launch_standalone_bayesian_optimization.py

# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import logging

from syne_tune.config_space import uniform, randint, choice

from syne_tune.optimizer.schedulers.searchers.bayesopt.datatypes.common import (
    dictionarize_objective,
)
from syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_factory import (
    make_hyperparameter_ranges,
)
from syne_tune.optimizer.schedulers.searchers.bayesopt.utils.test_objects import (
    create_tuning_job_state,
)
from syne_tune.optimizer.schedulers.searchers.gp_fifo_searcher import GPFIFOSearcher
from syne_tune.optimizer.schedulers.searchers.gp_searcher_utils import encode_state

if __name__ == "__main__":
    logging.getLogger().setLevel(logging.INFO)

    random_seed = 31415927

    # toy example of 3 hp's
    config_space = {
        "hp_1": uniform(-5.0, 5.0),
        "hp_2": randint(-5, 5),
        "hp_3": choice(["a", "b", "c"]),
    }
    hp_ranges = make_hyperparameter_ranges(config_space)
    batch_size = 16
    num_init_candidates_for_batch = 10
    state = create_tuning_job_state(
        hp_ranges=hp_ranges,
        cand_tuples=[
            (-3.0, -4, "a"),
            (2.2, -3, "b"),
            (-4.9, -1, "b"),
            (-1.9, -1, "c"),
            (-3.5, 3, "a"),
        ],
        metrics=[dictionarize_objective(x) for x in (15.0, 27.0, 13.0, 39.0, 35.0)],
    )

    gp_searcher = GPFIFOSearcher(
        state.hp_ranges.config_space,
        points_to_evaluate=None,
        random_seed=random_seed,
        metric="objective",
        debug_log=False,
    )
    gp_searcher_state = gp_searcher.get_state()
    gp_searcher_state["state"] = encode_state(state)
    gp_searcher = gp_searcher.clone_from_state(gp_searcher_state)

    next_candidate_list = gp_searcher.get_batch_configs(
        batch_size=batch_size,
        num_init_candidates_for_batch=num_init_candidates_for_batch,
    )

    assert len(next_candidate_list) == batch_size