Combine after_load, after_lp and after_mip into Sample dataclass

miplearn/features.py

@@ -106,6 +106,13 @@ class Features:
     mip_solve: Optional["MIPSolveStats"] = None
 
 
+@dataclass
+class Sample:
+    after_load: Optional[Features] = None
+    after_lp: Optional[Features] = None
+    after_mip: Optional[Features] = None
+
+
 class FeaturesExtractor:
     def __init__(
         self,

miplearn/instance/base.py

@@ -8,7 +8,7 @@ from typing import Any, List, Optional, Hashable, TYPE_CHECKING
 
 from overrides import EnforceOverrides
 
-from miplearn.features import TrainingSample, Features
+from miplearn.features import TrainingSample, Features, Sample
 from miplearn.types import VariableName, Category
 
 logger = logging.getLogger(__name__)
@@ -33,9 +33,7 @@ class Instance(ABC, EnforceOverrides):
     def __init__(self) -> None:
         self.training_data: List[TrainingSample] = []
         self.features: Features = Features()
-        self.features_after_load: List[Features] = []
-        self.features_after_lp: List[Features] = []
-        self.features_after_mip: List[Features] = []
+        self.samples: List[Sample] = []
 
     @abstractmethod
     def to_model(self) -> Any:

miplearn/instance/picklegz.py

@@ -123,9 +123,7 @@ class PickleGzInstance(Instance):
             self.instance = obj
             self.features = self.instance.features
             self.training_data = self.instance.training_data
-            self.features_after_load = self.instance.features_after_load
-            self.features_after_lp = self.instance.features_after_lp
-            self.features_after_mip = self.instance.features_after_mip
+            self.samples = self.instance.samples
 
     @overrides
     def free(self) -> None:

miplearn/solvers/learning.py

@@ -13,7 +13,7 @@ from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
 from miplearn.components.dynamic_user_cuts import UserCutsComponent
 from miplearn.components.objective import ObjectiveValueComponent
 from miplearn.components.primal import PrimalSolutionComponent
-from miplearn.features import FeaturesExtractor, TrainingSample
+from miplearn.features import FeaturesExtractor, TrainingSample, Sample
 from miplearn.instance.base import Instance
 from miplearn.instance.picklegz import PickleGzInstance
 from miplearn.solvers import _RedirectOutput
@@ -140,6 +140,7 @@ class LearningSolver:
         # -------------------------------------------------------
         training_sample = TrainingSample()
         instance.training_data += [training_sample]
+        sample = Sample()
 
         # Initialize stats
         # -------------------------------------------------------
@@ -158,7 +159,7 @@ class LearningSolver:
         logger.info("Extracting features (after-load)...")
         features = FeaturesExtractor(self.internal_solver).extract(instance)
         instance.features.__dict__ = features.__dict__
-        instance.features_after_load.append(features)
+        sample.after_load = features
 
         callback_args = (
             self,
@@ -193,7 +194,7 @@ class LearningSolver:
             logger.info("Extracting features (after-lp)...")
             features = FeaturesExtractor(self.internal_solver).extract(instance)
             features.lp_solve = lp_stats
-            instance.features_after_lp.append(features)
+            sample.after_lp = features
 
         # Callback wrappers
         # -------------------------------------------------------
@@ -254,7 +255,7 @@ class LearningSolver:
         logger.info("Extracting features (after-mip)...")
         features = FeaturesExtractor(self.internal_solver).extract(instance)
         features.mip_solve = mip_stats
-        instance.features_after_mip.append(features)
+        sample.after_mip = features
 
         # Add some information to training_sample
         # -------------------------------------------------------