Source code for lale.search.search_space

# Copyright 2019 IBM Corporation
#
# 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
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import math
import logging
import numpy

from typing import Any, Dict, List, Set, Iterable, Iterator, Optional, Tuple, Union
from hyperopt import hp
from hyperopt.pyll import scope
from lale.util.VisitorMeta import AbstractVisitorMeta
from lale.search.PGO import FrequencyDistribution

logging.basicConfig(level=logging.WARNING)
logger = logging.getLogger(__name__)

PGO_input_type = Union[FrequencyDistribution, Iterable[Tuple[Any, int]], None]
[docs]class SearchSpace(metaclass=AbstractVisitorMeta): def __init__(self): pass
[docs]class SearchSpaceEnum(SearchSpace): pgo:Optional[FrequencyDistribution] vals:List[Any] def __init__(self, vals:Iterable[Any], pgo:PGO_input_type=None): super(SearchSpaceEnum, self).__init__() self.vals = sorted(vals, key=str) if pgo is None or isinstance(pgo, FrequencyDistribution): self.pgo = pgo else: self.pgo = FrequencyDistribution.asEnumValues(pgo, self.vals)
[docs]class SearchSpaceConstant(SearchSpaceEnum): def __init__(self, v, pgo:PGO_input_type=None): super(SearchSpaceConstant, self).__init__([v], pgo=pgo)
[docs]class SearchSpaceBool(SearchSpaceEnum): def __init__(self, pgo:PGO_input_type=None): super(SearchSpaceBool, self).__init__([True, False], pgo=pgo)
[docs]class SearchSpaceNumber(SearchSpace): minimum:Optional[float] exclusiveMinumum:bool maximum:Optional[float] exclusiveMaximum:bool discrete:bool distribution:str pgo:Optional[FrequencyDistribution] def __init__(self, minimum=None, exclusiveMinimum:bool=False, maximum=None, exclusiveMaximum:bool=False, discrete:bool=False, distribution="uniform", pgo:PGO_input_type=None) -> None: super(SearchSpaceNumber, self).__init__() self.minimum = minimum self.exclusiveMinimum = exclusiveMinimum self.maximum = maximum self.exclusiveMaximum = exclusiveMaximum self.distribution = distribution self.discrete = discrete if pgo is None or isinstance(pgo, FrequencyDistribution): self.pgo = pgo else: if discrete: self.pgo = FrequencyDistribution.asIntegerValues(pgo, inclusive_min=self.getInclusiveMin(), inclusive_max=self.getInclusiveMax()) else: self.pgo = FrequencyDistribution.asFloatValues(pgo, inclusive_min=self.getInclusiveMin(), inclusive_max=self.getInclusiveMax())
[docs] def getInclusiveMax(self): """ Return the maximum as an inclusive maximum (exclusive maxima are adjusted accordingly) """ max = self.maximum if self.exclusiveMaximum: if self.discrete: max = max - 1 else: max = numpy.nextafter(max, float('-inf')) return max
[docs] def getInclusiveMin(self): """ Return the maximum as an inclusive minimum (exclusive minima are adjusted accordingly) """ min = self.minimum if self.exclusiveMinimum: if self.discrete: min = min + 1 else: min = numpy.nextafter(min, float('+inf')) return min
[docs]class SearchSpaceArray(SearchSpace): def __init__(self, minimum:int=0, *, maximum:int, contents:SearchSpace, is_tuple=False) -> None: super(SearchSpaceArray, self).__init__() self.minimum = minimum self.maximum = maximum self.contents = contents self.is_tuple = is_tuple
[docs]class SearchSpaceList(SearchSpace): def __init__(self, contents:List[SearchSpace], is_tuple=False) -> None: super(SearchSpaceList, self).__init__() self.contents = contents self.is_tuple = is_tuple
[docs]class SearchSpaceObject(SearchSpace): def __init__(self, longName:str, keys:List[str], choices:Iterable[Any]) -> None: super(SearchSpaceObject, self).__init__() self.longName = longName self.keys = keys self.choices = choices