PCA主成分分析+SVM实现人脸识别
原文地址：
http://ihoge.cn/2018/PCA+SVM人脸识别.html
加载数据
这里使用的测试数据共包含40位人员照片，每个人10张照片。也可登陆http://www.cl.cam.ac.uk/research/dtg/attarchive/facesataglance.html 查看400张照片的缩略图。
import time
import logging
from sklearn.datasets import fetch_olivetti_faces
logging.basicConfig(level = logging.INFO, format="%(asctime)s %(message)s") # 这里INFO必须大写
data_home = 'code/datasets/'
logging.info("开始加载数据")
faces = fetch_olivetti_faces(data_home=data_home)
logging.info("加载完成")这里做下简单的解释：
加载的图片保存在faces变量里，sklaern已经把每张照片处理成剪切掉头发部分并且64x64大小且人脸居中显示。在真实生产环境中这一步很重要，否则模型将被大量的噪声干扰（即照片背景，变化的发型等，这些特征都应该排除在输入特征之外）。最后要成功下载数据集还需要安装Python图片图里工具Pillow否则无法对图片解码。下面输出下数据的概要信息：
import numpy as np
X = faces.data
y = faces.target
targets = np.unique(faces.target)
target_names = np.array(["p%d" % t for t in targets]) #给每个人做标签
n_targets = target_name.shape[0]
n_samples, h, w = faces.images.shape
print('Samples count:{}\nTarget count:{}'.format(n_samples, n_targets))
print('Image size:{}x{}\nData shape:{}'.format(w, h, X.shape))Samples count:400
Target count:40
Image size:64x64
Data shape:(400, 4096)
由输出可知，共有40人，照片总量400，输入特征(64x64=4096)个。
为了直观观察数据，从每个人物的照片里随机选择一张显示，定义下画图工具：
其中输入参数images是一个二维数据，每一行都是一个图片数据。在加载数据时，fech_ollivetti_faces()函数已经自动做了预处理，图片的每个像素的RBG值都转换成了[0,1]浮点数。因此，画出来的照片也是黑白的。子图片识别领域一般用黑白照片就可以了，减少计算量的同时也更加准确。
%matplotlib inline
from matplotlib import pyplot as plt
def plot_gallery(images, titles, h, w, n_row=2, n_col=5):
# 显示图片阵列：
plt.figure(figsize=(2*n_col, 2.2*n_row),dpi=140)
plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.01)
for i in range(n_row * n_col):
plt.subplot(n_row, n_col, i+1)
plt.imshow(images[i].reshape((h,w)), cmap=plt.cm.gray)
plt.title(titles[i])
plt.axis('off')
n_row = 2
n_col = 6
sample_images = None
sample_titles = []
for i in range(n_targets):
people_images = X[y==i] # 注意这里传入i
people_sample_index = np.random.randint(0, people_images.shape[0], 1)
people_sample_image = people_images[people_sample_index, :]
if sample_images is not None:
sample_images = np.concatenate((sample_images, people_sample_image), axis=0)
else:
sample_images =people_sample_image
sample_titles.append(target_names[i]) # 这里target_names是在前面生成的标签
plot_gallery(sample_images, sample_titles, h, w, n_row, n_col)
#代码中X[y=i]可以选择除特定的所有照片，随机选出来的照片放在sample.images数组对象里，最后调用之前定义的函数把照片画出来。from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)支持向量机第一次尝试
直接食用支持向量机来实现人脸识别：
from time import time
from sklearn.svm import SVC
t = time()
clf = SVC(class_weight='balanced')
clf.fit(X_train, y_train)
print("耗时：{}秒".format(time() - t))耗时：1.0220119953155518秒
1、接着对人脸数据进行预测：使用confusion_matrix查看准确性：
from sklearn.metrics import confusion_matrix
y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred, labels=range(n_targets))
print("confusion_matrix:\n")
# np.set_printoptions(threshold=np.nan)
print(cm[:10])confusion_matrix:
[[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
上面np.set_printoptions()是为了确保cm完整输出，这是因为这个数组是40x40的，默认情况下不会全部输出。??
2、使用classification_report查看准确性
但是很明显输出结果效果很差。 因为confusion_matrix理想的输出是矩阵的对角线上有数组，其他地方都为0，而且这里很多图片都被预测成索引为12的类别了。我买再来看下classification_report的结果：
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred, target_names = target_names)) #这里y_test和y_pred不要颠倒。 precision recall f1-score support
p0 0.00 0.00 0.00 1
p1 0.00 0.00 0.00 3
p2 0.00 0.00 0.00 2
p3 0.00 0.00 0.00 1
p4 0.00 0.00 0.00 1
p5 0.00 0.00 0.00 1
p6 0.00 0.00 0.00 4
p7 0.00 0.00 0.00 2
p8 0.00 0.00 0.00 4
p9 0.00 0.00 0.00 2
p10 0.00 0.00 0.00 1
p11 0.00 0.00 0.00 0
p12 0.00 0.00 0.00 4
p13 0.00 0.00 0.00 4
p14 0.00 0.00 0.00 1
p15 0.00 0.00 0.00 1
p16 0.00 0.00 0.00 3
p17 0.00 0.00 0.00 2
p18 0.00 0.00 0.00 2
p19 0.00 0.00 0.00 2
p20 0.00 0.00 0.00 1
p21 0.00 0.00 0.00 2
p22 0.00 0.00 0.00 3
p23 0.00 0.00 0.00 2
p24 0.00 0.00 0.00 3
p25 0.00 0.00 0.00 3
p26 0.00 0.00 0.00 2
p27 0.00 0.00 0.00 2
p28 0.00 0.00 0.00 0
p29 0.00 0.00 0.00 2
p30 0.00 0.00 0.00 2
p31 0.00 0.00 0.00 3
p32 0.00 0.00 0.00 2
p33 0.00 0.00 0.00 2
p34 0.00 0.00 0.00 0
p35 0.00 0.00 0.00 2
p36 0.00 0.00 0.00 3
p37 0.00 0.00 0.00 1
p38 0.00 0.00 0.00 2
p39 0.00 0.00 0.00 2
avg / total 0.00 0.00 0.00 80
/Users/hadoop/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/Users/hadoop/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1137: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.
'recall', 'true', average, warn_for)
结果不出预料，果然很差。
这是因为：这里把每个像素都作为一个输入特征来处理，这样那个数据噪声太严重了，模型根本没有办法对训练数据集进行拟合。这里有4096个特征向量，可是数据集大小才400个，比特征个数少了太多，而且分出20%作为测试集。这种情况下根本无法进行准确的训练和预测。
使用PCA来处理数据集
选择 k k 值
解决上述问题的办法有两种，一个是加大数据样本量（在这里这个不太现实），或者使用PCA给数据降维，值选择前k个最重要的特征。
这里我们根据PCA算法来计算失真程度来确定k值。
在sklearn里，可以从PCA模型的explained_variance_ratio_变量里获取经PCA处理后的数据还原率。这是一个数组，所有元素求和即可知道选择的 k k 值的数据还原率。随着 k k 的增大，数值会无限接近于1。
利用这一特征，可以让 k k 取值10～300之间，每个30取一次样。针对这里的情况选择失真度小于5%即可。
from sklearn.decomposition import PCA
print("Exploring explained variance ratio for dataset ...")
candidate_components = range(10, 300, 30)
explained_ratios = []
t = time()
for c in candidate_components:
pca = PCA(n_components=c)
X_pca = pca.fit_transform(X)
explained_ratios.append(np.sum(pca.explained_variance_ratio_))
print('Done in {0:.2f}s'.format(time()-t))Exploring explained variance ratio for dataset ...
Done in 2.17s
plt.figure(figsize=(8, 5), dpi=100)
plt.grid()
plt.plot(candidate_components, explained_ratios)
plt.xlabel('Number of PCA Components')
plt.ylabel('Explained Variance Ratio')
plt.title('Explained variance ratio for PCA')
plt.yticks(np.arange(0.5, 1.05, .05))
plt.xticks(np.arange(0, 300, 20));由上图可知，若要保留95%的数据还原率， k k 值选择120即可。为了更直观的看不同 k k 值的区别，这里画出来体验下：
def title_prefix(prefix, title):
return "{}: {}".format(prefix, title)
n_row = 1
n_col = 5
sample_images = sample_images[0:5]
sample_titles = sample_titles[0:5]
plotting_images = sample_images
plotting_titles = [title_prefix('orig', t) for t in sample_titles]
candidate_components = [120, 75, 37, 19, 8]
for c in candidate_components:
print("Fitting and projecting on PCA(n_components={}) ...".format(c))
t = time()
pca = PCA(n_components=c)
pca.fit(X)
X_sample_pca = pca.transform(sample_images)
X_sample_inv = pca.inverse_transform(X_sample_pca)
plotting_images = np.concatenate((plotting_images, X_sample_inv), axis=0)
sample_title_pca = [title_prefix('{}'.format(c), t) for t in sample_titles]
plotting_titles = np.concatenate((plotting_titles, sample_title_pca), axis=0)
print("Done in {0:.2f}s".format(time() - t))
print("Plotting sample image with different number of PCA conpoments ...")
plot_gallery(plotting_images, plotting_titles, h, w,
n_row * (len(candidate_components) + 1), n_col)Fitting and projecting on PCA(n_components=120) ...
Done in 0.18s
Fitting and projecting on PCA(n_components=75) ...
Done in 0.14s
Fitting and projecting on PCA(n_components=37) ...
Done in 0.11s
Fitting and projecting on PCA(n_components=19) ...
Done in 0.07s
Fitting and projecting on PCA(n_components=8) ...
Done in 0.06s
Plotting sample image with different number of PCA conpoments ...
利用GridSearchCV选出最优参数
接下来选择 k=120 k = 120 作为PCA的参数对数据集和测试集进行特征提取，然后调用GridSearchCV选出最优参数
n_components = 120
print("Fitting PCA by using training data ...")
t = time()
pca = PCA(n_components=n_components, svd_solver='randomized', whiten=True).fit(X_train)
print("Done in {0:.2f}s".format(time() - t))
print("Projecting input data for PCA ...")
t = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("Done in {0:.2f}s".format(time() - t))Fitting PCA by using training data ...
Done in 0.16s
Projecting input data for PCA ...
Done in 0.01s
from sklearn.model_selection import GridSearchCV
print("Searching the best parameters for SVC ...")
param_grid = {'C': [1, 5, 10, 50],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01]}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid, verbose=2, n_jobs=4)# 参数n_jobs=4表示启动4个进程
clf = clf.fit(X_train_pca, y_train)
print("Best parameters found by grid search:")
print(clf.best_params_)Searching the best parameters for SVC ...
Fitting 3 folds for each of 20 candidates, totalling 60 fits
[CV] C=1, gamma=0.0001 ...............................................
[CV] C=1, gamma=0.0001 ...............................................
[CV] C=1, gamma=0.0001 ...............................................
[CV] C=1, gamma=0.0005 ...............................................
[CV] ................................ C=1, gamma=0.0001, total= 0.1s
[CV] C=1, gamma=0.0005 ...............................................
[CV] ................................ C=1, gamma=0.0001, total= 0.1s
[CV] C=1, gamma=0.0005 ...............................................
[CV] ................................ C=1, gamma=0.0001, total= 0.1s
[CV] C=1, gamma=0.001 ................................................
[CV] ................................ C=1, gamma=0.0005, total= 0.1s
[CV] C=1, gamma=0.001 ................................................
[CV] ................................ C=1, gamma=0.0005, total= 0.1s
[CV] C=1, gamma=0.001 ................................................
[CV] ................................ C=1, gamma=0.0005, total= 0.1s
[CV] C=1, gamma=0.01 .................................................
[CV] ................................. C=1, gamma=0.001, total= 0.1s
[CV] C=5, gamma=0.0001 ...............................................
[CV] ................................. C=1, gamma=0.001, total= 0.1s
[CV] C=5, gamma=0.0005 ...............................................
[CV] ................................. C=1, gamma=0.001, total= 0.1s
[CV] C=1, gamma=0.005 ................................................
[CV] .................................. C=1, gamma=0.01, total= 0.1s
[CV] C=1, gamma=0.01 .................................................
[CV] ................................ C=5, gamma=0.0001, total= 0.1s
[CV] C=5, gamma=0.0001 ...............................................
[CV] ................................ C=5, gamma=0.0005, total= 0.1s
[CV] C=5, gamma=0.001 ................................................
[CV] ................................. C=1, gamma=0.005, total= 0.1s
[CV] C=1, gamma=0.005 ................................................
[CV] .................................. C=1, gamma=0.01, total= 0.1s
[CV] C=1, gamma=0.01 .................................................
[CV] ................................ C=5, gamma=0.0001, total= 0.1s
[CV] C=5, gamma=0.0005 ...............................................
[CV] ................................. C=5, gamma=0.001, total= 0.1s
[CV] C=5, gamma=0.001 ................................................
[CV] ................................. C=1, gamma=0.005, total= 0.1s
[CV] C=1, gamma=0.005 ................................................
[CV] ................................ C=5, gamma=0.0005, total= 0.1s
[CV] C=5, gamma=0.0005 ...............................................
[CV] .................................. C=1, gamma=0.01, total= 0.1s
[CV] C=5, gamma=0.0001 ...............................................
[CV] ................................. C=5, gamma=0.001, total= 0.1s
[CV] C=5, gamma=0.001 ................................................
[CV] ................................. C=1, gamma=0.005, total= 0.1s
[CV] ................................ C=5, gamma=0.0005, total= 0.1s
[CV] C=5, gamma=0.005 ................................................
[CV] C=5, gamma=0.01 .................................................
[CV] ................................ C=5, gamma=0.0001, total= 0.1s
[CV] C=10, gamma=0.0001 ..............................................
[CV] ................................. C=5, gamma=0.001, total= 0.1s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................................. C=5, gamma=0.005, total= 0.1s
[CV] C=5, gamma=0.005 ................................................
[CV] .................................. C=5, gamma=0.01, total= 0.1s
[CV] C=5, gamma=0.01 .................................................
[CV] ............................... C=10, gamma=0.0001, total= 0.1s
[CV] C=10, gamma=0.0005 ..............................................
[CV] ................................ C=10, gamma=0.001, total= 0.1s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................................. C=5, gamma=0.005, total= 0.1s
[CV] C=5, gamma=0.005 ................................................
[CV] ............................... C=10, gamma=0.0005, total= 0.1s
[CV] C=10, gamma=0.0005 ..............................................
[CV] .................................. C=5, gamma=0.01, total= 0.1s
[CV] C=10, gamma=0.0001 ..............................................
[CV] ................................ C=10, gamma=0.001, total= 0.1s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................................. C=5, gamma=0.005, total= 0.1s
[CV] C=5, gamma=0.01 .................................................
[CV] ............................... C=10, gamma=0.0001, total= 0.1s
[CV] C=10, gamma=0.0001 ..............................................
[CV] ............................... C=10, gamma=0.0005, total= 0.1s
[CV] C=10, gamma=0.0005 ..............................................
[CV] ................................ C=10, gamma=0.001, total= 0.1s
[CV] C=10, gamma=0.005 ...............................................
[CV] .................................. C=5, gamma=0.01, total= 0.1s
[CV] C=10, gamma=0.005 ...............................................
[CV] ............................... C=10, gamma=0.0001, total= 0.1s
[CV] C=10, gamma=0.01 ................................................
[CV] ............................... C=10, gamma=0.0005, total= 0.1s
[CV] C=50, gamma=0.0005 ..............................................
[CV] ................................ C=10, gamma=0.005, total= 0.1s
[CV] C=50, gamma=0.001 ...............................................
[CV] ................................ C=10, gamma=0.005, total= 0.1s
[CV] C=10, gamma=0.005 ...............................................
[CV] ................................. C=10, gamma=0.01, total= 0.1s
[CV] C=50, gamma=0.0001 ..............................................
[CV] ............................... C=50, gamma=0.0005, total= 0.1s
[CV] C=50, gamma=0.0005 ..............................................
[CV] ................................ C=50, gamma=0.001, total= 0.1s
[CV] C=50, gamma=0.001 ...............................................
[CV] ............................... C=50, gamma=0.0001, total= 0.1s
[CV] ................................ C=10, gamma=0.005, total= 0.1s
[CV] C=10, gamma=0.01 ................................................
[CV] ............................... C=50, gamma=0.0005, total= 0.1s
[CV] C=50, gamma=0.0001 ..............................................
[CV] C=50, gamma=0.0005 ..............................................
[CV] ................................ C=50, gamma=0.001, total= 0.1s
[CV] ................................. C=10, gamma=0.01, total= 0.1s
[CV] C=10, gamma=0.01 ................................................
[CV] C=50, gamma=0.005 ...............................................
[CV] ............................... C=50, gamma=0.0001, total= 0.1s
[CV] C=50, gamma=0.0001 ..............................................
[CV] ............................... C=50, gamma=0.0005, total= 0.1s
[CV] C=50, gamma=0.001 ...............................................
[CV] ................................. C=10, gamma=0.01, total= 0.1s
[CV] ................................ C=50, gamma=0.005, total= 0.1s
[CV] C=50, gamma=0.005 ...............................................
[CV] C=50, gamma=0.005 ...............................................
[CV] ............................... C=50, gamma=0.0001, total= 0.1s
[CV] ................................ C=50, gamma=0.001, total= 0.1s
[CV] ................................ C=50, gamma=0.005, total= 0.1s
[CV] ................................ C=50, gamma=0.005, total= 0.1s
[CV] C=50, gamma=0.01 ................................................
[CV] ................................. C=50, gamma=0.01, total= 0.0s
[CV] C=50, gamma=0.01 ................................................
[CV] ................................. C=50, gamma=0.01, total= 0.0s
[CV] C=50, gamma=0.01 ................................................
[CV] ................................. C=50, gamma=0.01, total= 0.0s
Best parameters found by grid search:
{'C': 10, 'gamma': 0.0005}
[Parallel(n_jobs=4)]: Done 60 out of 60 | elapsed: 1.9s finished
测试模型准确性
接着使用这一模型对测试集进行预测，并分别使用confusion_matrix和classification_report查看其效果
t = time()
y_pred = clf.best_estimator_.predict(X_test_pca)
cm = confusion_matrix(y_test, y_pred, labels=range(n_targets))
print("Done in {0:.2f}.\n".format(time()-t))
print("confusion matrix:")
np.set_printoptions(threshold=np.nan)
print(cm[:10])Done in 0.01.
confusion matrix:
[[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred, target_names=target_names)) #这里注意y_test和y_pred位置不要颠倒 precision recall f1-score support
p0 1.00 1.00 1.00 1
p1 1.00 1.00 1.00 3
p2 1.00 0.50 0.67 2
p3 1.00 1.00 1.00 1
p4 1.00 1.00 1.00 1
p5 1.00 1.00 1.00 1
p6 1.00 0.75 0.86 4
p7 1.00 1.00 1.00 2
p8 1.00 1.00 1.00 4
p9 1.00 1.00 1.00 2
p10 1.00 1.00 1.00 1
p11 1.00 1.00 1.00 4
p12 1.00 1.00 1.00 4
p13 1.00 1.00 1.00 1
p14 1.00 1.00 1.00 1
p15 0.75 1.00 0.86 3
p16 1.00 1.00 1.00 2
p17 1.00 1.00 1.00 2
p18 1.00 1.00 1.00 2
p19 1.00 1.00 1.00 1
p20 1.00 1.00 1.00 2
p21 1.00 1.00 1.00 3
p22 1.00 1.00 1.00 2
p23 1.00 1.00 1.00 3
p24 0.75 1.00 0.86 3
p25 1.00 1.00 1.00 2
p26 1.00 1.00 1.00 2
p27 1.00 1.00 1.00 2
p28 1.00 1.00 1.00 2
p29 1.00 1.00 1.00 3
p30 1.00 1.00 1.00 2
p31 1.00 1.00 1.00 2
p32 1.00 1.00 1.00 2
p33 1.00 1.00 1.00 3
p34 1.00 1.00 1.00 1
p35 1.00 1.00 1.00 2
p36 1.00 1.00 1.00 2
avg / total 0.98 0.97 0.97 80
/Users/hadoop/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1428: UserWarning: labels size, 37, does not match size of target_names, 40
.format(len(labels), len(target_names))
疑问：
效果非常乐观，但是仍有个问题：怎么确定p0～p37分别对应的是哪个一个人？