Modularize LearningSolver into components; implement branch-priority

miplearn/branching.py

@@ -0,0 +1,63 @@
+# 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>
+
+from . import Component
+from .transformers import PerVariableTransformer
+from abc import ABC, abstractmethod
+import numpy as np
+
+
+class BranchPriorityComponent(Component):
+    def __init__(self,
+                 initial_priority=None,
+                 collect_training_data=True):
+        self.priority = initial_priority
+        self.transformer = PerVariableTransformer()
+        self.collect_training_data = collect_training_data
+    
+    def before_solve(self, solver, instance, model):
+        assert solver.is_persistent, "BranchPriorityComponent requires a persistent solver"
+        var_split = self.transformer.split_variables(instance, model)
+        for category in var_split.keys():
+            var_index_pairs = var_split[category]
+            if self.priority is not None:
+                from gurobipy import GRB
+                for (i, (var, index)) in enumerate(var_index_pairs):
+                    gvar = solver.internal_solver._pyomo_var_to_solver_var_map[var[index]]
+                    gvar.setAttr(GRB.Attr.BranchPriority, int(self.priority[index]))        
+
+                    
+    def after_solve(self, solver, instance, model):
+        if self.collect_training_data:
+            import subprocess, tempfile, os
+            src_dirname = os.path.dirname(os.path.realpath(__file__))
+            model_file = tempfile.NamedTemporaryFile(suffix=".lp")
+            priority_file = tempfile.NamedTemporaryFile()
+            solver.internal_solver.write(model_file.name)
+            subprocess.run(["julia",
+                            "%s/scripts/branchpriority.jl" % src_dirname,
+                            model_file.name,
+                            priority_file.name],
+                           check=True)
+            self._merge(np.genfromtxt(priority_file.name,
+                                      delimiter=',',
+                                      dtype=int))
+        
+    
+    def fit(self, solver):
+        pass
+
+    
+    def merge(self, other):
+        if other.priority is not None:
+            self._merge(other.priority)
+            
+            
+    def _merge(self, priority):
+        assert isinstance(priority, np.ndarray)
+        if self.priority is None:
+            self.priority = priority
+        else:
+            assert self.priority.shape == priority.shape
+            self.priority += priority