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Explanation of Parameters in ZOOpt

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Yu-Ren Liu edited this page Jan 8, 2018 · 6 revisions

​​​With the aim of supporting machine learning tasks, ZOOpt includes a set of methods that are efficient and performance-guaranteed, with addons handling noise and high-dimensionality. This page explains parameters of ZOOpt.

Parameters in Dimension, Objective and Parameter

To handle different tasks, users are to set specific parameters in Dimension, Objective and Parameter objects. Constructors of these classes are listed here for looking up. This part can be skipped if you don't want to know all details of the parameters in these classes.

Dimension

class Dimension:
    """
    This class describes dimension information of the search space.
    """
     def __init__(self, size=0, regs=[], tys=[], order=[]):
  • size is an integer indicating the dimension size.
  • regs is a list contains the search space of each dimension (search space is a two-element list showing the range of each dimension, e.g., [-1, 1] for the range from -1 to 1).
  • tys is a list of boolean value, True means continuous in this dimension and False means discrete.
  • order is a list of boolean value, True means this dimension has a partial order relation and False means not. The boolean value in order is effective only when this dimension is discrete. By default, order=[False] * size. In most cases, order is optional.

Objective

class Objective:
    """
    This class represents the objective function and its associated variables
    """
    def __init__(self, func=None, dim=None, constraint=None, resample_func=None):
  • func is the objective function to be optimized. Indispensable parameter for all tasks.
  • dim is a Dimension object describing information of the search space. Indispensable parameter for all tasks.
  • constraint is set for subset selection algorithm POSS. Optional parameter.
  • resample_func and balance_rate is set for SSRACOS, a noise handling variant of general optimization method SRACOS. Optional parameters

Parameter

class Parameter:
    """
        This class contains all parameters used for optimization.
    """
    def __init__(self, algorithm=None, budget=0, init_samples=None, time_budget=None, terminal_value=None, sequential=True,
            	 precision=None, uncertain_bits=None, intermediate_result=False, intermediate_freq=100, autoset=True,
                 noise_handling=False, resampling=False, suppression=False, ponss=False, ponss_theta=None, ponss_b=None,
                 non_update_allowed=500, resample_times=100, balance_rate=0.5, high_dim_handling=False, reducedim=False, num_sre=5,
                 low_dimension=None, withdraw_alpha=Dimension(1, [[-1, 1]], [True]), variance_A=None):
  • budget is the only indispensable parameter of all tasks, it means the number of calls to the objective function.
  • autoset is True by default. If autoset=False, users should control all the algorithm parameters.
  • algorithm is the optimization algorithm that ZOOpt uses, can be 'racos' or 'poss'. By default it is set to 'racos'. When the solution space is binary and a constraint function has been set, the default algorithm is 'poss'.
  • init_samples is a list of samples (Solution objects) provided by user. By default it is None and the algorithm will randomly sample initial solutions. If the users do have some initial samples, set the samples to init_samples, and these samples will be added into the first sampled solution set.
  • time_budget set the time limit of the optimization algorithm. If running time exceeds this value, the optimization algorithm will return the best solution immediately.
  • terminal_value is set for early stop. The optimization procedure will stop if the function value reaches terminal_value
  • sequential switches between RACOS and SRACOS optimization algorithms. sequential equals to True by default and ZOOpt will use SRACOS. Otherwise, ZOOpt will use RACOS.
  • precision sets the precision of the result.
  • uncertain_bits sets the number of uncertain bits in RACOS, SRACOS, and SSRACOS.
  • intermediate_result and intermediate_freq are set for showing intermediate results during the optimization progress. The procedure will show the best solution every intermediate_freq calls to the objective function if intermediate_result=True.
  • noise_handling, resampling, suppression, ponss, ponss_theta, ponss_b, non_update_allowed, resample_times, balance_rate are set for noise handling.
  • high_dim_handling, reducedim, num_sre, low_dimension, withdraw_alpha, variance_A are set for high-dimensionality handling. Details of parameter setting for noise handling and high-dimensionality handling in ZOOpt will be discussed in the next part.

Parameter setting in different tasks

We will introduce the most important parameters in different tasks and omit the others.

Optimize a function with the continuous search space

A Dimension object should be paid attention to in this example. ty of the Dimension object should be set [True] * dim_size, which means it's search space is continuous.

dim_size = 10
dim = Dimension(dim_size, [[-1, 1]] * dim_size, [True] * dim_size)

Optimize a function with the discrete search space

In this example, ty of the Dimension object should be set [False] * dim_size, which means it's search space is discrete.

dim_size = 10
dim = Dimension(dim_size, [[-1, 1]] * dim_size, [False] * dim_size)

If the search space of a dimension is discrete and has partial order relation, order of this dimension should be set to True.

dim_size = 10
dim = Dimension(dim_size, [[-1, 1]] * dim_size, [False] * dim_size, [True] * dim_size)

Optimize a function with the mixed search space

In this example, the search space is mixed with continuous subspace and discrete subspace.

dim = Dimension(3, [[-1, 1]] * 3, [False, False, True], [False, True, False])

It means the dimension size is 3, the range of each dimension is [-1, 1]. The first dimension is discrete and does not have partial order relation. The second dimension is discrete and has partial order relation. The third dimension is continuous.

Optimize a noisy function

Three noise handling methods are implemented in ZOOpt, respectively are resampling, value suppression for SRACOS (SSRACOS) and threshold selection for POSS (PONSS).

Resampling

Resamping is a generic nosie handling method of all optimization algorithms. It evalueates one sample several times to obtain a stable mean value.

parameter = Parameter(budget=100000, noise_handling=True, resampling=True, resample_times=10)

To use resampling in ZOOpt, noise_handling and resampling should be set to True. resample_times, times of evaluating one sample, should also be provided by users.

Value Suppression for SRACOS (SSRACOS)

Value suppression is a noise handling method proposed recently.

parameter = Parameter(budget=100000, noise_handling=True, suppression=True, non_update_allowed=500, resample_times=100, balance_rate=0.5)

To use SSRACOS in ZOOpt, noise_handling and suppression should be set to True. non_update_allowed, resample_times and balance_rate should be provided by users. It means if the best solution doesn't change for non_update_allowed budgets, the best solution will be re-evaluated for resample_times times. balance_rate is a parameter for exponential weight average of several evaluations of one sample.

Threshold Selection for POSS (PONSS)

PONSS is a variant of POSS and designed to solve noisy subset selection problems.

parameter = Parameter(budget=20000, algorithm='poss', noise_handling=True, ponss=True, ponss_theta=0.5, ponss_b=8)

To use PONSS in ZOOpt, noise_handling and ponss should be set to True. ponss_theta and ponss_b are parameters used in PONSS algorithm and should be provided by users. ponss_theta stands for the threshold. ponss_b limits the number of solutions in the population set.

Optimize a high-dimensionality function

ZOOpt implements a high-dimensionality handling method called sequential random embedding (SRE).

parameter = Parameter(budget=100000, high_dim_handling=True, reducedim=True, num_sre=5, low_dimension=Dimension(10, [[-1, 1]] * 10, [True] * 10))

To use SRE in ZOOpt, high_dim_handling and reducedim should be set to True. num_sre, low_dimension and withdraw_alpha are parameters used in SRE and should be provided by users. num_sre means the number of sequential random embedding. low_dimension stands for the low dimension SRE projects to. withdraw_alpha and variance_A are optimal parameters. withdraw_alpha, a withdraw variable to the previous solution, is a Dimension object with only one dimension. variance_A specifies the variance of the projection matrix A. By default, withdraw_alpha equals to Dimension(1, [[-1, 1]], [True]) and variance_A equals to 1/d (d is the dimension size of the low_dimension). In most cases, it's not necessary for users to provide them.

Other useful parameters in the Parameter object

Set the Time Limit of the Optimization Procedure

parameter = Parameter(..., time_budget=3600, ...)

In this example, time budget is 3600s and it means if the overall running time exceeds 3600s, the optimization procedure will stop early and return the best solution so far.

Print intermediate results

parameter = Parameter(..., intermediate_result=True, intermediate_freq=100, ...)

intermediate_result and intermediate_freq are set for showing intermediate results during the optimization progress. The procedure will show the best solution every intermediate_freq calls to the objective function if intermediate_result=True. intermediate_freq is set to 100 by default.

In this example, the optimization procedure will print the best solution every 100 budgets.

​Set train_size and positive_size in RACOS, SRACOS and SSRACOS manully

parameter = Parameter(budget=20000)
parameter.set_train_size(22)
parameter.set_positive_size(2)

train_size represents the size of the binary classification data set, which is a component of RACOS, SRACOS and SSRACOS. positive_size represents the size of the positive data among all data. negetive_size is set to train_size - positive_size automatically. It shouldn't be set manually.

In most cases, default setting can work well and there's no need to set them manually.

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