result

naiwny bayes2.py

@@ -0,0 +1,27 @@
+from sklearn.naive_bayes import GaussianNB
+import pandas as pd
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+r_in = './train/train.tsv'
+
+r_ind_ev = './dev-0/in.tsv'
+tsv_read = pd.read_table(r_in, error_bad_lines=False, sep='\t', header=None)
+tsv_read_dev = pd.read_table(r_ind_ev, error_bad_lines=False, sep='\t', header=None)
+
+y_train = tsv_read[0].values
+X_train = tsv_read[1].values
+X_dev = tsv_read_dev[0].values
+
+vectorizer = TfidfVectorizer()
+counts = vectorizer.fit_transform(X_train)
+
+
+classifier = MultinomialNB()
+classifier.fit(counts, y_train)
+
+counts2 = vectorizer.transform(X_dev)
+predictions = classifier.predict(counts2)
+
+predictions.tofile("./dev-0/out.tsv", sep='\n')
+