Source code for ax.core.parameter_constraint

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# pyre-strict

from __future__ import annotations

from typing import Dict, List, Union

from ax.core.parameter import ChoiceParameter, FixedParameter, Parameter, RangeParameter
from ax.core.types import ComparisonOp
from ax.utils.common.base import SortableBase


[docs]class ParameterConstraint(SortableBase): """Base class for linear parameter constraints. Constraints are expressed using a map from parameter name to weight followed by a bound. The constraint is satisfied if w * v <= b where: w is the vector of parameter weights. v is a vector of parameter values. b is the specified bound. * is the dot product operator. """ def __init__(self, constraint_dict: Dict[str, float], bound: float) -> None: """Initialize ParameterConstraint Args: constraint_dict: Map from parameter name to weight. bound: Bound of the inequality of the constraint. """ self._constraint_dict = constraint_dict self._bound = bound @property def constraint_dict(self) -> Dict[str, float]: """Get mapping from parameter names to weights.""" return self._constraint_dict @property def bound(self) -> float: """Get bound of the inequality of the constraint.""" return self._bound @bound.setter def bound(self, bound: float) -> None: """Set bound.""" self._bound = bound
[docs] def check(self, parameter_dict: Dict[str, Union[int, float]]) -> bool: """Whether or not the set of parameter values satisfies the constraint. Does a weighted sum of the parameter values based on the constraint_dict and checks that the sum is less than the bound. Args: parameter_dict: Map from parameter name to parameter value. Returns: Whether the constraint is satisfied. """ for parameter_name in self.constraint_dict.keys(): if parameter_name not in parameter_dict.keys(): raise ValueError(f"`{parameter_name}` not present in param_dict.") weighted_sum = sum( float(parameter_dict[param]) * weight for param, weight in self.constraint_dict.items() ) # Expected `int` for 2nd anonymous parameter to call `int.__le__` but got # `float`. return weighted_sum <= self._bound + 1e-8 # allow for numerical imprecision
[docs] def clone(self) -> ParameterConstraint: """Clone.""" return ParameterConstraint( constraint_dict=self._constraint_dict.copy(), bound=self._bound )
[docs] def clone_with_transformed_parameters( self, transformed_parameters: Dict[str, Parameter] ) -> ParameterConstraint: """Clone, but replaced parameters with transformed versions.""" return self.clone()
def __repr__(self) -> str: return ( "ParameterConstraint(" + " + ".join( "{}*{}".format(v, k) for k, v in sorted(self.constraint_dict.items()) ) + " <= {})".format(self._bound) ) @property def _unique_id(self) -> str: return str(self)
[docs]class OrderConstraint(ParameterConstraint): """Constraint object for specifying one parameter to be smaller than another.""" _bound: float def __init__(self, lower_parameter: Parameter, upper_parameter: Parameter) -> None: """Initialize OrderConstraint Args: lower_parameter: Parameter that should have the lower value. upper_parameter: Parameter that should have the higher value. Note: The constraint p1 <= p2 can be expressed in matrix notation as [1, -1] * [p1, p2]^T <= 0. """ validate_constraint_parameters([lower_parameter, upper_parameter]) self._lower_parameter = lower_parameter self._upper_parameter = upper_parameter self._bound = 0.0 @property def lower_parameter(self) -> Parameter: """Parameter with lower value.""" return self._lower_parameter @property def upper_parameter(self) -> Parameter: """Parameter with higher value.""" return self._upper_parameter @property def parameters(self) -> List[Parameter]: """Parameters.""" return [self.lower_parameter, self.upper_parameter] @property def constraint_dict(self) -> Dict[str, float]: """Weights on parameters for linear constraint representation.""" return {self.lower_parameter.name: 1.0, self.upper_parameter.name: -1.0}
[docs] def clone(self) -> OrderConstraint: """Clone.""" return OrderConstraint( lower_parameter=self.lower_parameter.clone(), upper_parameter=self._upper_parameter.clone(), )
[docs] def clone_with_transformed_parameters( self, transformed_parameters: Dict[str, Parameter] ) -> OrderConstraint: """Clone, but replace parameters with transformed versions.""" return OrderConstraint( lower_parameter=transformed_parameters[self.lower_parameter.name], upper_parameter=transformed_parameters[self._upper_parameter.name], )
def __repr__(self) -> str: return "OrderConstraint({} <= {})".format( self.lower_parameter.name, self.upper_parameter.name )
[docs]class SumConstraint(ParameterConstraint): """Constraint on the sum of parameters being greater or less than a bound.""" def __init__( self, parameters: List[Parameter], is_upper_bound: bool, bound: float ) -> None: """Initialize SumConstraint Args: parameters: List of parameters whose sum to constrain on. is_upper_bound: Whether the bound is an upper or lower bound on the sum. bound: The bound on the sum. """ validate_constraint_parameters(parameters) self._parameters = parameters self._is_upper_bound: bool = is_upper_bound self._parameter_names: List[str] = [parameter.name for parameter in parameters] self._bound: float = self._inequality_weight * bound self._constraint_dict: Dict[str, float] = { name: self._inequality_weight for name in self._parameter_names } @property def parameters(self) -> List[Parameter]: """Parameters.""" return self._parameters @property def constraint_dict(self) -> Dict[str, float]: """Weights on parameters for linear constraint representation.""" return self._constraint_dict @property def op(self) -> ComparisonOp: """Whether the sum is constrained by a <= or >= inequality.""" return ComparisonOp.LEQ if self._is_upper_bound else ComparisonOp.GEQ
[docs] def clone(self) -> SumConstraint: """Clone. To use the same constraint, we need to reconstruct the original bound. We do this by re-applying the original bound weighting. """ return SumConstraint( parameters=[p.clone() for p in self._parameters], is_upper_bound=self._is_upper_bound, bound=self._inequality_weight * self._bound, )
[docs] def clone_with_transformed_parameters( self, transformed_parameters: Dict[str, Parameter] ) -> SumConstraint: """Clone, but replace parameters with transformed versions.""" return SumConstraint( parameters=[transformed_parameters[p.name] for p in self._parameters], is_upper_bound=self._is_upper_bound, bound=self._inequality_weight * self._bound, )
@property def _inequality_weight(self) -> float: """Multiplier of all terms in the inequality. If the constraint is an upper bound, it is v1 + v2 ... v_n <= b If the constraint is an lower bound, it is -v1 + -v2 ... -v_n <= -b This property returns 1 or -1 depending on the scenario """ return 1.0 if self._is_upper_bound else -1.0 def __repr__(self) -> str: symbol = ">=" if self.op == ComparisonOp.GEQ else "<=" return ( "SumConstraint(" + " + ".join(self._parameter_names) + " {} {})".format( symbol, self._bound if self.op == ComparisonOp.LEQ else -self._bound ) )
[docs]def validate_constraint_parameters(parameters: List[Parameter]) -> None: """Basic validation of parameters used in a constraint. Args: parameters: Parameters used in constraint. Raises: ValueError if the parameters are not valid for use. """ unique_parameter_names = {p.name for p in parameters} if len(unique_parameter_names) != len(parameters): raise ValueError("Duplicate parameter in constraint.") for parameter in parameters: if not parameter.is_numeric: raise ValueError( "Parameter constraints only supported for numeric parameters." ) # Constraints on FixedParameters are non-sensical. if isinstance(parameter, FixedParameter): raise ValueError("Parameter constraints not supported for FixedParameter.") # ChoiceParameters are transformed either using OneHotEncoding # or the OrderedChoice transform. Both are non-linear, and # Ax models only support linear constraints. if isinstance(parameter, ChoiceParameter): raise ValueError("Parameter constraints not supported for ChoiceParameter.") # Log parameters require a non-linear transformation, and Ax # models only support linear constraints. if isinstance(parameter, RangeParameter) and parameter.log_scale is True: raise ValueError( "Parameter constraints not allowed on log scale parameters." )