Make classifiers and regressors clonable

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)