downloaded geval

main.py

@@ -0,0 +1,38 @@
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import CountVectorizer
+import pandas as pd
+import numpy as np
+
+cv = CountVectorizer(strip_accents='ascii', token_pattern=u'(?ui)\\b\\w*[a-z]+\\w*\\b', lowercase=True)
+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)
+
+naive_bayes.fit(x_train.cv, 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:
+    Y_dev = np.array([float(x.rstrip('\n')) for x in dev_exp_f.readlines()])
+
+
+X_dev = pd.DataFrame(ball_dev)
+X_dev.cv=cv.transform(X_dev)
+
+Y_dev_predicted = naive_bayes.predict(X_dev.cv)
+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)
+
+Y_test_predicted = naive_bayes.predict(X_test.cv)
+pd.DataFrame(Y_test_predicted).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)