test: Added basic test for EnsembleDistanceClassifier

- Also renamed EnsembleDistanceMetricClassifier to EnsembleDistanceClassifier

distclassipy/__init__.py

@@ -24,15 +24,15 @@
 
 from .classifier import (
     DistanceMetricClassifier,
-    EnsembleDistanceMetricClassifier,
+    EnsembleDistanceClassifier,
 )
 from .distances import Distance, _ALL_METRICS
 
 __version__ = "0.2.0"
 
 __all__ = [
     "DistanceMetricClassifier",
-    "EnsembleDistanceMetricClassifier",
+    "EnsembleDistanceClassifier",
     "Distance",
     "_ALL_METRICS",
 ]

distclassipy/classifier.py

@@ -506,7 +506,7 @@ def find_best_metrics(
     return quantile_scores_df, best_metrics_per_quantile, group_bins
 
 
-class EnsembleDistanceMetricClassifier(BaseEstimator, ClassifierMixin):
+class EnsembleDistanceClassifier(BaseEstimator, ClassifierMixin):
     """An ensemble classifier that uses different metrics for each quantile."""
 
     def __init__(
@@ -541,7 +541,7 @@ def __init__(
 
     def fit(
         self, X: np.ndarray, y: np.ndarray, n_quantiles: int = 4
-    ) -> "EnsembleDistanceMetricClassifier":
+    ) -> "EnsembleDistanceClassifier":
         """Fit the ensemble classifier using the best metrics for each quantile.
 
         Parameters

tests/test_classifier.py

@@ -1,9 +1,13 @@
-from distclassipy.classifier import DistanceMetricClassifier
+from distclassipy.classifier import (
+    DistanceMetricClassifier,
+    EnsembleDistanceClassifier,
+)
 
 import numpy as np
 
 import pytest
 
+from sklearn.datasets import make_classification
 from sklearn.utils.estimator_checks import check_estimator
 
 
@@ -134,3 +138,18 @@ def test_confidence_calculation():
     clf.predict_and_analyse(X)
     distance_confidence = clf.calculate_confidence()
     assert distance_confidence.shape == (3, len(np.unique(y)))
+
+
+# Test basic functionality of EnsembleDistanceClassifier
+def test_ensemble_distance_classifier():
+    X, y = make_classification(
+        n_samples=1000,
+        n_features=4,
+        n_informative=2,
+        shuffle=True,
+    )
+    clf = EnsembleDistanceClassifier(feat_idx=0)
+    clf.fit(X, y)
+    predictions = clf.predict(X)
+    assert len(predictions) == len(y)
+    assert set(predictions).issubset(set(y))