solution

.gitignore

@@ -1,4 +1,4 @@
-
+.idea/*
 *~
 *.swp
 *.bak

run.py

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+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+
+with open('train/in.tsv', 'r', encoding='utf-8') as f:
+    x_train = pd.DataFrame([line.strip().split('\t') for line in f.readlines()], columns=['text', 'text_id'])
+with open('dev-0/in.tsv', 'r', encoding='utf-8') as f:
+    x_dev = pd.DataFrame([line.strip().split('\t') for line in f.readlines()], columns=['text', 'text_id'])
+with open('train/in.tsv', 'r', encoding='utf-8') as f:
+    x_test = pd.DataFrame([line.strip().split('\t') for line in f.readlines()], columns=['text', 'text_id'])
+
+y_train = pd.read_csv('train/expected.tsv', sep='\t', names=['paranormal'], encoding='utf-8')
+tfidf_vectorizer = TfidfVectorizer(max_df=0.95, max_features=500)
+x_train_vectorized = tfidf_vectorizer.fit_transform(x_train['text'].values)
+
+mnb_model = MultinomialNB().fit(x_train_vectorized, y_train.values.ravel())
+
+# Dev data
+x_dev_prepared = tfidf_vectorizer.transform(x_dev['text'].values)
+predictions = mnb_model.predict(x_dev_prepared)
+with open('dev-0/out.tsv', 'w') as f:
+    for pred in predictions:
+        f.write(f'{pred}\n')
+
+# Test data
+x_test_vectorized = tfidf_vectorizer.transform(x_test['text'].values)
+predictions = mnb_model.predict(x_test_vectorized)
+with open('test-A/out.tsv', 'w') as f:
+    for pred in predictions:
+        f.write(f'{pred}\n')