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Ajout DBSCAN

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This commit is contained in:
Paul Faure 2022-01-06 17:01:07 +01:00
parent e213447913
commit 262b4cef0c
4 changed files with 142 additions and 22 deletions
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33
mydatalib.py
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@ -28,27 +28,34 @@ def scale_data(data):
def apply_kmeans(data, k: int = 3, init="k-means++"):
tps1 = time.time()
model_km = cluster.KMeans(n_clusters=k, init=init)
model_km.fit(data)
model = cluster.KMeans(n_clusters=k, init=init)
model.fit(data)
tps2 = time.time()
return (model_km, round((tps2 - tps1)*1000, 2))
return (model, round((tps2 - tps1)*1000, 2))
def apply_agglomerative_clustering(data, k: int = 3, linkage="complete"):
tps1 = time.time()
model_agg = cluster.AgglomerativeClustering(
model = cluster.AgglomerativeClustering(
n_clusters=k, affinity='euclidean', linkage=linkage)
model_agg.fit(data)
model.fit(data)
tps2 = time.time()
return (model_agg, round((tps2 - tps1)*1000, 2))
return (model, round((tps2 - tps1)*1000, 2))
def evaluate_kmeans(data, model_km):
silh = metrics.silhouette_score(data, model_km.labels_, metric='euclidean')
return (silh, model_km.inertia_, model_km.n_iter_)
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_agglomerative_clustering(data, model_agg):
silh = metrics.silhouette_score(
data, model_agg.labels_, metric='euclidean')
return silh
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)

20
tp1-read-plot-5iss.py
View file

@ -7,8 +7,8 @@ Created on Fri Nov 19 23:08:23 2021
from myplotlib import print_1d_data, print_2d_data, print_3d_data
from mydatalib import extract_data_2d, extract_data_3d, scale_data
from mydatalib import apply_kmeans, evaluate_kmeans
from mydatalib import (extract_data_2d, extract_data_3d, scale_data,
apply_kmeans, evaluate)
path = './artificial/'
@ -34,6 +34,8 @@ print_2d_data(data_scaled, dataset_name=dataset_name +
k = []
durations = []
silouettes = []
daviess = []
calinskis = []
inerties = []
iterations = []
for i in range(2, 50):
@ -44,13 +46,15 @@ for i in range(2, 50):
method_name="k-means", k=i, c=model.labels_,
stop=False, save=save)
# Evaluation de la solution de clustering
(silouette, inertie, iteration) = evaluate_kmeans(data_scaled, model)
(silouette, davies, calinski) = evaluate(data_scaled, model)
# Enregistrement des valeurs
k += [i]
durations += [duration]
silouettes += [silouette]
inerties += [inertie]
iterations += [iteration]
daviess += [davies]
calinskis += [calinski]
inerties += [model.inertia_]
iterations += [model.n_iter_]
# Affichage des résultats
print_1d_data(k, k, x_name="k", y_name="k", dataset_name=dataset_name,
@ -61,6 +65,12 @@ print_1d_data(k, durations, x_name="k", y_name="temps_de_calcul", y_unit="ms",
print_1d_data(k, silouettes, x_name="k", y_name="coeficient_de_silhouette",
dataset_name=dataset_name, method_name="k-means",
stop=False, save=save)
print_1d_data(k, daviess, x_name="k", y_name="coeficient_de_Davies",
dataset_name=dataset_name, method_name="k-means",
stop=False, save=save)
print_1d_data(k, calinskis, x_name="k", y_name="coeficient_de_Calinski",
dataset_name=dataset_name, method_name="k-means",
stop=False, save=save)
print_1d_data(k, inerties, x_name="k", y_name="inertie",
dataset_name=dataset_name, method_name="k-means",
stop=False, save=save)

17
tp2-read-standardization-dendrogram-agglo-1val.py
View file

@ -6,9 +6,8 @@ Created on Sat Nov 20 21:28:40 2021
"""
from myplotlib import print_1d_data, print_2d_data, print_dendrogramme
from mydatalib import extract_data_2d, scale_data
from mydatalib import apply_agglomerative_clustering
from mydatalib import evaluate_agglomerative_clustering
from mydatalib import (extract_data_2d, scale_data,
apply_agglomerative_clustering, evaluate)
##################################################################
@ -61,6 +60,8 @@ print(" Création clusters : linkage " +
k = []
durations = []
silouettes = []
daviess = []
calinskis = []
for i in range(2, k_max):
# Application du clustering agglomeratif
(model, duration) = apply_agglomerative_clustering(
@ -70,11 +71,13 @@ for i in range(2, k_max):
method_name="agglomerative_" + linkage, k=i,
stop=False, save=save, c=model.labels_)
# Evaluation de la solution de clustering
silouette = evaluate_agglomerative_clustering(data_scaled, model)
(silouette, davies, calinski) = evaluate(data_scaled, model)
# Enregistrement des valeurs
k += [i]
durations += [duration]
silouettes += [silouette]
daviess += [davies]
calinskis += [calinski]
# Affichage des résultats
print_1d_data(k, k, x_name="k", y_name="k", dataset_name=dataset_name,
@ -85,3 +88,9 @@ print_1d_data(k, durations, x_name="k", y_name="temps_de_calcul", y_unit="ms",
print_1d_data(k, silouettes, x_name="k", y_name="coeficient_de_silhouette",
dataset_name=dataset_name,
method_name="agglomerative_" + linkage, stop=False, save=save)
print_1d_data(k, daviess, x_name="k", y_name="coeficient_de_Davies",
dataset_name=dataset_name,
method_name="agglomerative_" + linkage, stop=False, save=save)
print_1d_data(k, calinskis, x_name="k", y_name="coeficient_de_Calinski",
dataset_name=dataset_name,
method_name="agglomerative_" + linkage, stop=False, save=save)

94
tp3-dbscan.py Normal file
View file

@ -0,0 +1,94 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 8 16:07:28 2021
@author: pfaure
"""
from sklearn.neighbors import NearestNeighbors
import numpy as np
from myplotlib import print_1d_data, print_2d_data
from mydatalib import extract_data_2d, scale_data, apply_DBSCAN, evaluate
path = './artificial/'
dataset_name = "banana"
save = True
print("-----------------------------------------------------------")
print(" Chargement du dataset : " + dataset_name)
data = extract_data_2d(path + dataset_name)
print_2d_data(data, dataset_name=dataset_name +
"_brutes", stop=False, save=save)
print("-----------------------------------------------------------")
print(" Mise à l'échelle")
data_scaled = scale_data(data)
print_2d_data(data_scaled, dataset_name=dataset_name +
"_scaled", stop=False, save=save)
print("-----------------------------------------------------------")
print(" Calcul du voisinage")
n = 50
neighbors = NearestNeighbors(n_neighbors=n)
neighbors.fit(data)
distances, indices = neighbors.kneighbors(data)
distances = list(map(lambda x: sum(x[1:n-1])/(len(x)-1), distances))
print(distances)
distances = np.sort(distances, axis=0)
print(distances)
print_1d_data(distances, range(1, len(distances)+1), x_name="distance_moyenne",
y_name="nombre_de_points", stop=False, save=False)
print("-----------------------------------------------------------")
print(" Création clusters : DBSCAN")
params = []
for i in range(1, 20):
params += [(i/100, 5)]
durations = []
silouettes = []
daviess = []
calinskis = []
clusters = []
noise_points = []
for (distance, min_pts) in params:
# Application du clustering agglomeratif
(model, duration) = apply_DBSCAN(data, distance, min_pts)
cl_pred = model.labels_
# Affichage des clusters# Affichage des clusters
print_2d_data(data_scaled, dataset_name=dataset_name,
method_name="DBSCAN-Eps=" +
str(distance)+"-Minpt="+str(min_pts),
k=0, stop=False, save=save, c=cl_pred)
# Evaluation de la solution de clustering
(silouette, davies, calinski) = evaluate(data_scaled, model)
# Enregistrement des valeurs
durations += [duration]
silouettes += [silouette]
daviess += [davies]
calinskis += [calinski]
clusters += [len(set(cl_pred)) - (1 if -1 in cl_pred else 0)]
noise_points += [list(cl_pred).count(-1)]
# Affichage des résultats
params = [str(i) for i in params]
print_1d_data(params, durations, x_name="(eps,min_pts)",
y_name="temps_de_calcul", y_unit="ms", dataset_name=dataset_name,
method_name="DBSCAN", stop=False, save=save)
print_1d_data(params, silouettes, x_name="(eps,min_pts)",
y_name="coeficient_de_silhouette", dataset_name=dataset_name,
method_name="DBSCAN", stop=False, save=save)
print_1d_data(params, daviess, x_name="(eps,min_pts)",
y_name="coeficient_de_Davies", dataset_name=dataset_name,
method_name="DBSCAN", stop=False, save=save)
print_1d_data(params, calinskis, x_name="(eps,min_pts)",
y_name="coeficient_de_Calinski", dataset_name=dataset_name,
method_name="DBSCAN", stop=False, save=save)
print_1d_data(params, clusters, x_name="(eps,min_pts)",
y_name="nombre_de_clusters", dataset_name=dataset_name,
method_name="DBSCAN", stop=False, save=save)
print_1d_data(params, noise_points, x_name="(eps,min_pts)",
y_name="points_de_bruit", dataset_name=dataset_name,
method_name="DBSCAN", stop=False, save=save)