main.py

@@ -1,52 +0,0 @@
-import pandas as pd
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-
-def main():
-    clf = MultinomialNB()
-    vectorizer = TfidfVectorizer()
-    x_train, y_train = get_training_data(vectorizer)
-    clf.fit(x_train, y_train)
-
-    x_dev = get_dev_data(vectorizer)
-    Y_dev_predicted = clf.predict(x_dev)
-    save_to_tsv(Y_dev_predicted, 'dev-0/out.tsv')
-
-    x_test = get_test_data(vectorizer)
-    Y_test_predicted = clf.predict(x_test)
-    save_to_tsv(Y_test_predicted, 'test-A/out.tsv')
-
-
-def save_to_tsv(data, path):
-    pd.DataFrame(data).to_csv(path, sep='\t', index=False, header=False)
-
-
-def get_training_data(vectorizer):
-    train_dataset = pd.read_csv('train/train.tsv/train.tsv', sep='\t', header=None, error_bad_lines=False)
-
-    y_train = train_dataset[0]
-    X_train = train_dataset[1]
-    x_train = [str(item) for item in X_train.to_numpy()]
-
-    x_train = vectorizer.fit_transform(x_train)
-
-    return x_train, y_train
-
-
-def get_dev_data(vectorizer):
-    dev_dataset = pd.read_csv('dev-0/in.tsv', sep='\t', header=None, error_bad_lines=False)
-    X_dev = [str(item) for item in dev_dataset.to_numpy()]
-
-    return vectorizer.transform(X_dev)
-
-
-def get_test_data(vectorizer):
-    test_dataset = pd.read_csv('test-A/in.tsv', sep='\t', header=None, error_bad_lines=False)
-    X_test = [str(item) for item in test_dataset.to_numpy()]
-
-    return vectorizer.transform(X_test)
-
-
-if __name__ == '__main__':
-    main()