顾老师新书《全栈软件测试工程师宝典》
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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")
本结果数据来源:
sklearn.datasets.load_breast_cancer()
2.1 线性模型
总体
总体
整体运行速度相当慢
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