Dodanie rozwiązania

result.py

@@ -0,0 +1,28 @@
+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