repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
keras | keras-master/keras/mixed_precision/get_layer_policy.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 1,528 | 35.404762 | 80 | py |
keras | keras-master/keras/mixed_precision/loss_scale.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 2,071 | 33.533333 | 80 | py |
keras | keras-master/keras/mixed_precision/layer_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 18,563 | 41.774194 | 80 | py |
keras | keras-master/keras/mixed_precision/policy.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 24,502 | 40.601019 | 80 | py |
keras | keras-master/keras/mixed_precision/loss_scale_benchmark.py | # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 6,177 | 39.379085 | 97 | py |
keras | keras-master/keras/mixed_precision/__init__.py | # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 844 | 39.238095 | 80 | py |
keras | keras-master/keras/mixed_precision/autocast_variable.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 19,740 | 34.958106 | 146 | py |
keras | keras-master/keras/mixed_precision/get_layer_policy_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 1,650 | 34.12766 | 80 | py |
keras | keras-master/keras/mixed_precision/loss_scale_optimizer.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 51,264 | 40.611201 | 110 | py |
keras | keras-master/keras/mixed_precision/device_compatibility_check_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 5,467 | 37.507042 | 83 | py |
keras | keras-master/keras/mixed_precision/model_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 35,294 | 39.756351 | 80 | py |
keras | keras-master/keras/mixed_precision/layer_correctness_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 11,512 | 43.451737 | 80 | py |
keras | keras-master/keras/applications/mobilenet_v2.py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 20,697 | 38.05283 | 87 | py |
keras | keras-master/keras/applications/efficientnet.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 25,089 | 31.5 | 87 | py |
keras | keras-master/keras/applications/resnet.py | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 21,207 | 35.191126 | 87 | py |
keras | keras-master/keras/applications/vgg16.py | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 9,586 | 37.971545 | 87 | py |
keras | keras-master/keras/applications/densenet.py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 16,084 | 36.320186 | 87 | py |
keras | keras-master/keras/applications/imagenet_utils.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 15,197 | 33.778032 | 80 | py |
keras | keras-master/keras/applications/resnet_v2.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 6,741 | 32.879397 | 87 | py |
keras | keras-master/keras/applications/vgg19.py | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 9,818 | 38.276 | 87 | py |
keras | keras-master/keras/applications/inception_v3.py | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 16,038 | 36.562061 | 87 | py |
keras | keras-master/keras/applications/mobilenet_v3.py | # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 23,489 | 38.412752 | 87 | py |
keras | keras-master/keras/applications/nasnet.py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 30,441 | 36.215159 | 87 | py |
keras | keras-master/keras/applications/__init__.py | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 765 | 46.875 | 80 | py |
keras | keras-master/keras/applications/imagenet_utils_test.py | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 9,851 | 32.39661 | 80 | py |
keras | keras-master/keras/applications/applications_load_weight_test.py | # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 4,840 | 40.732759 | 80 | py |
keras | keras-master/keras/applications/xception.py | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 13,000 | 38.159639 | 87 | py |
keras | keras-master/keras/applications/inception_resnet_v2.py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 15,195 | 37.470886 | 87 | py |
keras | keras-master/keras/applications/mobilenet.py | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 19,722 | 42.157549 | 87 | py |
keras | keras-master/keras/applications/applications_test.py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 5,219 | 34.27027 | 80 | py |
keras | keras-master/keras/applications/efficientnet_weight_update_util.py | # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 13,222 | 34.834688 | 80 | py |
ReCO | ReCO-master/test.py | # -*- coding: utf-8 -*-
"""
@Time : 2020/6/23 下午1:43
@FileName: test.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import argparse
import torch
from model import Bert4ReCO
from utils import *
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", type=str, default='bert-base-chinese... | 1,629 | 30.346154 | 110 | py |
ReCO | ReCO-master/prepare_data.py | # -*- coding: utf-8 -*-
"""
@Time : 2020/6/23 上午10:15
@FileName: prepare_data.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import json
import random
from transformers import BertTokenizer
from utils import *
tokenizer = None
def get_shuffled_answer(alternatives):
answers_index = [0, 1, 2]
... | 2,068 | 35.298246 | 87 | py |
ReCO | ReCO-master/utils.py | # -*- coding: utf-8 -*-
"""
@Time : 2019/11/20 下午6:14
@FileName: utils.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import os
import pickle
import re
import numpy as np
import multiprocessing
from joblib import Parallel, delayed
from tqdm import tqdm
def multi_process(func, lst, num_cores=multiproc... | 1,783 | 23.438356 | 95 | py |
ReCO | ReCO-master/model.py | # -*- coding: utf-8 -*-
"""
@Time : 2020/6/23 上午10:13
@FileName: model.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModel
class Bert4ReCO(nn.Module):
def __init__(self, model_type):
super().... | 1,073 | 28.833333 | 93 | py |
ReCO | ReCO-master/train.py | # -*- coding: utf-8 -*-
"""
@Time : 2019/11/21 下午7:14
@FileName: train.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import argparse
import torch
from model import Bert4ReCO
from prepare_data import prepare_bert_data
from utils import *
import torch.distributed as dist
torch.manual_seed(100)
np.rand... | 5,074 | 33.290541 | 114 | py |
ReCO | ReCO-master/BiDAF/inference.py | # -*- coding: utf-8 -*-
import argparse
import cPickle
import codecs
import torch
from utils import *
from preprocess import seg_data, transform_data_to_id
parser = argparse.ArgumentParser(description='inference procedure, note you should train the data at first')
parser.add_argument('--data', type=str,
... | 2,636 | 34.635135 | 113 | py |
ReCO | ReCO-master/BiDAF/utils.py | # -*- coding: utf-8 -*-
import numpy as np
def pad_answer(batch):
output = []
length_info = [len(x[0]) for x in batch]
max_length = max(length_info)
for one in batch:
output.append([x + [0] * (max_length - len(x)) for x in one])
return output
def get_model_parameters(model):
total = ... | 2,196 | 32.8 | 118 | py |
ReCO | ReCO-master/BiDAF/MwAN.py | # -*- coding: utf-8 -*-
import torch
from torch import nn
from torch.nn import functional as F
class MwAN(nn.Module):
def __init__(self, vocab_size, embedding_size, encoder_size, drop_out=0.2):
super(MwAN, self).__init__()
self.drop_out=drop_out
self.embedding = nn.Embedding(vocab_size + 1... | 4,704 | 44.679612 | 108 | py |
ReCO | ReCO-master/BiDAF/__init__.py | # -*- coding: utf-8 -*-
| 24 | 11.5 | 23 | py |
ReCO | ReCO-master/BiDAF/train.py | # -*- coding: utf-8 -*-
import argparse
import cPickle
import torch
from MwAN import MwAN
from preprocess import process_data
from utils import *
parser = argparse.ArgumentParser(description='PyTorch implementation for Multiway Attention Networks for Modeling '
'Sentence P... | 4,466 | 38.184211 | 120 | py |
ReCO | ReCO-master/BiDAF/preprocess.py | # -*- coding: utf-8 -*-
import cPickle
import json
import jieba
def seg_line(line):
return list(jieba.cut(line))
def seg_data(path):
print 'start process ', path
data = []
with open(path, 'r') as f:
for line in f:
dic = json.loads(line, encoding='utf-8')
question = d... | 3,805 | 32.095652 | 117 | py |
ReCO | ReCO-master/BiDAF/BiDAF.py | # -*- coding: utf-8 -*-
"""
@Time : 2019/11/21 下午4:42
@FileName: BiDAF.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class BiDAF(nn.Module):
def __init__(self, vocab_size, embedding_size, encoder_size, drop_out=0.2):
supe... | 4,904 | 40.923077 | 109 | py |
ReCO | ReCO-master/InHouseBert/prepare_data.py | # -*- coding: utf-8 -*-
"""
@Time : 2020/6/23 上午10:15
@FileName: prepare_data.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import json
import random
from transformers import BertTokenizer, AutoTokenizer, XLNetTokenizer
from utils import *
import sentencepiece as spm
tokenizer = spm.SentencePiecePro... | 2,106 | 35.327586 | 87 | py |
ReCO | ReCO-master/InHouseBert/model.py | # -*- coding: utf-8 -*-
"""
@Time : 2020/6/24 下午6:18
@FileName: model.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import warnings
import apex
import torch
import torch.nn as nn
from apex.contrib.multihead_attn import SelfMultiheadAttn
from apex.mlp import MLP
from torch.nn import functional as F
w... | 4,192 | 37.118182 | 119 | py |
ReCO | ReCO-master/InHouseBert/__init__.py | # -*- coding: utf-8 -*-
"""
@Time : 2020/6/24 下午6:10
@FileName: __init__.py.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
""" | 139 | 19 | 37 | py |
ReCO | ReCO-master/InHouseBert/train.py | # -*- coding: utf-8 -*-
"""
@Time : 2020/6/24 下午6:16
@FileName: train.py
@author: 王炳宁
@contact: wangbingning@sogou-inc.com
"""
import argparse
import sys
sys.path.append("../..")
sys.path.append("..")
from tasks.ReCO.model import BERT
from utils import *
import torch.distributed as dist
torch.manual_seed(100)... | 5,292 | 32.713376 | 114 | py |
mining-legal-arguments | mining-legal-arguments-main/importance_model.py | from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import os
import json
from cassis import *
from collections import Co... | 13,432 | 39.098507 | 173 | py |
mining-legal-arguments | mining-legal-arguments-main/evaluate.py | #!/usr/bin/env python
# coding: utf-8
from collections import Counter
from prettytable import PrettyTable
import os
from transformers import AutoTokenizer
import torch
from torch.utils.data import Dataset
import pandas as pd
from datasets import load_dataset, load_metric
import csv
from ast import literal_eval
impor... | 20,634 | 44.855556 | 227 | py |
mining-legal-arguments | mining-legal-arguments-main/create_arg_mining_dataset.py | from cassis import *
from collections import Counter
from prettytable import PrettyTable
import os
import pandas as pd
import numpy as np
original_data = True
id2label_argType = ['O', 'B-Subsumtion', 'I-Subsumtion', 'B-Entscheidung des EGMR', 'I-Entscheidung des EGMR',
'B-Vorherige Rechtsprechung des EG... | 13,984 | 45.616667 | 140 | py |
mining-legal-arguments | mining-legal-arguments-main/create_confusion_matrix.py | #!/usr/bin/env python
# coding: utf-8
import pandas as pd
from ast import literal_eval
from sklearn.metrics import confusion_matrix
from confusion_matrix_pretty_print import pretty_plot_confusion_matrix
id2label_argType = ['B-Distinguishing',
'B-Einschätzungsspielraum',
'B-Entscheidung des EGMR',
'B-Konsens der pr... | 3,478 | 38.534091 | 195 | py |
mining-legal-arguments | mining-legal-arguments-main/multiTaskModel.py | #!/usr/bin/env python
# coding: utf-8
from collections import Counter
from prettytable import PrettyTable
import os
from transformers import AutoTokenizer
import torch
from torch.utils.data import Dataset
import pandas as pd
from datasets import load_dataset, load_metric
import csv
from ast import literal_eval
import... | 25,615 | 37.232836 | 144 | py |
mining-legal-arguments | mining-legal-arguments-main/compare_f1s.py | #!/usr/bin/env python
# coding: utf-8
import pandas as pd
import csv
from ast import literal_eval
from collections import Counter
import numpy as np
from tabulate import tabulate
from multiTaskModel import compute_f1
id2label_argType = ['B-Distinguishing',
'B-Einschätzungsspielraum',
'B-Entscheidung des EGMR',
... | 7,473 | 42.202312 | 175 | py |
mining-legal-arguments | mining-legal-arguments-main/confusion_matrix_pretty_print.py | # -*- coding: utf-8 -*-
"""
plot a pretty confusion matrix with seaborn
Created on Mon Jun 25 14:17:37 2018
@author: Wagner Cipriano - wagnerbhbr - gmail - CEFETMG / MMC
REFerences:
https://www.mathworks.com/help/nnet/ref/plotconfusion.html
https://stackoverflow.com/questions/28200786/how-to-plot-scikit-learn-class... | 11,369 | 35.796117 | 265 | py |
imbalanced-learn | imbalanced-learn-master/conftest.py | # This file is here so that when running from the root folder
# ./imblearn is added to sys.path by pytest.
# See https://docs.pytest.org/en/latest/pythonpath.html for more details.
# For example, this allows to build extensions in place and run pytest
# doc/modules/clustering.rst and use imblearn from the local folder
... | 798 | 32.291667 | 73 | py |
imbalanced-learn | imbalanced-learn-master/setup.py | #! /usr/bin/env python
"""Toolbox for imbalanced dataset in machine learning."""
import codecs
import os
from setuptools import find_packages, setup
try:
import builtins
except ImportError:
# Python 2 compat: just to be able to declare that Python >=3.7 is needed.
import __builtin__ as builtins
# This i... | 2,646 | 32.0875 | 85 | py |
imbalanced-learn | imbalanced-learn-master/examples/pipeline/plot_pipeline_classification.py | """
====================================
Usage of pipeline embedding samplers
====================================
An example of the :class:~imblearn.pipeline.Pipeline` object (or
:func:`~imblearn.pipeline.make_pipeline` helper function) working with
transformers and resamplers.
"""
# Authors: Christos Aridas
# ... | 2,006 | 25.407895 | 86 | py |
imbalanced-learn | imbalanced-learn-master/examples/evaluation/plot_metrics.py | """
=======================================
Metrics specific to imbalanced learning
=======================================
Specific metrics have been developed to evaluate classifier which
has been trained using imbalanced data. :mod:`imblearn` provides mainly
two additional metrics which are not implemented in :mod:... | 2,900 | 25.135135 | 88 | py |
imbalanced-learn | imbalanced-learn-master/examples/evaluation/plot_classification_report.py | """
=============================================
Evaluate classification by compiling a report
=============================================
Specific metrics have been developed to evaluate classifier which has been
trained using imbalanced data. :mod:`imblearn` provides a classification report
similar to :mod:`sklea... | 1,584 | 25.416667 | 84 | py |
imbalanced-learn | imbalanced-learn-master/examples/combine/plot_comparison_combine.py | """
==================================================
Compare sampler combining over- and under-sampling
==================================================
This example shows the effect of applying an under-sampling algorithms after
SMOTE over-sampling. In the literature, Tomek's link and edited nearest
neighbours ar... | 3,820 | 30.065041 | 88 | py |
imbalanced-learn | imbalanced-learn-master/examples/model_selection/plot_validation_curve.py | """
==========================
Plotting Validation Curves
==========================
In this example the impact of the :class:`~imblearn.over_sampling.SMOTE`'s
`k_neighbors` parameter is examined. In the plot you can see the validation
scores of a SMOTE-CART classifier for different values of the
:class:`~imblearn.ove... | 3,153 | 24.435484 | 87 | py |
imbalanced-learn | imbalanced-learn-master/examples/api/plot_sampling_strategy_usage.py | """
====================================================
How to use ``sampling_strategy`` in imbalanced-learn
====================================================
This example shows the different usage of the parameter ``sampling_strategy``
for the different family of samplers (i.e. over-sampling, under-sampling. or
c... | 6,099 | 30.443299 | 88 | py |
imbalanced-learn | imbalanced-learn-master/examples/datasets/plot_make_imbalance.py | """
============================
Create an imbalanced dataset
============================
An illustration of the :func:`~imblearn.datasets.make_imbalance` function to
create an imbalanced dataset from a balanced dataset. We show the ability of
:func:`~imblearn.datasets.make_imbalance` of dealing with Pandas DataFrame... | 2,474 | 23.264706 | 76 | py |
imbalanced-learn | imbalanced-learn-master/examples/over-sampling/plot_shrinkage_effect.py | """
======================================================
Effect of the shrinkage factor in random over-sampling
======================================================
This example shows the effect of the shrinkage factor used to generate the
smoothed bootstrap using the
:class:`~imblearn.over_sampling.RandomOverSamp... | 3,956 | 30.656 | 87 | py |
imbalanced-learn | imbalanced-learn-master/examples/over-sampling/plot_illustration_generation_sample.py | """
============================================
Sample generator used in SMOTE-like samplers
============================================
This example illustrates how a new sample is generated taking into account the
neighbourhood of this sample. A new sample is generated by selecting the
randomly 2 samples of the sa... | 2,010 | 26.547945 | 88 | py |
imbalanced-learn | imbalanced-learn-master/examples/over-sampling/plot_comparison_over_sampling.py | """
==============================
Compare over-sampling samplers
==============================
The following example attends to make a qualitative comparison between the
different over-sampling algorithms available in the imbalanced-learn package.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License... | 10,987 | 30.304843 | 86 | py |
imbalanced-learn | imbalanced-learn-master/examples/under-sampling/plot_illustration_nearmiss.py | """
============================
Sample selection in NearMiss
============================
This example illustrates the different way of selecting example in
:class:`~imblearn.under_sampling.NearMiss`.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
# %%
print(__doc__)
import seaborn as sn... | 5,767 | 25.827907 | 81 | py |
imbalanced-learn | imbalanced-learn-master/examples/under-sampling/plot_illustration_tomek_links.py | """
==============================================
Illustration of the definition of a Tomek link
==============================================
This example illustrates what is a Tomek link.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
# %%
print(__doc__)
import matplotlib.pyplot as pl... | 3,180 | 22.389706 | 79 | py |
imbalanced-learn | imbalanced-learn-master/examples/under-sampling/plot_comparison_under_sampling.py | """
===============================
Compare under-sampling samplers
===============================
The following example attends to make a qualitative comparison between the
different under-sampling algorithms available in the imbalanced-learn package.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Lic... | 9,708 | 30.11859 | 88 | py |
imbalanced-learn | imbalanced-learn-master/examples/ensemble/plot_bagging_classifier.py | """
=================================
Bagging classifiers using sampler
=================================
In this example, we show how
:class:`~imblearn.ensemble.BalancedBaggingClassifier` can be used to create a
large variety of classifiers by giving different samplers.
We will give several examples that have been p... | 6,020 | 32.636872 | 88 | py |
imbalanced-learn | imbalanced-learn-master/examples/ensemble/plot_comparison_ensemble_classifier.py | """
=============================================
Compare ensemble classifiers using resampling
=============================================
Ensemble classifiers have shown to improve classification performance compare
to single learner. However, they will be affected by class imbalance. This
example shows the benefi... | 7,338 | 30.497854 | 85 | py |
imbalanced-learn | imbalanced-learn-master/examples/applications/plot_impact_imbalanced_classes.py | """
==========================================================
Fitting model on imbalanced datasets and how to fight bias
==========================================================
This example illustrates the problem induced by learning on datasets having
imbalanced classes. Subsequently, we compare different approac... | 12,377 | 32.274194 | 87 | py |
imbalanced-learn | imbalanced-learn-master/examples/applications/porto_seguro_keras_under_sampling.py | """
==========================================================
Porto Seguro: balancing samples in mini-batches with Keras
==========================================================
This example compares two strategies to train a neural-network on the Porto
Seguro Kaggle data set [1]_. The data set is imbalanced and we... | 8,747 | 32.776062 | 88 | py |
imbalanced-learn | imbalanced-learn-master/examples/applications/plot_topic_classication.py | """
=================================================
Example of topic classification in text documents
=================================================
This example shows how to balance the text data before to train a classifier.
Note that for this example, the data are slightly imbalanced but it can happen
that fo... | 3,358 | 30.101852 | 83 | py |
imbalanced-learn | imbalanced-learn-master/examples/applications/plot_multi_class_under_sampling.py | """
=============================================
Multiclass classification with under-sampling
=============================================
Some balancing methods allow for balancing dataset with multiples classes.
We provide an example to illustrate the use of those methods which do
not differ from the binary case.... | 1,494 | 28.313725 | 85 | py |
imbalanced-learn | imbalanced-learn-master/examples/applications/plot_over_sampling_benchmark_lfw.py | """
==========================================================
Benchmark over-sampling methods in a face recognition task
==========================================================
In this face recognition example two faces are used from the LFW
(Faces in the Wild) dataset. Several implemented over-sampling
methods ar... | 4,750 | 29.651613 | 79 | py |
imbalanced-learn | imbalanced-learn-master/examples/applications/plot_outlier_rejections.py | """
===============================================================
Customized sampler to implement an outlier rejections estimator
===============================================================
This example illustrates the use of a custom sampler to implement an outlier
rejections estimator. It can be used easily wi... | 4,353 | 34.688525 | 85 | py |
imbalanced-learn | imbalanced-learn-master/maint_tools/test_docstring.py | import importlib
import inspect
import pkgutil
import re
from inspect import signature
from typing import Optional
import pytest
import imblearn
from imblearn.utils.testing import all_estimators
numpydoc_validation = pytest.importorskip("numpydoc.validate")
# List of whitelisted modules and methods; regexp are supp... | 8,925 | 29.360544 | 85 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/base.py | """Base class for sampling"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
from abc import ABCMeta, abstractmethod
import numpy as np
import sklearn
from sklearn.base import BaseEstimator
try:
# scikit-learn >= 1.2
from sklearn.base import OneToOneFeature... | 12,954 | 29.845238 | 88 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/exceptions.py | """
The :mod:`imblearn.exceptions` module includes all custom warnings and error
classes and functions used across imbalanced-learn.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
def raise_isinstance_error(variable_name, possible_type, variable):
"""Raise consistent error message for ... | 785 | 22.818182 | 76 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/_config.py | """This is copy of sklearn/_config.py
# TODO: remove this file when scikit-learn minimum version is 1.3
We remove the array_api_dispatch for the moment.
"""
import os
import threading
from contextlib import contextmanager as contextmanager
import sklearn
from sklearn.utils import parse_version
sklearn_version = parse... | 13,655 | 38.582609 | 87 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/_version.py | """
``imbalanced-learn`` is a set of python methods to deal with imbalanced
datset in machine learning and pattern recognition.
"""
# Based on NiLearn package
# License: simplified BSD
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
# X.Y
# X.Y.Z # ... | 629 | 23.230769 | 71 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/_min_dependencies.py | """All minimum dependencies for imbalanced-learn."""
import argparse
NUMPY_MIN_VERSION = "1.17.3"
SCIPY_MIN_VERSION = "1.5.0"
PANDAS_MIN_VERSION = "1.0.5"
SKLEARN_MIN_VERSION = "1.0.2"
TENSORFLOW_MIN_VERSION = "2.4.3"
KERAS_MIN_VERSION = "2.4.3"
JOBLIB_MIN_VERSION = "1.1.1"
THREADPOOLCTL_MIN_VERSION = "2.0.0"
PYTEST_M... | 2,240 | 36.35 | 86 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/__init__.py | """Toolbox for imbalanced dataset in machine learning.
``imbalanced-learn`` is a set of python methods to deal with imbalanced
datset in machine learning and pattern recognition.
Subpackages
-----------
combine
Module which provides methods based on over-sampling and under-sampling.
ensemble
Module which prov... | 3,963 | 30.967742 | 83 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/pipeline.py | """
The :mod:`imblearn.pipeline` module implements utilities to build a
composite estimator, as a chain of transforms, samples and estimators.
"""
# Adapted from scikit-learn
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Lars Buitinck
# C... | 18,767 | 38.428571 | 85 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/base.py | """
Base class for the under-sampling method.
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
import numbers
from collections.abc import Mapping
from ..base import BaseSampler
from ..utils._param_validation import Interval, StrOptions
class BaseUnderSampler(BaseSampler):
"""Base class ... | 3,904 | 33.557522 | 100 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/__init__.py | """
The :mod:`imblearn.under_sampling` provides methods to under-sample
a dataset.
"""
from ._prototype_generation import ClusterCentroids
from ._prototype_selection import (
AllKNN,
CondensedNearestNeighbour,
EditedNearestNeighbours,
InstanceHardnessThreshold,
NearMiss,
NeighbourhoodCleaningRu... | 733 | 21.242424 | 67 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_edited_nearest_neighbours.py | """Classes to perform under-sampling based on the edited nearest neighbour
method."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Dayvid Oliveira
# Christos Aridas
# License: MIT
import numbers
from collections import Counter
import numpy as np
from sklearn.utils import _safe_indexing
... | 21,490 | 33.440705 | 87 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_nearmiss.py | """Class to perform under-sampling based on nearmiss methods."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numbers
import warnings
from collections import Counter
import numpy as np
from sklearn.utils import _safe_indexing
from ...utils import Substituti... | 11,121 | 34.307937 | 83 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_one_sided_selection.py | """Class to perform under-sampling based on one-sided selection method."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numbers
import warnings
from collections import Counter
import numpy as np
from sklearn.base import clone
from sklearn.neighbors import KN... | 8,259 | 35.22807 | 88 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_tomek_links.py | """Class to perform under-sampling by removing Tomek's links."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Fernando Nogueira
# Christos Aridas
# License: MIT
import numbers
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import _safe_indexing
fro... | 5,115 | 30.776398 | 79 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_instance_hardness_threshold.py | """Class to perform under-sampling based on the instance hardness
threshold."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Dayvid Oliveira
# Christos Aridas
# License: MIT
import numbers
from collections import Counter
import numpy as np
from sklearn.base import ClassifierMixin, clone... | 6,500 | 30.712195 | 87 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/__init__.py | """
The :mod:`imblearn.under_sampling.prototype_selection` submodule contains
methods that select samples in order to balance the dataset.
"""
from ._condensed_nearest_neighbour import CondensedNearestNeighbour
from ._edited_nearest_neighbours import (
AllKNN,
EditedNearestNeighbours,
RepeatedEditedNearest... | 928 | 28.967742 | 73 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_random_under_sampler.py | """Class to perform random under-sampling."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numpy as np
from sklearn.utils import _safe_indexing, check_random_state
from ...utils import Substitution, check_target_type
from ...utils._docstring import _random_s... | 4,601 | 31.181818 | 79 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_condensed_nearest_neighbour.py | """Class to perform under-sampling based on the condensed nearest neighbour
method."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numbers
import warnings
from collections import Counter
import numpy as np
from scipy.sparse import issparse
from sklearn.base... | 9,474 | 35.583012 | 88 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py | """Class performing under-sampling based on the neighbourhood cleaning rule."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numbers
import warnings
from collections import Counter
import numpy as np
from sklearn.base import clone
from sklearn.neighbors impo... | 9,614 | 36.123552 | 88 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/tests/test_edited_nearest_neighbours.py | """Test the module edited nearest neighbour."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numpy as np
from sklearn.datasets import make_classification
from sklearn.neighbors import NearestNeighbors
from sklearn.utils._testing import assert_array_equal
from... | 4,212 | 28.879433 | 74 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/tests/test_nearmiss.py | """Test the module nearmiss."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils._testing import assert_array_equal
from imblearn.under_sampling import NearMiss
X = np.array(
[
... | 6,989 | 32.127962 | 75 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/tests/test_repeated_edited_nearest_neighbours.py | """Test the module repeated edited nearest neighbour."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numpy as np
import pytest
from sklearn.neighbors import NearestNeighbors
from sklearn.utils._testing import assert_array_equal
from imblearn.under_sampling i... | 8,727 | 24.746313 | 86 | py |
imbalanced-learn | imbalanced-learn-master/imblearn/under_sampling/_prototype_selection/tests/test_instance_hardness_threshold.py | """Test the module ."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.naive_bayes import GaussianNB as NB
from sklearn.utils._testing import assert_array_eq... | 3,118 | 31.489583 | 79 | py |
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