集成学习——Gradient Tree Boosting

最新推荐文章于 2024-07-22 08:12:53 发布

小小蒲公英

最新推荐文章于 2024-07-22 08:12:53 发布

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分类专栏：机器学习

本文链接：https://blog.youkuaiyun.com/weixin_39777626/article/details/79859562

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本文详细介绍了如何使用梯度提升决策树（Gradient Boosting Decision Tree）进行分类与回归任务，并通过多个实验展示了不同参数对模型性能的影响。

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import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,cross_validation,ensemble

加载数据集

#糖尿病病人
def load_data_regression():
    diabetes=datasets.load_diabetes()
    return cross_validation.train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0)

#手写识别数据集Digit Dataset
def load_data_classification():
    digits=datasets.load_digits()
    return cross_validation.train_test_split(digits.data,digits.target,test_size=0.25,random_state=0)

GradientBoostClassifier梯度提升决策树

模型原型

class sklearn.ensemble.GradientBoostClassifier(loss=’deviance’,learning_rate=0.1,n_estimators=100,subsample=1.0, min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_depth=3,init=None,random_state=None, max_feature=None,verbose=0,max_leaf_nodes=None,warm_start=False,presort=’auto’)
参数

loss:指定损失函数
- ’deviance’:对数损失函数
- ‘exponential’:指数损失函数
learning_rate
n_estimators:指定基础决策树的数量（默认为100）.GBDT对过拟合有很好的鲁棒性，因此该值越大越好
subsample:指定了提取原始训练集中的一个子集用于训练基础决策树
min_samples_split
min_samples_leaf
min_weight_fraction_leaf
max_depth
init:用于执行初始的预测（如果为None，则使用loss.init_estimator）
random_state
max_feature
verbose
max_leaf_nodes
warm_start
presort

属性

featureimportances
oobimprovement
trainscore
init
estimators_

方法

fit(X,y[,samples_weight,monitor])
predict(X)
predict_log_proba(X)
predict_proba(X)
score(X,y[,samples_weight])
staged_predict(X)
staged_predict_proba(X)

使用GradientBoostingClassifier类

def test_GradientBoostingClassifier(*data):
    X_train,X_test,y_train,y_test=data
    clf=ensemble.GradientBoostingClassifier()
    clf.fit(X_train,y_train)
    print('Training Score:%f'%clf.score(X_train,y_train))
    print('Testing Score:%f'%clf.score(X_test,y_test))

X_train,X_test,y_train,y_test=load_data_classification()
test_GradientBoostingClassifier(X_train,X_test,y_train,y_test)

个体决策树的数量的影响

def test_GradientBoostingClassifier_num(*data):
    X_train,X_test,y_train,y_test=data
    nums=np.arange(1,100,step=2)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    training_scores=[]
    testing_scores=[]
    for num in nums:
        clf=ensemble.GradientBoostingClassifier(n_estimators=num)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(nums,training_scores,label='Training Score')
    ax.plot(nums,testing_scores,label='Testing Score')
    ax.set_xlabel('estimator num')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(0,1.05)
    plt.suptitle('GradientBoostingClassifier')
    plt.show()

test_GradientBoostingClassifier_num(X_train,X_test,y_train,y_test)

个体决策树的最大树深度的影响

def test_GradientBoostingClassifier_maxdepth(*data):
    X_train,X_test,y_train,y_test=data
    maxdepths=np.arange(1,20)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    training_scores=[]
    testing_scores=[]
    for maxdepth in maxdepths:
        clf=ensemble.GradientBoostingClassifier(max_depth=maxdepth,
            max_leaf_nodes=None)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(maxdepths,training_scores,label='Training Score')
    ax.plot(maxdepths,testing_scores,label='Testing Score')
    ax.set_xlabel('max_depth')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(0,1.05)
    plt.suptitle('GradientBoostingClassifier')
    plt.show()

test_GradientBoostingClassifier_maxdepth(X_train,X_test,y_train,y_test)

学习率的影响

def test_GradientBoostingClassifier_learning(*data):
    X_train,X_test,y_train,y_test=data
    learnings=np.linspace(0.01,1.0)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    training_scores=[]
    testing_scores=[]
    for learning in learnings:
        clf=ensemble.GradientBoostingClassifier(learning_rate=learning)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(learnings,training_scores,label='Training Score')
    ax.plot(learnings,testing_scores,label='Testing Score')
    ax.set_xlabel('learning_rate')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(0,1.05)
    plt.suptitle('GradientBoostingClassifier')
    plt.show()

test_GradientBoostingClassifier_learning(X_train,X_test,y_train,y_test)

subsample的影响强调内容

def test_GradientBoostingClassifier_subsample(*data):
    X_train,X_test,y_train,y_test=data
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    subsamples=np.linspace(0.01,1.0)
    training_scores=[]
    testing_scores=[]
    for subsample  in subsamples:
        clf=ensemble.GradientBoostingClassifier(subsample=subsample)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(subsamples,training_scores,label='Training Score')
    ax.plot(subsamples,testing_scores,label='Testing Score')
    ax.set_xlabel('subsample')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(0,1.05)
    plt.suptitle('GradientBoostingClassifier')
    plt.show()

test_GradientBoostingClassifier_subsample(X_train,X_test,y_train,y_test)

max_features参数的影响

def test_GradientBoostingClassifier_max_features(*data):
    X_train,X_test,y_train,y_test=data
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    max_features=np.linspace(0.01,1.0)
    training_scores=[]
    testing_scores=[]
    for features in max_features:
        clf=ensemble.GradientBoostingClassifier(max_features=features)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(max_features,training_scores,label='Training Score')
    ax.plot(max_features,testing_scores,label='Testing Score')
    ax.set_xlabel('max_features')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(0,1.05)
    plt.suptitle('GradientBoostingClassifier')
    plt.show()

test_GradientBoostingClassifier_max_features(X_train,X_test,y_train,y_test)

GradientBoostRegressor

模型原型

class sklearn.ensemble.GradientBoostRegressor(loss=’ls’,learning_rate=0.1,n_estimators=100,subsample=1.0, min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_depth=3,init=None,random_state=None, max_feature=None,alpha=0.9,verbose=0,max_leaf_nodes=None,warm_start=False,presort=’auto’)
参数

loss:指定损失函数
- ’ls’:平方损失函数
- ‘lad’:绝对值损失函数
- ‘huber’:平方损失函数和绝对值损失函数结合，通过alpha参数指定比例
learning_rate
n_estimators:指定基础决策树的数量（默认为100）.GBDT对过拟合有很好的鲁棒性，因此该值越大越好
subsample:指定了提取原始训练集中的一个子集用于训练基础决策树
min_samples_split
min_samples_leaf
min_weight_fraction_leaf
max_depth
init:用于执行初始的预测（如果为None，则使用loss.init_estimator）
random_state
max_feature
alpha
verbose
max_leaf_nodes
warm_start
presort

属性

featureimportances
oobimprovement
trainscore
init
estimators_

方法

fit(X,y[,samples_weight,monitor])
predict(X)
predict_log_proba(X)
predict_proba(X)
score(X,y[,samples_weight])
staged_predict(X)

使用GradientBoostingRegressor类

def test_GradientBoostingRegressor(*data):
    X_train,X_test,y_train,y_test=data
    regr=ensemble.GradientBoostingRegressor()
    regr.fit(X_train,y_train)
    print('Training Score:%f'%regr.score(X_train,y_train))
    print('Testing Score:%f'%regr.score(X_test,y_test))

X_train,X_test,y_train,y_test=load_data_regression()
test_GradientBoostingRegressor(X_train,X_test,y_train,y_test)

个体回归树的数量的影响

def test_GradientBoostingRegressor_num(*data):
    X_train,X_test,y_train,y_test=data
    nums=np.arange(1,200,step=2)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    training_scores=[]
    testing_scores=[]
    for num in nums:
        clf=ensemble.GradientBoostingRegressor(n_estimators=num)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(nums,training_scores,label='Training Score')
    ax.plot(nums,testing_scores,label='Testing Score')
    ax.set_xlabel('estimator num')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(0,1.05)
    plt.suptitle('GradientBoostingRegressor')
    plt.show()

test_GradientBoostingRegressor_num(X_train,X_test,y_train,y_test)

个体决策树的最大树深度的影响

def test_GradientBoostingRegressor_maxdepth(*data):
    X_train,X_test,y_train,y_test=data
    maxdepths=np.arange(1,20)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    training_scores=[]
    testing_scores=[]
    for maxdepth in maxdepths:
        clf=ensemble.GradientBoostingRegressor(max_depth=maxdepth,max_leaf_nodes=None)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(maxdepths,training_scores,label='Training Score')
    ax.plot(maxdepths,testing_scores,label='Testing Score')
    ax.set_xlabel('max_depth')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(-1,1.05)
    plt.suptitle('GradientBoostingRegressor')
    plt.show()

test_GradientBoostingRegressor_maxdepth(X_train,X_test,y_train,y_test)

学习率的影响

def test_GradientBoostingRegressor_learning(*data):
    X_train,X_test,y_train,y_test=data
    learnings=np.linspace(0.01,1.0)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    training_scores=[]
    testing_scores=[]
    for learning in learnings:
        clf=ensemble.GradientBoostingRegressor(learning_rate=learning)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(learnings,training_scores,label='Training Score')
    ax.plot(learnings,testing_scores,label='Testing Score')
    ax.set_xlabel('learning_rate')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(-1,1.05)
    plt.suptitle('GradientBoostingRegressor')
    plt.show()

test_GradientBoostingRegressor_learning(X_train,X_test,y_train,y_test)

subsample的影响

def test_GradientBoostingRegressor_subsample(*data):
    X_train,X_test,y_train,y_test=data
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    subsamples=np.linspace(0.01,1.0,num=20)
    training_scores=[]
    testing_scores=[]
    for subsample  in subsamples:
        clf=ensemble.GradientBoostingRegressor(subsample=subsample)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(subsamples,training_scores,label='Training Score')
    ax.plot(subsamples,testing_scores,label='Testing Score')
    ax.set_xlabel('subsample')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(-1,1.05)
    plt.suptitle('GradientBoostingRegressor')
    plt.show()

test_GradientBoostingRegressor_subsample(X_train,X_test,y_train,y_test)

损失函数的影响

def test_GradientBoostingRegressor_loss(*data):
    X_train,X_test,y_train,y_test=data
    fig=plt.figure()
    nums=np.arange(1,200,step=2)
    losses=['ls','lad','huber']
    #绘制huber
    ax=fig.add_subplot(2,1,1)
    alphas=np.linspace(0.01,1.0,endpoint=False,num=5)
    for alpha in alphas:
        training_scores=[]
        testing_scores=[]
        for num in nums:
            regr=ensemble.GradientBoostingRegressor(n_estimators=num,
                loss='huber',alpha=alpha)
            regr.fit(X_train,y_train)
            training_scores.append(regr.score(X_train,y_train))
            testing_scores.append(regr.score(X_test,y_test))
        ax.plot(nums,training_scores,
            label='Training Score:alpha=%f'%alpha)
        ax.plot(nums,testing_scores,
            label='Testing Score:alpha=%f'%alpha)
    ax.set_xlabel('estimator num')
    ax.set_ylabel('score')
    ax.legend(loc='lower right',framealpha=0.4)
    ax.set_ylim(0,1.05)
    ax.set_title('loss=%huber')
    plt.suptitle('GradientBoostingRegressor')
    #绘制ls和lad
    ax=fig.add_subplot(2,1,2)
    for loss in ['ls','lad']:
        training_scores=[]
        testing_scores=[]
        for num in nums:
            regr=ensemble.GradientBoostingRegressor(n_estimators=num,
                loss=loss)
            regr.fit(X_train,y_train)
            training_scores.append(regr.score(X_train,y_train))
            testing_scores.append(regr.score(X_test,y_test))
        ax.plot(nums,training_scores,label='Training Score:loss=%s'%loss)
        ax.plot(nums,testing_scores,label='Testing Score:loss=%s'%loss)
    ax.set_xlabel('estimator num')
    ax.set_ylabel('score')
    ax.legend(loc='lower right',framealpha=0.4)
    ax.set_ylim(0,1.05)
    ax.set_title('loss=ls,lad')
    plt.suptitle('GradientBoostingRegressor')
    plt.show()

test_GradientBoostingRegressor_loss(X_train,X_test,y_train,y_test)

max_features参数的影响

def test_GradientBoostingRegressor_max_features(*data):
    X_train,X_test,y_train,y_test=data
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    max_features=np.linspace(0.01,1.0)
    training_scores=[]
    testing_scores=[]
    for features in max_features:
        clf=ensemble.GradientBoostingRegressor(max_features=features)
        clf.fit(X_train,y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))
    ax.plot(max_features,training_scores,label='Training Score')
    ax.plot(max_features,testing_scores,label='Testing Score')
    ax.set_xlabel('max_features')
    ax.set_ylabel('score')
    ax.legend(loc='lower right')
    ax.set_ylim(0,1.05)
    plt.suptitle('GradientBoostingRegressor')
    plt.show()

test_GradientBoostingRegressor_max_features(X_train,X_test,
    y_train,y_test)