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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.steps.convert_tight</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 random
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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.steps.drop_redundant import DropRedundantInequalitiesStep
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from miplearn.extractors import InstanceIterator
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logger = logging.getLogger(__name__)
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class ConvertTightIneqsIntoEqsStep(Component):
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"""
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Component that predicts which inequality constraints are likely to be binding in
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the LP relaxation of the problem and converts them into equality constraints.
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This component always makes sure that the conversion process does not affect the
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feasibility of the problem. It can also, optionally, make sure that it does not affect
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the optimality, but this may be expensive.
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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=0.0,
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check_optimality=False,
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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.check_optimality = check_optimality
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self.converted = []
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self.original_sense = {}
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def before_solve(self, solver, instance, _):
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logger.info("Predicting tight LP constraints...")
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x, constraints = DropRedundantInequalitiesStep._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.n_converted = 0
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self.n_restored = 0
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self.n_kept = 0
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self.n_infeasible_iterations = 0
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self.n_suboptimal_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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s = solver.internal_solver.get_constraint_sense(cid)
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self.original_sense[cid] = s
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solver.internal_solver.set_constraint_sense(cid, "=")
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self.converted += [cid]
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self.n_converted += 1
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else:
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self.n_kept += 1
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logger.info(f"Converted {self.n_converted} inequalities")
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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["ConvertTight: Kept"] = self.n_kept
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stats["ConvertTight: Converted"] = self.n_converted
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stats["ConvertTight: Restored"] = self.n_restored
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stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
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stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
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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:conv_ineqs)"):
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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 x(self, instances):
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return DropRedundantInequalitiesStep._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:conv_ineqs:y)",
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disable=len(instances) < 5,
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):
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for (cid, slack) in instance.training_data[0]["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 0 <= 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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def iteration_cb(self, solver, instance, model):
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is_infeasible, is_suboptimal = False, False
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restored = []
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def check_pi(msense, csense, pi):
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if csense == "=":
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return True
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if msense == "max":
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if csense == "<":
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return pi >= 0
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else:
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return pi <= 0
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else:
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if csense == ">":
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return pi >= 0
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else:
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return pi <= 0
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def restore(cid):
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nonlocal restored
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csense = self.original_sense[cid]
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solver.internal_solver.set_constraint_sense(cid, csense)
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restored += [cid]
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if solver.internal_solver.is_infeasible():
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for cid in self.converted:
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pi = solver.internal_solver.get_dual(cid)
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if abs(pi) > 0:
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is_infeasible = True
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restore(cid)
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elif self.check_optimality:
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random.shuffle(self.converted)
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n_restored = 0
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for cid in self.converted:
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if n_restored >= 100:
|
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break
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pi = solver.internal_solver.get_dual(cid)
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csense = self.original_sense[cid]
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msense = solver.internal_solver.get_sense()
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if not check_pi(msense, csense, pi):
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is_suboptimal = True
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restore(cid)
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n_restored += 1
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|
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for cid in restored:
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self.converted.remove(cid)
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|
|
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if len(restored) > 0:
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self.n_restored += len(restored)
|
|
if is_infeasible:
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self.n_infeasible_iterations += 1
|
|
if is_suboptimal:
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self.n_suboptimal_iterations += 1
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logger.info(f"Restored {len(restored)} inequalities")
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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>
|
|
<section>
|
|
<h2 class="section-title" id="header-classes">Classes</h2>
|
|
<dl>
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|
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep"><code class="flex name class">
|
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<span>class <span class="ident">ConvertTightIneqsIntoEqsStep</span></span>
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<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=0.0, check_optimality=False)</span>
|
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</code></dt>
|
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<dd>
|
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<section class="desc"><p>Component that predicts which inequality constraints are likely to be binding in
|
|
the LP relaxation of the problem and converts them into equality constraints.</p>
|
|
<p>This component always makes sure that the conversion process does not affect the
|
|
feasibility of the problem. It can also, optionally, make sure that it does not affect
|
|
the optimality, but this may be expensive.</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 ConvertTightIneqsIntoEqsStep(Component):
|
|
"""
|
|
Component that predicts which inequality constraints are likely to be binding in
|
|
the LP relaxation of the problem and converts them into equality constraints.
|
|
|
|
This component always makes sure that the conversion process does not affect the
|
|
feasibility of the problem. It can also, optionally, make sure that it does not affect
|
|
the optimality, but this may be expensive.
|
|
|
|
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=0.0,
|
|
check_optimality=False,
|
|
):
|
|
self.classifiers = {}
|
|
self.classifier_prototype = classifier
|
|
self.threshold = threshold
|
|
self.slack_tolerance = slack_tolerance
|
|
self.check_optimality = check_optimality
|
|
self.converted = []
|
|
self.original_sense = {}
|
|
|
|
def before_solve(self, solver, instance, _):
|
|
logger.info("Predicting tight LP constraints...")
|
|
x, constraints = DropRedundantInequalitiesStep._x_test(
|
|
instance,
|
|
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
|
)
|
|
y = self.predict(x)
|
|
|
|
self.n_converted = 0
|
|
self.n_restored = 0
|
|
self.n_kept = 0
|
|
self.n_infeasible_iterations = 0
|
|
self.n_suboptimal_iterations = 0
|
|
for category in y.keys():
|
|
for i in range(len(y[category])):
|
|
if y[category][i][0] == 1:
|
|
cid = constraints[category][i]
|
|
s = solver.internal_solver.get_constraint_sense(cid)
|
|
self.original_sense[cid] = s
|
|
solver.internal_solver.set_constraint_sense(cid, "=")
|
|
self.converted += [cid]
|
|
self.n_converted += 1
|
|
else:
|
|
self.n_kept += 1
|
|
|
|
logger.info(f"Converted {self.n_converted} inequalities")
|
|
|
|
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["ConvertTight: Kept"] = self.n_kept
|
|
stats["ConvertTight: Converted"] = self.n_converted
|
|
stats["ConvertTight: Restored"] = self.n_restored
|
|
stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
|
|
stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_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:conv_ineqs)"):
|
|
if category not in self.classifiers:
|
|
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
|
self.classifiers[category].fit(x[category], y[category])
|
|
|
|
def x(self, instances):
|
|
return DropRedundantInequalitiesStep._x_train(instances)
|
|
|
|
def y(self, instances):
|
|
y = {}
|
|
for instance in tqdm(
|
|
InstanceIterator(instances),
|
|
desc="Extract (rlx:conv_ineqs:y)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in y:
|
|
y[category] = []
|
|
if 0 <= 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):
|
|
is_infeasible, is_suboptimal = False, False
|
|
restored = []
|
|
|
|
def check_pi(msense, csense, pi):
|
|
if csense == "=":
|
|
return True
|
|
if msense == "max":
|
|
if csense == "<":
|
|
return pi >= 0
|
|
else:
|
|
return pi <= 0
|
|
else:
|
|
if csense == ">":
|
|
return pi >= 0
|
|
else:
|
|
return pi <= 0
|
|
|
|
def restore(cid):
|
|
nonlocal restored
|
|
csense = self.original_sense[cid]
|
|
solver.internal_solver.set_constraint_sense(cid, csense)
|
|
restored += [cid]
|
|
|
|
if solver.internal_solver.is_infeasible():
|
|
for cid in self.converted:
|
|
pi = solver.internal_solver.get_dual(cid)
|
|
if abs(pi) > 0:
|
|
is_infeasible = True
|
|
restore(cid)
|
|
elif self.check_optimality:
|
|
random.shuffle(self.converted)
|
|
n_restored = 0
|
|
for cid in self.converted:
|
|
if n_restored >= 100:
|
|
break
|
|
pi = solver.internal_solver.get_dual(cid)
|
|
csense = self.original_sense[cid]
|
|
msense = solver.internal_solver.get_sense()
|
|
if not check_pi(msense, csense, pi):
|
|
is_suboptimal = True
|
|
restore(cid)
|
|
n_restored += 1
|
|
|
|
for cid in restored:
|
|
self.converted.remove(cid)
|
|
|
|
if len(restored) > 0:
|
|
self.n_restored += len(restored)
|
|
if is_infeasible:
|
|
self.n_infeasible_iterations += 1
|
|
if is_suboptimal:
|
|
self.n_suboptimal_iterations += 1
|
|
logger.info(f"Restored {len(restored)} inequalities")
|
|
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.convert_tight.ConvertTightIneqsIntoEqsStep.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.convert_tight.ConvertTightIneqsIntoEqsStep.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:conv_ineqs)"):
|
|
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.convert_tight.ConvertTightIneqsIntoEqsStep.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.convert_tight.ConvertTightIneqsIntoEqsStep.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 DropRedundantInequalitiesStep._x_train(instances)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.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:conv_ineqs:y)",
|
|
disable=len(instances) < 5,
|
|
):
|
|
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
|
category = instance.get_constraint_category(cid)
|
|
if category is None:
|
|
continue
|
|
if category not in y:
|
|
y[category] = []
|
|
if 0 <= 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.convert_tight.ConvertTightIneqsIntoEqsStep" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep">ConvertTightIneqsIntoEqsStep</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate">evaluate</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit">fit</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict">predict</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x">x</a></code></li>
|
|
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y">y</a></code></li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
</li>
|
|
</ul>
|
|
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