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


=========================
多标签分类
=========================

这个示例模拟了一个多标签文档分类问题。数据集是基于以下过程随机生成的：

    - 选择标签数量：n ~ 泊松分布(n_labels)
    - 选择n次类别c：c ~ 多项分布(theta)
    - 选择文档长度：k ~ 泊松分布(length)
    - 选择k次单词：w ~ 多项分布(theta_c)

在上述过程中，使用拒绝采样确保n大于2，并且文档长度不为零。同样，我们拒绝已经选择的类别。分配给两个类别的文档被绘制在两个彩色圆圈中。

分类通过投影到PCA和CCA找到的前两个主成分进行，以便于可视化，然后使用:class:`~sklearn.multiclass.OneVsRestClassifier` 元分类器，使用两个具有线性核的SVC来学习每个类别的判别模型。请注意，PCA用于执行无监督的降维，而CCA用于执行有监督的降维。

注意：在图中，“未标记样本”并不意味着我们不知道标签（如在半监督学习中），而是样本根本没有标签。

.. GENERATED FROM PYTHON SOURCE LINES 20-120



.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_multilabel_001.png
   :alt: With unlabeled samples + CCA, With unlabeled samples + PCA, Without unlabeled samples + CCA, Without unlabeled samples + PCA
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_multilabel_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX 许可证标识符：BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.cross_decomposition import CCA
    from sklearn.datasets import make_multilabel_classification
    from sklearn.decomposition import PCA
    from sklearn.multiclass import OneVsRestClassifier
    from sklearn.svm import SVC


    def plot_hyperplane(clf, min_x, max_x, linestyle, label):
        # 获得分离超平面
        w = clf.coef_[0]
        a = -w[0] / w[1]
        xx = np.linspace(min_x - 5, max_x + 5)  # make sure the line is long enough
        yy = a * xx - (clf.intercept_[0]) / w[1]
        plt.plot(xx, yy, linestyle, label=label)


    def plot_subfigure(X, Y, subplot, title, transform):
        if transform == "pca":
            X = PCA(n_components=2).fit_transform(X)
        elif transform == "cca":
            X = CCA(n_components=2).fit(X, Y).transform(X)
        else:
            raise ValueError

        min_x = np.min(X[:, 0])
        max_x = np.max(X[:, 0])

        min_y = np.min(X[:, 1])
        max_y = np.max(X[:, 1])

        classif = OneVsRestClassifier(SVC(kernel="linear"))
        classif.fit(X, Y)

        plt.subplot(2, 2, subplot)
        plt.title(title)

        zero_class = np.where(Y[:, 0])
        one_class = np.where(Y[:, 1])
        plt.scatter(X[:, 0], X[:, 1], s=40, c="gray", edgecolors=(0, 0, 0))
        plt.scatter(
            X[zero_class, 0],
            X[zero_class, 1],
            s=160,
            edgecolors="b",
            facecolors="none",
            linewidths=2,
            label="Class 1",
        )
        plt.scatter(
            X[one_class, 0],
            X[one_class, 1],
            s=80,
            edgecolors="orange",
            facecolors="none",
            linewidths=2,
            label="Class 2",
        )

        plot_hyperplane(
            classif.estimators_[0], min_x, max_x, "k--", "Boundary\nfor class 1"
        )
        plot_hyperplane(
            classif.estimators_[1], min_x, max_x, "k-.", "Boundary\nfor class 2"
        )
        plt.xticks(())
        plt.yticks(())

        plt.xlim(min_x - 0.5 * max_x, max_x + 0.5 * max_x)
        plt.ylim(min_y - 0.5 * max_y, max_y + 0.5 * max_y)
        if subplot == 2:
            plt.xlabel("First principal component")
            plt.ylabel("Second principal component")
            plt.legend(loc="upper left")


    plt.figure(figsize=(8, 6))

    X, Y = make_multilabel_classification(
        n_classes=2, n_labels=1, allow_unlabeled=True, random_state=1
    )

    plot_subfigure(X, Y, 1, "With unlabeled samples + CCA", "cca")
    plot_subfigure(X, Y, 2, "With unlabeled samples + PCA", "pca")

    X, Y = make_multilabel_classification(
        n_classes=2, n_labels=1, allow_unlabeled=False, random_state=1
    )

    plot_subfigure(X, Y, 3, "Without unlabeled samples + CCA", "cca")
    plot_subfigure(X, Y, 4, "Without unlabeled samples + PCA", "pca")

    plt.subplots_adjust(0.04, 0.02, 0.97, 0.94, 0.09, 0.2)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.094 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_multilabel.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/miscellaneous/plot_multilabel.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_multilabel.recommendations


.. only:: html

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

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