33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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data_raw = pd.read_csv('train/train.tsv', delimiter = '\t', header = None,usecols=[0, 1], names = ['y', 'X'])
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import string
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import os
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from sklearn.feature_extraction.text import TfidfVectorizer
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from stop_words import get_stop_words
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def preprocess(item):
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to_replace = '''~!@#$%^&*()_+-=[]{};\'":?/.>,<1234567890–”’'''
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for r in to_replace:
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item = item.replace(r, '')
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return item.lower()
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stop_words = get_stop_words('polish') + ['aby', 'tych', 'tym', 'tyle', 'tymi', 'też']
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vectorizer = TfidfVectorizer(stop_words=stop_words, preprocessor=preprocess)
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tfs = vectorizer.fit_transform(data_raw.X)
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from sklearn.naive_bayes import MultinomialNB
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clf = MultinomialNB()
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clf.fit(tfs,data_raw.y)
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for dir in ['dev-0/', 'test-A/']:
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test_raw = pd.read_csv(dir+'in.tsv', delimiter = '\t', header = None,usecols=[0], names = ['X'])
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X_test = vectorizer.transform(test_raw.X)
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y_predicted = clf.predict(X_test)
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np.savetxt(dir+"out.tsv", y_predicted, delimiter="\t")
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