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How to find Top features from Naive Bayes using sklearn pipeline

Hi all,

I am trying to apply Naive Bayes(MultinomialNB ) using pipelines and i came up with the code. However I am interested in finding top 10 positve and negative words , but not able to succeed. when I searched , I got the code for finding top features which i mentioned below. However when i tried using the code using pipeline i am getting the error which i mentioned below. I tried searching exhaustively , but got the code without using pipeline.But when i use the code with my output from pipeline, it is not working. COuld you please help me on how to find feature importance from pipeline output.

 # Pipeline dictionary
 pipelines = 
 'bow_MultinomialNB' : make_pipeline(
 CountVectorizer(), 
 preprocessing.Normalizer(),
 MultinomialNB()
 )
 


 # List tuneable hyperparameters of our pipeline
 pipelines['bow_MultinomialNB'].get_params()


 # BOW - MultinomialNB hyperparameters
 bow_MultinomialNB_hyperparameters = 
 'multinomialnb__alpha' : [1000,500,100,50,10,5,1,0.5,0.1,0.05,0.01,0.005,0.001,0.0005,0.0001]
 

 # Create hyperparameters dictionary
 hyperparameters = 
 'bow_MultinomialNB' : bow_MultinomialNB_hyperparameters
 


 tscv = TimeSeriesSplit(n_splits=3) #For time based splitting
 for name, pipeline in pipelines.items():
 print("NAME:",name)
 print("PIPELINE:",pipeline)


 %time
 # Create empty dictionary called fitted_models
 fitted_models = 

 # Loop through model pipelines, tuning each one and saving it to fitted_models
 for name, pipeline in pipelines.items():
 # Create cross-validation object from pipeline and hyperparameters

 model = GridSearchCV(pipeline, hyperparameters[name], cv=tscv, n_jobs=1,verbose=1)


 # Fit model on X_train, y_train

 model.fit(X_train, y_train)


 # Store model in fitted_models[name] 

 fitted_models[name] = model


 # Print 'name has been fitted'
 print(name, 'has been fitted.')

FEAURE IMPORTANCE:-

 pipelines['bow_MultinomialNB'].steps[2][1].classes__

 ---------------------------------------------------------------------------
 AttributeError Traceback (most recent call last)
 <ipython-input-125-7d45b007e86b> in <module>()
 ----> 1 pipelines['bow_MultinomialNB'].steps[2][1].classes_

 AttributeError: 'MultinomialNB' object has no attribute 'classes_'


 pipelines['bow_MultinomialNB'].steps[0][1].get_feature_names()
 ---------------------------------------------------------------------------
 NotFittedError Traceback (most recent call last)
 <ipython-input-126-2883929221d1> in <module>()
 ----> 1 pipelines['bow_MultinomialNB'].steps[0][1].get_feature_names()

 ~Anaconda3libsite-packagessklearnfeature_extractiontext.py in get_feature_names(self)
 958 def get_feature_names(self):
 959 """Array mapping from feature integer indices to feature name"""
 --> 960 self._check_vocabulary()
 961 
 962 return [t for t, i in sorted(six.iteritems(self.vocabulary_),

 ~Anaconda3libsite-packagessklearnfeature_extractiontext.py in _check_vocabulary(self)
 301 """Check if vocabulary is empty or missing (not fit-ed)"""
 302 msg = "%(name)s - Vocabulary wasn't fitted."
 --> 303 check_is_fitted(self, 'vocabulary_', msg=msg),
 304 
 305 if len(self.vocabulary_) == 0:

 ~Anaconda3libsite-packagessklearnutilsvalidation.py in check_is_fitted(estimator, attributes, msg, all_or_any)
 766 
 767 if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
 --> 768 raise NotFittedError(msg % 'name': type(estimator).__name__)
 769 
 770 

 NotFittedError: CountVectorizer - Vocabulary wasn't fitted.


 x=pipelines['bow_MultinomialNB'].steps[0][1]._validate_vocabulary()
 x.get_feature_names()

 ---------------------------------------------------------------------------
 AttributeError Traceback (most recent call last)
 <ipython-input-120-f620c754a34e> in <module>()
 ----> 1 x.get_feature_names()

 AttributeError: 'NoneType' object has no attribute 'get_feature_names'

Regards,
Shree