34 lines
858 B
Python
34 lines
858 B
Python
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import pandas as pd
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import make_pipeline
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from sklearn.metrics import accuracy_score
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df = pd.read_csv("train/train.tsv", sep="\t", header=None, error_bad_lines=False)
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dev_x = pd.read_csv("dev-0/in.tsv", sep="\t", header=None, error_bad_lines=False)
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test_x = pd.read_csv("test-A/in.tsv", sep="\t", header=None, error_bad_lines=False)
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x = df[1]
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y = df[0]
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model = make_pipeline(TfidfVectorizer(), MultinomialNB())
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model.fit(x,y)
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pred_dev = model.predict(dev_x[0])
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pred_test = model.predict(test_x[0])
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with open('dev-0/out.tsv', 'wt') as f:
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for pred in pred_dev:
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f.write(str(pred)+'\n')
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with open('test-A/out.tsv', 'wt') as f:
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for pred in pred_test:
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f.write(str(pred)+'\n')
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