Make classifiers and regressors clonable

miplearn/classifiers/__init__.py

@@ -6,6 +6,7 @@ from abc import ABC, abstractmethod
 from typing import Optional, Any, cast
 
 import numpy as np
+import sklearn
 
 
 class Classifier(ABC):
@@ -77,6 +78,13 @@ class Classifier(ABC):
         )
         return np.ndarray([])
 
+    @abstractmethod
+    def clone(self) -> "Classifier":
+        """
+        Returns an unfitted copy of this classifier with the same hyperparameters.
+        """
+        pass
+
 
 class Regressor(ABC):
     """
@@ -136,6 +144,13 @@ class Regressor(ABC):
         assert n_inputs_x == self.n_inputs
         return np.ndarray([])
 
+    @abstractmethod
+    def clone(self) -> "Regressor":
+        """
+        Returns an unfitted copy of this regressor with the same hyperparameters.
+        """
+        pass
+
 
 class ScikitLearnClassifier(Classifier):
     """
@@ -185,3 +200,8 @@ class ScikitLearnClassifier(Classifier):
             assert isinstance(sklearn_proba, np.ndarray)
             assert sklearn_proba.shape == (n_samples, 2)
             return sklearn_proba
+
+    def clone(self) -> "ScikitLearnClassifier":
+        return ScikitLearnClassifier(
+            clf=sklearn.base.clone(self.inner_clf),
+        )

miplearn/classifiers/adaptive.py

@@ -34,7 +34,7 @@ class CandidateClassifierSpecs:
 
     def __init__(
         self,
-        classifier: Callable[[], Classifier],
+        classifier: Classifier,
         min_samples: int = 0,
     ) -> None:
         self.min_samples = min_samples
@@ -64,13 +64,13 @@ class AdaptiveClassifier(Classifier):
         if candidates is None:
             candidates = {
                 "knn(100)": CandidateClassifierSpecs(
-                    classifier=lambda: ScikitLearnClassifier(
+                    classifier=ScikitLearnClassifier(
                         KNeighborsClassifier(n_neighbors=100)
                     ),
                     min_samples=100,
                 ),
                 "logistic": CandidateClassifierSpecs(
-                    classifier=lambda: ScikitLearnClassifier(
+                    classifier=ScikitLearnClassifier(
                         make_pipeline(
                             StandardScaler(),
                             LogisticRegression(),
@@ -79,7 +79,7 @@ class AdaptiveClassifier(Classifier):
                     min_samples=30,
                 ),
                 "counting": CandidateClassifierSpecs(
-                    classifier=lambda: CountingClassifier(),
+                    classifier=CountingClassifier(),
                 ),
             }
         self.candidates = candidates
@@ -101,7 +101,7 @@ class AdaptiveClassifier(Classifier):
         for (name, specs) in self.candidates.items():
             if n_samples < specs.min_samples:
                 continue
-            clf = specs.classifier()
+            clf = specs.classifier.clone()
             clf.fit(x_train, y_train)
             proba = clf.predict_proba(x_train)
             # FIXME: Switch to k-fold cross validation
@@ -115,3 +115,6 @@ class AdaptiveClassifier(Classifier):
         super().predict_proba(x_test)
         assert self.classifier is not None
         return self.classifier.predict_proba(x_test)
+
+    def clone(self) -> "AdaptiveClassifier":
+        return AdaptiveClassifier(self.candidates)

miplearn/classifiers/counting.py

@@ -40,3 +40,6 @@ class CountingClassifier(Classifier):
 
     def __repr__(self):
         return "CountingClassifier(mean=%s)" % self.mean
+
+    def clone(self) -> "CountingClassifier":
+        return CountingClassifier()

miplearn/classifiers/cv.py

@@ -46,9 +46,7 @@ class CrossValidatedClassifier(Classifier):
 
     def __init__(
         self,
-        classifier: Callable[[], ScikitLearnClassifier] = (
+        classifier: ScikitLearnClassifier = ScikitLearnClassifier(LogisticRegression()),
-            lambda: ScikitLearnClassifier(LogisticRegression())
-        ),
         threshold: float = 0.75,
         constant: Optional[List[bool]] = None,
         cv: int = 5,
@@ -60,7 +58,7 @@ class CrossValidatedClassifier(Classifier):
             constant = [True, False]
         self.n_classes = len(constant)
         self.classifier: Optional[ScikitLearnClassifier] = None
-        self.classifier_factory = classifier
+        self.classifier_prototype = classifier
         self.constant: List[bool] = constant
         self.threshold = threshold
         self.cv = cv
@@ -77,7 +75,7 @@ class CrossValidatedClassifier(Classifier):
         absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
 
         # Calculate cross validation score and decide which classifier to use
-        clf = self.classifier_factory()
+        clf = self.classifier_prototype.clone()
         assert clf is not None
         assert isinstance(clf, ScikitLearnClassifier), (
             f"The provided classifier callable must return a ScikitLearnClassifier. "
@@ -123,3 +121,12 @@ class CrossValidatedClassifier(Classifier):
         super().predict_proba(x_test)
         assert self.classifier is not None
         return self.classifier.predict_proba(x_test)
+
+    def clone(self) -> "CrossValidatedClassifier":
+        return CrossValidatedClassifier(
+            classifier=self.classifier_prototype,
+            threshold=self.threshold,
+            constant=self.constant,
+            cv=self.cv,
+            scoring=self.scoring,
+        )

miplearn/classifiers/threshold.py

@@ -49,6 +49,13 @@ class Threshold(ABC):
         """
         pass
 
+    @abstractmethod
+    def clone(self) -> "Threshold":
+        """
+        Returns an unfitted copy of this threshold with the same hyperparameters.
+        """
+        pass
+
 
 class MinProbabilityThreshold(Threshold):
     """
@@ -65,6 +72,9 @@ class MinProbabilityThreshold(Threshold):
     def predict(self, x_test: np.ndarray) -> List[float]:
         return self.min_probability
 
+    def clone(self) -> "MinProbabilityThreshold":
+        return MinProbabilityThreshold(self.min_probability)
+
 
 class MinPrecisionThreshold(Threshold):
     """
@@ -111,3 +121,8 @@ class MinPrecisionThreshold(Threshold):
             if precision[k] >= min_precision:
                 return thresholds[k]
         return float("inf")
+
+    def clone(self) -> "MinPrecisionThreshold":
+        return MinPrecisionThreshold(
+            min_precision=self.min_precision,
+        )

tests/classifiers/test_adaptive.py

@@ -15,7 +15,7 @@ def test_adaptive() -> None:
     clf = AdaptiveClassifier(
         candidates={
             "linear": CandidateClassifierSpecs(
-                classifier=lambda: ScikitLearnClassifier(
+                classifier=ScikitLearnClassifier(
                     SVC(
                         probability=True,
                         random_state=42,
@@ -23,7 +23,7 @@ def test_adaptive() -> None:
                 )
             ),
             "poly": CandidateClassifierSpecs(
-                classifier=lambda: ScikitLearnClassifier(
+                classifier=ScikitLearnClassifier(
                     SVC(
                         probability=True,
                         kernel="poly",

tests/classifiers/test_cv.py

@@ -20,7 +20,7 @@ def test_cv() -> None:
     # Support vector machines with linear kernels do not perform well on this
     # data set, so predictor should return the given constant.
     clf = CrossValidatedClassifier(
-        classifier=lambda: ScikitLearnClassifier(
+        classifier=ScikitLearnClassifier(
             SVC(
                 probability=True,
                 random_state=42,
@@ -41,7 +41,7 @@ def test_cv() -> None:
     # Support vector machines with quadratic kernels perform almost perfectly
     # on this data set, so predictor should return their prediction.
     clf = CrossValidatedClassifier(
-        classifier=lambda: ScikitLearnClassifier(
+        classifier=ScikitLearnClassifier(
             SVC(
                 probability=True,
                 kernel="poly",