main.py

@@ -1,41 +0,0 @@
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
-import pandas as pd
-import numpy as np
-from stop_words import get_stop_words
-
-stop_words = get_stop_words('polish')
-
-v = TfidfVectorizer(stop_words=None)
-naive_bayes=MultinomialNB()
-
-ball_train = pd.read_csv('train/train.tsv', sep='\t', error_bad_lines=False, header=None)
-
-y_train = pd.DataFrame(ball_train[0])
-x_train = pd.DataFrame(ball_train[1])
-x_np=x_train.to_numpy()
-x_np = [str(item) for item in x_np]
-
-x_train=v.fit_transform(x_np)
-
-naive_bayes.fit(x_train, y_train)
-
-ball_dev = pd.read_csv('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None)
-
-X_dev = pd.DataFrame(ball_dev)
-X_dev_np=X_dev.to_numpy()
-X_dev_np = [str(item) for item in X_dev_np]
-X_dev=v.transform(X_dev_np)
-
-Y_dev_predicted = naive_bayes.predict(X_dev)
-pd.DataFrame(Y_dev_predicted).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
-
-
-ball_test=pd.read_csv('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None)
-X_test = pd.DataFrame(ball_test)
-X_test_np=X_test.to_numpy()
-X_test_np = [str(item) for item in X_test_np]
-X_test=v.transform(X_test_np)
-
-Y_test_predicted = naive_bayes.predict(X_test)
-pd.DataFrame(Y_test_predicted).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)