Remove EnforceOverrides; automatically convert np.ndarray features

CHANGELOG.md

@@ -28,8 +28,6 @@
    def get_variable_category(self, var_name: str) -> Optional[Hashable]:
       pass  
    ````
-- Features are now represented as a list of floating point numbers, as indicated in the snippet above. This change was required for performance reasons. Returning numpy arrays is no longer supported, and raises an error.
-
 - Internal solvers must now be specified as objects, instead of strings. For example,
   ```python
   solver = LearningSolver(

miplearn/__init__.py

@@ -13,6 +13,13 @@ from .components.dynamic_user_cuts import UserCutsComponent
 from .components.objective import ObjectiveValueComponent
 from .components.primal import PrimalSolutionComponent
 from .components.static_lazy import StaticLazyConstraintsComponent
+from .features import (
+    Features,
+    TrainingSample,
+    ConstraintFeatures,
+    VariableFeatures,
+    InstanceFeatures,
+)
 from .instance.base import Instance
 from .instance.picklegz import (
     PickleGzInstance,
@@ -27,6 +34,3 @@ from .solvers.learning import LearningSolver
 from .solvers.pyomo.base import BasePyomoSolver
 from .solvers.pyomo.cplex import CplexPyomoSolver
 from .solvers.pyomo.gurobi import GurobiPyomoSolver
-
-# noinspection PyUnresolvedReferences
-from overrides import overrides

miplearn/components/component.py

@@ -16,7 +16,7 @@ if TYPE_CHECKING:
 
 
 # noinspection PyMethodMayBeStatic
-class Component(EnforceOverrides):
+class Component:
     """
     A Component is an object which adds functionality to a LearningSolver.
 

miplearn/features.py

@@ -8,6 +8,7 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, Optional, Set, List, Hashable
 
 from miplearn.types import Solution, VariableName, Category
+import numpy as np
 
 if TYPE_CHECKING:
     from miplearn.solvers.internal import InternalSolver
@@ -83,6 +84,8 @@ class FeaturesExtractor:
                     f"Found {type(category).__name__} instead for var={var_name}."
                 )
                 user_features = instance.get_variable_features(var_name)
+                if isinstance(user_features, np.ndarray):
+                    user_features = user_features.tolist()
                 assert isinstance(user_features, list), (
                     f"Variable features must be a list. "
                     f"Found {type(user_features).__name__} instead for "
@@ -115,6 +118,8 @@ class FeaturesExtractor:
                     f"Found {type(category).__name__} instead for cid={cid}.",
                 )
                 user_features = instance.get_constraint_features(cid)
+                if isinstance(user_features, np.ndarray):
+                    user_features = user_features.tolist()
                 assert isinstance(user_features, list), (
                     f"Constraint features must be a list. "
                     f"Found {type(user_features).__name__} instead for cid={cid}."
@@ -141,6 +146,8 @@ class FeaturesExtractor:
     ) -> InstanceFeatures:
         assert features.constraints is not None
         user_features = instance.get_instance_features()
+        if isinstance(user_features, np.ndarray):
+            user_features = user_features.tolist()
         assert isinstance(user_features, list), (
             f"Instance features must be a list. "
             f"Found {type(user_features).__name__} instead."

miplearn/instance/base.py

@@ -15,7 +15,7 @@ logger = logging.getLogger(__name__)
 
 
 # noinspection PyMethodMayBeStatic
-class Instance(ABC, EnforceOverrides):
+class Instance(ABC):
     """
     Abstract class holding all the data necessary to generate a concrete model of the
     proble.

miplearn/solvers/internal.py

@@ -24,7 +24,7 @@ from miplearn.types import (
 logger = logging.getLogger(__name__)
 
 
-class InternalSolver(ABC, EnforceOverrides):
+class InternalSolver(ABC):
     """
     Abstract class representing the MIP solver used internally by LearningSolver.
     """