tune_parameters_for_benchmark.py

from thesis_lib.standalone_analysis.parameter_tuning import *
from thesis_lib.testdata.definitions import benchmark_images

import skopt
from skopt.space import Real, Categorical, Integer
import dill
import os
import sys

if __name__ == '__main__':
    idx = int(sys.argv[1])
    name, recipe = list(benchmark_images.items())[idx]
    print(name)

    result_name = name+'_starfinder_opt.pkl'
    if not os.path.exists(result_name):
        starfinder_obj = make_starfinder_objective(recipe, name)
        starfinder_dims = [
            Real(-6., 3, name='threshold'),
            Real(2., 8, name='fwhm'),
            Real(2., 5.5, name='sigma_radius'),
            Real(-15., 0., name='roundlo'),
            Real(0., 15., name='roundhi'),
            Real(-15, 0., name='sharplo'),
            Real(0., 10., name='sharphi')
        ]

        starfinder_optimizer = skopt.Optimizer(
            dimensions=starfinder_dims,
            n_jobs=12,
            random_state=1,
            base_estimator='RF',
            n_initial_points=1000,
            initial_point_generator='random'
        )

        starfinder_result = run_optimizer(starfinder_optimizer, starfinder_obj, n_evaluations=1400, n_processes=12)

        with open(name+'_starfinder_opt.pkl', 'wb') as f:
            dill.dump(starfinder_result, f)
        with open(name+'starfinder_params.txt', 'w') as f:
            f.write(str(list(zip(starfinder_result.space.dimension_names, starfinder_result.x))))
    else:
        with open(result_name, 'rb') as f:
            starfinder_result = dill.load(f)

    x = starfinder_result.x
    config = Config()
    config.threshold_factor = x[0]
    config.fwhm_guess = x[1]
    config.sigma_radius = x[2]
    config.roundlo = x[3]
    config.roundhi = x[4]
    config.sharplo = x[5]
    config.sharphi = x[6]
    config.use_catalogue_positions = False
    config.exclude_border = True
    config.photometry_iterations = 1  # TODO this should also be optimized maybe

    epsf_obj = make_epsf_objective(config, recipe, name)
    epsf_dims = [
        Integer(5, 40, name='cutout_size'),
        Integer(1, 10, name='fitshape_half'),
        Real(0.30, 3., name='sigma'),
        Categorical([4, 5, 7, 10], name='iters'),
        Categorical([1, 2, 4], name='oversampling'),
        Real(0.5, 2, name='separation factor')
    ]

    epsf_optimizer = skopt.Optimizer(
        dimensions=epsf_dims,
        n_jobs=12,
        random_state=1,
        base_estimator='RF',
        n_initial_points=50,
        initial_point_generator='random'
    )
    epsf_result = run_optimizer(epsf_optimizer, epsf_obj, n_evaluations=200, n_processes=9)

    with open(name+'_epsf_opt.pkl', 'wb') as f:
        dill.dump(epsf_result, f)
    with open(name + 'epsf_params.txt', 'w') as f:
        f.write(str(list(zip(epsf_result.space.dimension_names, epsf_result.x))))