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)。

相关问题

X_train = df.loc[:25000, 'review'].values y_train = df.loc[:25000, 'sentiment'].values X_test = df.loc[25000:, 'review'].values y_test = df.loc[25000:, 'sentiment'].values from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import GridSearchCV tfidf = TfidfVectorizer(strip_accents=None, lowercase=False, preprocessor=None) param_grid = [{'vect__ngram_range': [(1, 1)], 'vect__stop_words': [stop, None], 'vect__tokenizer': [tokenizer, tokenizer_porter], 'clf__penalty': ['l1', 'l2'], 'clf__C': [1.0, 10.0, 100.0]}, {'vect__ngram_range': [(1, 1)], 'vect__stop_words': [stop, None], 'vect__tokenizer': [tokenizer, tokenizer_porter], 'vect__use_idf':[False], 'vect__norm':[None], 'clf__penalty': ['l1', 'l2'], 'clf__C': [1.0, 10.0, 100.0]}, ] lr_tfidf = Pipeline([('vect', tfidf), ('clf', ******)]) # find out how to use pipeline and choose a model to make the document classification gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid, scoring='accuracy', cv=5, verbose=2, n_jobs=-1) *号部分填什么

You can choose a classifier to use in the pipeline depending on your specific task and the nature of your data. Some commonly used classifiers for document classification include logistic regression, support vector machines (SVM), and naive Bayes.

For example, if you want to use logistic regression as your classifier, you can replace the asterisks with `LogisticRegression(random_state=0)`. The `random_state` parameter ensures that the results are reproducible.

The complete code would look like this:

from sklearn.linear_model import LogisticRegression

X_train = df.loc[:25000, 'review'].values 
y_train = df.loc[:25000, 'sentiment'].values 
X_test = df.loc[25000:, 'review'].values 
y_test = df.loc[25000:, 'sentiment'].values 

from sklearn.pipeline import Pipeline 
from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn.model_selection import GridSearchCV 

tfidf = TfidfVectorizer(strip_accents=None, 
                        lowercase=False, 
                        preprocessor=None) 

param_grid = [{'vect__ngram_range': [(1, 1)], 
               'vect__stop_words': [stop, None], 
               'vect__tokenizer': [tokenizer, tokenizer_porter], 
               'clf__penalty': ['l1', 'l2'], 
               'clf__C': [1.0, 10.0, 100.0]}, 
              
              {'vect__ngram_range': [(1, 1)], 
               'vect__stop_words': [stop, None], 
               'vect__tokenizer': [tokenizer, tokenizer_porter], 
               'vect__use_idf':[False], 
               'vect__norm':[None], 
               'clf__penalty': ['l1', 'l2'], 
               'clf__C': [1.0, 10.0, 100.0]}, 
              ] 

lr_tfidf = Pipeline([('vect', tfidf), 
                     ('clf', LogisticRegression(random_state=0))]) 

gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid, 
                           scoring='accuracy', 
                           cv=5, 
                           verbose=2, 
                           n_jobs=-1)

3.使用sklearn库调用CountVectorizer和TfidfTransformer函数计算TF-IDF值，将文本转化为词频矩阵。

可以按照以下步骤使用sklearn库调用CountVectorizer和TfidfTransformer函数计算TF-IDF值，将文本转化为词频矩阵：

1. 导入需要的库和模块：

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer

2. 创建一个CountVectorizer对象，用于将文本转换为词频矩阵：

count_vect = CountVectorizer()

3. 调用fit_transform方法将文本转换为词频矩阵：

count_matrix = count_vect.fit_transform(text_list)

其中，text_list是一个包含多个文本的列表。

4. 创建一个TfidfTransformer对象，用于计算TF-IDF值：

tfidf_transformer = TfidfTransformer()

5. 调用fit_transform方法将词频矩阵转换为TF-IDF矩阵：

tfidf_matrix = tfidf_transformer.fit_transform(count_matrix)

现在，tfidf_matrix就是我们需要的TF-IDF矩阵，可以用于后续的文本分析任务。