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MIPLearn/miplearn/extractors.py

70 lines
2.5 KiB

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