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Description:

optunizer 0.1.17

optunizer
Optuna extension for JSON and YAML configuration files
Installation
pip install optunizer

with PostresSQL connector

pip install optunizer[psycopg]

Running

Suppose you have some script/program (e.g. main.py) with config in YAML/JSON file (e.g. config.yaml) that returns some output (e.g. metrics.json)

main.py

import json
import yaml
config_file = 'config.yaml'
with open(config_file) as f:
params = yaml.safe_load(f)
metric = params['param1'] + params['param2']
metrics = {'metric': metric}
metrics_file = 'metrics.json'
with open(metrics_file, 'w') as f:
json.dump(metrics, f)

config.yaml

param1: 2
param2: 0.5
param3: c

metrics.json

{
"metric": 0.3
}

Make optunizer config file, e.g. optunizer.yaml

attrs: # track all fields in files
config.yaml: true
metrics.json: true
optunizer_sysinfo.json: true
class: optunizer.optimizer.Optimizer
load_if_exists: true
export_csv: optunizer_results.csv
export_metrics: optunizer_metrics.json
export_sysinfo: optunizer_sysinfo.json
study: optunizer_test
objectives: # Specify objectives, e.g. fields in metrics.json file
metric@metrics.json: minimize
params: # Specify params, e.g. fields in config.yaml file
param1@config.yaml:
method: suggest_int
method_kwargs:
high: 3
low: 0
param2@config.yaml:
method: suggest_float
method_kwargs:
high: 1.0
low: 0.01
log: true
param3@config.yaml:
method: suggest_categorical
method_kwargs:
choices: [a, b, c]
pruner: PatientPruner
pruner_kwargs: # Specify pruner, e.g. PatientPruner with NopPruner subpruner
min_delta: 0
patience: 0
wrapped_pruner: NopPruner
wrapped_pruner_kwargs: {}
sampler: PartialFixedSampler
sampler_kwargs: # Specify sampler, e.g. PartialFixedSampler with GridSampler subsampler
base_sampler: RandomSampler
base_sampler_kwargs: {}
# base_sampler: GridSampler
# base_sampler_kwargs:
# search_space:
# param1@config.yaml: [0, 1, 2]
# param2@config.yaml: [0.01, 0.5]
fixed_params:
param3@config.yaml: a
subprocess_kwargs: # Specify your command
args:
- python
- main.py
- config.yaml

Run optunizer

OPTUNA_CONFIG=optunizer.yaml python -m optunizer

or
python -m optunizer optunizer.yaml

Run optunizer streamlit viz

pip install optunizer[viz]
python -m optunizer app

There are several useful environment variables, that could be set in command line, .env or .env.secret files

OPTUNA_CONFIG=optunizer.yaml
OPTUNA_CONFIG_APP=app.yaml
OPTUNA_SHARED=.env
OPTUNA_SECRET=.env.secret
OPTUNA_URL=postgresql+psycopg2://USER:PASSWORD@IP:PORT/DB # see https://docs.sqlalchemy.org/en/14/core/engines.html
OPTUNA_STUDY=STUDY_NAME
OPTUNA_TRIALS=3
OPTUNA_TIMEOUT=3600
OPTUNA_LOAD_IF_EXISTS=1
OPTUNA_EXPORT_CSV=CSV_FILE_NAME
OPTUNA_EXPORT_METRICS=METRICS_FILE_NAME
OPTUNA_EXPORT_SYSINFO=SYSINFO_FILE_NAME