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环境简述:python3.6.4
根据python2.7版本代码进行勘误
Class RandomizedPCA is deprecated; RandomizedPCA was deprecated in
0.18 and will be removed in 0.20. Use PCA(svd_solver=’randomized’) instead. The new implementation DOES NOT store whiten Apply transform to get them.
ValueError: class_weight must be dict, ‘balanced’, or None, got:
‘auto’\
DeprecationWarning:
imreadis deprecated!
imreadis deprecated in SciPy 1.0.0, and will be removed in 1.2.0.
Code如下
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Tanghong
# 在python2.x版本中要使用Python3.x的特性,可以使用__future__模块导入相应的接口，减少对当前低版本影响
# from __future__ import print_function
# 计时，程序运行时间
from time import time
# 打印程序进展时的一些信息
import logging
# 最后识别出来的人脸通过绘图打印出来
import matplotlib.pyplot as plt
# DeprecationWarning: `imread` is deprecated! `imread` is deprecated in SciPy
# 1.0.0,and will be removed in 1.2.0.
from PIL import Image
from scipy import ndimage
# 当import 一个模块比如如下模块cross_validation时，会有删除横线，表示该模块在当前版本可能已经被删除,在新版本中改为model_selection模块
# DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection
# module into which all the refactored classes and functions are moved.
# Also note that the interface of the new CV iterators are different from that of this module.
# This module will be removed in 0.20."This module will be removed in 0.20.", DeprecationWarning)
# from sklearn.cross_validation import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_lfw_people
# grid_search已经被移除
# from sklearn.grid_search import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
# Class RandomizedPCA is deprecated; RandomizedPCA was deprecated in 0.18 and will be removed in 0.20.
# Use PCA(svd_solver='randomized') instead. The new implementation DOES NOT store whiten ``components_``.
# Apply transform to get them.
from sklearn.decomposition import PCA
from sklearn.svm import SVC
# 导入混淆矩阵模块confusion_matrix()
from sklearn.metrics import confusion_matrix
print(__doc__)
# Display progress logs on stdout程序进展的信息打印出来
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
###############################################################################
# Download the data, if not already on disk and load it as numpy arrays
# 下载人脸库 http://vis-www.cs.umass.edu/lfw/
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape
# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
# 获取特征向量矩阵
X = lfw_people.data
# 特征向量的维度(列数)或者称特征点的个数
n_features = X.shape[1]
# the label to predict is the id of the person
# 返回每一组的特征标记
y = lfw_people.target
target_names = lfw_people.target_names
# 返回多少类(多少行)，也就是多少个人进行人脸识别
n_classes = target_names.shape[0]
print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)
###############################################################################
# Split into a training set and a test set using a stratified k fold
# split into a training and testing set
# 将数据集拆分成四个部分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
###############################################################################
# PCA降维方法，减少特征值，降低复杂度。
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150
print("Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0]))
t0 = time()
pca = PCA(n_components=n_components, whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))
# 提取特征值
eigenfaces = pca.components_.reshape((n_components, h, w))
print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))
###############################################################################
# Train a SVM classification model
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
# clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
clf = GridSearchCV(SVC(kernel='rbf'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("Predicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("done in %0.3fs" % (time() - t0))
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
###############################################################################
# Qualitative evaluation of the predictions using matplotlib
def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
"""Helper function to plot a gallery of portraits"""
plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
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], size=12)
plt.xticks(())
plt.yticks(())
# plot the result of the prediction on a portion of the test set
def title(y_pred, y_test, target_names, i):
pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
return 'predicted: %s\ntrue: %s' % (pred_name, true_name)
prediction_titles = [title(y_pred, y_test, target_names, i)
for i in range(y_pred.shape[0])]
plot_gallery(X_test, prediction_titles, h, w)
# plot the gallery of the most significative eigenfaces
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
plt.show()Result如下：
None
Total dataset size:
n_samples: 1288
n_features: 1850
n_classes: 7
Extracting the top 150 eigenfaces from 966 faces
done in 0.862s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.062s
Fitting the classifier to the training set
done in 34.029s
Best estimator found by grid search:
SVC(C=1000.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=0.005, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
Predicting people's names on the test set
done in 0.079s
precision recall f1-score support
Ariel Sharon 1.00 0.63 0.77 19
Colin Powell 0.74 0.92 0.82 64
Donald Rumsfeld 0.91 0.70 0.79 30
George W Bush 0.82 0.92 0.87 130
Gerhard Schroeder 0.92 0.80 0.86 30
Hugo Chavez 1.00 0.55 0.71 22
Tony Blair 0.91 0.78 0.84 27
avg / total 0.85 0.84 0.83 322
[[ 12 4 0 3 0 0 0]
[ 0 59 0 5 0 0 0]
[ 0 2 21 6 0 0 1]
[ 0 8 2 120 0 0 0]
[ 0 2 0 3 24 0 1]
[ 0 4 0 5 1 12 0]
[ 0 1 0 4 1 0 21]]
