Python -将GridSearchCV与NLTK结合使用

from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# 假设我们有一个包含文本和对应标签的数据集
X = ['文本1', '文本2', '文本3', ...]
y = [标签1, 标签2, 标签3, ...]

# 定义文本预处理的步骤
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
preprocessor = TfidfVectorizer(stop_words=stop_words, tokenizer=lemmatizer.lemmatize)

# 定义分类器
classifier = SVC()

# 创建Pipeline对象
pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', classifier)
])

param_grid = {
    'preprocessor__max_features': [1000, 2000, 3000],
    'classifier__C': [0.1, 1, 10],
    'classifier__kernel': ['linear', 'rbf']
}

grid_search = GridSearchCV(pipeline, param_grid, cv=5)
grid_search.fit(X, y)

print("Best score: %0.3f" % grid_search.best_score_)
print("Best parameters set:")
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(param_grid.keys()):
    print("\t%s: %r" % (param_name, best_parameters[param_name]))