Python案例如何用FastText文本分类

wen python案例 2026-07-16 224

本文目录导读：

环境安装
完整代码实现
重要参数说明
注意事项

我将为你提供一个完整的 FastText 文本分类 Python 案例，FastText 是 Facebook 开发的一个高效文本分类工具。

环境安装

pip install fasttext
pip install pandas
pip install scikit-learn

完整代码实现

数据准备与预处理

import pandas as pd
import numpy as np
import fasttext
import fasttext.util
import re
import jieba
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
# 设置随机种子
np.random.seed(42)
class FastTextClassifier:
    def __init__(self):
        self.model = None
    def clean_text(self, text):
        """文本清洗"""
        # 移除特殊字符和数字
        text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z\s]', '', text)
        # 转换为小写
        text = text.lower()
        # 去除多余空格
        text = ' '.join(text.split())
        return text
    def tokenize_chinese(self, text):
        """中文分词"""
        return ' '.join(jieba.cut(text))
    def preprocess_data(self, texts, labels=None, is_chinese=True):
        """数据预处理"""
        processed_texts = []
        for text in texts:
            # 清洗文本
            text = self.clean_text(text)
            # 如果是中文，进行分词
            if is_chinese:
                text = self.tokenize_chinese(text)
            processed_texts.append(text)
        # 如果有标签，返回标签和文本
        if labels is not None:
            return processed_texts, labels
        return processed_texts
    def prepare_fasttext_data(self, texts, labels, output_file='train.txt'):
        """准备FastText训练数据"""
        with open(output_file, 'w', encoding='utf-8') as f:
            for text, label in zip(texts, labels):
                # FastText格式：__label__标签 文本
                line = f'__label__{label} {text}\n'
                f.write(line)
        print(f"数据已保存到 {output_file}")
        return output_file
# 创建示例数据集
def create_sample_data():
    """创建示例数据集"""
    texts = [
        "这部电影太精彩了，剧情紧凑，演员演技出色",
        "产品性价比很高，值得购买",
        "服务质量很差，客服态度不好",
        "这个手机电池续航能力很强",
        "今天天气真不错，适合出去游玩",
        "这家餐厅的菜品味道一般，价格偏贵",
        "学习编程可以提高逻辑思维能力",
        "这个软件操作简单，功能强大",
        "快递速度很快，包装也很完好",
        "这篇文章内容空洞，没有什么实质内容"
    ]
    labels = [
        "positive",
        "positive", 
        "negative",
        "positive",
        "positive",
        "negative",
        "neutral",
        "positive",
        "positive",
        "negative"
    ]
    return texts, labels
# 生成数据
texts, labels = create_sample_data()
# 创建分类器实例
classifier = FastTextClassifier()
# 预处理数据
processed_texts, processed_labels = classifier.preprocess_data(texts, labels)
# 分割数据集
train_texts, test_texts, train_labels, test_labels = train_test_split(
    processed_texts, processed_labels, test_size=0.2, random_state=42
)
# 准备FastText训练数据
classifier.prepare_fasttext_data(train_texts, train_labels, 'train.txt')
classifier.prepare_fasttext_data(test_texts, test_labels, 'test.txt')

训练模型

# 训练FastText模型
print("开始训练模型...")
classifier.model = fasttext.train_supervised(
    input='train.txt',
    lr=1.0,              # 学习率
    epoch=25,            # 训练轮数
    wordNgrams=2,        # 词N-gram
    dim=100,             # 词向量维度
    loss='softmax',      # 损失函数
    minCount=1,          # 最小词频
    minn=1,              # 字符n-gram的最小长度
    maxn=6,              # 字符n-gram的最大长度
    bucket=2000000,      # 字符n-gram的哈希桶数
    thread=4,            # 线程数
    verbose=1            # 是否显示训练信息
)
# 保存模型
classifier.model.save_model("fasttext_model.bin")
print("模型训练完成并保存！")

模型评估

# 计算训练集准确率
train_result = classifier.model.test('train.txt')
print(f"训练集准确率: {train_result[1]:.4f}")
# 计算测试集准确率
test_result = classifier.model.test('test.txt')
print(f"测试集准确率: {test_result[1]:.4f}")
# 测试单个文本
def predict_text(classifier, text):
    """预测单个文本的情感"""
    processed_text = classifier.clean_text(text)
    processed_text = classifier.tokenize_chinese(processed_text)
    if classifier.model is None:
        raise ValueError("模型未加载")
    # 预测
    prediction = classifier.model.predict(processed_text, k=3)
    labels, probabilities = prediction
    print(f"\n文本: {text}")
    print("预测结果:")
    for label, prob in zip(labels, probabilities):
        print(f"  {label.replace('__label__', '')}: {prob:.4f}")
    return labels[0].replace('__label__', ''), probabilities[0]
# 测试预测
test_sentences = [
    "这个产品非常好用，强烈推荐",
    "服务太差了，再也不来了",
    "一般般吧，没什么特别"
]
for sentence in test_sentences:
    predict_text(classifier, sentence)

批量预测与混淆矩阵

from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
def batch_predict(classifier, texts, true_labels):
    """批量预测并生成报告"""
    predictions = []
    probabilities = []
    for text in texts:
        pred_label, prob = predict_text(classifier, text)
        predictions.append(pred_label)
        probabilities.append(prob)
    # 生成分类报告
    print("\n分类报告:")
    print(classification_report(true_labels, predictions))
    # 计算准确率
    accuracy = accuracy_score(true_labels, predictions)
    print(f"准确率: {accuracy:.4f}")
    # 绘制混淆矩阵
    cm = confusion_matrix(true_labels, predictions, labels=['positive', 'negative', 'neutral'])
    plt.figure(figsize=(8, 6))
    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
                xticklabels=['positive', 'negative', 'neutral'],
                yticklabels=['positive', 'negative', 'neutral'])
    plt.title('混淆矩阵')
    plt.xlabel('预测标签')
    plt.ylabel('真实标签')
    plt.show()
    return predictions, probabilities
# 执行批量预测
batch_predict(classifier, test_texts, test_labels)

参数调优

def parameter_tuning(train_file, test_file):
    """FastText参数调优"""
    best_accuracy = 0
    best_params = {}
    # 测试不同参数组合
    learning_rates = [0.1, 0.5, 1.0]
    dimensions = [50, 100, 200]
    epochs = [10, 25, 50]
    for lr in learning_rates:
        for dim in dimensions:
            for epoch in epochs:
                print(f"测试参数: lr={lr}, dim={dim}, epoch={epoch}")
                model = fasttext.train_supervised(
                    input=train_file,
                    lr=lr,
                    dim=dim,
                    epoch=epoch,
                    wordNgrams=2,
                    loss='softmax'
                )
                result = model.test(test_file)
                accuracy = result[1]
                print(f"准确率: {accuracy:.4f}")
                if accuracy > best_accuracy:
                    best_accuracy = accuracy
                    best_params = {
                        'lr': lr,
                        'dim': dim,
                        'epoch': epoch
                    }
    print(f"\n最佳参数: {best_params}")
    print(f"最佳准确率: {best_accuracy:.4f}")
    return best_params
# 执行参数调优（可选，数据量大时执行）
# best_params = parameter_tuning('train.txt', 'test.txt')

完整使用示例

# 完整的FastText分类器使用示例
def main():
    # 1. 创建数据
    texts, labels = create_sample_data()
    # 2. 初始化分类器
    classifier = FastTextClassifier()
    # 3. 预处理数据
    processed_texts, processed_labels = classifier.preprocess_data(texts, labels)
    # 4. 分割数据
    train_texts, test_texts, train_labels, test_labels = train_test_split(
        processed_texts, processed_labels, test_size=0.3, random_state=42
    )
    # 5. 准备训练数据
    classifier.prepare_fasttext_data(train_texts, train_labels, 'train.txt')
    classifier.prepare_fasttext_data(test_texts, test_labels, 'test.txt')
    # 6. 训练模型
    print("训练模型...")
    classifier.model = fasttext.train_supervised(
        input='train.txt',
        lr=1.0,
        epoch=50,
        wordNgrams=3,
        dim=100
    )
    # 7. 评估模型
    print("\n模型评估结果:")
    print(f"测试集准确率: {classifier.model.test('test.txt')[1]:.4f}")
    # 8. 使用模型进行预测
    new_texts = [
        "这次购物体验非常愉快",
        "产品做工粗糙，不值得这个价格",
        "内容还不错，但需要改进"
    ]
    print("\n新文本预测:")
    for text in new_texts:
        predict_text(classifier, text)
    # 9. 保存和加载模型
    classifier.model.save_model("final_model.bin")
    print("\n模型已保存为 final_model.bin")
    # 重新加载模型
    loaded_model = fasttext.load_model("final_model.bin")
    print("模型加载成功！")
if __name__ == "__main__":
    main()