基于朴素贝叶斯的中文垃圾短信分类(含ui界面)

完整代码如下

基于朴素贝叶斯的中文垃圾短信分类、垃圾邮件分类。
可用于机器学习课程设计等。

import warnings
warnings.filterwarnings('ignore')
import os
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
import pandas as pd
import numpy as np
from sklearn import metrics
import joblib
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB


def read_stopwords(stopwords_path):
    """
    读取停用词库
    :param stopwords_path: 停用词库的路径
    :return: 停用词列表
    """
    with open(stopwords_path, 'r', encoding='utf-8') as f:
        stopwords = f.read()
    stopwords = stopwords.splitlines()
    return stopwords

def train():
    # 1.1 数据集的路径
    data_path = "./dataset/data140152/5f9ae242cae5285cd734b91e-momodel/sms_pub.csv"
    # 1.2 读取数据
    sms = pd.read_csv(data_path, encoding='utf-8')

    # 1.3 划分训练集和测试集 9：1
    X = np.array(sms.msg_new)
    y = np.array(sms.label)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)

    # 2.1 加载停用词库
    stopwords_path = r'./dataset/data140152/5f9ae242cae5285cd734b91e-momodel/scu_stopwords.txt'
    stopwords = read_stopwords(stopwords_path)

    # 3. 文本向量化
    # 3.1 设置匹配的正则表达式和停用词
    vect = CountVectorizer(token_pattern=r"(?u)\b\w+\b", stop_words=stopwords)
    X_train_dtm = vect.fit_transform(X_train)
    X_test_dtm = vect.transform(X_test)

    # 4.模型搭建
    # 4.1 创建朴素贝叶斯
    nb = MultinomialNB(alpha=10)
    # 4.2 开始训练
    nb.fit(X_train_dtm, y_train)
    # 4.3 对测试集的数据集进行预测
    y_pred = nb.predict(X_test_dtm)
    # 4.4 在测试集上评估训练的模型
    print("在测试集上的混淆矩阵：")
    print(metrics.confusion_matrix(y_test, y_pred))
    print("在测试集上的分类结果报告：")
    print(metrics.classification_report(y_test, y_pred))
    print("在测试集上的f1-score ：")
    print(metrics.f1_score(y_test, y_pred))
    accuracy = metrics.accuracy_score(y_test, y_pred)
    print("在测试集上的Accuracy:", accuracy)
    # 计算准确率
    accuracy = metrics.accuracy_score(y_test, y_pred)
    print("Accuracy:", accuracy)
    # 计算精确率
    precision = metrics.precision_score(y_test, y_pred)
    print("Precision:", precision)
    # 计算召回率
    recall = metrics.recall_score(y_test, y_pred)
    print("Recall:", recall)
    # 计算 F1 值
    f1 = metrics.f1_score(y_test, y_pred)
    print("F1 score:", f1)

    # 5. 搭建PIpeLine
    # 5.1. 构建PipleLine可以将数据处理和数据分类结合在一起，这样输入原始的数据就可以得到分类的结果，方便直接对原始数据进行预测。
    pipeline = Pipeline([
        ('cv', vect),
        ('classifier', nb),
    ])

    # 5.2 保存Pipeline
    joblib.dump(pipeline, 'sms_spam_pipeline.pkl')

    # 5.3 加载Pipeline
    new_pipeline = joblib.load('sms_spam_pipeline.pkl')

    # 5.4 使用加载的Pipeline进行预测
    print(new_pipeline.predict(["乌兰察布丰镇市法院成立爱心救助基金", "感谢致电杭州萧山全金釜韩国烧烤店，本店位于金城路xxx号。韩式烧烤等，价格实惠、欢迎惠顾【全金釜韩国烧烤店】"]))

if __name__ == "__main__":
    train()

另提供

ui界面、数据集、完整代码、课程设计报告等