PoSH-autosklearn源码分析

本文主要是介绍PoSH-autosklearn源码分析，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

论文：（2018ICML）
https://ml.informatik.uni-freiburg.de/papers/18-AUTOML-AutoChallenge.pdf

代码：
http://ml.informatik.uni-freiburg.de/downloads/automl_competition_2018.zip

数据：(codalab平台，需要注册)
https://competitions.codalab.org/competitions/17767#participate-get_data

TODO LIST

PoSH对时序数据是怎么处理的？

文章目录

特别苟的Manual design decisions
- 特征选择
- 少样本数不采用SH
Budget 的计算

特别苟的Manual design decisions

如果特征数>500, 用单变量特征选择（听起来很牛逼，看代码就知道怎么回事了）
如果样本数<1000, 不采用SuccessiveHaving。并且采用交叉验证的方式，而不是HoldOut

在附录A.3和A.4有比较详细的需求。

特征选择

特征选择
logic.project_data_via_feature_selection

imp = sklearn.preprocessing.Imputer(strategy='median')
pca = sklearn.feature_selection.SelectKBest(k=n_keep)
pipeline = sklearn.pipeline.Pipeline((('imp', imp), ('pca', pca)))

如果特征数>500, 强行将为500.
lib/logic.py:114

        D.feat_type = [ft for i, ft in enumerate(D.feat_type) if rval[2][i] == True]

更新feat_type

少样本数不采用SH

回到logic
lib/logic.py:227

    if min_budget == max_budget:res = SSB.run(len(autosklearn_portfolio), min_n_workers=1)else:res = SSB.run(1, min_n_workers=1)

如果样本数<1000, 则设置min_budget = max_budget，且不采用SH，强行迭代16次。

Budget 的计算

    max_budget = 1.0min_budget = 1.0 / 16eta = 4

eta是什么意思呢？可以回顾我的这篇文章：HpBandSter源码分析

n_iterations是用户指定的，stages是根据max_budget, min_budget, eta决定的，详见hpbandster.optimizers.bohb.BOHB#__init__

hpbandster/optimizers/bohb.py:101

self.max_SH_iter = -int(np.log(min_budget/max_budget)/np.log(eta)) + 1

max_budget=243
min_budget=9
eta=3

$s_{max}=log_{\eta}(R)$
$B=(S_{max}+1)R$

eta $\eta$ 其实是SuccessiveHaving的budget扩增倍数，从9到243，每次增加3倍，即：

9, 27, 81, 243

再看到PoSH的代码

    max_budget = 1.0min_budget = 1.0 / 16eta = 4

hp_util.SideShowBOHB

self.max_SH_iter = -int(np.log(min_budget / max_budget) / np.log(eta)) + 1
self.budgets = max_budget * np.power(eta,-np.linspace(self.max_SH_iter - 1,0, self.max_SH_iter))

self.max_SH_iter
Out[5]: 3
self.budgets
Out[6]: array([0.0625, 0.25  , 1.    ])

$\frac{1}{16} \Rightarrow \frac{4}{16} \Rightarrow \frac{16}{16}$

lib/hp_util.py:62

            self.budget_converter = {'libsvm_svc': lambda b: b,'random_forest': lambda b: int(b*128),'sgd': lambda b: int(b*512),'xgradient_boosting': lambda b: int(b*512),'extra_trees': lambda b: int(b*1024)}

budget与iterations的转换数与论文也是对应的

lib/logic.py:202

worker.run(background=True)

在用线程跑

portfolio.get_hydra_portfolio
获取混合文件夹

balancing的策略还是存在的

    SSB = hp_util.SideShowBOHB(configspace=worker.get_config_space(),initial_configs=autosklearn_portfolio,run_id=run_id,eta=eta, min_budget=min_budget, max_budget=max_budget,SH_only=True,       # suppresses Hyperband's outer loop and runs SuccessiveHalving onlynameserver=ns_host,nameserver_port=ns_port,ping_interval=sleep,job_queue_sizes=(-1, 0),dynamic_queue_size=True,)

eta
Out[4]: 4
min_budget
Out[5]: 0.0625
max_budget
Out[6]: 1.0
sleep
Out[7]: 5
run_id
Out[8]: '0'

SideShowBOHB是Master, AutoMLWorker是Worker。

class PortfolioBOHB(BOHB):""" subclasses the config_generator BOHB"""def __init__(self, initial_configs=None, *args, **kwargs):super().__init__(*args, **kwargs)if initial_configs is None:# dummy initial portfolioself.initial_configs = [self.configspace.sample_configuration().get_dictionary() for i in range(5)]else:self.initial_configs = initial_configs

继承BOHB，仅仅多个initial_configs

        cg = PortfolioBOHB(initial_configs=initial_configs,configspace=configspace,min_points_in_model=min_points_in_model,top_n_percent=top_n_percent,num_samples=num_samples,random_fraction=random_fraction,bandwidth_factor=bandwidth_factor,)

cg: ConfigGenerate

min_points_in_model
top_n_percent
Out[10]: 15
num_samples
Out[11]: 64
random_fraction
Out[12]: 0.5
bandwidth_factor
Out[13]: 3

min_points_in_model = None

hp_util.SideShowBOHB#get_next_iteration

iteration
Out[2]: 0
s
Out[3]: 2
n0
Out[4]: 16
ns
Out[5]: [16, 4, 1]
self.budgets[(-s - 1):]
Out[6]: array([0.0625, 0.25  , 1.    ])

s是HyperBand的bracket，代表stages数目。
ns代表配置数，依次递减。budgets预算数依次递增。

hpbandster.iterations.base.BaseIteration#add_configuration

self.config_sampler
Out[7]: <bound method PortfolioBOHB.get_config of <hp_util.PortfolioBOHB object at 0x7f1670208208>>

hp_util.PortfolioBOHB#get_config

    def get_config(self, budget):# return a portfolio member firstif len(self.initial_configs) > 0 and True:c = self.initial_configs.pop()return (c, {'portfolio_member': True})return (super().get_config(budget))