#!/usr/bin/python
from kaggle import api
from pandas import read_csv, DataFrame
from sklearn.model_selection import train_test_split


def download_and_save_dataset():
    api.authenticate() 
    api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', unzip=True)


def split_dataset(data: DataFrame):
    train_ratio, validation_ratio, test_ratio = 0.6, 0.2, 0.2
    data_x, data_y = data.iloc[:, :-1], data.iloc[:, -1:]

    x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=1 - train_ratio, random_state=123)

    x_val, x_test, y_val, y_test = train_test_split(x_test, y_test,
                                                    test_size=test_ratio / (test_ratio + validation_ratio),
                                                    random_state=123)

    return x_train, x_val, x_test, y_train, y_val, y_test


def main():
    # download_and_save_dataset()
    df = read_csv('./fake_job_postings.csv')
    print(df.describe(include='all'))
    print(df.shape)
    x_train, x_val, x_test, y_train, y_val, y_test = split_dataset(df)
    print(x_train.shape, x_val.shape, x_test.shape)
    print(y_train.shape, y_val.shape, y_test.shape)


if __name__ == '__main__':
    main()