#!/usr/bin/env python
# coding: utf-8

# In[208]:


import pandas as pd
import vowpalwabbit
from sklearn import preprocessing


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vw = vowpalwabbit.Workspace('--oaa 20')


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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'])


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Y_train['class'].unique()


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le = preprocessing.LabelEncoder()
le.fit(['business', 'culture', 'lifestyle', 'news', 'opinion', 'removed', 'sport'])
Y_train['class'] = le.fit_transform(Y_train['class'])


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for x, y in zip(X_train['text'], Y_train['class']):
    vw.learn(f'{y} | text:{x}')


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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')