#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from abc import ABCMeta, abstractmethod, abstractproperty
from enum import Enum
from typing import Dict, List, Optional, Type, Union
from ax.core.base import Base
from ax.core.types import TParamValue
FIXED_CHOICE_PARAM_ERROR = (
"ChoiceParameters require multiple feasible values. "
"Please use FixedParameter instead when setting a single possible value."
)
[docs]class ParameterType(Enum):
BOOL: int = 0
INT: int = 1
FLOAT: int = 2
STRING: int = 3
@property
def is_numeric(self) -> bool:
return self == ParameterType.INT or self == ParameterType.FLOAT
TParameterType = Union[Type[int], Type[float], Type[str], Type[bool]]
# pyre: PARAMETER_PYTHON_TYPE_MAP is declared to have type
# pyre: `Dict[ParameterType, Union[Type[bool], Type[float], Type[int],
# pyre: Type[str]]]` but is used as type `Dict[ParameterType,
# pyre-fixme[9]: Type[Union[float, str]]]`.
PARAMETER_PYTHON_TYPE_MAP: Dict[ParameterType, TParameterType] = {
ParameterType.INT: int,
ParameterType.FLOAT: float,
ParameterType.STRING: str,
ParameterType.BOOL: bool,
}
[docs]class Parameter(Base, metaclass=ABCMeta):
_is_fidelity: bool = False
_target_value: Optional[TParamValue] = None
def _cast(self, value: TParamValue) -> TParamValue:
if value is None:
return None
# pyre-fixme[29]: `Union[Type[bool], Type[float], Type[int], Type[str]]` is
# not a function.
return self.python_type(value)
[docs] @abstractmethod
def validate(self, value: TParamValue) -> bool:
pass # pragma: no cover
@property
def python_type(self) -> TParameterType:
"""The python type for the corresponding ParameterType enum.
Used primarily for casting values of unknown type to conform
to that of the parameter.
"""
return PARAMETER_PYTHON_TYPE_MAP[self.parameter_type]
[docs] def is_valid_type(self, value: TParamValue) -> bool:
"""Whether a given value's type is allowed by this parameter."""
return type(value) is self.python_type
@property
def is_numeric(self) -> bool:
return self.parameter_type.is_numeric
@property
def is_fidelity(self) -> bool:
return self._is_fidelity
@property
def target_value(self) -> Optional[TParamValue]:
return self._target_value
@abstractproperty
def parameter_type(self) -> ParameterType:
pass # pragma: no cover
@abstractproperty
def name(self) -> str:
pass # pragma: no cover
[docs] def clone(self) -> "Parameter":
pass # pragma: no cover
[docs]class RangeParameter(Parameter):
"""Parameter object that specifies a continuous numerical range of values."""
def __init__(
self,
name: str,
parameter_type: ParameterType,
lower: float,
upper: float,
log_scale: bool = False,
digits: Optional[int] = None,
is_fidelity: bool = False,
target_value: Optional[TParamValue] = None,
) -> None:
"""Initialize RangeParameter
Args:
name: Name of the parameter.
parameter_type: Enum indicating the type of parameter
value (e.g. string, int).
lower: Lower bound of the parameter range.
upper: Upper bound of the parameter range.
log_scale: Whether to sample in the log space when drawing
random values of the parameter.
digits: Number of digits to round values to for float type.
is_fidelity: Whether this parameter is a fidelity parameter.
target_value: Target value of this parameter if it's fidelity.
"""
self._name = name
self._parameter_type = parameter_type
self._digits = digits
self._lower = self._cast(lower)
self._upper = self._cast(upper)
self._log_scale = log_scale
self._is_fidelity = is_fidelity
self._target_value = target_value
self._validate_range_param(
parameter_type=parameter_type, lower=lower, upper=upper, log_scale=log_scale
)
def _validate_range_param(
self,
lower: TParamValue,
upper: TParamValue,
log_scale: bool,
parameter_type: Optional[ParameterType] = None,
) -> None:
if parameter_type and parameter_type not in (
ParameterType.INT,
ParameterType.FLOAT,
):
raise ValueError("RangeParameter type must be int or float.")
if lower >= upper:
raise ValueError("max must be strictly larger than min.")
if log_scale and lower <= 0:
raise ValueError("Cannot take log when min <= 0.")
if not (self.is_valid_type(lower)) or not (self.is_valid_type(upper)):
raise ValueError(
f"[{lower}, {upper}] is an invalid range for this parameter."
)
@property
def parameter_type(self) -> ParameterType:
return self._parameter_type
@property
def name(self) -> str:
return self._name
@property
def upper(self) -> float:
"""Upper bound of the parameter range.
Value is cast to parameter type upon set and also validated
to ensure the bound is strictly greater than lower bound.
"""
return self._upper
@property
def lower(self) -> float:
"""Lower bound of the parameter range.
Value is cast to parameter type upon set and also validated
to ensure the bound is strictly less than upper bound.
"""
return self._lower
@property
def digits(self) -> Optional[int]:
"""Number of digits to round values to for float type.
Upper and lower bound are re-cast after this property is changed.
"""
return self._digits
@property
def log_scale(self) -> bool:
"""Whether to sample in log space when drawing random values of the parameter.
"""
return self._log_scale
[docs] def update_range(
self, lower: Optional[float] = None, upper: Optional[float] = None
) -> "RangeParameter":
"""Set the range to the given values.
If lower or upper is not provided, it will be left at its current value.
Args:
lower: New value for the lower bound.
upper: New value for the upper bound.
"""
if lower is None:
lower = self._lower
if upper is None:
upper = self._upper
cast_lower = self._cast(lower)
cast_upper = self._cast(upper)
self._validate_range_param(
lower=cast_lower, upper=cast_upper, log_scale=self.log_scale
)
self._lower = cast_lower
self._upper = cast_upper
return self
[docs] def set_digits(self, digits: int) -> "RangeParameter":
self._digits = digits
# Re-scale min and max to new digits definition
cast_lower = self._cast(self._lower)
cast_upper = self._cast(self._upper)
if cast_lower >= cast_upper:
raise ValueError(
f"Lower bound {cast_lower} is >= upper bound {cast_upper}."
)
self._lower = cast_lower
self._upper = cast_upper
return self
[docs] def set_log_scale(self, log_scale: bool) -> "RangeParameter":
self._log_scale = log_scale
return self
[docs] def validate(self, value: TParamValue) -> bool:
"""Returns True if input is a valid value for the parameter.
Checks that value is of the right type and within
the valid range for the parameter. Returns False if value is None.
Args:
value: Value being checked.
Returns:
True if valid, False otherwise.
"""
if value is None:
return False
if not self.is_valid_type(value):
return False
return value >= self._lower and value <= self._upper
[docs] def is_valid_type(self, value: TParamValue) -> bool:
"""Same as default except allows floats whose value is an int
for Int parameters.
"""
if not (isinstance(value, float) or isinstance(value, int)):
return False
# This might have issues with ints > 2^24
if self.parameter_type is ParameterType.INT:
return isinstance(value, int) or float(value).is_integer()
return True
[docs] def clone(self) -> "RangeParameter":
return RangeParameter(
name=self._name,
parameter_type=self._parameter_type,
lower=self._lower,
upper=self._upper,
log_scale=self._log_scale,
digits=self._digits,
is_fidelity=self._is_fidelity,
)
def _cast(self, value: TParamValue) -> TParamValue:
if value is None:
return None
if self.parameter_type is ParameterType.FLOAT and self._digits is not None:
# pyre-fixme[6]: Expected `None` for 2nd param but got `Optional[int]`.
return round(float(value), self._digits)
# pyre-fixme[29]: `Union[Type[bool], Type[float], Type[int], Type[str]]` is
# not a function.
return self.python_type(value)
def __repr__(self) -> str:
ret_val = (
f"RangeParameter("
f"name='{self._name}', "
f"parameter_type={self.parameter_type.name}, "
f"range=[{self._lower}, {self._upper}]"
)
if self._log_scale:
ret_val += f", log_scale={self._log_scale}"
if self._digits:
ret_val += f", digits={self._digits}"
if self.is_fidelity:
ret_val += (
f", fidelity={self.is_fidelity}, target_value={self.target_value}"
)
return ret_val + ")"
[docs]class ChoiceParameter(Parameter):
"""Parameter object that specifies a discrete set of values."""
def __init__(
self,
name: str,
parameter_type: ParameterType,
values: List[TParamValue],
is_ordered: bool = False,
is_task: bool = False,
is_fidelity: bool = False,
target_value: Optional[TParamValue] = None,
) -> None:
"""Initialize ChoiceParameter.
Args:
name: Name of the parameter.
parameter_type: Enum indicating the type of parameter
value (e.g. string, int).
values: List of allowed values for the parameter.
is_ordered: If False, the parameter is a categorical variable.
is_task: Treat the parameter as a task parameter for modeling.
target_value: Target value of this parameter if it's fidelity.
"""
self._is_ordered = is_ordered
self._is_task = is_task
self._is_fidelity = is_fidelity
self._target_value = target_value
self._name = name
self._parameter_type = parameter_type
# A choice parameter with only one value is a FixedParameter.
if not len(values) > 1:
raise ValueError(FIXED_CHOICE_PARAM_ERROR)
self._values = self._cast_values(values)
@property
def is_ordered(self) -> bool:
return self._is_ordered
@property
def is_task(self) -> bool:
return self._is_task
@property
def values(self) -> List[TParamValue]:
return self._values
@property
def parameter_type(self) -> ParameterType:
return self._parameter_type
@property
def name(self) -> str:
return self._name
[docs] def set_values(self, values: List[TParamValue]) -> "ChoiceParameter":
"""Set the list of allowed values for parameter.
Cast all input values to the parameter type.
Args:
values: New list of allowed values.
"""
# A choice parameter with only one value is a FixedParameter.
if not len(values) > 1:
raise ValueError(FIXED_CHOICE_PARAM_ERROR)
self._values = self._cast_values(values)
return self
[docs] def add_values(self, values: List[TParamValue]) -> "ChoiceParameter":
"""Add input list to the set of allowed values for parameter.
Cast all input values to the parameter type.
Args:
values: Values being added to the allowed list.
"""
self._values.extend(self._cast_values(values))
return self
[docs] def validate(self, value: TParamValue) -> bool:
"""Checks that the input is in the list of allowed values.
Args:
value: Value being checked.
Returns:
True if valid, False otherwise.
"""
return value in self._values
def _cast_values(self, values: List[TParamValue]) -> List[TParamValue]:
return [self._cast(value) for value in values]
[docs] def clone(self) -> "ChoiceParameter":
return ChoiceParameter(
name=self._name,
parameter_type=self._parameter_type,
values=self._values,
is_task=self._is_task,
is_fidelity=self._is_fidelity,
)
def __repr__(self) -> str:
return (
f"ChoiceParameter("
f"name='{self._name}', "
f"parameter_type={self.parameter_type.name}, "
f"values={self._values})"
)
[docs]class FixedParameter(Parameter):
"""Parameter object that specifies a single fixed value."""
def __init__(
self,
name: str,
parameter_type: ParameterType,
value: TParamValue,
is_fidelity: bool = False,
target_value: Optional[TParamValue] = None,
) -> None:
"""Initialize FixedParameter
Args:
name: Name of the parameter.
parameter_type: Enum indicating the type of parameter
value (e.g. string, int).
value: The fixed value of the parameter.
target_value: Target value of this parameter if it's fidelity.
"""
self._name = name
self._parameter_type = parameter_type
self._value = self._cast(value)
self._is_fidelity = is_fidelity
self._target_value = target_value
@property
def value(self) -> TParamValue:
return self._value
@property
def parameter_type(self) -> ParameterType:
return self._parameter_type
@property
def name(self) -> str:
return self._name
[docs] def set_value(self, value: TParamValue) -> "FixedParameter":
self._value = self._cast(value)
return self
[docs] def validate(self, value: TParamValue) -> bool:
"""Checks that the input is equal to the fixed value.
Args:
value: Value being checked.
Returns:
True if valid, False otherwise.
"""
return value == self._value
[docs] def clone(self) -> "FixedParameter":
return FixedParameter(
name=self._name,
parameter_type=self._parameter_type,
value=self._value,
is_fidelity=self._is_fidelity,
)
def __repr__(self) -> str:
return (
f"FixedParameter("
f"name='{self._name}', "
f"parameter_type={self.parameter_type.name}, "
f"value={self._value})"
)
