result.py

@@ -1,28 +0,0 @@
-from sklearn import preprocessing
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.preprocessing import LabelEncoder
-from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-import numpy as np
-import lzma
-
-def openXZ(path):
-    with lzma.open(path, mode='rt') as f:
-        return f.readlines()
-
-def readFile(path):
-    with open(path) as source:
-          return source.readlines()
-
-lines = openXZ('./paranormal-or-skeptic-ISI-public/dev-0/in.tsv.xz')
-inData = openXZ('./paranormal-or-skeptic-ISI-public/train/in.tsv.xz')
-expData = readFile('./paranormal-or-skeptic-ISI-public/train/expected.tsv')
-expected = LabelEncoder().fit_transform(expData)
-pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
-model = pipeline.fit(inData, expected)
-result = model.predict(lines)
-np.savetxt('./paranormal-or-skeptic-ISI-public/dev-0/out.tsv', result, fmt='%d', delimiter='\n')
-lines = openXZ('./paranormal-or-skeptic-ISI-public/test-A/in.tsv.xz')
-result = model.predict(lines)
-np.savetxt('./paranormal-or-skeptic-ISI-public/test-A/out.tsv', result, fmt='%d', delimiter='\n')
-#dla dev wynik był: 0.7367223065250379, ściezki sa troche dziwne, ponieważ pracowałem na google colab