done

main.py

@@ -1,22 +1,24 @@
 from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
 import pandas as pd
 import numpy as np
+from stop_words import get_stop_words
 
-cv = CountVectorizer(strip_accents='ascii', token_pattern=u'(?ui)\\b\\w*[a-z]+\\w*\\b', lowercase=True)
+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)
-print(ball_train.head())
-print(len(ball_train))
-print(pd.DataFrame(ball_train[0]))
-print(pd.DataFrame(ball_train[1]))
 
 y_train = pd.DataFrame(ball_train[0])
 x_train = pd.DataFrame(ball_train[1])
-x_train.cv=cv.transform(x_train)
+x_np=x_train.to_numpy()
+x_np = [str(item) for item in x_np]
 
-naive_bayes.fit(x_train.cv, y_train)
+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)
 with open('dev-0/expected.tsv', 'r') as dev_exp_f:
@@ -24,15 +26,19 @@ with open('dev-0/expected.tsv', 'r') as dev_exp_f:
 
 
 X_dev = pd.DataFrame(ball_dev)
-X_dev.cv=cv.transform(X_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.cv)
+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.cv=cv.transform(X_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.cv)
+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)