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106 lines
3.9 KiB
106 lines
3.9 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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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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from tqdm.auto import tqdm, trange
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from p_tqdm import p_map
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import logging
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logger = logging.getLogger(__name__)
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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):
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assert hasattr(instance, "lp_solution")
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result = {}
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for var_name in instance.lp_solution:
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for index in instance.lp_solution[var_name]:
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category = instance.get_variable_category(var_name, index)
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if category is None:
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continue
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if category not in result:
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result[category] = []
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result[category] += [(var_name, index)]
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return result
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class VariableFeaturesExtractor(Extractor):
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def extract(self, instances):
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result = {}
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for instance in tqdm(instances,
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desc="Extract var features",
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disable=len(instances) < 5):
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instance_features = instance.get_instance_features()
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var_split = self.split_variables(instance)
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for (category, var_index_pairs) in var_split.items():
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if category not in result:
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result[category] = []
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for (var_name, index) in var_index_pairs:
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result[category] += [
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instance_features.tolist() + \
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instance.get_variable_features(var_name, index).tolist() + \
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[instance.lp_solution[var_name][index]]
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]
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for category in result:
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result[category] = np.array(result[category])
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return result
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class SolutionExtractor(Extractor):
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def __init__(self, relaxation=False):
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self.relaxation = relaxation
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def extract(self, instances):
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result = {}
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for instance in tqdm(instances,
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desc="Extract solution",
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disable=len(instances) < 5):
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var_split = self.split_variables(instance)
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for (category, var_index_pairs) in var_split.items():
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if category not in result:
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result[category] = []
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for (var_name, index) in var_index_pairs:
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if self.relaxation:
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v = instance.lp_solution[var_name][index]
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else:
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v = instance.solution[var_name][index]
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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:
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result[category] = np.array(result[category])
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return result
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class InstanceFeaturesExtractor(Extractor):
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def extract(self, instances, models=None):
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return np.vstack([
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np.hstack([
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instance.get_instance_features(),
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instance.lp_value,
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])
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for instance in instances
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])
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class ObjectiveValueExtractor(Extractor):
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def __init__(self, kind="lp"):
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assert kind in ["lower bound", "upper bound", "lp"]
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self.kind = kind
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def extract(self, instances, models=None):
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if self.kind == "lower bound":
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return np.array([[instance.lower_bound] for instance in instances])
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if self.kind == "upper bound":
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return np.array([[instance.upper_bound] for instance in instances])
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if self.kind == "lp":
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return np.array([[instance.lp_value] for instance in instances])
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