# -*- coding: utf-8 -*- # Copyright 2020 PyPAL 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 [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