Clean up the distance function kwargs for DISE

selector/methods/distance.py

@@ -22,6 +22,7 @@
 #
 # --
 """Module for Distance-Based Selection Methods."""
+import inspect
 import warnings
 
 import bitarray
@@ -461,7 +462,7 @@ class DISE(SelectionBase):
     """
 
     def __init__(
-        self, r0=None, ref_index=None, tol=0.05, n_iter=10, p=2.0, eps=0.0, fun_dist=None, **kwargs
+        self, r0=None, ref_index=None, tol=0.05, n_iter=10, p=2.0, eps=0.0, fun_dist=None
     ):
         """
         Initialize class.
@@ -496,13 +497,6 @@ def __init__(
         fun_dist: callable, optional
             Function for calculating the distances between sample points. When `fun_dist` is `None`,
             the Minkowski p-norm distance is used. Default is None.
-        kwargs: dict, optional
-            Additional keyword arguments to be passed to the distance function `fun_dist`.
-
-        Notes
-        -----
-        If `p` is also defined in `kwargs`, the value of `p` from the argument will be used. For
-        example, when `p=2` and `kwargs={"p": 3}`, the value of `p` will be 2.
 
         """
         self.r0 = r0
@@ -519,14 +513,6 @@ def __init__(
         #     self.fun_dist = fun_dist
         self.fun_dist = fun_dist
 
-        self.kwargs = kwargs
-        if "p" in self.kwargs.keys():
-            warnings.warn(
-                f"Value of p in kwargs is overwritten by: {self.p} as defined in the "
-                f"argument `p`."
-            )
-        self.kwargs["p"] = p
-
     def algorithm(self, x, max_size):
         """Return selected samples based on directed sphere exclusion algorithm.
 
@@ -545,10 +531,10 @@ def algorithm(self, x, max_size):
         """
         if self.fun_dist is None:
             distances = spatial.distance.squareform(
-                spatial.distance.pdist(x, metric="minkowski", p=self.kwargs.get("p"))
+                spatial.distance.pdist(x, metric="minkowski", p=self.p)
             )
         else:
-            distances = spatial.distance.squareform(self.fun_dist(x, **self.kwargs))
+            distances = self.fun_dist(x)
 
         # set up the ref_index as when is None
         if self.ref_index is None:

selector/methods/tests/test_distance.py

@@ -26,8 +26,10 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_equal, assert_raises
+from scipy.spatial.distance import pdist, squareform
 from sklearn.metrics import pairwise_distances
 
+
 from selector.methods.distance import DISE, MaxMin, MaxSum, OptiSim
 from selector.methods.tests.common import generate_synthetic_data
 
@@ -369,3 +371,18 @@ def test_directed_sphere_on_line_with_larger_radius():
     expected = [1, 5, 9]
     assert_equal(selected, expected)
     assert_equal(collector.r, 1.0)
+
+
+def test_directed_sphere_dist_func():
+    """Test Direct Sphere Exclusion with a distance function."""
+    # (0,0) as the reference point
+    x = np.array([[0, 0], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6]])
+    collector = DISE(r0=0.5,
+                     tol=0,
+                     ref_index=0,
+                     fun_dist=lambda x: squareform(pdist(x, metric="minkowski", p=0.1))
+                     )
+    selected = collector.select(x, size=3)
+    expected = [0, 3, 6]
+    assert_equal(selected, expected)
+    assert_equal(collector.r, 2.0)