s444386 bayes

run.py

@@ -0,0 +1,43 @@
+import lzma
+import pandas as pd
+import numpy as np
+import gzip
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import accuracy_score
+
+def readFile(filename):
+    X_dev = []
+    with open(filename, 'r', encoding="utf-8") as dev_in:
+        for line in dev_in:
+            text = line.split("\t")[0].strip()
+            X_dev.append(text)
+    return X_dev
+    
+def writePred(filename, predictions):
+    with open(filename, "w") as out_file:
+        for pred in predictions:
+            out_file.write(str(pred) + "\n")
+
+with gzip.open('train/train.tsv.gz', 'rb') as f:
+    data = pd.read_csv(f, sep='\t',error_bad_lines=False,names=['isBall','text'])
+
+x = data['text']
+y = data['isBall']
+
+x = np.asarray(x)
+y = np.asarray(y)
+
+model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+model.fit(x,y)
+
+dev = readFile('dev-0/in.tsv')
+pred = model.predict(dev)
+writePred('dev-0/out.tsv',pred)
+
+dev = readFile('test-A/in.tsv')
+pred = model.predict(dev)
+writePred('test-A/out.tsv',pred)
+
+