Extract all features ahead of time

miplearn/components/primal.py

@@ -236,7 +236,7 @@ class PrimalSolutionComponent(Component):
             PrimalSolutionComponent._extract(
                 instance,
                 sample,
-                instance.model_features["Variables"],
+                instance.features["Variables"],
                 extract_y=False,
             ),
         )

miplearn/features.py

@@ -2,30 +2,84 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import numbers
+import collections
 from typing import TYPE_CHECKING, Dict
 
 from miplearn.types import ModelFeatures, ConstraintFeatures
 
 if TYPE_CHECKING:
-    from miplearn import InternalSolver
+    from miplearn import InternalSolver, Instance
 
 
-class ModelFeaturesExtractor:
+class FeaturesExtractor:
     def __init__(
         self,
         internal_solver: "InternalSolver",
     ) -> None:
         self.solver = internal_solver
 
-    def extract(self) -> ModelFeatures:
+    def extract(self, instance: "Instance") -> ModelFeatures:
+        return {
+            "Constraints": self._extract_constraints(instance),
+            "Variables": self._extract_variables(instance),
+        }
+
+    def _extract_variables(self, instance: "Instance") -> Dict:
+        variables = self.solver.get_empty_solution()
+        for (var_name, var_dict) in variables.items():
+            for idx in var_dict.keys():
+                user_features = None
+                category = instance.get_variable_category(var_name, idx)
+                if category is not None:
+                    assert isinstance(category, collections.Hashable), (
+                        f"Variable category must be be hashable. "
+                        f"Found {type(category).__name__} instead for var={var_name}."
+                    )
+                    user_features = instance.get_variable_features(var_name, idx)
+                    assert isinstance(user_features, list), (
+                        f"Variable features must be a list. "
+                        f"Found {type(user_features).__name__} instead for "
+                        f"var={var_name}."
+                    )
+                    assert isinstance(user_features[0], numbers.Real), (
+                        f"Variable features must be a list of numbers."
+                        f"Found {type(user_features[0]).__name__} instead "
+                        f"for var={var_name}."
+                    )
+                var_dict[idx] = {
+                    "Category": category,
+                    "User features": user_features,
+                }
+        return variables
+
+    def _extract_constraints(
+        self,
+        instance: "Instance",
+    ) -> Dict[str, ConstraintFeatures]:
         constraints: Dict[str, ConstraintFeatures] = {}
         for cid in self.solver.get_constraint_ids():
+            user_features = None
+            category = instance.get_constraint_category(cid)
+            if category is not None:
+                assert isinstance(category, collections.Hashable), (
+                    f"Constraint category must be hashable. "
+                    f"Found {type(category).__name__} instead for cid={cid}.",
+                )
+                user_features = instance.get_constraint_features(cid)
+                assert isinstance(user_features, list), (
+                    f"Constraint features must be a list. "
+                    f"Found {type(user_features).__name__} instead for cid={cid}."
+                )
+                assert isinstance(user_features[0], float), (
+                    f"Constraint features must be a list of floats. "
+                    f"Found {type(user_features[0]).__name__} instead for cid={cid}."
+                )
             constraints[cid] = {
                 "RHS": self.solver.get_constraint_rhs(cid),
                 "LHS": self.solver.get_constraint_lhs(cid),
                 "Sense": self.solver.get_constraint_sense(cid),
+                "Category": category,
+                "User features": user_features,
             }
-        return {
+        return constraints
-            "Constraints": constraints,
-            "Variables": self.solver.get_empty_solution(),
-        }

miplearn/instance.py

@@ -12,6 +12,7 @@ import numpy as np
 from miplearn.types import TrainingSample, VarIndex, ModelFeatures
 
 
+# noinspection PyMethodMayBeStatic
 class Instance(ABC):
     """
     Abstract class holding all the data necessary to generate a concrete model of the
@@ -26,7 +27,7 @@ class Instance(ABC):
 
     def __init__(self) -> None:
         self.training_data: List[TrainingSample] = []
-        self.model_features: ModelFeatures = {}
+        self.features: ModelFeatures = {}
 
     @abstractmethod
     def to_model(self) -> Any:
@@ -94,10 +95,10 @@ class Instance(ABC):
         """
         return "default"
 
-    def get_constraint_features(self, cid):
+    def get_constraint_features(self, cid: str) -> Optional[List[float]]:
-        return np.zeros(1)
+        return [0.0]
 
-    def get_constraint_category(self, cid):
+    def get_constraint_category(self, cid: str) -> Optional[str]:
         return cid
 
     def has_static_lazy_constraints(self):

miplearn/solvers/learning.py

@@ -17,7 +17,7 @@ from miplearn.components.cuts import UserCutsComponent
 from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
 from miplearn.components.objective import ObjectiveValueComponent
 from miplearn.components.primal import PrimalSolutionComponent
-from miplearn.features import ModelFeaturesExtractor
+from miplearn.features import FeaturesExtractor
 from miplearn.instance import Instance
 from miplearn.solvers import _RedirectOutput
 from miplearn.solvers.internal import InternalSolver
@@ -174,9 +174,9 @@ class LearningSolver:
         assert isinstance(self.internal_solver, InternalSolver)
         self.internal_solver.set_instance(instance, model)
 
-        # Extract model features
+        # Extract features
-        extractor = ModelFeaturesExtractor(self.internal_solver)
+        extractor = FeaturesExtractor(self.internal_solver)
-        instance.model_features = extractor.extract()
+        instance.features = extractor.extract(instance)
 
         # Solve root LP relaxation
         if self.solve_lp:

miplearn/types.py

@@ -2,7 +2,7 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from typing import Optional, Dict, Callable, Any, Union, Tuple, List, Set
+from typing import Optional, Dict, Callable, Any, Union, Tuple, List, Set, Hashable
 
 from mypy_extensions import TypedDict
 
@@ -79,6 +79,8 @@ ConstraintFeatures = TypedDict(
         "RHS": float,
         "LHS": Dict[str, float],
         "Sense": str,
+        "Category": Optional[Hashable],
+        "User features": Optional[List[float]],
     },
     total=False,
 )

tests/components/test_primal.py

@@ -153,7 +153,7 @@ def test_predict() -> None:
             2: [2.0, 0.0],
         }[index]
     )
-    instance.model_features = {
+    instance.features = {
         "Variables": {
             "x": {
                 0: None,

tests/solvers/test_learning_solver.py

@@ -27,7 +27,7 @@ def test_learning_solver():
 
             solver.solve(instance)
 
-            assert hasattr(instance, "model_features")
+            assert hasattr(instance, "features")
 
             data = instance.training_data[0]
             assert data["Solution"]["x"][0] == 1.0

tests/test_features.py

@@ -3,37 +3,47 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from miplearn import GurobiSolver
-from miplearn.features import ModelFeaturesExtractor
+from miplearn.features import FeaturesExtractor
 from tests.fixtures.knapsack import get_knapsack_instance
 
 
 def test_knapsack() -> None:
     for solver_factory in [GurobiSolver]:
-        # Initialize model, instance and internal solver
         solver = solver_factory()
         instance = get_knapsack_instance(solver)
         model = instance.to_model()
         solver.set_instance(instance, model)
-
+        extractor = FeaturesExtractor(solver)
-        # Extract all model features
+        features = extractor.extract(instance)
-        extractor = ModelFeaturesExtractor(solver)
-        features = extractor.extract()
-
-        # Test constraint features
-        print(solver, features)
         assert features["Variables"] == {
             "x": {
-                0: None,
+                0: {
-                1: None,
+                    "Category": "default",
-                2: None,
+                    "User features": [23.0, 505.0],
-                3: None,
+                },
+                1: {
+                    "Category": "default",
+                    "User features": [26.0, 352.0],
+                },
+                2: {
+                    "Category": "default",
+                    "User features": [20.0, 458.0],
+                },
+                3: {
+                    "Category": "default",
+                    "User features": [18.0, 220.0],
+                },
             }
         }
-        assert features["Constraints"]["eq_capacity"]["LHS"] == {
+        assert features["Constraints"]["eq_capacity"] == {
-            "x[0]": 23.0,
+            "LHS": {
-            "x[1]": 26.0,
+                "x[0]": 23.0,
-            "x[2]": 20.0,
+                "x[1]": 26.0,
-            "x[3]": 18.0,
+                "x[2]": 20.0,
+                "x[3]": 18.0,
+            },
+            "Sense": "<",
+            "RHS": 67.0,
+            "Category": "eq_capacity",
+            "User features": [0.0],
         }
-        assert features["Constraints"]["eq_capacity"]["Sense"] == "<"
-        assert features["Constraints"]["eq_capacity"]["RHS"] == 67.0