.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/svm/plot_oneclass.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_svm_plot_oneclass.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_svm_plot_oneclass.py:


==========================================
One-class SVM with non-linear kernel (RBF)
==========================================

An example using a one-class SVM for novelty detection.

:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
classifying new data as similar or different to the training set.

.. GENERATED FROM PYTHON SOURCE LINES 15-38

.. code-block:: Python

    import numpy as np

    from sklearn import svm

    # Generate train data
    X = 0.3 * np.random.randn(100, 2)
    X_train = np.r_[X + 2, X - 2]
    # Generate some regular novel observations
    X = 0.3 * np.random.randn(20, 2)
    X_test = np.r_[X + 2, X - 2]
    # Generate some abnormal novel observations
    X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))

    # fit the model
    clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
    clf.fit(X_train)
    y_pred_train = clf.predict(X_train)
    y_pred_test = clf.predict(X_test)
    y_pred_outliers = clf.predict(X_outliers)
    n_error_train = y_pred_train[y_pred_train == -1].size
    n_error_test = y_pred_test[y_pred_test == -1].size
    n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size








.. GENERATED FROM PYTHON SOURCE LINES 39-103

.. code-block:: Python

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

    from sklearn.inspection import DecisionBoundaryDisplay

    _, ax = plt.subplots()

    # generate grid for the boundary display
    xx, yy = np.meshgrid(np.linspace(-5, 5, 10), np.linspace(-5, 5, 10))
    X = np.concatenate([xx.reshape(-1, 1), yy.reshape(-1, 1)], axis=1)
    DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        response_method="decision_function",
        plot_method="contourf",
        ax=ax,
        cmap="PuBu",
    )
    DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        response_method="decision_function",
        plot_method="contourf",
        ax=ax,
        levels=[0, 10000],
        colors="palevioletred",
    )
    DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        response_method="decision_function",
        plot_method="contour",
        ax=ax,
        levels=[0],
        colors="darkred",
        linewidths=2,
    )

    s = 40
    b1 = ax.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
    b2 = ax.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
    c = ax.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
    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),
    )
    ax.set(
        xlabel=(
            f"error train: {n_error_train}/200 ; errors novel regular: {n_error_test}/40 ;"
            f" errors novel abnormal: {n_error_outliers}/40"
        ),
        title="Novelty Detection",
        xlim=(-5, 5),
        ylim=(-5, 5),
    )
    plt.show()



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_oneclass_001.png
   :alt: Novelty Detection
   :srcset: /auto_examples/svm/images/sphx_glr_plot_oneclass_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.152 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_oneclass.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/svm/plot_oneclass.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/svm/plot_oneclass.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_oneclass.recommendations


.. only:: html

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

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