53 lines
1.3 KiB
Python
53 lines
1.3 KiB
Python
from sklearn.feature_extraction.text import TfidfVectorizer
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import GaussianNB
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nrows = 10000
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def load_data(path):
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in_df = pd.read_csv(f'{path}/in.tsv.xz', sep='\t', nrows=nrows, header=None)
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try:
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exp_df = pd.read_csv(f'{path}/expected.tsv', sep='\t', nrows=nrows, header=None)
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except:
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exp_df = None
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return in_df, exp_df
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def write_res(data, path):
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with open(path, 'w') as f:
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for line in data:
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f.write(f'{line}\n')
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print(f"Data written {path}/out.tsv")
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def main():
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in_df, exp_df = load_data('train')
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in_df = in_df.drop(in_df.columns[1], axis=1)
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vectorizer = TfidfVectorizer(lowercase=True, stop_words=['english'])
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X = vectorizer.fit_transform(in_df.to_numpy().ravel())
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vectorizer.get_feature_names_out()
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# in_df = in_df.reset_index()
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tfidfVector = vectorizer.transform(in_df[0])
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gnb = GaussianNB()
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gnb.fit(tfidfVector.todense(), exp_df)
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paths = ['dev-0', 'test-A']
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for path in paths:
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x = load_data(path)
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vec = vectorizer.transform(x[0][0])
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result = gnb.predict(vec.todense())
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write_res(result, f'{path}/out.tsv')
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if __name__ == '__main__':
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main() |