96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
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"""
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==============================================
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Face completion with a multi-output estimators
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==============================================
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This example shows the use of multi-output estimator to complete images.
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The goal is to predict the lower half of a face given its upper half.
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The first column of images shows true faces. The next columns illustrate
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how extremely randomized trees, k nearest neighbors, linear
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regression and ridge regression complete the lower half of those faces.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import fetch_olivetti_faces
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from sklearn.ensemble import ExtraTreesRegressor
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from sklearn.linear_model import LinearRegression, RidgeCV
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from sklearn.neighbors import KNeighborsRegressor
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from sklearn.utils.validation import check_random_state
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# Load the faces datasets
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data, targets = fetch_olivetti_faces(return_X_y=True)
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train = data[targets < 30]
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test = data[targets >= 30] # Test on independent people
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# Test on a subset of people
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n_faces = 5
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rng = check_random_state(4)
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face_ids = rng.randint(test.shape[0], size=(n_faces,))
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test = test[face_ids, :]
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n_pixels = data.shape[1]
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# Upper half of the faces
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X_train = train[:, : (n_pixels + 1) // 2]
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# Lower half of the faces
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y_train = train[:, n_pixels // 2 :]
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X_test = test[:, : (n_pixels + 1) // 2]
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y_test = test[:, n_pixels // 2 :]
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# Fit estimators
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ESTIMATORS = {
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"Extra trees": ExtraTreesRegressor(
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n_estimators=10, max_features=32, random_state=0
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),
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"K-nn": KNeighborsRegressor(),
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"Linear regression": LinearRegression(),
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"Ridge": RidgeCV(),
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}
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y_test_predict = dict()
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for name, estimator in ESTIMATORS.items():
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estimator.fit(X_train, y_train)
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y_test_predict[name] = estimator.predict(X_test)
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# Plot the completed faces
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image_shape = (64, 64)
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n_cols = 1 + len(ESTIMATORS)
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plt.figure(figsize=(2.0 * n_cols, 2.26 * n_faces))
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plt.suptitle("Face completion with multi-output estimators", size=16)
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for i in range(n_faces):
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true_face = np.hstack((X_test[i], y_test[i]))
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if i:
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sub = plt.subplot(n_faces, n_cols, i * n_cols + 1)
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else:
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sub = plt.subplot(n_faces, n_cols, i * n_cols + 1, title="true faces")
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sub.axis("off")
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sub.imshow(
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true_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest"
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)
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for j, est in enumerate(sorted(ESTIMATORS)):
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completed_face = np.hstack((X_test[i], y_test_predict[est][i]))
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if i:
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sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j)
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else:
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sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j, title=est)
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sub.axis("off")
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sub.imshow(
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completed_face.reshape(image_shape),
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cmap=plt.cm.gray,
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interpolation="nearest",
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)
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plt.show()
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