36 lines
998 B
Python
36 lines
998 B
Python
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import pandas as pd
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import numpy as np
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import gzip
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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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with gzip.open('train/train.tsv.gz', 'rb') as file:
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data = pd.read_csv(file, sep='\t', header=None, error_bad_lines=False)
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# dane do trenowania
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y_train = data[0]
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x_train = data[1]
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# dev
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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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#test
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x_test_A = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')
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x_test_A = x_test_A[0]
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# model
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model = make_pipeline(TfidfVectorizer(), MultinomialNB())
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model.fit(x_train, y_train)
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def predictions(zb, path_out, model):
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res = model.predict(zb)
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with open(path_out, 'wt') as file:
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for i in res:
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file.write(str(i) + '\n')
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predictions(x_dev,'dev-0/out.tsv', model)
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predictions(x_test_A,'test-A/out.tsv', model)
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