s444421

run.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[208]:
+
+
+import pandas as pd
+import vowpalwabbit
+from sklearn import preprocessing
+
+
+# In[209]:
+
+
+vw = vowpalwabbit.Workspace('--oaa 20')
+
+
+# In[210]:
+
+
+X_train = pd.read_csv('train\in.tsv', sep='\t', usecols=[2], names=['text'])
+Y_train = pd.read_csv('train\expected.tsv', sep='\t', usecols=[0], names=['class'])
+
+
+# In[211]:
+
+
+Y_train['class'].unique()
+
+
+# In[212]:
+
+
+le = preprocessing.LabelEncoder()
+le.fit(['business', 'culture', 'lifestyle', 'news', 'opinion', 'removed', 'sport'])
+Y_train['class'] = le.fit_transform(Y_train['class'])
+
+
+# In[213]:
+
+
+for x, y in zip(X_train['text'], Y_train['class']):
+    vw.learn(f'{y} | text:{x}')
+
+
+# In[216]:
+
+
+def make_prediction(path_in, path_out):
+    test_set = pd.read_csv(path_in, sep='\t', usecols=[2], names=['text'])
+    predictions = []
+    for x in X_dev0['text']:
+        predictions.append(vw.predict(f'| text:{x}'))
+    predictions = le.inverse_transform(predictions)
+    file = open(path_out, 'w')
+    for pred in predictions:
+        file.write(f'{pred}\n')
+    file.close()
+
+
+# In[217]:
+
+
+make_prediction('dev-0\in.tsv', 'dev-0\out.tsv')
+
+
+# In[218]:
+
+
+make_prediction('test-A\in.tsv', 'test-A\out.tsv')
+
+
+# In[219]:
+
+
+make_prediction('test-B\in.tsv', 'test-B\out.tsv')
+