41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
from sklearn.naive_bayes import MultinomialNB
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from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
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import pandas as pd
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import numpy as np
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from stop_words import get_stop_words
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stop_words = get_stop_words('polish')
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v = TfidfVectorizer(stop_words=None)
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naive_bayes=MultinomialNB()
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ball_train = pd.read_csv('train/train.tsv', sep='\t', error_bad_lines=False, header=None)
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y_train = pd.DataFrame(ball_train[0])
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x_train = pd.DataFrame(ball_train[1])
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x_np=x_train.to_numpy()
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x_np = [str(item) for item in x_np]
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x_train=v.fit_transform(x_np)
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naive_bayes.fit(x_train, y_train)
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ball_dev = pd.read_csv('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None)
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X_dev = pd.DataFrame(ball_dev)
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X_dev_np=X_dev.to_numpy()
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X_dev_np = [str(item) for item in X_dev_np]
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X_dev=v.transform(X_dev_np)
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Y_dev_predicted = naive_bayes.predict(X_dev)
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pd.DataFrame(Y_dev_predicted).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
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ball_test=pd.read_csv('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None)
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X_test = pd.DataFrame(ball_test)
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X_test_np=X_test.to_numpy()
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X_test_np = [str(item) for item in X_test_np]
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X_test=v.transform(X_test_np)
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Y_test_predicted = naive_bayes.predict(X_test)
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pd.DataFrame(Y_test_predicted).to_csv('test-A/out.tsv', sep='\t', index=False, header=False) |