Full solution

main.py

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+import gensim as gensim
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import GaussianNB, MultinomialNB
+from stop_words import get_stop_words
+import os
+import pandas as pd
+
+def read_train_file(inDirectory):
+    colnames = ['Y', 'X']
+    df_train = pd.read_csv(inDirectory, sep="\t", names=colnames)
+    return df_train
+
+def read_evaluate_file(inDirectory):
+    colnames = ['X']
+    df_evaulate = pd.read_csv(inDirectory, sep="\n", names=colnames)
+    return df_evaulate
+
+def naivBayersWithTFIDFTrain(inDirectory):
+    df_train = read_train_file(inDirectory)
+    train_vectorized_corpus = vectorizer.fit_transform(df_train['X'])
+    gnb.fit(train_vectorized_corpus, df_train['Y'])
+
+def naivBayersWithTFIDFEvaluate(inDirectory, outDirectory):
+    df_evaluate = read_evaluate_file(inDirectory)
+    evaluate_vectorized_corpus = vectorizer.transform(df_evaluate['X'])
+    evaluate = gnb.predict(evaluate_vectorized_corpus)
+    with open(outDirectory, 'w') as file:
+        for e in evaluate:
+            file.write("%i\n" % e)
+
+vectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
+gnb = MultinomialNB()
+
+naivBayersWithTFIDFTrain("train/train.tsv")
+naivBayersWithTFIDFEvaluate("dev-0/in.tsv", "dev-0/out.tsv")
+naivBayersWithTFIDFEvaluate("test-A/in.tsv", "test-A/out.tsv")
+
+
+os.system("./geval -t dev-0")