MIPLearn/miplearn/components/static_lazy.py

#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

import logging
from typing import Dict, Tuple, List, Hashable, Any, TYPE_CHECKING, Set

import numpy as np

from miplearn.classifiers import Classifier
from miplearn.classifiers.counting import CountingClassifier
from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
from miplearn.components.component import Component
from miplearn.features import TrainingSample, Features
from miplearn.types import LearningSolveStats

logger = logging.getLogger(__name__)

if TYPE_CHECKING:
    from miplearn.solvers.learning import LearningSolver, Instance


class LazyConstraint:
    def __init__(self, cid: str, obj: Any) -> None:
        self.cid = cid
        self.obj = obj


class StaticLazyConstraintsComponent(Component):
    """
    Component that decides which of the constraints tagged as lazy should
    be kept in the formulation, and which should be removed.
    """

    def __init__(
        self,
        classifier: Classifier = CountingClassifier(),
        threshold: Threshold = MinProbabilityThreshold([0.50, 0.50]),
        violation_tolerance: float = -0.5,
    ) -> None:
        assert isinstance(classifier, Classifier)
        self.classifier_prototype: Classifier = classifier
        self.threshold_prototype: Threshold = threshold
        self.classifiers: Dict[Hashable, Classifier] = {}
        self.thresholds: Dict[Hashable, Threshold] = {}
        self.pool: Dict[str, LazyConstraint] = {}
        self.violation_tolerance: float = violation_tolerance
        self.enforced_cids: Set[str] = set()
        self.n_restored: int = 0
        self.n_iterations: int = 0

    def before_solve_mip(
        self,
        solver: "LearningSolver",
        instance: "Instance",
        model: Any,
        stats: LearningSolveStats,
        features: Features,
        training_data: TrainingSample,
    ) -> None:
        assert solver.internal_solver is not None
        assert features.instance is not None
        assert features.constraints is not None

        if not features.instance.lazy_constraint_count == 0:
            logger.info("Instance does not have static lazy constraints. Skipping.")
        logger.info("Predicting required lazy constraints...")
        self.enforced_cids = set(self.sample_predict(instance, training_data))
        logger.info("Moving lazy constraints to the pool...")
        self.pool = {}
        for (cid, cdict) in features.constraints.items():
            if cdict.lazy and cid not in self.enforced_cids:
                self.pool[cid] = LazyConstraint(
                    cid=cid,
                    obj=solver.internal_solver.extract_constraint(cid),
                )
        logger.info(
            f"{len(self.enforced_cids)} lazy constraints kept; "
            f"{len(self.pool)} moved to the pool"
        )
        stats["LazyStatic: Removed"] = len(self.pool)
        stats["LazyStatic: Kept"] = len(self.enforced_cids)
        stats["LazyStatic: Restored"] = 0
        self.n_restored = 0
        self.n_iterations = 0

    def after_solve_mip(
        self,
        solver: "LearningSolver",
        instance: "Instance",
        model: Any,
        stats: LearningSolveStats,
        features: Features,
        training_data: TrainingSample,
    ) -> None:
        training_data.lazy_enforced = self.enforced_cids
        stats["LazyStatic: Restored"] = self.n_restored
        stats["LazyStatic: Iterations"] = self.n_iterations

    def iteration_cb(
        self,
        solver: "LearningSolver",
        instance: "Instance",
        model: Any,
    ) -> bool:
        if solver.use_lazy_cb:
            return False
        else:
            return self._check_and_add(solver)

    def lazy_cb(
        self,
        solver: "LearningSolver",
        instance: "Instance",
        model: Any,
    ) -> None:
        self._check_and_add(solver)

    def _check_and_add(self, solver: "LearningSolver") -> bool:
        assert solver.internal_solver is not None
        logger.info("Finding violated lazy constraints...")
        enforced: List[LazyConstraint] = []
        for (cid, c) in self.pool.items():
            if not solver.internal_solver.is_constraint_satisfied(
                c.obj,
                tol=self.violation_tolerance,
            ):
                enforced.append(c)
        logger.info(f"{len(enforced)} violations found")
        for c in enforced:
            del self.pool[c.cid]
            solver.internal_solver.add_constraint(c.obj)
            self.enforced_cids.add(c.cid)
            self.n_restored += 1
        logger.info(
            f"{len(enforced)} constraints restored; {len(self.pool)} in the pool"
        )
        if len(enforced) > 0:
            self.n_iterations += 1
            return True
        else:
            return False

    def sample_predict(
        self,
        instance: "Instance",
        sample: TrainingSample,
    ) -> List[str]:
        assert instance.features.constraints is not None

        x, y = self.sample_xy(instance, sample)
        category_to_cids: Dict[Hashable, List[str]] = {}
        for (cid, cfeatures) in instance.features.constraints.items():
            if cfeatures.category is None:
                continue
            category = cfeatures.category
            if category not in category_to_cids:
                category_to_cids[category] = []
            category_to_cids[category] += [cid]
        enforced_cids: List[str] = []
        for category in x.keys():
            if category not in self.classifiers:
                continue
            npx = np.array(x[category])
            proba = self.classifiers[category].predict_proba(npx)
            thr = self.thresholds[category].predict(npx)
            pred = list(proba[:, 1] > thr[1])
            for (i, is_selected) in enumerate(pred):
                if is_selected:
                    enforced_cids += [category_to_cids[category][i]]
        return enforced_cids

    def sample_xy(
        self,
        instance: "Instance",
        sample: TrainingSample,
    ) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
        assert instance.features.constraints is not None
        x: Dict = {}
        y: Dict = {}
        for (cid, cfeatures) in instance.features.constraints.items():
            if not cfeatures.lazy:
                continue
            category = cfeatures.category
            if category is None:
                continue
            if category not in x:
                x[category] = []
                y[category] = []
            x[category] += [cfeatures.user_features]
            if sample.lazy_enforced is not None:
                if cid in sample.lazy_enforced:
                    y[category] += [[False, True]]
                else:
                    y[category] += [[True, False]]
        return x, y

    def fit_xy(
        self,
        x: Dict[Hashable, np.ndarray],
        y: Dict[Hashable, np.ndarray],
    ) -> None:
        for c in y.keys():
            assert c in x
            self.classifiers[c] = self.classifier_prototype.clone()
            self.thresholds[c] = self.threshold_prototype.clone()
            self.classifiers[c].fit(x[c], y[c])
            self.thresholds[c].fit(self.classifiers[c], x[c], y[c])