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Version: v2603

General-Purpose Strategies

This page describes the general-purpose strategies that can be used with any Preset. For preset-specific strategies, see the documentation for each preset.

random​

Samples parameters randomly.

Parameters are sampled according to their type:

Parameter TypeSampling Method
IntParameterRandom selection based on step size when step is specified; uniform distribution over [low, high] otherwise
FloatParameterUniform distribution in log space when log=True, linear space otherwise
CategoricalParameterUniform random selection from choices
BoolParameterUniform random selection from True/False

Options:

ParameterDescriptionDefault
seedRandom seed for reproducibilityNone

The search continues until the trial budget (--n-trials) is exhausted.

grid​

Exhaustively searches all parameter combinations.

Generates value lists for each parameter and evaluates all combinations via itertools.product. Value list generation depends on the parameter type:

Parameter TypeValue List Generation
IntParameterrange(low, high+1, step) when step is specified; grid_points evenly spaced values otherwise
FloatParametergrid_points evenly spaced values in log space when log=True, linear space otherwise
CategoricalParameterAll choices
BoolParameter[True, False]

Options:

ParameterDescriptionDefault
grid_pointsNumber of grid points for parameters without an explicit step size5

The search automatically terminates when all combinations have been evaluated. Best suited for small search spaces.

optuna​

Samples parameters using Optuna's Sampler. By default, uses TPE (Tree-structured Parzen Estimator), which prioritizes exploring promising parameter regions based on past trial results.

Options:

ParameterDescriptionDefault
samplerOptuna sampler instanceTPESampler
storageOptuna storage URL (e.g., sqlite:///study.db)None
study_nameOptuna study nameNone

Specifying storage persists the search results, enabling interruption and resumption.