python调参神器hyperopt

一、安装

pip install hyperopt

二、说明

Hyperopt提供了一个优化接口，这个接口接受一个评估函数和参数空间，能计算出参数空间内的一个点的损失函数值。用户还要指定空间内参数的分布情况。 
Hyheropt四个重要的因素：指定需要最小化的函数，搜索的空间，采样的数据集(trails database)（可选），搜索的算法（可选）。 
首先，定义一个目标函数,接受一个变量,计算后返回一个函数的损失值，比如要最小化函数q(x,y) = x**2 + y**2

指定搜索的算法，算法也就是hyperopt的fmin函数的algo参数的取值。当前支持的算法由随机搜索(对应是hyperopt.rand.suggest)，模拟退火(对应是hyperopt.anneal.suggest)，TPE算法。

关于参数空间的设置，比如优化函数q，输入fmin(q,space=hp.uniform(‘a’,0,1)).hp.uniform函数的第一个参数是标签，每个超参数在参数空间内必须具有独一无二的标签。hp.uniform指定了参数的分布。其他的参数分布比如 

hp.choice返回一个选项，选项可以是list或者tuple.options可以是嵌套的表达式，用于组成条件参数。 
hp.pchoice(label,p_options)以一定的概率返回一个p_options的一个选项。这个选项使得函数在搜索过程中对每个选项的可能性不均匀。 
hp.uniform(label,low,high)参数在low和high之间均匀分布。 
hp.quniform(label,low,high,q),参数的取值是round(uniform(low,high)/q)*q，适用于那些离散的取值。 
hp.loguniform(label,low,high)绘制exp(uniform(low,high)),变量的取值范围是[exp(low),exp(high)] 
hp.randint(label,upper) 返回一个在[0,upper)前闭后开的区间内的随机整数。 

搜索空间可以含有list和dictionary.

from hyperopt import hp
list_space = [
hp.uniform(’a’, 0, 1),
hp.loguniform(’b’, 0, 1)]
tuple_space = (
hp.uniform(’a’, 0, 1),
hp.loguniform(’b’, 0, 1))
dict_space = {
’a’: hp.uniform(’a’, 0, 1),
’b’: hp.loguniform(’b’, 0, 1)}

三、简单例子

from hyperopt import  hp,fmin, rand, tpe, space_eval

def q (args) :
    x, y = args
    return x**2-2*x+1 + y**2

space = [hp.randint('x', 5), hp.randint('y', 5)]

best = fmin(q,space,algo=rand.suggest,max_evals=10)

print(best)

输出：

{'x': 2, 'y': 0}

四、xgboost举例

xgboost具有很多的参数，把xgboost的代码写成一个函数，然后传入fmin中进行参数优化，将交叉验证的auc作为优化目标。auc越大越好，由于fmin是求最小值，因此求-auc的最小值。所用的数据集是202列的数据集，第一列样本id，最后一列是label,中间200列是属性。

#coding:utf-8
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import xgboost as xgb
from random import shuffle
from xgboost.sklearn import XGBClassifier
from sklearn.cross_validation import cross_val_score
import pickle
import time
from hyperopt import fmin, tpe, hp,space_eval,rand,Trials,partial,STATUS_OK

def loadFile(fileName = "E://zalei//browsetop200Pca.csv"):
    data = pd.read_csv(fileName,header=None)
    data = data.values
    return data

data = loadFile()
label = data[:,-1]
attrs = data[:,:-1]
labels = label.reshape((1,-1))
label = labels.tolist()[0]

minmaxscaler = MinMaxScaler()
attrs = minmaxscaler.fit_transform(attrs)

index = range(0,len(label))
shuffle(index)
trainIndex = index[:int(len(label)*0.7)]
print len(trainIndex)
testIndex = index[int(len(label)*0.7):]
print len(testIndex)
attr_train = attrs[trainIndex,:]
print attr_train.shape
attr_test = attrs[testIndex,:]
print attr_test.shape
label_train = labels[:,trainIndex].tolist()[0]
print len(label_train)
label_test = labels[:,testIndex].tolist()[0]
print len(label_test)
print np.mat(label_train).reshape((-1,1)).shape


def GBM(argsDict):
    max_depth = argsDict["max_depth"] + 5
    n_estimators = argsDict['n_estimators'] * 5 + 50
    learning_rate = argsDict["learning_rate"] * 0.02 + 0.05
    subsample = argsDict["subsample"] * 0.1 + 0.7
    min_child_weight = argsDict["min_child_weight"]+1
    print "max_depth:" + str(max_depth)
    print "n_estimator:" + str(n_estimators)
    print "learning_rate:" + str(learning_rate)
    print "subsample:" + str(subsample)
    print "min_child_weight:" + str(min_child_weight)
    global attr_train,label_train

    gbm = xgb.XGBClassifier(nthread=4,    #进程数
                            max_depth=max_depth,  #最大深度
                            n_estimators=n_estimators,   #树的数量
                            learning_rate=learning_rate, #学习率
                            subsample=subsample,      #采样数
                            min_child_weight=min_child_weight,   #孩子数
                            max_delta_step = 10,  #10步不降则停止
                            objective="binary:logistic")

    metric = cross_val_score(gbm,attr_train,label_train,cv=5,scoring="roc_auc").mean()
    print metric
    return -metric

space = {"max_depth":hp.randint("max_depth",15),
         "n_estimators":hp.randint("n_estimators",10),  #[0,1,2,3,4,5] -> [50,]
         "learning_rate":hp.randint("learning_rate",6),  #[0,1,2,3,4,5] -> 0.05,0.06
         "subsample":hp.randint("subsample",4),#[0,1,2,3] -> [0.7,0.8,0.9,1.0]
         "min_child_weight":hp.randint("min_child_weight",5), #
        }
algo = partial(tpe.suggest,n_startup_jobs=1)
best = fmin(GBM,space,algo=algo,max_evals=4)#max_evals表示想要训练的最大模型数量，越大越容易找到最优解

print best
print GBM(best)

详细参考：http://blog.csdn.net/qq_34139222/article/details/60322995