fix conflict

8 files changed, 340 insertions, 9 deletions

diff --git a/src/python/test/test_cover_complex.py b/src/python/test/test_cover_complex.py
index 32bc5a26..260f6a5c 100755
--- a/src/python/test/test_cover_complex.py
+++ b/src/python/test/test_cover_complex.py
@@ -9,6 +9,7 @@
 """
 
 from gudhi import CoverComplex
+import pytest
 
 __author__ = "Vincent Rouvreau"
 __copyright__ = "Copyright (C) 2018 Inria"
@@ -24,7 +25,8 @@ def test_empty_constructor():
 def test_non_existing_file_read():
     # Try to open a non existing file
     cover = CoverComplex()
-    assert cover.read_point_cloud("pouetpouettralala.toubiloubabdou") == False
+    with pytest.raises(FileNotFoundError):
+        cover.read_point_cloud("pouetpouettralala.toubiloubabdou")
 
 
 def test_files_creation():
diff --git a/src/python/test/test_cubical_complex.py b/src/python/test/test_cubical_complex.py
index b20f77c1..dd7653c2 100755
--- a/src/python/test/test_cubical_complex.py
+++ b/src/python/test/test_cubical_complex.py
@@ -10,6 +10,7 @@
 
 from gudhi import CubicalComplex, PeriodicCubicalComplex
 import numpy as np
+import pytest
 
 __author__ = "Vincent Rouvreau"
 __copyright__ = "Copyright (C) 2016 Inria"
@@ -25,9 +26,8 @@ def test_empty_constructor():
 
 def test_non_existing_perseus_file_constructor():
     # Try to open a non existing file
-    cub = CubicalComplex(perseus_file="pouetpouettralala.toubiloubabdou")
-    assert cub.__is_defined() == False
-    assert cub.__is_persistence_defined() == False
+    with pytest.raises(FileNotFoundError):
+        cub = CubicalComplex(perseus_file="pouetpouettralala.toubiloubabdou")
 
 
 def test_dimension_or_perseus_file_constructor():
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
new file mode 100755
index 00000000..bff4c267
--- /dev/null
+++ b/src/python/test/test_dtm.py
@@ -0,0 +1,68 @@
+""" 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.dtm import DistanceToMeasure
+import numpy
+import pytest
+import torch
+
+
+def test_dtm_compare_euclidean():
+    pts = numpy.random.rand(1000, 4)
+    k = 6
+    dtm = DistanceToMeasure(k, implementation="ckdtree")
+    r0 = dtm.fit_transform(pts)
+    dtm = DistanceToMeasure(k, implementation="sklearn")
+    r1 = dtm.fit_transform(pts)
+    assert r1 == pytest.approx(r0)
+    dtm = DistanceToMeasure(k, implementation="sklearn", algorithm="brute")
+    r2 = dtm.fit_transform(pts)
+    assert r2 == pytest.approx(r0)
+    dtm = DistanceToMeasure(k, implementation="hnsw")
+    r3 = dtm.fit_transform(pts)
+    assert r3 == pytest.approx(r0, rel=0.1)
+    from scipy.spatial.distance import cdist
+
+    d = cdist(pts, pts)
+    dtm = DistanceToMeasure(k, metric="precomputed")
+    r4 = dtm.fit_transform(d)
+    assert r4 == pytest.approx(r0)
+    dtm = DistanceToMeasure(k, metric="precomputed", n_jobs=2)
+    r4b = dtm.fit_transform(d)
+    assert r4b == pytest.approx(r0)
+    dtm = DistanceToMeasure(k, implementation="keops")
+    r5 = dtm.fit_transform(pts)
+    assert r5 == pytest.approx(r0)
+    pts2 = torch.tensor(pts, requires_grad=True)
+    assert pts2.grad is None
+    dtm = DistanceToMeasure(k, implementation="keops", enable_autodiff=True)
+    r6 = dtm.fit_transform(pts2)
+    assert r6.detach().numpy() == pytest.approx(r0)
+    r6.sum().backward()
+    assert not torch.isnan(pts2.grad).any()
+    pts2 = torch.tensor(pts, requires_grad=True)
+    assert pts2.grad is None
+    dtm = DistanceToMeasure(k, implementation="ckdtree", enable_autodiff=True)
+    r7 = dtm.fit_transform(pts2)
+    assert r7.detach().numpy() == pytest.approx(r0)
+    r7.sum().backward()
+    assert not torch.isnan(pts2.grad).any()
+
+
+def test_dtm_precomputed():
+    dist = numpy.array([[1.0, 3, 8], [1, 5, 5], [0, 2, 3]])
+    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 = 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
new file mode 100755
index 00000000..a87ec212
--- /dev/null
+++ b/src/python/test/test_knn.py
@@ -0,0 +1,130 @@
+""" 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 KNearestNeighbors
+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 = 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 = 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 = (
+        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]]))
+    r = (
+        KNearestNeighbors(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops", enable_autodiff=True)
+        .fit(base)
+        .transform(query)
+    )
+    assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
+
+    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 = (
+        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 = 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 = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, sort_results=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]])
+    # Second time in parallel
+    knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=False, n_jobs=2, sort_results=True)
+    r = knn.fit_transform(dist)
+    assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]])
+    knn = KNearestNeighbors(2, metric="precomputed", return_index=True, return_distance=True, n_jobs=2)
+    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 = (
+        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 = (
+        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
+
+
+def test_knn_nop():
+    # This doesn't look super useful...
+    p = np.array([[0.0]])
+    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)
diff --git a/src/python/test/test_simplex_tree.py b/src/python/test/test_simplex_tree.py
index f7848379..2137d822 100755
--- a/src/python/test/test_simplex_tree.py
+++ b/src/python/test/test_simplex_tree.py
@@ -9,6 +9,7 @@
 """
 
 from gudhi import SimplexTree
+import pytest
 
 __author__ = "Vincent Rouvreau"
 __copyright__ = "Copyright (C) 2016 Inria"
@@ -45,7 +46,6 @@ def test_insertion():
     assert st.find([2, 3]) == False
 
     # filtration test
-    st.initialize_filtration()
     assert st.filtration([0, 1, 2]) == 4.0
     assert st.filtration([0, 2]) == 4.0
     assert st.filtration([1, 2]) == 4.0
@@ -92,7 +92,6 @@ def test_insertion():
     assert st.find([1]) == True
     assert st.find([2]) == True
 
-    st.initialize_filtration()
     assert st.persistence(persistence_dim_max=True) == [
         (1, (4.0, float("inf"))),
         (0, (0.0, float("inf"))),
@@ -150,7 +149,6 @@ def test_expansion():
     st.expansion(3)
     assert st.num_vertices() == 7
     assert st.num_simplices() == 22
-    st.initialize_filtration()
 
     assert list(st.get_filtration()) == [
         ([2], 0.1),
@@ -250,6 +248,87 @@ def test_make_filtration_non_decreasing():
     assert st.filtration([3, 4]) == 2.0
     assert st.filtration([4, 5]) == 2.0
 
+def test_extend_filtration():
+
+    # Inserted simplex:
+    #      5   4
+    #      o   o
+    #     / \ /
+    #    o   o
+    #   /2\ /3
+    #  o   o        
+    #  1   0        
+
+    st = SimplexTree()                                                                                                                     
+    st.insert([0,2])
+    st.insert([1,2])
+    st.insert([0,3])
+    st.insert([2,5])
+    st.insert([3,4])
+    st.insert([3,5])                                                                                                         
+    st.assign_filtration([0], 1.)                                                                                                                
+    st.assign_filtration([1], 2.)                                                                                                                
+    st.assign_filtration([2], 3.)                                                                                                                
+    st.assign_filtration([3], 4.)                                                                                                                
+    st.assign_filtration([4], 5.)                                                                                                                
+    st.assign_filtration([5], 6.)                                                                                                                
+
+    assert list(st.get_filtration()) == [                                                                                                                         
+        ([0, 2], 0.0), 
+        ([1, 2], 0.0), 
+        ([0, 3], 0.0), 
+        ([3, 4], 0.0), 
+        ([2, 5], 0.0), 
+        ([3, 5], 0.0), 
+        ([0], 1.0), 
+        ([1], 2.0), 
+        ([2], 3.0), 
+        ([3], 4.0), 
+        ([4], 5.0), 
+        ([5], 6.0)
+    ]
+        
+    st.extend_filtration()
+    
+    assert list(st.get_filtration()) == [                                                                                                                         
+        ([6], -3.0), 
+        ([0], -2.0), 
+        ([1], -1.8), 
+        ([2], -1.6), 
+        ([0, 2], -1.6), 
+        ([1, 2], -1.6), 
+        ([3], -1.4), 
+        ([0, 3], -1.4), 
+        ([4], -1.2), 
+        ([3, 4], -1.2), 
+        ([5], -1.0), 
+        ([2, 5], -1.0), 
+        ([3, 5], -1.0), 
+        ([5, 6], 1.0), 
+        ([4, 6], 1.2), 
+        ([3, 6], 1.4), 
+        ([3, 4, 6], 1.4),
+        ([3, 5, 6], 1.4), 
+        ([2, 6], 1.6), 
+        ([2, 5, 6], 1.6), 
+        ([1, 6], 1.8), 
+        ([1, 2, 6], 1.8), 
+        ([0, 6], 2.0), 
+        ([0, 2, 6], 2.0), 
+        ([0, 3, 6], 2.0)
+    ]
+
+    dgms = st.extended_persistence(min_persistence=-1.)
+
+    assert dgms[0][0][1][0] == pytest.approx(2.)
+    assert dgms[0][0][1][1] == pytest.approx(3.)
+    assert dgms[1][0][1][0] == pytest.approx(5.)
+    assert dgms[1][0][1][1] == pytest.approx(4.)
+    assert dgms[2][0][1][0] == pytest.approx(1.)
+    assert dgms[2][0][1][1] == pytest.approx(6.)
+    assert dgms[3][0][1][0] == pytest.approx(6.)
+    assert dgms[3][0][1][1] == pytest.approx(1.) 
+
 def test_simplices_iterator():
     st = SimplexTree()
     
diff --git a/src/python/test/test_subsampling.py b/src/python/test/test_subsampling.py
index fe0985fa..31f64e32 100755
--- a/src/python/test/test_subsampling.py
+++ b/src/python/test/test_subsampling.py
@@ -120,7 +120,6 @@ def test_simple_pick_n_random_points():
     # Go furter than point set on purpose
     for iter in range(1, 10):
         sub_set = gudhi.pick_n_random_points(points=point_set, nb_points=iter)
-        print(5)
         for sub in sub_set:
             found = False
             for point in point_set:
diff --git a/src/python/test/test_wasserstein_barycenter.py b/src/python/test/test_wasserstein_barycenter.py
new file mode 100755
index 00000000..f68c748e
--- /dev/null
+++ b/src/python/test/test_wasserstein_barycenter.py
@@ -0,0 +1,46 @@
+from gudhi.wasserstein.barycenter import lagrangian_barycenter
+import numpy as np
+
+""" 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):       Theo Lacombe
+
+    Copyright (C) 2019 Inria
+
+    Modification(s):
+      - YYYY/MM Author: Description of the modification
+"""
+
+__author__ = "Theo Lacombe"
+__copyright__ = "Copyright (C) 2019 Inria"
+__license__ = "MIT"
+
+
+def test_lagrangian_barycenter():
+
+    dg1 = np.array([[0.2, 0.5]])
+    dg2 = np.array([[0.2, 0.7]])
+    dg3 = np.array([[0.3, 0.6], [0.7, 0.8], [0.2, 0.3]])
+    dg4 = np.array([])
+    dg5 = np.array([])
+    dg6 = np.array([])
+    res = np.array([[0.27916667, 0.55416667], [0.7375,  0.7625], [0.2375, 0.2625]])
+
+    dg7 = np.array([[0.1, 0.15], [0.1, 0.7], [0.2, 0.22], [0.55, 0.84], [0.11, 0.91], [0.61, 0.75], [0.33, 0.46], [0.12, 0.41], [0.32, 0.48]])
+    dg8 = np.array([[0., 4.], [4, 8]])
+
+    # error crit.
+    eps = 1e-7
+
+
+    assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg1, dg2, dg3, dg4],init=3, verbose=False) - res) < eps
+    assert np.array_equal(lagrangian_barycenter(pdiagset=[dg4, dg5, dg6], verbose=False), np.empty(shape=(0,2)))
+    assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg7], verbose=False) - dg7) < eps
+    Y, log = lagrangian_barycenter(pdiagset=[dg4, dg8], verbose=True)
+    assert np.linalg.norm(Y - np.array([[1,3], [5, 7]])) < eps
+    assert np.abs(log["energy"] - 2) < eps
+    assert np.array_equal(log["groupings"][0] , np.array([[0, -1], [1, -1]]))
+    assert np.array_equal(log["groupings"][1] , np.array([[0, 0], [1, 1]]))
+    assert np.linalg.norm(lagrangian_barycenter(pdiagset=[dg8, dg4], init=np.array([[0.2, 0.6], [0.5, 0.7]]), verbose=False) - np.array([[1, 3], [5, 7]])) < eps
+    assert lagrangian_barycenter(pdiagset = []) is None
+
diff --git a/src/python/test/test_wasserstein_distance.py b/src/python/test/test_wasserstein_distance.py
index 0d70e11a..1a4acc1d 100755
--- a/src/python/test/test_wasserstein_distance.py
+++ b/src/python/test/test_wasserstein_distance.py
@@ -8,6 +8,7 @@
       - YYYY/MM Author: Description of the modification
 """
 
+from gudhi.wasserstein.wasserstein import _proj_on_diag
 from gudhi.wasserstein import wasserstein_distance as pot
 from gudhi.hera import wasserstein_distance as hera
 import numpy as np
@@ -17,6 +18,12 @@ __author__ = "Theo Lacombe"
 __copyright__ = "Copyright (C) 2019 Inria"
 __license__ = "MIT"
 
+def test_proj_on_diag():
+    dgm = np.array([[1., 1.], [1., 2.], [3., 5.]])
+    assert np.array_equal(_proj_on_diag(dgm), [[1., 1.], [1.5, 1.5], [4., 4.]])
+    empty = np.empty((0, 2))
+    assert np.array_equal(_proj_on_diag(empty), empty)
+
 def _basic_wasserstein(wasserstein_distance, delta, test_infinity=True, test_matching=True):
     diag1 = np.array([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]])
     diag2 = np.array([[2.8, 4.45], [9.5, 14.1]])
@@ -70,7 +77,7 @@ def _basic_wasserstein(wasserstein_distance, delta, test_infinity=True, test_mat
         assert np.array_equal(match , [[0, -1], [1, -1]])
         match = wasserstein_distance(diag1, diag2, matching=True, internal_p=2., order=2.)[1]
         assert np.array_equal(match, [[0, 0], [1, 1], [2, -1]])
-        
+
 
 
 def hera_wrap(delta):