.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/miscellaneous/plot_multioutput_face_completion.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_multioutput_face_completion.py: ============================================== Face completion with a multi-output estimators ============================================== This example shows the use of multi-output estimator to complete images. The goal is to predict the lower half of a face given its upper half. The first column of images shows true faces. The next columns illustrate how extremely randomized trees, k nearest neighbors, linear regression and ridge regression complete the lower half of those faces. .. GENERATED FROM PYTHON SOURCE LINES 14-99 .. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png :alt: Face completion with multi-output estimators, true faces, Extra trees, K-nn, Linear regression, Ridge :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_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.datasets import fetch_olivetti_faces from sklearn.ensemble import ExtraTreesRegressor from sklearn.linear_model import LinearRegression, RidgeCV from sklearn.neighbors import KNeighborsRegressor from sklearn.utils.validation import check_random_state # Load the faces datasets data, targets = fetch_olivetti_faces(return_X_y=True) train = data[targets < 30] test = data[targets >= 30] # Test on independent people # Test on a subset of people n_faces = 5 rng = check_random_state(4) face_ids = rng.randint(test.shape[0], size=(n_faces,)) test = test[face_ids, :] n_pixels = data.shape[1] # Upper half of the faces X_train = train[:, : (n_pixels + 1) // 2] # Lower half of the faces y_train = train[:, n_pixels // 2 :] X_test = test[:, : (n_pixels + 1) // 2] y_test = test[:, n_pixels // 2 :] # Fit estimators ESTIMATORS = { "Extra trees": ExtraTreesRegressor( n_estimators=10, max_features=32, random_state=0 ), "K-nn": KNeighborsRegressor(), "Linear regression": LinearRegression(), "Ridge": RidgeCV(), } y_test_predict = dict() for name, estimator in ESTIMATORS.items(): estimator.fit(X_train, y_train) y_test_predict[name] = estimator.predict(X_test) # Plot the completed faces image_shape = (64, 64) n_cols = 1 + len(ESTIMATORS) plt.figure(figsize=(2.0 * n_cols, 2.26 * n_faces)) plt.suptitle("Face completion with multi-output estimators", size=16) for i in range(n_faces): true_face = np.hstack((X_test[i], y_test[i])) if i: sub = plt.subplot(n_faces, n_cols, i * n_cols + 1) else: sub = plt.subplot(n_faces, n_cols, i * n_cols + 1, title="true faces") sub.axis("off") sub.imshow( true_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest" ) for j, est in enumerate(sorted(ESTIMATORS)): completed_face = np.hstack((X_test[i], y_test_predict[est][i])) if i: sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j) else: sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j, title=est) sub.axis("off") sub.imshow( completed_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest", ) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.853 seconds) .. _sphx_glr_download_auto_examples_miscellaneous_plot_multioutput_face_completion.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_multioutput_face_completion.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_multioutput_face_completion.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_multioutput_face_completion.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_multioutput_face_completion.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_multioutput_face_completion.zip ` .. include:: plot_multioutput_face_completion.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_