run.py

run.py

@@ -0,0 +1,31 @@
+import os
+import sklearn
+import pandas as pd
+from sklearn.metrics import accuracy_score
+from gzip import open as open_gz
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+
+def evaluation(x, path_out, model):
+    results = model.predict(x)
+
+    with open(path_out, 'wt') as file:
+        for r in results:
+            file.write(str(r) + '\n')
+            
+
+train = pd.read_csv('train/train.tsv', header = None, sep = '\t', error_bad_lines = False)
+
+x_train = train[1]
+y_train = train[0]
+x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')
+x_dev = x_dev[0]
+x_test = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')
+x_test = x_test[0]
+
+model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+model.fit(x_train, y_train)
+
+evaluation(x_dev,'dev-0/out.tsv', model)
+evaluation(x_test,'test-A/out.tsv', model)