Use longer names

4 files changed, 71 insertions, 35 deletions

diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 23c36b88..38368f29 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -7,10 +7,10 @@
 # Modification(s):
 #   - YYYY/MM Author: Description of the modification
 
-from .knn import KNN
+from .knn import KNearestNeighbors
 
 
-class DTM:
+class DistanceToMeasure:
     """
     Class to compute the distance to the empirical measure defined by a point set, as introduced in :cite:`dtm`.
     """
@@ -20,7 +20,7 @@ class DTM:
         Args:
             k (int): number of neighbors (possibly including the point itself).
             q (float): order used to compute the distance to measure. Defaults to 2.
-            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNN`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors.
+            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNearestNeighbors`, except that metric="neighbors" means that :func:`transform` expects an array with the distances to the k nearest neighbors.
         """
         self.k = k
         self.q = q
@@ -35,7 +35,9 @@ class DTM:
             X (numpy.array): coordinates for mass points.
         """
         if self.params.setdefault("metric", "euclidean") != "neighbors":
-            self.knn = KNN(self.k, return_index=False, return_distance=True, sort_results=False, **self.params)
+            self.knn = KNearestNeighbors(
+                self.k, return_index=False, return_distance=True, sort_results=False, **self.params
+            )
             self.knn.fit(X)
         return self
 
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index 8369f1f8..6642a3c2 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -10,7 +10,7 @@
 import numpy
 
 
-class KNN:
+class KNearestNeighbors:
     """
     Class wrapping several implementations for computing the k nearest neighbors in a point set.
     """
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index 93b13e1a..37934fdb 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -8,7 +8,7 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi.point_cloud.dtm import DTM
+from gudhi.point_cloud.dtm import DistanceToMeasure
 import numpy
 import pytest
 
@@ -16,35 +16,35 @@ import pytest
 def test_dtm_compare_euclidean():
     pts = numpy.random.rand(1000, 4)
     k = 3
-    dtm = DTM(k, implementation="ckdtree")
+    dtm = DistanceToMeasure(k, implementation="ckdtree")
     r0 = dtm.fit_transform(pts)
-    dtm = DTM(k, implementation="sklearn")
+    dtm = DistanceToMeasure(k, implementation="sklearn")
     r1 = dtm.fit_transform(pts)
     assert r1 == pytest.approx(r0)
-    dtm = DTM(k, implementation="sklearn", algorithm="brute")
+    dtm = DistanceToMeasure(k, implementation="sklearn", algorithm="brute")
     r2 = dtm.fit_transform(pts)
     assert r2 == pytest.approx(r0)
-    dtm = DTM(k, implementation="hnsw")
+    dtm = DistanceToMeasure(k, implementation="hnsw")
     r3 = dtm.fit_transform(pts)
     assert r3 == pytest.approx(r0)
     from scipy.spatial.distance import cdist
 
     d = cdist(pts, pts)
-    dtm = DTM(k, metric="precomputed")
+    dtm = DistanceToMeasure(k, metric="precomputed")
     r4 = dtm.fit_transform(d)
     assert r4 == pytest.approx(r0)
-    dtm = DTM(k, implementation="keops")
+    dtm = DistanceToMeasure(k, implementation="keops")
     r5 = dtm.fit_transform(pts)
     assert r5 == pytest.approx(r0)
 
 
 def test_dtm_precomputed():
     dist = numpy.array([[1.0, 3, 8], [1, 5, 5], [0, 2, 3]])
-    dtm = DTM(2, q=1, metric="neighbors")
+    dtm = DistanceToMeasure(2, q=1, metric="neighbors")
     r = dtm.fit_transform(dist)
     assert r == pytest.approx([2.0, 3, 1])
 
     dist = numpy.array([[2.0, 2], [0, 1], [3, 4]])
-    dtm = DTM(2, q=2, metric="neighbors")
+    dtm = DistanceToMeasure(2, q=2, metric="neighbors")
     r = dtm.fit_transform(dist)
     assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01)
diff --git a/src/python/test/test_knn.py b/src/python/test/test_knn.py
index e455fb48..6aac2006 100755
--- a/src/python/test/test_knn.py
+++ b/src/python/test/test_knn.py
@@ -8,7 +8,7 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi.point_cloud.knn import KNN
+from gudhi.point_cloud.knn import KNearestNeighbors
 import numpy as np
 import pytest
 
@@ -16,39 +16,39 @@ import pytest
 def test_knn_explicit():
     base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
     query = np.array([[1.0, 1], [2, 2], [4, 4]])
-    knn = KNN(2, metric="manhattan", return_distance=True, return_index=True)
+    knn = KNearestNeighbors(2, metric="manhattan", return_distance=True, return_index=True)
     knn.fit(base)
     r = knn.transform(query)
     assert r[0] == pytest.approx(np.array([[0, 1], [1, 0], [3, 2]]))
     assert r[1] == pytest.approx(np.array([[0.0, 1], [1, 2], [1, 2]]))
 
-    knn = KNN(2, metric="chebyshev", return_distance=True, return_index=False)
+    knn = KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False)
     knn.fit(base)
     r = knn.transform(query)
     assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
     r = (
-        KNN(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops")
+        KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops")
         .fit(base)
         .transform(query)
     )
     assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
 
-    knn = KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True)
+    knn = KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True)
     knn.fit(base)
     r = knn.transform(query)
     assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
     r = (
-        KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops")
+        KNearestNeighbors(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops")
         .fit(base)
         .transform(query)
     )
     assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
 
     dist = np.array([[0.0, 3, 8], [1, 0, 5], [1, 2, 0]])
-    knn = KNN(2, metric="precomputed", return_index=True, return_distance=False)
+    knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False)
     r = knn.fit_transform(dist)
     assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]])
-    knn = KNN(2, metric="precomputed", return_index=True, return_distance=True)
+    knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True)
     r = knn.fit_transform(dist)
     assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]])
     assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]])
@@ -57,16 +57,40 @@ def test_knn_explicit():
 def test_knn_compare():
     base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
     query = np.array([[1.0, 1], [2, 2], [4, 4]])
-    r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=False).fit(base).transform(query)
-    r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=False).fit(base).transform(query)
-    r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query)
-    r3 = KNN(2, implementation="keops", return_index=True, return_distance=False).fit(base).transform(query)
+    r0 = (
+        KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=False)
+        .fit(base)
+        .transform(query)
+    )
+    r1 = (
+        KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=False)
+        .fit(base)
+        .transform(query)
+    )
+    r2 = (
+        KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query)
+    )
+    r3 = (
+        KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=False)
+        .fit(base)
+        .transform(query)
+    )
     assert np.array_equal(r0, r1) and np.array_equal(r0, r2) and np.array_equal(r0, r3)
 
-    r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=True).fit(base).transform(query)
-    r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=True).fit(base).transform(query)
-    r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query)
-    r3 = KNN(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query)
+    r0 = (
+        KNearestNeighbors(2, implementation="ckdtree", return_index=True, return_distance=True)
+        .fit(base)
+        .transform(query)
+    )
+    r1 = (
+        KNearestNeighbors(2, implementation="sklearn", return_index=True, return_distance=True)
+        .fit(base)
+        .transform(query)
+    )
+    r2 = KNearestNeighbors(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query)
+    r3 = (
+        KNearestNeighbors(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query)
+    )
     assert np.array_equal(r0[0], r1[0]) and np.array_equal(r0[0], r2[0]) and np.array_equal(r0[0], r3[0])
     d0 = pytest.approx(r0[1])
     assert r1[1] == d0 and r2[1] == d0 and r3[1] == d0
@@ -75,8 +99,18 @@ def test_knn_compare():
 def test_knn_nop():
     # This doesn't look super useful...
     p = np.array([[0.0]])
-    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="sklearn").fit_transform(p)
-    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="ckdtree").fit_transform(p)
-    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5).fit_transform(p)
-    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="keops").fit_transform(p)
-    assert None is KNN(k=1, return_index=False, return_distance=False, metric="precomputed").fit_transform(p)
+    assert None is KNearestNeighbors(
+        k=1, return_index=False, return_distance=False, implementation="sklearn"
+    ).fit_transform(p)
+    assert None is KNearestNeighbors(
+        k=1, return_index=False, return_distance=False, implementation="ckdtree"
+    ).fit_transform(p)
+    assert None is KNearestNeighbors(
+        k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5
+    ).fit_transform(p)
+    assert None is KNearestNeighbors(
+        k=1, return_index=False, return_distance=False, implementation="keops"
+    ).fit_transform(p)
+    assert None is KNearestNeighbors(
+        k=1, return_index=False, return_distance=False, metric="precomputed"
+    ).fit_transform(p)