浓度先验类型变异分析贝叶斯高斯混合#

此示例绘制了从玩具数据集（三个高斯的混合）获得的椭圆体，该数据集由具有狄利克雷分布先验（weight_concentration_prior_type='dirichlet_distribution'）和狄利克雷过程先验（weight_concentration_prior_type='dirichlet_process'）的 BayesianGaussianMixture 类模型拟合。在每个图上，我们绘制了三个不同权重浓度先验值的结果。

BayesianGaussianMixture 类可以自动调整其混合成分的数量。参数 weight_concentration_prior 与非零权重的结果成分数量直接相关。为浓度先验指定较低的值将使模型将大部分权重放在少数成分上，并将剩余成分的权重设置为非常接近于零。较高的浓度先验值将允许更多成分在混合中处于活动状态。

狄利克雷过程先验允许定义无限数量的成分，并自动选择正确的成分数量：仅在必要时才激活成分。

相反，具有狄利克雷分布先验的经典有限混合模型将有利于更均匀加权的成分，因此倾向于将自然聚类划分为不必要的子成分。

$Finite mixture with a Dirichlet distribution prior and $\gamma_0=$$1.0e-03$, Finite mixture with a Dirichlet distribution prior and $\gamma_0=$$1.0e+00$, Finite mixture with a Dirichlet distribution prior and $\gamma_0=$$1.0e+03$$
$Infinite mixture with a Dirichlet process prior and$\gamma_0=$$1.0e+00$, Infinite mixture with a Dirichlet process prior and$\gamma_0=$$1.0e+03$, Infinite mixture with a Dirichlet process prior and$\gamma_0=$$1.0e+05$$

# Author: Thierry Guillemot <[email protected]>
# License: BSD 3 clause

import matplotlib as mpl
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np

from sklearn.mixture import BayesianGaussianMixture


def plot_ellipses(ax, weights, means, covars):
    for n in range(means.shape[0]):
        eig_vals, eig_vecs = np.linalg.eigh(covars[n])
        unit_eig_vec = eig_vecs[0] / np.linalg.norm(eig_vecs[0])
        angle = np.arctan2(unit_eig_vec[1], unit_eig_vec[0])
        # Ellipse needs degrees
        angle = 180 * angle / np.pi
        # eigenvector normalization
        eig_vals = 2 * np.sqrt(2) * np.sqrt(eig_vals)
        ell = mpl.patches.Ellipse(
            means[n], eig_vals[0], eig_vals[1], angle=180 + angle, edgecolor="black"
        )
        ell.set_clip_box(ax.bbox)
        ell.set_alpha(weights[n])
        ell.set_facecolor("#56B4E9")
        ax.add_artist(ell)


def plot_results(ax1, ax2, estimator, X, y, title, plot_title=False):
    ax1.set_title(title)
    ax1.scatter(X[:, 0], X[:, 1], s=5, marker="o", color=colors[y], alpha=0.8)
    ax1.set_xlim(-2.0, 2.0)
    ax1.set_ylim(-3.0, 3.0)
    ax1.set_xticks(())
    ax1.set_yticks(())
    plot_ellipses(ax1, estimator.weights_, estimator.means_, estimator.covariances_)

    ax2.get_xaxis().set_tick_params(direction="out")
    ax2.yaxis.grid(True, alpha=0.7)
    for k, w in enumerate(estimator.weights_):
        ax2.bar(
            k,
            w,
            width=0.9,
            color="#56B4E9",
            zorder=3,
            align="center",
            edgecolor="black",
        )
        ax2.text(k, w + 0.007, "%.1f%%" % (w * 100.0), horizontalalignment="center")
    ax2.set_xlim(-0.6, 2 * n_components - 0.4)
    ax2.set_ylim(0.0, 1.1)
    ax2.tick_params(axis="y", which="both", left=False, right=False, labelleft=False)
    ax2.tick_params(axis="x", which="both", top=False)

    if plot_title:
        ax1.set_ylabel("Estimated Mixtures")
        ax2.set_ylabel("Weight of each component")


# Parameters of the dataset
random_state, n_components, n_features = 2, 3, 2
colors = np.array(["#0072B2", "#F0E442", "#D55E00"])

covars = np.array(
    [[[0.7, 0.0], [0.0, 0.1]], [[0.5, 0.0], [0.0, 0.1]], [[0.5, 0.0], [0.0, 0.1]]]
)
samples = np.array([200, 500, 200])
means = np.array([[0.0, -0.70], [0.0, 0.0], [0.0, 0.70]])

# mean_precision_prior= 0.8 to minimize the influence of the prior
estimators = [
    (
        "Finite mixture with a Dirichlet distribution\nprior and " r"$\gamma_0=$",
        BayesianGaussianMixture(
            weight_concentration_prior_type="dirichlet_distribution",
            n_components=2 * n_components,
            reg_covar=0,
            init_params="random",
            max_iter=1500,
            mean_precision_prior=0.8,
            random_state=random_state,
        ),
        [0.001, 1, 1000],
    ),
    (
        "Infinite mixture with a Dirichlet process\n prior and" r"$\gamma_0=$",
        BayesianGaussianMixture(
            weight_concentration_prior_type="dirichlet_process",
            n_components=2 * n_components,
            reg_covar=0,
            init_params="random",
            max_iter=1500,
            mean_precision_prior=0.8,
            random_state=random_state,
        ),
        [1, 1000, 100000],
    ),
]

# Generate data
rng = np.random.RandomState(random_state)
X = np.vstack(
    [
        rng.multivariate_normal(means[j], covars[j], samples[j])
        for j in range(n_components)
    ]
)
y = np.concatenate([np.full(samples[j], j, dtype=int) for j in range(n_components)])

# Plot results in two different figures
for title, estimator, concentrations_prior in estimators:
    plt.figure(figsize=(4.7 * 3, 8))
    plt.subplots_adjust(
        bottom=0.04, top=0.90, hspace=0.05, wspace=0.05, left=0.03, right=0.99
    )

    gs = gridspec.GridSpec(3, len(concentrations_prior))
    for k, concentration in enumerate(concentrations_prior):
        estimator.weight_concentration_prior = concentration
        estimator.fit(X)
        plot_results(
            plt.subplot(gs[0:2, k]),
            plt.subplot(gs[2, k]),
            estimator,
            X,
            y,
            r"%s$%.1e$" % (title, concentration),
            plot_title=k == 0,
        )

plt.show()

**脚本总运行时间：**（0 分 6.631 秒）