How to get feature Importance in naive bayes?

Question

I have a dataset of reviews which has a class label of positive/negative. I am applying Naive Bayes to that reviews dataset. Firstly, I am converting into Bag of words. Here sorted_data['Text'] is reviews and final_counts is a sparse matrix

count_vect = CountVectorizer() 
final_counts = count_vect.fit_transform(sorted_data['Text'].values)

I am splitting the data into train and test dataset.

X_1, X_test, y_1, y_test = cross_validation.train_test_split(final_counts, labels, test_size=0.3, random_state=0)

I am applying the naive bayes algorithm as follows

optimal_alpha = 1
NB_optimal = BernoulliNB(alpha=optimal_aplha)

# fitting the model
NB_optimal.fit(X_tr, y_tr)

# predict the response
pred = NB_optimal.predict(X_test)

# evaluate accuracy
acc = accuracy_score(y_test, pred) * 100
print('\nThe accuracy of the NB classifier for k = %d is %f%%' % (optimal_aplha, acc))

Here X_test is test dataset in which pred variable gives us whether the vector in X_test is positive or negative class.

The X_test shape is (54626 rows, 82343 dimensions)

length of pred is 54626

My question is I want to get the words with highest probability in each vector so that I can get to know by the words that why it predicted as positive or negative class. Therefore, how to get the words which have highest probability in each vector?

Answer 1

You can get the important of each word out of the fit model by using the coefs_ or feature_log_prob_ attributes. For example

neg_class_prob_sorted = NB_optimal.feature_log_prob_[0, :].argsort()
pos_class_prob_sorted = NB_optimal.feature_log_prob_[1, :].argsort()

print(np.take(count_vect.get_feature_names(), neg_class_prob_sorted[:10]))
print(np.take(count_vect.get_feature_names(), pos_class_prob_sorted[:10]))

Prints the top ten most predictive words for each of your classes.

Answer 2

Try this:

pred_proba = NB_optimal.predict_proba(X_test)
words = np.take(count_vect.get_feature_names(), pred_proba.argmax(axis=1))