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main_bow_nb.py
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main_bow_nb.py
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from prepare_text import MovieReview
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.externals import joblib
from review_file import review_list
from test_review_file import test_review_list
def main():
# extract reviews from tsv files
labeled_training_data = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3) # 25,000 reviews
test_data = pd.read_csv("testData.tsv", header=0, delimiter="\t", quoting=3) # 25, 000 reviews
print "Creating BOW...."" "
vectorizer = CountVectorizer(analyzer = "word", tokenizer = None, preprocessor = None, stop_words = None, max_features = 5000)
trained_data_features = vectorizer.fit_transform(review_list)
trained_data_features = trained_data_features.toarray() # convert to numpy array for faster processing
print "Supervised Learning - Naive Bayes"
nb_model = MultinomialNB(alpha = 0.01)
nb_model = nb_model.fit(trained_data_features, labeled_training_data["sentiment"]) # using BOW as feaures and the given labels as repsonse variables
print "---------------------------------"
print " "
print "Predicting on test data: "
# BOW for test set
test_data_features = vectorizer.transform(test_review_list)
test_data_features = test_data_features.toarray()
# use the trained forest to make predictions
predictions = nb_model.predict(test_data_features)
# prepare output submission file
prediction_output = pd.DataFrame( data = {"id":test_data["id"], "sentiment":predictions} ) # create pandas dataframe
prediction_output.to_csv("BOW_NB.csv", index=False, quoting=3)# write to csv file
joblib.dump(vectorizer, 'bow_model.pkl')
joblib.dump(nb_model, 'nb_bow_model.pkl')
if __name__ == '__main__':
main()