mirror of
https://github.com/gsi-upm/senpy
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47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
#
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# Copyright 2014 Grupo de Sistemas Inteligentes (GSI) DIT, UPM
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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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_out()))
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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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