bayes

bayes.py

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+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+import pandas as pd
+
+# czytanie danych treningowych
+train_data = pd.read_csv('train/train.tsv', sep='\t', header=None, error_bad_lines=False)
+y_train = train_data.iloc[:, 0]
+x_train = train_data.iloc[:, 1]
+
+# wektoryzacji tfidf
+tfidf = TfidfVectorizer()
+tfidf_train = tfidf.fit_transform(x_train)
+
+# naiwny klasyfikator Bayesa - trenowanie
+bayes = MultinomialNB()
+bayes.fit(tfidf_train, y_train)
+
+# naiwny klasyfikator Bayesa - ewaluacja zbioru dev
+dev_data = pd.read_csv('dev-0/in.tsv', sep='\n', header=None)
+x_dev = dev_data.iloc[:, 0]
+tfidf_dev = tfidf.transform(x_dev)
+dev_predictions = bayes.predict(tfidf_dev)
+dev_out_file = open('dev-0/out.tsv', 'w')
+for prediction in dev_predictions:
+    dev_out_file.write(str(prediction) + '\n')
+
+# naiwny klasyfikator Bayesa - ewaluacja zbioru test
+test_data = pd.read_csv('test-A/in.tsv', sep='\n', header=None)
+x_test = dev_data.iloc[:, 0]
+tfidf_test = tfidf.transform(x_test)
+test_predictions = bayes.predict(tfidf_test)
+test_out_file = open('test-A/out.tsv', 'w')
+for prediction in test_predictions:
+    test_out_file.write(str(prediction) + '\n')
+
+
+
+
+
+