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149 lines
6.3 KiB
149 lines
6.3 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from copy import deepcopy
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import sys
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from .component import Component
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from ..classifiers.adaptive import AdaptiveClassifier
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from ..classifiers.threshold import MinPrecisionThreshold, DynamicThreshold
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from ..components import classifier_evaluation_dict
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from ..extractors import *
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logger = logging.getLogger(__name__)
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class PrimalSolutionComponent(Component):
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"""
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A component that predicts primal solutions.
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"""
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def __init__(self,
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classifier=AdaptiveClassifier(),
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mode="exact",
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threshold=MinPrecisionThreshold(0.98)):
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self.mode = mode
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self.classifiers = {}
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self.thresholds = {}
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self.threshold_prototype = threshold
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self.classifier_prototype = classifier
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def before_solve(self, solver, instance, model):
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logger.info("Predicting primal solution...")
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solution = self.predict(instance)
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if self.mode == "heuristic":
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solver.internal_solver.fix(solution)
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else:
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solver.internal_solver.set_warm_start(solution)
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def after_solve(self, solver, instance, model, results):
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pass
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def x(self, training_instances):
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return VariableFeaturesExtractor().extract(training_instances)
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def y(self, training_instances):
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return SolutionExtractor().extract(training_instances)
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def fit(self, training_instances, n_jobs=1):
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logger.debug("Extracting features...")
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features = VariableFeaturesExtractor().extract(training_instances)
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solutions = SolutionExtractor().extract(training_instances)
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for category in tqdm(features.keys(),
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desc="Fit (primal)",
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disable=not sys.stdout.isatty(),
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):
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x_train = features[category]
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for label in [0, 1]:
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y_train = solutions[category][:, label].astype(int)
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# If all samples are either positive or negative, make constant predictions
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y_avg = np.average(y_train)
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if y_avg < 0.001 or y_avg >= 0.999:
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self.classifiers[category, label] = round(y_avg)
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self.thresholds[category, label] = 0.50
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continue
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# Create a copy of classifier prototype and train it
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if isinstance(self.classifier_prototype, list):
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clf = deepcopy(self.classifier_prototype[label])
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else:
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clf = deepcopy(self.classifier_prototype)
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clf.fit(x_train, y_train)
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# Find threshold (dynamic or static)
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if isinstance(self.threshold_prototype, DynamicThreshold):
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self.thresholds[category, label] = self.threshold_prototype.find(clf, x_train, y_train)
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else:
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self.thresholds[category, label] = deepcopy(self.threshold_prototype)
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self.classifiers[category, label] = clf
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def predict(self, instance):
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solution = {}
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x_test = VariableFeaturesExtractor().extract([instance])
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var_split = Extractor.split_variables(instance)
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for category in var_split.keys():
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n = len(var_split[category])
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for (i, (var, index)) in enumerate(var_split[category]):
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if var not in solution.keys():
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solution[var] = {}
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solution[var][index] = None
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for label in [0, 1]:
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if (category, label) not in self.classifiers.keys():
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continue
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clf = self.classifiers[category, label]
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if isinstance(clf, float) or isinstance(clf, int):
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ws = np.array([[1 - clf, clf] for _ in range(n)])
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else:
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ws = clf.predict_proba(x_test[category])
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assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (n, ws.shape)
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for (i, (var, index)) in enumerate(var_split[category]):
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if ws[i, 1] >= self.thresholds[category, label]:
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solution[var][index] = label
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return solution
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def evaluate(self, instances):
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ev = {"Fix zero": {},
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"Fix one": {}}
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for instance_idx in tqdm(range(len(instances)),
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desc="Evaluate (primal)",
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disable=not sys.stdout.isatty(),
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):
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instance = instances[instance_idx]
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solution_actual = instance.solution
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solution_pred = self.predict(instance)
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vars_all, vars_one, vars_zero = set(), set(), set()
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pred_one_positive, pred_zero_positive = set(), set()
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for (varname, var_dict) in solution_actual.items():
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for (idx, value) in var_dict.items():
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vars_all.add((varname, idx))
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if value > 0.5:
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vars_one.add((varname, idx))
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else:
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vars_zero.add((varname, idx))
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if solution_pred[varname][idx] is not None:
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if solution_pred[varname][idx] > 0.5:
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pred_one_positive.add((varname, idx))
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else:
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pred_zero_positive.add((varname, idx))
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pred_one_negative = vars_all - pred_one_positive
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pred_zero_negative = vars_all - pred_zero_positive
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tp_zero = len(pred_zero_positive & vars_zero)
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fp_zero = len(pred_zero_positive & vars_one)
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tn_zero = len(pred_zero_negative & vars_one)
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fn_zero = len(pred_zero_negative & vars_zero)
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tp_one = len(pred_one_positive & vars_one)
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fp_one = len(pred_one_positive & vars_zero)
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tn_one = len(pred_one_negative & vars_zero)
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fn_one = len(pred_one_negative & vars_one)
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ev["Fix zero"][instance_idx] = classifier_evaluation_dict(tp_zero, tn_zero, fp_zero, fn_zero)
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ev["Fix one"][instance_idx] = classifier_evaluation_dict(tp_one, tn_one, fp_one, fn_one)
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return ev
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