.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_dbscan.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_cluster_plot_dbscan.py: =================================== DBSCAN聚类算法演示 =================================== DBSCAN（基于密度的噪声应用空间聚类）在高密度区域中找到核心样本，并从中扩展聚类。 该算法适用于包含相似密度聚类的数据。 请参阅示例 :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` ，了解不同聚类算法在二维数据集上的演示。 .. GENERATED FROM PYTHON SOURCE LINES 14-18 数据生成 --------------- 我们使用 :class:`~sklearn.datasets.make_blobs` 来创建 3 个合成簇。 .. GENERATED FROM PYTHON SOURCE LINES 18-29 .. code-block:: Python from sklearn.datasets import make_blobs from sklearn.preprocessing import StandardScaler centers = [[1, 1], [-1, -1], [1, -1]] X, labels_true = make_blobs( n_samples=750, centers=centers, cluster_std=0.4, random_state=0 ) X = StandardScaler().fit_transform(X) .. GENERATED FROM PYTHON SOURCE LINES 30-31 我们可以将结果数据可视化： .. GENERATED FROM PYTHON SOURCE LINES 31-38 .. code-block:: Python import matplotlib.pyplot as plt plt.scatter(X[:, 0], X[:, 1]) plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png :alt: plot dbscan :srcset: /auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 39-43 计算 DBSCAN -------------- 可以使用 `labels_` 属性访问 :class:`~sklearn.cluster.DBSCAN` 分配的标签。噪声样本被赋予标签 math:`-1` 。 .. GENERATED FROM PYTHON SOURCE LINES 43-59 .. code-block:: Python import numpy as np from sklearn import metrics from sklearn.cluster import DBSCAN db = DBSCAN(eps=0.3, min_samples=10).fit(X) labels = db.labels_ # 标签中的簇数量，如果存在噪声则忽略。 n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0) n_noise_ = list(labels).count(-1) print("Estimated number of clusters: %d" % n_clusters_) print("Estimated number of noise points: %d" % n_noise_) .. rst-class:: sphx-glr-script-out .. code-block:: none Estimated number of clusters: 3 Estimated number of noise points: 18 .. GENERATED FROM PYTHON SOURCE LINES 60-67 聚类算法本质上是无监督学习方法。然而，由于 :class:`~sklearn.datasets.make_blobs` 提供了合成簇的真实标签，因此可以使用利用这种“监督”真实信息的评估指标来量化生成簇的质量。这类指标的例子包括同质性、完整性、V-测度、兰德指数、调整兰德指数和调整互信息（AMI）。 如果不知道真实标签，只能使用模型结果本身进行评估。在这种情况下，轮廓系数非常有用。 有关更多信息，请参见 :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py` 示例或 :ref:`clustering_evaluation` 模块。 .. GENERATED FROM PYTHON SOURCE LINES 67-78 .. code-block:: Python print(f"Homogeneity: {metrics.homogeneity_score(labels_true, labels):.3f}") print(f"Completeness: {metrics.completeness_score(labels_true, labels):.3f}") print(f"V-measure: {metrics.v_measure_score(labels_true, labels):.3f}") print(f"Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):.3f}") print( "Adjusted Mutual Information:" f" {metrics.adjusted_mutual_info_score(labels_true, labels):.3f}" ) print(f"Silhouette Coefficient: {metrics.silhouette_score(X, labels):.3f}") .. rst-class:: sphx-glr-script-out .. code-block:: none Homogeneity: 0.953 Completeness: 0.883 V-measure: 0.917 Adjusted Rand Index: 0.952 Adjusted Mutual Information: 0.916 Silhouette Coefficient: 0.626 .. GENERATED FROM PYTHON SOURCE LINES 79-83 绘制结果 ------------ 核心样本（大点）和非核心样本（小点）根据分配的簇进行颜色编码。被标记为噪声的样本用黑色表示。 .. GENERATED FROM PYTHON SOURCE LINES 83-118 .. code-block:: Python unique_labels = set(labels) core_samples_mask = np.zeros_like(labels, dtype=bool) core_samples_mask[db.core_sample_indices_] = True colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))] for k, col in zip(unique_labels, colors): if k == -1: # 黑色用于噪声。 col = [0, 0, 0, 1] class_member_mask = labels == k xy = X[class_member_mask & core_samples_mask] plt.plot( xy[:, 0], xy[:, 1], "o", markerfacecolor=tuple(col), markeredgecolor="k", markersize=14, ) xy = X[class_member_mask & ~core_samples_mask] plt.plot( xy[:, 0], xy[:, 1], "o", markerfacecolor=tuple(col), markeredgecolor="k", markersize=6, ) plt.title(f"Estimated number of clusters: {n_clusters_}") plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_dbscan_002.png :alt: Estimated number of clusters: 3 :srcset: /auto_examples/cluster/images/sphx_glr_plot_dbscan_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.082 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_dbscan.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_dbscan.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_dbscan.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_dbscan.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_dbscan.zip ` .. include:: plot_dbscan.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_