## File: plot_multioutput_face_completion.py

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scikit-learn 0.23.2-5
 `12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697` ``````""" ============================================== 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. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import fetch_olivetti_faces from sklearn.utils.validation import check_random_state from sklearn.ensemble import ExtraTreesRegressor from sklearn.neighbors import KNeighborsRegressor from sklearn.linear_model import LinearRegression from sklearn.linear_model import RidgeCV # 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. * 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() ``````