|
@@ -1,94 +0,0 @@
|
1
|
|
-#!/usr/bin/env python3
|
2
|
|
-# -*- coding: utf-8 -*-
|
3
|
|
-"""
|
4
|
|
-Created on Wed Dec 8 16:07:28 2021
|
5
|
|
-
|
6
|
|
-@author: pfaure
|
7
|
|
-"""
|
8
|
|
-
|
9
|
|
-from sklearn.neighbors import NearestNeighbors
|
10
|
|
-import numpy as np
|
11
|
|
-
|
12
|
|
-from myplotlib import print_1d_data, print_2d_data
|
13
|
|
-from mydatalib import extract_data_2d, scale_data, apply_DBSCAN, evaluate
|
14
|
|
-
|
15
|
|
-path = './artificial/'
|
16
|
|
-dataset_name = "banana"
|
17
|
|
-save = True
|
18
|
|
-
|
19
|
|
-print("-----------------------------------------------------------")
|
20
|
|
-print(" Chargement du dataset : " + dataset_name)
|
21
|
|
-data = extract_data_2d(path + dataset_name)
|
22
|
|
-print_2d_data(data, dataset_name=dataset_name +
|
23
|
|
- "_brutes", stop=False, save=save)
|
24
|
|
-
|
25
|
|
-print("-----------------------------------------------------------")
|
26
|
|
-print(" Mise à l'échelle")
|
27
|
|
-data_scaled = scale_data(data)
|
28
|
|
-print_2d_data(data_scaled, dataset_name=dataset_name +
|
29
|
|
- "_scaled", stop=False, save=save)
|
30
|
|
-
|
31
|
|
-print("-----------------------------------------------------------")
|
32
|
|
-print(" Calcul du voisinage")
|
33
|
|
-n = 50
|
34
|
|
-neighbors = NearestNeighbors(n_neighbors=n)
|
35
|
|
-neighbors.fit(data)
|
36
|
|
-distances, indices = neighbors.kneighbors(data)
|
37
|
|
-distances = list(map(lambda x: sum(x[1:n-1])/(len(x)-1), distances))
|
38
|
|
-print(distances)
|
39
|
|
-distances = np.sort(distances, axis=0)
|
40
|
|
-print(distances)
|
41
|
|
-print_1d_data(distances, range(1, len(distances)+1), x_name="distance_moyenne",
|
42
|
|
- y_name="nombre_de_points", stop=False, save=False)
|
43
|
|
-
|
44
|
|
-
|
45
|
|
-print("-----------------------------------------------------------")
|
46
|
|
-print(" Création clusters : DBSCAN")
|
47
|
|
-params = []
|
48
|
|
-for i in range(1, 20):
|
49
|
|
- params += [(i/100, 5)]
|
50
|
|
-durations = []
|
51
|
|
-silouettes = []
|
52
|
|
-daviess = []
|
53
|
|
-calinskis = []
|
54
|
|
-clusters = []
|
55
|
|
-noise_points = []
|
56
|
|
-for (distance, min_pts) in params:
|
57
|
|
- # Application du clustering agglomeratif
|
58
|
|
- (model, duration) = apply_DBSCAN(data, distance, min_pts)
|
59
|
|
- cl_pred = model.labels_
|
60
|
|
- # Affichage des clusters# Affichage des clusters
|
61
|
|
- print_2d_data(data_scaled, dataset_name=dataset_name,
|
62
|
|
- method_name="DBSCAN-Eps=" +
|
63
|
|
- str(distance)+"-Minpt="+str(min_pts),
|
64
|
|
- k=0, stop=False, save=save, c=cl_pred)
|
65
|
|
- # Evaluation de la solution de clustering
|
66
|
|
- (silouette, davies, calinski) = evaluate(data_scaled, model)
|
67
|
|
- # Enregistrement des valeurs
|
68
|
|
- durations += [duration]
|
69
|
|
- silouettes += [silouette]
|
70
|
|
- daviess += [davies]
|
71
|
|
- calinskis += [calinski]
|
72
|
|
- clusters += [len(set(cl_pred)) - (1 if -1 in cl_pred else 0)]
|
73
|
|
- noise_points += [list(cl_pred).count(-1)]
|
74
|
|
-
|
75
|
|
-# Affichage des résultats
|
76
|
|
-params = [str(i) for i in params]
|
77
|
|
-print_1d_data(params, durations, x_name="(eps,min_pts)",
|
78
|
|
- y_name="temps_de_calcul", y_unit="ms", dataset_name=dataset_name,
|
79
|
|
- method_name="DBSCAN", stop=False, save=save)
|
80
|
|
-print_1d_data(params, silouettes, x_name="(eps,min_pts)",
|
81
|
|
- y_name="coeficient_de_silhouette", dataset_name=dataset_name,
|
82
|
|
- method_name="DBSCAN", stop=False, save=save)
|
83
|
|
-print_1d_data(params, daviess, x_name="(eps,min_pts)",
|
84
|
|
- y_name="coeficient_de_Davies", dataset_name=dataset_name,
|
85
|
|
- method_name="DBSCAN", stop=False, save=save)
|
86
|
|
-print_1d_data(params, calinskis, x_name="(eps,min_pts)",
|
87
|
|
- y_name="coeficient_de_Calinski", dataset_name=dataset_name,
|
88
|
|
- method_name="DBSCAN", stop=False, save=save)
|
89
|
|
-print_1d_data(params, clusters, x_name="(eps,min_pts)",
|
90
|
|
- y_name="nombre_de_clusters", dataset_name=dataset_name,
|
91
|
|
- method_name="DBSCAN", stop=False, save=save)
|
92
|
|
-print_1d_data(params, noise_points, x_name="(eps,min_pts)",
|
93
|
|
- y_name="points_de_bruit", dataset_name=dataset_name,
|
94
|
|
- method_name="DBSCAN", stop=False, save=save)
|