Refactor PrimalSolutionComponent

miplearn/components/primal.py

@@ -13,6 +13,7 @@ from typing import (
     Any,
     TYPE_CHECKING,
     Tuple,
+    cast,
 )
 
 import numpy as np
@@ -63,29 +64,30 @@ class PrimalSolutionComponent(Component):
         self._n_one = 0
 
     def before_solve_mip(self, solver, instance, model):
-        if len(self.thresholds) > 0:
-            logger.info("Predicting primal solution...")
-            solution = self.predict(instance)
-
-            # Collect prediction statistics
-            self._n_free = 0
-            self._n_zero = 0
-            self._n_one = 0
-            for (var, var_dict) in solution.items():
-                for (idx, value) in var_dict.items():
-                    if value is None:
-                        self._n_free += 1
-                    else:
-                        if value < 0.5:
-                            self._n_zero += 1
-                        else:
-                            self._n_one += 1
-
-            # Provide solution to the solver
-            if self.mode == "heuristic":
-                solver.internal_solver.fix(solution)
-            else:
-                solver.internal_solver.set_warm_start(solution)
+        pass
+        # if len(self.thresholds) > 0:
+        #     logger.info("Predicting primal solution...")
+        #     solution = self.predict(instance)
+        #
+        #     # Collect prediction statistics
+        #     self._n_free = 0
+        #     self._n_zero = 0
+        #     self._n_one = 0
+        #     for (var, var_dict) in solution.items():
+        #         for (idx, value) in var_dict.items():
+        #             if value is None:
+        #                 self._n_free += 1
+        #             else:
+        #                 if value < 0.5:
+        #                     self._n_zero += 1
+        #                 else:
+        #                     self._n_one += 1
+        #
+        #     # Provide solution to the solver
+        #     if self.mode == "heuristic":
+        #         solver.internal_solver.fix(solution)
+        #     else:
+        #         solver.internal_solver.set_warm_start(solution)
 
     def after_solve_mip(
         self,
@@ -214,43 +216,56 @@ class PrimalSolutionComponent(Component):
         if "Solution" not in sample:
             return x, y
         assert sample["Solution"] is not None
-        for (var, var_dict) in sample["Solution"].items():
-            for (idx, opt_value) in var_dict.items():
-                assert opt_value is not None
-                assert 0.0 - 1e-5 <= opt_value <= 1.0 + 1e-5, (
-                    f"Variable {var} has non-binary value {opt_value} in the optimal "
-                    f"solution. Predicting values of non-binary variables is not "
-                    f"currently supported. Please set its category to None."
+        return cast(
+            Tuple[Dict, Dict],
+            PrimalSolutionComponent._extract(
+                instance,
+                sample,
+                sample["Solution"],
+                extract_y=True,
+            ),
         )
-                category = instance.get_variable_category(var, idx)
-                if category is None:
-                    continue
-                if category not in x.keys():
-                    x[category] = []
-                    y[category] = []
-                features: Any = instance.get_variable_features(var, idx)
-                assert isinstance(features, list)
-                if "LP solution" in sample and sample["LP solution"] is not None:
-                    lp_value = sample["LP solution"][var][idx]
-                    if lp_value is not None:
-                        features += [sample["LP solution"][var][idx]]
-                x[category] += [features]
-                y[category] += [[opt_value < 0.5, opt_value >= 0.5]]
-        return x, y
 
     @staticmethod
     def x_sample(
         instance: Any,
         sample: TrainingSample,
     ) -> Dict:
+        return cast(
+            Dict,
+            PrimalSolutionComponent._extract(
+                instance,
+                sample,
+                instance.model_features["Variables"],
+                extract_y=False,
+            ),
+        )
+
+    @staticmethod
+    def _extract(
+        instance: Any,
+        sample: TrainingSample,
+        variables: Dict,
+        extract_y: bool,
+    ) -> Union[Dict, Tuple[Dict, Dict]]:
         x: Dict = {}
-        for (var, var_dict) in instance.model_features["Variables"].items():
-            for idx in var_dict.keys():
+        y: Dict = {}
+        for (var, var_dict) in variables.items():
+            for (idx, opt_value) in var_dict.items():
+                if extract_y:
+                    assert opt_value is not None
+                    assert 0.0 - 1e-5 <= opt_value <= 1.0 + 1e-5, (
+                        f"Variable {var} has non-binary value {opt_value} in the "
+                        "optimal solution. Predicting values of non-binary "
+                        "variables is not currently supported. Please set its "
+                        "category to None."
+                    )
                 category = instance.get_variable_category(var, idx)
                 if category is None:
                     continue
                 if category not in x.keys():
                     x[category] = []
+                    y[category] = []
                 features: Any = instance.get_variable_features(var, idx)
                 assert isinstance(features, list)
                 if "LP solution" in sample and sample["LP solution"] is not None:
@@ -258,6 +273,9 @@ class PrimalSolutionComponent(Component):
                     if lp_value is not None:
                         features += [sample["LP solution"][var][idx]]
                 x[category] += [features]
-        for category in x.keys():
-            x[category] = np.array(x[category])
+                if extract_y:
+                    y[category] += [[opt_value < 0.5, opt_value >= 0.5]]
+        if extract_y:
+            return x, y
+        else:
             return x