LearningSolver: add fix_variables option; simplify ws_predictor option

miplearn/solvers.py

@@ -6,6 +6,7 @@ from .transformers import PerVariableTransformer
 from .warmstart import LogisticWarmStartPredictor
 import pyomo.environ as pe
 import numpy as np
+from copy import deepcopy
 
 
 class LearningSolver:
@@ -17,13 +18,15 @@ class LearningSolver:
     def __init__(self,
                  threads=4,
                  parent_solver=pe.SolverFactory('cbc'),
-                 ws_predictor_factory=LogisticWarmStartPredictor):
+                 ws_predictor=LogisticWarmStartPredictor(),
+                 fix_variables=False):
         self.parent_solver = parent_solver
         self.parent_solver.options["threads"] = threads
-        self.ws_predictor_factory = ws_predictor_factory
+        self.fix_variables = fix_variables
         self.x_train = {}
         self.y_train = {}
         self.ws_predictors = {}
+        self.ws_predictor_prototype = ws_predictor
 
     def solve(self, instance, tee=False):
         # Convert instance into concrete model
@@ -52,10 +55,16 @@ class LearningSolver:
                 assert ws.shape == (len(var_index_pairs), 2)
                 for i in range(len(var_index_pairs)):
                     var, index = var_index_pairs[i]
-                    if ws[i,0] == 1:
+                    if self.fix_variables:
-                        var[index].value = 0
+                        if ws[i,0] == 1:
-                    elif ws[i,1] == 1:
+                            var[index].fix(0)
-                        var[index].value = 1
+                        elif ws[i,1] == 1:
+                            var[index].fix(1)
+                    else:
+                        if ws[i,0] == 1:
+                            var[index].value = 0
+                        elif ws[i,1] == 1:
+                            var[index].value = 1
 
         # Solve MILP
         self._solve(model, tee=tee)
@@ -76,7 +85,7 @@ class LearningSolver:
         for category in x_train_dict.keys():
             x_train = x_train_dict[category]
             y_train = y_train_dict[category]
-            self.ws_predictors[category] = self.ws_predictor_factory()
+            self.ws_predictors[category] = deepcopy(self.ws_predictor_prototype)
             self.ws_predictors[category].fit(x_train, y_train)
 
     def _solve(self, model, tee=False):