.. 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 Click :ref:`here ` 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 13-18 .. code-block:: default # Authors: Clay Woolam # Andreas Mueller # License: BSD .. GENERATED FROM PYTHON SOURCE LINES 19-23 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 23-34 .. code-block:: default 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:: default 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="upper right") 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 .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Text(0.5, 1.0, 'Raw data (2 classes=outer and inner)') .. 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:: default 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:: default 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="upper right") 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 0.125 seconds) .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.1.X?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-python :download:`Download Python source code: plot_label_propagation_structure.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_label_propagation_structure.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_