吳裕雄 python 機器學習——集成學習梯度提升決策樹GradientBoostingClassifier分類模型

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets,ensemble
from sklearn.model_selection import train_test_split

def load_data_classification():
    '''
    加載用於分類問題的數據集
    '''
    # 使用 scikit-learn 自帶的 digits 數據集
    digits=datasets.load_digits() 
    # 分層采樣拆分成訓練集和測試集，測試集大小為原始數據集大小的 1/4
    return train_test_split(digits.data,digits.target,test_size=0.25,random_state=0,stratify=digits.target) 

#集成學習梯度提升決策樹GradientBoostingClassifier分類模型
def test_GradientBoostingClassifier(*data):
    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))

# 獲取分類數據
X_train,X_test,y_train,y_test=load_data_classification() 
# 調用 test_GradientBoostingClassifier
test_GradientBoostingClassifier(X_train,X_test,y_train,y_test) 

def test_GradientBoostingClassifier_num(*data):
    '''
    測試 GradientBoostingClassifier 的預測性能隨 n_estimators 參數的影響
    '''
    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")
    plt.show()
    
# 調用 test_GradientBoostingClassifier_num
test_GradientBoostingClassifier_num(X_train,X_test,y_train,y_test)

def test_GradientBoostingClassifier_maxdepth(*data):
    '''
    測試 GradientBoostingClassifier 的預測性能隨 max_depth 參數的影響
    '''
    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")
    plt.show()
    
# 調用 test_GradientBoostingClassifier_maxdepth
test_GradientBoostingClassifier_maxdepth(X_train,X_test,y_train,y_test)

def test_GradientBoostingClassifier_learning(*data):
    '''
    測試 GradientBoostingClassifier 的預測性能隨學習率參數的影響
    '''
    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")
    plt.show()
    
# 調用 test_GradientBoostingClassifier_learning
test_GradientBoostingClassifier_learning(X_train,X_test,y_train,y_test)

def test_GradientBoostingClassifier_subsample(*data):
    '''
    測試 GradientBoostingClassifier 的預測性能隨 subsample 參數的影響
    '''
    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")
    plt.show()
    
# 調用 test_GradientBoostingClassifier_subsample
test_GradientBoostingClassifier_subsample(X_train,X_test,y_train,y_test)

def test_GradientBoostingClassifier_max_features(*data):
    '''
    測試 GradientBoostingClassifier 的預測性能隨 max_features 參數的影響
    '''
    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()
    
# 調用 test_GradientBoostingClassifier_max_features
test_GradientBoostingClassifier_max_features(X_train,X_test,y_train,y_test)