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624 lines
27 KiB
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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.lazy_static</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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import sys
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from copy import deepcopy
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import numpy as np
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from tqdm.auto import tqdm
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components.component import Component
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logger = logging.getLogger(__name__)
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class LazyConstraint:
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def __init__(self, cid, obj):
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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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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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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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def before_solve(self, solver, instance, model):
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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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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(
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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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pass
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def iteration_cb(self, solver, instance, model):
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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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def lazy_cb(self, solver, instance, model):
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self._check_and_add(instance, 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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if not solver.internal_solver.is_constraint_satisfied(
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c.obj, 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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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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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] = deepcopy(self.classifier_prototype)
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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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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</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</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.lazy_static.LazyConstraint"><code class="flex name class">
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<span>class <span class="ident">LazyConstraint</span></span>
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<span>(</span><span>cid, obj)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
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<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class LazyConstraint:
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def __init__(self, cid, obj):
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self.cid = cid
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self.obj = obj</code></pre>
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</details>
|
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</dd>
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<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent"><code class="flex name class">
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<span>class <span class="ident">StaticLazyConstraintsComponent</span></span>
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<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05, use_two_phase_gap=True, large_gap=0.01, violation_tolerance=-0.5)</span>
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</code></dt>
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<dd>
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<section class="desc"><p>A Component is an object which adds functionality to a LearningSolver.</p>
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<p>For better code maintainability, LearningSolver simply delegates most of its
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functionality to Components. Each Component is responsible for exactly one ML
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|
strategy.</p></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class StaticLazyConstraintsComponent(Component):
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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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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
|
|
|
|
def before_solve(self, solver, instance, model):
|
|
self.pool = []
|
|
if not solver.use_lazy_cb and self.use_two_phase_gap:
|
|
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)
|
|
|
|
instance.found_violated_lazy_constraints = []
|
|
if instance.has_static_lazy_constraints():
|
|
self._extract_and_predict_static(solver, instance)
|
|
|
|
def after_solve(
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|
self,
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|
solver,
|
|
instance,
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|
model,
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|
stats,
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|
training_data,
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|
):
|
|
pass
|
|
|
|
def iteration_cb(self, solver, instance, model):
|
|
if solver.use_lazy_cb:
|
|
return False
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|
else:
|
|
should_repeat = self._check_and_add(instance, solver)
|
|
if should_repeat:
|
|
return True
|
|
else:
|
|
if self.is_gap_large:
|
|
logger.info("Restoring gap tolerance to %f", self.original_gap)
|
|
solver.internal_solver.set_gap_tolerance(self.original_gap)
|
|
self.is_gap_large = False
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return True
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else:
|
|
return False
|
|
|
|
def lazy_cb(self, solver, instance, model):
|
|
self._check_and_add(instance, solver)
|
|
|
|
def _check_and_add(self, instance, solver):
|
|
logger.debug("Finding violated lazy constraints...")
|
|
constraints_to_add = []
|
|
for c in self.pool:
|
|
if not solver.internal_solver.is_constraint_satisfied(
|
|
c.obj, tol=self.violation_tolerance
|
|
):
|
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constraints_to_add.append(c)
|
|
for c in constraints_to_add:
|
|
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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if len(constraints_to_add) > 0:
|
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logger.info(
|
|
"%8d lazy constraints added %8d in the pool"
|
|
% (len(constraints_to_add), len(self.pool))
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)
|
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return True
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|
else:
|
|
return False
|
|
|
|
def fit(self, training_instances):
|
|
training_instances = [
|
|
t
|
|
for t in training_instances
|
|
if hasattr(t, "found_violated_lazy_constraints")
|
|
]
|
|
|
|
logger.debug("Extracting x and y...")
|
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x = self.x(training_instances)
|
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y = self.y(training_instances)
|
|
|
|
logger.debug("Fitting...")
|
|
for category in tqdm(
|
|
x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
|
|
):
|
|
if category not in self.classifiers:
|
|
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
|
self.classifiers[category].fit(x[category], y[category])
|
|
|
|
def predict(self, instance):
|
|
pass
|
|
|
|
def evaluate(self, instances):
|
|
pass
|
|
|
|
def _extract_and_predict_static(self, solver, instance):
|
|
x = {}
|
|
constraints = {}
|
|
logger.info("Extracting lazy constraints...")
|
|
for cid in solver.internal_solver.get_constraint_ids():
|
|
if instance.is_constraint_lazy(cid):
|
|
category = instance.get_constraint_category(cid)
|
|
if category not in x:
|
|
x[category] = []
|
|
constraints[category] = []
|
|
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))
|
|
logger.info("Predicting required lazy constraints...")
|
|
n_added = 0
|
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for (category, x_values) in x.items():
|
|
if category not in self.classifiers:
|
|
continue
|
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if isinstance(x_values[0], np.ndarray):
|
|
x[category] = np.array(x_values)
|
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proba = self.classifiers[category].predict_proba(x[category])
|
|
for i in range(len(proba)):
|
|
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)
|
|
solver.internal_solver.add_constraint(c.obj)
|
|
instance.found_violated_lazy_constraints += [c.cid]
|
|
logger.info(
|
|
"%8d lazy constraints added %8d in the pool"
|
|
% (
|
|
n_added,
|
|
len(self.pool),
|
|
)
|
|
)
|
|
|
|
def _collect_constraints(self, train_instances):
|
|
constraints = {}
|
|
for instance in train_instances:
|
|
for cid in instance.found_violated_lazy_constraints:
|
|
category = instance.get_constraint_category(cid)
|
|
if category not in constraints:
|
|
constraints[category] = set()
|
|
constraints[category].add(cid)
|
|
for (category, cids) in constraints.items():
|
|
constraints[category] = sorted(list(cids))
|
|
return constraints
|
|
|
|
def x(self, train_instances):
|
|
result = {}
|
|
constraints = self._collect_constraints(train_instances)
|
|
for (category, cids) in constraints.items():
|
|
result[category] = []
|
|
for instance in train_instances:
|
|
for cid in cids:
|
|
result[category].append(instance.get_constraint_features(cid))
|
|
return result
|
|
|
|
def y(self, train_instances):
|
|
result = {}
|
|
constraints = self._collect_constraints(train_instances)
|
|
for (category, cids) in constraints.items():
|
|
result[category] = []
|
|
for instance in train_instances:
|
|
for cid in cids:
|
|
if cid in instance.found_violated_lazy_constraints:
|
|
result[category].append([0, 1])
|
|
else:
|
|
result[category].append([1, 0])
|
|
return result</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.lazy_static.StaticLazyConstraintsComponent.evaluate"><code class="name flex">
|
|
<span>def <span class="ident">evaluate</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 evaluate(self, instances):
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.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):
|
|
training_instances = [
|
|
t
|
|
for t in training_instances
|
|
if hasattr(t, "found_violated_lazy_constraints")
|
|
]
|
|
|
|
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 (lazy)", disable=not sys.stdout.isatty()
|
|
):
|
|
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.lazy_static.StaticLazyConstraintsComponent.lazy_cb"><code class="name flex">
|
|
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def lazy_cb(self, solver, instance, model):
|
|
self._check_and_add(instance, solver)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict"><code class="name flex">
|
|
<span>def <span class="ident">predict</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 predict(self, instance):
|
|
pass</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.x"><code class="name flex">
|
|
<span>def <span class="ident">x</span></span>(<span>self, train_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, train_instances):
|
|
result = {}
|
|
constraints = self._collect_constraints(train_instances)
|
|
for (category, cids) in constraints.items():
|
|
result[category] = []
|
|
for instance in train_instances:
|
|
for cid in cids:
|
|
result[category].append(instance.get_constraint_features(cid))
|
|
return result</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.y"><code class="name flex">
|
|
<span>def <span class="ident">y</span></span>(<span>self, train_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, train_instances):
|
|
result = {}
|
|
constraints = self._collect_constraints(train_instances)
|
|
for (category, cids) in constraints.items():
|
|
result[category] = []
|
|
for instance in train_instances:
|
|
for cid in cids:
|
|
if cid in instance.found_violated_lazy_constraints:
|
|
result[category].append([0, 1])
|
|
else:
|
|
result[category].append([1, 0])
|
|
return result</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" href="index.html">miplearn.components</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.components.lazy_static.LazyConstraint" href="#miplearn.components.lazy_static.LazyConstraint">LazyConstraint</a></code></h4>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent">StaticLazyConstraintsComponent</a></code></h4>
|
|
<ul class="two-column">
|
|
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate">evaluate</a></code></li>
|
|
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit">fit</a></code></li>
|
|
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb">lazy_cb</a></code></li>
|
|
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict">predict</a></code></li>
|
|
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.x" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.x">x</a></code></li>
|
|
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.y" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.y">y</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
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