机器学习实战—人脸自动补全（多目标预测）
目标
通过上半部分的人脸图案来预测下边部分人脸，进行人脸补全。
实质是一个多目标预测问题，对每一个目标点都会进行模型建模，然后通过相应模型对各个点进行预测
数据集
采用Olivetti人脸数据集包含400张灰度的64*64像素的人脸图像，每个图像被展平为大小为4096的一维向量，40个不同的人拍照十次。
from sklearn.neighbors import KNeighborsRegressor from sklearn.linear_model import LinearRegression,Ridge,Lasso from sklearn.ensemble import ExtraTreesRegressor
from sklearn import datasets • 1
faces = datasets.fetch_olivetti_faces() • 1
faces
{'data': array([[0.30991736, 0.3677686 , 0.41735536, ..., 0.15289256, 0.16115703, 0.1570248 ], [0.45454547, 0.47107437, 0.5123967 , ..., 0.15289256, 0.15289256, 0.15289256], [0.3181818 , 0.40082645, 0.49173555, ..., 0.14049587, 0.14876033, 0.15289256], ..., [0.5 , 0.53305787, 0.607438 , ..., 0.17768595, 0.14876033, 0.19008264], [0.21487603, 0.21900827, 0.21900827, ..., 0.57438016, 0.59090906, 0.60330576], [0.5165289 , 0.46280992, 0.28099173, ..., 0.35950413, 0.3553719 , 0.38429752]], dtype=float32), 'images': array([[[0.30991736, 0.3677686 , 0.41735536, ..., 0.37190083, 0.3305785 , 0.30578512], [0.3429752 , 0.40495867, 0.43801653, ..., 0.37190083, 0.338843 , 0.3140496 ], [0.3429752 , 0.41735536, 0.45041323, ..., 0.38016528, 0.338843 , 0.29752067], ..., [0.21487603, 0.20661157, 0.2231405 , ..., 0.15289256, 0.16528925, 0.17355372], [0.20247933, 0.2107438 , 0.2107438 , ..., 0.14876033, 0.16115703, 0.16528925], [0.20247933, 0.20661157, 0.20247933, ..., 0.15289256, 0.16115703, 0.1570248 ]], [[0.45454547, 0.47107437, 0.5123967 , ..., 0.19008264, 0.18595041, 0.18595041], [0.446281 , 0.48347107, 0.5206612 , ..., 0.21487603, 0.2107438 , 0.2107438 ], [0.49586776, 0.5165289 , 0.53305787, ..., 0.20247933, 0.20661157, 0.20661157], ..., [0.77272725, 0.78099173, 0.7933884 , ..., 0.1446281 , 0.1446281 , 0.1446281 ], [0.77272725, 0.7768595 , 0.7892562 , ..., 0.13636364, 0.13636364, 0.13636364], [0.7644628 , 0.7892562 , 0.78099173, ..., 0.15289256, 0.15289256, 0.15289256]], [[0.3181818 , 0.40082645, 0.49173555, ..., 0.40082645, 0.3553719 , 0.30991736], [0.30991736, 0.3966942 , 0.47933885, ..., 0.40495867, 0.37603307, 0.30165288], [0.26859504, 0.34710744, 0.45454547, ..., 0.3966942 , 0.37190083, 0.30991736], ..., [0.1322314 , 0.09917355, 0.08264463, ..., 0.13636364, 0.14876033, 0.15289256], [0.11570248, 0.09504132, 0.0785124 , ..., 0.1446281 , 0.1446281 , 0.1570248 ], [0.11157025, 0.09090909, 0.0785124 , ..., 0.14049587, 0.14876033, 0.15289256]], ..., [[0.5 , 0.53305787, 0.607438 , ..., 0.28512397, 0.23966943, 0.21487603], [0.49173555, 0.5413223 , 0.60330576, ..., 0.29752067, 0.20247933, 0.20661157], [0.46694216, 0.55785125, 0.6198347 , ..., 0.29752067, 0.17768595, 0.18595041], ..., [0.03305785, 0.46280992, 0.5289256 , ..., 0.17355372, 0.17355372, 0.1694215 ], [0.1570248 , 0.5247934 , 0.53305787, ..., 0.16528925, 0.1570248 , 0.18595041], [0.45454547, 0.5206612 , 0.53305787, ..., 0.17768595, 0.14876033, 0.19008264]], [[0.21487603, 0.21900827, 0.21900827, ..., 0.71487606, 0.71487606, 0.6942149 ], [0.20247933, 0.20661157, 0.20661157, ..., 0.7107438 , 0.7066116 , 0.6942149 ], [0.2107438 , 0.20661157, 0.20661157, ..., 0.6859504 , 0.69008267, 0.6942149 ], ..., [0.2644628 , 0.25619835, 0.2603306 , ..., 0.5413223 , 0.57438016, 0.59090906], [0.26859504, 0.2644628 , 0.26859504, ..., 0.56198347, 0.58264464, 0.59504133], [0.27272728, 0.26859504, 0.27272728, ..., 0.57438016, 0.59090906, 0.60330576]], [[0.5165289 , 0.46280992, 0.28099173, ..., 0.5785124 , 0.5413223 , 0.60330576], [0.5165289 , 0.45041323, 0.29338843, ..., 0.58264464, 0.553719 , 0.5785124 ], [0.5165289 , 0.44214877, 0.29338843, ..., 0.59917355, 0.5785124 , 0.54545456], ..., [0.39256197, 0.41322315, 0.38842976, ..., 0.33471075, 0.37190083, 0.3966942 ], [0.39256197, 0.38429752, 0.40495867, ..., 0.3305785 , 0.35950413, 0.37603307], [0.3677686 , 0.40495867, 0.3966942 , ..., 0.35950413, 0.3553719 , 0.38429752]]], dtype=float32), 'target': array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39]), 'DESCR': 'Modified Olivetti faces dataset.\n\nThe original database was available from\n\n http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html\n\nThe version retrieved here comes in MATLAB format from the personal\nweb page of Sam Roweis:\n\n http://www.cs.nyu.edu/~roweis/\n\nThere are ten different images of each of 40 distinct subjects. For some\nsubjects, the images were taken at different times, varying the lighting,\nfacial expressions (open / closed eyes, smiling / not smiling) and facial\ndetails (glasses / no glasses). All the images were taken against a dark\nhomogeneous background with the subjects in an upright, frontal position (with\ntolerance for some side movement).\n\nThe original dataset consisted of 92 x 112, while the Roweis version\nconsists of 64x64 images.\n'}
data = faces.data target = faces.target data.shape
(400, 4096) • 1
faces.images.shape • 1
(400, 64, 64)
import matplotlib.pyplot as plt %matplotlib inline • 1 • 2
# 打印一张人脸图片 plt.imshow(data[100].reshape((64,64)),cmap="gray") • 1 • 2
数据切分
切分特征数据数据和标签数据，特征是上半边脸，标签是下半边脸
# 特征是上半边脸 faces_up = data[:,:2048] # 需要预测的目标：标签是下半边脸 faces_down = data[:,2048:]
plt.figure(figsize=(2,2)) plt.imshow(faces_up[10].reshape((32,64)),cmap="gray") • 1 • 2
<matplotlib.image.AxesImage at 0x25eca1c8828>
plt.figure(figsize=(2,2)) plt.imshow(faces_down[10].reshape((32,64)),cmap="gray")
划分数据集
# 数据切分 from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(faces_up,faces_down,test_size=0.02) • 1
y_train[1]
array([0.5082645 , 0.5082645 , 0.5123967 , ..., 0.16115703, 0.17768595, 0.1694215 ], dtype=float32)
建立不同的回归模型并训练
此处分别用KNN回归模型，线性回归，岭回归，lasso回归，极端随机森林回归这几种不同的模型来进行建模
estimators = { "knn":KNeighborsRegressor(), "linear":LinearRegression(), "ridge":Ridge(), "lasso":Lasso(), "extra":ExtraTreesRegressor() #极端随机森林回归 }
# 定义一个字典，用于保存每个算法预测结果 faces_pre = dict() for key,estimator in estimators.items(): # 对算法进行模型训练 estimator.fit(x_train,y_train) # 预测 y_ = estimator.predict(x_test) # 把预测的结果保存 faces_pre[key] = y_ # 得分 score = estimator.score(x_test, y_test) print(key, score)
knn 0.4880642098170732 linear 0.18894319531680143 ridge 0.5157197923145055 lasso -0.2100687498661858 extra 0.35087195680524175
faces_pre
{'knn': array([[0.4471074 , 0.41652894, 0.42066115, ..., 0.54793394, 0.5355372 , 0.546281 ], [0.34876034, 0.34214878, 0.346281 , ..., 0.42727274, 0.42809922, 0.43057853], [0.5355372 , 0.546281 , 0.58016527, ..., 0.56611574, 0.56280994, 0.5644628 ], ..., [0.64793384, 0.67685956, 0.7049587 , ..., 0.41487604, 0.3586777 , 0.36776862], [0.3942149 , 0.41322312, 0.43553716, ..., 0.45785123, 0.43471074, 0.39173552], [0.47520667, 0.47024792, 0.51404965, ..., 0.631405 , 0.6256199 , 0.59173554]], dtype=float32), 'linear': array([[0.42212042, 0.35969752, 0.39748642, ..., 0.63096315, 0.5628751 , 0.5159277 ], [0.4241521 , 0.26758337, 0.16570012, ..., 0.09656662, 0.13010818, 0.19814485], [0.62213266, 0.441006 , 0.48480797, ..., 0.5819658 , 0.69699645, 0.44033697], ..., [0.71544605, 0.6732123 , 0.7088314 , ..., 0.37067276, 0.39097485, 0.45659465], [0.2940399 , 0.3306437 , 0.32395566, ..., 0.19252078, 0.21714431, 0.24263924], [0.4138433 , 0.47978985, 0.5166639 , ..., 0.5562554 , 0.4086836 , 0.42044348]], dtype=float32), 'ridge': array([[0.4290133 , 0.37331253, 0.4017402 , ..., 0.5793132 , 0.53899723, 0.4968022 ], [0.3253019 , 0.2301054 , 0.17614344, ..., 0.33642793, 0.3497425 , 0.3560007 ], [0.5519007 , 0.46847916, 0.5257808 , ..., 0.6301012 , 0.69831306, 0.5881569 ], ..., [0.6989316 , 0.6826698 , 0.7077453 , ..., 0.29566136, 0.32281214, 0.3521443 ], [0.31752783, 0.33159164, 0.33879474, ..., 0.24723864, 0.23903543, 0.23862499], [0.39791593, 0.4184358 , 0.52279156, ..., 0.58981174, 0.50477254, 0.5145724 ]], dtype=float32), 'lasso': array([[0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098, 0.307535 ], [0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098, 0.307535 ], [0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098, 0.307535 ], ..., [0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098, 0.307535 ], [0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098, 0.307535 ], [0.5130819 , 0.5360938 , 0.56652683, ..., 0.31880376, 0.31096098, 0.307535 ]], dtype=float32), 'extra': array([[0.42644627, 0.39462809, 0.40661157, ..., 0.5409091 , 0.53388429, 0.53966941], [0.30619835, 0.33347108, 0.35661157, ..., 0.43057852, 0.42066116, 0.40909091], [0.43842976, 0.47768595, 0.58347108, ..., 0.45867768, 0.40041323, 0.39380165], ..., [0.64049588, 0.65702479, 0.6731405 , ..., 0.36157025, 0.37272727, 0.38429752], [0.3161157 , 0.3144628 , 0.37066115, ..., 0.41239669, 0.40206612, 0.37685951], [0.43471075, 0.47272727, 0.51818182, ..., 0.54090908, 0.503719 , 0.50041322]])}
faces_pre["knn"]
array([[0.4471074 , 0.41652894, 0.42066115, ..., 0.54793394, 0.5355372 , 0.546281 ], [0.34876034, 0.34214878, 0.346281 , ..., 0.42727274, 0.42809922, 0.43057853], [0.5355372 , 0.546281 , 0.58016527, ..., 0.56611574, 0.56280994, 0.5644628 ], ..., [0.64793384, 0.67685956, 0.7049587 , ..., 0.41487604, 0.3586777 , 0.36776862], [0.3942149 , 0.41322312, 0.43553716, ..., 0.45785123, 0.43471074, 0.39173552], [0.47520667, 0.47024792, 0.51404965, ..., 0.631405 , 0.6256199 , 0.59173554]], dtype=float32)
不同模型预测的人脸结果与实际的对比
import numpy as np
plt.figure(figsize=(6*3,8*3)) for i in range(8): axes = plt.subplot(8,6,i*6+1) axes.axis("off") face_up = x_test[i] face_down = y_test[i] face = np.concatenate([face_up,face_down]) axes.imshow(face.reshape((64,64)),cmap="gray") if i==0: axes.set_title("True") # 把机器学习预测出来的下半边脸和上半边脸拼接 for j,key in enumerate(faces_pre): axes = plt.subplot(8,6,i*6+2+j) axes.axis("off") if i==0: axes.set_title(key) face_up = x_test[i] y_pre = faces_pre[key] face_down_pre = y_pre[i] face =np.concatenate([face_up,face_down_pre]) axes.imshow(face.reshape((64,64)),cmap="gray")
通过对比发现，上述案例中通过KNN预测的结果的脸型要好一些只是有明显的分界线，需要进一步处理，线性回归与岭回归预测的结果没有明显分界线，但是实际预测效果没有那么好；lasso回归，极端随机森林预测出的人脸结果不理想。
