feat: added weightedknn

Author: DwayneAcosta

src/capymoa/classifier/__init__.py

@@ -18,6 +18,7 @@
 from ._samknn import SAMkNN
 from ._dynamic_weighted_majority import DynamicWeightedMajority
 from ._csmote import CSMOTE
+from ._weightedknn import WeightedkNN
 
 __all__ = [
     "AdaptiveRandomForestClassifier",
@@ -39,5 +40,6 @@
     "HoeffdingAdaptiveTree",
     "SAMkNN",
     "DynamicWeightedMajority",
-    "CSMOTE"
+    "CSMOTE",
+    "WeightedkNN"
 ]

src/capymoa/classifier/_weightedknn.py

@@ -0,0 +1,54 @@
+from capymoa.base import MOAClassifier
+from moa.classifiers.lazy import WeightedkNN as _MOA_WeightedkNN
+from capymoa.stream import Schema
+from capymoa._utils import build_cli_str_from_mapping_and_locals
+
+
+class WeightedkNN(MOAClassifier):
+    """WeightedKNN
+    Reference:
+    
+    'Effective Weighted k-Nearest Neighbors for Dynamic Data Streams'
+    Maroua Bahri
+    IEEE International Conference on Big Data (Big Data), 2022
+    <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10020652>
+    Example usages:
+    >>> from capymoa.datasets import ElectricityTiny
+    >>> from capymoa.classifier import WeightedkNN
+    >>> from capymoa.evaluation import prequential_evaluation
+    >>> stream = ElectricityTiny()
+    >>> schema = stream.get_schema()
+    >>> learner = WeightedkNN(schema)
+    >>> results = prequential_evaluation(stream, learner, max_instances=1000)
+    >>> results["cumulative"].accuracy()
+    74.7
+    """
+
+
+    def __init__(
+        self,
+        schema: Schema,
+        k: int = 10,
+        limit: int = 1000
+    ):
+
+        """ Weighted KNN Classifier
+        :param schema: The schema of the stream.
+        :param k: The number of neighbors.
+        :param w: The maximum number of instances to store.
+        """
+
+
+        mapping = {
+            "k": "-k",
+            "limit": "-w",
+        }
+
+
+        config_str = build_cli_str_from_mapping_and_locals(mapping, locals())
+        self.moa_learner = _MOA_WeightedkNN()
+        super(WeightedkNN, self).__init__(
+            schema=schema,
+            CLI=config_str,
+            moa_learner=self.moa_learner,
+        )

tests/test_classifiers.py

@@ -20,6 +20,7 @@
     CSMOTE,
     LeveragingBagging,
     OnlineAdwinBagging,
+    WeightedkNN
 )
 from capymoa.base import Classifier
 from capymoa.base import MOAClassifier
@@ -197,6 +198,12 @@ class ClassifierTestCase:
         85.25,
         92.0,
     ),
+    ClassifierTestCase(
+        "WeightedkNN",
+        partial(WeightedkNN),
+        78.15,
+        70,
+    ),
 ]
 
 
@@ -286,3 +293,4 @@ def test_classifiers(test_case: ClassifierTestCase, subtests: SubTests):
     if isinstance(learner, MOAClassifier) and test_case.cli_string is not None:
         cli_str = _extract_moa_learner_CLI(learner).strip("()")
         assert cli_str == test_case.cli_string, "CLI does not match expected value"
+