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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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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@ -1,249 +0,0 @@
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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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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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self.n_restored = 0
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self.n_infeasible_iterations = 0
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self.n_suboptimal_iterations = 0
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def before_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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features,
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training_data,
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):
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self.n_restored = 0
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self.n_infeasible_iterations = 0
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self.n_suboptimal_iterations = 0
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logger.info("Predicting tight LP constraints...")
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x, constraints = DropRedundantInequalitiesStep.x(
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instance,
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constraint_ids=solver.internal_solver.get_constraint_ids(),
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)
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y = self.predict(x)
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n_converted = 0
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n_kept = 0
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for category in y.keys():
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for i in range(len(y[category])):
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if y[category][i][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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n_converted += 1
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else:
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n_kept += 1
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stats["ConvertTight: Kept"] = n_kept
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stats["ConvertTight: Converted"] = n_converted
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logger.info(f"Converted {n_converted} inequalities")
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def after_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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features,
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training_data,
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):
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if training_data.slacks is None:
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training_data.slacks = solver.internal_solver.get_inequality_slacks()
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stats["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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@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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instances,
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desc="Extract (drop: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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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] += [[False, True]]
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else:
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y[category] += [[True, False]]
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for category in y.keys():
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y[category] = np.array(y[category], dtype=np.bool8)
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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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for cid in restored:
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self.converted.remove(cid)
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if len(restored) > 0:
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self.n_restored += len(restored)
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if is_infeasible:
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self.n_infeasible_iterations += 1
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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
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@ -1,240 +0,0 @@
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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import logging
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from copy import deepcopy
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import numpy as np
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from p_tqdm import p_umap
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from tqdm import tqdm
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components import classifier_evaluation_dict
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from miplearn.components.component import Component
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from miplearn.components.static_lazy import LazyConstraint
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logger = logging.getLogger(__name__)
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class DropRedundantInequalitiesStep(Component):
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"""
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Component that predicts which inequalities are likely loose in the LP and removes
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them. Optionally, double checks after the problem is solved that all dropped
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inequalities were in fact redundant, and, if not, re-adds them to the problem.
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This component does not work on MIPs. All integrality constraints must be relaxed
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before this component is used.
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"""
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def __init__(
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self,
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classifier=CountingClassifier(),
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threshold=0.95,
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slack_tolerance=1e-5,
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check_feasibility=True,
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violation_tolerance=1e-5,
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max_iterations=3,
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):
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self.classifiers = {}
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self.classifier_prototype = classifier
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self.threshold = threshold
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self.slack_tolerance = slack_tolerance
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self.pool = []
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self.check_feasibility = check_feasibility
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self.violation_tolerance = violation_tolerance
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self.max_iterations = max_iterations
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self.current_iteration = 0
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self.n_iterations = 0
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self.n_restored = 0
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def before_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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features,
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training_data,
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):
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self.n_iterations = 0
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self.n_restored = 0
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self.current_iteration = 0
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logger.info("Predicting redundant LP constraints...")
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x, constraints = self.x(
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instance,
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constraint_ids=solver.internal_solver.get_constraint_ids(),
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)
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y = self.predict(x)
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self.pool = []
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n_dropped = 0
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n_kept = 0
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for category in y.keys():
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for i in range(len(y[category])):
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if y[category][i][1] == 1:
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cid = constraints[category][i]
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c = LazyConstraint(
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cid=cid,
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obj=solver.internal_solver.extract_constraint(cid),
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)
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self.pool += [c]
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n_dropped += 1
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else:
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n_kept += 1
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stats["DropRedundant: Kept"] = n_kept
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stats["DropRedundant: Dropped"] = n_dropped
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logger.info(f"Extracted {n_dropped} predicted constraints")
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def after_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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features,
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training_data,
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):
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if training_data.slacks is None:
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training_data.slacks = solver.internal_solver.get_inequality_slacks()
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stats["DropRedundant: Iterations"] = self.n_iterations
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stats["DropRedundant: Restored"] = self.n_restored
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def fit(self, training_instances, n_jobs=1):
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x, y = self.x_y(training_instances, n_jobs=n_jobs)
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for category in tqdm(x.keys(), desc="Fit (drop)"):
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if category not in self.classifiers:
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self.classifiers[category] = deepcopy(self.classifier_prototype)
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self.classifiers[category].fit(x[category], np.array(y[category]))
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@staticmethod
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def x(instance, constraint_ids):
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x = {}
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constraints = {}
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cids = constraint_ids
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for cid in cids:
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category = instance.get_constraint_category(cid)
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if category is None:
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continue
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if category not in x:
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x[category] = []
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constraints[category] = []
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x[category] += [instance.get_constraint_features(cid)]
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constraints[category] += [cid]
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for category in x.keys():
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x[category] = np.array(x[category])
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return x, constraints
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def x_y(self, instances, n_jobs=1):
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def _extract(instance):
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x = {}
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y = {}
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for training_data in instance.training_data:
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for (cid, slack) in training_data.slacks.items():
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category = instance.get_constraint_category(cid)
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if category is None:
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continue
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if category not in x:
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x[category] = []
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if category not in y:
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y[category] = []
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if slack > self.slack_tolerance:
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y[category] += [[False, True]]
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else:
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y[category] += [[True, False]]
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x[category] += [instance.get_constraint_features(cid)]
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return x, y
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if n_jobs == 1:
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results = [_extract(i) for i in tqdm(instances, desc="Extract (drop 1/3)")]
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else:
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results = p_umap(
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_extract,
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instances,
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num_cpus=n_jobs,
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desc="Extract (drop 1/3)",
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)
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x_combined = {}
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y_combined = {}
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for (x, y) in tqdm(results, desc="Extract (drop 2/3)"):
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for category in x.keys():
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if category not in x_combined:
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x_combined[category] = []
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y_combined[category] = []
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x_combined[category] += x[category]
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y_combined[category] += y[category]
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for category in tqdm(x_combined.keys(), desc="Extract (drop 3/3)"):
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x_combined[category] = np.array(x_combined[category])
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y_combined[category] = np.array(y_combined[category])
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return x_combined, y_combined
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def predict(self, x):
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y = {}
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for (category, x_cat) in x.items():
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if category not in self.classifiers:
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continue
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y[category] = []
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x_cat = np.array(x_cat)
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proba = self.classifiers[category].predict_proba(x_cat)
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for i in range(len(proba)):
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if proba[i][1] >= self.threshold:
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y[category] += [[False, True]]
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else:
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y[category] += [[True, False]]
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return y
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def evaluate(self, instance, n_jobs=1):
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x, y_true = self.x_y([instance], n_jobs=n_jobs)
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y_pred = self.predict(x)
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tp, tn, fp, fn = 0, 0, 0, 0
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for category in tqdm(
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y_true.keys(),
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disable=len(y_true) < 100,
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desc="Eval (drop)",
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):
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for i in range(len(y_true[category])):
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if (category in y_pred) and (y_pred[category][i][1] == 1):
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if y_true[category][i][1] == 1:
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tp += 1
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else:
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fp += 1
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else:
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if y_true[category][i][1] == 1:
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fn += 1
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else:
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tn += 1
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return classifier_evaluation_dict(tp, tn, fp, fn)
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def iteration_cb(self, solver, instance, model):
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if not self.check_feasibility:
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return False
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if self.current_iteration >= self.max_iterations:
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return False
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if solver.internal_solver.is_infeasible():
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return False
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self.current_iteration += 1
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logger.debug("Checking that dropped constraints are satisfied...")
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constraints_to_add = []
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for c in self.pool:
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if not solver.internal_solver.is_constraint_satisfied(
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c.obj,
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self.violation_tolerance,
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):
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constraints_to_add.append(c)
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for c in constraints_to_add:
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self.pool.remove(c)
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solver.internal_solver.add_constraint(c.obj)
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if len(constraints_to_add) > 0:
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self.n_restored += len(constraints_to_add)
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logger.info(
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"%8d constraints %8d in the pool"
|
||||
% (len(constraints_to_add), len(self.pool))
|
||||
)
|
||||
self.n_iterations += 1
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
@ -1,27 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
|
||||
from miplearn.components.component import Component
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RelaxIntegralityStep(Component):
|
||||
"""
|
||||
Component that relaxes all integrality constraints before the problem is solved.
|
||||
"""
|
||||
|
||||
def before_solve_mip(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
features,
|
||||
training_data,
|
||||
):
|
||||
logger.info("Relaxing integrality...")
|
||||
solver.internal_solver.relax()
|
||||
@ -1,3 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
@ -1,127 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
|
||||
from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
|
||||
from miplearn.instance.base import Instance
|
||||
from miplearn.problems.knapsack import GurobiKnapsackInstance
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_convert_tight_usage():
|
||||
instance = GurobiKnapsackInstance(
|
||||
weights=[3.0, 5.0, 10.0],
|
||||
prices=[1.0, 1.0, 1.0],
|
||||
capacity=16.0,
|
||||
)
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[
|
||||
RelaxIntegralityStep(),
|
||||
ConvertTightIneqsIntoEqsStep(),
|
||||
],
|
||||
)
|
||||
|
||||
# Solve original problem
|
||||
stats = solver.solve(instance)
|
||||
original_upper_bound = stats["Upper bound"]
|
||||
|
||||
# Should collect training data
|
||||
assert instance.training_data[0].slacks["eq_capacity"] == 0.0
|
||||
|
||||
# Fit and resolve
|
||||
solver.fit([instance])
|
||||
stats = solver.solve(instance)
|
||||
|
||||
# Objective value should be the same
|
||||
assert stats["Upper bound"] == original_upper_bound
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0
|
||||
|
||||
|
||||
class SampleInstance(Instance):
|
||||
def to_model(self):
|
||||
import gurobipy as grb
|
||||
|
||||
m = grb.Model("model")
|
||||
x1 = m.addVar(name="x1")
|
||||
x2 = m.addVar(name="x2")
|
||||
m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
|
||||
m.addConstr(x1 <= 2, name="c1")
|
||||
m.addConstr(x2 <= 2, name="c2")
|
||||
m.addConstr(x1 + x2 <= 3, name="c2")
|
||||
return m
|
||||
|
||||
|
||||
def test_convert_tight_infeasibility():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 1
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0
|
||||
|
||||
|
||||
def test_convert_tight_suboptimality():
|
||||
comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 1
|
||||
|
||||
|
||||
def test_convert_tight_optimal():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0
|
||||
@ -1,439 +0,0 @@
|
||||
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
|
||||
from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
|
||||
from miplearn.features import TrainingSample, Features
|
||||
from miplearn.instance.base import Instance
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
from tests.fixtures.infeasible import get_infeasible_instance
|
||||
from tests.fixtures.redundant import get_instance_with_redundancy
|
||||
|
||||
|
||||
def _setup():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.get_inequality_slacks = Mock(
|
||||
side_effect=lambda: {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}
|
||||
)
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
internal.is_infeasible = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
return solver, internal, instance, classifiers
|
||||
|
||||
|
||||
def test_drop_redundant():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep()
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve_mip(
|
||||
solver=solver,
|
||||
instance=instance,
|
||||
model=None,
|
||||
stats={},
|
||||
features=Features(),
|
||||
training_data=TrainingSample(),
|
||||
)
|
||||
|
||||
# Should query list of constraints
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should query category and features for each constraint in the model
|
||||
assert instance.get_constraint_category.call_count == 4
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For constraint with non-null categories, should ask for features
|
||||
assert instance.get_constraint_features.call_count == 3
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether constraint should be removed
|
||||
type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
|
||||
type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
|
||||
np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
|
||||
np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
|
||||
|
||||
# Should ask internal solver to remove constraints predicted as redundant
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls after_solve
|
||||
training_data = TrainingSample()
|
||||
component.after_solve_mip(
|
||||
solver=solver,
|
||||
instance=instance,
|
||||
model=None,
|
||||
stats={},
|
||||
features=Features(),
|
||||
training_data=training_data,
|
||||
)
|
||||
|
||||
# Should query slack for all inequalities
|
||||
internal.get_inequality_slacks.assert_called_once()
|
||||
|
||||
# Should store constraint slacks in instance object
|
||||
assert training_data.slacks == {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}
|
||||
|
||||
|
||||
def test_drop_redundant_with_check_feasibility():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep(
|
||||
check_feasibility=True,
|
||||
violation_tolerance=1e-3,
|
||||
)
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver call before_solve
|
||||
component.before_solve_mip(
|
||||
solver=solver,
|
||||
instance=instance,
|
||||
model=None,
|
||||
stats={},
|
||||
features=Features(),
|
||||
training_data=TrainingSample(),
|
||||
)
|
||||
|
||||
# Assert constraints are extracted
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls iteration_cb (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
|
||||
# Should ask LearningSolver to repeat
|
||||
assert should_repeat
|
||||
|
||||
# Should ask solver if removed constraints are satisfied (mock always returns false)
|
||||
internal.is_constraint_satisfied.assert_has_calls(
|
||||
[
|
||||
call("<c3>", 1e-3),
|
||||
call("<c4>", 1e-3),
|
||||
]
|
||||
)
|
||||
|
||||
# Should add constraints back to LP relaxation
|
||||
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
||||
|
||||
# LearningSolver calls iteration_cb (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
|
||||
def test_x_y_fit_predict_evaluate():
|
||||
instances = [Mock(spec=Instance), Mock(spec=Instance)]
|
||||
component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
np.array([0.20, 0.80]),
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.50, 0.50],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
# First mock instance
|
||||
instances[0].training_data = [
|
||||
TrainingSample(
|
||||
slacks={
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
)
|
||||
]
|
||||
instances[0].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[0].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
# Second mock instance
|
||||
instances[1].training_data = [
|
||||
TrainingSample(
|
||||
slacks={
|
||||
"c1": 0.00,
|
||||
"c3": 0.30,
|
||||
"c4": 0.00,
|
||||
"c5": 0.00,
|
||||
}
|
||||
)
|
||||
]
|
||||
instances[1].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[1].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c3": np.array([0.3, 0.4]),
|
||||
"c4": np.array([0.7]),
|
||||
"c5": np.array([0.8]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[0.3, 0.4],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[0.7],
|
||||
[0.8],
|
||||
]
|
||||
),
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[True, False],
|
||||
[True, False],
|
||||
[False, True],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[False, True],
|
||||
[True, False],
|
||||
[True, False],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
actual_x, actual_y = component.x_y(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])
|
||||
|
||||
# Should pass along X and Y matrices to classifiers
|
||||
component.fit(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
actual_x = component.classifiers[category].fit.call_args[0][0]
|
||||
actual_y = component.classifiers[category].fit.call_args[0][1]
|
||||
np.testing.assert_array_equal(actual_x, expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y, expected_y[category])
|
||||
|
||||
assert component.predict(expected_x) == {
|
||||
"type-a": [
|
||||
[False, True],
|
||||
],
|
||||
"type-b": [
|
||||
[True, False],
|
||||
[False, True],
|
||||
],
|
||||
}
|
||||
|
||||
ev = component.evaluate(instances[1])
|
||||
assert ev["True positive"] == 1
|
||||
assert ev["True negative"] == 1
|
||||
assert ev["False positive"] == 1
|
||||
assert ev["False negative"] == 0
|
||||
|
||||
|
||||
def test_x_multiple_solves():
|
||||
instance = Mock(spec=Instance)
|
||||
instance.training_data = [
|
||||
TrainingSample(
|
||||
slacks={
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
),
|
||||
TrainingSample(
|
||||
slacks={
|
||||
"c1": 0.00,
|
||||
"c2": 0.00,
|
||||
"c3": 1.00,
|
||||
"c4": 0.0,
|
||||
}
|
||||
),
|
||||
]
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[1.0],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
expected_y = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[False, True],
|
||||
[True, False],
|
||||
[True, False],
|
||||
[False, True],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[False, True],
|
||||
[True, False],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
component = DropRedundantInequalitiesStep()
|
||||
actual_x, actual_y = component.x_y([instance])
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])
|
||||
|
||||
|
||||
def test_usage():
|
||||
for internal_solver in [GurobiSolver]:
|
||||
for instance in [
|
||||
get_instance_with_redundancy(internal_solver),
|
||||
get_infeasible_instance(internal_solver),
|
||||
]:
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
components=[
|
||||
RelaxIntegralityStep(),
|
||||
DropRedundantInequalitiesStep(),
|
||||
],
|
||||
)
|
||||
# The following should not crash
|
||||
solver.solve(instance)
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
Loading…
Reference in new issue