import lzma
import csv
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline


def readInput(dir):
    X = []
    if 'xz' in dir:
        with lzma.open(dir) as f:
            for line in f:
                text = line.decode('utf-8')
                text = text.split('\t')
                X.append(text)
    else:
        with open(dir, encoding='utf8', errors='ignore') as f:
            for line in f:
                X. append(line.replace('\n',''))
    return X

def writeOutput(output, dir):
    with open(dir, 'w', newline='') as f:
        writer = csv.writer(f)
        writer.writerows(output)


if __name__ == '__main__':
    train = pd.DataFrame(readInput('train/train.tsv.xz'), 
        columns=['Beginning', 'End', 'Title', 'Source', 'X'])
    train['Y'] = train.apply(lambda x: (float(x.Beginning) + float(x.End))/2, axis=1)
    train = train.drop(columns=['Beginning', 'End', 'Title', 'Source'])

    estimators = [('tfidf', TfidfVectorizer()), ('linearRegression', LinearRegression())]
    model = Pipeline(estimators)
    model.fit(train.X, train.Y)

    # dev-0
    testX = readInput('dev-0/in.tsv')
    writeOutput(model.predict(testX), 'dev-0/out.tsv')

    # dev-1
    testX = readInput('dev-1/in.tsv')
    writeOutput(model.predict(testX), 'dev-1/out.tsv')

    # test-A
    testX = readInput('test-A/in.tsv')
    writeOutput(model.predict(testX), 'test-A/out.tsv')