#!/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


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 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 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)