import os
import sklearn
import pandas as pd
from gzip import open as open_gz
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

def predict_year(x, path_out, model):
    results = model.predict(x)
    with open(path_out, 'wt') as file:
        for r in results:
            file.write(str(r) + '\n')       

def read_file(filename):
    result = []
    with open(filename, 'r', encoding="utf-8") as file:
        for line in file:
            text = line.split("\t")[0].strip()
            result.append(text)
    return result

with open('train/train.tsv', 'r', encoding='utf8') as file:
    train = pd.read_csv(file, sep='\t', names=['Start', 'End', 'Title', 'Author', 'Text'])
    
train = train[0:12000]
train_x = train['Text']
#train['Date'] = (train['Start'].astype(float) + train['End'].astype(float))/2
train_y = train['Start']

model = make_pipeline(TfidfVectorizer(), LinearRegression())
model.fit(train_x, train_y)

x_dev_0 = read_file('dev-0/in.tsv')
predict_year(x_dev_0, 'dev-0/out.tsv', model)
x_dev_1 = read_file('dev-1/in.tsv')
predict_year(x_dev_1,'dev-1/out.tsv', model)
x_test = read_file('test-A/in.tsv')
predict_year(x_test,'test-A/out.tsv', model)