aup.Proposer.SpearmintProposer

Re-implementation of Spearmint. Most of the Spearmint code has not been changed Mainly wrap main.py

Be aware - all variables are vectorized except size=1 case. (different from spearmint original implementation)

The original source is forked from Spearmint github commit 70309f0.

Configuration

General parameters

Name	Default value	Explanation
proposer		spearmint
engine	GPEIOptChooser
engine_config		Options for spearmint chooser
grid_size	20000	Option for spearmint
n_samples		Total number of trials to sample
random_seed	0	[Optional] seed for random generator
spearmint_dir	spearmint	Spearmint working directory

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 SpearmintProposer(config)

Bases: aup.Proposer.AbstractProposer.AbstractProposer

failed(job)

Mark job as failed in proposer history.

Parameters

job (Job) – Failed job

get_param(**kwargs)

Get new proposed parameter values

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()

update(score, job)

Update scores in proposer history

Parameters

• score (float) – score returned by Job

• job (Job) – Finished job

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