.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/applications/plot_outlier_detection_wine.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_applications_plot_outlier_detection_wine.py>`
        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_applications_plot_outlier_detection_wine.py:


====================================
真实数据集上的异常值检测
====================================

本示例说明了在真实数据集上进行稳健协方差估计的必要性。
这对于异常值检测和更好地理解数据结构都很有用。

我们从葡萄酒数据集中选择了两组两个变量，作为使用多种异常值检测工具进行分析的示例。
为了便于可视化，我们使用了二维示例，但需要注意的是，在高维情况下，事情并不那么简单，这一点将在后文中指出。

在下面的两个示例中，主要结果是经验协方差估计作为一种非稳健方法，受观测值的异质结构影响很大。
尽管稳健协方差估计能够聚焦于数据分布的主要模式，但它坚持认为数据应服从高斯分布，从而导致对数据结构的某些偏差估计，但在某种程度上仍然是准确的。
一类支持向量机（One-Class SVM）不假设数据分布的任何参数形式，因此可以更好地建模数据的复杂形状。

.. GENERATED FROM PYTHON SOURCE LINES 16-20

.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier：BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 21-25

第一个例子
-------------

第一个示例说明了当存在离群点时，最小协方差行列式稳健估计器如何帮助集中于相关簇。在这里，经验协方差估计被主簇之外的点所偏斜。当然，一些筛选工具会指出存在两个簇（支持向量机、高斯混合模型、单变量离群点检测等）。但如果这是一个高维示例，这些方法都不容易应用。

.. GENERATED FROM PYTHON SOURCE LINES 25-37

.. code-block:: Python

    from sklearn.covariance import EllipticEnvelope
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.svm import OneClassSVM

    estimators = {
        "Empirical Covariance": EllipticEnvelope(support_fraction=1.0, contamination=0.25),
        "Robust Covariance (Minimum Covariance Determinant)": EllipticEnvelope(
            contamination=0.25
        ),
        "OCSVM": OneClassSVM(nu=0.25, gamma=0.35),
    }








.. GENERATED FROM PYTHON SOURCE LINES 38-82

.. code-block:: Python

    import matplotlib.lines as mlines
    import matplotlib.pyplot as plt

    from sklearn.datasets import load_wine

    X = load_wine()["data"][:, [1, 2]]  # two clusters

    fig, ax = plt.subplots()
    colors = ["tab:blue", "tab:orange", "tab:red"]
    # 学习一个用于异常检测的前沿方法，结合多个分类器
    legend_lines = []
    for color, (name, estimator) in zip(colors, estimators.items()):
        estimator.fit(X)
        DecisionBoundaryDisplay.from_estimator(
            estimator,
            X,
            response_method="decision_function",
            plot_method="contour",
            levels=[0],
            colors=color,
            ax=ax,
        )
        legend_lines.append(mlines.Line2D([], [], color=color, label=name))


    ax.scatter(X[:, 0], X[:, 1], color="black")
    bbox_args = dict(boxstyle="round", fc="0.8")
    arrow_args = dict(arrowstyle="->")
    ax.annotate(
        "outlying points",
        xy=(4, 2),
        xycoords="data",
        textcoords="data",
        xytext=(3, 1.25),
        bbox=bbox_args,
        arrowprops=arrow_args,
    )
    ax.legend(handles=legend_lines, loc="upper center")
    _ = ax.set(
        xlabel="ash",
        ylabel="malic_acid",
        title="Outlier detection on a real data set (wine recognition)",
    )




.. image-sg:: /auto_examples/applications/images/sphx_glr_plot_outlier_detection_wine_001.png
   :alt: Outlier detection on a real data set (wine recognition)
   :srcset: /auto_examples/applications/images/sphx_glr_plot_outlier_detection_wine_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 83-87

第二个例子
--------------

第二个示例展示了最小协方差行列式（Minimum Covariance Determinant）稳健协方差估计器集中于数据分布主要模式的能力：尽管由于香蕉形分布，协方差难以估计，但位置似乎被很好地估计了。不管怎样，我们可以去除一些离群观测值。单类支持向量机（One-Class SVM）能够捕捉真实的数据结构，但难点在于调整其核带宽参数，以在数据散布矩阵的形状和数据过拟合风险之间取得良好的平衡。

.. GENERATED FROM PYTHON SOURCE LINES 87-116

.. code-block:: Python

    X = load_wine()["data"][:, [6, 9]]  # "banana"-shaped

    fig, ax = plt.subplots()
    colors = ["tab:blue", "tab:orange", "tab:red"]
    # 学习一个用于异常检测的前沿方法，结合多个分类器
    legend_lines = []
    for color, (name, estimator) in zip(colors, estimators.items()):
        estimator.fit(X)
        DecisionBoundaryDisplay.from_estimator(
            estimator,
            X,
            response_method="decision_function",
            plot_method="contour",
            levels=[0],
            colors=color,
            ax=ax,
        )
        legend_lines.append(mlines.Line2D([], [], color=color, label=name))


    ax.scatter(X[:, 0], X[:, 1], color="black")
    ax.legend(handles=legend_lines, loc="upper center")
    ax.set(
        xlabel="flavanoids",
        ylabel="color_intensity",
        title="Outlier detection on a real data set (wine recognition)",
    )

    plt.show()



.. image-sg:: /auto_examples/applications/images/sphx_glr_plot_outlier_detection_wine_002.png
   :alt: Outlier detection on a real data set (wine recognition)
   :srcset: /auto_examples/applications/images/sphx_glr_plot_outlier_detection_wine_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.164 seconds)


.. _sphx_glr_download_auto_examples_applications_plot_outlier_detection_wine.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/applications/plot_outlier_detection_wine.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_outlier_detection_wine.ipynb <plot_outlier_detection_wine.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_outlier_detection_wine.py <plot_outlier_detection_wine.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_outlier_detection_wine.zip <plot_outlier_detection_wine.zip>`


.. include:: plot_outlier_detection_wine.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_