From 68839b95e7751afd04155cd2565cc53362f01fa2 Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Sat, 28 Mar 2020 10:41:50 +0100 Subject: Missing test --- src/python/CMakeLists.txt | 1 + src/python/test/test_knn.py | 82 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 83 insertions(+) create mode 100755 src/python/test/test_knn.py (limited to 'src') diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt index ec0ab1ca..d7a6a4db 100644 --- a/src/python/CMakeLists.txt +++ b/src/python/CMakeLists.txt @@ -411,6 +411,7 @@ if(PYTHONINTERP_FOUND) # DTM if(SCIPY_FOUND AND SKLEARN_FOUND AND TORCH_FOUND AND HNSWLIB_FOUND AND PYKEOPS_FOUND) + add_gudhi_py_test(test_knn) add_gudhi_py_test(test_dtm) endif() diff --git a/src/python/test/test_knn.py b/src/python/test/test_knn.py new file mode 100755 index 00000000..e455fb48 --- /dev/null +++ b/src/python/test/test_knn.py @@ -0,0 +1,82 @@ +""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. + See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. + Author(s): Marc Glisse + + Copyright (C) 2020 Inria + + Modification(s): + - YYYY/MM Author: Description of the modification +""" + +from gudhi.point_cloud.knn import KNN +import numpy as np +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.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.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") + .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.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") + .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) + 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) + 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]]) + + +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) + 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) + 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 + + +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) -- cgit v1.2.3