run.py

@@ -1,42 +0,0 @@
-import pandas as pd
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-
-# * Training data loading
-with open('train/in.tsv', 'r', encoding='utf-8') as f:
-    x_train = pd.DataFrame(f.readlines(), columns=['text'])
-y_train = pd.read_csv('train/expected.tsv', sep='\t',
-                      names=['paranormal'], encoding='utf-8')
-# *Validation data loading
-with open('dev-0/in.tsv', 'r', encoding='utf-8') as f:
-    x_dev = pd.DataFrame(f.readlines(), columns=['text'])
-# * Test data loading
-with open('test-A/in.tsv', 'r', encoding='utf-8') as f:
-    x_test = pd.DataFrame(f.readlines(), columns=['text'])
-
-# * Training data preparation
-tfidf_vectorizer = TfidfVectorizer(max_df=0.95, max_features=500)
-x_train_vectorized = tfidf_vectorizer.fit_transform(
-    x_train['text'].values)
-
-# * Model training
-mnb_model = MultinomialNB().fit(x_train_vectorized, y_train.values.ravel())
-
-# * Validation data preparation
-x_dev_prepared = tfidf_vectorizer.transform(x_dev['text'].values)
-# * Validation data predictions
-predictions = mnb_model.predict(x_dev_prepared)
-# * Validation predicitons saving
-with open('dev-0/out.tsv', 'w') as f:
-    for pred in predictions:
-        f.write(f'{pred}\n')
-
-# * Test data preparation
-x_test_vectorized = tfidf_vectorizer.transform(x_test['text'].values)
-# * Test data predictions
-predictions = mnb_model.predict(x_test_vectorized)
-# * Test predictions saving
-with open('test-A/out.tsv', 'w') as f:
-    for pred in predictions:
-        f.write(f'{pred}\n')