帮我用python写出一个基于PCA方法和SVM分类的新闻文本分类代码
时间: 2024-02-21 21:00:33 浏览: 34
好的,以下是一个基于PCA方法和SVM分类的新闻文本分类的Python代码示例:
```python
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
# 加载新闻数据集
newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
newsgroups_test = fetch_20newsgroups(subset='test', remove=('headers', 'footers', 'quotes'))
# 提取TF-IDF特征
vectorizer = TfidfVectorizer(stop_words='english')
X_train = vectorizer.fit_transform(newsgroups_train.data)
X_test = vectorizer.transform(newsgroups_test.data)
y_train = newsgroups_train.target
y_test = newsgroups_test.target
# 使用PCA进行降维
pca = PCA(n_components=1000)
X_train_pca = pca.fit_transform(X_train.toarray())
X_test_pca = pca.transform(X_test.toarray())
# 定义SVM分类器
svm = SVC(kernel='linear', random_state=42)
# 构建Pipeline
pipeline = Pipeline([
('pca', pca),
('svm', svm)
])
# 定义GridSearchCV参数
param_grid = {
'svm__C': [0.1, 1, 10],
'svm__gamma': [0.1, 1, 10],
}
# 进行GridSearchCV
grid_search = GridSearchCV(pipeline, param_grid, cv=5, n_jobs=-1)
grid_search.fit(X_train, y_train)
# 输出测试集准确率
accuracy = grid_search.score(X_test, y_test)
print("Test set accuracy: {:.2f}".format(accuracy))
```
这个示例中,我们首先使用TfidfVectorizer提取新闻文本的TF-IDF特征。然后使用PCA对特征矩阵进行降维,将维度降到1000。接下来,我们定义了一个SVM分类器,并将PCA和SVM分类器构建成Pipeline。然后我们使用GridSearchCV进行参数调优,并输出测试集准确率。
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