Use longer names

1 files changed, 55 insertions, 21 deletions

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)