cotengra.hyperoptimizers.hyper_cmaes

Hyper parameter optimization using cmaes, as implemented by

https://github.com/CyberAgentAILab/cmaes.

Classes

LCBOptimizer	Lower Confidence Bound Optimizer.
Param	A basic parameter class for mapping various types of parameters to
ParamFloat	A basic parameter class for mapping various types of parameters to
ParamFloatExp	An exponentially distributed (i.e. uniform in logspace) parameter.
ParamInt	A basic parameter class for mapping various types of parameters to
ParamString	A basic parameter class for mapping various types of parameters to
ParamBool	A basic parameter class for mapping various types of parameters to
HyperCMAESSampler

Functions

cmaes_init_optimizers(self, methods, space[, sigma, ...])
cmaes_get_setting(self)
cmaes_report_result(self, settings, trial, score)

Module Contents

class cotengra.hyperoptimizers.hyper_cmaes.LCBOptimizer(options, exploration=1.0, temperature=1.0, seed=None)

Lower Confidence Bound Optimizer.

This optimizer selects the option with the lowest lower confidence bound.

options

index

nopt

counts

values

total = 0

exploration = 1.0

temperature = 1.0

rng

ask()

Suggest an option based on the lower confidence bound.

tell(option, score)

class cotengra.hyperoptimizers.hyper_cmaes.Param(name)

A basic parameter class for mapping various types of parameters to and from uniform optimization space of [-1, 1].

name

size = 1

abstract get_raw_bounds()

abstract convert_raw(vi)

class cotengra.hyperoptimizers.hyper_cmaes.ParamFloat(min, max, **kwargs)

Bases: Param

A basic parameter class for mapping various types of parameters to and from uniform optimization space of [-1, 1].

min

max

convert_raw(x)

class cotengra.hyperoptimizers.hyper_cmaes.ParamFloatExp(min, max, power=0.5, **kwargs)

Bases: ParamFloat

An exponentially distributed (i.e. uniform in logspace) parameter.

power = 0.5

convert_raw(x)

class cotengra.hyperoptimizers.hyper_cmaes.ParamInt(min, max, **kwargs)

Bases: Param

A basic parameter class for mapping various types of parameters to and from uniform optimization space of [-1, 1].

min

max

convert_raw(x)

class cotengra.hyperoptimizers.hyper_cmaes.ParamString(options, name)

Bases: Param

A basic parameter class for mapping various types of parameters to and from uniform optimization space of [-1, 1].

options

size

name

convert_raw(x)

class cotengra.hyperoptimizers.hyper_cmaes.ParamBool(name)

Bases: Param

A basic parameter class for mapping various types of parameters to and from uniform optimization space of [-1, 1].

size = 2

name

convert_raw(x)

class cotengra.hyperoptimizers.hyper_cmaes.HyperCMAESSampler(space, sigma=1.0, lr_adapt=True, separable=False, exponential_param_power=None, **kwargs)

params = []

opt

_trial_counter = 0

_trial_store

_batch = []

ask()

tell(trial_number, value)

cotengra.hyperoptimizers.hyper_cmaes.cmaes_init_optimizers(self, methods, space, sigma=1.0, lr_adapt=True, method_exploration=1.0, method_temperature=1.0, exponential_param_power=None, **cmaes_opts)

cotengra.hyperoptimizers.hyper_cmaes.cmaes_get_setting(self)

cotengra.hyperoptimizers.hyper_cmaes.cmaes_report_result(self, settings, trial, score)