Vfrdtyky

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