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- 机器学习之Gradient Tree Boosting中GBDT-- GradientBoostingClassifier
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 3 22:24:34 2018
@author: muli
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,cross_validation,ensemble
def load_data_classification():
'''
加载用于分类问题的数据集
:return: 一个元组，用于分类问题。元组元素依次为：训练样本集、测试样本集、训练样本集对应的标记、测试样本集对应的标记
'''
digits=datasets.load_digits() # 使用 scikit-learn 自带的 digits 数据集
return cross_validation.train_test_split(digits.data,digits.target,
test_size=0.25,random_state=0,stratify=digits.target) # 分层采样拆分成训练集和测试集，测试集大小为原始数据集大小的 1/4
def test_GradientBoostingClassifier(*data):
'''
测试 GradientBoostingClassifier 的用法
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
X_train,X_test,y_train,y_test=data
clf=ensemble.GradientBoostingClassifier()
clf.fit(X_train,y_train)
print("Traing Score:%f"%clf.score(X_train,y_train))
print("Testing Score:%f"%clf.score(X_test,y_test))
def test_GradientBoostingClassifier_num(*data):
'''
测试 GradientBoostingClassifier 的预测性能随 n_estimators 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
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)
testing_scores=[]
training_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")
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_GradientBoostingClassifier_maxdepth(*data):
'''
测试 GradientBoostingClassifier 的预测性能随 max_depth 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
X_train,X_test,y_train,y_test=data
maxdepths=np.arange(1,20)
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
testing_scores=[]
training_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")
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_GradientBoostingClassifier_learning(*data):
'''
测试 GradientBoostingClassifier 的预测性能随 学习率 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
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)
testing_scores=[]
training_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")
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_GradientBoostingClassifier_subsample(*data):
'''
测试 GradientBoostingClassifier 的预测性能随 subsample 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
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)
testing_scores=[]
training_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="Training Score")
ax.set_xlabel("subsample")
ax.set_ylabel("score")
ax.legend(loc="lower right")
ax.set_ylim(0,1.05)
plt.suptitle("GradientBoostingClassifier")
# 设置 X 轴的网格线，风格为 点画线
plt.grid(axis='x',linestyle='-.')
plt.show()
def test_GradientBoostingClassifier_max_features(*data):
'''
测试 GradientBoostingClassifier 的预测性能随 max_features 参数的影响
:param data: 可变参数。它是一个元组，这里要求其元素依次为：训练样本集、测试样本集、训练样本的标记、测试样本的标记
:return: None
'''
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)
testing_scores=[]
training_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="Training 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()
if __name__=='__main__':
X_train,X_test,y_train,y_test=load_data_classification() # 获取分类数据
# test_GradientBoostingClassifier(X_train,X_test,y_train,y_test) # 调用 test_GradientBoostingClassifier
# test_GradientBoostingClassifier_num(X_train,X_test,y_train,y_test) # 调用 test_GradientBoostingClassifier_num
# test_GradientBoostingClassifier_maxdepth(X_train,X_test,y_train,y_test) # 调用 test_GradientBoostingClassifier_maxdepth
# test_GradientBoostingClassifier_learning(X_train,X_test,y_train,y_test) # 调用 test_GradientBoostingClassifier_learning
# test_GradientBoostingClassifier_subsample(X_train,X_test,y_train,y_test) # 调用 test_GradientBoostingClassifier_subsample
test_GradientBoostingClassifier_max_features(X_train,X_test,y_train,y_test) # 调用 test_GradientBoostingClassifier_max_features
- 如图：
