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31 lines
1023 B
Python
31 lines
1023 B
Python
from sklearn.pipeline import Pipeline
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.model_selection import train_test_split
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from mydata import text, labels
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X_train, X_test, y_train, y_test = train_test_split(text, labels, test_size=0.12, random_state=42)
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from sklearn.naive_bayes import MultinomialNB
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count_vec = CountVectorizer(tokenizer=lambda x: x.split())
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clf3 = MultinomialNB()
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pipeline = Pipeline([('cv', count_vec),
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('clf', clf3)])
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pipeline.fit(X_train, y_train)
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print('Feature names: {}'.format(count_vec.get_feature_names()))
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print('Class count: {}'.format(clf3.class_count_))
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if __name__ == '__main__':
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print('--Results--')
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tests = [
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(['The sentiment for senpy should be positive :)', ], 1),
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(['The sentiment for anything else should be negative :()', ], -1)
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]
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for features, expected in tests:
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result = pipeline.predict(features)
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print('Input: {}\nExpected: {}\nGot: {}'.format(features[0], expected, result))
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