mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-06 01:18:52 -06:00
StaticLazy: Refactor
This commit is contained in:
@@ -151,8 +151,8 @@ class Component:
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def fit_xy(
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self,
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x: Dict[str, np.ndarray],
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y: Dict[str, np.ndarray],
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x: Dict[Hashable, np.ndarray],
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y: Dict[Hashable, np.ndarray],
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) -> None:
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"""
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Given two dictionaries x and y, mapping the name of the category to matrices
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@@ -4,7 +4,7 @@
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import logging
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import sys
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from typing import Dict, Tuple, Optional
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from typing import Dict, Tuple, Optional, List, Hashable, Any, TYPE_CHECKING, Set
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import numpy as np
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from tqdm.auto import tqdm
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@@ -12,203 +12,163 @@ from tqdm.auto import tqdm
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from miplearn import Classifier
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components.component import Component
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from miplearn.types import TrainingSample, Features
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from miplearn.types import TrainingSample, Features, LearningSolveStats
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logger = logging.getLogger(__name__)
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if TYPE_CHECKING:
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from miplearn.solvers.learning import LearningSolver, Instance
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class LazyConstraint:
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def __init__(self, cid, obj):
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def __init__(self, cid: str, obj: Any) -> None:
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self.cid = cid
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self.obj = obj
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class StaticLazyConstraintsComponent(Component):
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"""
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Component that decides which of the constraints tagged as lazy should
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be kept in the formulation, and which should be removed.
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"""
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def __init__(
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self,
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classifier=CountingClassifier(),
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threshold=0.05,
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use_two_phase_gap=True,
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large_gap=1e-2,
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violation_tolerance=-0.5,
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):
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classifier: Classifier = CountingClassifier(),
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threshold: float = 0.05,
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violation_tolerance: float = -0.5,
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) -> None:
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assert isinstance(classifier, Classifier)
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self.threshold = threshold
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self.classifier_prototype = classifier
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self.classifiers = {}
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self.pool = []
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self.original_gap = None
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self.large_gap = large_gap
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self.is_gap_large = False
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self.use_two_phase_gap = use_two_phase_gap
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self.violation_tolerance = violation_tolerance
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self.threshold: float = threshold
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self.classifier_prototype: Classifier = classifier
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self.classifiers: Dict[Hashable, Classifier] = {}
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self.pool: Dict[str, LazyConstraint] = {}
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self.violation_tolerance: float = violation_tolerance
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self.enforced_cids: Set[str] = set()
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self.n_restored: int = 0
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self.n_iterations: int = 0
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def before_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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features,
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training_data,
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):
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self.pool = []
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if not solver.use_lazy_cb and self.use_two_phase_gap:
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logger.info("Increasing gap tolerance to %f", self.large_gap)
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self.original_gap = solver.gap_tolerance
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self.is_gap_large = True
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solver.internal_solver.set_gap_tolerance(self.large_gap)
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solver: "LearningSolver",
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instance: "Instance",
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model: Any,
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stats: LearningSolveStats,
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features: Features,
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training_data: TrainingSample,
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) -> None:
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assert solver.internal_solver is not None
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if not features["Instance"]["Lazy constraint count"] == 0:
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logger.info("Instance does not have static lazy constraints. Skipping.")
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logger.info("Predicting required lazy constraints...")
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self.enforced_cids = set(self.sample_predict(features, training_data))
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logger.info("Moving lazy constraints to the pool...")
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self.pool = {}
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for (cid, cdict) in features["Constraints"].items():
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if cdict["Lazy"] and cid not in self.enforced_cids:
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self.pool[cid] = LazyConstraint(
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cid=cid,
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obj=solver.internal_solver.extract_constraint(cid),
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)
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logger.info(
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f"{len(self.enforced_cids)} lazy constraints kept; "
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f"{len(self.pool)} moved to the pool"
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)
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stats["LazyStatic: Removed"] = len(self.pool)
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stats["LazyStatic: Kept"] = len(self.enforced_cids)
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stats["LazyStatic: Restored"] = 0
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self.n_restored = 0
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self.n_iterations = 0
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instance.found_violated_lazy_constraints = []
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if instance.has_static_lazy_constraints():
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self._extract_and_predict_static(solver, instance)
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def after_solve_mip(
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self,
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solver: "LearningSolver",
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instance: "Instance",
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model: Any,
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stats: LearningSolveStats,
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features: Features,
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training_data: TrainingSample,
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) -> None:
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training_data["LazyStatic: Enforced"] = self.enforced_cids
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stats["LazyStatic: Restored"] = self.n_restored
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stats["LazyStatic: Iterations"] = self.n_iterations
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def iteration_cb(self, solver, instance, model):
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def iteration_cb(
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self,
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solver: "LearningSolver",
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instance: "Instance",
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model: Any,
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) -> bool:
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if solver.use_lazy_cb:
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return False
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else:
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should_repeat = self._check_and_add(instance, solver)
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if should_repeat:
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return True
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else:
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if self.is_gap_large:
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logger.info("Restoring gap tolerance to %f", self.original_gap)
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solver.internal_solver.set_gap_tolerance(self.original_gap)
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self.is_gap_large = False
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return True
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else:
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return False
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return self._check_and_add(solver)
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def lazy_cb(self, solver, instance, model):
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self._check_and_add(instance, solver)
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def lazy_cb(
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self,
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solver: "LearningSolver",
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instance: "Instance",
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model: Any,
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) -> None:
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self._check_and_add(solver)
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def _check_and_add(self, instance, solver):
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logger.debug("Finding violated lazy constraints...")
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constraints_to_add = []
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for c in self.pool:
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def _check_and_add(self, solver: "LearningSolver") -> bool:
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assert solver.internal_solver is not None
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logger.info("Finding violated lazy constraints...")
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enforced: List[LazyConstraint] = []
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for (cid, c) in self.pool.items():
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if not solver.internal_solver.is_constraint_satisfied(
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c.obj, tol=self.violation_tolerance
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c.obj,
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tol=self.violation_tolerance,
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):
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constraints_to_add.append(c)
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for c in constraints_to_add:
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self.pool.remove(c)
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enforced.append(c)
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logger.info(f"{len(enforced)} violations found")
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for c in enforced:
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del self.pool[c.cid]
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solver.internal_solver.add_constraint(c.obj)
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instance.found_violated_lazy_constraints += [c.cid]
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if len(constraints_to_add) > 0:
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logger.info(
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"%8d lazy constraints added %8d in the pool"
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% (len(constraints_to_add), len(self.pool))
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)
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self.enforced_cids.add(c.cid)
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self.n_restored += 1
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logger.info(
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f"{len(enforced)} constraints restored; {len(self.pool)} in the pool"
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)
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if len(enforced) > 0:
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self.n_iterations += 1
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return True
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else:
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return False
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def fit(self, training_instances):
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training_instances = [
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t
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for t in training_instances
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if hasattr(t, "found_violated_lazy_constraints")
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]
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logger.debug("Extracting x and y...")
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x = self.x(training_instances)
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y = self.y(training_instances)
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logger.debug("Fitting...")
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for category in tqdm(
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x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
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):
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if category not in self.classifiers:
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self.classifiers[category] = self.classifier_prototype.clone()
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self.classifiers[category].fit(x[category], y[category])
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def predict(self, instance):
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pass
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def evaluate(self, instances):
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pass
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def _extract_and_predict_static(self, solver, instance):
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x = {}
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constraints = {}
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logger.info("Extracting lazy constraints...")
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for cid in solver.internal_solver.get_constraint_ids():
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if instance.is_constraint_lazy(cid):
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category = instance.get_constraint_category(cid)
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if category not in x:
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x[category] = []
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constraints[category] = []
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x[category] += [instance.get_constraint_features(cid)]
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c = LazyConstraint(
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cid=cid,
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obj=solver.internal_solver.extract_constraint(cid),
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)
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constraints[category] += [c]
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self.pool.append(c)
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logger.info("%8d lazy constraints extracted" % len(self.pool))
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logger.info("Predicting required lazy constraints...")
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n_added = 0
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for (category, x_values) in x.items():
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def sample_predict(
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self,
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features: Features,
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sample: TrainingSample,
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) -> List[str]:
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x, y = self.sample_xy(features, sample)
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category_to_cids: Dict[Hashable, List[str]] = {}
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for (cid, cdict) in features["Constraints"].items():
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if "Category" not in cdict or cdict["Category"] is None:
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continue
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category = cdict["Category"]
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if category not in category_to_cids:
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category_to_cids[category] = []
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category_to_cids[category] += [cid]
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enforced_cids: List[str] = []
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for category in x.keys():
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if category not in self.classifiers:
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continue
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if isinstance(x_values[0], np.ndarray):
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x[category] = np.array(x_values)
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proba = self.classifiers[category].predict_proba(x[category])
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for i in range(len(proba)):
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if proba[i][1] > self.threshold:
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n_added += 1
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c = constraints[category][i]
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self.pool.remove(c)
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solver.internal_solver.add_constraint(c.obj)
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instance.found_violated_lazy_constraints += [c.cid]
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logger.info(
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"%8d lazy constraints added %8d in the pool"
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% (
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n_added,
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len(self.pool),
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)
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)
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def _collect_constraints(self, train_instances):
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constraints = {}
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for instance in train_instances:
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for cid in instance.found_violated_lazy_constraints:
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category = instance.get_constraint_category(cid)
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if category not in constraints:
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constraints[category] = set()
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constraints[category].add(cid)
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for (category, cids) in constraints.items():
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constraints[category] = sorted(list(cids))
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return constraints
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def x(self, train_instances):
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result = {}
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constraints = self._collect_constraints(train_instances)
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for (category, cids) in constraints.items():
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result[category] = []
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for instance in train_instances:
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for cid in cids:
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result[category].append(instance.get_constraint_features(cid))
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return result
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def y(self, train_instances):
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result = {}
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constraints = self._collect_constraints(train_instances)
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for (category, cids) in constraints.items():
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result[category] = []
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for instance in train_instances:
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for cid in cids:
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if cid in instance.found_violated_lazy_constraints:
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result[category].append([0, 1])
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else:
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result[category].append([1, 0])
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return result
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clf = self.classifiers[category]
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proba = clf.predict_proba(np.array(x[category]))
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pred = list(proba[:, 1] > self.threshold)
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for (i, is_selected) in enumerate(pred):
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if is_selected:
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enforced_cids += [category_to_cids[category][i]]
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return enforced_cids
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@staticmethod
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def sample_xy(
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features: Features,
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sample: TrainingSample,
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) -> Tuple[Dict, Dict]:
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) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
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x: Dict = {}
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y: Dict = {}
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for (cid, cfeatures) in features["Constraints"].items():
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@@ -227,3 +187,13 @@ class StaticLazyConstraintsComponent(Component):
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else:
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y[category] += [[True, False]]
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return x, y
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def fit_xy(
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self,
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x: Dict[Hashable, np.ndarray],
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y: Dict[Hashable, np.ndarray],
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) -> None:
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for c in y.keys():
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assert c in x
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self.classifiers[c] = self.classifier_prototype.clone()
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self.classifiers[c].fit(x[c], y[c])
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@@ -58,8 +58,8 @@ class ObjectiveValueComponent(Component):
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def fit_xy(
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self,
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x: Dict[str, np.ndarray],
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y: Dict[str, np.ndarray],
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x: Dict[Hashable, np.ndarray],
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y: Dict[Hashable, np.ndarray],
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) -> None:
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for c in ["Upper bound", "Lower bound"]:
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if c in y:
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@@ -84,9 +84,9 @@ class ObjectiveValueComponent(Component):
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def sample_xy(
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features: Features,
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sample: TrainingSample,
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) -> Tuple[Dict[str, List[List[float]]], Dict[str, List[List[float]]]]:
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x: Dict[str, List[List[float]]] = {}
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y: Dict[str, List[List[float]]] = {}
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) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
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x: Dict[Hashable, List[List[float]]] = {}
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y: Dict[Hashable, List[List[float]]] = {}
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f = list(features["Instance"]["User features"])
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if "LP value" in sample and sample["LP value"] is not None:
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f += [sample["LP value"]]
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@@ -148,7 +148,7 @@ class PrimalSolutionComponent(Component):
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def sample_xy(
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features: Features,
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sample: TrainingSample,
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) -> Tuple[Dict, Dict]:
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) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
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x: Dict = {}
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y: Dict = {}
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solution: Optional[Solution] = None
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@@ -227,8 +227,8 @@ class PrimalSolutionComponent(Component):
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def fit_xy(
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self,
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x: Dict[str, np.ndarray],
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y: Dict[str, np.ndarray],
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x: Dict[Hashable, np.ndarray],
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y: Dict[Hashable, np.ndarray],
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) -> None:
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for category in x.keys():
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clf = self.classifier_prototype.clone()
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