Pre-final Version

main.py

@@ -1,34 +1,36 @@
 import numpy as np
 from sklearn.preprocessing import LabelEncoder
 from sklearn.naive_bayes import MultinomialNB
-from sklearn.pipeline import Pipeline
+from sklearn.pipeline import make_pipeline
 from sklearn.feature_extraction.text import TfidfVectorizer
 
-def train_model(train_in, train_expected):
+def Create_model(X_tsv, Y_tsv):
-    with open(train_expected, 'r', encoding='utf-8') as f:
-        exp = f.readlines()
 
-    with open(train_in, 'r', encoding='utf-8') as f:
+    with open(X_tsv) as f:
-        train_data = f.readlines()
+        X = f.readlines()
 
-    exp_encoded = LabelEncoder().fit_transform(exp)
+    with open(Y_tsv) as f:
-    pipeline = Pipeline(steps=[
+        Y = f.readlines() 
-        ('tfidf', TfidfVectorizer()),
-        ('naive-bayes', MultinomialNB())
-    ])
 
-    return pipeline.fit(train_data, exp_encoded)
+    Y = LabelEncoder().fit_transform(Y)
+    pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
+
+    return pipeline.fit(X, Y)
 
 
-def predict(model, in_file, out_file):
+def predict(model, X_tsv, file_name):
-    with open(in_file, 'r', encoding='utf-8') as f:
+
-        lines = f.readlines()
+    with open(X_tsv) as f:
-    prediction = model.predict(lines)
+        X = f.readlines()
-    np.savetxt(out_file, prediction, fmt='%d')
+
+    prediction = model.predict(X)
+    np.savetxt(file_name, prediction, fmt='%d')
 
 
 def main():
-    model = train_model("train/in.tsv", "train/expected.tsv")
+    
+    model = Create_model("train/in.tsv", "train/expected.tsv")
+
     predict(model, "dev-0/in.tsv", "dev-0/out.tsv")
     predict(model, "test-A/in.tsv", "test-A/out.tsv")