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

# In[6]:


import vowpalwabbit
import pandas as pd
import re


# In[7]:


def prediction(path_in, path_out, model, categories):
    data = pd.read_csv(path_in, header=None, sep='\t')
    data = data.drop(1, axis=1)
    data.columns = ['year', 'text']

    data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)

    with open(path_out, 'w', encoding='utf-8') as file:
        for example in data['train_input']:
            predicted = model.predict(example)
            text_predicted = dict((value, key) for key, value in map_dict.items()).get(predicted)
            file.write(str(text_predicted) + '\n')


# In[8]:


def to_vowpalwabbit(row, categories):
    text = row['text'].replace('\n', ' ').lower().strip()
    text = re.sub("[^a-zA-Z -']", '', text)
    text = re.sub(" +", ' ', text)
    year = row['year']
    try:
        category = categories[row['category']]
    except KeyError:
        category = ''

    vw = f"{category} | year:{year} text:{text}\n"

    return vw


# In[9]:


x_train = pd.read_csv('train/in.tsv', header=None, sep='\t')
x_train = x_train.drop(1, axis=1)
x_train.columns = ['year', 'text']

y_train = pd.read_csv('train/expected.tsv', header=None, sep='\t')
y_train.columns = ['category']

data = pd.concat([x_train, y_train], axis=1)

categories = {}

for i, x in enumerate(data['category'].unique()):
    categories[x] = i+1

print(categories)
    
data['train_input'] = data.apply(lambda row: to_vowpalwabbit(row, categories), axis=1)

model = vowpalwabbit.Workspace('--oaa 3 --quiet')

for example in data['train_input']:
    model.learn(example)

prediction('dev-0/in.tsv', 'dev-0/out.tsv', model, categories)
prediction('test-A/in.tsv', 'test-A/out.tsv', model, categories)
prediction('test-B/in.tsv', 'test-B/out.tsv', model, categories)