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97 lines
3.8 KiB
97 lines
3.8 KiB
# MIPLearn: A Machine-Learning Framework for 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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from .transformers import PerVariableTransformer
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from .warmstart import LogisticWarmStartPredictor
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import pyomo.environ as pe
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import numpy as np
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from copy import deepcopy
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class LearningSolver:
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"""
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Mixed-Integer Linear Programming (MIP) solver that extracts information from previous runs,
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using Machine Learning methods, to accelerate the solution of new (yet unseen) instances.
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"""
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def __init__(self,
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threads=4,
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parent_solver=pe.SolverFactory('cbc'),
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ws_predictor=LogisticWarmStartPredictor(),
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fix_variables=False):
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self.parent_solver = parent_solver
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self.parent_solver.options["threads"] = threads
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self.fix_variables = fix_variables
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self.x_train = {}
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self.y_train = {}
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self.ws_predictors = {}
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self.ws_predictor_prototype = ws_predictor
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def solve(self, instance, tee=False):
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# Convert instance into concrete model
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model = instance.to_model()
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# Split decision variables according to their category
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transformer = PerVariableTransformer()
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var_split = transformer.split_variables(instance, model)
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# Build x_test and update x_train
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x_test = {}
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for category in var_split.keys():
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var_index_pairs = var_split[category]
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x = transformer.transform_instance(instance, var_index_pairs)
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x_test[category] = x
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if category not in self.x_train.keys():
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self.x_train[category] = x
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else:
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self.x_train[category] = np.vstack([self.x_train[category], x])
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# Predict warm start
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for category in var_split.keys():
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if category in self.ws_predictors.keys():
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var_index_pairs = var_split[category]
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ws = self.ws_predictors[category].predict(x_test[category])
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assert ws.shape == (len(var_index_pairs), 2)
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for i in range(len(var_index_pairs)):
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var, index = var_index_pairs[i]
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if self.fix_variables:
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if ws[i,0] == 1:
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var[index].fix(0)
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elif ws[i,1] == 1:
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var[index].fix(1)
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else:
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if ws[i,0] == 1:
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var[index].value = 0
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elif ws[i,1] == 1:
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var[index].value = 1
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# Solve MILP
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self._solve(model, tee=tee)
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# Update y_train
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for category in var_split.keys():
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var_index_pairs = var_split[category]
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y = transformer.transform_solution(var_index_pairs)
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if category not in self.y_train.keys():
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self.y_train[category] = y
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else:
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self.y_train[category] = np.vstack([self.y_train[category], y])
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def fit(self, x_train_dict=None, y_train_dict=None):
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if x_train_dict is None:
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x_train_dict = self.x_train
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y_train_dict = self.y_train
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for category in x_train_dict.keys():
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x_train = x_train_dict[category]
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y_train = y_train_dict[category]
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self.ws_predictors[category] = deepcopy(self.ws_predictor_prototype)
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self.ws_predictors[category].fit(x_train, y_train)
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def _solve(self, model, tee=False):
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if hasattr(self.parent_solver, "set_instance"):
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self.parent_solver.set_instance(model)
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self.parent_solver.solve(tee=tee, warmstart=True)
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else:
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self.parent_solver.solve(model, tee=tee, warmstart=True)
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