【Python-ML】SKlearn库学习曲线和验证曲线

# -*- coding: utf-8 -*- ''' Created on 2018年1月18日 @author: Jason.F @summary: 判别过拟合和欠拟合 学习曲线Learning Curve：评估样本量和指标的关系 验证曲线validation Curve：评估参数和指标的关系 ''' import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline import matplotlib.pyplot as plt from sklearn.learning_curve import learning_curve import numpy as np from sklearn.learning_curve import validation_curve #导入数据 df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data',header=None) X=df.loc[:,2:].values y=df.loc[:,1].values le=LabelEncoder() y=le.fit_transform(y)#类标整数化 print (le.transform(['M','B'])) #划分训练集合测试集 X_train,X_test,y_train,y_test = train_test_split (X,y,test_size=0.20,random_state=1) #标准化、模型训练串联 pipe_lr=Pipeline([('scl',StandardScaler()),('clf',LogisticRegression(random_state=1,penalty='l2'))])#case1：学习曲线 #构建学习曲线评估器，train_sizes：控制用于生成学习曲线的样本的绝对或相对数量 train_sizes,train_scores,test_scores=learning_curve(estimator=pipe_lr,X=X_train,y=y_train,train_sizes=np.linspace(0.1,1.0,10),cv=10,n_jobs=1) #统计结果 train_mean= np.mean(train_scores,axis=1) train_std = np.std(train_scores,axis=1) test_mean =np.mean(test_scores,axis=1) test_std=np.std(test_scores,axis=1) #绘制效果 plt.plot(train_sizes,train_mean,color='blue',marker='o',markersize=5,label='training accuracy') plt.fill_between(train_sizes,train_mean+train_std,train_mean-train_std,alpha=0.15,color='blue') plt.plot(train_sizes,test_mean,color='green',linestyle='--',marker='s',markersize=5,label='test accuracy') plt.fill_between(train_sizes,test_mean+test_std,test_mean-test_std,alpha=0.15,color='green') plt.grid() plt.xlabel('Number of training samples') plt.ylabel('Accuracy') plt.legend(loc='lower right') plt.ylim([0.8,1.0]) plt.show()#case2：验证曲线 param_range=[0.001,0.01,0.1,1.0,10.0,100.0] #10折，验证正则化参数C train_scores,test_scores =validation_curve(estimator=pipe_lr,X=X_train,y=y_train,param_name='clf__C',param_range=param_range,cv=10) #统计结果 train_mean= np.mean(train_scores,axis=1) train_std = np.std(train_scores,axis=1) test_mean =np.mean(test_scores,axis=1) test_std=np.std(test_scores,axis=1) plt.plot(param_range,train_mean,color='blue',marker='o',markersize=5,label='training accuracy') plt.fill_between(param_range,train_mean+train_std,train_mean-train_std,alpha=0.15,color='blue') plt.plot(param_range,test_mean,color='green',linestyle='--',marker='s',markersize=5,label='test accuracy') plt.fill_between(param_range,test_mean+test_std,test_mean-test_std,alpha=0.15,color='green') plt.grid() plt.xscale('log') plt.xlabel('Parameter C') plt.ylabel('Accuracy') plt.legend(loc='lower right') plt.ylim([0.8,1.0]) plt.show()

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