一、模型验证方法如下：
- 通过交叉验证得分：model_sleection.cross_val_score(estimator,X)
- 对每个输入数据点产生交叉验证估计：model_selection.cross_val_predict(estimator,X)
- 计算并绘制模型的学习率曲线：model_selection.learning_curve(estimator,X,y)
- 计算并绘制模型的验证曲线：model_selection.validation(estimator,...)
- 通过排序评估交叉验证的得分在重要性：model_selection.permutation_test_score(...)
①通过交叉验证得分：model_sleection.cross_val_score(estimator,X)
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn import datasets,svm
digits=datasets.load_digits()
X=digits.data
y=digits.target
svc=svm.SVC(kernel='linear')
C_s=np.logspace(-10,0,10)
print("参数列表长度",len(C_s))
scores=list()
scores_std=list()
n_folds=3
for C in C_s:
svc.C=C
this_scores=cross_val_score(svc,X,y,cv=n_folds,n_jobs=1)
#print(this_scores)
scores.append(np.mean(this_scores))
scores_std.append(np.std(this_scores))
#绘制交叉验证的曲线
import matplotlib.pyplot as plt
plt.figure(1,figsize=(4,3))
plt.clf()
plt.semilogx(C_s,scores)
plt.semilogx(C_s,np.array(scores)+np.array(scores_std),'b--')
plt.semilogx(C_s,np.array(scores)-np.array(scores_std),'b--')
locs,labels=plt.yticks()
plt.yticks(locs,list(map(lambda x:"%g" %x,locs)))
plt.ylabel("CV score")
plt.xlabel("Parameter C")
plt.ylim(0,1.1)
plt.show()
结果图
②对每个输入数据点产生交叉验证估计：model_selection.cross_val_predict(estimator,X)
from sklearn import datasets,linear_model from sklearn.model_selection import cross_val_predict disbetes=datasets.load_diabetes() X=disbetes.data[:150] y=disbetes.target[:150] lasso=linear_model.Lasso() y_pred=cross_val_predict(lasso,X,y) print(y_pred) 结果： [ 174.26933996 117.6539241 164.60228641 155.65049088 132.68647979 128.49511245 120.76146877 141.069413 164.18904498 182.37394949 111.04181265 127.94311443 135.0869234 162.83066014 135.3573514 157.64516523 178.95843326 163.3919841 143.85237903 144.29748882 133.58117218 124.77928571 132.90918003 208.52927 153.61908967 154.16616341 118.95351821 163.50467541 145.89406196 168.3308101 155.87411031 123.45960148 185.70459144 133.38468582 117.2789469 150.27895019 174.1541028 160.03235091 192.31389633 161.58568256 154.2224809 119.35517679 146.15706413 133.82056934 179.68118754 137.96619936 146.07788398 126.77579723 123.32101099 166.26710247 146.41559964 161.67261029 147.47731459 138.44595305 144.85421048 113.77990664 185.54970402 115.31624749 142.23672103 171.07792136 132.5394716 177.80524864 116.5616502 134.25230846 142.88707475 173.2830912 154.31273504 149.16680759 144.88238997 121.97783103 110.38457621 180.25559631 199.06141058 151.1195546 161.14217698 153.96960812 150.77179755 113.30903579 165.15755771 115.85735727 174.19267171 150.12027233 115.47891783 153.38967232 115.31573467 156.49909623 92.62211515 178.15649994 131.59320715 134.46166754 116.97678633 190.00790119 166.01173292 126.25944471 134.29256991 144.71971963 190.9769591 182.39199466 154.45325308 148.30325558 151.72036937 124.12825466 138.6011155 137.75891286 123.0917243 131.74735403 112.07367481 124.56956904 156.78432061 128.63135591 93.68260079 130.54324394 131.8693231 154.5708257 179.81343019 165.78130755 150.04779033 162.37974736 143.92996797 143.15645843 125.20161377 145.99590279 155.3505536 145.97574185 134.66120515 163.92450638 101.92329396 139.33014324 122.71377023 152.20573113 153.36931089 116.76545147 131.96936127 109.74817383 132.57453994 159.38030328 109.31343881 147.69926269 156.3664255 161.12509958 128.16523686 156.78446286 154.04375702 124.83705022 143.85606595 143.23651701 147.76316913 154.21572891 129.07895017 157.79644923]
③、计算并绘制模型的学习率曲线：model_selection.learning_curve(estimator,X,y)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plt_learning_curve(estimator,title,X,y,ylim=None,cv=None,n_jobs=1,train_size=np.linspace(.1,1.0,5)):
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes,train_scores,test_scores=learning_curve(
estimator,X,y,cv=cv,n_jobs=n_jobs,train_sizes=train_size)
train_scores_mean=np.mean(train_scores,axis=1)
train_scores_std=np.std(train_scores,axis=1)
test_scores_mean=np.mean(test_scores,axis=1)
test_scores_std=np.std(test_scores,axis=1)
plt.grid()
plt.fill_between(train_sizes,train_scores_mean-train_scores_std,train_scores_mean+train_scores_std,alpha=0.1,color="r")
plt.fill_between(train_sizes,test_scores_mean-test_scores_std,test_scores_mean+test_scores_std,alpha=0.1,color="g")
plt.plot(train_sizes,train_scores_mean,"o-",color="r",label="Training score")
plt.plot(train_sizes,test_scores_mean,"o-",color="g",label="Cross-validation score")
plt.legend(loc="best")
return plt
digits=load_digits()
X,y=digits.data,digits.target
title="Learning Curves(Nativr Bayes)"
cv=ShuffleSplit(n_splits=100,test_size=0.2,random_state=0)
estimator=GaussianNB()
plt_learning_curve(estimator,title,X,y,ylim=(0.7,1.0),cv=cv,n_jobs=1)
title="Learnming Curves (SVM,RBF kernel,$gamma=0.001$)"
cv=ShuffleSplit(n_splits=10,test_size=0.2,random_state=0)
estimator=SVC(gamma=0.001)
plt_learning_curve(estimator,title,X,y,(0.7,1.01),cv=cv,n_jobs=1)
plt.show()
④、计算并绘制模型的验证曲线：model_selection.validation(estimator,...)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_digits
from sklearn.svm import SVC
from sklearn.model_selection import validation_curve
digits = load_digits()
param_range=np.logspace(-6,-1,5)
train_scores,test_scores=validation_curve(SVC(),X,y,param_name="gamma",param_range=param_range,
cv=10,scoring="accuracy",n_jobs=1)
train_scores_mean=np.mean(train_scores,axis=1)
train_scores_std=np.std(train_scores,axis=1)
test_scores_mean=np.mean(test_scores,axis=1)
test_scores_std=np.std(test_scores,axis=1)
plt.title("Validation Curve with SVM")
plt.xlabel("$gamma$")
plt.ylabel("Score")
plt.ylim(0.0,1.1)
lw=2
plt.semilogx(param_range,train_scores_mean,label="Training score",color="darkorange",lw=lw)
plt.fill_between(param_range,train_scores_mean-train_scores_std,train_scores_mean+train_scores_std,
alpha=0.2,color="darkorange",lw=lw)
plt.semilogx(param_range,test_scores_mean,label="Cross-validation Score",color="navy",lw=lw)
plt.fill_between(param_range,test_scores_mean-test_scores_std,test_scores_mean+test_scores_std,
alpha=0.2,color="navy",lw=lw)
plt.legend(loc="best")
plt.show()
⑤、通过排序评估交叉验证的得分在重要性：model_selection.permutation_test_score(...)---现在用的很少
二、模型评估方法
sklearn模型预测性能的评估方法
- Estimator对象的score方法
- 在交叉验证中使用的scoring参数
Estimator对象的score方法
score(self,X,y,y_true)函数在内部会调用predict函数获得预测响应y_predict,然后与传人的真实响应进行比较，计算得分
使用estimator的score函数来苹果模型的性能，默认情况下
分类器对应于准确率：sklearn.metrics.accuracy_score
回归器对应于R2得分：sklearn.metrics.r2_score
在交叉验证中使用scoring参数
上面的两个模型选择工具中都有一个参数“scoring”，该参数用来指定在进行网格搜索或计算交叉验证得分的时候，用什么标砖度量“estimator”的预测性能。默认情况下，该参数为“None”就表示“GridSearchCV”与“cross_val_score”都会去调用“estimator”自己的“score”函数，我们也可以为“scoring”参数指定别的性能度量标准，他必须是一个可调用对象，sklearn.metric不仅为我们提供了一系列预定义的可调用对象，而且好支持自定义评估标准。
在交叉验证中使用预定义scoring参数：
#在交叉验证中使用预定义scoring参数
from sklearn import svm,datasets from sklearn.model_selection import cross_val_score iris=datasets.load_iris() X,y=iris.data,iris.target clf=svm.SVC(probability=True,random_state=0) print(cross_val_score(clf,X,y,scoring="neg_log_loss"))
#结果[-0.0757138 -0.16816241 -0.07091847]
model=svm.SVC() print(cross_val_score(model,X,y,scoring="wrong_choice"))
#结果：
ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_median_absolute_error', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc']
”scoring“的可用类型都存放在sklearn.metric.SCORES字典对象中
在交叉验证中海可以使用自定义scoring参数 ，具体讲解在http://www.studyai.com/course/play/9dd4fa59779d454991f55ac4c85889eb
三、sklearn分类器评估指标总体概况
使用sklearn.metric包中的性能度量函数有：
- 分类器性能指标
- 回归器性能指标
- 聚类其性能指标
- 两两距离测度
分类器性能度量指标
总的来说，主要分为以下3类
- 精度-召回率-F度量：Precision-Recall-F_measures
- 损失函数：Loss Function
- 接收机操作曲线：ROC Curves
只限于二分类单标签分类问题的评估指标
- matthews_corrcoef(y_true,y_pred[],...):计算二元分类中的Matthews相关系数（MCC）
- precision_recall_curve(y_true,probas_pred)：在不同的概率阈值下计算precision-recall点，形成曲线
- roc_curve(y_true,y_score[,pos_label,...]):计算ROC曲线
可用于二分类多标签分类问题的评估指标
- average_precision_score(y_true,y_score[,...]) 计算预测得分的平均精度（mAP）
- roc_auc_score(y_true,y_score[,average,...])计算预测得分的AUC值
可用于多分类问题的评估指标（紫色的可用于多标签分类问题）
- cohen_kappa_score(y1,y2[,labels,weights])
- confusion_matrix(y_true,y_pred[,labels,...])
- hinge_loss(y_true,pred_decision[,labels,...])
- accuracy_score(y_true,y_pred[,normalize,...])
- classification_report(y_true,y_pred[,...])
- f1_score(y_true,y_pres[,labels,...])
- fbeta_score(y_true,,y_pres,beta[,labels,...])
- hamming_loss(y_true,y_pres[,labels,...])
- jaccard_similarity_score(y_true,y_pres[,...])
- log_loss(y_true,y_pres[,eps,normalize,...])
- zero_one_loss(y_true,y_pres[,normalize,...])
- precision_recall_fsconfe_support(y_true,y_pres)
多分类性能评估指标
将二分类指标拓展到多分类或多标签问题中：
分类器性能评估指标：
- 接收机操作曲线Reciever Operating Curves-》可用于二分类问题
- 解卡德指数（相似性系数）Jaccard similarity coefficient-》可用于多分类问题
- MCC指标（相关性系数）Matthews correlation coefficient-》可用于二分类问题
四、分类器评估标准
准确率：返回被正确分类的样本比例（default）或者数量（normalize=False）
#准确率 import numpy as np from sklearn.metrics import accuracy_score y_pred=[0,2,1,3] y_true=[0,1,2,3] print(accuracy_score(y_true,y_pred)) print(accuracy_score(y_true,y_pred,normalize=False)) #0.5 #2
混淆矩阵
from sklearn.metrics import confusion_matrix y_true=[2,0,2,2,0,1] y_pred=[0,0,2,2,0,2] print(confusion_matrix(y_true,y_pred)) y_true=["cat","ant","cat","cat","ant","bird"] y_pred=["ant","ant","cat","cat","ant","cat"] print(confusion_matrix(y_true,y_pred,labels=["ant","cat","bird"])) #[[2 0 0][0 0 1][1 0 2]]
#[[2 0 0][1 2 0][0 1 0]]
二元分类问题：
#precision-recall-F-measures from sklearn import metrics y_pred=[0,1,0,0] y_true=[0,1,0,1] print(metrics.precision_score(y_true,y_pred)) #1.0 print(metrics.recall_score(y_true,y_pred)) #0.5 print(metrics.f1_score(y_true,y_pred)) #0.666666666667 print(metrics.fbeta_score(y_true,y_pred,beta=0.5)) #0.833333333333 print(metrics.fbeta_score(y_true,y_pred,beta=1)) #0.666666666667 print(metrics.fbeta_score(y_true,y_pred,beta=2)) #0.555555555556 print(metrics.precision_recall_fscore_support(y_true,y_pred,beta=0.5)) #(array([ 0.66666667, 1. ]), array([ 1. , 0.5]), array([ 0.71428571, 0.83333333]), array([2, 2], dtype=int32)) import numpy as np from sklearn.metrics import precision_recall_curve from sklearn.metrics import average_precision_score y_true=np.array([0,0,1,1]) y_score=np.array([0.1,0.4,0.35,0.8]) precision,recall,threahold=precision_recall_curve(y_true,y_score) print(precision) #[ 0.66666667 0.5 1. 1. ] print(recall) [ 1. 0.5 0.5 0. ] print(threahold) #[ 0.35 0.4 0.8 ] print(average_precision_score(y_true,y_score)) #0.791666666667
多类别多标签分类问题
把其中的一类看成是正类，其他所有类看成是负类，每一类都可以看作是正类是都可以产生P，R，F，此时，可以按照5中方式来组合每一个类的结果，这5种方式是：macro，weighted，micro，samples，average=None
from sklearn import metrics y_true=[0,1,2,0,1,2] y_pred=[0,2,1,0,0,1] print(metrics.precision_score(y_true,y_pred,average="macro"))
#0.222222222222
print(metrics.recall_score(y_true,y_pred,average="micro"))
#0.333333333333
print(metrics.f1_score(y_true,y_pred,average="weighted"))
#0.266666666667
print(metrics.fbeta_score(y_true,y_pred,average="macro",beta=0.5))
#0.238095238095
print(metrics.precision_recall_fscore_support(y_true,y_pred,beta=0.5,average="None"))
#(array([ 0.66666667, 0. , 0. ]), array([ 1., 0., 0.]), array([ 0.71428571, 0. , 0. ]), array([2, 2, 2], dtype=int32))
print(metrics.recall_score(y_true,y_pred,average="micro",labels=[1,2]))
#0.0
from sklearn.metrics import classification_report y_true=[0,1,2,0,1,2] y_pred=[0,2,1,0,0,1] target_names=["class0","class1","class2"] print(classification_report(y_true,y_pred,target_names=target_names))
结果为：
precision recall f1-score support
class0 0.67 1.00 0.80 2
class1 0.00 0.00 0.00 2
class2 0.00 0.00 0.00 2
avg / total 0.22 0.33 0.27 6
Roc曲线
更多ROC曲线内容：http://v.youku.com/v_show/id_XMjcyMzg0MzgwMA==.html?spm=a2h0k.8191407.0.0&from=s1.8-1-1.2
ROC曲线只需知道true positive rate（TPR）和false positive rate（FPR），TPR，FPR被看作是分类器的某个参数的函数。
TPR定义了在全部的正样本中，分类器找到了多少个真真的正样本
FPR定义了在全部的负样本中，分类器把多少负样本错误的分为正样本