77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Fri Nov 19 23:08:23 2021
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@author: pfaure
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"""
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from myplotlib import print_1d_data, print_2d_data, print_3d_data
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from mydatalib import (extract_data_2d, extract_data_3d, scale_data,
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apply_kmeans, evaluate)
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path = './artificial/'
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dataset_name = "xclara"
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save = True
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print("-----------------------------------------------------------")
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print(" Chargement du dataset : " + dataset_name)
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data = extract_data_2d(path + dataset_name)
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print_2d_data(data, dataset_name=dataset_name+"_brute", stop=False, save=save)
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print("-----------------------------------------------------------")
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print(" Mise à l'échelle")
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data_scaled = scale_data(data)
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print_2d_data(data_scaled, dataset_name=dataset_name +
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"_scaled", stop=False, save=save)
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# Application de k-means pour plusieurs valeurs de k
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# et evaluation de la solution
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k = []
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durations = []
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silouettes = []
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daviess = []
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calinskis = []
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inerties = []
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iterations = []
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for i in range(2, 50):
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# Application de k-means
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(model, duration) = apply_kmeans(data_scaled, k=i, init="k-means++")
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# Affichage des clusters
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print_2d_data(data_scaled, dataset_name=dataset_name,
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method_name="k-means", k=i, c=model.labels_,
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stop=False, save=save)
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# Evaluation de la solution de clustering
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(silouette, davies, calinski) = evaluate(data_scaled, model)
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# Enregistrement des valeurs
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k += [i]
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durations += [duration]
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silouettes += [silouette]
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daviess += [davies]
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calinskis += [calinski]
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inerties += [model.inertia_]
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iterations += [model.n_iter_]
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# Affichage des résultats
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print_1d_data(k, k, x_name="k", y_name="k", dataset_name=dataset_name,
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method_name="k-means", stop=False, save=save)
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print_1d_data(k, durations, x_name="k", y_name="temps_de_calcul", y_unit="ms",
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dataset_name=dataset_name, method_name="k-means",
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stop=False, save=save)
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print_1d_data(k, silouettes, x_name="k", y_name="coeficient_de_silhouette",
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dataset_name=dataset_name, method_name="k-means",
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stop=False, save=save)
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print_1d_data(k, daviess, x_name="k", y_name="coeficient_de_Davies",
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dataset_name=dataset_name, method_name="k-means",
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stop=False, save=save)
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print_1d_data(k, calinskis, x_name="k", y_name="coeficient_de_Calinski",
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dataset_name=dataset_name, method_name="k-means",
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stop=False, save=save)
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print_1d_data(k, inerties, x_name="k", y_name="inertie",
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dataset_name=dataset_name, method_name="k-means",
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stop=False, save=save)
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print_1d_data(k, iterations, x_name="k", y_name="nombre_d_iterations",
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dataset_name=dataset_name, method_name="k-means",
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stop=True, save=save)
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