3_face_completion_with_random_forest.zip_Random Forest_completio

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 RandomForestRegressor from sklearn.tree import DecisionTreeRegressor #加载脸部数据 data = fetch_olivetti_faces() targets = data.target #划分训练、测试集 data = data.images.reshape((len(data.images), -1)) train = data[targets < 30] test = data[targets >= 30] #从测试集中随机找出5个用来作预测并且对其可视化 n_faces = 5 rng = check_random_state(4) face_ids = rng.randint(test.shape[0], size=(n_faces, )) test = test[face_ids, :] #数据处理 n_pixels = data.shape[1] X_train = train[:, :(n_pixels + 1) // 2] #上半部分脸 y_train = train[:, n_pixels // 2:] #下半部分脸 X_test = test[:, :(n_pixels + 1) // 2] y_test = test[:, n_pixels // 2:] #训练模型并预测 ESTIMATORS = { "Random Forest": RandomForestRegressor(n_estimators=100, max_features=32, random_state=0), "Decision Tree Regressor": DecisionTreeRegressor() } y_test_predict = dict() for name, estimator in ESTIMATORS.items(): estimator.fit(X_train, y_train) y_test_predict[name] = estimator.predict(X_test) # 可视化补全的脸 image_shape = (64, 64) #图片像素 n_cols = 1 + len(ESTIMATORS) plt.figure(figsize=(2. * n_cols, 2.26 * n_faces)) plt.suptitle("Face completion with RandomForests and DecisionTreeReression", size=8) 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()