Improve logging

miplearn/components/dynamic_common.py

@@ -13,6 +13,10 @@ from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
 from miplearn.features import TrainingSample
 
+import logging
+
+logger = logging.getLogger(__name__)
+
 if TYPE_CHECKING:
     from miplearn.solvers.learning import Instance
 
@@ -89,6 +93,9 @@ class DynamicConstraintsComponent(Component):
         sample: TrainingSample,
     ) -> List[Hashable]:
         pred: List[Hashable] = []
+        if len(self.known_cids) == 0:
+            logger.info("Classifiers not fitted. Skipping.")
+            return pred
         x, _, cids = self.sample_xy_with_cids(instance, sample)
         for category in x.keys():
             assert category in self.classifiers

miplearn/components/dynamic_lazy.py

@@ -65,7 +65,7 @@ class DynamicLazyConstraintsComponent(Component):
         training_data: TrainingSample,
     ) -> None:
         training_data.lazy_enforced = set()
-        logger.info("Predicting violated lazy constraints...")
+        logger.info("Predicting violated (dynamic) lazy constraints...")
         cids = self.dynamic.sample_predict(instance, training_data)
         logger.info("Enforcing %d lazy constraints..." % len(cids))
         self.enforce(cids, instance, model, solver)

miplearn/components/dynamic_user_cuts.py

@@ -93,7 +93,8 @@ class UserCutsComponent(Component):
     ) -> None:
         training_data.user_cuts_enforced = set(self.enforced)
         stats["UserCuts: Added in callback"] = self.n_added_in_callback
-        logger.info(f"{self.n_added_in_callback} user cuts added in callback")
+        if self.n_added_in_callback > 0:
+            logger.info(f"{self.n_added_in_callback} user cuts added in callback")
 
     # Delegate ML methods to self.dynamic
     # -------------------------------------------------------------------

miplearn/components/primal.py

@@ -69,12 +69,14 @@ class PrimalSolutionComponent(Component):
         features: Features,
         training_data: TrainingSample,
     ) -> None:
+        logger.info("Predicting primal solution...")
+
         # Do nothing if models are not trained
         if len(self.classifiers) == 0:
+            logger.info("Classifiers not fitted. Skipping.")
             return
 
         # Predict solution and provide it to the solver
-        logger.info("Predicting MIP solution...")
         solution = self.sample_predict(instance, training_data)
         assert solver.internal_solver is not None
         if self.mode == "heuristic":
@@ -130,6 +132,8 @@ class PrimalSolutionComponent(Component):
         category_offset: Dict[Hashable, int] = {cat: 0 for cat in x.keys()}
         for (var_name, var_features) in instance.features.variables.items():
             category = var_features.category
+            if category not in category_offset:
+                continue
             offset = category_offset[category]
             category_offset[category] += 1
             if y_pred[category][offset, 0]:

miplearn/components/static_lazy.py

@@ -64,9 +64,9 @@ class StaticLazyConstraintsComponent(Component):
         assert features.instance is not None
         assert features.constraints is not None
 
-        if not features.instance.lazy_constraint_count == 0:
+        logger.info("Predicting violated (static) lazy constraints...")
+        if features.instance.lazy_constraint_count == 0:
             logger.info("Instance does not have static lazy constraints. Skipping.")
-        logger.info("Predicting required lazy constraints...")
         self.enforced_cids = set(self.sample_predict(instance, training_data))
         logger.info("Moving lazy constraints to the pool...")
         self.pool = {}

miplearn/solvers/learning.py

@@ -155,6 +155,7 @@ class LearningSolver:
 
         # Extract features
         # -------------------------------------------------------
+        logger.info("Extracting features...")
         FeaturesExtractor(self.internal_solver).extract(instance)
 
         callback_args = (