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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.

Face completion with multi-output estimators, true faces, Extra trees, K-nn, Linear regression, Ridge

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
importnumpyasnp
fromsklearn.datasetsimport fetch_olivetti_faces
fromsklearn.ensembleimport ExtraTreesRegressor
fromsklearn.linear_modelimport LinearRegression , RidgeCV
fromsklearn.neighborsimport KNeighborsRegressor
fromsklearn.utils.validationimport 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 ()