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663 lines
28 KiB
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.steps.drop_redundant</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># 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 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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from miplearn.extractors import InstanceIterator
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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=False,
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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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def before_solve(self, solver, instance, _):
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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_test(
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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.total_dropped = 0
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self.total_restored = 0
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self.total_kept = 0
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self.total_iterations = 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][0] == 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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self.total_dropped += 1
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else:
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self.total_kept += 1
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logger.info(f"Extracted {self.total_dropped} predicted constraints")
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def after_solve(
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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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training_data,
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):
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if "slacks" not in training_data.keys():
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training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
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stats.update(
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{
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"DropRedundant: Kept": self.total_kept,
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"DropRedundant: Dropped": self.total_dropped,
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"DropRedundant: Restored": self.total_restored,
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"DropRedundant: Iterations": self.total_iterations,
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}
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)
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def fit(self, training_instances):
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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(x.keys(), desc="Fit (rlx:drop_ineq)"):
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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], y[category])
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@staticmethod
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def _x_test(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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@staticmethod
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def _x_train(instances):
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x = {}
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for instance in tqdm(
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InstanceIterator(instances),
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desc="Extract (rlx:drop_ineq:x)",
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disable=len(instances) < 5,
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):
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for training_data in instance.training_data:
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cids = training_data["slacks"].keys()
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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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x[category] += [instance.get_constraint_features(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
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def x(self, instances):
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return self._x_train(instances)
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def y(self, instances):
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y = {}
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for instance in tqdm(
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InstanceIterator(instances),
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desc="Extract (rlx:drop_ineq:y)",
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disable=len(instances) < 5,
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):
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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 y:
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y[category] = []
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if slack > self.slack_tolerance:
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y[category] += [[1]]
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else:
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y[category] += [[0]]
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return y
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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] += [[1]]
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else:
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y[category] += [[0]]
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return y
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def evaluate(self, instance):
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x = self.x([instance])
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y_true = self.y([instance])
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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 y_true.keys():
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for i in range(len(y_true[category])):
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if y_pred[category][i][0] == 1:
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if y_true[category][i][0] == 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][0] == 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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|
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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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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.total_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.total_iterations += 1
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return True
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else:
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return False</code></pre>
|
|
</details>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
</section>
|
|
<section>
|
|
</section>
|
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<section>
|
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<h2 class="section-title" id="header-classes">Classes</h2>
|
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<dl>
|
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<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep"><code class="flex name class">
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<span>class <span class="ident">DropRedundantInequalitiesStep</span></span>
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<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=1e-05, check_feasibility=False, violation_tolerance=1e-05, max_iterations=3)</span>
|
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</code></dt>
|
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<dd>
|
|
<section class="desc"><p>Component that predicts which inequalities are likely loose in the LP and removes
|
|
them. Optionally, double checks after the problem is solved that all dropped
|
|
inequalities were in fact redundant, and, if not, re-adds them to the problem.</p>
|
|
<p>This component does not work on MIPs. All integrality constraints must be relaxed
|
|
before this component is used.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class DropRedundantInequalitiesStep(Component):
|
|
"""
|
|
Component that predicts which inequalities are likely loose in the LP and removes
|
|
them. Optionally, double checks after the problem is solved that all dropped
|
|
inequalities were in fact redundant, and, if not, re-adds them to the problem.
|
|
|
|
This component does not work on MIPs. All integrality constraints must be relaxed
|
|
before this component is used.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
classifier=CountingClassifier(),
|
|
threshold=0.95,
|
|
slack_tolerance=1e-5,
|
|
check_feasibility=False,
|
|
violation_tolerance=1e-5,
|
|
max_iterations=3,
|
|
):
|
|
self.classifiers = {}
|
|
self.classifier_prototype = classifier
|
|
self.threshold = threshold
|
|
self.slack_tolerance = slack_tolerance
|
|
self.pool = []
|
|
self.check_feasibility = check_feasibility
|
|
self.violation_tolerance = violation_tolerance
|
|
self.max_iterations = max_iterations
|
|
self.current_iteration = 0
|
|
|
|
def before_solve(self, solver, instance, _):
|
|
self.current_iteration = 0
|
|
|
|
logger.info("Predicting redundant LP constraints...")
|
|
x, constraints = self._x_test(
|
|
instance,
|
|
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
|
)
|
|
y = self.predict(x)
|
|
|
|
self.total_dropped = 0
|
|
self.total_restored = 0
|
|
self.total_kept = 0
|
|
self.total_iterations = 0
|
|
for category in y.keys():
|
|
for i in range(len(y[category])):
|
|
if y[category][i][0] == 1:
|
|
cid = constraints[category][i]
|
|
c = LazyConstraint(
|
|
cid=cid,
|
|
obj=solver.internal_solver.extract_constraint(cid),
|
|
)
|
|
self.pool += [c]
|
|
self.total_dropped += 1
|
|
else:
|
|
self.total_kept += 1
|
|
logger.info(f"Extracted {self.total_dropped} predicted constraints")
|
|
|
|
def after_solve(
|
|
self,
|
|
solver,
|
|
instance,
|
|
model,
|
|
stats,
|
|
training_data,
|
|
):
|
|
if "slacks" not in training_data.keys():
|
|
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
|
stats.update(
|
|
{
|
|
"DropRedundant: Kept": self.total_kept,
|
|
"DropRedundant: Dropped": self.total_dropped,
|
|
"DropRedundant: Restored": self.total_restored,
|
|
"DropRedundant: Iterations": self.total_iterations,
|
|
}
|
|
)
|
|
|
|
def fit(self, training_instances):
|
|
logger.debug("Extracting x and y...")
|
|
x = self.x(training_instances)
|
|
y = self.y(training_instances)
|
|
logger.debug("Fitting...")
|
|
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
|
if category not in self.classifiers:
|
|
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
|
self.classifiers[category].fit(x[category], y[category])
|
|
|
|
@staticmethod
|
|
def _x_test(instance, constraint_ids):
|
|
x = {}
|
|
constraints = {}
|
|
cids = constraint_ids
|
|
for cid in cids:
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in x:
|
|
x[category] = []
|
|
constraints[category] = []
|
|
x[category] += [instance.get_constraint_features(cid)]
|
|
constraints[category] += [cid]
|
|
for category in x.keys():
|
|
x[category] = np.array(x[category])
|
|
return x, constraints
|
|
|
|
@staticmethod
|
|
def _x_train(instances):
|
|
x = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (rlx:drop_ineq:x)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
for training_data in instance.training_data:
|
|
cids = training_data["slacks"].keys()
|
|
for cid in cids:
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in x:
|
|
x[category] = []
|
|
x[category] += [instance.get_constraint_features(cid)]
|
|
for category in x.keys():
|
|
x[category] = np.array(x[category])
|
|
return x
|
|
|
|
def x(self, instances):
|
|
return self._x_train(instances)
|
|
|
|
def y(self, instances):
|
|
y = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (rlx:drop_ineq:y)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
for training_data in instance.training_data:
|
|
for (cid, slack) in training_data["slacks"].items():
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in y:
|
|
y[category] = []
|
|
if slack > self.slack_tolerance:
|
|
y[category] += [[1]]
|
|
else:
|
|
y[category] += [[0]]
|
|
return y
|
|
|
|
def predict(self, x):
|
|
y = {}
|
|
for (category, x_cat) in x.items():
|
|
if category not in self.classifiers:
|
|
continue
|
|
y[category] = []
|
|
x_cat = np.array(x_cat)
|
|
proba = self.classifiers[category].predict_proba(x_cat)
|
|
for i in range(len(proba)):
|
|
if proba[i][1] >= self.threshold:
|
|
y[category] += [[1]]
|
|
else:
|
|
y[category] += [[0]]
|
|
return y
|
|
|
|
def evaluate(self, instance):
|
|
x = self.x([instance])
|
|
y_true = self.y([instance])
|
|
y_pred = self.predict(x)
|
|
tp, tn, fp, fn = 0, 0, 0, 0
|
|
for category in y_true.keys():
|
|
for i in range(len(y_true[category])):
|
|
if y_pred[category][i][0] == 1:
|
|
if y_true[category][i][0] == 1:
|
|
tp += 1
|
|
else:
|
|
fp += 1
|
|
else:
|
|
if y_true[category][i][0] == 1:
|
|
fn += 1
|
|
else:
|
|
tn += 1
|
|
return classifier_evaluation_dict(tp, tn, fp, fn)
|
|
|
|
def iteration_cb(self, solver, instance, model):
|
|
if not self.check_feasibility:
|
|
return False
|
|
if self.current_iteration >= self.max_iterations:
|
|
return False
|
|
self.current_iteration += 1
|
|
logger.debug("Checking that dropped constraints are satisfied...")
|
|
constraints_to_add = []
|
|
for c in self.pool:
|
|
if not solver.internal_solver.is_constraint_satisfied(
|
|
c.obj,
|
|
self.violation_tolerance,
|
|
):
|
|
constraints_to_add.append(c)
|
|
for c in constraints_to_add:
|
|
self.pool.remove(c)
|
|
solver.internal_solver.add_constraint(c.obj)
|
|
if len(constraints_to_add) > 0:
|
|
self.total_restored += len(constraints_to_add)
|
|
logger.info(
|
|
"%8d constraints %8d in the pool"
|
|
% (len(constraints_to_add), len(self.pool))
|
|
)
|
|
self.total_iterations += 1
|
|
return True
|
|
else:
|
|
return False</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate"><code class="name flex">
|
|
<span>def <span class="ident">evaluate</span></span>(<span>self, instance)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def evaluate(self, instance):
|
|
x = self.x([instance])
|
|
y_true = self.y([instance])
|
|
y_pred = self.predict(x)
|
|
tp, tn, fp, fn = 0, 0, 0, 0
|
|
for category in y_true.keys():
|
|
for i in range(len(y_true[category])):
|
|
if y_pred[category][i][0] == 1:
|
|
if y_true[category][i][0] == 1:
|
|
tp += 1
|
|
else:
|
|
fp += 1
|
|
else:
|
|
if y_true[category][i][0] == 1:
|
|
fn += 1
|
|
else:
|
|
tn += 1
|
|
return classifier_evaluation_dict(tp, tn, fp, fn)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit"><code class="name flex">
|
|
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def fit(self, training_instances):
|
|
logger.debug("Extracting x and y...")
|
|
x = self.x(training_instances)
|
|
y = self.y(training_instances)
|
|
logger.debug("Fitting...")
|
|
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
|
if category not in self.classifiers:
|
|
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
|
self.classifiers[category].fit(x[category], y[category])</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict"><code class="name flex">
|
|
<span>def <span class="ident">predict</span></span>(<span>self, x)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def predict(self, x):
|
|
y = {}
|
|
for (category, x_cat) in x.items():
|
|
if category not in self.classifiers:
|
|
continue
|
|
y[category] = []
|
|
x_cat = np.array(x_cat)
|
|
proba = self.classifiers[category].predict_proba(x_cat)
|
|
for i in range(len(proba)):
|
|
if proba[i][1] >= self.threshold:
|
|
y[category] += [[1]]
|
|
else:
|
|
y[category] += [[0]]
|
|
return y</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x"><code class="name flex">
|
|
<span>def <span class="ident">x</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def x(self, instances):
|
|
return self._x_train(instances)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y"><code class="name flex">
|
|
<span>def <span class="ident">y</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def y(self, instances):
|
|
y = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (rlx:drop_ineq:y)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
for training_data in instance.training_data:
|
|
for (cid, slack) in training_data["slacks"].items():
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in y:
|
|
y[category] = []
|
|
if slack > self.slack_tolerance:
|
|
y[category] += [[1]]
|
|
else:
|
|
y[category] += [[0]]
|
|
return y</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
<h3>Inherited members</h3>
|
|
<ul class="hlist">
|
|
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
|
|
<ul class="hlist">
|
|
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
|
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
|
</article>
|
|
<nav id="sidebar">
|
|
<h1>Index</h1>
|
|
<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
|
<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate">evaluate</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit">fit</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict">predict</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x">x</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y">y</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
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