32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
import lzma
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import nltk
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.feature_extraction.text import TfidfVectorizer
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with lzma.open("train/in.tsv.xz", "rt", encoding="utf-8") as train_file:
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in_train = [x.strip().lower() for x in train_file.readlines()]
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with open("train/expected.tsv", "r", encoding="utf-8") as train_file:
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out_train = [int(x.strip()) for x in train_file.readlines()]
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with lzma.open("dev-0/in.tsv.xz", "rt", encoding="utf-8") as dev_file:
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in_dev = [x.strip().lower() for x in dev_file.readlines()]
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with lzma.open("test-A/in.tsv.xz", "rt", encoding="utf-8") as test_file:
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in_test = [x.strip().lower() for x in test_file.readlines()]
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tfidf_vectorizer=TfidfVectorizer()
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IN_train = tfidf_vectorizer.fit_transform(in_train)
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classifier = MultinomialNB()
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y_pred = classifier.fit(IN_train, out_train)
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y_prediction = y_pred.predict(tfidf_vectorizer.transform(in_test))
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with open("test-A/out.tsv", "w", encoding="utf-8") as test_out_file:
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for single_pred in y_prediction:
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test_out_file.writelines(f"{str(single_pred)}\n")
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pred_dev = y_pred.predict(tfidf_vectorizer.transform(in_test))
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with open("dev-0/out.tsv", "w", encoding="utf-8") as dev_out_file:
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for single_pred in pred_dev:
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dev_out_file.writelines(f"{str(single_pred)}\n")
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