.. 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 ` 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_miscellaneous_plot_multilabel.py: ========================= Multilabel classification ========================= This example simulates a multi-label document classification problem. The dataset is generated randomly based on the following process: - pick the number of labels: n ~ Poisson(n_labels) - n times, choose a class c: c ~ Multinomial(theta) - pick the document length: k ~ Poisson(length) - k times, choose a word: w ~ Multinomial(theta_c) In the above process, rejection sampling is used to make sure that n is more than 2, and that the document length is never zero. Likewise, we reject classes which have already been chosen. The documents that are assigned to both classes are plotted surrounded by two colored circles. The classification is performed by projecting to the first two principal components found by PCA and CCA for visualisation purposes, followed by using the :class:`~sklearn.multiclass.OneVsRestClassifier` metaclassifier using two SVCs with linear kernels to learn a discriminative model for each class. Note that PCA is used to perform an unsupervised dimensionality reduction, while CCA is used to perform a supervised one. Note: in the plot, "unlabeled samples" does not mean that we don't know the labels (as in semi-supervised learning) but that the samples simply do *not* have a label. .. GENERATED FROM PYTHON SOURCE LINES 31-131 .. 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 # Authors: The scikit-learn developers # SPDX-License-Identifier: 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): # get the separating hyperplane 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 0.177 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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_multilabel.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/miscellaneous/plot_multilabel.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_multilabel.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_multilabel.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_multilabel.zip ` .. include:: plot_multilabel.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_