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91 lines
3.8 KiB
91 lines
3.8 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import numbers
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import collections
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from typing import TYPE_CHECKING, Dict
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from miplearn.types import Features, ConstraintFeatures, InstanceFeatures
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if TYPE_CHECKING:
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from miplearn import InternalSolver, Instance
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class FeaturesExtractor:
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def __init__(
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self,
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internal_solver: "InternalSolver",
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) -> None:
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self.solver = internal_solver
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def extract(self, instance: "Instance") -> Features:
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return {
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"Instance": self._extract_instance(instance),
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"Constraints": self._extract_constraints(instance),
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"Variables": self._extract_variables(instance),
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}
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def _extract_variables(self, instance: "Instance") -> Dict:
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variables = self.solver.get_empty_solution()
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for (var_name, var_dict) in variables.items():
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for idx in var_dict.keys():
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user_features = None
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category = instance.get_variable_category(var_name, idx)
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if category is not None:
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assert isinstance(category, collections.Hashable), (
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f"Variable category must be be hashable. "
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f"Found {type(category).__name__} instead for var={var_name}."
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)
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user_features = instance.get_variable_features(var_name, idx)
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assert isinstance(user_features, list), (
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f"Variable features must be a list. "
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f"Found {type(user_features).__name__} instead for "
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f"var={var_name}."
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)
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assert isinstance(user_features[0], numbers.Real), (
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f"Variable features must be a list of numbers."
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f"Found {type(user_features[0]).__name__} instead "
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f"for var={var_name}."
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)
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var_dict[idx] = {
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"Category": category,
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"User features": user_features,
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}
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return variables
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def _extract_constraints(
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self,
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instance: "Instance",
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) -> Dict[str, ConstraintFeatures]:
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constraints: Dict[str, ConstraintFeatures] = {}
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for cid in self.solver.get_constraint_ids():
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user_features = None
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category = instance.get_constraint_category(cid)
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if category is not None:
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assert isinstance(category, collections.Hashable), (
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f"Constraint category must be hashable. "
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f"Found {type(category).__name__} instead for cid={cid}.",
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)
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user_features = instance.get_constraint_features(cid)
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assert isinstance(user_features, list), (
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f"Constraint features must be a list. "
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f"Found {type(user_features).__name__} instead for cid={cid}."
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)
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assert isinstance(user_features[0], float), (
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f"Constraint features must be a list of floats. "
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f"Found {type(user_features[0]).__name__} instead for cid={cid}."
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)
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constraints[cid] = {
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"RHS": self.solver.get_constraint_rhs(cid),
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"LHS": self.solver.get_constraint_lhs(cid),
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"Sense": self.solver.get_constraint_sense(cid),
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"Category": category,
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"User features": user_features,
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}
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return constraints
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@staticmethod
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def _extract_instance(instance: "Instance") -> InstanceFeatures:
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return {"User features": instance.get_instance_features()}
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