Documentation
Classes
ParameterSampler

ParameterSampler

Generator on parameters sampled from given distributions.

Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new ParameterSampler(opts?: object): ParameterSampler;

Parameters

NameTypeDescription
opts?object-
opts.n_iter?numberNumber of parameter settings that are produced.
opts.param_distributions?anyDictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
opts.random_state?numberPseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns

ParameterSampler

Defined in: generated/model_selection/ParameterSampler.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/model_selection/ParameterSampler.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/model_selection/ParameterSampler.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/model_selection/ParameterSampler.ts:21 (opens in a new tab)

id

string

Defined in: generated/model_selection/ParameterSampler.ts:18 (opens in a new tab)

opts

any

Defined in: generated/model_selection/ParameterSampler.ts:19 (opens in a new tab)

Accessors

params

Yields* dictionaries mapping each estimator parameter to as sampled value.

Signature

params(): Promise<any>;

Returns

Promise<any>

Defined in: generated/model_selection/ParameterSampler.ts:119 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/model_selection/ParameterSampler.ts:45 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/model_selection/ParameterSampler.ts:49 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/model_selection/ParameterSampler.ts:102 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/model_selection/ParameterSampler.ts:58 (opens in a new tab)