【阿旭机器学习实战】系列文章主要介绍机器学习的各种算法模型及其实战案例，欢迎点赞，关注共同学习交流。

本文通过5种不同的机器学习模型：KNN回归模型，线性回归，岭回归，lasso回归，极端随机森林回归，对人脸进行自动补全，并且比较其预测结果。

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

faces = datasets.fetch_olivetti_faces()

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)

faces.images.shape

(400, 64, 64)

import matplotlib.pyplot as plt
%matplotlib inline

# 打印一张人脸图片
plt.imshow(data[100].reshape((64,64)),cmap="gray")

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")

<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)

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。

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【阿旭机器学习实战】【15】人脸自动补全(多目标回归)，并比较5种不同模型的预测效果

目录