from sklearn import preprocessing
import numpy as np
import pandas as pd
import kaggle
import pandas
import os

path_to_data = './data'


def download_data():
    kaggle.api.authenticate()
    kaggle.api.dataset_download_files('thedevastator/airbnb-prices-in-european-cities', path=path_to_data, unzip=True)


def read_data_from_file(filename):
    return pandas.read_csv(filename)


def split_data(data):
    return np.split(data.sample(frac=1, random_state=42), [int(.6 * len(data)), int(.8 * len(data))])


def calculate_stats(data, col_name):
    col_values = data[col_name]
    return [
        len(data),
        np.min(col_values),
        np.max(col_values),
        np.std(col_values),
        np.median(col_values)
    ]


def calculate_value_counts(data, col_name):
    return data[col_name].value_counts()


def normalize_data(data):
    data = data.iloc[:, 1:]

    numeric_columns = ['realSum', 'person_capacity', 'multi', 'biz', 'cleanliness_rating', 'guest_satisfaction_overall', 'bedrooms', 'dist', 'metro_dist', 'attr_index', 'attr_index_norm', 'rest_index', 'rest_index_norm', 'lng', 'lat']
    non_numeric_columns = ['room_type', 'room_shared', 'room_private', 'host_is_superhost', ]
    numeric_data = data[numeric_columns]
    non_numeric_data = data[non_numeric_columns]

    enc = preprocessing.OrdinalEncoder()
    non_numeric_data_norm = enc.fit_transform(non_numeric_data)

    scaler = preprocessing.MinMaxScaler()
    numeric_data_norm = scaler.fit_transform(numeric_data)

    data[numeric_columns] = numeric_data_norm
    data[non_numeric_columns] = non_numeric_data_norm

    y_col = 'guest_satisfaction_overall'
    new_order = list(data.columns.drop(y_col)) + [y_col]
    new_df = data.reindex(columns=new_order)

    return new_df


if __name__ == '__main__':
    # 1.1
    if not os.path.isdir('data'):
        download_data()
    whole_set = read_data_from_file(path_to_data + '/barcelona_weekends.csv')
    print(whole_set.head())

    # 1.2
    train_set, dev_set, test_set = split_data(whole_set)

    # 1.3
    whole_set_stats = calculate_stats(whole_set, 'realSum')
    train_set_stats = calculate_stats(train_set, 'realSum')
    dev_set_stats = calculate_stats(dev_set, 'realSum')
    test_set_stats = calculate_stats(test_set, 'realSum')

    columns = ['size', 'minimum', 'maximum', 'standard deviation', 'median']
    rows = ['whole set', 'train', 'dev', 'test']
    print(pd.DataFrame(
        data=np.array([whole_set_stats, train_set_stats, dev_set_stats, test_set_stats]),
        index=rows,
        columns=columns),
        end='\n\n')

    print('Whole set', calculate_value_counts(whole_set, 'person_capacity'), end='\n\n')
    print('Train', calculate_value_counts(train_set, 'person_capacity'), end='\n\n')
    print('Dev', calculate_value_counts(dev_set, 'person_capacity'), end='\n\n')
    print('Test', calculate_value_counts(test_set, 'person_capacity'), end='\n\n')

    # 1.4 & 1.5
    whole_set_normalized = normalize_data(whole_set)
    train_set_normalized = normalize_data(train_set)
    dev_set_normalized = normalize_data(dev_set)
    test_set_normalized = normalize_data(test_set)
    print(whole_set_normalized, '\n\n')

    # Save all sets into files
    train_set_normalized.to_csv('./barcelona_weekends_datasets/train_set.csv', index=False)
    dev_set_normalized.to_csv('./barcelona_weekends_datasets/dev_set.csv', index=False)
    test_set_normalized.to_csv('./barcelona_weekends_datasets/test_set.csv', index=False)