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imports.html
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imports.html
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<article id="content">
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<h1 class="title">Module <code>ktrain.text.shallownlp.imports</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import logging
import os
import warnings
from ...imports import SUPPRESS_DEP_WARNINGS
# os.environ['DISABLE_V2_BEHAVIOR'] = '1'
if SUPPRESS_DEP_WARNINGS:
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("tensorflow_hub").setLevel(logging.ERROR)
warnings.simplefilter(action="ignore", category=FutureWarning)
try:
import tensorflow as tf
TF_INSTALLED = True
except ImportError:
TF_INSTALLED = False
if TF_INSTALLED:
tf.autograph.set_verbosity(1)
import os.path
import re
import string
import numpy as np
from scipy.sparse import coo_matrix, spmatrix
from sklearn.base import BaseEstimator
try: # sklearn<0.24.x
from sklearn.linear_model.base import LinearClassifierMixin, SparseCoefMixin
except ImportError: # sklearn>=0.24.x
from sklearn.linear_model._base import LinearClassifierMixin, SparseCoefMixin
import syntok.segmenter as segmenter
from joblib import dump, load
from sklearn.datasets import load_files
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import LinearSVC
# ktrain imported locally in ner.py
# import ktrain
# pandas imported locally in classifier.py
# import pandas as pd
try:
import langdetect
LANGDETECT = True
except:
LANGDETECT = False
try:
import cchardet as chardet
CHARDET = True
except:
CHARDET = False
try:
import jieba
JIEBA = True
except:
JIEBA = False</code></pre>
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<h1>Index</h1>
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<ul></ul>
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<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.text.shallownlp" href="index.html">ktrain.text.shallownlp</a></code></li>
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