40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
import gzip
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import io
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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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from sklearn.feature_extraction.text import CountVectorizer
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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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def read_data_gz(baseUrl):
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f = gzip.open(baseUrl,'r')
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data_unzip = f.read()
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data = pd.read_table(io.StringIO(data_unzip.decode('utf-8')), error_bad_lines=False, header= None)
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return data
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baseUrl = '/home/przemek/ekstrakcja/sport-text-classification-ball-ISI-public/'
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data = read_data_gz(baseUrl + 'train/train.tsv.gz')
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y_train = data[0].values
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x_train = data[1].values
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model = make_pipeline(TfidfVectorizer(), MultinomialNB())
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model.fit(x_train, y_train)
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# dev-0
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x_dev = pd.read_table(baseUrl + 'dev-0/in.tsv', error_bad_lines=False, header= None)
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x_dev = x_dev[0].values
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y_pred = model.predict(x_dev)
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y_pred.tofile(baseUrl + 'dev-0/out.tsv', sep='\n')
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# --------------
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# test-A
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x_testA = pd.read_table(baseUrl + '/test-A/in.tsv', error_bad_lines=False, header= None)
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x_testA= x_testA[0].values
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y_predA = model.predict(x_testA)
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y_predA.tofile(baseUrl + 'test-A/out.tsv', sep='\n')
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# -------------- |