Components: Switch from factory methods to prototype objects

docs/customization.md

@@ -79,7 +79,7 @@ from miplearn import PrimalSolutionComponent, MinPrecisionThreshold
 
 PrimalSolutionComponent(
     mode="heuristic",
-    threshold=lambda: MinPrecisionThreshold([0.80, 0.95]),
+    threshold=MinPrecisionThreshold([0.80, 0.95]),
 )
 ```
 
@@ -159,14 +159,14 @@ dtype: float64
 
 By default, given a training set of instantes, MIPLearn trains a fixed set of ML classifiers and regressors, then selects the best one based on cross-validation performance. Alternatively, the user may specify which ML model a component should use through the `classifier` or `regressor` contructor parameters. Scikit-learn classifiers and regressors are currently supported. A future version of the package will add compatibility with Keras models.
 
-The example below shows how to construct a `PrimalSolutionComponent` which internally uses scikit-learn's `KNeighborsClassifiers`. Any other scikit-learn classifier or pipeline can be used. The classifier needs to be provided as a lambda function because the component may need to create multiple copies of it. It needs to be wrapped in `ScikitLearnClassifier` to ensure that all the proper data transformations are applied.
+The example below shows how to construct a `PrimalSolutionComponent` which internally uses scikit-learn's `KNeighborsClassifiers`. Any other scikit-learn classifier or pipeline can be used. It needs to be wrapped in `ScikitLearnClassifier` to ensure that all the proper data transformations are applied.
 
 ```python
 from miplearn import PrimalSolutionComponent, ScikitLearnClassifier
 from sklearn.neighbors import KNeighborsClassifier
 
 comp = PrimalSolutionComponent(
-    classifier=lambda: ScikitLearnClassifier(
+    classifier=ScikitLearnClassifier(
         KNeighborsClassifier(n_neighbors=5),
     ),
 )

miplearn/components/cuts.py

@@ -4,7 +4,6 @@
 
 import logging
 import sys
-from copy import deepcopy
 from typing import Any, Dict
 
 import numpy as np
@@ -29,6 +28,7 @@ class UserCutsComponent(Component):
         classifier: Classifier = CountingClassifier(),
         threshold: float = 0.05,
     ):
+        assert isinstance(classifier, Classifier)
         self.threshold: float = threshold
         self.classifier_prototype: Classifier = classifier
         self.classifiers: Dict[Any, Classifier] = {}
@@ -63,7 +63,7 @@ class UserCutsComponent(Component):
                 continue
             for v in instance.found_violated_user_cuts:
                 if v not in self.classifiers:
-                    self.classifiers[v] = deepcopy(self.classifier_prototype)
+                    self.classifiers[v] = self.classifier_prototype.clone()
                     violation_to_instance_idx[v] = []
                 violation_to_instance_idx[v] += [idx]
 

miplearn/components/lazy_dynamic.py

@@ -4,7 +4,6 @@
 
 import logging
 import sys
-from copy import deepcopy
 from typing import Any, Dict
 
 import numpy as np
@@ -29,6 +28,7 @@ class DynamicLazyConstraintsComponent(Component):
         classifier: Classifier = CountingClassifier(),
         threshold: float = 0.05,
     ):
+        assert isinstance(classifier, Classifier)
         self.threshold: float = threshold
         self.classifier_prototype: Classifier = classifier
         self.classifiers: Dict[Any, Classifier] = {}
@@ -75,7 +75,7 @@ class DynamicLazyConstraintsComponent(Component):
                 if isinstance(v, list):
                     v = tuple(v)
                 if v not in self.classifiers:
-                    self.classifiers[v] = deepcopy(self.classifier_prototype)
+                    self.classifiers[v] = self.classifier_prototype.clone()
                     violation_to_instance_idx[v] = []
                 violation_to_instance_idx[v] += [idx]
 

miplearn/components/lazy_static.py

@@ -4,12 +4,12 @@
 
 import logging
 import sys
-from copy import deepcopy
-from typing import Any, Dict, Tuple, Optional
+from typing import Dict, Tuple, Optional
 
 import numpy as np
 from tqdm.auto import tqdm
 
+from miplearn import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.components.component import Component
 from miplearn.types import TrainingSample, Features
@@ -32,6 +32,7 @@ class StaticLazyConstraintsComponent(Component):
         large_gap=1e-2,
         violation_tolerance=-0.5,
     ):
+        assert isinstance(classifier, Classifier)
         self.threshold = threshold
         self.classifier_prototype = classifier
         self.classifiers = {}
@@ -120,7 +121,7 @@ class StaticLazyConstraintsComponent(Component):
             x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
         ):
             if category not in self.classifiers:
-                self.classifiers[category] = deepcopy(self.classifier_prototype)
+                self.classifiers[category] = self.classifier_prototype.clone()
             self.classifiers[category].fit(x[category], y[category])
 
     def predict(self, instance):

miplearn/components/objective.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from typing import List, Dict, Union, Callable, Optional, Any, TYPE_CHECKING, Tuple
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING, Tuple
 
 import numpy as np
 from sklearn.linear_model import LinearRegression
@@ -16,10 +16,11 @@ from sklearn.metrics import (
 )
 
 from miplearn.classifiers import Regressor
+from miplearn.classifiers.sklearn import ScikitLearnRegressor
 from miplearn.components.component import Component
 from miplearn.extractors import InstanceIterator
 from miplearn.instance import Instance
-from miplearn.types import MIPSolveStats, TrainingSample, LearningSolveStats, Features
+from miplearn.types import TrainingSample, LearningSolveStats, Features
 
 if TYPE_CHECKING:
     from miplearn.solvers.learning import LearningSolver
@@ -34,13 +35,15 @@ class ObjectiveValueComponent(Component):
 
     def __init__(
         self,
-        lb_regressor: Callable[[], Regressor] = LinearRegression,
-        ub_regressor: Callable[[], Regressor] = LinearRegression,
+        lb_regressor: Regressor = ScikitLearnRegressor(LinearRegression()),
+        ub_regressor: Regressor = ScikitLearnRegressor(LinearRegression()),
     ) -> None:
+        assert isinstance(lb_regressor, Regressor)
+        assert isinstance(ub_regressor, Regressor)
         self.ub_regressor: Optional[Regressor] = None
         self.lb_regressor: Optional[Regressor] = None
-        self.lb_regressor_factory = lb_regressor
-        self.ub_regressor_factory = ub_regressor
+        self.lb_regressor_prototype = lb_regressor
+        self.ub_regressor_prototype = ub_regressor
         self._predicted_ub: Optional[float] = None
         self._predicted_lb: Optional[float] = None
 
@@ -77,8 +80,8 @@ class ObjectiveValueComponent(Component):
             stats["Objective: predicted LB"] = self._predicted_lb
 
     def fit(self, training_instances: Union[List[str], List[Instance]]) -> None:
-        self.lb_regressor = self.lb_regressor_factory()
-        self.ub_regressor = self.ub_regressor_factory()
+        self.lb_regressor = self.lb_regressor_prototype.clone()
+        self.ub_regressor = self.ub_regressor_prototype.clone()
         logger.debug("Extracting features...")
         x_train = self.x(training_instances)
         y_train = self.y(training_instances)

miplearn/components/primal.py

@@ -50,18 +50,18 @@ class PrimalSolutionComponent(Component):
 
     def __init__(
         self,
-        classifier: Callable[[], Classifier] = lambda: AdaptiveClassifier(),
+        classifier: Classifier = AdaptiveClassifier(),
         mode: str = "exact",
-        threshold: Callable[[], Threshold] = lambda: MinPrecisionThreshold(
-            [0.98, 0.98]
-        ),
+        threshold: Threshold = MinPrecisionThreshold([0.98, 0.98]),
     ) -> None:
+        assert isinstance(classifier, Classifier)
+        assert isinstance(threshold, Threshold)
         assert mode in ["exact", "heuristic"]
         self.mode = mode
         self.classifiers: Dict[Hashable, Classifier] = {}
         self.thresholds: Dict[Hashable, Threshold] = {}
-        self.threshold_factory = threshold
-        self.classifier_factory = classifier
+        self.threshold_prototype = threshold
+        self.classifier_prototype = classifier
         self.stats: Dict[str, float] = {}
         self._n_free = 0
         self._n_zero = 0
@@ -114,8 +114,8 @@ class PrimalSolutionComponent(Component):
         y: Dict[str, np.ndarray],
     ) -> None:
         for category in x.keys():
-            clf = self.classifier_factory()
-            thr = self.threshold_factory()
+            clf = self.classifier_prototype.clone()
+            thr = self.threshold_prototype.clone()
             clf.fit(x[category], y[category])
             thr.fit(clf, x[category], y[category])
             self.classifiers[category] = clf

tests/components/test_lazy_dynamic.py

@@ -22,6 +22,7 @@ def test_lazy_fit():
     instances[0].found_violated_lazy_constraints = ["a", "b"]
     instances[1].found_violated_lazy_constraints = ["b", "c"]
     classifier = Mock(spec=Classifier)
+    classifier.clone = lambda: Mock(spec=Classifier)
     component = DynamicLazyConstraintsComponent(classifier=classifier)
 
     component.fit(instances)

tests/components/test_objective.py

@@ -38,11 +38,13 @@ def test_x_y_predict() -> None:
     # Construct mock regressors
     lb_regressor = Mock(spec=Regressor)
     lb_regressor.predict = Mock(return_value=np.array([[5.0], [6.0]]))
+    lb_regressor.clone = lambda: lb_regressor
     ub_regressor = Mock(spec=Regressor)
     ub_regressor.predict = Mock(return_value=np.array([[3.0], [3.0]]))
+    ub_regressor.clone = lambda: ub_regressor
     comp = ObjectiveValueComponent(
-        lb_regressor=lambda: lb_regressor,
-        ub_regressor=lambda: ub_regressor,
+        lb_regressor=lb_regressor,
+        ub_regressor=ub_regressor,
     )
 
     # Should build x correctly
@@ -77,9 +79,10 @@ def test_obj_evaluate():
     instances, models = get_test_pyomo_instances()
     reg = Mock(spec=Regressor)
     reg.predict = Mock(return_value=np.array([[1000.0], [1000.0]]))
+    reg.clone = lambda: reg
     comp = ObjectiveValueComponent(
-        lb_regressor=lambda: reg,
-        ub_regressor=lambda: reg,
+        lb_regressor=reg,
+        ub_regressor=reg,
     )
     comp.fit(instances)
     ev = comp.evaluate(instances)

tests/components/test_primal.py

@@ -189,10 +189,11 @@ def test_predict() -> None:
 
 
 def test_fit_xy():
-    comp = PrimalSolutionComponent(
-        classifier=lambda: Mock(spec=Classifier),
-        threshold=lambda: Mock(spec=Threshold),
-    )
+    clf = Mock(spec=Classifier)
+    clf.clone = lambda: Mock(spec=Classifier)
+    thr = Mock(spec=Threshold)
+    thr.clone = lambda: Mock(spec=Threshold)
+    comp = PrimalSolutionComponent(classifier=clf, threshold=thr)
     x = {
         "type-a": np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
         "type-b": np.array([[7.0, 8.0, 9.0]]),