.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/neighbors/plot_lof_outlier_detection.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_neighbors_plot_lof_outlier_detection.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_neighbors_plot_lof_outlier_detection.py:


=================================================
使用局部离群因子（LOF）进行离群点检测
=================================================

局部离群因子（LOF）算法是一种无监督的异常检测方法，它通过计算给定数据点相对于其邻居的局部密度偏差来检测离群点。它将那些密度显著低于其邻居的样本视为离群点。此示例展示了如何使用LOF进行离群点检测，这是scikit-learn中此估计器的默认用例。请注意，当LOF用于离群点检测时，它没有 `predict` 、 `decision_function` 和 `score_samples` 方法。有关离群点检测和新颖性检测之间的区别以及如何使用LOF进行新颖性检测的详细信息，请参见：ref:`用户指南 <outlier_detection>` 。

考虑的邻居数量（参数 `n_neighbors` ）通常设置为1）大于一个簇必须包含的最小样本数，以便其他样本可以相对于该簇成为局部离群点，以及2）小于可能成为局部离群点的最大邻近样本数。在实践中，通常无法获得此类信息，并且选择 `n_neighbors=20` 通常效果良好。

.. GENERATED FROM PYTHON SOURCE LINES 13-15

生成包含异常值的数据
-----------------------

.. GENERATED FROM PYTHON SOURCE LINES 18-31

.. code-block:: Python

    import numpy as np

    np.random.seed(42)

    X_inliers = 0.3 * np.random.randn(100, 2)
    X_inliers = np.r_[X_inliers + 2, X_inliers - 2]
    X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
    X = np.r_[X_inliers, X_outliers]

    n_outliers = len(X_outliers)
    ground_truth = np.ones(len(X), dtype=int)
    ground_truth[-n_outliers:] = -1








.. GENERATED FROM PYTHON SOURCE LINES 32-35

拟合用于异常检测的模型（默认）

使用 `fit_predict` 来计算训练样本的预测标签（当 LOF 用于异常检测时，估计器没有 `predict` 、 `decision_function` 和 `score_samples` 方法）。

.. GENERATED FROM PYTHON SOURCE LINES 35-43

.. code-block:: Python


    from sklearn.neighbors import LocalOutlierFactor

    clf = LocalOutlierFactor(n_neighbors=20, contamination=0.1)
    y_pred = clf.fit_predict(X)
    n_errors = (y_pred != ground_truth).sum()
    X_scores = clf.negative_outlier_factor_








.. GENERATED FROM PYTHON SOURCE LINES 44-46

绘制结果
------------

.. GENERATED FROM PYTHON SOURCE LINES 48-78

.. code-block:: Python

    import matplotlib.pyplot as plt
    from matplotlib.legend_handler import HandlerPathCollection


    def update_legend_marker_size(handle, orig):
        "自定义图例标记的大小"
        handle.update_from(orig)
        handle.set_sizes([20])


    plt.scatter(X[:, 0], X[:, 1], color="k", s=3.0, label="Data points")
    # 绘制半径与异常值分数成比例的圆圈
    radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min())
    scatter = plt.scatter(
        X[:, 0],
        X[:, 1],
        s=1000 * radius,
        edgecolors="r",
        facecolors="none",
        label="Outlier scores",
    )
    plt.axis("tight")
    plt.xlim((-5, 5))
    plt.ylim((-5, 5))
    plt.xlabel("prediction errors: %d" % (n_errors))
    plt.legend(
        handler_map={scatter: HandlerPathCollection(update_func=update_legend_marker_size)}
    )
    plt.title("Local Outlier Factor (LOF)")
    plt.show()



.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_lof_outlier_detection_001.png
   :alt: Local Outlier Factor (LOF)
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_lof_outlier_detection_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.036 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_lof_outlier_detection.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/neighbors/plot_lof_outlier_detection.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_lof_outlier_detection.recommendations


.. only:: html

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

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