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


=================================================
使用局部离群因子（LOF）进行新颖性检测
=================================================

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识别异常点。它将密度显著低于其邻居的样本视为异常点。本示例展示了如何使用LOF进行新颖性检测。请注意，当LOF用于新颖性检测时，绝对不能在训练集上使用predict、decision_function和score_samples方法，因为这会导致错误的结果。您必须仅在未见过的新数据（不在训练集中）上使用这些方法。有关离群检测和新颖性检测之间的区别以及如何使用LOF进行离群检测的详细信息，请参见用户指南。

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；2）小于可能成为局部离群点的最大邻近样本数。在实际操作中，通常无法获得此类信息，并且n_neighbors=20通常效果良好。

.. GENERATED FROM PYTHON SOURCE LINES 11-72



.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_lof_novelty_detection_001.png
   :alt: Novelty Detection with LOF
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_lof_novelty_detection_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


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

    from sklearn.neighbors import LocalOutlierFactor

    np.random.seed(42)

    xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
    # 生成正常（非异常）训练观测值
    X = 0.3 * np.random.randn(100, 2)
    X_train = np.r_[X + 2, X - 2]
    # 生成新的正常（非异常）观测值
    X = 0.3 * np.random.randn(20, 2)
    X_test = np.r_[X + 2, X - 2]
    # 生成一些异常的新奇观测
    X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))

    # 拟合模型以进行新颖性检测（novelty=True）
    clf = LocalOutlierFactor(n_neighbors=20, novelty=True, contamination=0.1)
    clf.fit(X_train)
    # 请勿在 X_train 上使用 predict、decision_function 和 score_samples，因为这会给出错误的结果，而应仅在新的未见过的数据（未在 X_train 中使用）上使用，例如 X_test、X_outliers 或 meshgrid。
    y_pred_test = clf.predict(X_test)
    y_pred_outliers = clf.predict(X_outliers)
    n_error_test = y_pred_test[y_pred_test == -1].size
    n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size

    # 绘制学习到的边界、点和最近的向量到平面
    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    plt.title("Novelty Detection with LOF")
    plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)
    a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors="darkred")
    plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors="palevioletred")

    s = 40
    b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
    b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
    c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
    plt.axis("tight")
    plt.xlim((-5, 5))
    plt.ylim((-5, 5))
    plt.legend(
        [mlines.Line2D([], [], color="darkred"), b1, b2, c],
        [
            "learned frontier",
            "training observations",
            "new regular observations",
            "new abnormal observations",
        ],
        loc="upper left",
        prop=matplotlib.font_manager.FontProperties(size=11),
    )
    plt.xlabel(
        "errors novel regular: %d/40 ; errors novel abnormal: %d/40"
        % (n_error_test, n_error_outliers)
    )
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.325 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_lof_novelty_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_novelty_detection.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_lof_novelty_detection.recommendations


.. only:: html

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

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