/
imports.html
323 lines (286 loc) · 14.9 KB
/
imports.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.9.2" />
<title>ktrain.imports API documentation</title>
<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.imports</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># --------------------------
# Tensorflow Keras imports
# --------------------------
import logging
import os
import re
import warnings
from distutils.util import strtobool
from packaging import version
os.environ[
"NUMEXPR_MAX_THREADS"
] = "8" # suppress warning from NumExpr on machines with many CPUs
# TensorFlow
SUPPRESS_DEP_WARNINGS = strtobool(os.environ.get("SUPPRESS_DEP_WARNINGS", "1"))
if (
SUPPRESS_DEP_WARNINGS
): # 2021-11-12: copied this here to properly suppress TF/CUDA warnings in Kaggle notebooks, etc.
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
DISABLE_V2_BEHAVIOR = strtobool(os.environ.get("DISABLE_V2_BEHAVIOR", "0"))
try:
if DISABLE_V2_BEHAVIOR:
# TF2-transition
ACC_NAME = "acc"
VAL_ACC_NAME = "val_acc"
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from tensorflow.compat.v1 import keras
print("Using DISABLE_V2_BEHAVIOR with TensorFlow")
else:
# TF2
ACC_NAME = "accuracy"
VAL_ACC_NAME = "val_accuracy"
import tensorflow as tf
from tensorflow import keras
K = keras.backend
# suppress autograph warnings
tf.autograph.set_verbosity(1)
if version.parse(tf.__version__) < version.parse("2.0"):
raise Exception(
"As of v0.8.x, ktrain needs TensorFlow 2. Please upgrade TensorFlow."
)
os.environ["TF_KERAS"] = "1" # to use keras_bert package below with tf.Keras
except ImportError:
warnings.warn(
"TensorFlow is not installed. You can still use ktrain's scikit-learn models and pretrained PyTorch models: "
+ "text.zsl.ZeroShotClassifier, text.translation.Translator, text.summarization.TransformerSummarizer, "
+ "text.speech.Transcriber, and text.eda.TopicModel. To train neural network models, you will need to install TensorFlow: "
+ "pip install tensorflow"
)
keras = None
K = None
# for TF backwards compatibility (e.g., support for TF 2.3.x):
try:
MobileNetV3Small = keras.applications.MobileNetV3Small
pre_mobilenetv3small = keras.applications.mobilenet_v3.preprocess_input
HAS_MOBILENETV3 = True
except:
HAS_MOBILENETV3 = False
# ----------------------------------------------------------
# standards
# ----------------------------------------------------------
import codecs
import copy
import csv
import glob
import gzip
import itertools
import json
import math
import mimetypes
import operator
import os
import os.path
import pickle
import random
import re
import shutil
import string
# import warnings # imported above
import sys
import tempfile
import urllib.request
import zipfile
from abc import ABC, abstractmethod
from collections import Counter
from distutils.version import StrictVersion
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import rgb2hex
# ----------------------------------------------------------
# external dependencies
# ----------------------------------------------------------
plt.ion() # interactive mode
# from sklearn.externals import joblib
import joblib
import pandas as pd
import sklearn
from scipy import sparse # utils
from scipy.sparse import csr_matrix
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.datasets import load_files
from sklearn.decomposition import NMF, LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.manifold import TSNE
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
try:
# fastprogress >= v0.2.0
from fastprogress.fastprogress import master_bar, progress_bar
except:
# fastprogress < v0.2.0
from fastprogress import master_bar, progress_bar
import requests
# verify=False added to avoid headaches from some corporate networks
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
import cchardet as chardet
import jieba
# multilingual text processing
import langdetect
# text processing
import syntok.segmenter as segmenter
# 'bert' text classification model
try:
import keras_bert
from keras_bert import Tokenizer as BERT_Tokenizer
except ImportError:
warnings.warn(
"keras_bert (and/or its TensorFlow dependency) is not installed. keras_bert is only needed only for 'bert' text classification model"
)
# transformers for models in 'text' module
logging.getLogger("transformers").setLevel(logging.ERROR)
try:
import transformers
except ImportError:
warnings.warn(
"transformers not installed - needed by various models in 'text' module"
)
try:
from PIL import Image
PIL_INSTALLED = True
except:
PIL_INSTALLED = False
SG_ERRMSG = (
"ktrain currently uses a forked version of stellargraph v0.8.2. "
+ "Please install with: "
+ "pip install https://github.com/amaiya/stellargraph/archive/refs/heads/no_tf_dep_082.zip"
)
ALLENNLP_ERRMSG = (
"To use ELMo embedings, please install allenlp:\n" + "pip install allennlp"
)
# ELI5
KTRAIN_ELI5_TAG = "0.10.1-1"
# Suppress Warnings
def set_global_logging_level(level=logging.ERROR, prefices=[""]):
"""
Override logging levels of different modules based on their name as a prefix.
It needs to be invoked after the modules have been loaded so that their loggers have been initialized.
Args:
- level: desired level. e.g. logging.INFO. Optional. Default is logging.ERROR
- prefices: list of one or more str prefices to match (e.g. ["transformers", "torch"]). Optional.
Default is `[""]` to match all active loggers.
The match is a case-sensitive `module_name.startswith(prefix)`
"""
prefix_re = re.compile(rf'^(?:{ "|".join(prefices) })')
for name in logging.root.manager.loggerDict:
if re.match(prefix_re, name):
logging.getLogger(name).setLevel(level)
if SUPPRESS_DEP_WARNINGS:
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
warnings.simplefilter(action="ignore", category=FutureWarning)
# elevate warnings to errors for debugging dependencies
# warnings.simplefilter('error', FutureWarning)
set_global_logging_level(
logging.ERROR,
[
"transformers",
"nlp",
"torch",
"tensorflow",
"tensorboard",
"wandb",
"mosestokenizer",
"shap",
],
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.imports.set_global_logging_level"><code class="name flex">
<span>def <span class="ident">set_global_logging_level</span></span>(<span>level=40, prefices=[''])</span>
</code></dt>
<dd>
<div class="desc"><p>Override logging levels of different modules based on their name as a prefix.
It needs to be invoked after the modules have been loaded so that their loggers have been initialized.</p>
<h2 id="args">Args</h2>
<ul>
<li>level: desired level. e.g. logging.INFO. Optional. Default is logging.ERROR</li>
<li>prefices: list of one or more str prefices to match (e.g. ["transformers", "torch"]). Optional.
Default is <code>[""]</code> to match all active loggers.
The match is a case-sensitive <code>module_name.startswith(prefix)</code></li>
</ul></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def set_global_logging_level(level=logging.ERROR, prefices=[""]):
"""
Override logging levels of different modules based on their name as a prefix.
It needs to be invoked after the modules have been loaded so that their loggers have been initialized.
Args:
- level: desired level. e.g. logging.INFO. Optional. Default is logging.ERROR
- prefices: list of one or more str prefices to match (e.g. ["transformers", "torch"]). Optional.
Default is `[""]` to match all active loggers.
The match is a case-sensitive `module_name.startswith(prefix)`
"""
prefix_re = re.compile(rf'^(?:{ "|".join(prefices) })')
for name in logging.root.manager.loggerDict:
if re.match(prefix_re, name):
logging.getLogger(name).setLevel(level)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain" href="index.html">ktrain</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.imports.set_global_logging_level" href="#ktrain.imports.set_global_logging_level">set_global_logging_level</a></code></li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.9.2</a>.</p>
</footer>
</body>
</html>