pytextclassifier

pytextclassifier, Python Text Classifier. It can be applied to the fields of sentiment polarity analysis, text risk classification and so on, and it supports multiple classification algorithms and clustering algorithms.

文本分类器，提供多种文本分类和聚类算法，支持文本极性情感分类，文本风险类型分类等文本分类和聚类应用，开箱即用。python3开发。

Guide

• Feature
• Install
• Usage
• Dataset
• Contact
• Cite
• Reference

Feature

pytextclassifier is a python Open Source Toolkit for text classification. The goal is to implement text analysis algorithm, so as to achieve the use in the production environment.

pytextclassifier has the characteristics of clear algorithm, high performance and customizable corpus.

Functions：

Classifier

• LogisticRegression
• Random Forest
• Decision Tree
• K-Nearest Neighbours
• Naive bayes
• Xgboost
• Support Vector Machine(SVM)
• TextCNN
• TextRNN_Att
• Fasttext
• Bert

Cluster

• MiniBatchKmeans

While providing rich functions, pytextclassifier internal modules adhere to low coupling, model adherence to inert loading, dictionary publication, and easy to use.

Install

• Requirements and Installation

pip3 install pytextclassifier

or

git clone https://github.com/shibing624/pytextclassifier.git
cd pytextclassifier
python3 setup.py install

Usage

Text Classifier

English Text Classifier

Including model training, saving, predict, test, for example examples/base_demo.py:

import sys

sys.path.append('..')
from pytextclassifier import TextClassifier

if __name__ == '__main__':
    m = TextClassifier()
    # model_name is choose classifier, default lr, support lr, random_forest, textcnn, fasttext, textrnn_att, bert
    print(m)
    data = [
        ('education', 'Student debt to cost Britain billions within decades'),
        ('education', 'Chinese education for TV experiment'),
        ('sports', 'Middle East and Asia boost investment in top level sports'),
        ('sports', 'Summit Series look launches HBO Canada sports doc series: Mudhar')
    ]
    m.train(data)

    predict_label, predict_proba = m.predict(
        ['Abbott government spends $8 million on higher education media blitz',
         'Middle East and Asia boost investment in top level sports'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')
    del m

    new_m = TextClassifier()
    new_m.load_model()
    predict_label, predict_proba = new_m.predict([
        'Abbott government spends $8 million on higher education media blitz'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')

    test_data = [
        ('education', 'Abbott government spends $8 million on higher education media blitz'),
        ('sports', 'Middle East and Asia boost investment in top level sports'),
    ]
    acc_score = new_m.evaluate(test_data)
    print(f'acc_score: {acc_score}')

output:

TextClassifier instance (lr)
predict_label: ['education' 'sports'], predict_proba: [0.5378236358492112, 0.5989408491490308]
predict_label: ['education'], predict_proba: [0.5378236358492112]
acc_score: 1.0

Chinese Text Classifier(中文文本分类)

Text classification compatible with Chinese and English corpora, for example examples/chinese_text_demo.py

import sys

sys.path.append('..')
from pytextclassifier import TextClassifier

if __name__ == '__main__':
    m = TextClassifier()
    # model_name is choose classifier, default lr, support lr, random_forest, textcnn, fasttext, textrnn_att, bert
    data = [
        ('education', '名师指导托福语法技巧：名词的复数形式'),
        ('education', '中国高考成绩海外认可 是“狼来了”吗？'),
        ('sports', '图文：法网孟菲尔斯苦战进16强 孟菲尔斯怒吼'),
        ('sports', '四川丹棱举行全国长距登山挑战赛 近万人参与'),
        ('sports', '米兰客场8战不败国米10年连胜')
    ]
    m.train(data)
    print(m)
    predict_label, predict_proba = m.predict(['福建春季公务员考试报名18日截止 2月6日考试',
                                              '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')
    del m

    new_m = TextClassifier()
    new_m.load_model()
    predict_label, predict_proba = new_m.predict(['福建春季公务员考试报名18日截止 2月6日考试',
                                                  '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')

    test_data = [
        ('education', '福建春季公务员考试报名18日截止 2月6日考试'),
        ('sports', '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜'),
    ]
    acc_score = new_m.evaluate(test_data)
    print(f'acc_score: {acc_score}')  # 1.0

    #### load data from file
    print('-' * 42)
    m = TextClassifier()
    data_file = 'thucnews_train_10w.txt'
    m.train(data_file)

    predict_label, predict_proba = m.predict(
        ['顺义北京苏活88平米起精装房在售',
         '美EB-5项目“15日快速移民”将推迟'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')

output:

TextClassifier instance (lr)
predict_label: ['education' 'sports'], predict_proba: [0.5, 0.5989415812731275]
predict_label: ['education' 'sports'], predict_proba: [0.5, 0.5989415812731275]
acc_score: 1.0
------------------------------------------
predict_label: ['realty' 'education'], predict_proba: [0.9746881260530019, 0.5150055067574651]

Visual Feature Importance

Show feature weights of model, and prediction word weight, for example examples/visual_feature_importance.ipynb

import sys

sys.path.append('..')
from pytextclassifier import TextClassifier
import jieba

tc = TextClassifier()
data = [
    ('education', '名师指导托福语法技巧：名词的复数形式'),
    ('education', '中国高考成绩海外认可 是“狼来了”吗？'),
    ('sports', '图文：法网孟菲尔斯苦战进16强 孟菲尔斯怒吼'),
    ('sports', '四川丹棱举行全国长距登山挑战赛 近万人参与'),
    ('sports', '米兰客场8战不败国米10年连胜')
]
tc.train(data)
import eli5
infer_data = ['高考指导托福语法技巧国际认可',
              '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜']
eli5.show_weights(tc.model, vec=tc.vectorizer)
seg_infer_data = [' '.join(jieba.lcut(i)) for i in infer_data]
eli5.show_prediction(tc.model, seg_infer_data[0], vec=tc.vectorizer)

output:

Deep Classification model

工具支持多种常用深度分类模型，包括Fasttext、TextCNN、RextRNN_Att、Bert分类模型。

训练和预测Fasttext模型示例examples/fasttext_classification_demo.py

import sys

sys.path.append('..')
from pytextclassifier import TextClassifier

if __name__ == '__main__':
    model_name = 'fasttext'
    m = TextClassifier(model_name)
    # model_name is choose classifier, default lr, support lr, random_forest, textcnn, fasttext, textrnn_att, bert
    data = [
        ('education', '名师指导托福语法技巧：名词的复数形式'),
        ('education', '中国高考成绩海外认可 是“狼来了”吗？'),
        ('sports', '图文：法网孟菲尔斯苦战进16强 孟菲尔斯怒吼'),
        ('sports', '四川丹棱举行全国长距登山挑战赛 近万人参与'),
        ('sports', '米兰客场8战不败国米10年连胜')
    ]
    m.train(data)
    print(m)
    predict_label, predict_proba = m.predict(['福建春季公务员考试报名18日截止 2月6日考试',
                                              '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')
    del m

    new_m = TextClassifier(model_name)
    new_m.load_model()
    predict_label, predict_proba = new_m.predict(['福建春季公务员考试报名18日截止 2月6日考试',
                                                  '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')

    test_data = [
        ('education', '福建春季公务员考试报名18日截止 2月6日考试'),
        ('sports', '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜'),
    ]
    acc_score = new_m.evaluate(test_data)
    print(f'acc_score: {acc_score}')

训练样本也支持文件加载，示例examples/data_file_classfication_demo.py

import sys

sys.path.append('..')
from pytextclassifier import TextClassifier, load_data

if __name__ == '__main__':
    model_name = 'fasttext'  # or lr, textcnn, bert
    m = TextClassifier(model_name)
    # model_name is choose classifier, default lr, support lr, random_forest, textcnn, fasttext, textrnn_att, bert
    data_file = 'thucnews_train_10w.txt'
    m.train(data_file)

    predict_label, predict_proba = m.predict(
        ['顺义北京苏活88平米起精装房在售',
         '美EB-5项目“15日快速移民”将推迟'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')
    del m

    new_m = TextClassifier(model_name)
    new_m.load_model()
    predict_label, predict_proba = new_m.predict(
        ['顺义北京苏活88平米起精装房在售',
         '美EB-5项目“15日快速移民”将推迟'])
    print(f'predict_label: {predict_label}, predict_proba: {predict_proba}')
    x, y, df = load_data(data_file)
    test_data = df[:100]
    acc_score = new_m.evaluate(test_data)
    print(f'acc_score: {acc_score}')

模型效果

1. THUCNews中文文本数据集（1.56GB）：官方下载地址。
2. 抽样的THUCNews中文文本10分类数据集（6MB），地址：examples/thucnews_train_10w.txt。

各模型在THUCNews中文文本10分类数据集评估，模型效果如下：

模型调研

提供分类模型快速调研工具tools，文件树：

pytextclassifier/tools
├── bert_classification.py
├── fasttext_classification.py
├── lr_classification.py
├── textcnn_classification.py
└── textrnn_att_classification.py

每个文件对应一个模型，各模型完全独立，可以直接运行，也方便修改，支持通过argparse 修改--data_path等参数。

直接在终端调用fasttext模型训练：

python -m pytextclassifier.tools.fasttext_classification

Text Cluster

Text clustering, for example cluster_demo.py

from pytextclassifier.textcluster import TextCluster

if __name__ == '__main__':
    m = TextCluster(n_clusters=2)
    print(m)
    data = [
        'Student debt to cost Britain billions within decades',
        'Chinese education for TV experiment',
        'Abbott government spends $8 million on higher education',
        'Middle East and Asia boost investment in top level sports',
        'Summit Series look launches HBO Canada sports doc series: Mudhar'
    ]
    X_vec, labels = m.train(data)
    r = m.predict(['Abbott government spends $8 million on higher education media blitz',
                   'Middle East and Asia boost investment in top level sports'])
    print(r)
    m.show_clusters(X_vec, labels, image_file='cluster.png')
    del m

    new_m = TextCluster(n_clusters=2)
    new_m.load_model()
    r = new_m.predict(['Abbott government spends $8 million on higher education media blitz',
                       'Middle East and Asia boost investment in top level sports'])
    print(r)

    ########### load chinese train data from file
    from sklearn.feature_extraction.text import TfidfVectorizer

    vec = TfidfVectorizer(ngram_range=(1, 2))
    tcluster = TextCluster(vectorizer=vec, n_clusters=10)
    data = tcluster.load_file_data('thucnews_train_10w.txt')
    X_vec, labels = tcluster.train(data[:5000])
    tcluster.show_clusters(X_vec, labels, 'cluster_train_seg_samples.png')
    r = tcluster.predict(data[:30])
    print(r)

output:

TextCluster instance (MiniBatchKMeans(n_clusters=2, n_init=10), <pytextclassifier.utils.tokenizer.Tokenizer object at 0x7f80bd4682b0>, TfidfVectorizer(ngram_range=(1, 2)))
[1 0]
[1 0]

clustering plot image:

Contact

• Issue(建议)：
• 邮件我：xuming: xuming624@qq.com
• 微信我：加我微信号：xuming624, 进Python-NLP交流群，备注：姓名-公司名-NLP

Cite

如果你在研究中使用了pytextclassifier，请按如下格式引用：

@software{pytextclassifier,
  author = {Xu Ming},
  title = {pytextclassifier: A Tool for Text Classifier},
  year = {2021},
  url = {https://github.com/shibing624/pytextclassifier},
}

License

授权协议为 The Apache License 2.0，可免费用做商业用途。请在产品说明中附加pytextclassifier的链接和授权协议。

Contribute

项目代码还很粗糙，如果大家对代码有所改进，欢迎提交回本项目，在提交之前，注意以下两点：

• 在tests添加相应的单元测试
• 使用python setup.py test来运行所有单元测试，确保所有单测都是通过的

之后即可提交PR。

Reference

• SentimentPolarityAnalysis