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)