【Python-ML】SKlearn库集成学习器Boosting

# -*- coding: utf-8 -*- ''' Created on 2018年1月19日@author: Jason.F @summary: Boosting，无放回抽样，串行训练基学习器，用整个训练集来训练弱学习机，训练样本在每次迭代中都会重新赋予一个权重，在上一弱学习机错误的基础上进行学习进而构建一个更强大的分类器。 ''' import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder from sklearn.cross_validation import train_test_split from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt#导入数据和数据处理 df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',header=None) df_wine.columns=['Class label','Alcohol','Malic acid','Ash','Alcalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted wines','Proline'] print ('class labels:',np.unique(df_wine['Class label'])) df_wine=df_wine[df_wine['Class label']!=1]#选择2和3类别 y=df_wine['Class label'].values X=df_wine[['Alcohol','Hue']].values #选择Alcohol和 Hue两个特征 le=LabelEncoder() y=le.fit_transform(y) X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.40,random_state=1) #引用BaggingClassifier训练 tree=DecisionTreeClassifier(criterion='entropy',max_depth=None)#构建基学习器 ada=AdaBoostClassifier(base_estimator=tree,n_estimators=500,learning_rate=0.1,random_state=0) #评分，比较baggin集成器和单颗未剪枝决策树的性能差异 #单颗树评估 tree=tree.fit(X_train,y_train) y_train_pred=tree.predict(X_train) y_test_pred=tree.predict(X_test) tree_train=accuracy_score(y_train, y_train_pred) tree_test=accuracy_score(y_test, y_test_pred) print ('Decision tree train/test accuracies %.3f/%.3f'%(tree_train,tree_test))#未剪枝，过拟合 #集成器评估 ada=ada.fit(X_train,y_train) y_train_pred=ada.predict(X_train) y_test_pred=ada.predict(X_test) ada_train=accuracy_score(y_train, y_train_pred) ada_test=accuracy_score(y_test, y_test_pred) print ('Adaboost train/test accuracies %.3f/%.3f'%(ada_train,ada_test)) #可视化决策区域 x_min = X_train[:,0].min()-1 x_max = X_train[:,0].max()+1 y_min = X_train[:,1].min()-1 y_max = X_train[:,1].max()+1 xx,yy =np.meshgrid(np.arange(x_min,x_max,0.1),np.arange(y_min,y_max,0.1)) f,axarr= plt.subplots(nrows=1,ncols=2,sharex='col',sharey='row',figsize=(8,3)) for idx,clf,tt in zip([0,1],[tree,ada],['Decision Tree','Adaboost']):clf.fit(X_train,y_train)Z=clf.predict(np.c_[xx.ravel(),yy.ravel()])Z=Z.reshape(xx.shape)axarr[idx].contourf(xx,yy,Z,alpha=0.3)axarr[idx].scatter(X_train[y_train==0,0],X_train[y_train==0,1],c='blue',marker='^')axarr[idx].scatter(X_train[y_train==1,0],X_train[y_train==1,1],c='red',marker='o')axarr[idx].set_title(tt) axarr[0].set_ylabel('Alcohol',fontsize=12) plt.text(10.2,-1.2,s='Hue',ha='center',va='center',fontsize=12) plt.show()

结果：

('class labels:', array([1, 2, 3], dtype=int64)) Decision tree train/test accuracies 1.000/0.833 Adaboost train/test accuracies 1.000/0.833

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