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feat: add custom and hp tuning (#388)
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| Original file line number | Diff line number | Diff line change |
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| # -*- coding: utf-8 -*- | ||
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| # Copyright 2021 Google LLC | ||
| # | ||
| # 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. | ||
| # | ||
|
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| import abc | ||
| from typing import Dict, List, Optional, Sequence, Tuple, Union | ||
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| import proto | ||
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| from google.cloud.aiplatform.compat.types import study as gca_study_compat | ||
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| _SCALE_TYPE_MAP = { | ||
| "linear": gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE, | ||
| "log": gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_LOG_SCALE, | ||
| "reverse_log": gca_study_compat.StudySpec.ParameterSpec.ScaleType.UNIT_REVERSE_LOG_SCALE, | ||
| "unspecified": gca_study_compat.StudySpec.ParameterSpec.ScaleType.SCALE_TYPE_UNSPECIFIED, | ||
| } | ||
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| class _ParameterSpec(metaclass=abc.ABCMeta): | ||
| """Base class represents a single parameter to optimize.""" | ||
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| def __init__( | ||
| self, | ||
| conditional_parameter_spec: Optional[Dict[str, "_ParameterSpec"]] = None, | ||
| parent_values: Optional[List[Union[float, int, str]]] = None, | ||
| ): | ||
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| self.conditional_parameter_spec = conditional_parameter_spec | ||
| self.parent_values = parent_values | ||
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| @property | ||
| @classmethod | ||
| @abc.abstractmethod | ||
| def _proto_parameter_value_class(self) -> proto.Message: | ||
| """The proto representation of this parameter.""" | ||
| pass | ||
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| @property | ||
| @classmethod | ||
| @abc.abstractmethod | ||
| def _parameter_value_map(self) -> Tuple[Tuple[str, str]]: | ||
| """A Tuple map of parameter key to underlying proto key.""" | ||
| pass | ||
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| @property | ||
| @classmethod | ||
| @abc.abstractmethod | ||
| def _parameter_spec_value_key(self) -> Tuple[Tuple[str, str]]: | ||
| """The ParameterSpec key this parameter should be assigned.""" | ||
| pass | ||
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| @property | ||
| def _proto_parameter_value_spec(self) -> proto.Message: | ||
| """Converts this parameter to it's parameter value representation.""" | ||
| proto_parameter_value_spec = self._proto_parameter_value_class() | ||
| for self_attr_key, proto_attr_key in self._parameter_value_map: | ||
| setattr( | ||
| proto_parameter_value_spec, proto_attr_key, getattr(self, self_attr_key) | ||
| ) | ||
| return proto_parameter_value_spec | ||
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| def _to_parameter_spec( | ||
| self, parameter_id: str | ||
| ) -> gca_study_compat.StudySpec.ParameterSpec: | ||
| """Converts this parameter to ParameterSpec.""" | ||
| # TODO: Conditional parameters | ||
| parameter_spec = gca_study_compat.StudySpec.ParameterSpec( | ||
| parameter_id=parameter_id, | ||
| scale_type=_SCALE_TYPE_MAP.get(getattr(self, "scale", "unspecified")), | ||
| ) | ||
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| setattr( | ||
| parameter_spec, | ||
| self._parameter_spec_value_key, | ||
| self._proto_parameter_value_spec, | ||
| ) | ||
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| return parameter_spec | ||
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| class DoubleParameterSpec(_ParameterSpec): | ||
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| _proto_parameter_value_class = ( | ||
| gca_study_compat.StudySpec.ParameterSpec.DoubleValueSpec | ||
| ) | ||
| _parameter_value_map = (("min", "min_value"), ("max", "max_value")) | ||
| _parameter_spec_value_key = "double_value_spec" | ||
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| def __init__( | ||
| self, min: float, max: float, scale: str, | ||
| ): | ||
| """ | ||
| Value specification for a parameter in ``DOUBLE`` type. | ||
| Args: | ||
| min (float): | ||
| Required. Inclusive minimum value of the | ||
| parameter. | ||
| max (float): | ||
| Required. Inclusive maximum value of the | ||
| parameter. | ||
| scale (str): | ||
| Required. The type of scaling that should be applied to this parameter. | ||
| Accepts: 'linear', 'log', 'reverse_log' | ||
| """ | ||
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| super().__init__() | ||
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| self.min = min | ||
| self.max = max | ||
| self.scale = scale | ||
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| class IntegerParameterSpec(_ParameterSpec): | ||
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| _proto_parameter_value_class = ( | ||
| gca_study_compat.StudySpec.ParameterSpec.IntegerValueSpec | ||
| ) | ||
| _parameter_value_map = (("min", "min_value"), ("max", "max_value")) | ||
| _parameter_spec_value_key = "integer_value_spec" | ||
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| def __init__( | ||
| self, min: int, max: int, scale: str, | ||
| ): | ||
| """ | ||
| Value specification for a parameter in ``INTEGER`` type. | ||
| Args: | ||
| min (float): | ||
| Required. Inclusive minimum value of the | ||
| parameter. | ||
| max (float): | ||
| Required. Inclusive maximum value of the | ||
| parameter. | ||
| scale (str): | ||
| Required. The type of scaling that should be applied to this parameter. | ||
| Accepts: 'linear', 'log', 'reverse_log' | ||
| """ | ||
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| super().__init__() | ||
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| self.min = min | ||
| self.max = max | ||
| self.scale = scale | ||
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| class CategoricalParameterSpec(_ParameterSpec): | ||
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| _proto_parameter_value_class = ( | ||
| gca_study_compat.StudySpec.ParameterSpec.CategoricalValueSpec | ||
| ) | ||
| _parameter_value_map = (("values", "values"),) | ||
| _parameter_spec_value_key = "categorical_value_spec" | ||
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| def __init__( | ||
| self, values: Sequence[str], | ||
| ): | ||
| """Value specification for a parameter in ``CATEGORICAL`` type. | ||
| Args: | ||
| values (Sequence[str]): | ||
| Required. The list of possible categories. | ||
| """ | ||
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| super().__init__() | ||
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| self.values = values | ||
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| class DiscreteParameterSpec(_ParameterSpec): | ||
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| _proto_parameter_value_class = ( | ||
| gca_study_compat.StudySpec.ParameterSpec.DiscreteValueSpec | ||
| ) | ||
| _parameter_value_map = (("values", "values"),) | ||
| _parameter_spec_value_key = "discrete_value_spec" | ||
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| def __init__( | ||
| self, values: Sequence[float], scale: str, | ||
| ): | ||
| """Value specification for a parameter in ``DISCRETE`` type. | ||
| values (Sequence[float]): | ||
| Required. A list of possible values. | ||
| The list should be in increasing order and at | ||
| least 1e-10 apart. For instance, this parameter | ||
| might have possible settings of 1.5, 2.5, and | ||
| 4.0. This list should not contain more than | ||
| 1,000 values. | ||
| scale (str): | ||
| Required. The type of scaling that should be applied to this parameter. | ||
| Accepts: 'linear', 'log', 'reverse_log' | ||
| """ | ||
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| super().__init__() | ||
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| self.values = values | ||
| self.scale = scale |
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