Run autopep8
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@ -5,12 +5,12 @@ from sklearn.naive_bayes import GaussianNB, MultinomialNB
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from sklearn.pipeline import make_pipeline, Pipeline
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from sklearn.pipeline import make_pipeline, Pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.feature_extraction.text import TfidfVectorizer
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from nltk.corpus import stopwords
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from nltk.corpus import stopwords
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# pogarsza wynik z 0.73 na 0.7
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# pogarsza wynik z 0.73 na 0.7
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def preprocess(line, stop_words):
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def preprocess(line, stop_words):
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return " ".join([word for word in line.split() if word not in stop_words])
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return " ".join([word for word in line.split() if word not in stop_words])
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def train_model(train_in, train_expected):
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def train_model(train_in, train_expected):
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@ -27,11 +27,11 @@ def train_model(train_in, train_expected):
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# model = MultinomialNB()
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# model = MultinomialNB()
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# return model.fit(vectors, exp_encoded)
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# return model.fit(vectors, exp_encoded)
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# MemoryError
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# MemoryError
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pipeline = Pipeline(steps=[
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pipeline = Pipeline(steps=[
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('tfidf', TfidfVectorizer()),
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('tfidf', TfidfVectorizer()),
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('naive-bayes', MultinomialNB())
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('naive-bayes', MultinomialNB())
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])
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])
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return pipeline.fit(train_data, exp_encoded)
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return pipeline.fit(train_data, exp_encoded)
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@ -42,11 +42,13 @@ def predict(model, in_file, out_file):
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prediction = model.predict(lines)
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prediction = model.predict(lines)
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np.savetxt(out_file, prediction, fmt='%d')
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np.savetxt(out_file, prediction, fmt='%d')
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def main():
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def main():
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#stop_words = set(stopwords.words('english'))
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#stop_words = set(stopwords.words('english'))
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model = train_model("train/in.tsv", "train/expected.tsv")
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model = train_model("train/in.tsv", "train/expected.tsv")
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predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
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predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
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predict(model, "test-A/in.tsv", "test-A/out.tsv")
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predict(model, "test-A/in.tsv", "test-A/out.tsv")
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if __name__ == '__main__':
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if __name__ == '__main__':
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main()
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main()
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