#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec  3 16:29:12 2021

@author: pfaure
"""
from scipy.io import arff
import numpy as np
import time
from sklearn import cluster, metrics, preprocessing
import pandas as pd


def extract_data_2d(data_path):
    databrut = arff.loadarff(open(data_path + ".arff", 'r'))
    return np.array([[x[0], x[1]] for x in databrut[0]])


def extract_data_3d(data_path):
    databrut = arff.loadarff(open(data_path + ".arff", 'r'))
    return np.array([[x[0], x[1], x[2]] for x in databrut[0]])


def extract_data_txt(data_path):
    databrut = open(data_path + ".txt").readlines()
    ret = []
    for x in databrut:
        line = list(map(float, x.split()))
        ret += [line]
    return np.array(ret)


def extract_data_csv(data_path: str, first_col: int, last_col: int):
    databrut = pd.read_csv(data_path + ".csv")
    ret = []
    for x in range(first_col, last_col+1):
        ret += [databrut.iloc[:, x]]
    ret = np.array(ret)
    return (list(databrut.iloc[:, 0]), np.array(list(map(list, zip(*ret)))))


def scale_data(data):
    scaler = preprocessing.StandardScaler()
    return scaler.fit_transform(data)


def apply_kmeans(data, k: int = 3, init="k-means++"):
    tps1 = time.time()
    model = cluster.KMeans(n_clusters=k, init=init)
    model.fit(data)
    tps2 = time.time()
    return model, round((tps2 - tps1) * 1000, 2)


def apply_agglomerative_clustering(data, k: int = 3, linkage="complete"):
    tps1 = time.time()
    model = cluster.AgglomerativeClustering(
        n_clusters=k, affinity='euclidean', linkage=linkage)
    model.fit(data)
    tps2 = time.time()
    return model, round((tps2 - tps1) * 1000, 2)


def apply_DBSCAN(data, eps, min_pts):
    tps1 = time.time()
    model = cluster.DBSCAN(eps=eps, min_samples=min_pts)
    model.fit(data)
    tps2 = time.time()
    return model, round((tps2 - tps1) * 1000, 2)


def apply_mean_shift(data, bandwidth: float):
    tps1 = time.time()
    model = cluster.MeanShift(bandwidth=bandwidth)
    model.fit(data)
    tps2 = time.time()
    return model, round((tps2 - tps1) * 1000, 2)


def evaluate(data, model):
    try:
        silh = metrics.silhouette_score(data, model.labels_)
        davies = metrics.davies_bouldin_score(data, model.labels_)
        calinski = metrics.calinski_harabasz_score(data, model.labels_)
        return silh, davies, calinski
    except ValueError:
        return None, None, None