28 lines
1.2 KiB
Python
28 lines
1.2 KiB
Python
from sklearn import preprocessing
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.preprocessing import LabelEncoder
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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import numpy as np
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import lzma
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def openXZ(path):
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with lzma.open(path, mode='rt') as f:
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return f.readlines()
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def readFile(path):
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with open(path) as source:
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return source.readlines()
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lines = openXZ('./paranormal-or-skeptic-ISI-public/dev-0/in.tsv.xz')
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inData = openXZ('./paranormal-or-skeptic-ISI-public/train/in.tsv.xz')
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expData = readFile('./paranormal-or-skeptic-ISI-public/train/expected.tsv')
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expected = LabelEncoder().fit_transform(expData)
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pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
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model = pipeline.fit(inData, expected)
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result = model.predict(lines)
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np.savetxt('./paranormal-or-skeptic-ISI-public/dev-0/out.tsv', result, fmt='%d', delimiter='\n')
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lines = openXZ('./paranormal-or-skeptic-ISI-public/test-A/in.tsv.xz')
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result = model.predict(lines)
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np.savetxt('./paranormal-or-skeptic-ISI-public/test-A/out.tsv', result, fmt='%d', delimiter='\n')
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#dla dev wynik był: 0.7367223065250379, ściezki sa troche dziwne, ponieważ pracowałem na google colab |