31 lines
955 B
Python
31 lines
955 B
Python
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import os
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import sklearn
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import pandas as pd
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from sklearn.metrics import accuracy_score
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from gzip import open as open_gz
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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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def evaluation(x, path_out, model):
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results = model.predict(x)
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with open(path_out, 'wt') as file:
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for r in results:
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file.write(str(r) + '\n')
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train = pd.read_csv('train/train.tsv', header = None, sep = '\t', error_bad_lines = False)
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x_train = train[1]
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y_train = train[0]
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x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')
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x_dev = x_dev[0]
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x_test = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')
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x_test = x_test[0]
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model = make_pipeline(TfidfVectorizer(), MultinomialNB())
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model.fit(x_train, y_train)
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evaluation(x_dev,'dev-0/out.tsv', model)
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evaluation(x_test,'test-A/out.tsv', model)
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