import os
import urllib.request
from os.path import exists

import pandas
from keras.layers import Dense
from keras.models import Sequential
import pandas as pd
import numpy as np
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


def download_file():
	file_exist = exists('/adult.csv')
	if not file_exist:
		url = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
		filename = "adult.data"
		urllib.request.urlretrieve(url, filename)
		convert_data_to_csv()


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)
	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"]

	X_train_data, X_dev_data, X_test_data = train_dev_test(data)

	print("Data set: ", data.shape)
	print("Train Data set: ", X_train_data.shape)
	print("Dev Data set: ", X_dev_data.shape)
	print("Test Data set: ", X_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("X_train.csv", dtype={"income": "category"})
	dev_data = pd.read_csv("X_dev.csv", dtype={"income": "category"})
	test_data = pd.read_csv("X_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', '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', 'capital-gain', 'capital-loss', 'hours-per-week']] = data[
		['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']].apply(pd.to_numeric)


def train_dev_test(data):
	X = data.copy()
	y = pandas.DataFrame(data.pop('education-num'))
	X_train, X_temp, Y_train, Y_temp = train_test_split(X, y, test_size=0.3, random_state=1)
	X_dev, X_test, Y_dev, Y_test = train_test_split(X_temp, Y_temp, test_size=0.3, random_state=1)

	X_train.to_csv('X_train.csv', index=False)
	X_dev.to_csv('X_dev.csv', index=False)
	X_test.to_csv('X_test.csv', index=False)
	Y_test.to_csv('Y_test.csv', index=False)
	Y_train.to_csv('Y_train.csv', index=False)
	Y_dev.to_csv('Y_dev.csv', index=False)
	return X_train, X_dev, X_test


def create_model():
	data = pd.read_csv('X_train.csv')
	X = data.copy()
	y = data["education-num"]
	X_train_encoded = pd.get_dummies(X)
	y_train_cat = to_categorical(y)
	model = Sequential()
	model.add(Dense(64, activation='relu', input_dim=X_train_encoded.shape[1]))
	model.add(Dense(17, activation='softmax'))
	model.compile(optimizer='adam',
				  loss='categorical_crossentropy',
				  metrics=['accuracy'])
	model.fit(X_train_encoded, y_train_cat, epochs=10, batch_size=32, validation_data=(X_train_encoded, y_train_cat))
	model.save('model.h5')


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