机器学习测试笔记（24）——综合_学习曲线

2023-02-14 363

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简介： 机器学习测试笔记（24）——综合_学习曲线

1 画学习曲线

1.1函数和类调用图

1.2 代码

class LearningCurve:
    def__init__(self,data):
       self.data = data
#定义一个绘制学习曲线的函数
    defplot_learning_curve(self,estimator, title,X,y,ylim=None,cv=None,n_jobs=2,train_sizes=np.linspace(.1,1.0,5)):
if ylim is not None:
           plt.ylim(*ylim)
       plt.xlabel(u"训练样本")
       plt.ylabel(u"得分")
       plt.ylim(0,1.1)
        tarining_sizes,train_scores,test_scores =learning_curve(estimator,X,y,cv=cv,n_jobs=n_jobs,train_sizes=train_sizes)
       train_scores_mean = np.mean(train_scores,axis=1)
       test_scores_mean = np.mean(test_scores,axis=1)
       plt.grid()
       plt.plot(tarining_sizes,train_scores_mean,'o-',label=u"训练得分",c='r')
       plt.plot(tarining_sizes,test_scores_mean,'o-',label=u"交叉验证得分",c='g')
       plt.legend(loc='lower right')
return plt
#画学习曲线
    deflearning_curve(self,model_name,pram,title):
       ML = Machine_Learn()
       mytype = ML.get_pram_type(model_name)
       X,y = ML.get_data(self.data)
       cv = ShuffleSplit(n_splits=100,test_size=0.2,random_state=0)
       mytitle =u"学习曲线("+title+")"
       print(model_name)
if len(pram) == 0:
           plt.figure()
           clf = ML.get_model(model_name)
           self.plot_learning_curve(clf,mytitle,X, y,ylim=(0.9,1.01),cv=cv)
else:
           plt.figure()
           pramdic = eval(pram[0])
            i = 0
           maxj=0
           i,j = ML.Get_line_and_Column(pramdic)
           m =0
for key,values in pramdic.items():
                valuedics =values.split(",")
for valuedic in valuedics:
                    clf =ML.judg_clf(key,mytype,model_name,valuedic)
                    plt.subplot(i,j,m+1)
                   plt.title(key+"="+valuedic)
                   self.plot_learning_curve(clf,mytitle,X, y,ylim=(0.9,1.01),cv=cv)
                   m=m+1
       ML.set_ply_font_info_and_show(title)
#准备画学习曲线
    defDraw_learn_curve(self,scattertype):             
       ML = Machine_Learn()
       prams,model_name,title_name = ML.get_algorithm_type(scattertype)
       i = 0
for pram in prams:
           self.learning_curve(model_name[i],pram,title_name[i])
           i = i+1

1.3调用

if __name__=="__main__":
lc = LearningCurve("breast_cancer")
   lc.Draw_learn_curve("Liner")
   lc.Draw_learn_curve("RandomForest")
   lc.Draw_learn_curve("DecisionTree")
   lc.Draw_learn_curve("KNeighbors")
   lc.Draw_learn_curve("Bayes")
   lc.Draw_learn_curve("SVM")
   lc.Draw_learn_curve("Neural_network")
   lc.Draw_learn_curve("Ensemble")