机器学习之XGboost代码实现

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/qq_37960402/article/details/88638082

XGboost代码实现
XGboost的原生语言是c++，所以它的运行速度比较快。这里将会举例介绍一下python中如何调用XGboost，如果想了解XGboost的理论部分，可以参考这篇博客

数据集：以鸢尾花数据集为例
例1

# /usr/bin/python
# -*- encoding:utf-8 -*-
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import xgboost as xgb

def show_accuracy(a, b, tip):
    acc = a.ravel() == b.ravel()
    print (acc)
    print (tip + '正确率：\t', float(acc.sum()) / a.size)

if __name__ == "__main__":
    #加载iris数据集
    data=load_iris()
    X=data.data
    Y=data.target
    X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.25,random_state=1)  #训练集和测试集
    data_train = xgb.DMatrix(X_train,label=y_train)
    data_test = xgb.DMatrix(X_test,label=y_test)
    print (data_train)
    print (type(data_train))
    # 设置参数
    param = {'max_depth': 3, 'eta': 1, 'silent': 1, 'objective': 'multi:softmax','num_class': 3} # logitraw
    watchlist = [(data_test, 'eval'), (data_train, 'train')]
    n_round = 7
    bst = xgb.train(param, data_train, num_boost_round=4, evals=watchlist)  
    y_hat = bst.predict(data_test)
    show_accuracy(y_hat, y_test, 'XGBoost ')

结果显示：

xgb.train(),bst.predicty

bst = xgb.train(param, data_train, num_boost_round=4, evals=watchlist)
y_hat = bst.predict(data_test)

xgb.train()和xgb.predict是xgboost训练和测试的方式
xgb.train()函数原型：

xgboost.train(params,dtrain,num_boost_round=10,evals=(),obj=None,feval=None,maximize=False,early_stopping_rounds=None,evals_result=None,verbose_eval=True,learning_rates=None,xgb_model=None)  

1、params ：这是一个字典，里面包含着训练中的参数关键字和对应的值

param = {‘max_depth’: 3, ‘eta’: 1, ‘silent’: 1, ‘objective’: ‘multi:softmax’,‘num_class’: 3}

• ‘max_depth’：树的深度，默认值是6
• ‘eta’：步长用于防止过拟合，范围是0～1，默认值0.3
• ‘silent’: 设置成1则没有运行信息输出，设置为0有运行信息输出.
• ‘objective’：指定你想要的类型的学习者,包括线性回归、逻辑回归、泊松回归等，默认值设置为reg:linear
  • “reg:linear” ：线性回归（默认）.
  • “reg:logistic”：逻辑回归
  • “binary:logistic”：二分类的逻辑回归------输出概率值
  • “binary:logitraw”：二分类的逻辑回归，输出的结果为wTx。
  • “multi:softmax”：使用SoftMax进行多分类
  • “num_class”：设置类的数量。 仅用于多类目标。
  • “multi:softprob”：与softmax类似，预测输出ndata * nclass元素的向量，结果包含属于每个类的每个数据点的预测概率。
  • “rank:pairwise”：set xgboost to do ranking task by minimizing the pairwise loss

2、 dtrain ：训练的数据
3、num_boost_round：提升迭代的个数
4、evals ：用于对训练过程中进行评估列表中的元素。形式是evals = [(dtrain,’train’), (dval,’val’)]或者是evals = [ (dtrain,’train’)]
5、obj：自定义目标函数
6、feval：自定义评估函数
7、maximize： 是否对评估函数进行最大化
8、early_stopping_rounds：早期停止次数 ，假设为100，验证集的误差迭代到一定程度在100次内不能再继续降低，就停止迭代。
9、evals_result ：字典，存储在watchlist 中的元素的评估结果
10、verbose_eval(可以输入布尔型或数值型)：如果为True, 则对evals中元素的评估结果会输出在结果中；如果输入数字，假设为5，则每隔5个迭代输出一次。
11、learning_rates ：每一次提升的学习率的列表
12、xgb_model：在训练之前用于加载的xgb model

去掉evals，结果变为：
watchlist = [(data_test, ‘eval’), (data_train, ‘train’)]
evals=watchlist

#bst = xgb.train(param, data_train, num_boost_round=4, evals=watchlist)
bst = xgb.train(param, data_train, num_boost_round=4) #去掉watchlist
结果如下：

例2
我们要自定义损失函数的梯度和二阶导，这里不使用iris数据集了，因为样本数量至于150个，太少了。这里来预测蘑菇是否有毒。
代码如下：

# /usr/bin/python
# -*- encoding:utf-8 -*-
import xgboost as xgb
import numpy as np
# 1、xgBoost的基本使用
# 2、自定义损失函数的梯度和二阶导
# 3、binary:logistic/logitraw

# 定义f: theta * x
def log_reg(y_hat, y):
    p = 1.0 / (1.0 + np.exp(-y_hat))   #认为自定义损失函数
    g = p - y.get_label()  #一阶导
    h = p * (1.0-p)        #二阶导
    return g, h

def error_rate(y_hat, y):
    return 'error', float(sum(y.get_label() != (y_hat > 0.5))) / len(y_hat)

if __name__ == "__main__":
    # 读取数据
    data_train = xgb.DMatrix('14.agaricus_train.txt')
    data_test = xgb.DMatrix('14.agaricus_test.txt')
    print (data_train)
    print (type(data_train))

    # 设置参数
    param = {'max_depth': 3, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'} # logitraw
    watchlist = [(data_test, 'eval'), (data_train, 'train')]
    n_round = 7
    bst = xgb.train(param, data_train, num_boost_round=n_round, evals=watchlist, obj=log_reg, feval=error_rate)#feval 误差率

    # 计算错误率
    y_hat = bst.predict(data_test)
    y = data_test.get_label()
    print (y_hat)
    print (y)
    error = sum(y != (y_hat > 0.5))
    error_rate = float(error) / len(y_hat)
    print ('样本总数：\t', len(y_hat))
    print ('错误数目：\t%4d' % error)
    print ('错误率：\t%.5f%%' % (100*error_rate))

例三：将几种算法 逻辑回归、XGboost、随机森林、bagging、adaboost作了一下对比，所得结果不能作为评判这些算法好坏的标准。

# /usr/bin/python
# -*- coding:utf-8 -*-

import xgboost as xgb
import numpy as np
import scipy.sparse
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
#稀疏数据存储
def read_data(path):
    y = []
    row = []
    col = []
    values = []
    r = 0       # 首行
    for d in open(path):
        d = d.strip().split()      # 以空格分开
        y.append(int(d[0]))
        d = d[1:]
        for c in d:
            key, value = c.split(':')
            row.append(r)
            col.append(int(key))
            values.append(float(value))
        r += 1
    x = scipy.sparse.csr_matrix((values, (row, col))).toarray()
    y = np.array(y)
    return x, y


def show_accuracy(a, b, tip):
    acc = a.ravel() == b.ravel()
    #print (acc)
    print (tip + '正确率：\t', float(acc.sum()) / a.size)

if __name__ == '__main__':
    x_train, y_train = read_data('14.agaricus_train.txt')
    x_test, y_test = read_data('14.agaricus_test.txt')
    #x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.6)

    # Logistic回归
    lr = LogisticRegression(penalty='l1')
    lr.fit(x_train, y_train.ravel())
    y_hat = lr.predict(x_test)
    print ("##############Logistic回归#############")
    show_accuracy(y_hat, y_test, 'Logistic回归 ')

    # XGBoost
    data_train = xgb.DMatrix(x_train, label=y_train)
    data_test = xgb.DMatrix(x_test, label=y_test)
    watch_list = [(data_test, 'eval'), (data_train, 'train')]
    param = {'max_depth': 2, 'eta': 1, 'silent': 0, 'objective': 'multi:softmax', 'num_class': 3}
    bst = xgb.train(param, data_train, num_boost_round=4, evals=watch_list)  #evals=watch_list动态卡一看
    y_hat = bst.predict(data_test)
    print("##############XGboost#############")
    show_accuracy(y_hat, y_test, 'XGBoost ')

    #随机森林
    rnd_clf = RandomForestClassifier(n_estimators=10, max_leaf_nodes=7, n_jobs=1)
    rnd_clf.fit(x_train, y_train)
    y_hat = rnd_clf.predict(x_test)
    print("############随机森林 ###############")
    show_accuracy(y_hat, y_test, '随机森林 ')
    
    #bagging
    tree = DecisionTreeClassifier(criterion='entropy', max_depth=None)
    clf = BaggingClassifier(base_estimator=tree, n_estimators=6, max_samples=1.0, max_features=1.0, bootstrap=True,
                            bootstrap_features=False, n_jobs=1, random_state=1)
    clf.fit(x_train, y_train)
    y_hat = clf.predict(x_test)
    print("##############bagging#############")
    show_accuracy(y_hat, y_test, 'bagging ')
    
    #adaboost
    ada_real = AdaBoostClassifier(base_estimator=tree, learning_rate=0.5,
                                  n_estimators=6, algorithm='SAMME.R')  # 相比于ada_discrete只改变了Algorithm参数
    ada_real.fit(x_train, y_train)
    y_hat = clf.predict(x_test)
    print("##############adaboost#############")
    show_accuracy(y_hat, y_test, 'adaboost')

结果如下