# 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.
from syne_tune.experiments.default_baselines import (
RandomSearch,
BayesianOptimization,
ASHA,
MOBSTER,
)
[docs]
class Methods:
RS = "RS"
ASHA = "ASHA"
BO = "BO"
BO_WARP = "BO-WARP"
BO_BOXCOX = "BO-BOXCOX"
BO_WARP_BOXCOX = "BO-WARP-BOXCOX"
MOBSTER = "MOBSTER"
MOBSTER_WARP = "MOBSTER-WARP"
MOBSTER_BOXCOX = "MOBSTER-BOXCOX"
MOBSTER_WARP_BOXCOX = "MOBSTER-WARP-BOXCOX"
methods = {
Methods.RS: lambda method_arguments: RandomSearch(method_arguments),
Methods.ASHA: lambda method_arguments: ASHA(
method_arguments,
type="promotion",
),
Methods.BO: lambda method_arguments: BayesianOptimization(method_arguments),
Methods.BO_WARP: lambda method_arguments: BayesianOptimization(
method_arguments,
search_options=dict(input_warping=True),
),
Methods.BO_BOXCOX: lambda method_arguments: BayesianOptimization(
method_arguments,
search_options=dict(boxcox_transform=True),
),
Methods.BO_WARP_BOXCOX: lambda method_arguments: BayesianOptimization(
method_arguments,
search_options=dict(input_warping=True, boxcox_transform=True),
),
Methods.MOBSTER: lambda method_arguments: MOBSTER(
method_arguments,
type="promotion",
),
Methods.MOBSTER_WARP: lambda method_arguments: MOBSTER(
method_arguments,
type="promotion",
search_options=dict(input_warping=True),
),
Methods.MOBSTER_BOXCOX: lambda method_arguments: MOBSTER(
method_arguments,
type="promotion",
search_options=dict(boxcox_transform=True),
),
Methods.MOBSTER_WARP_BOXCOX: lambda method_arguments: MOBSTER(
method_arguments,
type="promotion",
search_options=dict(input_warping=True, boxcox_transform=True),
),
}