Adjust metrics (back to scipy distance)

aaanalysis/_utils/metrics.py

@@ -2,9 +2,10 @@
 This is a script for utility functions for statistical measures.
 """
 import numpy as np
-from sklearn.metrics import roc_auc_score, pairwise_distances
+from sklearn.metrics import roc_auc_score
 from scipy.stats import entropy, gaussian_kde
 from collections import OrderedDict
+from scipy.spatial import distance
 
 
 # AUC adjusted
@@ -54,7 +55,7 @@ def bic_score_(X, labels=None):
 
     # Compute variance over all clusters
     list_masks = [labels == label for label in center_labels]
-    sum_squared_dist = sum([sum(pairwise_distances(X[mask], [center], metric='euclidean') ** 2) for mask, center in zip(list_masks, centers)])
+    sum_squared_dist = sum([sum(distance.cdist(X[mask], [center], 'euclidean') ** 2) for mask, center in zip(list_masks, centers)])
 
     # Compute between-cluster variance
     denominator = max((n_samples - n_classes) * n_features, epsilon)

aaanalysis/feature_engineering/_aaclust.py

@@ -152,7 +152,7 @@ def __init__(self,
         self._model_class = model_class
         self._model_kwargs = model_kwargs
         # Output parameters (set during model fitting)
-        self.model : Optional[ClusterMixin] = None
+        self.model: Optional[ClusterMixin] = None
         self.n_clusters: Optional[int] = None
         self.labels_: Optional[ut.ArrayLike1D] = None
         self.centers_: Optional[ut.ArrayLike1D] = None

tests/unit/aaclust_tests/test_aaclust_eval.py

@@ -80,10 +80,10 @@ def test_labels_none(self):
         with pytest.raises(ValueError):
             aa.AAclust().eval(X)
 
-    @settings(deadline=1000)
+    @settings(deadline=1500)
     @given(X=npst.arrays(dtype=np.float64, shape=npst.array_shapes(min_dims=2, max_dims=2, min_side=10, max_side=50),
-                         elements=some.floats(allow_nan=False, allow_infinity=False, min_value=1)),
-        labels_length=some.integers(min_value=10, max_value=50))
+                         elements=some.floats(allow_nan=False, allow_infinity=False, min_value=1, max_value=10000)),
+           labels_length=some.integers(min_value=10, max_value=50))
     def test_matching_X_labels(self, X, labels_length):
         """Test evaluate with matching X and labels dimensions."""
         # Random label assignments