.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/datasets/plot_random_multilabel_dataset.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_datasets_plot_random_multilabel_dataset.py: ============================================== Plot randomly generated multilabel dataset ============================================== This illustrates the :func:`~sklearn.datasets.make_multilabel_classification` dataset generator. Each sample consists of counts of two features (up to 50 in total), which are differently distributed in each of two classes. Points are labeled as follows, where Y means the class is present: ===== ===== ===== ====== 1 2 3 Color ===== ===== ===== ====== Y N N Red N Y N Blue N N Y Yellow Y Y N Purple Y N Y Orange Y Y N Green Y Y Y Brown ===== ===== ===== ====== A star marks the expected sample for each class; its size reflects the probability of selecting that class label. The left and right examples highlight the ``n_labels`` parameter: more of the samples in the right plot have 2 or 3 labels. Note that this two-dimensional example is very degenerate: generally the number of features would be much greater than the "document length", while here we have much larger documents than vocabulary. Similarly, with ``n_classes > n_features``, it is much less likely that a feature distinguishes a particular class. .. GENERATED FROM PYTHON SOURCE LINES 37-110 .. image-sg:: /auto_examples/datasets/images/sphx_glr_plot_random_multilabel_dataset_001.png :alt: n_labels=1, length=50, n_labels=3, length=50 :srcset: /auto_examples/datasets/images/sphx_glr_plot_random_multilabel_dataset_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none The data was generated from (random_state=521): Class P(C) P(w0|C) P(w1|C) red 0.32 0.55 0.45 blue 0.26 0.79 0.21 yellow 0.42 0.49 0.51 | .. 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.datasets import make_multilabel_classification as make_ml_clf COLORS = np.array( [ "!", "#FF3333", # red "#0198E1", # blue "#BF5FFF", # purple "#FCD116", # yellow "#FF7216", # orange "#4DBD33", # green "#87421F", # brown ] ) # Use same random seed for multiple calls to make_multilabel_classification to # ensure same distributions RANDOM_SEED = np.random.randint(2**10) def plot_2d(ax, n_labels=1, n_classes=3, length=50): X, Y, p_c, p_w_c = make_ml_clf( n_samples=150, n_features=2, n_classes=n_classes, n_labels=n_labels, length=length, allow_unlabeled=False, return_distributions=True, random_state=RANDOM_SEED, ) ax.scatter( X[:, 0], X[:, 1], color=COLORS.take((Y * [1, 2, 4]).sum(axis=1)), marker="." ) ax.scatter( p_w_c[0] * length, p_w_c[1] * length, marker="*", linewidth=0.5, edgecolor="black", s=20 + 1500 * p_c**2, color=COLORS.take([1, 2, 4]), ) ax.set_xlabel("Feature 0 count") return p_c, p_w_c _, (ax1, ax2) = plt.subplots(1, 2, sharex="row", sharey="row", figsize=(8, 4)) plt.subplots_adjust(bottom=0.15) p_c, p_w_c = plot_2d(ax1, n_labels=1) ax1.set_title("n_labels=1, length=50") ax1.set_ylabel("Feature 1 count") plot_2d(ax2, n_labels=3) ax2.set_title("n_labels=3, length=50") ax2.set_xlim(left=0, auto=True) ax2.set_ylim(bottom=0, auto=True) plt.show() print("The data was generated from (random_state=%d):" % RANDOM_SEED) print("Class", "P(C)", "P(w0|C)", "P(w1|C)", sep="\t") for k, p, p_w in zip(["red", "blue", "yellow"], p_c, p_w_c.T): print("%s\t%0.2f\t%0.2f\t%0.2f" % (k, p, p_w[0], p_w[1])) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.133 seconds) .. _sphx_glr_download_auto_examples_datasets_plot_random_multilabel_dataset.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/datasets/plot_random_multilabel_dataset.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/datasets/plot_random_multilabel_dataset.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_random_multilabel_dataset.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_random_multilabel_dataset.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_random_multilabel_dataset.zip ` .. include:: plot_random_multilabel_dataset.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_