import os
import urllib.request
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


def download_file():
    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
    filename = "adult.data"
    urllib.request.urlretrieve(url, filename)
    csv_file = convert_data_to_csv()
    return csv_file


def convert_data_to_csv():
    data_file = "adult.data"
    csv_file = "adult.csv"
    df = pd.read_csv(data_file, header=None)
    df.to_csv(csv_file, index=False)
    delete_data_file()
    return csv_file


def delete_data_file():
    filename = "adult.data"
    os.remove(filename)


def add_subsets_to_csv_file(data):
    data.columns = ["age", "workclass", "fnlwgt", "education", "education-num", "marital-status", "occupation",
                    "relationship", "race", "sex", "capital-gain", "capital-loss", "hours-per-week", "native-country",
                    "income"]

    train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
    if len(train_data) > len(test_data):
        train_data, dev_data = train_test_split(train_data, test_size=0.25, random_state=42)
    else:
        dev_data = pd.DataFrame()

    train_data.to_csv("adult_train.csv", index=False)
    dev_data.to_csv("adult_dev.csv", index=False)
    test_data.to_csv("adult_test.csv", index=False)

    print("Data set: ", data.shape)
    print("Train Data set: ", train_data.shape)
    print("Dev Data set: ", dev_data.shape)
    print("Test Data set: ", test_data.shape)
    return data


def check_if_data_set_has_division_into_subsets(file_name):
    data = pd.read_csv(file_name)

    if "train" not in data.columns or "dev" not in data.columns or "test" not in data.columns:
        data_set = add_subsets_to_csv_file(data)
        data_set.to_csv(file_name, index=False)


def get_statistics(data):
    train_data = pd.read_csv("adult_train.csv", dtype={"income": "category"})
    dev_data = pd.read_csv("adult_dev.csv", dtype={"income": "category"})
    test_data = pd.read_csv("adult_test.csv", dtype={"income": "category"})

    print("Wielkość zbioru: ", len(data))
    print("Wielkość zbioru treningowego: ", len(train_data))
    print("Wielkość zbioru walidacyjnego: ", len(dev_data))
    print("Wielkość zbioru testowego: ", len(test_data))
    print("Średnia wartość wieku: ", np.mean(data["age"]))
    print("Minimalna wartość wieku: ", np.min(data["age"]))
    print("Maksymalna wartość wieku: ", np.max(data["age"]))
    print("Odchylenie standardowe wartości wieku: ", np.std(data["age"]))
    print("Mediana wartości wieku: ", np.median(data["age"]))

    print("Rozkład częstości klas: ")
    freq_dist_all = data['income'].value_counts()
    print('Rozkład częstości etykiet klas na całym zbiorze danych:')
    print(freq_dist_all)

    freq_dist_train = train_data['income'].value_counts()
    print('Rozkład częstości etykiet klas na zbiorze treningowym:')
    print(freq_dist_train)

    freq_dist_test = test_data['income'].value_counts()
    print('Rozkład częstości etykiet klas na zbiorze testowym:')
    print(freq_dist_test)

    freq_dist_dev = dev_data['income'].value_counts()
    print('Rozkład częstości etykiet klas na zbiorze walidacyjnym:')
    print(freq_dist_dev)


def normalization(data):
    numeric_features = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
    numeric_data = data[numeric_features]

    scaler = StandardScaler()
    normalized_data = scaler.fit_transform(numeric_data)

    data[numeric_features] = normalized_data

    print(data.head())


def clean(data):
    data.replace('?', np.nan, inplace=True)
    data.dropna(inplace=True)
    data.drop_duplicates(inplace=True)
    data[['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']] = data[
        ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']].apply(pd.to_numeric)


if __name__ == '__main__':
    csv_file_name = download_file()
    check_if_data_set_has_division_into_subsets(csv_file_name)
    data = pd.read_csv(csv_file_name, dtype={"income": "category"})
    get_statistics(data)
    normalization(data)
    clean(data)