`# -- coding: utf-8 --
"""SVM.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1a993aXFZd3z39U7eqp6J0Ndvuhrzu1-q
"""
import numpy as np
import time
from scipy.stats import sem
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.model_selection import KFold,GridSearchCV
from sklearn.svm import SVC
from sklearn.metrics import classification_report #对分类的结果进行综合性的报告
from sklearn.metrics import confusion_matrix
from sklearn.datasets import fetch_olivetti_faces #用sklearn自带的数据集 400张人脸
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
faces = fetch_olivetti_faces()
print(faces.DESCR)
print(faces.keys())
print(faces.images.shape)
print(faces.data.shape)
print(faces.target.shape)
print("max",np.max(faces.data))
print(np.min(faces.data))
print(np.mean(faces.data))
"""执行过程：SVC --> BaseSVC --> BaseLibSVM --> init初始化参数
SVC：多分类器
- C： C-SVC的惩罚参数C，默认值是1.0
- kernel ：核函数，默认是rbf，可为如下：
– 'linear'，线性：u'v
– 'poly'，多项式：(gammau'v + coef0)^degree
– 'rbf'，RBF函数：exp(-gamma|u-v|^2)
–'sigmoid'：tanh(gammau'v + coef0) - degree ：多项式poly函数的维度，默认是3，其他核函数会被忽略
- gamma ： ‘rbf’,‘poly’ 和‘sigmoid’的核函数参数。默认是’auto’，则会选择1/- - n_features
- coef0 ：核函数的常数项，对于‘poly’和 ‘sigmoid’有用
- max_iter ：最大迭代次数，-1为无限制
- tol ：停止训练的误差值大小，默认为1e-3
- decision_function_shape :’ovo‘, ‘ovr’ or None, default=‘ovr’(one vs rest)
- random_state ：随机数种子，数据洗牌时的种子值，int值
主要调节的参数有：C、kernel、degree、gamma、coef0。
"""
交叉验证
def evaluate_cross_validation(clf,X,y,K):
create a k-fold cross validation iterator
cv = KFold(K,shuffle=True,random_state=0)
score method of the estimator (accuracy)
scores = cross_val_score(clf,X,y,cv=cv)
这里的clf==之前建的SVC
print(scores)
print("Mean score: {0:.3f} (+/-{1:.3f})".format(
np.mean(scores), sem(scores)))
"""KFold:sklearn/model_selection/_split.py
KFold（n_split, shuffle, random_state）
- n_split:要划分的折数
- shuffle: 每次划分前，是否对数据进行shuffle洗牌打乱
- random_state:数据打乱的随机数种子
KFold过程
- 1、将数据集平均分割成K等份
- 2、使用1份数据作为测试数据，其余K-1份作为训练数据
- 3、计算测试准确率
- 4、使用不同的测试集，重复上面步骤
scores:sklearn/model_selection/_validation.py
作用：验证某个模型在某个训练集上的稳定性，输出k个预测精度。
输入：clf：分类器；X：数据；y：标签；cv：交叉验证，可为k或KFold
输出：k个划分的预测结果（分类准确率）
执行过程：调用文件内的cross_validate函数，启用多个job线程并行，每个线程处理cv分割出的一份训练数据和验证数据。
线程调用文件内_fit_and_score函数，_fit_and_score使用分类器的estimator.fit(X_train, y_train)进行训练，使用文件内_score函数计算预测结果。
"""
不用交叉验证
def train_and_evaluate(clf,X_train,X_test,y_train,y_test):
clf.fit(X_train,y_train)
print("Accuracy on training set:")
print(clf.score(X_train, y_train))
print("Accuracy on testing set:")
print(clf.score(X_test, y_test))
y_pred = clf.predict(X_test)
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))
from google.colab import drive
drive.mount('/content/drive')
"""作用：根据真实值和预测值计算分类精度的综合报告
输入：y_true：1 维数组，真实数据的分类标签
y_pred：1 维数组，模型预测的分类标签
输出：每个分类标签的精确度，召回率和 F1-score。
精确度：precision，正确预测为正的，占全部预测为正的比例，TP / (TP+FP)
召回率：recall，正确预测为正的，占全部实际为正的比例，TP / (TP+FN)
F1-score：精确率和召回率的调和平均数，2 * p*r / (p+r)
"""
戴眼镜的人的标注
# the index ranges of images of people with glasses
glasses = [
(10, 19), (30, 32), (37, 38), (50, 59), (63, 64),
(69, 69), (120, 121), (124, 129), (130, 139), (160, 161),
(164, 169), (180, 182), (185, 185), (189, 189), (190, 192),
(194, 194), (196, 199), (260, 269), (270, 279), (300, 309),
(330, 339), (358, 359), (360, 369)
]
戴眼镜的标为1，不戴眼镜的标为0
def create_target(num_sample,segments):
y = np.zeros(num_sample)
for (start,end) in segments:
y[start:end+1] = 1
return y
num_samples = faces.target.shape[0]
target_glasses = create_target(num_samples,glasses)
1 sklearn.model_selection import train_test_split
svc_1 = SVC(kernel = 'linear')
print(svc_1)
原始的：faces.target
X_train,X_test,y_train,y_test = train_test_split(
faces.data,faces.target,test_size=0.25,random_state=0
)
evaluate_cross_validation(svc_1,X_train,y_train,5)
train_and_evaluate(svc_1,X_train,X_test,y_train,y_test)
2 sklearn.model_selection import train_test_split
svc_2 = SVC(kernel='linear')
经过筛选的target：target_glasses（分为0和1类） 原始的：faces.target
X_train,X_test,y_train,y_test = train_test_split(
faces.data,target_glasses,test_size=0.25,random_state=0
)
evaluate_cross_validation(svc_2,X_train,y_train,5)
train_and_evaluate(svc_2,X_train,X_test,y_train,y_test)
3
def print_faces(images, target, top_n):
# set up figure size in inches
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
for i in range(top_n):
# we will print images in matrix 20x20
p = fig.add_subplot(20, 20, i + 1, xticks=[], yticks=[])
p.imshow(images[i], cmap=plt.cm.bone)
# label the image with target value 加本文target[i]预测的标签
p.text(0, 14, str(target[i]))
p.text(0, 60, str(i))
X_test = faces.data[30:40]
y_test = target_glasses[30:40]
print (y_test.shape[0])
select = np.ones(target_glasses.shape[0])
select[30:40] = 0
X_train = faces.data[select == 1]
y_train = target_glasses[select == 1]
print (y_train.shape[0])
svc_3 = SVC(kernel='linear')
train_and_evaluate(svc_3, X_train, X_test, y_train, y_test)
y_pred = svc_3.predict(X_test)
eval_faces = [np.reshape(a, (64, 64)) for a in X_test]
print_faces(eval_faces, y_pred, 10)`