Make Sample abstract; create MemorySample

miplearn/features/sample.py

@@ -2,10 +2,25 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+from abc import ABC, abstractmethod
 from typing import Dict, Optional, Any
 
 
-class Sample:
+class Sample(ABC):
+    """Abstract dictionary-like class that stores training data."""
+
+    @abstractmethod
+    def get(self, key: str) -> Optional[Any]:
+        pass
+
+    @abstractmethod
+    def put(self, key: str, value: Any) -> None:
+        pass
+
+
+class MemorySample(Sample):
+    """Dictionary-like class that stores training data in-memory."""
+
     def __init__(
         self,
         data: Optional[Dict[str, Any]] = None,

miplearn/solvers/learning.py

@@ -15,7 +15,7 @@ from miplearn.components.dynamic_user_cuts import UserCutsComponent
 from miplearn.components.objective import ObjectiveValueComponent
 from miplearn.components.primal import PrimalSolutionComponent
 from miplearn.features.extractor import FeaturesExtractor
-from miplearn.features.sample import Sample
+from miplearn.features.sample import Sample, MemorySample
 from miplearn.instance.base import Instance
 from miplearn.instance.picklegz import PickleGzInstance
 from miplearn.solvers import _RedirectOutput
@@ -150,7 +150,7 @@ class LearningSolver:
 
         # Initialize training sample
         # -------------------------------------------------------
-        sample = Sample()
+        sample = MemorySample()
         instance.push_sample(sample)
 
         # Initialize stats

tests/components/test_dynamic_lazy.py

@@ -11,7 +11,7 @@ from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import MinProbabilityThreshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
-from miplearn.features.sample import Sample
+from miplearn.features.sample import Sample, MemorySample
 from miplearn.instance.base import Instance
 from miplearn.solvers.tests import assert_equals
 
@@ -22,13 +22,13 @@ E = 0.1
 def training_instances() -> List[Instance]:
     instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]
     samples_0 = [
-        Sample(
+        MemorySample(
             {
                 "lazy_enforced": {"c1", "c2"},
                 "instance_features_user": [5.0],
             },
         ),
-        Sample(
+        MemorySample(
             {
                 "lazy_enforced": {"c2", "c3"},
                 "instance_features_user": [5.0],
@@ -53,7 +53,7 @@ def training_instances() -> List[Instance]:
         }
     )
     samples_1 = [
-        Sample(
+        MemorySample(
             {
                 "lazy_enforced": {"c3", "c4"},
                 "instance_features_user": [8.0],

tests/components/test_objective.py

@@ -10,14 +10,14 @@ from numpy.testing import assert_array_equal
 
 from miplearn.classifiers import Regressor
 from miplearn.components.objective import ObjectiveValueComponent
-from miplearn.features.sample import Sample
+from miplearn.features.sample import Sample, MemorySample
 from miplearn.solvers.learning import LearningSolver
 from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
 
 
 @pytest.fixture
 def sample() -> Sample:
-    sample = Sample(
+    sample = MemorySample(
         {
             "mip_lower_bound": 1.0,
             "mip_upper_bound": 2.0,

tests/components/test_primal.py

@@ -12,7 +12,7 @@ from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.primal import PrimalSolutionComponent
-from miplearn.features.sample import Sample
+from miplearn.features.sample import Sample, MemorySample
 from miplearn.problems.tsp import TravelingSalesmanGenerator
 from miplearn.solvers.learning import LearningSolver
 from miplearn.solvers.tests import assert_equals
@@ -20,7 +20,7 @@ from miplearn.solvers.tests import assert_equals
 
 @pytest.fixture
 def sample() -> Sample:
-    sample = Sample(
+    sample = MemorySample(
         {
             "var_names": ["x[0]", "x[1]", "x[2]", "x[3]"],
             "var_categories": ["default", None, "default", "default"],

tests/components/test_static_lazy.py

@@ -11,7 +11,7 @@ from numpy.testing import assert_array_equal
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import Threshold, MinProbabilityThreshold
 from miplearn.components.static_lazy import StaticLazyConstraintsComponent
-from miplearn.features.sample import Sample
+from miplearn.features.sample import Sample, MemorySample
 from miplearn.instance.base import Instance
 from miplearn.solvers.internal import InternalSolver, Constraints
 from miplearn.solvers.learning import LearningSolver
@@ -22,7 +22,7 @@ from miplearn.types import (
 
 @pytest.fixture
 def sample() -> Sample:
-    sample = Sample(
+    sample = MemorySample(
         {
             "constr_categories": [
                 "type-a",

tests/test_features.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 from miplearn.features.extractor import FeaturesExtractor
-from miplearn.features.sample import Sample
+from miplearn.features.sample import Sample, MemorySample
 from miplearn.solvers.internal import Variables, Constraints
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.tests import assert_equals
@@ -19,7 +19,7 @@ def test_knapsack() -> None:
     model = instance.to_model()
     solver.set_instance(instance, model)
     extractor = FeaturesExtractor()
-    sample = Sample()
+    sample = MemorySample()
 
     # after-load
     # -------------------------------------------------------