.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/semi_supervised/plot_label_propagation_structure.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 Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py: ============================================== Label Propagation learning a complex structure ============================================== Example of LabelPropagation learning a complex internal structure to demonstrate "manifold learning". The outer circle should be labeled "red" and the inner circle "blue". Because both label groups lie inside their own distinct shape, we can see that the labels propagate correctly around the circle. .. GENERATED FROM PYTHON SOURCE LINES 13-17 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 18-22 We generate a dataset with two concentric circles. In addition, a label is associated with each sample of the dataset that is: 0 (belonging to the outer circle), 1 (belonging to the inner circle), and -1 (unknown). Here, all labels but two are tagged as unknown. .. GENERATED FROM PYTHON SOURCE LINES 22-34 .. code-block:: Python import numpy as np from sklearn.datasets import make_circles n_samples = 200 X, y = make_circles(n_samples=n_samples, shuffle=False) outer, inner = 0, 1 labels = np.full(n_samples, -1.0) labels[0] = outer labels[-1] = inner .. GENERATED FROM PYTHON SOURCE LINES 35-36 Plot raw data .. GENERATED FROM PYTHON SOURCE LINES 36-67 .. code-block:: Python import matplotlib.pyplot as plt plt.figure(figsize=(4, 4)) plt.scatter( X[labels == outer, 0], X[labels == outer, 1], color="navy", marker="s", lw=0, label="outer labeled", s=10, ) plt.scatter( X[labels == inner, 0], X[labels == inner, 1], color="c", marker="s", lw=0, label="inner labeled", s=10, ) plt.scatter( X[labels == -1, 0], X[labels == -1, 1], color="darkorange", marker=".", label="unlabeled", ) plt.legend(scatterpoints=1, shadow=False, loc="center") _ = plt.title("Raw data (2 classes=outer and inner)") .. image-sg:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_001.png :alt: Raw data (2 classes=outer and inner) :srcset: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 68-70 The aim of :class:`~sklearn.semi_supervised.LabelSpreading` is to associate a label to sample where the label is initially unknown. .. GENERATED FROM PYTHON SOURCE LINES 71-76 .. code-block:: Python from sklearn.semi_supervised import LabelSpreading label_spread = LabelSpreading(kernel="knn", alpha=0.8) label_spread.fit(X, labels) .. raw:: html

LabelSpreading(alpha=0.8, kernel='knn')

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.. GENERATED FROM PYTHON SOURCE LINES 77-79 Now, we can check which labels have been associated with each sample when the label was unknown. .. GENERATED FROM PYTHON SOURCE LINES 79-106 .. code-block:: Python output_labels = label_spread.transduction_ output_label_array = np.asarray(output_labels) outer_numbers = np.where(output_label_array == outer)[0] inner_numbers = np.where(output_label_array == inner)[0] plt.figure(figsize=(4, 4)) plt.scatter( X[outer_numbers, 0], X[outer_numbers, 1], color="navy", marker="s", lw=0, s=10, label="outer learned", ) plt.scatter( X[inner_numbers, 0], X[inner_numbers, 1], color="c", marker="s", lw=0, s=10, label="inner learned", ) plt.legend(scatterpoints=1, shadow=False, loc="center") plt.title("Labels learned with Label Spreading (KNN)") plt.show() .. image-sg:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_002.png :alt: Labels learned with Label Spreading (KNN) :srcset: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.136 seconds) .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.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/semi_supervised/plot_label_propagation_structure.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_label_propagation_structure.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_label_propagation_structure.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_label_propagation_structure.zip ` .. include:: plot_label_propagation_structure.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_