aup.Proposer.RandomProposer

Random sampling of the parameters

Configuration

General parameters

Name	Default value	Explanation
proposer		random
n_samples		Total number of trials to sample
random_seed	0	[Optional] seed for random generator

Specific parameters for parameter_config

Name	Explanation
name	name of the variable, will be used in the job config, i.e. training code
type	type of the parameter to be sampled: choose from “float”,”int”,”choice”
range	range of the parameter. For “choice”, list all the feasible values

APIs

class RandomProposer(config, random_seed=0)

Bases: aup.Proposer.AbstractProposer.AbstractProposer

Random proposer

Parameters

• config – experiment configuration contains the details searching space

• random_seed – default random seed if not in config

get_param(**kwargs)

Get the next parameter set :return: parameter name and value pairs in dict

reload(path)

Reload Proposer state from path

Parameters

path (str) – path to reload

save(path)

Save Proposer state to path.

Some proposer can not generate new parameters after saving.

Parameters

path (str) – path to save

static setup_config()

Set up experiment configuration :return: experiment config in dict.

verify_config(config)

Verify the input configuration is enough for the proposer

Parameters

config (dict) – Experiment configuration of parameter_config

Returns

config

Return type

dict