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#!/usr/bin/env python
# encoding: utf-8
# The MIT License (MIT)
# Copyright (c) 2018-2020 CNRS
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# AUTHORS
# Hervé BREDIN - http://herve.niderb.fr
# Hadrien TITEUX - https://github.com/hadware
from typing import Iterable, Any
from .pipeline import Pipeline
from collections.abc import Mapping
class Parameter:
"""Base hyper-parameter"""
pass
class Categorical(Parameter):
"""Categorical hyper-parameter
The value is sampled from `choices`.
Parameters
----------
choices : iterable
Candidates of hyper-parameter value.
"""
def __init__(self, choices: Iterable):
super().__init__()
self.choices = list(choices)
# def __call__(self, name: str, trial: Trial):
# return trial.suggest_categorical(name, self.choices)
class DiscreteUniform(Parameter):
"""Discrete uniform hyper-parameter
The value is sampled from the range [low, high],
and the step of discretization is `q`.
Parameters
----------
low : `float`
Lower endpoint of the range of suggested values.
`low` is included in the range.
high : `float`
Upper endpoint of the range of suggested values.
`high` is included in the range.
q : `float`
A step of discretization.
"""
def __init__(self, low: float, high: float, q: float):
super().__init__()
self.low = float(low)
self.high = float(high)
self.q = float(q)
# def __call__(self, name: str, trial: Trial):
# return trial.suggest_discrete_uniform(name, self.low, self.high, self.q)
class Integer(Parameter):
"""Integer hyper-parameter
The value is sampled from the integers in [low, high].
Parameters
----------
low : `int`
Lower endpoint of the range of suggested values.
`low` is included in the range.
high : `int`
Upper endpoint of the range of suggested values.
`high` is included in the range.
"""
def __init__(self, low: int, high: int):
super().__init__()
self.low = int(low)
self.high = int(high)
# def __call__(self, name: str, trial: Trial):
# return trial.suggest_int(name, self.low, self.high)
class LogUniform(Parameter):
"""Log-uniform hyper-parameter
The value is sampled from the range [low, high) in the log domain.
Parameters
----------
low : `float`
Lower endpoint of the range of suggested values.
`low` is included in the range.
high : `float`
Upper endpoint of the range of suggested values.
`high` is excluded from the range.
"""
def __init__(self, low: float, high: float):
super().__init__()
self.low = float(low)
self.high = float(high)
# def __call__(self, name: str, trial: Trial):
# return trial.suggest_loguniform(name, self.low, self.high)
class Uniform(Parameter):
"""Uniform hyper-parameter
The value is sampled from the range [low, high) in the linear domain.
Parameters
----------
low : `float`
Lower endpoint of the range of suggested values.
`low` is included in the range.
high : `float`
Upper endpoint of the range of suggested values.
`high` is excluded from the range.
"""
def __init__(self, low: float, high: float):
super().__init__()
self.low = float(low)
self.high = float(high)
# def __call__(self, name: str, trial: Trial):
# return trial.suggest_uniform(name, self.low, self.high)
class Frozen(Parameter):
"""Frozen hyper-parameter
The value is fixed a priori
Parameters
----------
value :
Fixed value.
"""
def __init__(self, value: Any):
super().__init__()
self.value = value
# def __call__(self, name: str, trial: Trial):
# return self.value
class ParamDict(Pipeline, Mapping):
"""Dict-like structured hyper-parameter
Usage
-----
>>> params = ParamDict(param1=Uniform(0.0, 1.0), param2=Uniform(-1.0, 1.0))
>>> params = ParamDict(**{"param1": Uniform(0.0, 1.0), "param2": Uniform(-1.0, 1.0)})
"""
def __init__(self, **params):
super().__init__()
self.__params = params
for param_name, param_value in params.items():
setattr(self, param_name, param_value)
def __len__(self):
return len(self.__params)
def __iter__(self):
return iter(self.__params)
def __getitem__(self, param_name):
return getattr(self, param_name)
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