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241 lines
8.3 KiB
241 lines
8.3 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 logging
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from copy import deepcopy
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import numpy as np
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from p_tqdm import p_umap
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from tqdm import tqdm
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components import classifier_evaluation_dict
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from miplearn.components.component import Component
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from miplearn.components.lazy_static import LazyConstraint
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logger = logging.getLogger(__name__)
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class DropRedundantInequalitiesStep(Component):
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"""
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Component that predicts which inequalities are likely loose in the LP and removes
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them. Optionally, double checks after the problem is solved that all dropped
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inequalities were in fact redundant, and, if not, re-adds them to the problem.
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This component does not work on MIPs. All integrality constraints must be relaxed
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before this component is used.
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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=0.95,
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slack_tolerance=1e-5,
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check_feasibility=True,
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violation_tolerance=1e-5,
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max_iterations=3,
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):
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self.classifiers = {}
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self.classifier_prototype = classifier
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self.threshold = threshold
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self.slack_tolerance = slack_tolerance
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self.pool = []
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self.check_feasibility = check_feasibility
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self.violation_tolerance = violation_tolerance
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self.max_iterations = max_iterations
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self.current_iteration = 0
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self.n_iterations = 0
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self.n_restored = 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.n_iterations = 0
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self.n_restored = 0
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self.current_iteration = 0
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logger.info("Predicting redundant LP constraints...")
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x, constraints = self.x(
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instance,
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constraint_ids=solver.internal_solver.get_constraint_ids(),
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)
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y = self.predict(x)
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self.pool = []
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n_dropped = 0
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n_kept = 0
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for category in y.keys():
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for i in range(len(y[category])):
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if y[category][i][1] == 1:
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cid = constraints[category][i]
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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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self.pool += [c]
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n_dropped += 1
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else:
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n_kept += 1
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stats["DropRedundant: Kept"] = n_kept
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stats["DropRedundant: Dropped"] = n_dropped
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logger.info(f"Extracted {n_dropped} predicted constraints")
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def after_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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if training_data.slacks is None:
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training_data.slacks = solver.internal_solver.get_inequality_slacks()
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stats["DropRedundant: Iterations"] = self.n_iterations
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stats["DropRedundant: Restored"] = self.n_restored
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def fit(self, training_instances, n_jobs=1):
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x, y = self.x_y(training_instances, n_jobs=n_jobs)
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for category in tqdm(x.keys(), desc="Fit (drop)"):
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if category not in self.classifiers:
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self.classifiers[category] = deepcopy(self.classifier_prototype)
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self.classifiers[category].fit(x[category], np.array(y[category]))
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@staticmethod
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def x(instance, constraint_ids):
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x = {}
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constraints = {}
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cids = constraint_ids
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for cid in cids:
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category = instance.get_constraint_category(cid)
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if category is None:
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continue
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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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constraints[category] += [cid]
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for category in x.keys():
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x[category] = np.array(x[category])
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return x, constraints
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def x_y(self, instances, n_jobs=1):
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def _extract(instance):
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x = {}
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y = {}
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for training_data in instance.training_data:
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for (cid, slack) in training_data.slacks.items():
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category = instance.get_constraint_category(cid)
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if category is None:
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continue
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if category not in x:
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x[category] = []
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if category not in y:
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y[category] = []
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if slack > self.slack_tolerance:
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y[category] += [[False, True]]
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else:
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y[category] += [[True, False]]
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x[category] += [instance.get_constraint_features(cid)]
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return x, y
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if n_jobs == 1:
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results = [_extract(i) for i in tqdm(instances, desc="Extract (drop 1/3)")]
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else:
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results = p_umap(
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_extract,
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instances,
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num_cpus=n_jobs,
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desc="Extract (drop 1/3)",
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)
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x_combined = {}
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y_combined = {}
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for (x, y) in tqdm(results, desc="Extract (drop 2/3)"):
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for category in x.keys():
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if category not in x_combined:
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x_combined[category] = []
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y_combined[category] = []
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x_combined[category] += x[category]
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y_combined[category] += y[category]
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for category in tqdm(x_combined.keys(), desc="Extract (drop 3/3)"):
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x_combined[category] = np.array(x_combined[category])
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y_combined[category] = np.array(y_combined[category])
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return x_combined, y_combined
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def predict(self, x):
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y = {}
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for (category, x_cat) in x.items():
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if category not in self.classifiers:
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continue
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y[category] = []
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x_cat = np.array(x_cat)
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proba = self.classifiers[category].predict_proba(x_cat)
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for i in range(len(proba)):
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if proba[i][1] >= self.threshold:
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y[category] += [[False, True]]
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else:
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y[category] += [[True, False]]
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return y
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def evaluate(self, instance, n_jobs=1):
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x, y_true = self.x_y([instance], n_jobs=n_jobs)
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y_pred = self.predict(x)
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tp, tn, fp, fn = 0, 0, 0, 0
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for category in tqdm(
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y_true.keys(),
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disable=len(y_true) < 100,
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desc="Eval (drop)",
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):
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for i in range(len(y_true[category])):
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if (category in y_pred) and (y_pred[category][i][1] == 1):
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if y_true[category][i][1] == 1:
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tp += 1
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else:
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fp += 1
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else:
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if y_true[category][i][1] == 1:
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fn += 1
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else:
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tn += 1
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return classifier_evaluation_dict(tp, tn, fp, fn)
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def iteration_cb(self, solver, instance, model):
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if not self.check_feasibility:
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return False
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if self.current_iteration >= self.max_iterations:
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return False
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if solver.internal_solver.is_infeasible():
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return False
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self.current_iteration += 1
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logger.debug("Checking that dropped constraints are satisfied...")
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constraints_to_add = []
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for c in self.pool:
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if not solver.internal_solver.is_constraint_satisfied(
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c.obj,
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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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solver.internal_solver.add_constraint(c.obj)
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if len(constraints_to_add) > 0:
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self.n_restored += len(constraints_to_add)
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logger.info(
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"%8d constraints %8d in the pool"
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% (len(constraints_to_add), len(self.pool))
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)
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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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