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98 lines
3.5 KiB
98 lines
3.5 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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import numpy as np
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from abc import ABC, abstractmethod
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from pyomo.core import Var
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class Extractor(ABC):
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@abstractmethod
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def extract(self, instances, models):
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pass
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@staticmethod
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def split_variables(instance, model):
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result = {}
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for var in model.component_objects(Var):
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for index in var:
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category = instance.get_variable_category(var, index)
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if category is None:
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continue
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if category not in result.keys():
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result[category] = []
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result[category] += [(var, index)]
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return result
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@staticmethod
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def merge(partial_results, vertical=False):
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results = {}
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all_categories = set()
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for pr in partial_results:
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all_categories |= pr.keys()
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for category in all_categories:
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results[category] = []
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for pr in partial_results:
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if category in pr.keys():
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results[category] += [pr[category]]
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if vertical:
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results[category] = np.vstack(results[category])
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else:
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results[category] = np.hstack(results[category])
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return results
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class UserFeaturesExtractor(Extractor):
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def extract(self,
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instances,
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models=None,
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):
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result = {}
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if models is None:
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models = [instance.to_model() for instance in instances]
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for (index, instance) in enumerate(instances):
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model = models[index]
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instance_features = instance.get_instance_features()
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var_split = self.split_variables(instance, model)
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for (category, var_index_pairs) in var_split.items():
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if category not in result.keys():
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result[category] = []
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for (var, index) in var_index_pairs:
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result[category] += [np.hstack([
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instance_features,
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instance.get_variable_features(var, index),
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])]
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for category in result.keys():
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result[category] = np.vstack(result[category])
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return result
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class SolutionExtractor(Extractor):
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def extract(self, instances, models):
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result = {}
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for (index, instance) in enumerate(instances):
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model = models[index]
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var_split = self.split_variables(instance, model)
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for (category, var_index_pairs) in var_split.items():
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if category not in result.keys():
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result[category] = []
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for (var, index) in var_index_pairs:
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v = var[index].value
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if v is None:
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result[category] += [[0, 0]]
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else:
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result[category] += [[1 - v, v]]
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for category in result.keys():
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result[category] = np.vstack(result[category])
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return result
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class CombinedExtractor(Extractor):
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def __init__(self, extractors):
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self.extractors = extractors
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def extract(self, instances, models):
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return self.merge([ex.extract(instances, models)
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for ex in self.extractors])
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