#TFIDF from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer import pickle # tfidf向量保存 # tfidf = TfidfVectorizer() tfidftransformer_path = './tfidftransformer.pkl' tfidf = pickle.load(open(tfidftransformer_path,"rb")) x_train_df = tfidf.fit_transform(x_train) x_train_df = tfidf.transform(x_train) # tfidf向量保存 # tfidftransformer_path = './tfidftransformer.pkl' # with open(tfidftransformer_path, 'wb') as fw: # pickle.dump(tfidf, fw) # 先前已执行过一次 x_test_df = tfidf.transform(x_test)
时间: 2024-02-29 09:55:14 浏览: 28
这段代码是关于使用 sklearn 库中的 CountVectorizer 和 TfidfVectorizer 进行文本特征提取的过程。其中,使用 pickle 库对 tfidf 变量进行保存和加载,这样可以避免每次执行程序时都需要重新计算 tfidf。通过调用 fit_transform() 和 transform() 方法,将训练数据集和测试数据集转换成 tfidf 特征向量。这些特征向量可以作为机器学习算法的输入。
相关问题
data = ["This two-wheeler is really good on slippery roads"] sentce=["This is really good"] from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.metrics.pairwise import cosine_similarity vectorizer = CountVectorizer() X_train_termcounts = vectorizer.fit_transform(data) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_termcounts) print ("\nTfidf of training data:", X_train_tfidf.toarray()) X_input_termcounts = vectorizer.transform(sentce) X_input_tfidf = tfidf_transformer.transform(X_input_termcounts) print ("\nTfidf of training data:", X_input_tfidf.toarray()) print("\nCosine of data:",cosine_similarity(X_train_tfidf,X_input_tfidf))代码注释
下面是对这段代码的注释:
```
data = ["This two-wheeler is really good on slippery roads"] # 定义训练数据
sentce=["This is really good"] # 定义输入句子
from sklearn.feature_extraction.text import CountVectorizer # 导入文本特征提取库
from sklearn.feature_extraction.text import TfidfTransformer # 导入TF-IDF转换库
from sklearn.metrics.pairwise import cosine_similarity # 导入余弦相似度计算库
vectorizer = CountVectorizer() # 创建词袋模型
X_train_termcounts = vectorizer.fit_transform(data) # 对训练数据进行特征提取和向量化
tfidf_transformer = TfidfTransformer() # 创建TF-IDF转换器
X_train_tfidf = tfidf_transformer.fit_transform(X_train_termcounts) # 对训练数据进行TF-IDF转换
print ("\nTfidf of training data:", X_train_tfidf.toarray()) # 打印训练数据的TF-IDF矩阵
X_input_termcounts = vectorizer.transform(sentce) # 对输入句子进行特征提取和向量化
X_input_tfidf = tfidf_transformer.transform(X_input_termcounts) # 对输入句子进行TF-IDF转换
print ("\nTfidf of input data:", X_input_tfidf.toarray()) # 打印输入句子的TF-IDF矩阵
print("\nCosine of data:",cosine_similarity(X_train_tfidf,X_input_tfidf)) # 计算训练数据和输入句子的余弦相似度
```
这段代码通过使用CountVectorizer和TfidfTransformer库对文本数据进行特征提取和向量化,并使用cosine_similarity计算训练数据和输入句子的余弦相似度。输出结果是训练数据和输入句子的TF-IDF矩阵以及它们之间的余弦相似度。
指出代码的错误:from sklearn.feature_extraction.text import CountVectorizer vect = CountVectorizer() vect_train=vect.fit_transform(x_train) from sklearn.feature_extraction.text import TfidfVectorizer tfidf=TfidfVectorizer() tfidf_train=tfidf.fit_transform(vect_train)
看起来你把CountVectorizer和TfidfVectorizer混淆了,应该把tfidf_train=tfidf.fit_transform(x_train)而不是tfidf_train=tfidf.fit_transform(vect_train)。
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