# -*- coding: utf-8 -*-
# Copyright 2022 PyePAL authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
"""Provides some scheduling functions
that can be used to implement the _should_optimize_hyperparameters function"""
import math
import numpy as np
__all__ = ["linear", "exp_decay"]
[docs]def linear(iteration: int, frequency: int = 10) -> bool:
"""Optimize hyperparameters at equally spaced intervals
Args:
iteration (int): current iteration
frequency (int, optional): Spacing between the True outputs. Defaults to 10.
Returns:
bool: True if iteration can be divided by frequency without remainder
"""
if iteration == 1:
return True
if iteration % frequency == 0:
return True
return False
[docs]def exp_decay(iteration: int, base: int = 10) -> bool:
"""Optimize hyperparameters at logartihmically spaced intervals
Args:
iteration (int): current iteration
base (int, optional): Base of the logarithm. Defaults to 10.
Returns:
bool: True if iteration is on the log scaled grid
"""
if iteration == 1:
return True
result = math.log(iteration, base)
if np.abs(result - round(result)) < 0.00001:
return True
return False
