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64 lines
2.1 KiB
64 lines
2.1 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 pyomo.core import Var
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class PerVariableTransformer:
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"""
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Class that converts a miplearn.Instance into a matrix of features that is suitable
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for training machine learning models that make one decision per decision variable.
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"""
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def __init__(self):
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pass
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def transform_instance(self, instance, var_index_pairs):
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instance_features = self._get_instance_features(instance)
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variable_features = self._get_variable_features(instance, var_index_pairs)
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return np.vstack([
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np.hstack([instance_features, vf])
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for vf in variable_features
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])
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@staticmethod
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def _get_instance_features(instance):
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features = instance.get_instance_features()
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assert isinstance(features, np.ndarray)
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return features
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@staticmethod
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def _get_variable_features(instance, var_index_pairs):
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features = []
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expected_shape = None
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for (var, index) in var_index_pairs:
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vf = instance.get_variable_features(var, index)
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assert isinstance(vf, np.ndarray)
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if expected_shape is None:
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assert len(vf.shape) == 1
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expected_shape = vf.shape
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else:
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assert vf.shape == expected_shape
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features += [vf]
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return np.array(features)
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@staticmethod
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def transform_solution(var_index_pairs):
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y = []
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for (var, index) in var_index_pairs:
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y += [[1 - var[index].value, var[index].value]]
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return np.array(y)
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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 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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