from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
pipe = Pipeline([('count_vec', CountVectorizer()),
                 ('lr', LogisticRegression(solver='liblinear'))])
pipe_params = {'remove_stopwords': [None, 'english'],'ngram_vec': [(1,1,)(2,2), (1,3)],'lr__C': [0.01, 1]}
gs = GridSearchCV(pipe, param_grid=pipe_params, cv=3)
gs_fit=gs.fit(count_vec['label'])
pd.df(gs_fit.cv_results).sort_values('mean_test_score',ascending=False).head

TypeError                                 Traceback (most recent call last)
<ipython-input-20-e9e666a843e5> in <module>
     11
     12 gs = GridSearchCV(pipe, param_grid=pipe_params, cv=3)
---> 13 gs_fit=gs.fit(count_vec['label'])
     14 pd.df(gs_fit.cv_results).sort_values('mean_test_score',ascending=False).head()
     15

TypeError: 'CountVectorizer' object is not subscriptable`