Optuna Sweeper plugin

  

This plugin enables Hydra applications to utilize Optuna for the optimization of the parameters of experiments.

Installation

This plugin requires hydra-core>=1.1.0. Please install it with the following command:

pip install hydra-core --upgrade

You can install the plugin via pip:

pip install hydra-optuna-sweeper --upgrade

There are several standard approaches for configuring plugins. Check this page for more information.

Usage

Please set hydra/sweeper to optuna in your config file.

defaults:
  - override hydra/sweeper: optuna

Alternatively, add hydra/sweeper=optuna option to your command line.

The default configuration is here.

Example 1: Single-Objective Optimization

We include an example in this directory. example/sphere.py implements a simple benchmark function to be minimized.

You can discover the Optuna sweeper parameters with:

python example/sphere.py hydra/sweeper=optuna --cfg hydra -p hydra.sweeper

# @package hydra.sweeper
sampler:
  _target_: optuna.samplers.TPESampler
  seed: 123
  consider_prior: true
  prior_weight: 1.0
  consider_magic_clip: true
  consider_endpoints: false
  n_startup_trials: 10
  n_ei_candidates: 24
  multivariate: false
  warn_independent_sampling: true
_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
direction: minimize
storage: null
study_name: sphere
n_trials: 20
n_jobs: 1
search_space:
  x:
    type: float
    low: -5.5
    high: 5.5
    step: 0.5
  'y':
    type: categorical
    choices:
    - -5
    - 0
    - 5

The function decorated with @hydra.main() returns a float which we want to minimize, the minimum is 0 and reached for:

x: 0
y: 0

To run optimization, clone the code and run the following command in the plugins/hydra_optuna_sweeper directory:

python example/sphere.py --multirun

You can also override the search space parametrization:

python example/sphere.py --multirun 'x=interval(-5.0, 5.0)' 'y=interval(0, 10)'

You might find the optimization_results.yaml file (i.e. best params and best value) under multirun logs folder:

name: optuna
best_params:
  x: 0.0
  'y': 0
best_value: 0.0

Sampler configuration

This plugin supports Optuna's samplers. You can change the sampler by overriding hydra/sweeper/sampler or change sampler settings within hydra.sweeper.sampler.

Search space configuration

This plugin supports Optuna's distributions to configure search spaces. They can be defined either through commandline override or config file.

Configuring through commandline override

Hydra provides a override parser that support rich syntax. Please refer to OverrideGrammer/Basic and OverrideGrammer/Extended for details.

Interval override

By default, interval is converted to UniformDistribution. You can use IntUniformDistribution, LogUniformDistribution or IntLogUniformDistribution by casting the interval to int and tagging it with log.

Example for interval override

python example/sphere.py --multirun 'x=int(interval(-5.0, 5.0))' 'y=tag(log, interval(1, 10))'

The output is as follows:

[HYDRA] Study name: sphere
[HYDRA] Storage: None
[HYDRA] Sampler: TPESampler
[HYDRA] Directions: ['minimize']
[HYDRA] Launching 1 jobs locally
[HYDRA]     #0 : x=-3 y=1.6859762540733367
[HYDRA] Launching 1 jobs locally
[HYDRA]     #1 : x=1 y=5.237816870668193
...
[HYDRA] Best parameters: {'x': 0, 'y': 1.0929184723430116}
[HYDRA] Best value: 1.1944707871885822

Range override

range is converted to IntUniformDistribution. If you apply shuffle to range, CategoricalDistribution is used instead.

Example for range override

python example/sphere.py --multirun 'x=range(-5.0, 5.0)' 'y=shuffle(range(-5, 5))'

The output is as follows:

[HYDRA] Study name: sphere
[HYDRA] Storage: None
[HYDRA] Sampler: TPESampler
[HYDRA] Directions: ['minimize']
[HYDRA] Launching 1 jobs locally
[HYDRA]     #0 : x=-3 y=-4
[HYDRA] Launching 1 jobs locally
[HYDRA]     #1 : x=1 y=-1
...
[HYDRA] Best parameters: {'x': 0, 'y': -1}
[HYDRA] Best value: 1.0

Choice override

choice is converted to CategoricalDistribution.

Example for choice override

python example/sphere.py --multirun 'x=choice(-5.0, 0.0, 5.0)' 'y=choice(0, 1, 2, 3, 4, 5)'

The output is as follows:

[HYDRA] Study name: sphere
[HYDRA] Storage: None
[HYDRA] Sampler: TPESampler
[HYDRA] Directions: ['minimize']
[HYDRA] Launching 1 jobs locally
[HYDRA]     #0 : x=5.0 y=5
[HYDRA] Launching 1 jobs locally
[HYDRA]     #1 : x=5.0 y=2
...
[HYDRA] Best parameters: {'x': 0.0, 'y': 0}
[HYDRA] Best value: 0.0

Configuring through config file

Int parameters

int parameters can be defined with the following fields:

• type: int
• low: lower bound
• high: upper bound
• step: discretization step (optional)
• log: if true, space is converted to the log domain

If log is false, the parameter is mapped to IntUniformDistribution. Otherwise, the parameter is mapped to IntLogUniformDistribution. Please note that step can not be set if log=true.

Float parameters

float parameters can be defined with the following fields:

• type: float
• low: lower bound
• high: upper bound
• step: discretization step
• log: if true, space is converted to the log domain

If log is false, the parameter is mapped to UniformDistribution or DiscreteUniformDistribution depending on the presence or absence of the step field, respectively. Otherwise, the parameter is mapped to LogUniformDistribution. Please note that step can not be set if log=true.

Categorical parameters

categorical parameters can be defined with the following fields:

• type: categorical
• choices: a list of parameter value candidates

The parameters are mapped to CategoricalDistribution.

Example 2: Multi-Objective Optimization

In the same directory, example/multi-objective.py implements a simple benchmark function, which has two objective values. We want to minimize two objectives simultaneously.

You can discover the Optuna sweeper parameters with:

python example/multi-objective.py hydra/sweeper=optuna --cfg hydra -p hydra.sweeper

Configuration of the multi-objective optimization example

# @package hydra.sweeper
sampler:
  _target_: optuna.samplers.NSGAIISampler
  seed: 123
  population_size: 50
  mutation_prob: null
  crossover_prob: 0.9
  swapping_prob: 0.5
  constraints_func: null
_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
direction:
- minimize
- minimize
storage: null
study_name: multi-objective
n_trials: 20
n_jobs: 1
search_space:
  x:
    type: float
    low: 0
    high: 5
    step: 0.5
  'y':
    type: float
    low: 0
    high: 3
    step: 0.5

To run the optimization, use the following command in the plugins/hydra_optuna_sweeper directory:

python example/multi-objective.py --multirun

For problems with trade-offs between two different objectives, there may be no single solution that simultaneously minimizes both objectives. Instead, we obtained a set of solutions, namely Pareto optimal solutions, that show the best trade-offs possible between the objectives. In the following figure, the blue dots show the Pareto optimal solutions in the optimization results.