16、toy数据集上不同聚类算法的比较

import time

import warnings

import numpy as np

import matplotlib.pyplot as plt

from sklearn import cluster, datasets, mixture

from sklearn.neighbors import kneighbors_graph

from sklearn.preprocessing import StandardScaler

from itertools import cycle, islice

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

np.random.seed(0)

# 生成数据集

n_samples = 1500

noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,

                                      noise=.05)

noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)

blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)

no_structure = np.random.rand(n_samples, 2), None

# 各向异性分布数据

random_state = 170

X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)

transformation = [[0.6, -0.6], [-0.4, 0.8]]

X_aniso = np.dot(X, transformation)

aniso = (X_aniso, y)

# 变方差斑点

varied = datasets.make_blobs(n_samples=n_samples,

                            cluster_std=[1.0, 2.5, 0.5],

                            random_state=random_state)

# 设置群集参数

plt.figure(figsize=(9 * 2 + 3, 12.5))

plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,

                    hspace=.01)

plot_num = 1

default_base = {'quantile': .3,

                'eps': .3,

                'damping': .9,

                'preference': -200,

                'n_neighbors': 10,

                'n_clusters': 3,

                'min_samples': 20,

                'xi': 0.05,

                'min_cluster_size': 0.1}

datasets = [

    (noisy_circles, {'damping': .77, 'preference': -240,

                    'quantile': .2, 'n_clusters': 2,

                    'min_samples': 20, 'xi': 0.25}),

    (noisy_moons, {'damping': .75, 'preference': -220, 'n_clusters': 2}),

    (varied, {'eps': .18, 'n_neighbors': 2,

              'min_samples': 5, 'xi': 0.035, 'min_cluster_size': .2}),

    (aniso, {'eps': .15, 'n_neighbors': 2,

            'min_samples': 20, 'xi': 0.1, 'min_cluster_size': .2}),

    (blobs, {}),

    (no_structure, {})]

for i_dataset, (dataset, algo_params) in enumerate(datasets):

    # 使用特定于数据集的值更新参数

    params = default_base.copy()

    params.update(algo_params)

    X, y = dataset

    # 规范化数据集以方便参数选择

    X = StandardScaler().fit_transform(X)

    # 均值漂移估计带宽

    bandwidth = cluster.estimate_bandwidth(X, quantile=params['quantile'])

    # 结构化Ward的连通矩阵

    connectivity = kneighbors_graph(

        X, n_neighbors=params['n_neighbors'], include_self=False)

    # 使连通对称

    connectivity = 0.5 * (connectivity + connectivity.T)

    # 创建群集对象

    ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)

    two_means = cluster.MiniBatchKMeans(n_clusters=params['n_clusters'])

    ward = cluster.AgglomerativeClustering(

        n_clusters=params['n_clusters'], linkage='ward',

        connectivity=connectivity)

    spectral = cluster.SpectralClustering(

        n_clusters=params['n_clusters'], eigen_solver='arpack',

        affinity="nearest_neighbors")

    dbscan = cluster.DBSCAN(eps=params['eps'])

    optics = cluster.OPTICS(min_samples=params['min_samples'],

                            xi=params['xi'],

                            min_cluster_size=params['min_cluster_size'])

    affinity_propagation = cluster.AffinityPropagation(

        damping=params['damping'], preference=params['preference'])

    average_linkage = cluster.AgglomerativeClustering(

        linkage="average", affinity="cityblock",

        n_clusters=params['n_clusters'], connectivity=connectivity)

    birch = cluster.Birch(n_clusters=params['n_clusters'])

    gmm = mixture.GaussianMixture(

        n_components=params['n_clusters'], covariance_type='full')

    clustering_algorithms = (

        ('小型化', two_means),

        ('亲和传播', affinity_propagation),

        ('平均数移位', ms),

        ('光谱聚类', spectral),

        ('Ward', ward),

        ('凝聚剂聚类', average_linkage),

        ('DBSCAN', dbscan),

        ('OPTICS', optics),

        ('Birch', birch),

        ('高斯混合物', gmm)

    )

    for name, algorithm in clustering_algorithms:

        t0 = time.time()

        # 捕获与kneighs_graph相关的警告

        with warnings.catch_warnings():

            warnings.filterwarnings(

                "ignore",

                message="the number of connected components of the " +

                "connectivity matrix is [0-9]{1,2}" +

                " > 1. Completing it to avoid stopping the tree early.",

                category=UserWarning)

            warnings.filterwarnings(

                "ignore",

                message="Graph is not fully connected, spectral embedding" +

                " may not work as expected.",

                category=UserWarning)

            algorithm.fit(X)

        t1 = time.time()

        if hasattr(algorithm, 'labels_'):

            y_pred = algorithm.labels_.astype(np.int)

        else:

            y_pred = algorithm.predict(X)

        plt.subplot(len(datasets), len(clustering_algorithms), plot_num)

        if i_dataset == 0:

            plt.title(name, size=18)

        colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',

                                            '#f781bf', '#a65628', '#984ea3',

                                            '#999999', '#e41a1c', '#dede00']),

                                      int(max(y_pred) + 1))))

        # 为异常值添加黑色（如果有）

        colors = np.append(colors, ["#000000"])

        plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])

        plt.xlim(-2.5, 2.5)

        plt.ylim(-2.5, 2.5)

        plt.xticks(())

        plt.yticks(())

        plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),

                transform=plt.gca().transAxes, size=15,

                horizontalalignment='right')

        plot_num += 1

plt.show()