687e2d9727d95fc958372bd5

[ "MIT" ]

null

import pygame, sys from pygame.locals import * pygame.init() DISPLAYSURF = pygame.display.set_mode((800,600)) pygame.display.set_caption("Hello, Matt!")

19.375

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1

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5

14ca7a2c931659c7559aff5719e05931fe519d53

177

py

Python

genda/plotting/__init__.py

jeffhsu3/genda

5adbb5b5620c592849fa4a61126b934e1857cd77

[ "BSD-3-Clause" ]

5

2016-01-12T15:12:18.000Z

2022-02-10T21:57:39.000Z

genda/plotting/__init__.py

jeffhsu3/genda

5adbb5b5620c592849fa4a61126b934e1857cd77

[ "BSD-3-Clause" ]

5

2015-01-20T04:22:50.000Z

2018-10-02T19:39:12.000Z

genda/plotting/__init__.py

jeffhsu3/genda

5adbb5b5620c592849fa4a61126b934e1857cd77

[ "BSD-3-Clause" ]

1

2022-03-04T06:49:39.000Z

#from scipy.stats import linregress, pearsonr #from matplotlib import figure from matplotlib.path import Path from .annotation import (snp_arrow) from .plotting_utils import *

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14d621b788c00b4ca1ae2b2d85c84b8525b6814a

31

py

Python

kivymd/uix/snackbar/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

kivymd/uix/snackbar/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

kivymd/uix/snackbar/__init__.py

AnEx07/KivyMD

e4004a570ad3f1874b3540cc1b0c243b3037bba8

[ "MIT" ]

null

from .snackbar import Snackbar

15.5

30

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0.962963

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null

0

1

0

1

0

5

14e510b70b0366a9d9556e02fcea8c515f7b80a4

89

py

Python

Str_Repr/test2.py

sagarsaliya/code_snippets

692ccae54a47a16c8286d91b08707150c20531fc

[ "MIT" ]

9,588

2017-03-21T16:07:40.000Z

2022-03-31T08:43:39.000Z

Str_Repr/test2.py

JaredDelora/code_snippets

ed3c42ff06bb31da1f9f00689fa76d90babddc97

[ "MIT" ]

135

2017-04-29T15:28:11.000Z

2022-03-27T19:20:49.000Z

Str_Repr/test2.py

JaredDelora/code_snippets

ed3c42ff06bb31da1f9f00689fa76d90babddc97

[ "MIT" ]

20,939

2017-03-27T14:42:56.000Z

2022-03-31T16:41:14.000Z

a = [1,2,3,4] b = 'sample string' print str(a) print repr(a) print str(b) print repr(b)

11.125

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2.8

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8

20

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09483e25a5e07eaf517998c442ae2e6a0db7ecd9

391

py

Python

src/gfl/core/net/standalone/fetch.py

mingt2019/GFL

b8e027d2e8cdcc27c85a00744f8790d6db3cc4a3

[ "MIT" ]

123

2020-06-05T13:30:38.000Z

2022-03-30T08:39:43.000Z

src/gfl/core/net/standalone/fetch.py

GalaxyLearning/PFL

b8e027d2e8cdcc27c85a00744f8790d6db3cc4a3

[ "MIT" ]

13

2020-06-19T13:09:47.000Z

2021-12-22T03:09:24.000Z

src/gfl/core/net/standalone/fetch.py

GalaxyLearning/GFL

b8e027d2e8cdcc27c85a00744f8790d6db3cc4a3

[ "MIT" ]

35

2020-06-08T15:52:21.000Z

2022-03-25T11:52:42.000Z

from typing import List from gfl.core.net.abstract import NetFetch class StandaloneFetch(NetFetch): @classmethod def fetch_job(cls, job_id: str): pass @classmethod def list_job_id(cls) -> List[str]: pass @classmethod def fetch_dataset(cls, dataset_id: str): pass @classmethod def list_dataset_id(cls) -> List[str]: pass

17

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45

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1

0

5

095503a8213d4d96c79cd1c86f2cef588822381d

4,244

py

Python

test_autolens/unit/plot/test_ray_tracing_plots.py

rakaar/PyAutoLens

bc140c5d196c426092c1178b8abfa492c6fab859

[ "MIT" ]

null

test_autolens/unit/plot/test_ray_tracing_plots.py

rakaar/PyAutoLens

bc140c5d196c426092c1178b8abfa492c6fab859

[ "MIT" ]

null

test_autolens/unit/plot/test_ray_tracing_plots.py

rakaar/PyAutoLens

bc140c5d196c426092c1178b8abfa492c6fab859

[ "MIT" ]

null

from os import path import pytest import autolens as al import autolens.plot as aplt directory = path.dirname(path.realpath(__file__)) @pytest.fixture(name="plot_path") def make_fit_imaging_plotter_setup(): return path.join( "{}".format(path.dirname(path.realpath(__file__))), "files", "plots", "ray_tracing", ) def test__all_individual_plotters( tracer_x2_plane_7x7, sub_grid_7x7, mask_7x7, include_all, plot_path, plot_patch ): aplt.Tracer.image( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "image.png") in plot_patch.paths aplt.Tracer.convergence( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "convergence.png") in plot_patch.paths aplt.Tracer.potential( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "potential.png") in plot_patch.paths aplt.Tracer.deflections_y( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "deflections_y.png") in plot_patch.paths aplt.Tracer.deflections_x( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "deflections_x.png") in plot_patch.paths aplt.Tracer.magnification( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "magnification.png") in plot_patch.paths tracer_x2_plane_7x7.planes[0].galaxies[0].hyper_galaxy = al.HyperGalaxy() tracer_x2_plane_7x7.planes[0].galaxies[0].hyper_model_image = al.Array.ones( shape_2d=(7, 7), pixel_scales=0.1 ) tracer_x2_plane_7x7.planes[0].galaxies[0].hyper_galaxy_image = al.Array.ones( shape_2d=(7, 7), pixel_scales=0.1 ) aplt.Tracer.contribution_map( tracer=tracer_x2_plane_7x7, mask=mask_7x7, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "contribution_map.png") in plot_patch.paths def test__tracer_sub_plot_output( tracer_x2_plane_7x7, sub_grid_7x7, include_all, plot_path, plot_patch ): aplt.Tracer.subplot_tracer( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, include=include_all, sub_plotter=aplt.SubPlotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "subplot_tracer.png") in plot_patch.paths def test__tracer_individuals__dependent_on_input( tracer_x2_plane_7x7, sub_grid_7x7, include_all, plot_path, plot_patch ): aplt.Tracer.individual( tracer=tracer_x2_plane_7x7, grid=sub_grid_7x7, plot_image=True, plot_source_plane=True, plot_potential=True, plot_magnification=True, include=include_all, plotter=aplt.Plotter(output=aplt.Output(plot_path, format="png")), ) assert path.join(plot_path, "image.png") in plot_patch.paths assert path.join(plot_path, "source_plane.png") in plot_patch.paths assert path.join(plot_path, "convergence.png") not in plot_patch.paths assert path.join(plot_path, "potential.png") in plot_patch.paths assert path.join(plot_path, "deflections_y.png") not in plot_patch.paths assert path.join(plot_path, "deflections_x.png") not in plot_patch.paths assert path.join(plot_path, "magnification.png") in plot_patch.paths

31.205882

84

0.67672

577

4,244

4.662045

0.138648

0.083271

0.072491

0.089219

0.8171

0.788848

0.74684

0.69777

0.655762

0

0.026189

0.217248

4,244

135

85

31.437037

0.783564

0

0.460784

0

0.070333

0

0.147059

1

0.039216

false

0

0.039216

0.009804

0.088235

0

null

0

1

0

1

0

null

0

5

eeb47e549fc14e7570ce74c38d988419eaf542e4

51

py

Python

pyxb/bundles/opengis/gml_3_3/lro.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

123

2015-01-12T06:43:22.000Z

2022-03-20T18:06:46.000Z

pyxb/bundles/opengis/gml_3_3/lro.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

103

2015-01-08T18:35:57.000Z

2022-01-18T01:44:14.000Z

pyxb/bundles/opengis/gml_3_3/lro.py

eLBati/pyxb

14737c23a125fd12c954823ad64fc4497816fae3

[ "Apache-2.0" ]

54

2015-02-15T17:12:00.000Z

2022-03-07T23:02:32.000Z

from pyxb.bundles.opengis.gml_3_3.raw.lro import *

25.5

50

0.803922

10

51

3.9

0.9

0

0.042553

0.078431

51

1

51

0.787234

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

eeb8315a0743e10883152820c33a94b4b101c21a

213

py

Python

thinkific/contents.py

OmniPro-Group/thinkific-python

7aeb4b86ad5357f6a078c270416d68b311d107b6

[ "MIT" ]

2

2019-12-30T13:47:02.000Z

2021-07-03T07:21:37.000Z

thinkific/contents.py

OmniPro-Group/thinkific-python

7aeb4b86ad5357f6a078c270416d68b311d107b6

[ "MIT" ]

2

2021-09-08T10:22:52.000Z

2021-09-14T13:39:17.000Z

thinkific/contents.py

OmniPro-Group/thinkific-python

7aeb4b86ad5357f6a078c270416d68b311d107b6

[ "MIT" ]

3

2021-05-28T10:46:34.000Z

2022-01-26T03:42:27.000Z

from .client import Client class Contents: def __init__(self, client): self.__client = client def retrieve_content(self, id: int): return self.__client.request('get','/contents/%s' %id)

21.3

62

0.661972

27

213

4.888889

0.592593

0.227273

0

0.215962

213

9

63

23.666667

0.790419

0

0.070755

0

1

0.333333

false

0

0.166667

0.833333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

eee75ab1f69534190c9e13e1c4b38e6327dec2f7

245

py

Python

mathfunc.py

foreverfamily/typepython

29fa125ceac28714b0ee0a963677bebebdd533a8

[ "Apache-2.0" ]

1

2017-12-04T03:32:59.000Z

mathfunc.py

foreverfamily/typepython

29fa125ceac28714b0ee0a963677bebebdd533a8

[ "Apache-2.0" ]

null

mathfunc.py

foreverfamily/typepython

29fa125ceac28714b0ee0a963677bebebdd533a8

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2017/12/7 12:46 # @Author : yulu # @File : mathfunc def add(a, b): return a+b def minus(a, b): return a-b def multi(a, b): return a*b def divide(a, b): return a/b

15.3125

28

0.542857

45

245

2.955556

0.533333

0.120301

0.240602

0.270677

0.368421

0.293233

0

0.067039

0.269388

245

16

29

15.3125

0.675978

0.428571

0

1

0.5

false

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

eefcb13d844ad8d52d48d429d50498172b9bbb2b

81

py

Python

20181026-TestingInJupyter/src/keep_odds.py

chmp/misc-exp

2edc2ed598eb59f4ccb426e7a5c1a23343a6974b

[ "MIT" ]

6

2017-10-31T20:54:37.000Z

2020-10-23T19:03:00.000Z

20181026-TestingInJupyter/src/keep_odds.py

chmp/misc-exp

2edc2ed598eb59f4ccb426e7a5c1a23343a6974b

[ "MIT" ]

7

2020-03-24T16:14:34.000Z

2021-03-18T20:51:37.000Z

20181026-TestingInJupyter/src/keep_odds.py

chmp/misc-exp

2edc2ed598eb59f4ccb426e7a5c1a23343a6974b

[ "MIT" ]

1

2019-07-29T07:55:49.000Z

def keep_odds(iterable): return [item for item in iterable if item % 2 == 1]

27

55

0.679012

14

81

3.857143

0.785714

0

0.031746

0.222222

81

2

56

40.5

0.825397

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e12c39db1ad5d6f40e69954748348ba4899e4819

19

py

Python

myvenv/Lib/site-packages/stripe/version.py

Fa67/saleor-shop

76110349162c54c8bfcae61983bb59ba8fb0f778

[ "BSD-3-Clause" ]

null

myvenv/Lib/site-packages/stripe/version.py

Fa67/saleor-shop

76110349162c54c8bfcae61983bb59ba8fb0f778

[ "BSD-3-Clause" ]

5

2020-03-24T16:37:25.000Z

2021-06-10T21:24:54.000Z

upibo-venv/Lib/site-packages/stripe/version.py

smbpgroup/upibo

625dcda9f9692c62aeb9fe8f7123a5d407c610ae

[ "BSD-3-Clause" ]

null

VERSION = '1.77.1'

9.5

18

0.578947

4

19

2.75

0.75

0

0.25

0.157895

19

1

19

0.4375

0

0.315789

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

01601cac9ac8918cc584150f47c810e860449a9a

1,048

py

Python

Constants/Servers.py

jeui123/pyrelay

43203343217c784183342dead933e0dc88c161d0

[ "MIT" ]

26

2020-07-24T05:47:02.000Z

2022-03-31T16:03:13.000Z

Constants/Servers.py

jeui123/pyrelay

43203343217c784183342dead933e0dc88c161d0

[ "MIT" ]

17

2020-07-27T08:11:19.000Z

2022-03-29T05:26:16.000Z

Constants/Servers.py

jeui123/pyrelay

43203343217c784183342dead933e0dc88c161d0

[ "MIT" ]

16

2021-01-20T14:30:37.000Z

2022-03-18T05:31:51.000Z

nameToIp = {'USEast2': '52.87.248.5', 'EUEast': '18.184.218.174', 'EUSouthWest': '35.180.67.120', 'EUNorth': '18.159.133.120', 'USEast': '54.234.226.24', 'USWest4': '54.235.235.140', 'EUWest2': '52.16.86.215', 'Asia': '3.0.147.127', 'USSouth3': '52.207.206.31', 'EUWest': '15.237.60.223', 'USWest': '54.86.47.176', 'USMidWest2': '3.140.254.133', 'USMidWest': '3.133.12.23', 'USSouth': '3.82.126.16', 'USWest3': '18.144.30.153', 'USSouthWest': '54.153.13.68', 'USNorthWest': '34.238.176.119', 'Australia': '13.236.87.250'} ipToName = {'52.87.248.5': 'USEast2', '18.184.218.174': 'EUEast', '35.180.67.120': 'EUSouthWest', '18.159.133.120': 'EUNorth', '54.234.226.24': 'USEast', '54.235.235.140': 'USWest4', '52.16.86.215': 'EUWest2', '3.0.147.127': 'Asia', '52.207.206.31': 'USSouth3', '15.237.60.223': 'EUWest', '54.86.47.176': 'USWest', '3.140.254.133': 'USMidWest2', '3.133.12.23': 'USMidWest', '3.82.126.16': 'USSouth', '18.144.30.153': 'USWest3', '54.153.13.68': 'USSouthWest', '34.238.176.119': 'USNorthWest', '13.236.87.250': 'Australia'}

262

522

0.610687

182

1,048

3.516484

0.39011

0.0125

0.021875

0.025

0

0.369835

0.076336

1,048

3

523

349.333333

0.291322

0

0.701149

0

1

0

false

0

null

0

1

0

1

0

1

0

1

0

null

0

5

01878e770e55f1997b0db3371361596a09b09119

3,738

py

Python

tests/test_templatetags.py

narnikgamarnikus/telegram_bots

f58ee16d61cd1b14cdf5c39649f63a851c1419e4

[ "MIT" ]

1

2019-06-29T03:22:47.000Z

tests/test_templatetags.py

narnikgamarnikus/telegram_bots

f58ee16d61cd1b14cdf5c39649f63a851c1419e4

[ "MIT" ]

null

tests/test_templatetags.py

narnikgamarnikus/telegram_bots

f58ee16d61cd1b14cdf5c39649f63a851c1419e4

[ "MIT" ]

1

2021-12-30T10:28:36.000Z

from test_plus.test import TestCase from django.test import RequestFactory import mock import telepot from django.contrib.auth import get_user_model from telegram_bots.models import Bot, Authorization from django.template import Context, Template User = get_user_model() class TestSubscribeButton(TestCase): def setUp(self): self.owner = User.objects.create_user( username='testuser', email='testuser@example.com', password='password' ) telepot.Bot.setWebhook = mock.MagicMock(return_value=True) telepot.Bot.getMe = mock.MagicMock( return_value={ 'id': 559897142, 'username': 'global_crypto_signal_bot', 'first_name': 'Crypto Signals Bot' } ) self.bot = Bot.objects.create( owner=self.owner, api_key="559897142:AAH6v_q2dTuz8tOGcy_MoBrBkGiy9LYtlMc" ) self.request_factory = RequestFactory() self.user = User.objects.create_user( username='testuser2', email='testuser2@example.com', password='password' ) def test_with_valid_data(self): request = self.request_factory.get('/?ref=123123123') request.user = self.user out = Template( "{% load telegram_bots %}" "{% subscribe_button button_class='btn btn-primary' button_text='Subscribe' bot=bot user=user %}" ).render(Context({ 'request': request, 'user': self.user, 'bot': self.bot }) ) authorization = Authorization.objects.get( user=self.user.telegramuser, bot=self.bot ) self.assertEqual( out, '<a href="/subscribe/{}/" class="{}">{}</a>'.format( authorization.activation_url.split('/')[2], 'btn btn-primary', 'Subscribe' ) ) class TestUnsubscribeButton(TestCase): def setUp(self): self.owner = User.objects.create_user( username='testuser', email='testuser@example.com', password='password' ) telepot.Bot.setWebhook = mock.MagicMock(return_value=True) telepot.Bot.getMe = mock.MagicMock( return_value={ 'id': 559897142, 'username': 'global_crypto_signal_bot', 'first_name': 'Crypto Signals Bot' } ) self.bot = Bot.objects.create( owner=self.owner, api_key="559897142:AAH6v_q2dTuz8tOGcy_MoBrBkGiy9LYtlMc" ) self.request_factory = RequestFactory() self.user = User.objects.create_user( username='testuser2', email='testuser2@example.com', password='password' ) def test_with_valid_data(self): request = self.request_factory.get('/?ref=123123123') request.user = self.user out = Template( "{% load telegram_bots %}" "{% unsubscribe_button button_class='btn btn-primary' button_text='Unsubscribe' bot=bot user=user %}" ).render(Context({ 'request': request, 'user': self.user, 'bot': self.bot }) ) authorization = Authorization.objects.get( user=self.user.telegramuser, bot=self.bot ) self.assertEqual( out, '<a href="/unsubscribe/{}/" class="{}">{}</a>'.format( authorization.deactivation_url.split('/')[2], 'btn btn-primary', 'Unsubscribe' ) )

31.15

113

0.548689

348

3,738

5.755747

0.235632

0.027958

0.029955

0.041937

0.78682

0.764853

0.724913

0

0.027441

0.337079

3,738

120

114

31.15

0.780872

0

0.660377

0

0.213961

0.072747

0

0.018868

1

0.037736

false

0.037736

0.066038

0

0.122642

0

null

0

1

0

null

0

5

6d6b5b4c243f2dd7074167473838bc9becba57d3

56

py

Python

script.py

takafumifujita/Casimir-programming

ffb2caf3418f8816ce8d5ed09d0774ed9c53a672

[ "MIT" ]

null

script.py

takafumifujita/Casimir-programming

ffb2caf3418f8816ce8d5ed09d0774ed9c53a672

[ "MIT" ]

null

script.py

takafumifujita/Casimir-programming

ffb2caf3418f8816ce8d5ed09d0774ed9c53a672

[ "MIT" ]

null

print("hi") from test2 import circum print(circum(1))

9.333333

24

0.714286

9

56

4.444444

0.777778

0

0.041667

0.142857

56

5

25

11.2

0.791667

0

0.035714

0

1

0

true

0

0.333333

0

0.333333

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6da08516534c60dae6946a79690ab3036beecc26

24

py

Python

kivymd/effects/__init__.py

nssuryawanshi10/KivyMD

86fe70dfce00b7206527fdd01f23f70debff00dc

[ "MIT" ]

668

2018-08-31T12:38:18.000Z

2020-07-31T21:29:10.000Z

kivymd/effects/__init__.py

nssuryawanshi10/KivyMD

86fe70dfce00b7206527fdd01f23f70debff00dc

[ "MIT" ]

377

2018-10-23T15:46:29.000Z

2020-08-01T14:03:36.000Z

kivymd/effects/__init__.py

nssuryawanshi10/KivyMD

86fe70dfce00b7206527fdd01f23f70debff00dc

[ "MIT" ]

275

2018-09-04T19:27:51.000Z

2020-07-31T01:14:48.000Z

""" Effects ======= """

4.8

7

0.291667

1

24

7

1

0

0.166667

24

4

8

6

0.35

0.625

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

099921c75cae4cea1f04a8d0e410aad47f8d27b0

159

py

Python

tests/book_Python-unittest/tests/test_calc.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

tests/book_Python-unittest/tests/test_calc.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

tests/book_Python-unittest/tests/test_calc.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

import calc def test_add(): if calc.add(1, 2) == 3: print("Test add(a, b) is OK") else: print("Test add(a, b) is FAIL") test_add()

13.25

39

0.522013

28

159

2.892857

0.571429

0.345679

0.296296

0.320988

0.395062

0

0.027273

0.308176

159

11

40

14.454545

0.709091

0

0.264151

0

1

0.142857

true

0

0.142857

0

0.285714

1

0

null

1

0

null

0

1

0

5

09adcdef4c222caa2abca6c2d7a6d21f0bd81cc2

253

bzl

Python

configs/dependency-tracking/ubuntu1604.bzl

sgreenstein/bazel-toolchains

1727f840b24e6760abcfd5ed9e839ae57a46d6e4

[ "Apache-2.0" ]

null

configs/dependency-tracking/ubuntu1604.bzl

sgreenstein/bazel-toolchains

1727f840b24e6760abcfd5ed9e839ae57a46d6e4

[ "Apache-2.0" ]

null

configs/dependency-tracking/ubuntu1604.bzl

sgreenstein/bazel-toolchains

1727f840b24e6760abcfd5ed9e839ae57a46d6e4

[ "Apache-2.0" ]

null

"""Information tracking the latest published configs.""" bazel = "3.2.0" registry = "marketplace.gcr.io" repository = "google/rbe-ubuntu16-04" digest = "sha256:5e750dd878df9fcf4e185c6f52b9826090f6e532b097f286913a428290622332" configs_version = "11.0.0"

36.142857

82

0.790514

27

253

7.37037

0.888889

0

0.262931

0.083004

253

6

83

42.166667

0.594828

0.197628

0

0.619289

0.472081

0

1

0

false

0

1

0

null

0

1

0

1

0

1

null

0

5

09efa2b1a9ae263d21457a48dd743ecd40d780d3

122

py

Python

science_magic/__init__.py

ucsc-astro/science_magic

fbdd0c05427bf6b885d098a03bfcbdcf8c01698a

[ "MIT" ]

null

science_magic/__init__.py

ucsc-astro/science_magic

fbdd0c05427bf6b885d098a03bfcbdcf8c01698a

[ "MIT" ]

null

science_magic/__init__.py

ucsc-astro/science_magic

fbdd0c05427bf6b885d098a03bfcbdcf8c01698a

[ "MIT" ]

1

2018-04-25T16:40:33.000Z

from .science_magic import ScienceMagic def load_ipython_extension(ipython): ipython.register_magics(ScienceMagic)

17.428571

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0.827869

14

122

6.928571

0.785714

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0.114754

122

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20.333333

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false

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0.333333

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0.666667

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null

0

1

0

null

0

1

0

1

0

1

0

5

61d633033b4962178540e438bddaf68e8a00dbf5

256

py

Python

bradfield/test_x3_03_square.py

savarin/algorithms

4d1f8f2361de12a02f376883f648697562d177ae

[ "MIT" ]

1

2020-06-16T23:22:54.000Z

bradfield/test_x3_03_square.py

savarin/algorithms

4d1f8f2361de12a02f376883f648697562d177ae

[ "MIT" ]

null

bradfield/test_x3_03_square.py

savarin/algorithms

4d1f8f2361de12a02f376883f648697562d177ae

[ "MIT" ]

null

from x3_03_square import is_square def test_is_square(): assert is_square(1) == True assert is_square(4) == True assert is_square(100) == True assert is_square(2) == False assert is_square(5) == False assert is_square(99) == False

25.6

34

0.679688

41

256

3.97561

0.414634

0.392638

0.515337

0.331288

0

0.059701

0.214844

256

10

35

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0.751244

0

0.75

1

0.125

true

0

0.125

0

0.25

0

null

1

0

null

0

1

0

1

0

5

61e6a76da05b36745959e4a8825c4992bb6119f9

153

py

Python

dfapi/services/runners/__init__.py

altest-com/dnfas-api

56b4dfbef33fd9ad6e6504d1cb88105069b57d70

[ "MIT" ]

null

dfapi/services/runners/__init__.py

altest-com/dnfas-api

56b4dfbef33fd9ad6e6504d1cb88105069b57d70

[ "MIT" ]

1

2020-03-31T17:20:57.000Z

2020-04-01T17:40:31.000Z

dfapi/services/runners/__init__.py

altest-com/dnfas-api

56b4dfbef33fd9ad6e6504d1cb88105069b57d70

[ "MIT" ]

null

from .task import TaskRunner from .vdf import VdfTaskRunner from .vhf import VhfTaskRunner from .fcl import FclTaskRunner from .pga import PgaTaskRunner

25.5

30

0.836601

20

153

6.4

0.6

0

0.130719

153

5

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30.6

0.962406

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

61ed0154361143ccf16238697f60c036c491f98f

63,572

py

Python

python/dp_accounting/privacy_loss_mechanism.py

Jolly608090/differential-privacy

74d5be96d4abe6820ef4838c00a1b78c72ae01af

[ "Apache-2.0" ]

1

2022-03-02T20:02:34.000Z

python/dp_accounting/privacy_loss_mechanism.py

fbalicchia/differential-privacy

099080e49c4c047802d785bc818898c0caf84d45

[ "Apache-2.0" ]

null

python/dp_accounting/privacy_loss_mechanism.py

fbalicchia/differential-privacy

099080e49c4c047802d785bc818898c0caf84d45

[ "Apache-2.0" ]

null

# Copyright 2020 Google LLC. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Implementing Privacy Loss of Mechanisms. This file implements privacy loss of several additive noise mechanisms, including Gaussian Mechanism, Laplace Mechanism and Discrete Laplace Mechanism. Please refer to the supplementary material below for more details: ../docs/Privacy_Loss_Distributions.pdf """ import abc import dataclasses import enum import math from typing import Iterable, Mapping, Optional, Union import numpy as np from scipy import stats from dp_accounting import common class AdjacencyType(enum.Enum): """Designates the type of adjacency for computing privacy loss distributions. ADD: the 'add' adjacency type specifies that the privacy loss distribution for a mechanism M is to be computed with mu_upper = M(D) and mu_lower = M(D'), where D' contains one more datapoint than D. REMOVE: the 'remove' adjacency type specifies that the privacy loss distribution for a mechanism M is to be computed with mu_upper = M(D) and mu_lower = M(D'), where D' contains one less datapoint than D. Note: The rest of code currently assumes existence of only these two adjacency types. If a new adjacency type is added and used, the API in this file will pretend that it is same as REMOVE. """ ADD = 'ADD' REMOVE = 'REMOVE' @dataclasses.dataclass class TailPrivacyLossDistribution(object): """Representation of the tail of privacy loss distribution. Attributes: lower_x_truncation: the minimum value of x that should be considered after the tail is discarded. upper_x_truncation: the maximum value of x that should be considered after the tail is discarded. tail_probability_mass_function: the probability mass of the privacy loss distribution that has to be added due to the discarded tail; each key is a privacy loss value and the corresponding value is the probability mass that the value occurs. """ lower_x_truncation: float upper_x_truncation: float tail_probability_mass_function: Mapping[float, float] class AdditiveNoisePrivacyLoss(metaclass=abc.ABCMeta): """Superclass for privacy loss of additive noise mechanisms. An additive noise mechanism for computing a scalar-valued function f is a mechanism that outputs the sum of the true value of the function and a noise drawn from a certain distribution mu. This class allows one to compute several quantities related to the privacy loss of additive noise mechanisms. We assume that the noise mu is such that the algorithm is more private as the sensitivity of f decreases. (Recall that the sensitivity of f is the maximum absolute change in f when an input to a single user changes.) Under this assumption, the privacy loss distribution of the mechanism is exactly generated as follows: - Let mu_lower(x) := mu(x - sensitivity), i.e., right shifted by sensitivity - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). When mu is discrete, mu(x) refers to the probability mass of mu at x, and when mu is continuous, mu(x) is the probability density of mu at x; mu_upper and mu_lower are defined analogously. Support for sub-sampling (Refer to supplementary material for more details): An additive noise mechanism with Poisson sub-sampling first samples a subset of data points including each data point independently with probability q, and outputs the sum of the true value of the function and a noise drawn from a certain distribution mu. Here, we consider differential privacy with respect to the addition/removal relation. With sub-sampling probability of q, the privacy loss distribution is generated as follows: For ADD adjacency type: - Let mu_lower(x) := q * mu(x - sensitivity) + (1-q) * mu(x) - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). For REMOVE adjacency type: - Let mu_upper(x) := q * mu(x + sensitivity) + (1-q) * mu(x) - Sample x ~ mu_lower = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). Note: When q = 1, the result privacy loss distributions for both ADD and REMOVE adjacency types are identical. This class also assumes the privacy loss is non-increasing as x increases. Attributes: sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) discrete_noise: a value indicating whether the noise is discrete. If this is True, then it is assumed that the noise can only take integer values. If False, then it is assumed that the noise is continuous, i.e., the probability mass at any given point is zero. sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. """ def __init__(self, sensitivity: float, discrete_noise: bool, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE): if sensitivity <= 0: raise ValueError( f'Sensitivity is not a positive real number: {sensitivity}') if sampling_prob <= 0 or sampling_prob > 1: raise ValueError( f'Sampling probability is not in (0,1] : {sampling_prob}') self.sensitivity = sensitivity self.discrete_noise = discrete_noise self.sampling_prob = sampling_prob self.adjacency_type = adjacency_type def mu_upper_cdf( self, x: Union[float, Iterable[float]]) -> Union[float, np.ndarray]: """Computes the cumulative density function of the mu_upper distribution. For ADD adjacency type, for any sub-sampling probability: mu_upper(x) := mu For REMOVE adjacency type, with sub-sampling probability q: mu_upper(x) := (1-q) * mu(x) + q * mu(x + sensitivity) Args: x: the point or points at which the cumulative density function is to be calculated. Returns: The cumulative density function of the mu_upper distribution at x, i.e., the probability that mu_upper is less than or equal to x. """ if self.adjacency_type == AdjacencyType.ADD: return self.noise_cdf(x) else: # Case: self.adjacency_type == AdjacencyType.REMOVE # For performance, the case of sampling_prob=1 is handled separately. if self.sampling_prob == 1.0: return self.noise_cdf(np.add(x, self.sensitivity)) return ((1 - self.sampling_prob) * self.noise_cdf(x) + self.sampling_prob * self.noise_cdf(np.add(x, self.sensitivity))) def mu_lower_cdf( self, x: Union[float, Iterable[float]]) -> Union[float, np.ndarray]: """Computes the cumulative density function of the mu_lower distribution. For ADD adjacency type, with sub-sampling probability q: mu_lower(x) := (1-q) * mu(x) + q * mu(x - sensitivity) For REMOVE adjacency type, for any sub-sampling probability: mu_lower(x) := mu(x) Args: x: the point or points at which the cumulative density function is to be calculated. Returns: The cumulative density function of the mu_lower distribution at x, i.e., the probability that mu_lower is less than or equal to x. """ if self.adjacency_type == AdjacencyType.ADD: # For performance, the case of sampling_prob=1 is handled separately. if self.sampling_prob == 1.0: return self.noise_cdf(np.add(x, -self.sensitivity)) return ((1 - self.sampling_prob) * self.noise_cdf(x) + self.sampling_prob * self.noise_cdf(np.add(x, -self.sensitivity))) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return self.noise_cdf(x) def get_delta_for_epsilon(self, epsilon): """Computes the epsilon-hockey stick divergence of the mechanism. The epsilon-hockey stick divergence of the mechanism is the value of delta for which the mechanism is (epsilon, delta)-differentially private. (See Observation 1 in the supplementary material.) This function assumes the privacy loss is non-increasing as x increases. Under this assumption, the hockey stick divergence is simply mu_upper_cdf(inverse_privacy_loss(epsilon)) - exp(epsilon) * mu_lower_cdf(inverse_privacy_loss(epsilon) - sensitivity), because the privacy loss at a point x is at least epsilon iff x <= inverse_privacy_loss(epsilon). When adjacency_type is ADD and epsilon >= -log(1 - sampling_prob), the hockey stick divergence is 0, since mu_lower_cdf*exp(epsilon) is pointwise greater than mu_upper_cdf. When adjacency_type is REMOVE and epsilon <= log(1 - sampling_prob), the hockey stick divergence is 1-exp(epsilon), since mu_lower_cdf*exp(epsilon) is pointwise lower than mu_upper_cdf. Args: epsilon: the epsilon in epsilon-hockey stick divergence. Returns: A non-negative real number which is the epsilon-hockey stick divergence of the mechanism. """ if self.sampling_prob != 1.0: if (self.adjacency_type == AdjacencyType.ADD and epsilon >= -math.log(1 - self.sampling_prob)): return 0.0 if (self.adjacency_type == AdjacencyType.REMOVE and epsilon <= math.log(1 - self.sampling_prob)): return 1.0 - math.exp(epsilon) x_cutoff = self.inverse_privacy_loss(epsilon) return (self.mu_upper_cdf(x_cutoff) - math.exp(epsilon) * self.mu_lower_cdf(x_cutoff)) @abc.abstractmethod def privacy_loss_tail(self) -> TailPrivacyLossDistribution: """Computes the privacy loss at the tail of the distribution. Returns: A TailPrivacyLossDistribution instance representing the tail of the privacy loss distribution. Raises: NotImplementedError: If not implemented by the subclass. """ raise NotImplementedError def privacy_loss(self, x: float) -> float: """Computes the privacy loss at a given point. For ADD adjacency type, with sub-sampling probability of q: the privacy loss at x is - log(1-q + q*exp(-privacy_loss_without_subsampling(x))). For REMOVE adjacency type, with sub-sampling probability of q: the privacy loss at x is log(1-q + q*exp(privacy_loss_without_subsampling(x))). Args: x: the point at which the privacy loss is computed. Returns: The privacy loss at point x. Raises: NotImplementedError: If privacy_loss_without_subsampling is not implemented by the subclass. ValueError: If privacy loss is undefined at x. """ privacy_loss_without_subsampling = self.privacy_loss_without_subsampling(x) # For performance, the case of sampling_prob=1 is handled separately. if self.sampling_prob == 1.0: return privacy_loss_without_subsampling if self.adjacency_type == AdjacencyType.ADD: return -math.log(1 - self.sampling_prob + self.sampling_prob * math.exp(-privacy_loss_without_subsampling)) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return math.log(1 - self.sampling_prob + self.sampling_prob * math.exp(privacy_loss_without_subsampling)) @abc.abstractmethod def privacy_loss_without_subsampling(self, x: float) -> float: """Computes the privacy loss at a given point without sub-sampling. Args: x: the point at which the privacy loss is computed. Returns: The privacy loss at point x without sub-sampling, which is given as: For ADD adjacency type: ln(mu(x - sensitivity) / mu(x)). If mu(x - sensitivity) == 0 and mu(x) > 0, this is -infinity. If mu(x - sensitivity) > 0 and mu(x) == 0, this is +infinity. If mu(x - sensitivity) == 0 and mu(x) == 0, this is undefined (ValueError is raised in this case). For REMOVE adjacency type: ln(mu(x + sensitivity) / mu(x)). Similar conventions (regarding corner cases) apply as above. Raises: NotImplementedError: If not implemented by the subclass. """ raise NotImplementedError def inverse_privacy_loss(self, privacy_loss: float) -> float: """Computes the inverse of a given privacy loss. Args: privacy_loss: the privacy loss value. Returns: The largest float x such that the privacy loss at x is at least privacy_loss. For the ADD adjacency type, with sub-sampling probability of q: the inverse privacy loss is given as inverse_privacy_loss_without_subsampling(-log(1 + (exp(-privacy_loss)-1)/q)), When privacy_loss >= -log(1-q), the inverse privacy loss is inverse_privacy_loss_without_subsampling(+infinity), When privacy_loss == -infinity, the inverse privacy loss is inverse_privacy_loss_without_subsampling(-infinity). For the REMOVE adjacency type, with sub-sampling probability of q: the inverse privacy loss is given as inverse_privacy_loss_without_subsampling(log(1 + (exp(privacy_loss)-1)/q)), When privacy_loss <= log(1-q), the inverse privacy loss is inverse_privacy_loss_without_subsampling(-infinity), When privacy_loss == infinity, the inverse privacy loss is inverse_privacy_loss_without_subsampling(+infinity). Raises: NotImplementedError: If inverse_privacy_loss_without_subsampling is not implemented by the subclass. ValueError: If inverse_privacy_loss_without_subsampling raises a ValueError """ # For performance, the case of sampling_prob=1 is handled separately. if self.sampling_prob == 1.0: return self.inverse_privacy_loss_without_subsampling(privacy_loss) if self.adjacency_type == AdjacencyType.ADD: if math.isclose(privacy_loss, - math.log(1 - self.sampling_prob)): return self.inverse_privacy_loss_without_subsampling(math.inf) if privacy_loss > - math.log(1 - self.sampling_prob): raise ValueError(f'privacy_loss ({privacy_loss}) is larger than ' f'-log(1 - sampling_prob) ' f'({-math.log(1 - self.sampling_prob)}') return self.inverse_privacy_loss_without_subsampling( -math.log(1 + (math.exp(-privacy_loss) - 1) / self.sampling_prob)) else: # Case: self.adjacency_type == AdjacencyType.REMOVE if math.isclose(privacy_loss, math.log(1 - self.sampling_prob)): return self.inverse_privacy_loss_without_subsampling(-math.inf) if privacy_loss <= math.log(1 - self.sampling_prob): raise ValueError(f'privacy_loss ({privacy_loss}) is smaller than ' f'log(1 - sampling_prob) ' f'({math.log(1 - self.sampling_prob)}') return self.inverse_privacy_loss_without_subsampling( math.log(1 + (math.exp(privacy_loss) - 1) / self.sampling_prob)) @abc.abstractmethod def inverse_privacy_loss_without_subsampling(self, privacy_loss: float) -> float: """Computes the inverse of a given privacy loss without sub-sampling. Args: privacy_loss: the privacy loss value. Returns: The largest float x such that the privacy loss at x without sub-sampling, is at least privacy_loss. Raises: NotImplementedError: If not implemented by the subclass. """ raise NotImplementedError @abc.abstractmethod def noise_cdf(self, x: Union[float, Iterable[float]]) -> Union[float, np.ndarray]: """Computes the cumulative density function of the noise distribution mu. Args: x: the point or points at which the cumulative density function is to be calculated. Returns: The cumulative density function of that noise at x, i.e., the probability that mu is less than or equal to x. Raises: NotImplementedError: If not implemented by the subclass. """ raise NotImplementedError @classmethod @abc.abstractmethod def from_privacy_guarantee( cls, privacy_parameters: common.DifferentialPrivacyParameters, sensitivity: float = 1, pessimistic_estimate: bool = True, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE ) -> 'AdditiveNoisePrivacyLoss': """Creates the privacy loss for the mechanism with a given privacy. Computes parameters achieving given privacy with REMOVE relation, irrespective of adjacency_type, since for all epsilon > 0, the hockey-stick divergence for PLD with respect to the REMOVE adjacency type is at least that for PLD with respect to ADD adjacency type. The returned object has the specified adjacency_type. Args: privacy_parameters: the desired privacy guarantee of the mechanism. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) pessimistic_estimate: a value indicating whether the rounding is done in such a way that the resulting epsilon-hockey stick divergence computation gives an upper estimate to the real value. sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. Returns: The privacy loss of the mechanism with the given privacy guarantee. Raises: NotImplementedError: If not implemented by the subclass. """ raise NotImplementedError class LaplacePrivacyLoss(AdditiveNoisePrivacyLoss): """Privacy loss of the Laplace mechanism. The Laplace mechanism for computing a scalar-valued function f simply outputs the sum of the true value of the function and a noise drawn from the Laplace distribution. Recall that the Laplace distribution with parameter b has probability density function 0.5/b * exp(-|x|/b) at x for any real number x. The privacy loss distribution of the Laplace mechanism is equivalent to the privacy loss distribution between the Laplace distribution and the same distribution but shifted by the sensitivity of f. Specifically, the privacy loss distribution of the Laplace mechanism is generated as follows: - Let mu = Lap(0, b) be the Laplace noise PDF as given above. - Let mu_lower(x) := mu(x - sensitivity), i.e., right shifted by sensitivity - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)), which is equal to (|x - sensitivity| - |x|) / parameter. Case of sub-sampling (Refer to supplementary material for more details): The Laplace mechanism with sub-sampling for computing a scalar-valued function f, first samples a subset of data points including each data point independently with probability q, and returns the sum of the true values and a noise drawn from the Laplace distribution. Here, we consider differential privacy with respect to the addition/removal relation. When the sub-sampling probability is q, the worst-case privacy loss distribution is generated as follows: For ADD adjacency type: - Let mu_lower(x) := q * mu(x - sensitivity) + (1-q) * mu(x) - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). For REMOVE adjacency type: - Let mu_upper(x) := q * mu(x + sensitivity) + (1-q) * mu(x) - Sample x ~ mu_lower = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). Note: When q = 1, the result privacy loss distributions for both ADD and REMOVE adjacency types are identical. """ def __init__(self, parameter: float, sensitivity: float = 1, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE) -> None: """Initializes the privacy loss of the Laplace mechanism. Args: parameter: the parameter of the Laplace distribution. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. """ if parameter <= 0: raise ValueError(f'Parameter is not a positive real number: {parameter}') self._parameter = parameter self._laplace_random_variable = stats.laplace(scale=parameter) super().__init__(sensitivity, False, sampling_prob, adjacency_type) def privacy_loss_tail(self) -> TailPrivacyLossDistribution: """Computes the privacy loss at the tail of the Laplace distribution. For ADD adjacency type: lower_x_truncation = 0 and upper_x_truncation = sensitivity For REMOVE adjacency type: lower_x_truncation = -sensitivity and upper_x_truncation = 0 The probability masses below lower_x_truncation and above upper_x_truncation are computed using mu_upper_cdf. Returns: A TailPrivacyLossDistribution instance representing the tail of the privacy loss distribution. """ if self.adjacency_type == AdjacencyType.ADD: lower_x_truncation, upper_x_truncation = 0.0, self.sensitivity else: # Case: self.adjacency_type == AdjacencyType.REMOVE lower_x_truncation, upper_x_truncation = -self.sensitivity, 0.0 return TailPrivacyLossDistribution( lower_x_truncation, upper_x_truncation, { self.privacy_loss(lower_x_truncation): self.mu_upper_cdf(lower_x_truncation), self.privacy_loss(upper_x_truncation): 1 - self.mu_upper_cdf(upper_x_truncation) }) def privacy_loss_without_subsampling(self, x: float) -> float: """Computes the privacy loss of the Laplace mechanism without sub-sampling at a given point. Args: x: the point at which the privacy loss is computed. Returns: The privacy loss of the Laplace mechanism without sub-sampling at point x, which is given as For ADD adjacency type: (|x - sensitivity| - |x|) / parameter. For REMOVE adjacency type: (|x| - |x + sensitivity|) / parameter. """ if self.adjacency_type == AdjacencyType.ADD: return (abs(x - self.sensitivity) - abs(x)) / self._parameter else: # Case: self.adjacency_type == AdjacencyType.REMOVE return (abs(x) - abs(x + self.sensitivity)) / self._parameter def inverse_privacy_loss_without_subsampling(self, privacy_loss: float) -> float: """Computes the inverse of a given privacy loss for the Laplace mechanism without sub-sampling. Args: privacy_loss: the privacy loss value. Returns: The largest float x such that the privacy loss at x is at least privacy_loss. For ADD adjacency type: If privacy_loss <= - sensitivity / parameter, x is equal to infinity. If - sensitivity / parameter < privacy_loss <= sensitivity / parameter, x is equal to 0.5 * (sensitivity - privacy_loss * parameter). If privacy_loss > sensitivity / parameter, no such x exists and the function returns -infinity. For REMOVE adjacency type: For any value of privacy_loss, x is equal to the corresponding value for ADD adjacency type decreased by sensitivity. """ loss_threshold = privacy_loss * self._parameter if loss_threshold > self.sensitivity: return -math.inf if loss_threshold <= -self.sensitivity: return math.inf if self.adjacency_type == AdjacencyType.ADD: return 0.5 * (self.sensitivity - loss_threshold) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return 0.5 * (-self.sensitivity - loss_threshold) def noise_cdf(self, x: Union[float, Iterable[float]]) -> Union[float, np.ndarray]: """Computes the cumulative density function of the Laplace distribution. Args: x: the point or points at which the cumulative density function is to be calculated. Returns: The cumulative density function of the Laplace noise at x, i.e., the probability that the Laplace noise is less than or equal to x. """ return self._laplace_random_variable.cdf(x) @classmethod def from_privacy_guarantee( cls, privacy_parameters: common.DifferentialPrivacyParameters, sensitivity: float = 1, pessimistic_estimate: bool = True, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE ) -> 'LaplacePrivacyLoss': """Creates the privacy loss for Laplace mechanism with given privacy. Without sub-sampling, the parameter of the Laplace mechanism is simply sensitivity / epsilon. With sub-sampling probability of q, the parameter is given as sensitivity / log(1 + (exp(epsilon) - 1)/q). Note: Only the REMOVE adjacency type is used in determining the parameter, since for all epsilon > 0, the hockey-stick divergence for PLD with respect to the REMOVE adjacency type is at least that for PLD with respect to ADD adjacency type. Args: privacy_parameters: the desired privacy guarantee of the mechanism. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) pessimistic_estimate: a value indicating whether the rounding is done in such a way that the resulting epsilon-hockey stick divergence computation gives an upper estimate to the real value. sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. Returns: The privacy loss of the Laplace mechanism with the given privacy guarantee. """ parameter = ( sensitivity / np.log(1 + (np.exp(privacy_parameters.epsilon) - 1) / sampling_prob)) return LaplacePrivacyLoss( parameter, sensitivity=sensitivity, sampling_prob=sampling_prob, adjacency_type=adjacency_type) @property def parameter(self) -> float: """The parameter of the corresponding Laplace noise.""" return self._parameter class GaussianPrivacyLoss(AdditiveNoisePrivacyLoss): """Privacy loss of the Gaussian mechanism. The Gaussian mechanism for computing a scalar-valued function f simply outputs the sum of the true value of the function and a noise drawn from the Gaussian distribution. Recall that the (centered) Gaussian distribution with standard deviation sigma has probability density function 1/(sigma * sqrt(2 * pi)) * exp(-0.5 x^2/sigma^2) at x for any real number x. The privacy loss distribution of the Gaussian mechanism is equivalent to the privacy loss distribution between the Gaussian distribution and the same distribution but shifted by the sensitivity of f. Specifically, the privacy loss distribution of the Gaussian mechanism is generated as follows: - Let mu = N(0, sigma^2) be the Gaussian noise PDF as given above. - Let mu_lower(x) := mu(x - sensitivity), i.e., right shifted by sensitivity - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). Case of sub-sampling (Refer to supplementary material for more details): The Gaussian mechanism with sub-sampling for computing a scalar-valued function f, first samples a subset of data points including each data point independently with probability q, and returns the sum of the true values and a noise drawn from the Gaussian distribution. Here, we consider differential privacy with respect to the addition/removal relation. When the sub-sampling probability is q, the worst-case privacy loss distribution is generated as follows: For ADD adjacency type: - Let mu_lower(x) := q * mu(x - sensitivity) + (1-q) * mu(x) - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). For REMOVE adjacency type: - Let mu_upper(x) := q * mu(x + sensitivity) + (1-q) * mu(x) - Sample x ~ mu_lower = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). Note: When q = 1, the result privacy loss distributions for both ADD and REMOVE adjacency types are identical. """ def __init__(self, standard_deviation: float, sensitivity: float = 1, pessimistic_estimate: bool = True, log_mass_truncation_bound: float = -50, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE) -> None: """Initializes the privacy loss of the Gaussian mechanism. Args: standard_deviation: the standard_deviation of the Gaussian distribution. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) pessimistic_estimate: a value indicating whether the rounding is done in such a way that the resulting epsilon-hockey stick divergence computation gives an upper estimate to the real value. log_mass_truncation_bound: the ln of the probability mass that might be discarded from the noise distribution. The larger this number, the more error it may introduce in divergence calculations. sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. """ if standard_deviation <= 0: raise ValueError(f'Standard deviation is not a positive real number: ' f'{standard_deviation}') if log_mass_truncation_bound > 0: raise ValueError(f'Log mass truncation bound is not a non-positive real ' f'number: {log_mass_truncation_bound}') self._standard_deviation = standard_deviation self._gaussian_random_variable = stats.norm(scale=standard_deviation) self._pessimistic_estimate = pessimistic_estimate self._log_mass_truncation_bound = log_mass_truncation_bound super().__init__(sensitivity, False, sampling_prob, adjacency_type) def privacy_loss_tail(self) -> TailPrivacyLossDistribution: """Computes the privacy loss at the tail of the Gaussian distribution. For REMOVE adjacency type: lower_x_truncation is set such that CDF(lower_x_truncation) = 0.5 * exp(log_mass_truncation_bound), and upper_x_truncation is set to be -lower_x_truncation. Finally, lower_x_truncation is shifted by -1 * sensitivity. Recall that here mu_upper(x) := (1-q).mu(x) + q.mu(x + sensitivity), where q=sampling_prob. The truncations chosen above ensure that the tails of both mu(x) and mu(x+sensitivity) are smaller than 0.5 * exp(log_mass_truncation_bound). This ensures that the considered tails of mu_upper are no larger than exp(log_mass_truncation_bound). This is computationally cheaper than computing exact tail thresholds for mu_upper. For ADD adjacency type: lower_x_truncation is set such that CDF(lower_x_truncation) = 0.5 * exp(log_mass_truncation_bound), and upper_x_truncation is set to be -lower_x_truncation. Finally, upper_x_truncation is shifted by +1 * sensitivity. Recall that here mu_upper(x) := mu(x) for any value of sampling_prob. The truncations chosen ensures that the tails of mu(x) (and hence of mu_upper) are no larger than 0.5 * exp(log_mass_truncation_bound). While it was not strictly necessary to shift upper_x_truncation by +1 * sensitivity in this case, this choice leads to the same discretized privacy loss distribution for both ADD and REMOVE adjacency types, in the case where sampling_prob = 1. If pessimistic_estimate is True, the privacy losses for x < lower_x_truncation and x > upper_x_truncation are rounded up and added to tail_probability_mass_function. In the case x < lower_x_truncation, the privacy loss is rounded up to infinity. In the case x > upper_x_truncation, it is rounded up to the privacy loss at upper_x_truncation. On the other hand, if pessimistic_estimate is False, the privacy losses for x < lower_x_truncation and x > upper_x_truncation are rounded down and added to tail_probability_mass_function. In the case x < lower_x_truncation, the privacy loss is rounded down to the privacy loss at lower_x_truncation. In the case x > upper_x_truncation, it is rounded down to -infinity and hence not included in tail_probability_mass_function, Returns: A TailPrivacyLossDistribution instance representing the tail of the privacy loss distribution. """ lower_x_truncation = self._gaussian_random_variable.ppf( 0.5 * math.exp(self._log_mass_truncation_bound)) upper_x_truncation = -lower_x_truncation if self.adjacency_type == AdjacencyType.ADD: upper_x_truncation += self.sensitivity else: # Case: self.adjacency_type == AdjacencyType.REMOVE lower_x_truncation -= self.sensitivity if self._pessimistic_estimate: tail_probability_mass_function = { math.inf: self.mu_upper_cdf(lower_x_truncation), self.privacy_loss(upper_x_truncation): 1 - self.mu_upper_cdf(upper_x_truncation) } else: tail_probability_mass_function = { self.privacy_loss(lower_x_truncation): self.mu_upper_cdf(lower_x_truncation), } return TailPrivacyLossDistribution(lower_x_truncation, upper_x_truncation, tail_probability_mass_function) def privacy_loss_without_subsampling(self, x: float) -> float: """Computes the privacy loss of the Gaussian mechanism without sub-sampling at a given point. Args: x: the point at which the privacy loss is computed. Returns: The privacy loss of the Laplace mechanism at point x, which is given as For ADD adjacency type: (|x - sensitivity| - |x|) / parameter. For REMOVE adjacency type: (|x| - |x + sensitivity|) / parameter. The privacy loss of the Gaussian mechanism without sub-sampling at point x, which is given as For ADD adjacency type: sensitivity * (0.5 * sensitivity - x) / standard_deviation^2. For REMOVE adjacency type: sensitivity * (- 0.5 * sensitivity - x) / standard_deviation^2. """ if self.adjacency_type == AdjacencyType.ADD: return (self.sensitivity * (0.5 * self.sensitivity - x) / (self._standard_deviation**2)) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return (self.sensitivity * (-0.5 * self.sensitivity - x) / (self._standard_deviation**2)) def inverse_privacy_loss_without_subsampling(self, privacy_loss: float) -> float: """Computes the inverse of a given privacy loss for the Gaussian mechanism without sub-sampling. Args: privacy_loss: the privacy loss value. Returns: The largest float x such that the privacy loss at x is at least privacy_loss. This is equal to For ADD adjacency type: 0.5 * sensitivity - privacy_loss * standard_deviation^2 / sensitivity. For REMOVE adjacency type: -0.5 * sensitivity - privacy_loss * standard_deviation^2 / sensitivity. """ if self.adjacency_type == AdjacencyType.ADD: return (0.5 * self.sensitivity - privacy_loss * (self._standard_deviation**2) / self.sensitivity) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return (-0.5 * self.sensitivity - privacy_loss * (self._standard_deviation**2) / self.sensitivity) def noise_cdf(self, x: Union[float, Iterable[float]]) -> Union[float, np.ndarray]: """Computes the cumulative density function of the Gaussian distribution. Args: x: the point or points at which the cumulative density function is to be calculated. Returns: The cumulative density function of the Gaussian noise at x, i.e., the probability that the Gaussian noise is less than or equal to x. """ return self._gaussian_random_variable.cdf(x) @classmethod def from_privacy_guarantee( cls, privacy_parameters: common.DifferentialPrivacyParameters, sensitivity: float = 1, pessimistic_estimate: bool = True, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE ) -> 'GaussianPrivacyLoss': """Creates the privacy loss for Gaussian mechanism with desired privacy. Uses binary search to find the smallest possible standard deviation of the Gaussian noise for which the mechanism is (epsilon, delta)-differentially private, with respect to the REMOVE relation. Note: Only the REMOVE adjacency type is used in determining the parameter, since for all epsilon > 0, the hockey-stick divergence for PLD with respect to the REMOVE adjacency type is at least that for PLD with respect to ADD adjacency type. Args: privacy_parameters: the desired privacy guarantee of the mechanism. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) pessimistic_estimate: a value indicating whether the rounding is done in such a way that the resulting epsilon-hockey stick divergence computation gives an upper estimate to the real value. sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. Returns: The privacy loss of the Gaussian mechanism with the given privacy guarantee. """ if privacy_parameters.delta == 0: raise ValueError('delta=0 is not allowed for the Gaussian mechanism') # The initial standard deviation is set to # sqrt(2 * ln(1.5/delta)) * sensitivity / epsilon. It is known that, when # epsilon is no more than one, the Gaussian mechanism with this standard # deviation is (epsilon, delta)-DP. See e.g. Appendix A in Dwork and Roth # book, "The Algorithmic Foundations of Differential Privacy". search_parameters = common.BinarySearchParameters( 0, math.inf, initial_guess=math.sqrt(2 * math.log(1.5 / privacy_parameters.delta)) * sensitivity / privacy_parameters.epsilon) def _get_delta_for_standard_deviation(current_standard_deviation): return GaussianPrivacyLoss( current_standard_deviation, sensitivity=sensitivity, sampling_prob=sampling_prob, adjacency_type=AdjacencyType.REMOVE).get_delta_for_epsilon( privacy_parameters.epsilon) standard_deviation = common.inverse_monotone_function( _get_delta_for_standard_deviation, privacy_parameters.delta, search_parameters) return GaussianPrivacyLoss( standard_deviation, sensitivity=sensitivity, pessimistic_estimate=pessimistic_estimate, sampling_prob=sampling_prob, adjacency_type=adjacency_type) @property def standard_deviation(self) -> float: """The standard deviation of the corresponding Gaussian noise.""" return self._standard_deviation class DiscreteLaplacePrivacyLoss(AdditiveNoisePrivacyLoss): """Privacy loss of the discrete Laplace mechanism. The discrete Laplace mechanism for computing an integer-valued function f simply outputs the sum of the true value of the function and a noise drawn from the discrete Laplace distribution. Recall that the discrete Laplace distribution with parameter a > 0 has probability mass function Z * exp(-a * |x|) at x for any integer x, where Z = (e^a - 1) / (e^a + 1). This class represents the privacy loss for the aforementioned discrete Laplace mechanism with a given parameter, and a given sensitivity of the function f. It is assumed that the function f only outputs an integer. The privacy loss distribution of the discrete Laplace mechanism is equivalent to that between the discrete Laplace distribution and the same distribution but shifted by the sensitivity. Specifically, the privacy loss distribution of the discrete Laplace mechanism is generated as follows: - Let mu = DLap(0, a) be the discrete Laplace noise PMF as given above. - Let mu_lower(x) := mu(x - sensitivity), i.e., right shifted by sensitivity - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)), which is equal to parameter * (|x - sensitivity| - |x|). Case of sub-sampling (Refer to supplementary material for more details): The discrete Laplace mechanism with sub-sampling for computing a scalar integer-valued function f, first samples a subset of data points including each data point independently with probability q, and returns the sum of the true values and a noise drawn from the discrete Laplace distribution. Here, we consider differential privacy with respect to the addition/removal relation. When the sub-sampling probability is q, the worst-case privacy loss distribution is generated as follows: For ADD adjacency type: - Let mu_lower(x) := q * mu(x - sensitivity) + (1-q) * mu(x) - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). For REMOVE adjacency type: - Let mu_upper(x) := q * mu(x + sensitivity) + (1-q) * mu(x) - Sample x ~ mu_lower = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). Note: When q = 1, the result privacy loss distributions for both ADD and REMOVE adjacency types are identical. """ def __init__(self, parameter: float, sensitivity: int = 1, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE) -> None: """Initializes the privacy loss of the discrete Laplace mechanism. Args: parameter: the parameter of the discrete Laplace distribution. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. """ if parameter <= 0: raise ValueError(f'Parameter is not a positive real number: {parameter}') if not isinstance(sensitivity, int): raise ValueError(f'Sensitivity is not an integer : {sensitivity}') self._parameter = parameter self._discrete_laplace_random_variable = stats.dlaplace(parameter) super().__init__(sensitivity, True, sampling_prob, adjacency_type) def privacy_loss_tail(self) -> TailPrivacyLossDistribution: """Computes privacy loss at the tail of the discrete Laplace distribution. For ADD adjacency type: lower_x_truncation = 1 and upper_x_truncation = sensitivity-1 For REMOVE adjacency type: lower_x_truncation = -sensitivity+1 and upper_x_truncation = -1 The probability mass below lower_x_truncation and above upper_x_truncation are computed using mu_upper_cdf. Returns: A TailPrivacyLossDistribution instance representing the tail of the privacy loss distribution. """ if self.adjacency_type == AdjacencyType.ADD: lower_x_truncation, upper_x_truncation = 1, self.sensitivity - 1 else: # Case: self.adjacency_type == AdjacencyType.REMOVE lower_x_truncation, upper_x_truncation = 1 - self.sensitivity, -1 return TailPrivacyLossDistribution( lower_x_truncation, upper_x_truncation, { self.privacy_loss(lower_x_truncation - 1): self.mu_upper_cdf(lower_x_truncation - 1), self.privacy_loss(upper_x_truncation + 1): 1 - self.mu_upper_cdf(upper_x_truncation) }) def privacy_loss_without_subsampling(self, x: float) -> float: """Computes privacy loss of the discrete Laplace mechanism without sub-sampling at a given point. Args: x: the point at which the privacy loss is computed. Returns: The privacy loss of the discrete Laplace mechanism without sub-sampling at integer value x, which is given as For ADD adjacency type: parameter * (|x - sensitivity| - |x|). For REMOVE adjacency type: parameter * (|x| - |x + sensitivity|). """ if not isinstance(x, int): raise ValueError(f'Privacy loss at x is undefined for x = {x}') if self.adjacency_type == AdjacencyType.ADD: return (abs(x - self.sensitivity) - abs(x)) * self._parameter else: # Case: self.adjacency_type == AdjacencyType.REMOVE return (abs(x) - abs(x + self.sensitivity)) * self._parameter def inverse_privacy_loss_without_subsampling(self, privacy_loss: float) -> float: """Computes the inverse of a given privacy loss for the discrete Laplace mechanism. Args: privacy_loss: the privacy loss value. Returns: The largest float x such that the privacy loss at x is at least privacy_loss. For ADD adjacency type: If privacy_loss <= - sensitivity * parameter, x is equal to infinity. If - sensitivity * parameter < privacy_loss <= sensitivity * parameter, x is equal to floor(0.5 * (sensitivity - privacy_loss / parameter)). If privacy_loss > sensitivity * parameter, no such x exists and the function returns -infinity. For REMOVE adjacency type: For any value of privacy_loss, x is equal to the corresponding value for ADD adjacency type decreased by sensitivity. """ loss_threshold = privacy_loss / self._parameter if loss_threshold > self.sensitivity: return -math.inf if loss_threshold <= -self.sensitivity: return math.inf if self.adjacency_type == AdjacencyType.ADD: return math.floor(0.5 * (self.sensitivity - loss_threshold)) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return math.floor(0.5 * (-self.sensitivity - loss_threshold)) def noise_cdf(self, x: Union[float, Iterable[float]]) -> Union[float, np.ndarray]: """Computes cumulative density function of the discrete Laplace distribution. Args: x: the point or points at which the cumulative density function is to be calculated. Returns: The cumulative density function of the discrete Laplace noise at x, i.e., the probability that the discrete Laplace noise is less than or equal to x. """ return self._discrete_laplace_random_variable.cdf(x) @classmethod def from_privacy_guarantee( cls, privacy_parameters: common.DifferentialPrivacyParameters, sensitivity: int = 1, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE ) -> 'DiscreteLaplacePrivacyLoss': """Creates privacy loss for discrete Laplace mechanism with desired privacy. Without sub-sampling, the parameter of the Laplace mechanism is simply epsilon / sensitivity. With sub-sampling probability of q, the parameter is given as below. log(1 + (exp(epsilon) - 1)/q) / sensitivity, Note: Only the REMOVE adjacency type is used in determining the parameter, since for all epsilon > 0, the hockey-stick divergence for PLD with respect to the REMOVE adjacency type is at least that for PLD with respect to ADD adjacency type. Args: privacy_parameters: the desired privacy guarantee of the mechanism. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. Returns: The privacy loss of the discrete Laplace mechanism with the given privacy guarantee. """ if not isinstance(sensitivity, int): raise ValueError(f'Sensitivity is not an integer : {sensitivity}') if sensitivity <= 0: raise ValueError( f'Sensitivity is not a positive real number: {sensitivity}') if sampling_prob <= 0 or math.isclose(sampling_prob, 0): raise ValueError( f'Sampling probability ({sampling_prob}) is equal or too close to 0.') parameter = ( np.log(1 + (np.exp(privacy_parameters.epsilon) - 1) / sampling_prob) / sensitivity) return DiscreteLaplacePrivacyLoss( parameter, sensitivity=sensitivity, sampling_prob=sampling_prob, adjacency_type=adjacency_type) @property def parameter(self) -> float: """The parameter of the corresponding Discrete Laplace noise.""" return self._parameter class DiscreteGaussianPrivacyLoss(AdditiveNoisePrivacyLoss): """Privacy loss of the discrete Gaussian mechanism. The discrete Gaussian mechanism for computing a scalar-valued function f simply outputs the sum of the true value of the function and a noise drawn from the discrete Gaussian distribution. Recall that the (centered) discrete Gaussian distribution with parameter sigma has probability mass function proportional to exp(-0.5 x^2/sigma^2) at x for any integer x. Since its normalization factor and cumulative density function do not have a closed form, we will instead consider the truncated version where the noise x is restricted to only be in [-truncated_bound, truncated_bound]. The privacy loss distribution of the discrete Gaussian mechanism is equivalent to the privacy loss distribution between the discrete Gaussian distribution and the same distribution but shifted by the sensitivity of f. Specifically, the privacy loss distribution of the discrete Gaussian mechanism is generated as follows: - Let mu = N_Z(0, sigma^2, truncation_bound) be the discrete Gaussian noise PMF as given above. - Let mu_lower(x) := mu(x - sensitivity), i.e., right shifted by sensitivity - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). Note that since we consider the truncated version of the noise, we set the privacy loss to infinity when x < -truncation_bound + sensitivity. Case of sub-sampling (Refer to supplementary material for more details): The discrete Gaussian mechanism with sub-sampling for computing a scalar integer-valued function f, first samples a subset of data points including each data point independently with probability q, and returns the sum of the true values and a noise drawn from the discrete Gaussian distribution. Here, we consider differential privacy with respect to the addition/removal relation. When the sub-sampling probability is q, the worst-case privacy loss distribution is generated as follows: For ADD adjacency type: - Let mu_lower(x) := q * mu(x - sensitivity) + (1-q) * mu(x) - Sample x ~ mu_upper = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). For REMOVE adjacency type: - Let mu_upper(x) := q * mu(x + sensitivity) + (1-q) * mu(x) - Sample x ~ mu_lower = mu and let the privacy loss be ln(mu_upper(x) / mu_lower(x)). Note: When q = 1, the result privacy loss distributions for both ADD and REMOVE adjacency types are identical. Reference: Canonne, Kamath, Steinke. "The Discrete Gaussian for Differential Privacy". In NeurIPS 2020. """ def __init__(self, sigma: float, sensitivity: int = 1, truncation_bound: Optional[int] = None, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE) -> None: """Initializes the privacy loss of the discrete Gaussian mechanism. Args: sigma: the parameter of the discrete Gaussian distribution. Note that unlike the (continuous) Gaussian distribution this is not equal to the standard deviation of the noise. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) truncation_bound: bound for truncating the noise, i.e. the noise will only have a support in [-truncation_bound, truncation_bound]. When not specified, truncation_bound will be chosen in such a way that the mass of the noise outside of this range is at most 1e-30. sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. """ if sigma <= 0: raise ValueError(f'Sigma is not a positive real number: {sigma}') if not isinstance(sensitivity, int): raise ValueError(f'Sensitivity is not an integer : {sensitivity}') self._sigma = sigma if truncation_bound is None: # Tail bound from Canonne et al. ensures that the mass that gets truncated # is at most 1e-30. (See Proposition 1 in the supplementary material.) self._truncation_bound = math.ceil(11.6 * sigma) else: self._truncation_bound = truncation_bound if 2 * self._truncation_bound < sensitivity: raise ValueError(f'Truncation bound ({truncation_bound}) is smaller ' f'than 0.5 * sensitivity (0.5 * {sensitivity})') # Create the PMF and CDF. self._offset = -1 * self._truncation_bound - 1 self._pmf_array = np.arange(-1 * self._truncation_bound, self._truncation_bound + 1) self._pmf_array = np.exp(-0.5 * (self._pmf_array)**2 / (sigma**2)) self._pmf_array = np.insert(self._pmf_array, 0, 0) self._cdf_array = np.add.accumulate(self._pmf_array) self._pmf_array /= self._cdf_array[-1] self._cdf_array /= self._cdf_array[-1] super().__init__(sensitivity, True, sampling_prob, adjacency_type) def privacy_loss_tail(self) -> TailPrivacyLossDistribution: """Computes the privacy loss at the tail of the discrete Gaussian distribution. The lower_x_truncation and upper_x_truncation are chosen such that for any x < lower_x_truncation, the privacy loss is +infinity (or undefined), and for any x > upper_x_truncation, the privacy loss is -infinity (or undefined). With sampling probability of q, the privacy loss tail is given as For ADD adjacency type: (if q == 1) lower_x_truncation = sensitivity - truncation_bound (if q < 1) lower_x_truncation = - truncation_bound In either case, upper_x_truncation = truncation_bound For REMOVE adjacency type: (if q == 1) upper_x_truncation = truncation_bound - sensitivity (if q < 1) upper_x_truncation = truncation_bound In either case, lower_x_truncation = - truncation_bound Returns: A TailPrivacyLossDistribution instance representing the tail of the privacy loss distribution. """ if self.adjacency_type == AdjacencyType.ADD: upper_x_truncation = self._truncation_bound if self.sampling_prob == 1.0: lower_x_truncation = self.sensitivity - self._truncation_bound else: lower_x_truncation = -1 * self._truncation_bound else: # Case: self.adjacency_type == AdjacencyType.REMOVE lower_x_truncation = -1 * self._truncation_bound if self.sampling_prob == 1.0: upper_x_truncation = self._truncation_bound - self.sensitivity else: upper_x_truncation = self._truncation_bound return TailPrivacyLossDistribution( lower_x_truncation, upper_x_truncation, {math.inf: self.mu_upper_cdf(lower_x_truncation - 1)}) def privacy_loss_without_subsampling(self, x: float) -> float: """Computes the privacy loss of the discrete Gaussian mechanism without sub-sampling at a given point. Args: x: the point at which the privacy loss is computed. Returns: The privacy loss of the discrete Gaussian mechanism at integer value x, which is given as For ADD adjacency type: If x lies in [-truncation_bound + sensitivity, truncation_bound], it is equal to sensitivity * (0.5 * sensitivity - x) / sigma^2. If x lies in [-truncation_bound, -truncation_bound + sensitivity), it is equal to infinity. If x lies in (truncation_bound, trunction_bound + sensitivity], it is equal to -infinity. Otherwise, the privacy loss is undefined (ValueError is raised). For REMOVE adjacency type: Same as the case of ADD with x replaced by x + sensitivity. Raises: ValueError: if the privacy loss is undefined. """ def privacy_loss_without_subsampling_for_add(x: float) -> float: if (not isinstance(x, int) or x < -1 * self._truncation_bound or x > self._truncation_bound + self.sensitivity): actual_x = ( x if self.adjacency_type == AdjacencyType.ADD else x - self.sensitivity) raise ValueError(f'Privacy loss at x is undefined for x = {actual_x}') if x > self._truncation_bound: return -math.inf if x < self.sensitivity - self._truncation_bound: return math.inf return self.sensitivity * (0.5 * self.sensitivity - x) / (self._sigma**2) if self.adjacency_type == AdjacencyType.ADD: return privacy_loss_without_subsampling_for_add(x) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return privacy_loss_without_subsampling_for_add(x + self.sensitivity) def inverse_privacy_loss_without_subsampling(self, privacy_loss: float) -> float: """Computes the inverse of a given privacy loss for the discrete Gaussian mechanism without sub-sampling. Args: privacy_loss: the privacy loss value. Returns: The largest int x such that the privacy loss at x is at least privacy_loss, which is given as For ADD adjacency type: floor(0.5 * sensitivity - privacy_loss * sigma^2 / sensitivity) clipped to the interval [sensitivity - truncation_bound - 1, truncation_bound]. For REMOVE adjacency type: Same as that for ADD decreased by sensitivity. """ def inverse_privacy_loss_without_subsampling_for_add( privacy_loss: float) -> float: if privacy_loss == -math.inf: return self._truncation_bound return math.floor( np.clip( 0.5 * self.sensitivity - privacy_loss * (self._sigma**2) / self.sensitivity, self.sensitivity - self._truncation_bound - 1, self._truncation_bound)) if self.adjacency_type == AdjacencyType.ADD: return inverse_privacy_loss_without_subsampling_for_add(privacy_loss) else: # Case: self.adjacency_type == AdjacencyType.REMOVE return (inverse_privacy_loss_without_subsampling_for_add(privacy_loss) - self.sensitivity) def noise_cdf(self, x: Union[float, Iterable[float]]) -> Union[float, np.ndarray]: """Computes the cumulative density function of the discrete Gaussian distribution. Args: x: the point or points at which the cumulative density function is to be calculated. Returns: The cumulative density function of the discrete Gaussian noise at x, i.e., the probability that the discrete Gaussian noise is less than or equal to x. """ clipped_x = np.clip(x, -1 * self._truncation_bound - 1, self._truncation_bound) indices = np.floor(clipped_x).astype('int') - self._offset return self._cdf_array[indices] @classmethod def from_privacy_guarantee( cls, privacy_parameters: common.DifferentialPrivacyParameters, sensitivity: int = 1, sampling_prob: float = 1.0, adjacency_type: AdjacencyType = AdjacencyType.REMOVE ) -> 'DiscreteGaussianPrivacyLoss': """Creates the privacy loss for discrete Gaussian mechanism with desired privacy. Uses binary search to find the smallest possible standard deviation of the discrete Gaussian noise for which the protocol is (epsilon, delta)-DP. Note: Only the REMOVE adjacency type is used in determining the parameter, since for all epsilon > 0, the hockey-stick divergence for PLD with respect to the REMOVE adjacency type is at least that for PLD with respect to ADD adjacency type. Args: privacy_parameters: the desired privacy guarantee of the mechanism. sensitivity: the sensitivity of function f. (i.e. the maximum absolute change in f when an input to a single user changes.) sampling_prob: sub-sampling probability, a value in (0,1]. adjacency_type: type of adjacency relation to used for defining the privacy loss distribution. Returns: The privacy loss of the discrete Gaussian mechanism with the given privacy guarantee. """ if not isinstance(sensitivity, int): raise ValueError(f'Sensitivity is not an integer : {sensitivity}') if privacy_parameters.delta == 0: raise ValueError('delta=0 is not allowed for discrete Gaussian mechanism') # The initial standard deviation is set to # sqrt(2 * ln(1.5/delta)) * sensitivity / epsilon. It is known that, when # epsilon is no more than one, the (continuous) Gaussian mechanism with this # standard deviation is (epsilon, delta)-DP. See e.g. Appendix A in Dwork # and Roth book, "The Algorithmic Foundations of Differential Privacy". search_parameters = common.BinarySearchParameters( 0, math.inf, initial_guess=math.sqrt(2 * math.log(1.5 / privacy_parameters.delta)) * sensitivity / privacy_parameters.epsilon) def _get_delta_for_sigma(current_sigma): return DiscreteGaussianPrivacyLoss( current_sigma, sensitivity=sensitivity, sampling_prob=sampling_prob, adjacency_type=AdjacencyType.REMOVE).get_delta_for_epsilon( privacy_parameters.epsilon) sigma = common.inverse_monotone_function(_get_delta_for_sigma, privacy_parameters.delta, search_parameters) return DiscreteGaussianPrivacyLoss( sigma, sensitivity=sensitivity, sampling_prob=sampling_prob, adjacency_type=adjacency_type) def standard_deviation(self) -> float: """The standard deviation of the corresponding discrete Gaussian noise.""" return math.sqrt( sum(((i + self._offset)**2) * probability_mass for i, probability_mass in enumerate(self._pmf_array)))

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61fe633bddbc707187c07d7a53a70568a37a2605

51

py

Python

cupy/cuda/nvtx.py

prkhrsrvstv1/cupy

ea86c8225b575af9d2855fb77a306cf86fd098ea

[ "MIT" ]

6,180

2016-11-01T14:22:30.000Z

2022-03-31T08:39:20.000Z

cupy/cuda/nvtx.py

prkhrsrvstv1/cupy

ea86c8225b575af9d2855fb77a306cf86fd098ea

[ "MIT" ]

6,281

2016-12-22T07:42:31.000Z

2022-03-31T19:57:02.000Z

cupy/cuda/nvtx.py

prkhrsrvstv1/cupy

ea86c8225b575af9d2855fb77a306cf86fd098ea

[ "MIT" ]

829

2017-02-23T05:46:12.000Z

2022-03-27T17:40:03.000Z

from cupy_backends.cuda.libs.nvtx import * # NOQA

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112a1f58be79bbc63320ea1d9446910cf16940ab

63

py

Python

example/gogo_client.py

v1c77/gogo

41e39a303ae2ea6f2ab6f471b35f6787a8ee619c

[ "WTFPL" ]

null

example/gogo_client.py

v1c77/gogo

41e39a303ae2ea6f2ab6f471b35f6787a8ee619c

[ "WTFPL" ]

4

2018-08-07T06:53:32.000Z

2018-08-07T16:11:07.000Z

example/gogo_client.py

v1c77/gogo

41e39a303ae2ea6f2ab6f471b35f6787a8ee619c

[ "WTFPL" ]

null

# -*- coding: utf-8 -*- # just a pure grpc client is ok. pass

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5

112b92a2e3b1d5cf4c1b0d9f5756182f5e2747e6

142

py

Python

__init__.py

tommo/toolwindowmanager-pyqt

8fb5815dd2c2784eaacafc0a5d53339007cb0170

[ "MIT" ]

7

2016-02-13T18:47:23.000Z

2020-07-03T13:47:49.000Z

__init__.py

tommo/toolwindowmanager-pyqt

8fb5815dd2c2784eaacafc0a5d53339007cb0170

[ "MIT" ]

1

2018-06-13T04:55:27.000Z

2021-11-05T05:52:51.000Z

__init__.py

tommo/toolwindowmanager-pyqt

8fb5815dd2c2784eaacafc0a5d53339007cb0170

[ "MIT" ]

4

2016-02-15T13:32:46.000Z

2019-12-12T17:22:31.000Z

from PyQt4 import QtCore, QtGui, uic from ToolWindowManager import ToolWindowManager, ToolWindowManagerArea, ToolWindowManagerWrapper

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5

11506bb1e9094f0c99daa7e83f07255941a358f9

246

py

Python

api/allennlp_demo/transformer_qa/test_api.py

dragon18456/allennlp-demo

3070703c584ad9f8f0e6efb7dfae971769944bfd

[ "Apache-2.0" ]

190

2018-06-09T14:07:26.000Z

2022-03-29T09:09:49.000Z

api/allennlp_demo/transformer_qa/test_api.py

dragon18456/allennlp-demo

3070703c584ad9f8f0e6efb7dfae971769944bfd

[ "Apache-2.0" ]

784

2018-06-11T22:42:49.000Z

2022-03-28T17:06:12.000Z

api/allennlp_demo/transformer_qa/test_api.py

dragon18456/allennlp-demo

3070703c584ad9f8f0e6efb7dfae971769944bfd

[ "Apache-2.0" ]

92

2018-07-07T15:45:45.000Z

2022-03-15T00:13:02.000Z

from allennlp_demo.common.testing import RcModelEndpointTestCase from allennlp_demo.transformer_qa.api import TransformerQaModelEndpoint class TestTransformerQaModelEndpoint(RcModelEndpointTestCase): endpoint = TransformerQaModelEndpoint()

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fec043b70c0e730c09887d0bf467dd71c88eb7d9

51

py

Python

qika/apps/__init__.py

XiYanXian/qikaACG

a465c211ed0f77263d8eca33a3422592b80010e4

[ "Apache-2.0" ]

6

2020-04-18T13:21:52.000Z

2021-05-28T04:59:15.000Z

qika/apps/__init__.py

XiYanXian/qikaACG

a465c211ed0f77263d8eca33a3422592b80010e4

[ "Apache-2.0" ]

7

2020-06-05T22:36:33.000Z

2022-03-11T23:57:38.000Z

qika/apps/__init__.py

XiYanXian/qikaACG

a465c211ed0f77263d8eca33a3422592b80010e4

[ "Apache-2.0" ]

1

2020-04-09T06:34:52.000Z

""" @file: __init__.py.py @date: 2019/07/29 """

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1

0

null

0

1

0

5

fec310f486ddc07a8d6a214cc2f6fc93904250dd

155

py

Python

finance/admin.py

Hamifthi/gym-app

514d7efa4f7777ab9d2e0481311c1c15542756c1

[ "MIT" ]

null

finance/admin.py

Hamifthi/gym-app

514d7efa4f7777ab9d2e0481311c1c15542756c1

[ "MIT" ]

4

2021-03-30T12:49:27.000Z

2021-06-10T18:27:47.000Z

finance/admin.py

Hamifthi/gym-app

514d7efa4f7777ab9d2e0481311c1c15542756c1

[ "MIT" ]

null

from django.contrib import admin from .models import Income, Expense # Register your models here. admin.site.register(Expense) admin.site.register(Income)

25.833333

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0

1

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1

0

5

feccb9fec9331b62d5e38b45b707fa081b786fd2

126

py

Python

Flexo/AzureFS.py

jcapellman/DevOpsResearch

067e1fc691febca043259bd757fa93b771637002

[ "MIT" ]

null

Flexo/AzureFS.py

jcapellman/DevOpsResearch

067e1fc691febca043259bd757fa93b771637002

[ "MIT" ]

null

Flexo/AzureFS.py

jcapellman/DevOpsResearch

067e1fc691febca043259bd757fa93b771637002

[ "MIT" ]

null

import JarredFS class AzureFS(JarredFS): def __init__(self): pass def SaveFile(self): print("Azure")

15.75

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0.619048

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126

5.285714

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8

25

15.75

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0

1

0.333333

false

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0.166667

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null

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1

0

null

0

1

0

1

0

1

0

5

fecf67368d0ec6e791a8eb0ceb77fe413d4cfae4

88

py

Python

ThreeBotPackages/zerobot/webinterface/bottle/__init__.py

grimpy/jumpscaleX_threebot

81aab3f049b2b353c247cd2c9eecd759a34a64c3

[ "Apache-2.0" ]

null

ThreeBotPackages/zerobot/webinterface/bottle/__init__.py

grimpy/jumpscaleX_threebot

81aab3f049b2b353c247cd2c9eecd759a34a64c3

[ "Apache-2.0" ]

null

ThreeBotPackages/zerobot/webinterface/bottle/__init__.py

grimpy/jumpscaleX_threebot

81aab3f049b2b353c247cd2c9eecd759a34a64c3

[ "Apache-2.0" ]

null

from . import auth, bcdb, chat, gedis, info, wiki from .rooter import app_with_session

22

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88

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0

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true

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null

0

1

0

1

0

1

0

5

3a00427336624f1e049b83c0594f31caf8cb869b

3,281

py

Python

articles/migrations/0013_auto_20200607_1104.py

GilbertTan19/Empire_of_Movies-deploy

e9e05530a25e76523e624591c966dccf84898ace

[ "MIT" ]

null

articles/migrations/0013_auto_20200607_1104.py

GilbertTan19/Empire_of_Movies-deploy

e9e05530a25e76523e624591c966dccf84898ace

[ "MIT" ]

2

2021-03-30T14:31:18.000Z

2021-04-08T21:22:09.000Z

articles/migrations/0013_auto_20200607_1104.py

GilbertTan19/Empire_of_Movies-deploy

e9e05530a25e76523e624591c966dccf84898ace

[ "MIT" ]

5

2020-07-13T03:17:07.000Z

2020-07-22T03:15:57.000Z

# Generated by Django 3.0.1 on 2020-06-07 03:04 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('articles', '0012_auto_20200606_1955'), ] operations = [ migrations.AddField( model_name='movie', name='companies', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='movie', name='contentRating', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='movie', name='countries', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='movie', name='directors', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='movie', name='genres', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='movie', name='imDbRating', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='movie', name='image', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='movie', name='keywords', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='movie', name='languages', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='movie', name='releaseDate', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='movie', name='runtimeStr', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='movie', name='stars', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='movie', name='tagline', field=models.CharField(blank=True, max_length=255, null=True), ), migrations.AddField( model_name='movie', name='writers', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='movie', name='author', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='movie', name='synopsis', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='movie', name='title', field=models.CharField(blank=True, max_length=255, null=True), ), ]

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null

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5

3a46f2f85f2a61751a3869ad23516fc11f9ad9cc

274

py

Python

Python/7 - kyu/7 kyu - Square Every Digit.py

danielbom/codewars

d45b5a813c6f1d952a50d22f0b2fcea4ef3d0e27

[ "MIT" ]

null

Python/7 - kyu/7 kyu - Square Every Digit.py

danielbom/codewars

d45b5a813c6f1d952a50d22f0b2fcea4ef3d0e27

[ "MIT" ]

null

Python/7 - kyu/7 kyu - Square Every Digit.py

danielbom/codewars

d45b5a813c6f1d952a50d22f0b2fcea4ef3d0e27

[ "MIT" ]

null

# https://www.codewars.com/kata/square-every-digit/train/python # My solution def square_digits(num): return int(''.join(map(lambda c: str(int(c)**2),str(num)))) # Other ways def square_digits(num): return int(''.join(str(int(d)**2) for d in str(num)))

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0

1

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0

5

3a47fb98171c3660fe28e2da1ca349e299963113

97

py

Python

Scripts/isort-script.py

T3chy/WordAnalysis

5ac2f2409a4be24b74468be0c907946f6276af26

[ "PSF-2.0" ]

null

Scripts/isort-script.py

T3chy/WordAnalysis

5ac2f2409a4be24b74468be0c907946f6276af26

[ "PSF-2.0" ]

null

Scripts/isort-script.py

T3chy/WordAnalysis

5ac2f2409a4be24b74468be0c907946f6276af26

[ "PSF-2.0" ]

null

if __name__ == '__main__': import sys import isort.main sys.exit(isort.main.main())

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null

0

1

0

1

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5

28a7689a66f7622013919766f5578a454be5007e

119

py

Python

djenga/views/__init__.py

2ps/djenga

85ac2c7b0b0e80b55aff43f027814d05b9b0532c

[ "BSD-3-Clause" ]

6

2015-01-18T10:31:13.000Z

2019-06-14T17:39:58.000Z

djenga/views/__init__.py

2ps/djenga

85ac2c7b0b0e80b55aff43f027814d05b9b0532c

[ "BSD-3-Clause" ]

12

2015-05-03T06:58:00.000Z

2019-06-26T21:58:16.000Z

djenga/views/__init__.py

2ps/djenga

85ac2c7b0b0e80b55aff43f027814d05b9b0532c

[ "BSD-3-Clause" ]

1

2018-04-27T20:36:29.000Z

from .csv_views import CsvView from .csv_views import ZippedCsvView __all__ = [ 'CsvView', 'ZippedCsvView', ]

14.875

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6.076923

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7

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false

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0.333333

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0

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0

null

0

1

0

1

0

null

0

1

0

5

28dbaf33d8acc69a5622f80bd354523283d9499f

137

py

Python

requirerole/__init__.py

notodinair/RedV3-Cogs

47747ccc33617dcaa3851ff12c6f95aee675d1e6

[ "MIT" ]

1

2020-06-08T13:39:30.000Z

requirerole/__init__.py

Tominous/Swift-Cogs

47747ccc33617dcaa3851ff12c6f95aee675d1e6

[ "MIT" ]

null

requirerole/__init__.py

Tominous/Swift-Cogs

47747ccc33617dcaa3851ff12c6f95aee675d1e6

[ "MIT" ]

1

2020-06-08T13:39:32.000Z

from redbot.core.bot import Red from requirerole.requirerole import RequireRole def setup(bot: Red): bot.add_cog(RequireRole(bot))

19.571429

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0

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null

0

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0

1

0

1

0

5

3a609a31c73b1f3b1fa5c0700b3e8faea251cc3b

108

py

Python

tests/roots/test-advanced/apidoc_dummy_package/apidoc_dummy_submodule_a.py

dhellmann/apidoc

7fc95da3f1e5912bd6e98aa71b57535257788916

[ "BSD-2-Clause" ]

null

tests/roots/test-advanced/apidoc_dummy_package/apidoc_dummy_submodule_a.py

dhellmann/apidoc

7fc95da3f1e5912bd6e98aa71b57535257788916

[ "BSD-2-Clause" ]

null

tests/roots/test-advanced/apidoc_dummy_package/apidoc_dummy_submodule_a.py

dhellmann/apidoc

7fc95da3f1e5912bd6e98aa71b57535257788916

[ "BSD-2-Clause" ]

null

from __future__ import print_function class Bar(object): def foo(self): print('Hello, world')

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3aa8465ad57919d36ddce06446fec9192a450cce

113

py

Python

katas/kyu_7/more_than_zero.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_7/more_than_zero.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_7/more_than_zero.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

def corrections(x): return '{} is {} than zero.'.format( x, 'more' if x > 0 else 'equal to or less')

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null

0

1

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3ad532d5a5c8afd2085609f31c43724745e6d3de

1,703

py

Python

fprint.py

HaebeTillmann/fprint

65d69708a0cfe0adaff2086629ac645d10a479ab

[ "MIT" ]

null

fprint.py

HaebeTillmann/fprint

65d69708a0cfe0adaff2086629ac645d10a479ab

[ "MIT" ]

null

fprint.py

HaebeTillmann/fprint

65d69708a0cfe0adaff2086629ac645d10a479ab

[ "MIT" ]

null

import sys colors = { "black": "30", "red": "31", "green": "32", "yellow" : "33", "blue" : "34", "purple" : "35", "cyan" : "36", "white" : "37" } styles = { "bold": "1", "underline" : "4", "italic": "3", "blink" : "5" } background_colors = { "black": "40", "red": "41", "green": "42", "yellow" : "43", "blue" : "44", "purple" : "45", "cyan" : "46", "white" : "47" } def fprint(text, text_color = None, text_style = None, background_color = None): color = "" style = "" back_color = "" if text_color is not None: color = colors[text_color] if text_style is not None or background_color is not None: color += ";" if text_style is not None: style = styles[text_style] if background_color is not None: style += ";" if background_color is not None: back_color = background_colors[background_color] prefix = f"\033[{color}{style}{back_color}m" print(f"{prefix}{text}\033[0m") def fstr(text, text_color = None, text_style = None, background_color = None): color = "" style = "" back_color = "" if text_color is not None: color = colors[text_color] if text_style is not None or background_color is not None: color += ";" if text_style is not None: style = styles[text_style] if background_color is not None: style += ";" if background_color is not None: back_color = background_colors[background_color] prefix = f"\033[{color}{style}{back_color}m" return(f"{prefix}{text}\033[0m") def prev(): sys.stdout.write("\033[F")

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5

3ad72f7f6fe5ccb0ec4407ab2a35c1933e34fde2

3,867

py

Python

Ar_Script/ar_369_test_sort_id.py

archerckk/PyTest

610dd89df8d70c096f4670ca11ed2f0ca3196ca5

[ "MIT" ]

null

Ar_Script/ar_369_test_sort_id.py

archerckk/PyTest

610dd89df8d70c096f4670ca11ed2f0ca3196ca5

[ "MIT" ]

1

2020-01-19T01:19:57.000Z

Ar_Script/ar_369_test_sort_id.py

archerckk/PyTest

610dd89df8d70c096f4670ca11ed2f0ca3196ca5

[ "MIT" ]

null

target=""" 用户id:385---接听率:0.2466---礼物获得率:0.743009---视频平均时长:1534.1852---接听次数:40554---挂断率:0.1571---颜值:0--- 用户id:414---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:65--- 用户id:423---接听率:0.5455---礼物获得率:0.626732---视频平均时长:54.4352---接听次数:81108---挂断率:0.1571---颜值:0--- 用户id:424---接听率:0.2088---礼物获得率:5.919776---视频平均时长:28.8421---接听次数:14269---挂断率:0.1571---颜值:82--- 用户id:429---接听率:0.3743---礼物获得率:0.543625---视频平均时长:62.6643---接听次数:105140---挂断率:0.1571---颜值:75--- 用户id:456---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:50--- 用户id:458---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:80--- 用户id:563---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:98--- 用户id:576---接听率:0.2000---礼物获得率:---视频平均时长:269.0000---接听次数:751---挂断率:0.1571---颜值:65--- 用户id:598---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:100--- 用户id:712---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:100--- 用户id:727---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:728---接听率:0.0588---礼物获得率:1.970706---视频平均时长:144.5000---接听次数:1502---挂断率:0.1571---颜值:0--- 用户id:1539---接听率:0.3536---礼物获得率:0.750054---视频平均时长:83.0753---接听次数:69843---挂断率:0.1571---颜值:82--- 用户id:1577---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:1594---接听率:0.0741---礼物获得率:33.759361---视频平均时长:36.5000---接听次数:1502---挂断率:0.1571---颜值:0--- 用户id:1600---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:1603---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:1646---接听率:0.0769---礼物获得率:21.293387---视频平均时长:30.6667---接听次数:2253---挂断率:0.1571---颜值:51--- 用户id:1661---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:1667---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:1696---接听率:0.1067---礼物获得率:0.059920---视频平均时长:18.8750---接听次数:6008---挂断率:0.1571---颜值:0--- 用户id:1700---接听率:0.1667---礼物获得率:0.639148---视频平均时长:20.0000---接听次数:1502---挂断率:0.1571---颜值:80--- 用户id:1706---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:1708---接听率:0.3600---礼物获得率:0.046235---视频平均时长:150.6667---接听次数:13518---挂断率:0.1571---颜值:0--- 用户id:2211---接听率:0.1667---礼物获得率:---视频平均时长:0.0000---接听次数:751---挂断率:0.1571---颜值:0--- 用户id:2212---接听率:0.3929---礼物获得率:1.024089---视频平均时长:114.3636---接听次数:24783---挂断率:0.1571---颜值:0--- 用户id:2228---接听率:0.0000---礼物获得率:0---视频平均时长:0---接听次数:0---挂断率:0---颜值:0--- 用户id:2235---接听率:0.5714---礼物获得率:---视频平均时长:103.8750---接听次数:6008---挂断率:0.1571---颜值:0--- 用户id:2240---接听率:0.1165---礼物获得率:---视频平均时长:67.9167---接听次数:9012---挂断率:0.1571---颜值:0--- 用户id:2244---接听率:0.3889---礼物获得率:4.280008---视频平均时长:37.7143---接听次数:5257---挂断率:0.1571---颜值:0--- 用户id:2245---接听率:0.2222---礼物获得率:0.026631---视频平均时长:48.4167---接听次数:9012---挂断率:0.1571---颜值:0--- """ new_str=target.split('---') new_dict={} receive_rate_list=[] get_gift_rate=[] video_average=[] accept_call=[] refuse_times=[] face_score=[] for i in new_str: if i!='\n': key,value=i.split(':') if key=='接听率': receive_rate_list.append(value) elif key=='礼物获得率': get_gift_rate.append(value) elif key=='视频平均时长': video_average.append(float(value)) elif key=='接听次数': accept_call.append(value) elif key=='挂断率': refuse_times.append(value) elif key=='颜值': face_score.append(int(value)) receive_rate_list=sorted(receive_rate_list,reverse=True) get_gift_rate=sorted(get_gift_rate,reverse=True) video_average=sorted(video_average,reverse=True) accept_call=sorted(accept_call,reverse=True) refuse_times=sorted(refuse_times,reverse=True) face_score=sorted(face_score,reverse=True) for i in receive_rate_list: print(i,end=' ') for i in get_gift_rate: print(i,end=' ') print('平均时长:') for i in video_average: print(i,end=' ') print() for i in accept_call: print(i,end=' ') print() for i in refuse_times: print(i,end=' ') print() for i in face_score: print(i,end=' ') print()

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1

0

1

null

0

5

3aeaac33792cb126b43e90a0219492fda7592a1d

319

py

Python

conf_syrup/exceptions.py

stuntgoat/conf-syrup

d7bfb3752631e61cf178d7390e48200101bfda13

[ "MIT" ]

null

conf_syrup/exceptions.py

stuntgoat/conf-syrup

d7bfb3752631e61cf178d7390e48200101bfda13

[ "MIT" ]

null

conf_syrup/exceptions.py

stuntgoat/conf-syrup

d7bfb3752631e61cf178d7390e48200101bfda13

[ "MIT" ]

null

class ConfSyrupException(Exception): pass class InvalidOption(ConfSyrupException): pass class UnableToLoadSettings(ConfSyrupException): pass class ConflictingTypes(ConfSyrupException): pass class IOErrorWhileReading(ConfSyrupException): pass class CastError(ConfSyrupException): pass

13.291667

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319

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0.181452

0.435484

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0.166144

319

23

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0.5

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true

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null

0

1

0

null

0

1

0

5

c91a90eb2318af17910f2ab1c53d94a6318802fc

615

py

Python

g2lserver/models/__init__.py

oudrea/grammar2pddl

f5936c5b8f831c99c7378d88e86c7dd6983c7b12

[ "Apache-2.0" ]

11

2021-03-18T23:36:13.000Z

2022-02-27T11:06:15.000Z

g2lserver/models/__init__.py

oudrea/grammar2pddl

f5936c5b8f831c99c7378d88e86c7dd6983c7b12

[ "Apache-2.0" ]

3

2021-07-14T22:54:02.000Z

2022-02-22T05:12:28.000Z

g2lserver/models/__init__.py

oudrea/grammar2pddl

f5936c5b8f831c99c7378d88e86c7dd6983c7b12

[ "Apache-2.0" ]

1

2022-02-22T02:54:18.000Z

# coding: utf-8 # flake8: noqa from __future__ import absolute_import # import models into model package from g2lserver.models.grammar import Grammar from g2lserver.models.grammar_id import GrammarID from g2lserver.models.pipeline_feedback import PipelineFeedback from g2lserver.models.pipeline_feedback_feedback import PipelineFeedbackFeedback from g2lserver.models.pipeline_feedback_results import PipelineFeedbackResults from g2lserver.models.pipeline_generation_params import PipelineGenerationParams from g2lserver.models.pipelines import Pipelines from g2lserver.models.pipelines_inner import PipelinesInner

43.928571

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1

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5

c978ad2a3c6452d9bc248dca350a882c4a71a8e1

926

py

Python

uritools/classify.py

nagesh4193/uritools

71f6cc48efc042f02adf81f71c849896956ceeae

[ "MIT" ]

39

2015-06-30T21:03:42.000Z

2022-03-09T20:29:03.000Z

uritools/classify.py

nagesh4193/uritools

71f6cc48efc042f02adf81f71c849896956ceeae

[ "MIT" ]

38

2015-01-13T16:16:52.000Z

2021-04-27T21:04:40.000Z

uritools/classify.py

nagesh4193/uritools

71f6cc48efc042f02adf81f71c849896956ceeae

[ "MIT" ]

13

2015-07-30T20:58:45.000Z

2022-02-12T21:42:03.000Z

from .split import urisplit # TODO: use specialized checks/regexes for performance def isuri(uristring): """Return :const:`True` if `uristring` is a URI.""" return urisplit(uristring).isuri() def isabsuri(uristring): """Return :const:`True` if `uristring` is an absolute URI.""" return urisplit(uristring).isabsuri() def isnetpath(uristring): """Return :const:`True` if `uristring` is a network-path reference.""" return urisplit(uristring).isnetpath() def isabspath(uristring): """Return :const:`True` if `uristring` is an absolute-path reference.""" return urisplit(uristring).isabspath() def isrelpath(uristring): """Return :const:`True` if `uristring` is a relative-path reference.""" return urisplit(uristring).isrelpath() def issamedoc(uristring): """Return :const:`True` if `uristring` is a same-document reference.""" return urisplit(uristring).issamedoc()

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0.154428

926

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false

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0

1

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null

0

1

0

1

0

1

0

5

a3909747febacc371c8a8469596c9d43c9df7207

54

py

Python

tasks/__init__.py

zhudotexe/py-task-scheduler

6c226a0d80931c0111ad472bff9ae811c31ec616

[ "MIT" ]

null

tasks/__init__.py

zhudotexe/py-task-scheduler

6c226a0d80931c0111ad472bff9ae811c31ec616

[ "MIT" ]

null

tasks/__init__.py

zhudotexe/py-task-scheduler

6c226a0d80931c0111ad472bff9ae811c31ec616

[ "MIT" ]

null

from .scheduler import Scheduler, JSONSavingScheduler

27

53

0.87037

5

54

9.4

0.8

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54

1

54

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true

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0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

5

6e63db82dc0bc7c33a49b37fc903d15eeda71156

265

py

Python

DIP_0003P.py

abraaogleiber/Python_Procedural_OrientadoO

ad1bff9c1133b5e8c076c825a1201f4787aa4f03

[ "MIT" ]

null

DIP_0003P.py

abraaogleiber/Python_Procedural_OrientadoO

ad1bff9c1133b5e8c076c825a1201f4787aa4f03

[ "MIT" ]

null

DIP_0003P.py

abraaogleiber/Python_Procedural_OrientadoO

ad1bff9c1133b5e8c076c825a1201f4787aa4f03

[ "MIT" ]

null

#__________________ Script Python - versão 3.8 _____________________# # Autor |> Abraão A.da Silva # Data |> 05 de Março de 2021 # Paradigma |> Procedural # Objetivo |> Somando dois números #___________________________________________________________________#

33.125

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265

7

70

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0.943396

0

null

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null

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null

true

0

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null

0

1

0

1

0

null

0

1

0

5

6ea371895bcdec988ff416725b52f50e0ec5042f

134

py

Python

Easy/First Reverse.py

edaaydinea/Coderbyte

83d0fcde12c937b95e8b6f819df08b8b9e03ac55

[ "MIT" ]

6

2022-01-05T12:14:11.000Z

2022-03-10T16:28:23.000Z

Easy/First Reverse.py

edaaydinea/Coderbyte

83d0fcde12c937b95e8b6f819df08b8b9e03ac55

[ "MIT" ]

null

Easy/First Reverse.py

edaaydinea/Coderbyte

83d0fcde12c937b95e8b6f819df08b8b9e03ac55

[ "MIT" ]

null

def FirstReverse(strParam): # code goes here return strParam[::-1] # keep this function call here print(FirstReverse(input()))

16.75

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7

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1

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null

0

1

0

null

0

1

0

1

0

5

6ea450a416e53fcde4c3d19fddeed2c6999d7b1d

215

py

Python

xbrl/model/taxonomy/__init__.py

blinkace/pxp

9155103dc166674137bd0e2fddb609ca44875761

[ "MIT" ]

1

2022-01-27T14:53:23.000Z

xbrl/model/taxonomy/__init__.py

blinkace/pxp

9155103dc166674137bd0e2fddb609ca44875761

[ "MIT" ]

null

xbrl/model/taxonomy/__init__.py

blinkace/pxp

9155103dc166674137bd0e2fddb609ca44875761

[ "MIT" ]

null

from .concept import Concept, NoteConcept, PeriodType from .typeddimension import TypedDimension from .taxonomy import Taxonomy from .datatype import Datatype, ListBasedDatatype, ComplexDatatype, UnionBasedDatatype

43

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4

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true

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null

0

1

0

1

0

1

0

5

6eab1375d3197aad0da5c82ad3b6cb5b27a85b6e

15

py

Python

Shivani/Sum.py

63Shivani/Python-BootCamp

2ed0ef95af35d35c0602031670fecfc92d8cea0a

[ "MIT" ]

null

Shivani/Sum.py

63Shivani/Python-BootCamp

2ed0ef95af35d35c0602031670fecfc92d8cea0a

[ "MIT" ]

null

Shivani/Sum.py

63Shivani/Python-BootCamp

2ed0ef95af35d35c0602031670fecfc92d8cea0a

[ "MIT" ]

null

c=4+9 print(c)

5

8

0.6

5

15

1.8

0.8

0

0.153846

0.133333

15

2

9

7.5

0.538462

0

1

0

false

0

0.5

1

null

0

1

0

null

0

1

0

5

42c9f56958eb82abc2455e66c67e96a589753d9e

149

py

Python

src/lesson_email/imaplib_select_invalid.py

jasonwee/asus-rt-n14uhp-mrtg

4fa96c3406e32ea6631ce447db6d19d70b2cd061

[ "Apache-2.0" ]

3

2018-08-14T09:33:52.000Z

2022-03-21T12:31:58.000Z

src/lesson_email/imaplib_select_invalid.py

jasonwee/asus-rt-n14uhp-mrtg

4fa96c3406e32ea6631ce447db6d19d70b2cd061

[ "Apache-2.0" ]

null

src/lesson_email/imaplib_select_invalid.py

jasonwee/asus-rt-n14uhp-mrtg

4fa96c3406e32ea6631ce447db6d19d70b2cd061

[ "Apache-2.0" ]

null

import imaplib import imaplib_connect with imaplib_connect.open_connection() as c: typ, data = c.select('Does-Not-Exist') print(typ, data)

18.625

44

0.731544

22

149

4.818182

0.681818

0.245283

0

0.161074

149

7

45

21.285714

0.848

0

0.094595

0

1

0

true

0

0.4

0

0.4

0.2

1

0

null

1

0

1

0

null

0

1

0

1

0

5

42d23f117609e1b60ce5240735b776443e178316

217

py

Python

extra/demo/myapp/utilities.py

phoikoi/sisy

840c5463ab65488d34e99531f230e61f755d2d69

[ "MIT" ]

2

2017-12-05T17:21:12.000Z

2017-12-11T19:54:22.000Z

extra/demo/myapp/utilities.py

phoikoi/sisy

840c5463ab65488d34e99531f230e61f755d2d69

[ "MIT" ]

1

2020-06-05T17:55:33.000Z

extra/demo/myapp/utilities.py

phoikoi/sisy

840c5463ab65488d34e99531f230e61f755d2d69

[ "MIT" ]

null

def daily_maintenance(message): task = message['task'] print(f"Running daily maintenance function from task #{task.id} ({task.label})") print(f"This function's module path is {task.funcinfo['func_path']}")

54.25

84

0.709677

31

217

4.903226

0.612903

0.210526

0

0.138249

217

4

85

54.25

0.812834

0

0.610092

0.12844

0

1

0.25

false

0

0.25

0.5

0

null

1

0

1

0

1

0

null

0

1

0

1

0

5

6e0c08fa17498b9d0c937bdc6e5b65d5758c0033

229

py

Python

Tests/Runnable1/r_starargs_t.py

jwilk/Pyrex

83dfbae1261788933472e3f9c501ad74c61a37c5

[ "Apache-2.0" ]

5

2019-05-26T20:48:36.000Z

2021-07-09T01:38:38.000Z

Tests/Runnable1/r_starargs_t.py

jwilk/Pyrex

83dfbae1261788933472e3f9c501ad74c61a37c5

[ "Apache-2.0" ]

null

Tests/Runnable1/r_starargs_t.py

jwilk/Pyrex

83dfbae1261788933472e3f9c501ad74c61a37c5

[ "Apache-2.0" ]

1

2022-02-10T07:14:58.000Z

from r_starargs import swallow swallow("Brian", 42) swallow("Brian", 42, "African") swallow("Brian", airspeed = 42) swallow("Brian", airspeed = 42, species = "African", coconuts = 3) swallow("Brian", 42, "African", coconuts = 3)

32.714286

66

0.694323

30

229

5.266667

0.4

0.379747

0.265823

0

0.06

0.126638

229

6

67

38.166667

0.73

0

0.200873

0

1

0

true

0

0.166667

0

0.166667

0

null

1

0

1

0

null

0

1

0

5

2813d105bcc95b984811774af2d7efac4bab5dd6

36,935

py

Python

frimcla/StatisticalAnalysis/statisticalAnalysis.py

ManuGar/ObjectClassificationByTransferLearning

fc009fc5a71668355a94ea1a8f506fdde8e7bde0

[ "MIT" ]

3

2021-04-22T09:15:34.000Z

2022-01-05T09:50:18.000Z

frimcla/StatisticalAnalysis/statisticalAnalysis.py

ManuGar/ObjectClassificationByTransferLearning

fc009fc5a71668355a94ea1a8f506fdde8e7bde0

[ "MIT" ]

4

2020-09-25T22:46:39.000Z

2021-08-25T15:01:14.000Z

frimcla/StatisticalAnalysis/statisticalAnalysis.py

ManuGar/ObjectClassificationByTransferLearning

fc009fc5a71668355a94ea1a8f506fdde8e7bde0

[ "MIT" ]

3

2020-07-31T14:11:26.000Z

2021-11-24T01:53:01.000Z

import pandas as pd import numpy as np import matplotlib.pyplot as plt import operator from scipy.stats import shapiro,levene,ttest_ind,wilcoxon from multiprocessing.pool import ThreadPool from .stac.nonparametric_tests import quade_test,holm_test,friedman_test from .stac.parametric_tests import anova_test,bonferroni_test from tabulate import tabulate from sklearn.model_selection import RandomizedSearchCV from sklearn.metrics import accuracy_score,precision_score,roc_auc_score,recall_score,f1_score from sklearn.model_selection import KFold def cohen_d(x,y): nx = len(x) ny = len(y) dof = nx + ny - 2 return (np.mean(x) - np.mean(y)) / np.sqrt(((nx-1)*np.std(x, ddof=1) ** 2 + (ny-1)*np.std(y, ddof=1) ** 2) / dof) def SSbetween(accuracies): return float(sum(accuracies.sum(axis=1)**2))/len(accuracies[0]) - float(accuracies.sum()**2)/(len(accuracies[0])*len(accuracies)) def SSTotal(accuracies): sum_y_squared = sum([value**2 for value in accuracies.flatten()]) return sum_y_squared - float(accuracies.sum() ** 2) / (len(accuracies[0]) * len(accuracies)) def eta_sqrd(accuracies): return SSbetween(accuracies)/SSTotal(accuracies) def multipleAlgorithmsNonParametric(algorithms,accuracies, file, fileResults, alpha=0.05, verbose=False): algorithmsDataset = {x: y for (x, y) in zip(algorithms, accuracies)} filePath = file.name featureExtractor = filePath[filePath.rfind("_") + 1:filePath.rfind(("."))] if len(algorithms) < 5: if verbose: print("----------------------------------------------------------") print("Applying Quade test") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Applying Quade test\n") file.write("----------------------------------------------------------\n") (Fvalue, pvalue, rankings, pivots) = quade_test(*accuracies) else: if verbose: print("----------------------------------------------------------") print("Applying Friedman test") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Applying Friedman test\n") file.write("----------------------------------------------------------\n") (Fvalue, pvalue, rankings, pivots) = friedman_test(*accuracies) if verbose: print("F-value: %f, p-value: %s" % (Fvalue, pvalue)) file.write("F-value: %f, p-value: %s \n" % (Fvalue, pvalue)) if (pvalue < alpha): r = {x: y for (x, y) in zip(algorithms, rankings)} sorted_ranking = sorted(r.items(), key=operator.itemgetter(1)) sorted_ranking.reverse() (winner, _) = sorted_ranking[0] pivots = {x: y for (x, y) in zip(algorithms, pivots)} (comparions, zvalues, pvalues, adjustedpvalues) = holm_test(pivots, winner) res = zip(comparions, zvalues, pvalues, adjustedpvalues) if verbose: print("Null hypothesis is rejected; hence, models have different performance") print (tabulate(sorted_ranking, headers=['Technique', 'Ranking'])) print("Winner model: %s" % winner) print("----------------------------------------------------------") print("Applying Holm p-value adjustment procedure and analysing effect size") print("----------------------------------------------------------") print(tabulate(res, headers=['Comparison', 'Zvalue', 'p-value', 'adjusted p-value'])) file.write("Null hypothesis is rejected; hence, models have different performance \n") file.write(tabulate(sorted_ranking, headers=['Technique', 'Ranking']) + "\n") file.write("Winner model: %s \n" % winner) file.write("----------------------------------------------------------\n") file.write("Applying Holm p-value adjustment procedure and analysing effect size\n") file.write("----------------------------------------------------------\n") file.write(tabulate(res, headers=['Comparison', 'Zvalue', 'p-value', 'adjusted p-value'])+ "\n") fileResults.write(winner) for ele in algorithmsDataset[winner]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") for (c, p) in zip(comparions, adjustedpvalues): cohend = abs(cohen_d(algorithmsDataset[winner], algorithmsDataset[c[c.rfind(" ") + 1:]])) if (cohend <= 0.2): effectsize = "Small" elif (cohend <= 0.5): effectsize = "Medium" else: effectsize = "Large" if (p > alpha): #print("There are not significant differences between: %s and %s (Cohen's d=%s, %s)" % ( #winner, c[c.rfind(" ") + 1:], cohend, effectsize)) if verbose: print("We can't say that there is a significant difference in the performance of the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s)" % ( winner, np.mean(algorithmsDataset[winner]), np.std(algorithmsDataset[winner]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) file.write("We can't say that there is a significant difference in the performance of the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s) \n" % ( winner, np.mean(algorithmsDataset[winner]), np.std(algorithmsDataset[winner]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) else: if verbose: print("There is a significant difference between the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s)" % ( winner, np.mean(algorithmsDataset[winner]), np.std(algorithmsDataset[winner]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) file.write("There is a significant difference between the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s) \n" % ( winner, np.mean(algorithmsDataset[winner]), np.std(algorithmsDataset[winner]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) else: means = np.mean(accuracies, axis=1) maximum = max(means) if verbose: print("Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models") print("----------------------------------------------------------") print("Analysing effect size") print("----------------------------------------------------------") print("We take the model with the best mean (%s, mean: %f) and compare it with the other models: " % ( algorithms[means.tolist().index(maximum)], maximum)) file.write("Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models\n") file.write("----------------------------------------------------------\n") file.write("Analysing effect size\n") file.write("----------------------------------------------------------\n") file.write("We take the model with the best mean (%s, mean: %f) and compare it with the other models: \n" % ( algorithms[means.tolist().index(maximum)], maximum)) #f = fileResults confPath["classifier_path"] + conf["modelClassifiers"] +filePath[filePath.rfind("_"):filePath.rfind(("."))] classifier = (algorithms[means.tolist().index(maximum)]) fStatistical = open(filePath[:filePath.rfind("/")] + "/kfold-comparison_" + featureExtractor + ".csv", "r") fStatistical.readline() for line in fStatistical: line = line.split(",") if (line[0] == classifier): fileResults.write(line[0]) for it in line[1:]: fileResults.write("," + it) fStatistical.close() for i in range(0,len(algorithms)): if i != means.tolist().index(maximum): cohend = abs(cohen_d(algorithmsDataset[algorithms[means.tolist().index(maximum)]], algorithmsDataset[algorithms[i]])) if (cohend <= 0.2): effectsize = "Small" elif (cohend <= 0.5): effectsize = "Medium" else: effectsize = "Large" if verbose: print("Comparing effect size of %s and %s: Cohen's d=%s, %s" % (algorithms[means.tolist().index(maximum)],algorithms[i],cohend, effectsize)) file.write("Comparing effect size of %s and %s: Cohen's d=%s, %s \n" % (algorithms[means.tolist().index(maximum)],algorithms[i],cohend, effectsize)) eta= eta_sqrd(accuracies) if (eta <= 0.01): effectsize = "Small" elif (eta <= 0.06): effectsize = "Medium" else: effectsize = "Large" if verbose: print("Eta squared: %f (%s)" % (eta,effectsize)) file.write("Eta squared: %f (%s) \n" % (eta,effectsize)) def multipleAlgorithmsParametric(algorithms,accuracies, file, fileResults, alpha=0.05, verbose=False): algorithmsDataset = {x: y for (x, y) in zip(algorithms, accuracies)} (Fvalue, pvalue, pivots) = anova_test(*accuracies) if verbose: print("----------------------------------------------------------") print("Applying ANOVA test") print("----------------------------------------------------------") print("F-value: %f, p-value: %s" % (Fvalue, pvalue)) file.write("----------------------------------------------------------\n") file.write("Applying ANOVA test\n") file.write("----------------------------------------------------------\n") file.write("F-value: %f, p-value: %s \n" % (Fvalue, pvalue)) if (pvalue < alpha): if verbose: print("Null hypothesis is rejected; hence, models have different performance") print("----------------------------------------------------------") print("Applying Bonferroni-Dunn post-hoc and analysing effect size") print("----------------------------------------------------------") file.write("Null hypothesis is rejected; hence, models have different performance\n") file.write("----------------------------------------------------------\n") file.write("Applying Bonferroni-Dunn post-hoc and analysing effect size\n") file.write("----------------------------------------------------------\n") pivots = {x: y for (x, y) in zip(algorithms, pivots)} (comparions, zvalues, pvalues, adjustedpvalues) = bonferroni_test(pivots, len(accuracies[0])) res = zip(comparions, zvalues, pvalues, adjustedpvalues) if verbose: print(tabulate(res, headers=['Comparison', 'Zvalue', 'p-value', 'adjusted p-value'])) file.write(tabulate(res, headers=['Comparison', 'Zvalue', 'p-value', 'adjusted p-value'])+"\n") for (c, p) in zip(comparions, adjustedpvalues): cohend = abs(cohen_d(algorithmsDataset[c[0:c.find(" ")]], algorithmsDataset[c[c.rfind(" ") + 1:]])) if (cohend <= 0.2): effectsize = "Small" elif (cohend <= 0.5): effectsize = "Medium" else: effectsize = "Large" if (p > alpha): if verbose: print("We can't say that there is a significant difference in the performance of the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s)" % ( c[0:c.find(" ")], np.mean(algorithmsDataset[c[0:c.find(" ")]]), np.std(algorithmsDataset[c[0:c.find(" ")]]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) file.write("We can't say that there is a significant difference in the performance of the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s)\n" % ( c[0:c.find(" ")], np.mean(algorithmsDataset[c[0:c.find(" ")]]), np.std(algorithmsDataset[c[0:c.find(" ")]]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) #print("There are not significant differences between: %s and %s (Cohen's d=%s, %s)" % (c[0:c.find(" ")],c[c.rfind(" ") + 1:],cohend,effectsize)) # if(np.mean(algorithmsDataset[c[0:c.find(" ")]])>np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]])): # fileResults.write(c[0:c.find(" ")]) # for ele in algorithmsDataset[c[0:c.find(" ")]]: # fileResults.write(",") # fileResults.write(str(ele)) # fileResults.write("\n") # else: # fileResults.write(c[c.rfind(" ") + 1:]) # for ele in algorithmsDataset[c[c.rfind(" ") + 1:]]: # fileResults.write(",") # fileResults.write(str(ele)) # fileResults.write("\n") else: if verbose: print( "There is a significant difference between the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s)" % ( c[0:c.find(" ")], np.mean(algorithmsDataset[c[0:c.find(" ")]]), np.std(algorithmsDataset[c[0:c.find(" ")]]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) file.write( "There is a significant difference between the models: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) (Cohen's d=%s, %s)\n" % ( c[0:c.find(" ")], np.mean(algorithmsDataset[c[0:c.find(" ")]]), np.std(algorithmsDataset[c[0:c.find(" ")]]), c[c.rfind(" ") + 1:], np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]]), np.std(algorithmsDataset[c[c.rfind(" ") + 1:]]),cohend,effectsize)) # if (np.mean(algorithmsDataset[c[0:c.find(" ")]]) > np.mean(algorithmsDataset[c[c.rfind(" ") + 1:]])): # fileResults.write(c[0:c.find(" ")]) # for ele in algorithmsDataset[c[0:c.find(" ")]]: # fileResults.write(",") # fileResults.write(str(ele)) # fileResults.write("\n") # else: # fileResults.write(c[c.rfind(" ") + 1:]) # for ele in algorithmsDataset[c[c.rfind(" ") + 1:]]: # fileResults.write(",") # fileResults.write(str(ele)) # fileResults.write("\n") else: means = np.mean(accuracies, axis=1) maximum = max(means) if verbose: print("Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models") print("----------------------------------------------------------") print("Analysing effect size") print("----------------------------------------------------------") print("We take the model with the best mean (%s, mean: %f) and compare it with the other models: " % (algorithms[means.tolist().index(maximum)],maximum)) file.write("Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models\n") file.write("----------------------------------------------------------\n") file.write("Analysing effect size\n") file.write("----------------------------------------------------------\n") file.write("We take the model with the best mean (%s, mean: %f) and compare it with the other models: \n" % (algorithms[means.tolist().index(maximum)],maximum)) # filePath = file.name # featureExtractor = filePath[filePath.rfind("_") + 1:filePath.rfind(("."))] # classifier = (algorithms[means.tolist().index(maximum)]) # #f = fileResults # confPath["classifier_path"] + conf["modelClassifiers"] +filePath[filePath.rfind("_"):filePath.rfind(("."))] # # print(filePath[:filePath.rfind("/")] + "/kfold-comparison_" + featureExtractor + ".csv") # fStatistical = open(filePath[:filePath.rfind("/")] + "/kfold-comparison_" + featureExtractor + ".csv", "r") # fStatistical.readline() # for line in fStatistical: # line = line.split(",") # # print(line[0][1:len(line[0])-1]) # # print(""+classifier) # # print(line[0] == str(classifier)) # if (line[0] == classifier): # fileResults.write(line[0]) # for it in line[1:]: # fileResults.write(",") # fileResults.write(it) # fStatistical.close() for i in range(0,len(algorithms)): if i != means.tolist().index(maximum): cohend = abs(cohen_d(algorithmsDataset[algorithms[means.tolist().index(maximum)]], algorithmsDataset[algorithms[i]])) if (cohend <= 0.2): effectsize = "Small" elif (cohend <= 0.5): effectsize = "Medium" else: effectsize = "Large" if verbose: print("Comparing effect size of %s and %s: Cohen's d=%s, %s" % (algorithms[means.tolist().index(maximum)],algorithms[i],cohend, effectsize)) file.write("Comparing effect size of %s and %s: Cohen's d=%s, %s\n" % (algorithms[means.tolist().index(maximum)],algorithms[i],cohend, effectsize)) means = np.mean(accuracies, axis=1) maximum = max(means) if verbose: print("----------------------------------------------------------") print("We take the model with the best mean (%s, mean: %f) and compare it with the other models: " % ( algorithms[means.tolist().index(maximum)], maximum)) file.write("----------------------------------------------------------\n") file.write("We take the model with the best mean (%s, mean: %f) and compare it with the other models: \n" % ( algorithms[means.tolist().index(maximum)], maximum)) filePath = file.name featureExtractor = filePath[filePath.rfind("_") + 1:filePath.rfind(("."))] classifier = (algorithms[means.tolist().index(maximum)]) fStatistical = open(filePath[:filePath.rfind("/")] + "/kfold-comparison_" + featureExtractor + ".csv", "r") fStatistical.readline() for line in fStatistical: line = line.split(",") if (line[0] == classifier): fileResults.write(line[0]) for it in line[1:]: fileResults.write(",") fileResults.write(it) fStatistical.close() eta= eta_sqrd(accuracies) if (eta <= 0.01): effectsize = "Small" elif (eta <= 0.06): effectsize = "Medium" else: effectsize = "Large" if verbose: print("Eta squared: %f (%s)" % (eta,effectsize)) file.write("Eta squared: %f (%s)\n" % (eta,effectsize)) def twoAlgorithmsParametric(algorithms,accuracies,alpha,file, fileResults, verbose=False): (t,prob)=ttest_ind(accuracies[0], accuracies[1]) if verbose: print("Students' t: t=%f, p=%f" % (t,prob)) file.write("Students' t: t=%f, p=%f \n" % (t,prob)) # file.student("Students' t: t=%f, p=%f \n" % (t,prob)) if (prob > alpha): if verbose: print("Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models: %s and %s" % ( algorithms[0], algorithms[1])) file.write("Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models: %s and %s \n" % ( algorithms[0], algorithms[1])) if(np.mean(accuracies[0])>np.mean(accuracies[1])): fileResults.write(algorithms[0]) for ele in accuracies[0]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") else: fileResults.write(algorithms[1]) for ele in accuracies[1]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") else: if verbose: print("Null hypothesis is rejected; hence, there are significant differences between: %s (mean: %f, std: %f) and %s (mean: %f, std: %f)" % ( algorithms[0], np.mean(accuracies[0]),np.std(accuracies[0]),algorithms[1], np.mean(accuracies[1]),np.std(accuracies[1]))) file.write("Null hypothesis is rejected; hence, there are significant differences between: %s (mean: %f, std: %f) and %s (mean: %f, std: %f \n)" % ( algorithms[0], np.mean(accuracies[0]),np.std(accuracies[0]),algorithms[1], np.mean(accuracies[1]),np.std(accuracies[1]))) if (np.mean(accuracies[0]) > np.mean(accuracies[1])): fileResults.write(algorithms[0]) for ele in accuracies[0]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") else: fileResults.write(algorithms[1]) for ele in accuracies[1]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") if verbose: print("----------------------------------------------------------") print("Analysing effect size") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Analysing effect size\n") file.write("----------------------------------------------------------\n") cohend = abs(cohen_d(accuracies[0], accuracies[1])) if (cohend <= 0.2): effectsize = "Small" elif (cohend <= 0.5): effectsize = "Medium" else: effectsize = "Large" if (prob <= alpha): if verbose: print("Cohen's d=%s, %s" % (cohend, effectsize)) file.write("Cohen's d=%s, %s \n" % (cohend, effectsize)) def twoAlgorithmsNonParametric(algorithms,accuracies,alpha,file, fileResults, verbose=False): (t,prob)=wilcoxon(accuracies[0], accuracies[1]) if verbose: print("Wilconxon: t=%f, p=%f" % (t,prob)) file.write("Wilconxon: t=%f, p=%f \n" % (t,prob)) if (prob > alpha): if verbose: print( "Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models: %s and %s" % ( algorithms[0], algorithms[1])) file.write( "Null hypothesis is accepted; hence, we can't say that there is a significant difference in the performance of the models: %s and %s \n" % ( algorithms[0], algorithms[1])) if (np.mean(accuracies[0]) > np.mean(accuracies[1])): fileResults.write(algorithms[0]) for ele in accuracies[0]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") else: fileResults.write(algorithms[1]) for ele in accuracies[1]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") else: if verbose: print("Null hypothesis is rejected; hence, there are significant differences between: %s (mean: %f, std: %f) and %s (mean: %f, std: %f)" % ( algorithms[0], np.mean(accuracies[0]),np.std(accuracies[0]),algorithms[1], np.mean(accuracies[1]),np.std(accuracies[1]))) file.write("Null hypothesis is rejected; hence, there are significant differences between: %s (mean: %f, std: %f) and %s (mean: %f, std: %f) \n" % ( algorithms[0], np.mean(accuracies[0]),np.std(accuracies[0]),algorithms[1], np.mean(accuracies[1]),np.std(accuracies[1]))) if(np.mean(accuracies[0])>np.mean(accuracies[1])): fileResults.write( algorithms[0]) for ele in accuracies[0]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") else: fileResults.write(algorithms[1]) for ele in accuracies[1]: fileResults.write(",") fileResults.write(str(ele)) fileResults.write("\n") cohend = abs(cohen_d(accuracies[0], accuracies[1])) if verbose: print("----------------------------------------------------------") print("Analysing effect size") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Analysing effect size\n") file.write("----------------------------------------------------------\n") if (cohend <= 0.2): effectsize = "Small" elif (cohend <= 0.5): effectsize = "Medium" else: effectsize = "Large" if (prob <= alpha): if verbose: print("Cohen's d=%s, %s" % (cohend, effectsize)) file.write("Cohen's d=%s, %s \n" % (cohend, effectsize)) def meanStdReportAndPlot(algorithms,accuracies,file, verbose= False): if verbose: print("**********************************************************") print("Mean and std") print("**********************************************************") file.write("**********************************************************\n") file.write("Mean and std \n") file.write("**********************************************************\n") means = np.mean(accuracies, axis=1) stds = np.std(accuracies, axis=1) for (alg, mean, std) in zip(algorithms, means, stds): msg = "%s: %f (%f)" % (alg, mean, std) if verbose: print(msg) file.write(msg+"\n") fig = plt.figure() fig.suptitle('Algorithm Comparison') ax = fig.add_subplot(111) plt.boxplot(np.transpose(accuracies)) ax.set_xticklabels(algorithms) plt.savefig(file.name[:file.name.rfind("/")] + "/meansSTD" + file.name[file.name.rfind("_"):file.name.rfind(".")] + ".png") def checkParametricConditions(accuracies,alpha, file, verbose=False): (W, p) = shapiro(accuracies) if verbose: print("Checking independence ") print("Ok") print("Checking normality using Shapiro-Wilk's test for normality, alpha=0.05") print("W: %f, p:%f" % (W, p)) file.write("Checking independence \n") file.write("Ok\n") independence = True file.write("Checking normality using Shapiro-Wilk's test for normality, alpha=0.05\n") file.write("W: %f, p:%f \n" % (W, p)) if p < alpha: if verbose: print("The null hypothesis (normality) is rejected") file.write("The null hypothesis (normality) is rejected\n") normality = False else: if verbose: print("The null hypothesis (normality) is accepted") file.write("The null hypothesis (normality) is accepted\n") normality = True (W, p) = levene(*accuracies) if verbose: print("Checking heteroscedasticity using Levene's test, alpha=0.05") print("W: %f, p:%f" % (W, p)) file.write("Checking heteroscedasticity using Levene's test, alpha=0.05\n") file.write("W: %f, p:%f \n" % (W, p)) if p < alpha: if verbose: print("The null hypothesis (heteroscedasticity) is rejected") file.write("The null hypothesis (heteroscedasticity) is rejected\n") heteroscedasticity = False else: if verbose: print("The null hypothesis (heteroscedasticity) is accepted") file.write("The null hypothesis (heteroscedasticity) is accepted\n") heteroscedasticity = True parametric = independence and normality and heteroscedasticity return parametric # This is the main method employed to compare a dataset where the cross validation # process has been already carried out. def statisticalAnalysis(dataset, filePath, fileResults ,alpha=0.05, verbose=False): #path=dataset[:index] with open(filePath,"w") as file: #path+"/StatisticalComparison"+model[model.rfind("_"):]+".txt" df = pd.read_csv(dataset) algorithms = df.ix[0:,0].values accuracies = df.ix[0:, 1:].values if (len(algorithms)<2): if verbose: print("It is neccessary to compare at least two algorithms") file.write("It is neccessary to compare at least two algorithms\n") fileResults.write(algorithms[0]) for ele in accuracies[0]: fileResults.write(",") #str(ele) fileResults.write(str(ele)) fileResults.write("\n") return if verbose: print(dataset) print(algorithms) print("==========================================================") print("Report") print("==========================================================") file.write(dataset) #file.write(algorithms) file.write("\n==========================================================\n") file.write("Report\n") file.write("==========================================================\n") meanStdReportAndPlot(algorithms,accuracies,file, verbose) if verbose: print("**********************************************************") print("Statistical tests") print("**********************************************************") print("----------------------------------------------------------") print("Checking parametric conditions ") print("----------------------------------------------------------") file.write("**********************************************************\n") file.write("Statistical test\n") file.write("**********************************************************\n") file.write("----------------------------------------------------------\n") file.write("Cheking parametric conditions\n") file.write("----------------------------------------------------------\n") parametric = checkParametricConditions(accuracies,alpha,file) if parametric: if verbose: print("Conditions for a parametric test are fulfilled") file.write("Conditions for a parametric test are fulfilled\n") if(len(algorithms)==2): if verbose: print("----------------------------------------------------------") print("Working with 2 algorithms") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Working with 2 algorithms\n") file.write("----------------------------------------------------------\n") twoAlgorithmsParametric(algorithms,accuracies,alpha,file, fileResults, verbose) else: if verbose: print("----------------------------------------------------------") print("Working with more than 2 algorithms") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Working with more than 2 algorithms\n") file.write("----------------------------------------------------------\n") multipleAlgorithmsParametric(algorithms,accuracies,file, fileResults, alpha, verbose) else: if verbose: print("Conditions for a parametric test are not fulfilled, applying a non-parametric test") file.write("Conditions for a parametric test are not fulfilled, applying a non-parametric test\n") if (len(algorithms) == 2): if verbose: print("----------------------------------------------------------") print("Working with 2 algorithms") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Working with 2 algorithms\n") file.write("----------------------------------------------------------\n") twoAlgorithmsNonParametric(algorithms, accuracies,alpha,file, fileResults, verbose) else: if verbose: print("----------------------------------------------------------") print("Working with more than 2 algorithms") print("----------------------------------------------------------") file.write("----------------------------------------------------------\n") file.write("Working with more than 2 algorithms\n") file.write("----------------------------------------------------------\n") multipleAlgorithmsNonParametric(algorithms,accuracies,file, fileResults, alpha, verbose) file.close() def compare_method(tuple): iteration, train_index,test_index,data,labels, clf, params, name,metric = tuple trainData, testData = data[train_index], data[test_index] trainLabels, testLabels = labels[train_index], labels[test_index] #print("Iteration " + str(iteration) + " of " + name) if params is None: model = clf model.fit(trainData, trainLabels) else: try: model = RandomizedSearchCV(clf, param_distributions=params, n_iter=20,random_state=84) model.fit(trainData, trainLabels) except ValueError: model = RandomizedSearchCV(clf, param_distributions=params, n_iter=5,random_state=84) model.fit(trainData, trainLabels) predictions = model.predict(testData) try: if (metric == 'accuracy'): return (name, accuracy_score(testLabels, predictions)) elif (metric == 'recall'): return (name, recall_score(testLabels, predictions)) elif (metric == 'precision'): return (name, precision_score(testLabels, predictions)) elif (metric == 'f1'): return (name, f1_score(testLabels, predictions)) elif (metric == 'auroc'): return (name, roc_auc_score(testLabels, predictions)) except ValueError: print("In the multiclass problem, only accuracy can be used as metric") return def compare_methods(data,labels,listAlgorithms,listParameters,listAlgorithmNames,metric='accuracy',alpha=0.5): if(metric!='accuracy' and metric!='recall' and metric!='precision' and metric!='f1' and metric!='auroc'): print("Invalid metric") return kf = KFold(n_splits=10,shuffle=True,random_state=42) results = {name:[] for name in listAlgorithmNames} tuple = [(i,train_index,test_index,data,labels,x,y,z,metric) for i,(train_index,test_index) in enumerate(kf.split(data)) for (x, y, z) in zip(listAlgorithms, listParameters, listAlgorithmNames)] p = ThreadPool(len(listAlgorithms)) comparison=p.map(compare_method,tuple) for (name,comp) in comparison: results[name].append(comp) df = pd.DataFrame.from_dict(results, orient='index') df.to_csv('temp.csv') statisticalComparison('temp.csv',alpha=alpha)

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0.750188

0.714954

0.691178

0.652555

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0.009221

0.251279

36,935

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191

51.947961

0.663014

0.069988

0

0.682131

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0.318684

0.120498

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false

0

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null

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1

0

1

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null

0

5

281b744451fcf30e4f8e95a284d436d4f68d4ddc

48,634

py

Python

orc-schema/config.py

Lanceolata/log2hdfs

1ea1b572567dbe1e65f4134849b1970f21baf741

[ "MIT" ]

null

orc-schema/config.py

Lanceolata/log2hdfs

1ea1b572567dbe1e65f4134849b1970f21baf741

[ "MIT" ]

null

orc-schema/config.py

Lanceolata/log2hdfs

1ea1b572567dbe1e65f4134849b1970f21baf741

[ "MIT" ]

null

# ------------------------------------------------------------------- # v6-logs version = [ (0, 0, 'version', 'int') ] sign = [ (0, 0, 'uuid', 'string'), (1, 0, 'actionUuid', 'string'), (2, 0, 'actionHost', 'string'), (3, 0, 'actionUri', 'string'), (4, 0, 'type', 'string'), (5, 0, 'sessionId', 'string'), (6, 0, 'platform', 'string'), (7, 0, 'exBidRequestId', 'string'), (8, 0, 'exImpId', 'string'), (9, 0, 'exImpIndex', 'int'), (10, 0, 'requestTime', 'bigint'), (11, 0, 'responseTime', 'bigint'), (12, 0, 'test', 'boolean'), (13, 0, 'bidUuid', 'string'), (14, 0, 'bidActionUuid', 'string'), (15, 0, 'bidRequestTime', 'bigint'), (16, 0, 'bidLogShard', 'string'), (17, 4, 'subPlatform', 'string') ] user = [ (0, 0, 'pyid', 'string'), (1, 0, 'cookieId', 'string'), (2, 0, 'exid', 'string'), (3, 0, 'newUser', 'boolean'), (4, 0, 'dnt', 'boolean'), (5, 0, 'pyTags', 'string'), (6, 0, 'exTags', 'string'), (7, 0, 'pyTagsFromExTags', 'string'), (8, 0, 'placeholder1', 'string'), (9, 0, 'placeholder2', 'string'), (10, 0, 'placeholder3', 'string'), (11, 0, 'placeholder4', 'string'), (12, 0, 'baiduAmsTags', 'string') ] geo = [ (0, 0, 'ipv4', 'string'), (1, 0, 'ipv6', 'string'), (2, 0, 'utcMinutesOffset', 'int'), (3, 0, 'exid', 'string'), (4, 0, 'longitude', 'double'), (5, 0, 'latitude', 'double'), (6, 0, 'country', 'int'), (7, 0, 'province', 'int'), (8, 0, 'city', 'int'), (9, 0, 'gpsCountry', 'int'), (10, 0, 'gpsProvince', 'int'), (11, 0, 'gpsCity', 'int'), (12, 0, 'gpsStreet', 'string'), (13, 0, 'gpsDetail', 'string') ] site = [ (0, 0, 'flag', 'boolean'), (1, 0, 'url', 'string'), (2, 0, 'urlAnonymous', 'boolean'), (3, 0, 'referUrl', 'string'), (4, 0, 'mobileOptimized', 'boolean'), (5, 0, 'search', 'string'), (6, 0, 'keywords', 'string'), (7, 0, 'charset', 'string'), (8, 0, 'categories', 'string'), (9, 0, 'domain', 'string'), (10, 0, 'contentVerticalFirst', 'string'), (11, 0, 'contentVerticalSecond', 'string'), (12, 0, 'contentTitle', 'string'), (13, 0, 'contentKeywords', 'string') ] app = [ (0, 0, 'flag', 'boolean'), (1, 0, 'id', 'string'), (2, 0, 'name', 'string'), (3, 0, 'storeUrl', 'string'), (4, 0, 'version', 'string'), (5, 0, 'paid', 'boolean'), (6, 0, 'bundle', 'string'), (7, 0, 'keywords', 'string'), (8, 0, 'charset', 'string'), (9, 0, 'categories', 'string'), (10, 0, 'contentVerticalFirst', 'string'), (11, 0, 'contentVerticalSecond', 'string'), (12, 0, 'contentTitle', 'string'), (13, 0, 'contentKeywords', 'string') ] device = [ (0, 0, 'deviceType', 'string'), (1, 0, 'ua', 'string'), (2, 0, 'os', 'string'), (3, 0, 'osVersion', 'string'), (4, 0, 'browser', 'string'), (5, 0, 'browserVersion', 'string'), (6, 0, 'brand', 'string'), (7, 0, 'model', 'string'), (8, 0, 'screenWidth', 'int'), (9, 0, 'screenHeight', 'int'), (10, 0, 'carrier', 'string'), (11, 0, 'networkGeneration', 'string'), (12, 0, 'limitAdTracking', 'boolean'), (13, 0, 'mac', 'string'), (14, 0, 'macEnc', 'string'), (15, 0, 'imei', 'string'), (16, 0, 'imeiEnc', 'string'), (17, 0, 'imsi', 'string'), (18, 0, 'imsiEnc', 'string'), (19, 0, 'dpid', 'string'), (20, 0, 'dpidEnc', 'string'), (21, 0, 'adid', 'string'), (22, 0, 'adidEnc', 'string'), (23, 0, 'pxratio', 'double'), (24, 0, 'mobileDeviceType', 'string') ] adSlot = [ (0, 0, 'tagid', 'string'), (1, 0, 'type', 'string'), (2, 0, 'auctionType', 'string'), (3, 0, 'bannerViewType', 'string'), (4, 0, 'videoViewType', 'string'), (5, 0, 'nativeViewType', 'string'), (6, 0, 'width', 'int'), (7, 0, 'height', 'int'), (8, 0, 'optionalSizes', 'string'), (9, 0, 'placeholder1', 'string'), (10, 0, 'placeholder2', 'string'), (11, 0, 'placeholder3', 'string'), (12, 0, 'location', 'string'), (13, 0, 'secure', 'boolean'), (14, 0, 'floorPrice', 'double'), (15, 0, 'mimes', 'string'), (16, 0, 'expandables', 'string'), (17, 0, 'apiFrameworks', 'string'), (18, 0, 'minDuration', 'int'), (19, 0, 'maxDuration', 'int'), (20, 0, 'placeholder4', 'string'), (21, 0, 'minBitRate', 'double'), (22, 0, 'maxBitRate', 'double'), (23, 0, 'placeholder5', 'string'), (24, 0, 'placeholder6', 'string'), (25, 0, 'placeholder7', 'string'), (26, 0, 'placeholder8', 'string'), (27, 0, 'placeholder9', 'string'), (28, 0, 'pmp', 'boolean'), (29, 0, 'deals', 'string'), (30, 0, 'privateAuction', 'boolean'), (31, 0, 'placeholder11', 'string'), (32, 0, 'placeholder12', 'string'), (33, 0, 'fpTable', 'string'), (34, 0, 'floorPriceUse', 'double'), (35, 3, 'deeplinkSupportType', 'string') ] bid = [ (0, 0, 'placeholder', 'string'), (1, 0, 'bidPrice', 'double'), (2, 0, 'winPrice', 'double'), (3, 0, 'dealId', 'bigint'), (4, 0, 'unbidReason', 'string'), (5, 0, 'dealType', 'string'), (6, 0, 'orgWinPrice', 'double'), (7, 0, 'settlementType', 'string'), (8, 0, 'dspPrice', 'double'), (9, 0, 'carouselRuleId', 'bigint'), (10, 0, 'creativeGroupId', 'bigint'), (11, 1, 'pmdBidType', 'string') ] creative = [ (0, 0, 'clientId', 'bigint'), (1, 0, 'partnerId', 'bigint'), (2, 0, 'advertiserId', 'bigint'), (3, 0, 'advertiserCompanyId', 'bigint'), (4, 0, 'insertionOrderId', 'bigint'), (5, 0, 'lineItemId', 'bigint'), (6, 0, 'lineItemPath', 'string'), (7, 0, 'creativeId', 'string'), (8, 0, 'creativeExid', 'string'), (9, 0, 'creativeGroupId', 'string'), (10, 0, 'width', 'int'), (11, 0, 'height', 'int'), (12, 0, 'creativeType', 'string'), (13, 0, 'impressionUnits', 'string'), (14, 0, 'clickUnit', 'string'), (15, 0, 'thirdClickThroughUrl', 'string'), (16, 0, 'thirdImpressionTrackingUrls', 'string'), (17, 0, 'advertiserExid', 'string'), (18, 0, 'templateId', 'bigint'), (19, 0, 'impProductNumbers', 'string'), (20, 0, 'clickProductNumber', 'string'), (21, 5, 'subdivisionBudgetPath', 'string') ] algo = [ (0, 0, 'spamLevel', 'int'), (1, 0, 'cheatingReason', 'string'), (2, 0, 'bidModule', 'string'), (3, 0, 'bidModuleVersion', 'string'), (4, 0, 'bidData', 'string'), (5, 0, 'rankModule', 'string'), (6, 0, 'rankModuleVersion', 'string'), (7, 0, 'rankData', 'string'), (8, 0, 'creativeDecisionModuleModule', 'string'), (9, 0, 'creativeDecisionModuleVersion', 'string'), (10, 0, 'creativeDecisionData', 'string'), (11, 0, 'algoFilter', 'int'), (12, 0, 'algoBidTag1', 'int'), (13, 0, 'algoBidTag2', 'int') ] ts = [ (0, 0, 'grapeshot', 'string'), (1, 0, 'ias', 'string'), (2, 0, 'prebidAdmaster', 'string'), (3, 2, 'prebidMiaozhen', 'string') ] trace = [ (0, 0, 'apc', 'string'), (1, 0, 'adMatherSize', 'int'), (2, 0, 'dealSize', 'int'), (3, 0, 'frequencySize', 'int'), (4, 0, 'guard', 'string') ] ext = [ (0, 0, 'feedback', 'string') ] publisher = [ (0, 0, 'name', 'string') ] tax = [ (0, 0, 'exchangeLossRate', 'double'), (1, 0, 'mediaCostRate', 'double'), (2, 0, 'serviceFeeRate', 'double'), (3, 0, 'serviceFeeVatRate', 'double'), (4, 0, 'mediaCostVatRate', 'double') ] v6 = [ ('version', version), ('sign', sign), ('user', user), ('geo', geo), ('site', site), ('app', app), ('device', device), ('adSlot', adSlot), ('bid', bid), ('creative', creative), ('algo', algo), ('ts', ts), ('trace', trace), ('ext', ext), ('publisher', publisher) ] click_bid = [ ('ic', v6), ('bid', v6) ] impression_bid = [ ('ic', v6), ('bid', v6), ('tax', tax) ] # ------------------------------------------------------------------- # report report_base = [ (0, 0, 'partner_id', 'bigint'), (1, 0, 'advertiser_company_id', 'bigint'), (2, 0, 'advertiser_id', 'bigint'), (3, 0, 'order_id', 'bigint'), (4, 0, 'campaign_id', 'bigint'), (5, 0, 'sub_campaign_id', 'bigint'), (6, 0, 'exe_campaign_id', 'bigint'), (7, 0, 'vertical_tag_id', 'bigint'), (8, 0, 'conversion_pixel', 'bigint'), (9, 0, 'creative_size', 'string'), (10, 0, 'creative_id', 'string'), (11, 0, 'creative_type', 'string'), (12, 0, 'country_id', 'bigint'), (13, 0, 'province_id', 'bigint'), (14, 0, 'city_id', 'bigint'), (15, 0, 'inventory_type', 'string'), (16, 0, 'ad_slot_type', 'string'), (17, 0, 'banner_view_type', 'string'), (18, 0, 'video_view_type', 'string'), (19, 0, 'native_view_type', 'string'), (20, 0, 'ad_unit_id', 'string'), (21, 0, 'ad_unit_width', 'bigint'), (22, 0, 'ad_unit_height', 'bigint'), (23, 0, 'platform', 'string'), (24, 0, 'domain_category', 'string'), (25, 0, 'top_level_domain', 'string'), (26, 0, 'domain', 'string'), (27, 0, 'app_category', 'string'), (28, 0, 'app_name', 'string'), (29, 0, 'app_id', 'string'), (30, 0, 'content_vertical_first', 'string'), (31, 0, 'content_title', 'string'), (32, 0, 'deal_type', 'string'), (33, 0, 'deal_id', 'string'), (34, 0, 'device_type', 'string'), (35, 0, 'os', 'string'), (36, 0, 'browser', 'string'), (37, 0, 'brand', 'string'), (38, 0, 'model', 'string'), (39, 0, 'network_generation', 'string'), (40, 0, 'carrier', 'string'), (41, 0, 'mob_device_type', 'string'), (42, 0, 'mac', 'string'), (43, 0, 'mac_enc', 'string'), (44, 0, 'imei', 'string'), (45, 0, 'imei_enc', 'string'), (46, 0, 'imsi', 'string'), (47, 0, 'imsi_enc', 'string'), (48, 0, 'dpid', 'string'), (49, 0, 'dpidenc', 'string'), (50, 0, 'adid', 'string'), (51, 0, 'adid_enc', 'string'), (52, 0, 'raw_media_cost', 'double'), (53, 0, 'media_cost', 'double'), (54, 0, 'service_fee', 'double'), (55, 0, 'media_tax', 'double'), (56, 0, 'service_tax', 'double'), (57, 0, 'total_cost', 'double'), (58, 0, 'system_loss', 'double'), (59, 0, 'unbid', 'bigint'), (60, 0, 'bid', 'bigint'), (61, 0, 'imp', 'bigint'), (62, 0, 'click', 'bigint'), (63, 0, 'reach', 'bigint'), (64, 0, 'two_jump', 'bigint'), (65, 0, 'click_conversion', 'bigint'), (66, 0, 'imp_conversion', 'bigint'), (67, 0, 'vast_point1', 'bigint'), (68, 0, 'vast_point2', 'bigint'), (69, 0, 'vast_point3', 'bigint'), (70, 0, 'vast_point4', 'bigint'), (71, 0, 'vast_point5', 'bigint'), (72, 0, 'measurable_impression', 'bigint'), (73, 0, 'viewable_impression', 'bigint'), (74, 0, 'viewability', 'bigint'), (75, 0, 'bid_policy_data', 'bigint'), (76, 0, 'bid_policy_version_1', 'bigint'), (77, 0, 'bid_policy_version_2', 'bigint'), (78, 0, 'bid_policy_version_3', 'bigint'), (79, 0, 'bid_policy_version_4', 'bigint'), (80, 0, 'day', 'int'), (81, 0, 'hour', 'int'), (82, 0, 'spamlevel', 'int'), (83, 0, 'bidmodule', 'string'), (84, 0, 'bidmoduleversion', 'int'), (85, 0, 'rankmodule', 'string'), (86, 0, 'rankmoduleversion', 'int'), (87, 0, 'creativedecisionmodule', 'string'), (88, 0, 'creativedecisionmoduleversion', 'int'), (89, 0, 'algobidtag1', 'int'), (90, 0, 'algobidtag2', 'int'), (91, 0, 'uuid', 'string'), (92, 0, 'pyid', 'string'), (93, 0, 'pytags', 'string'), (94, 0, 'extags', 'string'), (95, 0, 'pyfromextags', 'string'), (96, 0, 'client_id', 'bigint'), (97, 0, 'campaign_division_id', 'bigint'), (98, 0, 'sub_campaign_division_id', 'bigint'), (99, 0, 'exe_campaign_division_id', 'bigint'), (100, 0 , 'sub_platform', 'string'), (101, 0, 'request_day', 'bigint'), (102, 0, 'request_hour', 'bigint'), ] report_base_day = [ (0, 0, 'partner_id', 'bigint'), (1, 0, 'advertiser_company_id', 'bigint'), (2, 0, 'advertiser_id', 'bigint'), (3, 0, 'order_id', 'bigint'), (4, 0, 'campaign_id', 'bigint'), (5, 0, 'sub_campaign_id', 'bigint'), (6, 0, 'exe_campaign_id', 'bigint'), (7, 0, 'vertical_tag_id', 'bigint'), (8, 0, 'conversion_pixel', 'bigint'), (9, 0, 'creative_size', 'string'), (10, 0, 'creative_id', 'string'), (11, 0, 'creative_type', 'string'), (12, 0, 'inventory_type', 'string'), (13, 0, 'ad_slot_type', 'string'), (14, 0, 'platform', 'string'), (15, 0, 'raw_media_cost', 'double'), (16, 0, 'media_cost', 'double'), (17, 0, 'service_fee', 'double'), (18, 0, 'media_tax', 'double'), (19, 0, 'service_tax', 'double'), (20, 0, 'total_cost', 'double'), (21, 0, 'system_loss', 'double'), (22, 0, 'bid', 'bigint'), (23, 0, 'imp', 'bigint'), (24, 0, 'click', 'bigint'), (25, 0, 'reach', 'bigint'), (26, 0, 'two_jump', 'bigint'), (27, 0, 'click_conversion', 'bigint'), (28, 0, 'imp_conversion', 'bigint'), (29, 0, 'day', 'int'), (30, 0, 'client_id', 'bigint'), (31, 0, 'campaign_division_id', 'bigint'), (32, 0, 'sub_campaign_division_id', 'bigint'), (33, 0, 'exe_campaign_division_id', 'bigint'), (34, 0 , 'sub_platform', 'string'), (35, 0, 'request_day', 'bigint'), (36, 0, 'request_hour', 'bigint'), ] report_conversion_click = [ (0, 0, 'partner_id', 'bigint'), (1, 0, 'advertiser_company_id', 'bigint'), (2, 0, 'advertiser_id', 'bigint'), (3, 0, 'order_id', 'bigint'), (4, 0, 'campaign_id', 'bigint'), (5, 0, 'sub_campaign_id', 'bigint'), (6, 0, 'exe_campaign_id', 'bigint'), (7, 0, 'vertical_tag_id', 'bigint'), (8, 0, 'conversion_pixel', 'bigint'), (9, 0, 'creative_size', 'string'), (10, 0, 'creative_id', 'string'), (11, 0, 'creative_type', 'string'), (12, 0, 'country_id', 'bigint'), (13, 0, 'province_id', 'bigint'), (14, 0, 'city_id', 'bigint'), (15, 0, 'inventory_type', 'string'), (16, 0, 'ad_slot_type', 'string'), (17, 0, 'banner_view_type', 'string'), (18, 0, 'video_view_type', 'string'), (19, 0, 'native_view_type', 'string'), (20, 0, 'ad_unit_id', 'string'), (21, 0, 'ad_unit_width', 'bigint'), (22, 0, 'ad_unit_height', 'bigint'), (23, 0, 'platform', 'string'), (24, 0, 'domain_category', 'string'), (25, 0, 'top_level_domain', 'string'), (26, 0, 'domain', 'string'), (27, 0, 'app_category', 'string'), (28, 0, 'app_name', 'string'), (29, 0, 'app_id', 'string'), (30, 0, 'content_vertical_first', 'string'), (31, 0, 'content_title', 'string'), (32, 0, 'deal_type', 'string'), (33, 0, 'deal_id', 'string'), (34, 0, 'device_type', 'string'), (35, 0, 'os', 'string'), (36, 0, 'browser', 'string'), (37, 0, 'brand', 'string'), (38, 0, 'model', 'string'), (39, 0, 'network_generation', 'string'), (40, 0, 'carrier', 'string'), (41, 0, 'mob_device_type', 'string'), (42, 0, 'mac', 'string'), (43, 0, 'mac_enc', 'string'), (44, 0, 'imei', 'string'), (45, 0, 'imei_enc', 'string'), (46, 0, 'imsi', 'string'), (47, 0, 'imsi_enc', 'string'), (48, 0, 'dpid', 'string'), (49, 0, 'dpid_enc', 'string'), (50, 0, 'adid', 'string'), (51, 0, 'adid_enc', 'string'), (52, 0, 'pyid', 'string'), (53, 0, 'daat', 'string'), (54, 0, 'imp_request_time', 'string'), (55, 0, 'clk_request_time', 'string'), (56, 0, 'cvt_request_time', 'string'), (57, 0, 'session_id', 'string'), (58, 0, 'orderno', 'string'), (59, 0, 'money', 'string'), (60, 0, 'product_list', 'string'), (61, 0, 'cvt_url', 'string'), (62, 0, 'cvt_refer_url', 'string'), (63, 0, 'clk_url', 'string'), (64, 0, 'imp_url', 'string'), (65, 0, 'pday', 'bigint'), (66, 0, 'cvt_action_id', 'string'), (67, 0, 'phour', 'bigint'), (68, 0, 'spamlevel', 'bigint'), (69, 0, 'bidmodule', 'string'), (70, 0, 'bidmoduleversion', 'string'), (71, 0, 'rankmodule', 'string'), (72, 0, 'rankmoduleversion', 'string'), (73, 0, 'creativedecisionmodule', 'string'), (74, 0, 'creativedecisionmoduleversion', 'string'), (75, 0, 'algobidtag1', 'bigint'), (76, 0, 'algobidtag2', 'bigint'), (77, 0, 'cvtpyid', 'string'), (78, 0, 'extags', 'string'), (79, 0, 'pytagsfromextags', 'string'), ] report_reach_click = [ (0, 0, 'action_id', 'string'), (1, 0, 'action_log_version', 'string'), (2, 0, 'action_type', 'string'), (3, 0, 'action_platform', 'string'), (4, 0, 'action_first_id', 'string'), (5, 0, 'action_session_id', 'string'), (6, 0, 'action_request_time', 'string'), (7, 0, 'action_response_time', 'string'), (8, 0, 'action_first_time', 'string'), (9, 0, 'action_prev_time', 'string'), (10, 0, 'action_is_ping', 'string'), (11, 0, 'action_is_test', 'string'), (12, 0, 'action_prev_id', 'string'), (13, 0, 'action_placeholder2', 'string'), (14, 0, 'action_extend_data', 'string'), (15, 0, 'user_id', 'string'), (16, 0, 'user_tid', 'string'), (17, 0, 'user_new', 'string'), (18, 0, 'user_placeholder1', 'string'), (19, 0, 'user_extend_data', 'string'), (20, 0, 'action_uri', 'string'), (21, 0, 'agent_url', 'string'), (22, 0, 'agent_anonymous_id', 'string'), (23, 0, 'agent_refer_url', 'string'), (24, 0, 'agent_type', 'string'), (25, 0, 'agent_ua', 'string'), (26, 0, 'agent_charset', 'string'), (27, 0, 'agent_app_id', 'string'), (28, 0, 'agent_categories', 'string'), (29, 0, 'agent_placeholder2', 'string'), (30, 0, 'agent_placeholder3', 'string'), (31, 0, 'agent_extend_data', 'string'), (32, 0, 'geo_ip', 'string'), (33, 0, 'geo_time_offset_minutes', 'string'), (34, 0, 'geo_id', 'string'), (35, 0, 'geo_tid', 'string'), (36, 0, 'geo_cell', 'string'), (37, 0, 'geo_l1', 'string'), (38, 0, 'geo_placeholder1', 'string'), (39, 0, 'geo_placeholder2', 'string'), (40, 0, 'geo_extend_data', 'string'), (41, 0, 'device_type', 'string'), (42, 0, 'device_os', 'string'), (43, 0, 'device_brand', 'string'), (44, 0, 'device_model', 'string'), (45, 0, 'device_carrier_id', 'string'), (46, 0, 'device_is_mobile_web_optimized', 'string'), (47, 0, 'device_ids', 'string'), (48, 0, 'device_placeholder3', 'string'), (49, 0, 'device_extend_data', 'string'), (50, 0, 'id_advertiser_company_id', 'string'), (51, 0, 'item', 'string'), (52, 0, 'strategy_id', 'string'), (53, 0, 'creative_id', 'string'), (54, 0, 'creative_unit_id', 'string'), (55, 0, 'ad_unit_id', 'string'), (56, 0, 'audience_decisions', 'string'), (57, 0, 'utm', 'string'), (58, 0, 'ad_unit_domain', 'string'), (59, 0, 'ad_unit_anonymous_id', 'string'), (60, 0, 'ad_unit_app_id', 'string'), (61, 0, 'ad_unit_location', 'string'), (62, 0, 'ad_unit_view_type', 'string'), (63, 0, 'jump', 'string'), (64, 0, 'param_invalid', 'string'), (65, 0, 'oem_name', 'string'), (66, 0, 'platform_mapping', 'string'), (67, 0, 'extend_data', 'string'), (68, 0, 'clk_version_version', 'string'), (69, 0, 'clk_sign_uuid', 'string'), (70, 0, 'clk_sign_action_uuid', 'string'), (71, 0, 'clk_sign_action_host', 'string'), (72, 0, 'clk_sign_action_uri', 'string'), (73, 0, 'clk_sign_type', 'string'), (74, 0, 'clk_sign_session_id', 'string'), (75, 0, 'clk_sign_platform', 'string'), (76, 0, 'clk_sign_ex_bid_request_id', 'string'), (77, 0, 'clk_sign_ex_imp_id', 'string'), (78, 0, 'clk_sign_ex_imp_index', 'string'), (79, 0, 'clk_sign_request_time', 'string'), (80, 0, 'clk_sign_response_time', 'string'), (81, 0, 'clk_sign_test', 'string'), (82, 0, 'clk_sign_bid_uuid', 'string'), (83, 0, 'clk_sign_bid_action_uuid', 'string'), (84, 0, 'clk_sign_bid_request_time', 'string'), (85, 0, 'clk_sign_bid_log_shard', 'string'), (86, 0, 'clk_user_pyid', 'string'), (87, 0, 'clk_user_cookie_id', 'string'), (88, 0, 'clk_user_exid', 'string'), (89, 0, 'clk_user_new_user', 'string'), (90, 0, 'clk_user_dnt', 'string'), (91, 0, 'clk_user_py_tags', 'string'), (92, 0, 'clk_user_ex_tags', 'string'), (93, 0, 'clk_user_py_tags_from_ex_tags', 'string'), (94, 0, 'clk_user_placeholder', 'string'), (95, 0, 'clk_user_placeholder2', 'string'), (96, 0, 'clk_user_placeholder3', 'string'), (97, 0, 'clk_user_placeholder4', 'string'), (98, 0, 'clk_user_baidu_ams_tags', 'string'), (99, 0, 'clk_geo_ipv4', 'string'), (100, 0, 'clk_geo_ipv6', 'string'), (101, 0, 'clk_geo_utc_minutes_offset', 'string'), (102, 0, 'clk_geo_exid', 'string'), (103, 0, 'clk_geo_longitude', 'string'), (104, 0, 'clk_geo_latitude', 'string'), (105, 0, 'clk_geo_country', 'string'), (106, 0, 'clk_geo_province', 'string'), (107, 0, 'clk_geo_city', 'string'), (108, 0, 'clk_geo_gps_country', 'string'), (109, 0, 'clk_geo_gps_province', 'string'), (110, 0, 'clk_geo_gps_city', 'string'), (111, 0, 'clk_geo_gps_street', 'string'), (112, 0, 'clk_geo_gps_detail', 'string'), (113, 0, 'clk_site_flag', 'string'), (114, 0, 'clk_site_url', 'string'), (115, 0, 'clk_site_url_anonymous', 'string'), (116, 0, 'clk_site_refer_url', 'string'), (117, 0, 'clk_site_mobile_optimized', 'string'), (118, 0, 'clk_site_search', 'string'), (119, 0, 'clk_site_keywords', 'string'), (120, 0, 'clk_site_charset', 'string'), (121, 0, 'clk_site_categories', 'string'), (122, 0, 'clk_site_domain', 'string'), (123, 0, 'clk_site_content_vertical_first', 'string'), (124, 0, 'clk_site_content_vertical_second', 'string'), (125, 0, 'clk_site_content_title', 'string'), (126, 0, 'clk_site_content_keywords', 'string'), (127, 0, 'clk_app_flag', 'string'), (128, 0, 'clk_app_id', 'string'), (129, 0, 'clk_app_name', 'string'), (130, 0, 'clk_app_store_url', 'string'), (131, 0, 'clk_app_version', 'string'), (132, 0, 'clk_app_paid', 'string'), (133, 0, 'clk_app_bundle', 'string'), (134, 0, 'clk_app_keywords', 'string'), (135, 0, 'clk_app_charset', 'string'), (136, 0, 'clk_app_categories', 'string'), (137, 0, 'clk_app_content_vertical_first', 'string'), (138, 0, 'clk_app_content_vertical_second', 'string'), (139, 0, 'clk_app_content_title', 'string'), (140, 0, 'clk_app_content_keywords', 'string'), (141, 0, 'clk_device_device_type', 'string'), (142, 0, 'clk_device_ua', 'string'), (143, 0, 'clk_device_os', 'string'), (144, 0, 'clk_device_os_version', 'string'), (145, 0, 'clk_device_browser', 'string'), (146, 0, 'clk_device_browser_version', 'string'), (147, 0, 'clk_device_brand', 'string'), (148, 0, 'clk_device_model', 'string'), (149, 0, 'clk_device_screen_width', 'string'), (150, 0, 'clk_device_screen_height', 'string'), (151, 0, 'clk_device_carrier', 'string'), (152, 0, 'clk_device_network_generation', 'string'), (153, 0, 'clk_device_limit_ad_tracking', 'string'), (154, 0, 'clk_device_mac', 'string'), (155, 0, 'clk_device_mac_enc', 'string'), (156, 0, 'clk_device_imei', 'string'), (157, 0, 'clk_device_imei_enc', 'string'), (158, 0, 'clk_device_imsi', 'string'), (159, 0, 'clk_device_imsi_enc', 'string'), (160, 0, 'clk_device_dpid', 'string'), (161, 0, 'clk_device_dpid_enc', 'string'), (162, 0, 'clk_device_adid', 'string'), (163, 0, 'clk_device_adid_enc', 'string'), (164, 0, 'clk_device_px_ratio', 'string'), (165, 0, 'clk_device_mobile_device_type', 'string'), (166, 0, 'clk_ad_slot_tag_id', 'string'), (167, 0, 'clk_ad_slot_type', 'string'), (168, 0, 'clk_ad_slot_auction_type', 'string'), (169, 0, 'clk_ad_slot_banner_view_type', 'string'), (170, 0, 'clk_ad_slot_video_view_type', 'string'), (171, 0, 'clk_ad_slot_native_view_type', 'string'), (172, 0, 'clk_ad_slot_width', 'string'), (173, 0, 'clk_ad_slot_height', 'string'), (174, 0, 'clk_ad_slot_optional_sizes', 'string'), (175, 0, 'clk_ad_slot_placeholder1', 'string'), (176, 0, 'clk_ad_slot_placeholder2', 'string'), (177, 0, 'clk_ad_slot_placeholder3', 'string'), (178, 0, 'clk_ad_slot_location', 'string'), (179, 0, 'clk_ad_slot_secure', 'string'), (180, 0, 'clk_ad_slot_floor_price', 'string'), (181, 0, 'clk_ad_slot_mimes', 'string'), (182, 0, 'clk_ad_slot_expandables', 'string'), (183, 0, 'clk_ad_slot_api_frameworks', 'string'), (184, 0, 'clk_ad_slot_min_duration', 'string'), (185, 0, 'clk_ad_slot_max_duration', 'string'), (186, 0, 'clk_ad_slot_placeholder4', 'string'), (187, 0, 'clk_ad_slot_min_bit_rate', 'string'), (188, 0, 'clk_ad_slot_max_bit_rate', 'string'), (189, 0, 'clk_ad_slot_placeholder5', 'string'), (190, 0, 'clk_ad_slot_placeholder6', 'string'), (191, 0, 'clk_ad_slot_placeholder7', 'string'), (192, 0, 'clk_ad_slot_placeholder8', 'string'), (193, 0, 'clk_ad_slot_placeholder9', 'string'), (194, 0, 'clk_ad_slot_pmp', 'string'), (195, 0, 'clk_ad_slot_deals', 'string'), (196, 0, 'clk_ad_slot_private_auction', 'string'), (197, 0, 'clk_ad_slot_placeholder11', 'string'), (198, 0, 'clk_ad_slot_placeholder12', 'string'), (199, 0, 'clk_bid_placeholder', 'string'), (200, 0, 'clk_bid_bid_price', 'string'), (201, 0, 'clk_bid_win_price', 'string'), (202, 0, 'clk_bid_deal_id', 'string'), (203, 0, 'clk_bid_unbid_reason', 'string'), (204, 0, 'clk_bid_deal_type', 'string'), (205, 0, 'clk_creative_client_id', 'string'), (206, 0, 'clk_creative_partner_id', 'string'), (207, 0, 'clk_creative_advertiser_id', 'string'), (208, 0, 'clk_creative_advertiser_company_id', 'string'), (209, 0, 'clk_creative_insertion_order_id', 'string'), (210, 0, 'clk_creative_line_item_id', 'string'), (211, 0, 'clk_creative_line_item_path', 'string'), (212, 0, 'clk_creative_creative_id', 'string'), (213, 0, 'clk_creative_creative_exid', 'string'), (214, 0, 'clk_creative_creative_group_id', 'string'), (215, 0, 'clk_creative_width', 'string'), (216, 0, 'clk_creative_height', 'string'), (217, 0, 'clk_creative_creative_type', 'string'), (218, 0, 'clk_creative_impression_units', 'string'), (219, 0, 'clk_creative_click_unit', 'string'), (220, 0, 'clk_creative_third_click_through_url', 'string'), (221, 0, 'clk_creative_third_impression_tracking_urls', 'string'), (222, 0, 'clk_creative_advertiser_exid', 'string'), (223, 0, 'clk_creative_template_id', 'string'), (224, 0, 'clk_creative_imp_product_numbers', 'string'), (225, 0, 'clk_creative_click_product_number', 'string'), (226, 0, 'clk_algo_spam_level', 'string'), (227, 0, 'clk_algo_cheating_reason', 'string'), (228, 0, 'clk_algo_bid_module', 'string'), (229, 0, 'clk_algo_bid_module_version', 'string'), (230, 0, 'clk_algo_bid_data', 'string'), (231, 0, 'clk_algo_rank_module', 'string'), (232, 0, 'clk_algo_rank_module_version', 'string'), (233, 0, 'clk_algo_rank_data', 'string'), (234, 0, 'clk_algo_creative_decision_module_module', 'string'), (235, 0, 'clk_algo_creative_decision_module_version', 'string'), (236, 0, 'clk_algo_creative_decision_data', 'string'), (237, 0, 'clk_algo_algo_filter', 'string'), (238, 0, 'clk_algo_algo_bid_tag1', 'string'), (239, 0, 'clk_algo_algo_bid_tag2', 'string'), (240, 0, 'clk_ts_grapeshot', 'string'), (241, 0, 'clk_ts_ias', 'string'), (242, 0, 'clk_trace_apc', 'string'), (243, 0, 'clk_trace_ad_mather_size', 'string'), (244, 0, 'clk_trace_deal_size', 'string'), (245, 0, 'clk_trace_frequency_size', 'string'), (246, 0, 'clk_trace_guard', 'string'), (247, 0, 'clk_ext_feedback', 'string'), (248, 0, 'clk_publisher_name', 'string'), ] report_second_jump = [ (0, 0, 'action_id', 'string'), (1, 0, 'action_log_version', 'string'), (2, 0, 'action_type', 'string'), (3, 0, 'action_platform', 'string'), (4, 0, 'action_first_id', 'string'), (5, 0, 'action_session_id', 'string'), (6, 0, 'action_request_time', 'string'), (7, 0, 'action_response_time', 'string'), (8, 0, 'action_first_time', 'string'), (9, 0, 'action_prev_time', 'string'), (10, 0, 'action_is_ping', 'string'), (11, 0, 'action_is_test', 'string'), (12, 0, 'action_prev_id', 'string'), (13, 0, 'action_placeholder2', 'string'), (14, 0, 'action_extend_data', 'string'), (15, 0, 'user_id', 'string'), (16, 0, 'user_tid', 'string'), (17, 0, 'user_new', 'string'), (18, 0, 'user_placeholder1', 'string'), (19, 0, 'user_extend_data', 'string'), (20, 0, 'action_uri', 'string'), (21, 0, 'agent_url', 'string'), (22, 0, 'agent_anonymous_id', 'string'), (23, 0, 'agent_refer_url', 'string'), (24, 0, 'agent_type', 'string'), (25, 0, 'agent_ua', 'string'), (26, 0, 'agent_charset', 'string'), (27, 0, 'agent_app_id', 'string'), (28, 0, 'agent_categories', 'string'), (29, 0, 'agent_placeholder2', 'string'), (30, 0, 'agent_placeholder3', 'string'), (31, 0, 'agent_extend_data', 'string'), (32, 0, 'geo_ip', 'string'), (33, 0, 'geo_time_offset_minutes', 'string'), (34, 0, 'geo_id', 'string'), (35, 0, 'geo_tid', 'string'), (36, 0, 'geo_cell', 'string'), (37, 0, 'geo_l1', 'string'), (38, 0, 'geo_placeholder1', 'string'), (39, 0, 'geo_placeholder2', 'string'), (40, 0, 'geo_extend_data', 'string'), (41, 0, 'device_type', 'string'), (42, 0, 'device_os', 'string'), (43, 0, 'device_brand', 'string'), (44, 0, 'device_model', 'string'), (45, 0, 'device_carrier_id', 'string'), (46, 0, 'device_is_mobile_web_optimized', 'string'), (47, 0, 'device_ids', 'string'), (48, 0, 'device_placeholder3', 'string'), (49, 0, 'device_extend_data', 'string'), (50, 0, 'id_advertiser_company_id', 'string'), (51, 0, 'item', 'string'), (52, 0, 'strategy_id', 'string'), (53, 0, 'creative_id', 'string'), (54, 0, 'creative_unit_id', 'string'), (55, 0, 'ad_unit_id', 'string'), (56, 0, 'audience_decisions', 'string'), (57, 0, 'utm', 'string'), (58, 0, 'ad_unit_domain', 'string'), (59, 0, 'ad_unit_anonymous_id', 'string'), (60, 0, 'ad_unit_app_id', 'string'), (61, 0, 'ad_unit_location', 'string'), (62, 0, 'ad_unit_view_type', 'string'), (63, 0, 'jump', 'string'), (64, 0, 'param_invalid', 'string'), (65, 0, 'oem_name', 'string'), (66, 0, 'platform_mapping', 'string'), (67, 0, 'extend_data', 'string'), (68, 0, 'clk_version_version', 'string'), (69, 0, 'clk_sign_uuid', 'string'), (70, 0, 'clk_sign_action_uuid', 'string'), (71, 0, 'clk_sign_action_host', 'string'), (72, 0, 'clk_sign_action_uri', 'string'), (73, 0, 'clk_sign_type', 'string'), (74, 0, 'clk_sign_session_id', 'string'), (75, 0, 'clk_sign_platform', 'string'), (76, 0, 'clk_sign_ex_bid_request_id', 'string'), (77, 0, 'clk_sign_ex_imp_id', 'string'), (78, 0, 'clk_sign_ex_imp_index', 'string'), (79, 0, 'clk_sign_request_time', 'string'), (80, 0, 'clk_sign_response_time', 'string'), (81, 0, 'clk_sign_test', 'string'), (82, 0, 'clk_sign_bid_uuid', 'string'), (83, 0, 'clk_sign_bid_action_uuid', 'string'), (84, 0, 'clk_sign_bid_request_time', 'string'), (85, 0, 'clk_sign_bid_log_shard', 'string'), (86, 0, 'clk_user_pyid', 'string'), (87, 0, 'clk_user_cookie_id', 'string'), (88, 0, 'clk_user_exid', 'string'), (89, 0, 'clk_user_new_user', 'string'), (90, 0, 'clk_user_dnt', 'string'), (91, 0, 'clk_user_py_tags', 'string'), (92, 0, 'clk_user_ex_tags', 'string'), (93, 0, 'clk_user_py_tags_from_ex_tags', 'string'), (94, 0, 'clk_user_placeholder', 'string'), (95, 0, 'clk_user_placeholder2', 'string'), (96, 0, 'clk_user_placeholder3', 'string'), (97, 0, 'clk_user_placeholder4', 'string'), (98, 0, 'clk_user_baidu_ams_tags', 'string'), (99, 0, 'clk_geo_ipv4', 'string'), (100, 0, 'clk_geo_ipv6', 'string'), (101, 0, 'clk_geo_utc_minutes_offset', 'string'), (102, 0, 'clk_geo_exid', 'string'), (103, 0, 'clk_geo_longitude', 'string'), (104, 0, 'clk_geo_latitude', 'string'), (105, 0, 'clk_geo_country', 'string'), (106, 0, 'clk_geo_province', 'string'), (107, 0, 'clk_geo_city', 'string'), (108, 0, 'clk_geo_gps_country', 'string'), (109, 0, 'clk_geo_gps_province', 'string'), (110, 0, 'clk_geo_gps_city', 'string'), (111, 0, 'clk_geo_gps_street', 'string'), (112, 0, 'clk_geo_gps_detail', 'string'), (113, 0, 'clk_site_flag', 'string'), (114, 0, 'clk_site_url', 'string'), (115, 0, 'clk_site_url_anonymous', 'string'), (116, 0, 'clk_site_refer_url', 'string'), (117, 0, 'clk_site_mobile_optimized', 'string'), (118, 0, 'clk_site_search', 'string'), (119, 0, 'clk_site_keywords', 'string'), (120, 0, 'clk_site_charset', 'string'), (121, 0, 'clk_site_categories', 'string'), (122, 0, 'clk_site_domain', 'string'), (123, 0, 'clk_site_content_vertical_first', 'string'), (124, 0, 'clk_site_content_vertical_second', 'string'), (125, 0, 'clk_site_content_title', 'string'), (126, 0, 'clk_site_content_keywords', 'string'), (127, 0, 'clk_app_flag', 'string'), (128, 0, 'clk_app_id', 'string'), (129, 0, 'clk_app_name', 'string'), (130, 0, 'clk_app_store_url', 'string'), (131, 0, 'clk_app_version', 'string'), (132, 0, 'clk_app_paid', 'string'), (133, 0, 'clk_app_bundle', 'string'), (134, 0, 'clk_app_keywords', 'string'), (135, 0, 'clk_app_charset', 'string'), (136, 0, 'clk_app_categories', 'string'), (137, 0, 'clk_app_content_vertical_first', 'string'), (138, 0, 'clk_app_content_vertical_second', 'string'), (139, 0, 'clk_app_content_title', 'string'), (140, 0, 'clk_app_content_keywords', 'string'), (141, 0, 'clk_device_device_type', 'string'), (142, 0, 'clk_device_ua', 'string'), (143, 0, 'clk_device_os', 'string'), (144, 0, 'clk_device_os_version', 'string'), (145, 0, 'clk_device_browser', 'string'), (146, 0, 'clk_device_browser_version', 'string'), (147, 0, 'clk_device_brand', 'string'), (148, 0, 'clk_device_model', 'string'), (149, 0, 'clk_device_screen_width', 'string'), (150, 0, 'clk_device_screen_height', 'string'), (151, 0, 'clk_device_carrier', 'string'), (152, 0, 'clk_device_network_generation', 'string'), (153, 0, 'clk_device_limit_ad_tracking', 'string'), (154, 0, 'clk_device_mac', 'string'), (155, 0, 'clk_device_mac_enc', 'string'), (156, 0, 'clk_device_imei', 'string'), (157, 0, 'clk_device_imei_enc', 'string'), (158, 0, 'clk_device_imsi', 'string'), (159, 0, 'clk_device_imsi_enc', 'string'), (160, 0, 'clk_device_dpid', 'string'), (161, 0, 'clk_device_dpid_enc', 'string'), (162, 0, 'clk_device_adid', 'string'), (163, 0, 'clk_device_adid_enc', 'string'), (164, 0, 'clk_device_px_ratio', 'string'), (165, 0, 'clk_device_mobile_device_type', 'string'), (166, 0, 'clk_ad_slot_tag_id', 'string'), (167, 0, 'clk_ad_slot_type', 'string'), (168, 0, 'clk_ad_slot_auction_type', 'string'), (169, 0, 'clk_ad_slot_banner_view_type', 'string'), (170, 0, 'clk_ad_slot_video_view_type', 'string'), (171, 0, 'clk_ad_slot_native_view_type', 'string'), (172, 0, 'clk_ad_slot_width', 'string'), (173, 0, 'clk_ad_slot_height', 'string'), (174, 0, 'clk_ad_slot_optional_sizes', 'string'), (175, 0, 'clk_ad_slot_placeholder1', 'string'), (176, 0, 'clk_ad_slot_placeholder2', 'string'), (177, 0, 'clk_ad_slot_placeholder3', 'string'), (178, 0, 'clk_ad_slot_location', 'string'), (179, 0, 'clk_ad_slot_secure', 'string'), (180, 0, 'clk_ad_slot_floor_price', 'string'), (181, 0, 'clk_ad_slot_mimes', 'string'), (182, 0, 'clk_ad_slot_expandables', 'string'), (183, 0, 'clk_ad_slot_api_frameworks', 'string'), (184, 0, 'clk_ad_slot_min_duration', 'string'), (185, 0, 'clk_ad_slot_max_duration', 'string'), (186, 0, 'clk_ad_slot_placeholder4', 'string'), (187, 0, 'clk_ad_slot_min_bit_rate', 'string'), (188, 0, 'clk_ad_slot_max_bit_rate', 'string'), (189, 0, 'clk_ad_slot_placeholder5', 'string'), (190, 0, 'clk_ad_slot_placeholder6', 'string'), (191, 0, 'clk_ad_slot_placeholder7', 'string'), (192, 0, 'clk_ad_slot_placeholder8', 'string'), (193, 0, 'clk_ad_slot_placeholder9', 'string'), (194, 0, 'clk_ad_slot_pmp', 'string'), (195, 0, 'clk_ad_slot_deals', 'string'), (196, 0, 'clk_ad_slot_private_auction', 'string'), (197, 0, 'clk_ad_slot_placeholder11', 'string'), (198, 0, 'clk_ad_slot_placeholder12', 'string'), (199, 0, 'clk_bid_placeholder', 'string'), (200, 0, 'clk_bid_bid_price', 'string'), (201, 0, 'clk_bid_win_price', 'string'), (202, 0, 'clk_bid_deal_id', 'string'), (203, 0, 'clk_bid_unbid_reason', 'string'), (204, 0, 'clk_bid_deal_type', 'string'), (205, 0, 'clk_creative_client_id', 'string'), (206, 0, 'clk_creative_partner_id', 'string'), (207, 0, 'clk_creative_advertiser_id', 'string'), (208, 0, 'clk_creative_advertiser_company_id', 'string'), (209, 0, 'clk_creative_insertion_order_id', 'string'), (210, 0, 'clk_creative_line_item_id', 'string'), (211, 0, 'clk_creative_line_item_path', 'string'), (212, 0, 'clk_creative_creative_id', 'string'), (213, 0, 'clk_creative_creative_exid', 'string'), (214, 0, 'clk_creative_creative_group_id', 'string'), (215, 0, 'clk_creative_width', 'string'), (216, 0, 'clk_creative_height', 'string'), (217, 0, 'clk_creative_creative_type', 'string'), (218, 0, 'clk_creative_impression_units', 'string'), (219, 0, 'clk_creative_click_unit', 'string'), (220, 0, 'clk_creative_third_click_through_url', 'string'), (221, 0, 'clk_creative_third_impression_tracking_urls', 'string'), (222, 0, 'clk_creative_advertiser_exid', 'string'), (223, 0, 'clk_creative_template_id', 'string'), (224, 0, 'clk_creative_imp_product_numbers', 'string'), (225, 0, 'clk_creative_click_product_number', 'string'), (226, 0, 'clk_algo_spam_level', 'string'), (227, 0, 'clk_algo_cheating_reason', 'string'), (228, 0, 'clk_algo_bid_module', 'string'), (229, 0, 'clk_algo_bid_module_version', 'string'), (230, 0, 'clk_algo_bid_data', 'string'), (231, 0, 'clk_algo_rank_module', 'string'), (232, 0, 'clk_algo_rank_module_version', 'string'), (233, 0, 'clk_algo_rank_data', 'string'), (234, 0, 'clk_algo_creative_decision_module_module', 'string'), (235, 0, 'clk_algo_creative_decision_module_version', 'string'), (236, 0, 'clk_algo_creative_decision_data', 'string'), (237, 0, 'clk_algo_algo_filter', 'string'), (238, 0, 'clk_algo_algo_bid_tag1', 'string'), (239, 0, 'clk_algo_algo_bid_tag2', 'string'), (240, 0, 'clk_ts_grapeshot', 'string'), (241, 0, 'clk_ts_ias', 'string'), (242, 0, 'clk_trace_apc', 'string'), (243, 0, 'clk_trace_ad_mather_size', 'string'), (244, 0, 'clk_trace_deal_size', 'string'), (245, 0, 'clk_trace_frequency_size', 'string'), (246, 0, 'clk_trace_guard', 'string'), (247, 0, 'clk_ext_feedback', 'string'), (248, 0, 'clk_publisher_name', 'string'), ] report_stats_service = [ (0, 0, 'imp_uuid', 'string'), (1, 0, 'partner_id', 'bigint'), (2, 0, 'advertiser_id', 'bigint'), (3, 0, 'order_id', 'bigint'), (4, 0, 'campaign_id', 'bigint'), (5, 0, 'sub_campaign_id', 'bigint'), (6, 0, 'exe_campaign_id', 'bigint'), (7, 0, 'service_provider', 'string'), (8, 0, 'service_category', 'string'), (9, 0, 'service_segments', 'string'), (10, 0, 'imp', 'bigint'), (11, 0, 'unit_price', 'double'), (12, 0, 'third_party_service_fee', 'double'), (13, 0, 'pday', 'bigint'), ] report_pdb_analysis = [ (0, 0, 'source', 'string'), (1, 0, 'day', 'bigint'), (2, 0, 'partner_id', 'bigint'), (3, 0, 'platform', 'string'), (4, 0, 'deal_id', 'string'), (5, 0, 'deal_type', 'string'), (6, 0, 'pyid', 'string'), (7, 0, 'request_time', 'string'), (8, 0, 'algoFilter', 'bigint'), (9, 0, 'dealSize', 'bigint'), (10, 0, 'frequencySize', 'bigint'), (11, 0, 'request', 'bigint'), (12, 0, 'optimization', 'bigint'), (13, 0, 'return', 'bigint'), (14, 0, 'imp', 'bigint'), (15, 0, 'click', 'bigint'), ] report_conversion_imp = [ (0, 0, 'partner_id', 'bigint'), (1, 0, 'advertiser_company_id', 'bigint'), (2, 0, 'advertiser_id', 'bigint'), (3, 0, 'order_id', 'bigint'), (4, 0, 'campaign_id', 'bigint'), (5, 0, 'sub_campaign_id', 'bigint'), (6, 0, 'exe_campaign_id', 'bigint'), (7, 0, 'vertical_tag_id', 'bigint'), (8, 0, 'conversion_pixel', 'bigint'), (9, 0, 'creative_size', 'string'), (10, 0, 'creative_id', 'string'), (11, 0, 'creative_type', 'string'), (12, 0, 'country_id', 'bigint'), (13, 0, 'province_id', 'bigint'), (14, 0, 'city_id', 'bigint'), (15, 0, 'inventory_type', 'string'), (16, 0, 'ad_slot_type', 'string'), (17, 0, 'banner_view_type', 'string'), (18, 0, 'video_view_type', 'string'), (19, 0, 'native_view_type', 'string'), (20, 0, 'ad_unit_id', 'string'), (21, 0, 'ad_unit_width', 'bigint'), (22, 0, 'ad_unit_height', 'bigint'), (23, 0, 'platform', 'string'), (24, 0, 'domain_category', 'string'), (25, 0, 'top_level_domain', 'string'), (26, 0, 'domain', 'string'), (27, 0, 'app_category', 'string'), (28, 0, 'app_name', 'string'), (29, 0, 'app_id', 'string'), (30, 0, 'content_vertical_first', 'string'), (31, 0, 'content_title', 'string'), (32, 0, 'deal_type', 'string'), (33, 0, 'deal_id', 'string'), (34, 0, 'device_type', 'string'), (35, 0, 'os', 'string'), (36, 0, 'browser', 'string'), (37, 0, 'brand', 'string'), (38, 0, 'model', 'string'), (39, 0, 'network_generation', 'string'), (40, 0, 'carrier', 'string'), (41, 0, 'mob_device_type', 'string'), (42, 0, 'mac', 'string'), (43, 0, 'mac_enc', 'string'), (44, 0, 'imei', 'string'), (45, 0, 'imei_enc', 'string'), (46, 0, 'imsi', 'string'), (47, 0, 'imsi_enc', 'string'), (48, 0, 'dpid', 'string'), (49, 0, 'dpid_enc', 'string'), (50, 0, 'adid', 'string'), (51, 0, 'adid_enc', 'string'), (52, 0, 'pyid', 'string'), (53, 0, 'daat', 'string'), (54, 0, 'imp_request_time', 'string'), (55, 0, 'clk_request_time', 'string'), (56, 0, 'cvt_request_time', 'string'), (57, 0, 'session_id', 'string'), (58, 0, 'money', 'string'), (59, 0, 'orderno', 'string'), (60, 0, 'product_list', 'string'), (61, 0, 'cvt_url', 'string'), (62, 0, 'cvt_refer_url', 'string'), (63, 0, 'clk_url', 'string'), (64, 0, 'imp_url', 'string'), (65, 0, 'day', 'bigint'), (66, 0, 'cvt_action_id', 'string'), (67, 0, 'hour', 'bigint'), (68, 0, 'spamlevel', 'bigint'), (69, 0, 'bidmodule', 'string'), (70, 0, 'bidmoduleversion', 'string'), (71, 0, 'rankmodule', 'string'), (72, 0, 'rankmoduleversion', 'string'), (73, 0, 'creativedecisionmodule', 'string'), (74, 0, 'creativedecisionmoduleversion', 'string'), (75, 0, 'algobidtag1', 'bigint'), (76, 0, 'algobidtag2', 'bigint'), (77, 0, 'cvtpyid', 'string'), (78, 0, 'extags', 'string'), (79, 0, 'pytagsfromextags', 'string'), (80, 0, 'uuid', 'string') ] report_other_cvt = [ (0, 0, 'partner_id', 'bigint'), (1, 0, 'advertiser_company_id', 'bigint'), (2, 0, 'advertiser_id', 'bigint'), (3, 0, 'order_id', 'bigint'), (4, 0, 'campaign_id', 'bigint'), (5, 0, 'sub_campaign_id', 'bigint'), (6, 0, 'exe_campaign_id', 'bigint'), (7, 0, 'vertical_tag_id', 'bigint'), (8, 0, 'conversion_pixel', 'bigint'), (9, 0, 'creative_size', 'string'), (10, 0, 'creative_id', 'string'), (11, 0, 'creative_type', 'string'), (12, 0, 'country_id', 'bigint'), (13, 0, 'province_id', 'bigint'), (14, 0, 'city_id', 'bigint'), (15, 0, 'inventory_type', 'string'), (16, 0, 'ad_slot_type', 'string'), (17, 0, 'banner_view_type', 'string'), (18, 0, 'video_view_type', 'string'), (19, 0, 'native_view_type', 'string'), (20, 0, 'ad_unit_id', 'string'), (21, 0, 'ad_unit_width', 'bigint'), (22, 0, 'ad_unit_height', 'bigint'), (23, 0, 'platform', 'string'), (24, 0, 'domain_category', 'string'), (25, 0, 'top_level_domain', 'string'), (26, 0, 'domain', 'string'), (27, 0, 'app_category', 'string'), (28, 0, 'app_name', 'string'), (29, 0, 'app_id', 'string'), (30, 0, 'content_vertical_first', 'string'), (31, 0, 'content_title', 'string'), (32, 0, 'deal_type', 'string'), (33, 0, 'deal_id', 'string'), (34, 0, 'device_type', 'string'), (35, 0, 'os', 'string'), (36, 0, 'browser', 'string'), (37, 0, 'brand', 'string'), (38, 0, 'model', 'string'), (39, 0, 'network_generation', 'string'), (40, 0, 'carrier', 'string'), (41, 0, 'mob_device_type', 'string'), (42, 0, 'mac', 'string'), (43, 0, 'mac_enc', 'string'), (44, 0, 'imei', 'string'), (45, 0, 'imei_enc', 'string'), (46, 0, 'imsi', 'string'), (47, 0, 'imsi_enc', 'string'), (48, 0, 'dpid', 'string'), (49, 0, 'dpid_enc', 'string'), (50, 0, 'adid', 'string'), (51, 0, 'adid_enc', 'string'), (52, 0, 'pyid', 'string'), (53, 0, 'daat', 'string'), (54, 0, 'imp_request_time', 'string'), (55, 0, 'clk_request_time', 'string'), (56, 0, 'cvt_request_time', 'string'), (57, 0, 'session_id', 'string'), (58, 0, 'money', 'string'), (59, 0, 'orderno', 'string'), (60, 0, 'product_list', 'string'), (61, 0, 'cvt_url', 'string'), (62, 0, 'cvt_refer_url', 'string'), (63, 0, 'clk_url', 'string'), (64, 0, 'imp_url', 'string'), (65, 0, 'pday', 'bigint'), (66, 0, 'cvt_action_id', 'string'), (67, 0, 'phour', 'bigint'), (68, 0, 'spamlevel', 'bigint'), (69, 0, 'bidmodule', 'bigint'), (70, 0, 'bidmoduleversion', 'bigint'), (71, 0, 'rankmodule', 'bigint'), (72, 0, 'rankmoduleversion', 'bigint'), (73, 0, 'creativedecisionmodule', 'bigint'), (74, 0, 'creativedecisionmoduleversion', 'bigint'), (75, 0, 'algobidtag1', 'bigint'), (76, 0, 'algobidtag2', 'bigint'), (77, 0, 'cvtpyid', 'string') ] rpt_effect_pdb_return_reason = [ (0, 0, 'day', 'bigint'), (1, 0, 'partner_id', 'bigint'), (2, 0, 'platform', 'string'), (3, 0, 'deal_id', 'string'), (4, 0, 'virtual_cost', 'double'), (5, 0, 'request', 'bigint'), (6, 0, 'optimization', 'bigint'), (7, 0, 'return', 'bigint'), (8, 0, 'imp', 'bigint'), (9, 0, 'click', 'bigint'), (10, 0, 'media_over_frequency', 'bigint'), (11, 0, 'returnby_media_frequency', 'bigint'), (12, 0, 'returnby_non_media_frequency', 'bigint'), (13, 0, 'returnby_union_frequency', 'bigint'), (14, 0, 'returnby_algo_optimization', 'bigint'), (15, 0, 'returnby_other_reasons', 'bigint'), ]

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6,255

48,634

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null

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1

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null

0

5

283f222931087e039b10a56bf02aa5de5e8bde30

96

py

Python

pytorch_optimizers/__init__.py

madsbk/pytorch_optimizers

10eefd83277237d3aa2788a2a5a47ba3294a6e50

[ "Apache-2.0" ]

null

pytorch_optimizers/__init__.py

madsbk/pytorch_optimizers

10eefd83277237d3aa2788a2a5a47ba3294a6e50

[ "Apache-2.0" ]

null

pytorch_optimizers/__init__.py

madsbk/pytorch_optimizers

10eefd83277237d3aa2788a2a5a47ba3294a6e50

[ "Apache-2.0" ]

null

from .adam import Adam from .adamw import AdamW from .radam import RAdam, PlainRAdam, FusedRAdam

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0.5

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0.135417

96

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null

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1

0

5

28443295e72e4b565ecf7211fde2f46aabd9f701

236

py

Python

helper_utils/helperutils/audio_file_checker.py

splitstrument/utils

8e33caec64dfd66369c3a19c069cf4a946a3fc95

[ "MIT" ]

null

helper_utils/helperutils/audio_file_checker.py

splitstrument/utils

8e33caec64dfd66369c3a19c069cf4a946a3fc95

[ "MIT" ]

null

helper_utils/helperutils/audio_file_checker.py

splitstrument/utils

8e33caec64dfd66369c3a19c069cf4a946a3fc95

[ "MIT" ]

null

import os accepted_audio_extensions = ['.wav', '.mp3', '.flac', '.aif', '.ogg'] def is_accepted_audio_file(filename): _, extension = os.path.splitext(filename) return extension.lower() in accepted_audio_extensions, extension

26.222222

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null

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1

0

5

284584960940e7e7b80f37fe05f0d30ba0dc04a0

6,525

py

Python

bmtk/tests/utils/reports/spike_trains/test_csv_adaptor.py

xpliu16/bmtk

a34dbdc5979985a9b9bfcdf2386c92ac0b6e844f

[ "BSD-3-Clause" ]

null

bmtk/tests/utils/reports/spike_trains/test_csv_adaptor.py

xpliu16/bmtk

a34dbdc5979985a9b9bfcdf2386c92ac0b6e844f

[ "BSD-3-Clause" ]

null

bmtk/tests/utils/reports/spike_trains/test_csv_adaptor.py

xpliu16/bmtk

a34dbdc5979985a9b9bfcdf2386c92ac0b6e844f

[ "BSD-3-Clause" ]

null

import pytest import numpy as np import tempfile import pandas as pd from six import string_types from bmtk.utils.reports.spike_trains.spike_train_buffer import STMemoryBuffer, STCSVBuffer from bmtk.utils.reports.spike_trains.spike_train_readers import CSVSTReader from bmtk.utils.reports.spike_trains.spikes_file_writers import write_csv, write_csv_itr from bmtk.utils.reports.spike_trains import sort_order def create_st_buffer(st_cls): # Helper for creating spike_trains object if issubclass(st_cls, STCSVBuffer): return st_cls(cache_dir=tempfile.mkdtemp()) else: return st_cls() @pytest.mark.parametrize('st_cls', [ STMemoryBuffer, STCSVBuffer ]) @pytest.mark.parametrize('write_fnc', [ write_csv, write_csv_itr ]) def test_write_csv(st_cls, write_fnc): st = create_st_buffer(st_cls) st.add_spikes(population='V1', node_ids=0, timestamps=np.linspace(0, 1.0, 100)) st.add_spikes(population='V1', node_ids=2, timestamps=np.linspace(2.0, 1.0, 10)) st.add_spike(population='V1', node_id=1, timestamp=3.0) st.add_spikes(population='V2', node_ids=[3, 3, 3], timestamps=[0.25, 0.5, 0.75]) tmpfile = tempfile.NamedTemporaryFile(suffix='.csv') write_fnc(tmpfile.name, st) df = pd.read_csv(tmpfile.name, sep=' ') assert(df.shape == (114, 3)) assert(set(df.columns) == {'timestamps', 'population', 'node_ids'}) assert(set(df['population'].unique()) == {'V1', 'V2'}) assert(np.allclose(np.sort(df[(df['population'] == 'V1') & (df['node_ids'] == 0)]['timestamps']), np.linspace(0, 1.0, 100), atol=1.0e-5)) assert(np.allclose(np.sort(df[(df['population'] == 'V2') & (df['node_ids'] == 3)]['timestamps']), [0.25, 0.5, 0.75])) @pytest.mark.parametrize('st_cls', [ STMemoryBuffer, STCSVBuffer ]) @pytest.mark.parametrize('write_fnc', [ write_csv, write_csv_itr ]) def test_write_csv_bytime(st_cls, write_fnc): # Check we can sort by timestamps st = create_st_buffer(st_cls) st.add_spikes(population='V1', node_ids=0, timestamps=[0.5, 0.3, 0.1, 0.2, 0.4]) tmpfile = tempfile.NamedTemporaryFile(suffix='.csv') write_fnc(tmpfile.name, st, sort_order=sort_order.by_time) df = pd.read_csv(tmpfile.name, sep=' ') assert(df.shape == (5, 3)) assert(np.all(df['population'].unique() == 'V1')) assert(np.all(df['node_ids'].unique() == 0)) assert(np.all(df['timestamps'] == [0.1, 0.2, 0.3, 0.4, 0.5])) @pytest.mark.parametrize('st_cls', [ STMemoryBuffer, STCSVBuffer ]) @pytest.mark.parametrize('write_fnc', [ write_csv, write_csv_itr ]) def test_write_csv_byid(st_cls, write_fnc): # Check we can sort by node_ids st = create_st_buffer(st_cls) st.add_spikes(population='V1', node_ids=[2, 4, 2, 1, 3, 3, 6, 0], timestamps=[0.1]*8) tmpfile = tempfile.NamedTemporaryFile(suffix='.csv') write_fnc(tmpfile.name, st, sort_order=sort_order.by_id) df = pd.read_csv(tmpfile.name, sep=' ') assert(df.shape == (8, 3)) assert(np.all(df['population'].unique() == 'V1')) assert(np.all(df['node_ids'] == [0, 1, 2, 2, 3, 3, 4, 6])) assert(np.all(df['timestamps'] == [0.1]*8)) def test_csv_reader(): df = pd.DataFrame({ 'node_ids': [0, 0, 0, 0, 2, 1, 2] + [10, 10, 10], 'population': ['V1']*7 + ['V2']*3, 'timestamps': [0.25, 0.5, 0.75, 1.0, 3.0, 0.001, 2.0] + [4.0, 4.0, 4.0] }) tmpfile = tempfile.NamedTemporaryFile(suffix='.csv') df.to_csv(tmpfile.name, sep=' ', columns=['timestamps', 'population', 'node_ids']) st = CSVSTReader(path=tmpfile.name, default_population='V1') assert(set(st.populations) == {'V1', 'V2'}) assert(st.n_spikes() == 7) assert(st.n_spikes(population='V1') == 7) assert(st.n_spikes(population='V2') == 3) assert(set(st.node_ids()) == {0, 1, 2}) assert(set(st.node_ids(population='V1')) == {0, 1, 2}) assert(np.all(st.node_ids(population='V2') == [10])) assert(np.allclose(np.sort(st.get_times(0)), [0.25, 0.50, 0.75, 1.0])) assert(np.allclose(st.get_times(1, population='V1'), [0.001])) assert(np.allclose(st.get_times(10, population='V2'), [4.0, 4.0, 4.0])) df = st.to_dataframe() assert(len(df) == 10) assert(set(df.columns) == {'timestamps', 'population', 'node_ids'}) df = st.to_dataframe(populations='V1', sort_order=sort_order.by_id, with_population_col=False) assert(len(df) == 7) assert(set(df.columns) == {'timestamps', 'node_ids'}) assert(np.all(np.diff(df['node_ids']) >= 0)) all_spikes = list(st.spikes()) assert(len(all_spikes) == 10) assert(isinstance(all_spikes[0][0], (float, float))) assert(isinstance(all_spikes[0][1], string_types)) assert(isinstance(all_spikes[0][2], (int, np.uint, np.integer))) def test_csv_reader_nopop(): df = pd.DataFrame({ 'node_ids': [0, 0, 0, 0, 2, 1, 2] + [10, 10, 10], # 'population': ['V1']*7 + ['V2']*3, 'timestamps': [0.25, 0.5, 0.75, 1.0, 3.0, 0.001, 2.0] + [4.0, 4.0, 4.0] }) tmpfile = tempfile.NamedTemporaryFile(suffix='.csv') df.to_csv(tmpfile.name, sep=' ', header=False, index=False, columns=['timestamps', 'node_ids']) st = CSVSTReader(path=tmpfile.name, default_population='V1') assert(set(st.populations) == {'V1'}) assert(st.n_spikes() == 10) assert(set(st.node_ids()) == {0, 1, 2, 10}) assert(np.allclose(np.sort(st.get_times(0)), [0.25, 0.50, 0.75, 1.0])) assert(np.allclose(st.get_times(1, population='V1'), [0.001])) assert(np.allclose(st.get_times(10, population='V1'), [4.0, 4.0, 4.0])) df = st.to_dataframe() assert(len(df) == 10) assert(set(df.columns) == {'timestamps', 'population', 'node_ids'}) df = st.to_dataframe(populations='V1', sort_order=sort_order.by_id, with_population_col=False) assert(len(df) == 10) assert(set(df.columns) == {'timestamps', 'node_ids'}) assert(np.all(np.diff(df['node_ids']) >= 0)) all_spikes = list(st.spikes()) assert(len(all_spikes) == 10) assert(isinstance(all_spikes[0][0], (float, float))) assert(isinstance(all_spikes[0][1], string_types)) assert(isinstance(all_spikes[0][2], (int, np.uint, np.integer))) if __name__ == '__main__': # test_write_csv(STMemoryBuffer, write_csv) # test_write_csv(STMemoryBuffer, write_csv_itr) # test_write_csv_bytime(STMemoryBuffer, write_csv_itr) # test_write_csv_byid(STMemoryBuffer, write_csv) # test_csv_reader() test_csv_reader_nopop()

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28520761a78f77950f6ca84abec753844ce21ece

4,322

py

Python

pymnn/pip_package/MNN/tools/mnn_fb/Op.py

xhuan28/MNN

81df3a48d79cbc0b75251d12934345948866f7be

[ "Apache-2.0" ]

3

2019-12-27T01:10:32.000Z

2021-05-14T08:10:40.000Z

pymnn/pip_package/MNN/tools/mnn_fb/Op.py

xhuan28/MNN

81df3a48d79cbc0b75251d12934345948866f7be

[ "Apache-2.0" ]

10

2019-07-04T01:40:13.000Z

2019-10-30T02:38:42.000Z

pymnn/pip_package/MNN/tools/mnn_fb/Op.py

xhuan28/MNN

81df3a48d79cbc0b75251d12934345948866f7be

[ "Apache-2.0" ]

1

2020-03-10T02:17:47.000Z

# automatically generated by the FlatBuffers compiler, do not modify # namespace: MNN import flatbuffers class Op(object): __slots__ = ['_tab'] @classmethod def GetRootAsOp(cls, buf, offset): n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) x = Op() x.Init(buf, n + offset) return x # Op def Init(self, buf, pos): self._tab = flatbuffers.table.Table(buf, pos) # Op def InputIndexes(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) return 0 # Op def InputIndexesAsNumpy(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) return 0 # Op def InputIndexesLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return self._tab.VectorLen(o) return 0 # Op def MainType(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) return 0 # Op def Main(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: from flatbuffers.table import Table obj = Table(bytearray(), 0) self._tab.Union(obj, o) return obj return None # Op def Name(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return self._tab.String(o + self._tab.Pos) return None # Op def OutputIndexes(self, j): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) if o != 0: a = self._tab.Vector(o) return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) return 0 # Op def OutputIndexesAsNumpy(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) if o != 0: return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) return 0 # Op def OutputIndexesLength(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) if o != 0: return self._tab.VectorLen(o) return 0 # Op def Type(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) if o != 0: return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) return 0 # Op def DefaultDimentionFormat(self): o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) if o != 0: return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos) return 1 def OpStart(builder): builder.StartObject(7) def OpAddInputIndexes(builder, inputIndexes): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(inputIndexes), 0) def OpStartInputIndexesVector(builder, numElems): return builder.StartVector(4, numElems, 4) def OpAddMainType(builder, mainType): builder.PrependUint8Slot(1, mainType, 0) def OpAddMain(builder, main): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(main), 0) def OpAddName(builder, name): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) def OpAddOutputIndexes(builder, outputIndexes): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(outputIndexes), 0) def OpStartOutputIndexesVector(builder, numElems): return builder.StartVector(4, numElems, 4) def OpAddType(builder, type): builder.PrependInt32Slot(5, type, 0) def OpAddDefaultDimentionFormat(builder, defaultDimentionFormat): builder.PrependInt8Slot(6, defaultDimentionFormat, 1) def OpEnd(builder): return builder.EndObject()

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2897b692218aa9ae48a1b45b96d4a1ca827e77ba

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Python

tests/test_tasks.py

florinapp/florin-notifier

8b3a807d06969e7bd521a35581979e5ed64dbe7b

[ "MIT" ]

null

tests/test_tasks.py

florinapp/florin-notifier

8b3a807d06969e7bd521a35581979e5ed64dbe7b

[ "MIT" ]

null

tests/test_tasks.py

florinapp/florin-notifier

8b3a807d06969e7bd521a35581979e5ed64dbe7b

[ "MIT" ]

null

import json import freezegun import pytest import mock import os import contextlib from redis import Redis from florin_notifier.tasks import notify_tangerine_transactions @pytest.fixture def redis(): r = Redis(host=os.getenv('REDIS_HOST', 'localhost'), port=os.getenv('REDIS_PORT', 6379)) for key in r.scan_iter("scrape:*"): r.delete(key) return r @pytest.fixture def tangerine_client(): m = mock.Mock() @contextlib.contextmanager def fake_ctx_mgr(): yield m.login = fake_ctx_mgr return m @pytest.fixture def email(): return mock.Mock() @pytest.fixture def sendgrid_client(): return mock.Mock() @freezegun.freeze_time('2017-11-10T12:00:00') def test_notify_tangerine_transactions___no_previous_scrapes(redis, tangerine_client, email): tangerine_client.list_transactions.return_value = [] notify_tangerine_transactions(['12345', '45678'], 'SECRET', 'foo@example.com', tangerine_client, email) assert redis.keys('scrape:tangerine*') == [b'scrape:tangerine:2017-11-10T12:00:00'] assert redis.get(b'scrape:tangerine:2017-11-10T12:00:00') == b'[]' @freezegun.freeze_time('2017-11-10T12:00:00.1111') def test_notify_tangerine_transactions___with_previous_scrape_on_a_different_day(redis, tangerine_client, email): txn_1 = { 'transaction_date': '2017-11-01T07:17:03', 'amount': -55.54, 'description': 'BUY STUFF', 'type': 'WITHDRAWAL', 'account_id': '12345', 'id': 123456789, 'posted_date': '2017-11-04T00:00:00', 'status': 'POSTED' } txn_2 = { 'transaction_date': '2017-11-10T07:17:03', 'amount': -100.99, 'description': 'BUY STUFF #2', 'type': 'WITHDRAWAL', 'account_id': '12345', 'id': 123456789, 'posted_date': '2017-11-04T00:00:00', 'status': 'POSTED' } redis.set('scrape:tangerine:2017-11-09T12:10:11', json.dumps([txn_1])) tangerine_client.list_transactions.return_value = [txn_2] notify_tangerine_transactions(['12345', '45678'], 'SECRET', 'foo@example.com', tangerine_client, email) assert sorted(redis.keys('scrape:tangerine*')) == [ b'scrape:tangerine:2017-11-09T12:10:11', b'scrape:tangerine:2017-11-10T12:00:00.111100'] assert email.send_new_transaction_email.call_args_list[0][0][:2] == ('foo@example.com', {'12345': [txn_2]}) @freezegun.freeze_time('2017-11-10T12:00:00.1111') def test_notify_tangerine_transactions___with_previous_scrape_on_a_different_day_multi_accts(redis, tangerine_client, email): txn_1 = { 'transaction_date': '2017-11-01T07:17:03', 'amount': -55.54, 'description': 'BUY STUFF', 'type': 'WITHDRAWAL', 'account_id': '12345', 'id': 123456789, 'posted_date': '2017-11-04T00:00:00', 'status': 'POSTED' } txn_2 = { 'transaction_date': '2017-11-10T07:17:03', 'amount': -100.99, 'description': 'BUY STUFF #2', 'type': 'WITHDRAWAL', 'account_id': '12345', 'id': 123456789, 'posted_date': '2017-11-04T00:00:00', 'status': 'POSTED' } txn_3 = { 'transaction_date': '2017-11-10T07:17:03', 'amount': -55.54, 'description': 'BUY STUFF', 'type': 'WITHDRAWAL', 'account_id': '45678', 'id': 123456789, 'posted_date': '2017-11-04T00:00:00', 'status': 'POSTED' } redis.set('scrape:tangerine:2017-11-09T12:10:11', json.dumps([txn_1])) tangerine_client.list_transactions.return_value = [txn_2, txn_3] notify_tangerine_transactions(['12345', '45678'], 'SECRET', 'foo@example.com', tangerine_client, email) assert sorted(redis.keys('scrape:tangerine*')) == [ b'scrape:tangerine:2017-11-09T12:10:11', b'scrape:tangerine:2017-11-10T12:00:00.111100'] assert email.send_new_transaction_email.call_args_list[0][0][:2] == ('foo@example.com', {'12345': [txn_2], '45678': [txn_3]})

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253c2e62a4f1ef8e610e5a90be0d5be93ee62de0

120

py

Python

decorate/__init__.py

thomas-smith-v/decorate

914e7b4828238359b24b5f5d6b35826d5e6c8882

[ "MIT" ]

null

decorate/__init__.py

thomas-smith-v/decorate

914e7b4828238359b24b5f5d6b35826d5e6c8882

[ "MIT" ]

null

decorate/__init__.py

thomas-smith-v/decorate

914e7b4828238359b24b5f5d6b35826d5e6c8882

[ "MIT" ]

null

from decorate.core import ( precall, postcall ) from decorate.debug import ( debug, debug_input, debug_output )

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254401064b1b7ea3ba5face9dec3e91974099154

191

py

Python

rl-toolkit/rlf/envs/__init__.py

clvrai/goal_prox_il

7c809b2ee575a69a14997068db06f3c1f3c8bd08

[ "MIT" ]

4

2021-11-17T20:19:34.000Z

2022-03-31T04:21:26.000Z

rl-toolkit/rlf/envs/__init__.py

clvrai/goal_prox_il

7c809b2ee575a69a14997068db06f3c1f3c8bd08

[ "MIT" ]

null

rl-toolkit/rlf/envs/__init__.py

clvrai/goal_prox_il

7c809b2ee575a69a14997068db06f3c1f3c8bd08

[ "MIT" ]

null

from rlf.envs.bit_flip import BIT_FLIP_ID from gym.envs.registration import register register( id=BIT_FLIP_ID, entry_point='tests.dev.her.bit_flip_env:BitFlipEnv', )

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2560578e23085038a46f97ce2be224b5358a4b88

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py

Python

shapely/examples/__init__.py

Jeremiah-England/Shapely

769b203f2b7cbeeb0a694c21440b4025a563f807

[ "BSD-3-Clause" ]

2,382

2015-01-04T03:16:59.000Z

2021-12-10T15:48:56.000Z

shapely/examples/__init__.py

Jeremiah-England/Shapely

769b203f2b7cbeeb0a694c21440b4025a563f807

[ "BSD-3-Clause" ]

1,009

2015-01-03T23:44:02.000Z

2021-12-10T16:02:42.000Z

shapely/examples/__init__.py

Jeremiah-England/Shapely

769b203f2b7cbeeb0a694c21440b4025a563f807

[ "BSD-3-Clause" ]

467

2015-01-19T23:18:33.000Z

2021-12-09T18:31:28.000Z

# Examples module

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25606fa7fe01161c1ab1d2e6e642bf1483440116

152

py

Python

instagram/admin.py

Muriithijoe/Instagram-project

0b94031c43fe961921e0b7ec055df5e645e06f52

[ "Unlicense" ]

null

instagram/admin.py

Muriithijoe/Instagram-project

0b94031c43fe961921e0b7ec055df5e645e06f52

[ "Unlicense" ]

null

instagram/admin.py

Muriithijoe/Instagram-project

0b94031c43fe961921e0b7ec055df5e645e06f52

[ "Unlicense" ]

null

from django.contrib import admin from .models import Profile,Post #Register your models here . admin.site.register(Profile) admin.site.register(Post)

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c261efe6e914809b607d732117130576702badcb

89

py

Python

src/ansible_navigator/__init__.py

LaudateCorpus1/ansible-navigator

28cdea13dba3e9039382eb993989db4b3e61b237

[ "Apache-2.0" ]

134

2021-03-26T17:44:49.000Z

2022-03-31T13:15:52.000Z

src/ansible_navigator/__init__.py

LaudateCorpus1/ansible-navigator

28cdea13dba3e9039382eb993989db4b3e61b237

[ "Apache-2.0" ]

631

2021-03-26T19:38:32.000Z

2022-03-31T22:57:36.000Z

src/ansible_navigator/__init__.py

LaudateCorpus1/ansible-navigator

28cdea13dba3e9039382eb993989db4b3e61b237

[ "Apache-2.0" ]

48

2021-03-26T17:44:29.000Z

2022-03-08T21:12:26.000Z

"""The ansible-navigator application.""" from ._version import __version__ # noqa: F401

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5

6c0d9c93724dc491a37ba1c2031c6dfee90ed2a8

140

py

Python

hackerrank/Python/Inner and Outer/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

4

2020-07-24T01:59:50.000Z

2021-07-24T15:14:08.000Z

hackerrank/Python/Inner and Outer/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

null

hackerrank/Python/Inner and Outer/solution.py

ATrain951/01.python-com_Qproject

c164dd093954d006538020bdf2e59e716b24d67c

[ "MIT" ]

null

import numpy a = numpy.array(input().split(), int) b = numpy.array(input().split(), int) print(numpy.inner(a, b)) print(numpy.outer(a, b))

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6c1a0778fcf27f5eb299da334d62241ed7900f9e

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py

Python

travis_doc/travis_doc.py

has2k1/travis_doc

1317091ebb224feb7627ab3e7a6a2e294afd203b

[ "BSD-3-Clause" ]

null

travis_doc/travis_doc.py

has2k1/travis_doc

1317091ebb224feb7627ab3e7a6a2e294afd203b

[ "BSD-3-Clause" ]

null

travis_doc/travis_doc.py

has2k1/travis_doc

1317091ebb224feb7627ab3e7a6a2e294afd203b

[ "BSD-3-Clause" ]

null

def function1(): """ Return 1 """ return 1 def function2(): """ Return 2 """ return 2

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6c2efdb2fa79f7fe8757e3c0ca7ce1ecc6db8a5b

97,152

py

Python

butterfly_data_generator/filename_to_code.py

hanayashiki/FasterRCNNForButterflyRecognition

ea1048626da0a9792387d1c93a27911ca6ce822c

[ "Apache-2.0" ]

1

2018-06-12T07:44:27.000Z

butterfly_data_generator/filename_to_code.py

hanayashiki/FasterRCNNForButterflyRecognition

ea1048626da0a9792387d1c93a27911ca6ce822c

[ "Apache-2.0" ]

null

butterfly_data_generator/filename_to_code.py

hanayashiki/FasterRCNNForButterflyRecognition

ea1048626da0a9792387d1c93a27911ca6ce822c

[ "Apache-2.0" ]

1

2021-01-14T04:44:38.000Z

wild_names = \ {'bg': 0, '中环蛱蝶': 1, '云粉蝶': 3, '云豹蛱蝶': 3, '亮灰蝶': 11, '伊诺小豹蛱蝶': 8, '侏粉蝶': 1, '依帕绢蝶': 2, '古北拟酒眼蝶': 3, '咖灰蝶': 3, '四川绢蝶': 1, '大卫粉蝶': 6, '大紫琉璃灰蝶': 28, '大翅绢粉蝶': 5, '婀灰蝶': 10, '宽边黄粉蝶': 24, '密纹飒弄蝶': 2, '小红蛱蝶': 3, '小黄斑弄蝶': 2, '尖翅翠蛱蝶': 7, '山豆粉蝶': 1, '巴黎翠凤蝶': 32, '扬眉线蛱蝶': 37, '拟稻眉眼蝶': 2, '斐豹蛱蝶': 1, '无斑珂弄蝶': 4, '曲斑珠蛱蝶': 2, '曲纹紫灰蝶': 3, '朴喙蝶': 3, '柑橘凤蝶': 23, '柱菲蛱蝶': 4, '柳紫闪蛱蝶': 5, '橙黄豆粉蝶': 2, '波太玄灰蝶': 9, '灿福蛱蝶': 6, '牧女珍眼蝶': 8, '玄灰蝶': 2, '玄珠带蛱蝶': 2, '玉带凤蝶': 1, '珍珠绢蝶': 3, '珍蛱蝶': 2, '琉璃蛱蝶': 3, '白眼蝶': 9, '白钩蛱蝶': 1, '直纹稻弄蝶': 7, '碧凤蝶': 45, '秀蛱蝶': 4, '突角小粉蝶': 5, '箭纹云粉蝶': 3, '箭纹绢粉蝶': 5, '素弄蝶': 26, '红基美凤蝶': 21, '红灰蝶': 7, '红襟粉蝶': 1, '线灰蝶': 11, '绢斑蝶': 1, '绢粉蝶': 2, '绢蛱蝶': 14, '维纳斯眼灰蝶': 1, '绿豹蛱蝶': 5, '网蛱蝶': 7, '美眼蛱蝶': 2, '翠蓝眼蛱蝶': 4, '翠袖锯眼蝶': 28, '老豹蛱蝶': 5, '艳灰蝶': 2, '花弄蝶': 18, '荨麻蛱蝶': 5, '菜粉蝶': 4, '菩萨酒眼蝶': 1, '蓝凤蝶': 10, '蓝灰蝶': 5, '蓝点紫斑蝶': 10, '虎斑蝶': 1, '虬眉带蛱蝶': 2, '蛇目褐蚬蝶': 13, '蟾福蛱蝶': 4, '西门珍眼蝶': 2, '边纹黛眼蝶': 1, '金裳凤蝶': 4, '钩翅眼蛱蝶': 2, '银斑豹蛱蝶': 92, '银豹蛱蝶': 2, '链环蛱蝶': 2, '锦瑟蛱蝶': 1, '镉黄迁粉蝶': 2, '阿芬眼蝶': 42, '隐纹谷弄蝶': 4, '雅弄蝶': 20, '青凤蝶': 8, '青海红珠灰蝶': 6, '黄环蛱蝶': 13, '黄钩蛱蝶': 20, '黎明豆粉蝶': 2, '黑网蛱蝶': 1} class_name_sequence = """ 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ALac0024006aa01b ALac0024007aa01c ALac0024007aa01d ALac0024008xx01c ALac0024008xx01d ALac0024008xx01a ALac0024008xx01b ALac0025001aa01a ALac0025001aa01b ALac0025001ab01a ALac0025001ab01b ALac0026001aa01a ALac0026001aa01b ALac0026001aa01c ALac0026001aa01d ALac0026001ab01a ALac0026001ab01b ALac0026001ab01c ALac0026001ab01d ALac0026001ac01c ALac0026001ac01d ALac0026002aa01a ALac0026002aa01b ALac0026003aa01a ALac0026003aa01b ALac0026003ab01a ALac0026003ab01b ALac0026003ab01c ALac0026003ab01d ALac0026004aa01a ALac0026004aa01b ALac0026004aa01c ALac0026004aa01d ALac0026005xx01a ALac0026005xx01b ALac0026006xx01a ALac0026006xx01b ALac0026006xx01c ALac0026006xx01d ALac0027001aa01a ALac0027001aa01b ALac0027001aa01c ALac0027001aa01d ALac0027002aa01b ALac0027002aa01c ALac0027002ab01c ALac0028001aa01a ALac0028001aa01c ALac0028001aa01d ALac0028001ab01a ALac0028001ab01b ALac0028001ab01c ALac0028001ab01d ALac0029001xx01a ALac0029001xx01b ALac0029001xx01c ALac0029001xx01d ALac0029002aa01a ALac0030001aa01a ALac0030002aa01a ALac0030002aa01b ALac0031001xx01a ALac0031001xx01b ALac0032001xx01a ALac0032002xx01a ALac0032002xx01b ALac0033001aa01a ALac0033001aa01b ALac0033001aa01c ALac0033001aa01d ALac0033002aa01a ALac0033002aa01b ALac0034001aa01a ALac0034001aa01c ALac0034001aa01d ALac0034001ab01a ALac0034001ab01b ALac0034001ab01c ALac0034001ab01d ALac0035001aa01a ALac0035001aa01b ALac0035001aa01c ALac0035001aa01d ALac0036001aa01a ALac0036001aa01b ALac0037001xx01a ALac0037001xx01b ALac0037001xx01c ALac0037001xx01d ALac0037002xx01a ALac0037002xx01b ALac0037002xx01c ALac0037002xx01d ALac0037003xx01c ALac0037003xx01d ALac0038001aa01a ALac0038001aa01b ALac0038001aa01c ALac0038001aa01d ALac0038001ab01a ALac0038001ab01b ALac0038001ab01c ALac0038001ab01d ALac0038002xx01c ALac0038002xx01d ALac0038003xx01a ALac0038003xx01b ALac0038003xx01c ALac0038003xx01d ALac0038004aa01a ALac0038004aa01b ALac0038004aa01c ALac0038004aa01d ALac0038005aa01a ALac0038005aa01b ALac0038006aa01a ALac0038006aa01b ALac0038008xx01a ALac0038008xx01b ALac0038008xx01c ALac0038008xx01d ALac0039001aa01a ALac0039001aa01b ALac0039001aa01c ALac0039001aa01d ALac0039002aa01a ALac0039002aa01b ALac0039003aa01b ALac0039003aa01c ALac0039004aa01a ALac0039004aa01b ALac0039004aa01c ALac0039004aa01d ALac0039005aa01a ALac0039005aa01c ALac0040001aa01a ALac0040001aa01b ALac0040001aa01c ALac0040001aa01d ALac0041001xx01c ALac0041001xx01d ALac0041002aa01a ALac0041002aa01b ALac0042001xx01c ALac0042001xx01d ALac0043001aa01a ALac0043001aa01c ALac0043001aa01d ALac0043002aa01a ALac0043002aa01b ALac0043002aa01c ALac0043002aa01d ALac0043002ab01a ALac0043002ab01b ALac0043002ab01c ALac0043002ab01d ALac0043003xx01a ALac0043003xx01b ALac0043003xx01c ALac0043003xx01d AMxx0001001xx01a AMxx0001001xx01c AMxx0001002aa01a AMxx0001002aa01b AMxx0001002aa01c AMxx0001002aa01d AMxx0001002ab01a AMxx0001002ab01b AMxx0001003aa01a AMxx0001003aa01b AMxx0001003aa01c AMxx0001003aa01d AMxx0001003ab01a AMxx0001003ab01b ALab0014002xx01a ALab0014002xx01b AAaa0007009aa01a AAaa0007009aa01c AAaa0007009aa01d AAaa0007009ab01b AAaa0007009ab01c AGaj0001001xx01a AGaj0001001xx01b AGaj0001001xx01c AGaj0001001xx01d AGaj0001002aa01c AGaj0001002ab01a AGaj0001002ac01a AGaj0001002ac01b AGaj0001002ac01c AGaj0001003aa01a AGaj0001003aa01b AGaj0001003ab01a AGaj0001003ab01b AGai0016010aa01a AGai0016010aa01b AGai0016010aa01c AGai0016010aa01d AGaf0006013aa01a AGaf0006013aa01b AGaf0006013aa02a AGaf0006013aa02b AGaf0006013aa02c AGaf0006013aa02d ACaa0004002aa01a ACaa0004002aa01b ACaa0004002ab01a ACaa0004002ab01b ACaa0004002ab01c ACaa0004002ab01d ACaa0004002ac01a ACaa0004002ac01b ACaa0004002aa01c """.strip().split("\n") assert len(file_name_sequence) == len(class_name_sequence) filename_to_code = dict((name,value) for name,value in zip(file_name_sequence, class_name_sequence))

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Python

CodeWars/7 Kyu/Complete The Pattern #5 - Even Ladder.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/7 Kyu/Complete The Pattern #5 - Even Ladder.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/7 Kyu/Complete The Pattern #5 - Even Ladder.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

def pattern(string): return '\n'.join(str(a) * a for a in range(2, string + 1, 2))

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