Primal: Compute statistics

miplearn/components/primal.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from typing import Union, Dict, Callable, List, Hashable, Optional
+from typing import Union, Dict, Callable, List, Hashable, Optional, Any, TYPE_CHECKING
 
 import numpy as np
 from tqdm.auto import tqdm
@@ -15,10 +15,13 @@ from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
 from miplearn.extractors import InstanceIterator
 from miplearn.instance import Instance
-from miplearn.types import TrainingSample, VarIndex, Solution
+from miplearn.types import TrainingSample, VarIndex, Solution, LearningSolveStats
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver
+
 
 class PrimalSolutionComponent(Component):
     """
@@ -44,11 +47,31 @@ class PrimalSolutionComponent(Component):
         self.thresholds: Dict[Hashable, Threshold] = {}
         self.threshold_factory = threshold
         self.classifier_factory = classifier
+        self.stats: Dict[str, float] = {}
+        self._n_free = 0
+        self._n_zero = 0
+        self._n_one = 0
 
     def before_solve(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:
@@ -56,13 +79,15 @@ class PrimalSolutionComponent(Component):
 
     def after_solve(
         self,
-        solver,
-        instance,
-        model,
-        stats,
-        training_data,
-    ):
-        pass
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        training_data: TrainingSample,
+    ) -> None:
+        stats["Primal: free"] = self._n_free
+        stats["Primal: zero"] = self._n_zero
+        stats["Primal: one"] = self._n_one
 
     def x(
         self,

miplearn/types.py

@@ -62,6 +62,9 @@ LearningSolveStats = TypedDict(
         "Upper bound": Optional[float],
         "Wallclock time": float,
         "Warm start value": Optional[float],
+        "Primal: free": int,
+        "Primal: zero": int,
+        "Primal: one": int,
     },
     total=False,
 )

tests/test_benchmark.py

@@ -29,7 +29,7 @@ def test_benchmark():
     benchmark = BenchmarkRunner(test_solvers)
     benchmark.fit(train_instances)
     benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
-    assert benchmark.results.values.shape == (12, 14)
+    assert benchmark.results.values.shape == (12, 17)
 
     benchmark.write_csv("/tmp/benchmark.csv")
     assert os.path.isfile("/tmp/benchmark.csv")