program.py

@@ -1,24 +0,0 @@
-import numpy as np
-from sklearn import preprocessing
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.pipeline import make_pipeline
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-le=preprocessing.LabelEncoder()
-
-
-with open("train/in.tsv") as f:
-    X = f.readlines()
-    with open("train/expected.tsv") as ff:
-        Y = ff.readlines() 
-        Y= le.fit_transform(Y)
-        with open("test-A/in.tsv") as d:
-            r = d.readlines() 
-            gnb = make_pipeline(TfidfVectorizer(),MultinomialNB())
-            model=gnb.fit(X, Y)
-            y_pred=model.predict(r)
-            y_pred=np.array(y_pred)
-            np.set_printoptions(threshold=np.inf)
-            t=np.array2string(y_pred.flatten(), precision=2, separator='\n',suppress_small=True)
-            f = open("test-A/out.tsv", "a")
-            f.write(t)