feat: add SGBT

src/capymoa/classifier/__init__.py

@@ -6,6 +6,7 @@
 from ._passive_aggressive_classifier import PassiveAggressiveClassifier
 from ._sgd_classifier import SGDClassifier
 from ._knn import KNN
+from ._sgbt import SGBT
 
 __all__ = [
     "AdaptiveRandomForest",
@@ -17,4 +18,5 @@
     "KNN",
     "PassiveAggressiveClassifier",
     "SGDClassifier",
+    "SGBT"
 ]

src/capymoa/classifier/_sgbt.py

@@ -0,0 +1,94 @@
+from __future__ import annotations
+from typing import Union
+
+from capymoa.base import (
+    MOAClassifier,
+)
+from capymoa.stream import Schema
+from capymoa._utils import build_cli_str_from_mapping_and_locals
+
+from moa.classifiers.meta import StreamingGradientBoostedTrees as _MOA_SGBT
+
+
+class SGBT(MOAClassifier):
+    """Streaming Gradient Boosted Trees (SGBT) Classifier
+
+    Streaming Gradient Boosted Trees (SGBT), which is trained using weighted squared loss elicited
+    in XGBoost. SGBT exploits trees with a replacement strategy to detect and recover from drifts,
+    thus enabling the ensemble to adapt without sacrificing the predictive performance.
+
+    Reference:
+
+    `Gradient boosted trees for evolving data streams.
+    Nuwan Gunasekara, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet.
+    Machine Learning, Springer, 2024.
+    <https://doi.org/10.1007/s10994-024-06517-y>`_
+
+    Example usages:
+
+    >>> from capymoa.datasets import ElectricityTiny
+    >>> from capymoa.classifier import SGBT
+    >>> from capymoa.evaluation import prequential_evaluation
+    >>> stream = ElectricityTiny()
+    >>> schema = stream.get_schema()
+    >>> learner = SGBT(schema)
+    >>> results = prequential_evaluation(stream, learner, max_instances=1000)
+    >>> results["cumulative"].accuracy()
+    86.3
+    >>> stream = ElectricityTiny()
+    >>> schema = stream.get_schema()
+    >>> learner = SGBT(schema, base_learner='meta.AdaptiveRandomForestRegressor -s 10', boosting_iterations=10)
+    >>> results = prequential_evaluation(stream, learner, max_instances=1000)
+    >>> results["cumulative"].accuracy()
+    86.8
+    """
+
+    def __init__(
+        self,
+        schema: Schema | None = None,
+        random_seed: int = 0,
+        base_learner = 'trees.FIMTDD -s VarianceReductionSplitCriterion -g 25 -c 0.05 -e -p',
+        boosting_iterations: int = 100,
+        percentage_of_features: int = 75,
+        learning_rate = 0.0125,
+        disable_one_hot: bool = False,
+        multiply_hessian_by: int = 1,
+        skip_training: int =1,
+        use_squared_loss: bool = False,
+    ):
+        """Streaming Gradient Boosted Trees (SGBT) Classifier
+
+        :param schema: The schema of the stream.
+        :param random_seed: The random seed passed to the MOA learner.
+        :param base_learner: The base learner to be trained. Default FIMTDD -s VarianceReductionSplitCriterion -g 25 -c 0.05 -e -p.
+        :param boosting_iterations: The number of boosting iterations.
+        :param percentage_of_features: The percentage of features to use.
+        :param learning_rate: The learning rate.
+        :param disable_one_hot: Whether to disable one-hot encoding for regressors that supports nominal attributes.
+        :param multiply_hessian_by: The multiply hessian by this parameter to generate weights for multiple iterations.
+        :param skip_training: Skip training of 1/skip_training instances. skip_training=1 means no skipping is performed (train on all instances).
+        :param use_squared_loss: Whether to use squared loss for classification.
+        """
+
+        mapping = {
+            "base_learner": "-l",
+            "boosting_iterations": "-s",
+            "percentage_of_features": "-m",
+            "learning_rate": "-L",
+            "disable_one_hot": "-H",
+            "multiply_hessian_by": "-M",
+            "skip_training": "-S",
+            "use_squared_loss": "-K",
+            "random_seed": "-r",
+        }
+
+        assert (type(base_learner) == str
+                ), "Only MOA CLI strings are supported for SGBT base_learner, at the moment."
+
+        config_str = build_cli_str_from_mapping_and_locals(mapping, locals())
+        super(SGBT, self).__init__(
+            moa_learner=_MOA_SGBT,
+            schema=schema,
+            CLI=config_str,
+            random_seed=random_seed,
+        )

tests/test_classifiers.py

@@ -5,7 +5,8 @@
     AdaptiveRandomForest,
     OnlineBagging,
     NaiveBayes,
-    KNN
+    KNN,
+    SGBT
 )
 from capymoa.base import Classifier
 from capymoa.base import MOAClassifier
@@ -38,6 +39,7 @@
         (partial(KNN), 81.6, 74.0, None),
         (partial(PassiveAggressiveClassifier), 84.7, 81.0, None),
         (partial(SGDClassifier), 84.7, 83.0, None),
+        (partial(SGBT), 88.75, 88.0, None),
     ],
     ids=[
         "OnlineBagging",
@@ -49,6 +51,7 @@
         "KNN",
         "PassiveAggressiveClassifier",
         "SGDClassifier",
+        "SGBT"
     ],
 )
 def test_classifiers(