adding SOKNL wrapper in regressor.py

src/capymoa/learner/regressor/regressors.py

@@ -10,6 +10,7 @@
 from moa.classifiers.meta import (
     AdaptiveRandomForestRegressor as MOA_AdaptiveRandomForestRegressor,
 )
+from moa.classifiers.meta import SelfOptimisingKNearestLeaves as MOA_SOKNL
 
 
 class KNNRegressor(MOARegressor):
@@ -125,3 +126,83 @@ def __init__(
             )
             self.moa_learner.prepareForUse()
             self.moa_learner.resetLearning()
+
+
+class SOKNL(MOARegressor):
+    def __init__(
+        self,
+        schema=None,
+        CLI=None,
+        random_seed=1,
+        tree_learner=None,
+        ensemble_size=100,
+        max_features=0.6,
+        lambda_param=6.0,  # m_features_mode=None, m_features_per_tree_size=60,
+        drift_detection_method=None,
+        warning_detection_method=None,
+        disable_drift_detection=False,
+        disable_background_learner=False,
+        self_optimising=True,
+        k_value=5,
+    ):
+        # Important: must create the MOA object before invoking the super class __init__
+        self.moa_learner = MOA_SOKNL()
+        super().__init__(
+            schema=schema,
+            CLI=CLI,
+            random_seed=random_seed,
+            moa_learner=self.moa_learner,
+        )
+
+        # Initialize instance attributes with default values, CLI was not set.
+        if self.CLI is None:
+            self.tree_learner = (
+                "(SelfOptimisingBaseTree -s VarianceReductionSplitCriterion -g 50 -c 0.01)"
+                if tree_learner is None
+                else tree_learner
+            )
+            self.ensemble_size = ensemble_size
+
+            self.max_features = max_features
+            if isinstance(self.max_features, float) and 0.0 <= self.max_features <= 1.0:
+                self.m_features_mode = "(Percentage (M * (m / 100)))"
+                self.m_features_per_tree_size = int(self.max_features * 100)
+            elif isinstance(self.max_features, int):
+                self.m_features_mode = "(Specified m (integer value))"
+                self.m_features_per_tree_size = max_features
+            elif self.max_features in ["sqrt"]:
+                self.m_features_mode = "(sqrt(M)+1)"
+                self.m_features_per_tree_size = -1  # or leave it unchanged
+            elif self.max_features is None:
+                self.m_features_mode = "(Percentage (M * (m / 100)))"
+                self.m_features_per_tree_size = 60
+            else:
+                # Handle other cases or raise an exception if needed
+                raise ValueError("Invalid value for max_features")
+
+            # self.m_features_mode = "(Percentage (M * (m / 100)))" if m_features_mode is None else m_features_mode
+            # self.m_features_per_tree_size = m_features_per_tree_size
+            self.lambda_param = lambda_param
+            self.drift_detection_method = (
+                "(ADWINChangeDetector -a 1.0E-3)"
+                if drift_detection_method is None
+                else drift_detection_method
+            )
+            self.warning_detection_method = (
+                "(ADWINChangeDetector -a 1.0E-2)"
+                if warning_detection_method is None
+                else warning_detection_method
+            )
+            self.disable_drift_detection = disable_drift_detection
+            self.disable_background_learner = disable_background_learner
+
+            self.self_optimising = self_optimising
+            self.k_value = k_value
+
+            self.moa_learner.getOptions().setViaCLIString(
+                f"-l {self.tree_learner} -s {self.ensemble_size} {'-f' if self_optimising else ''} -k {k_value} -o {self.m_features_mode} -m \
+                {self.m_features_per_tree_size} -a {self.lambda_param} -x {self.drift_detection_method} -p \
+                {self.warning_detection_method} {'-u' if self.disable_drift_detection else ''}  {'-q' if self.disable_background_learner else ''}"
+            )
+            self.moa_learner.prepareForUse()
+            self.moa_learner.resetLearning()