机器学习之随机森林RandomForestClassifier

# -*- coding: utf-8 -*-
"""
Created on Mon Dec  3 22:26:54 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_RandomForestClassifier(*data):
    '''
    测试 RandomForestClassifier 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.RandomForestClassifier()
    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_RandomForestClassifier_num(*data):
    '''
    测试 RandomForestClassifier 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:
        # 决策树的个数
        clf=ensemble.RandomForestClassifier(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("RandomForestClassifier")
    # 设置 X 轴的网格线，风格为 点画线
    plt.grid(axis='x',linestyle='-.')
    plt.show()


def test_RandomForestClassifier_max_depth(*data):
    '''
    测试 RandomForestClassifier 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=range(1,20)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    testing_scores=[]
    training_scores=[]
    for max_depth in maxdepths:
        clf=ensemble.RandomForestClassifier(max_depth=max_depth)
        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("RandomForestClassifier")
    # 设置 X 轴的网格线，风格为 点画线
    plt.grid(axis='x',linestyle='-.')
    plt.show()


def test_RandomForestClassifier_max_features(*data):
    '''
    测试 RandomForestClassifier 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
    max_features=np.linspace(0.01,1.0)
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    testing_scores=[]
    training_scores=[]
    for max_feature in max_features:
        clf=ensemble.RandomForestClassifier(max_features=max_feature)
        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_feature")
    ax.set_ylabel("score")
    ax.legend(loc="lower right")
    ax.set_ylim(0,1.05)
    plt.suptitle("RandomForestClassifier")
    # 设置 X 轴的网格线，风格为 点画线
    plt.grid(axis='x',linestyle='-.')
    plt.show()


if __name__=='__main__':
    X_train,X_test,y_train,y_test=load_data_classification()  # 获取分类数据
#    test_RandomForestClassifier(X_train,X_test,y_train,y_test)  # 调用 test_RandomForestClassifier
#    test_RandomForestClassifier_num(X_train,X_test,y_train,y_test)  # 调用 test_RandomForestClassifier_num
#    test_RandomForestClassifier_max_depth(X_train,X_test,y_train,y_test)  # 调用 test_RandomForestClassifier_max_depth
    test_RandomForestClassifier_max_features(X_train,X_test,y_train,y_test)  # 调用 test_RandomForestClassifier_max_features