Merge pull request #499 from VincentRouvreau/sklearn_cubical

Scikit learn like cubical interface

2 files changed, 98 insertions, 0 deletions

diff --git a/src/python/test/test_representations_preprocessing.py b/src/python/test/test_representations_preprocessing.py
new file mode 100644
index 00000000..838cf30c
--- /dev/null
+++ b/src/python/test/test_representations_preprocessing.py
@@ -0,0 +1,39 @@
+""" 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):       Vincent Rouvreau
+
+    Copyright (C) 2021 Inria
+
+    Modification(s):
+      - YYYY/MM Author: Description of the modification
+"""
+
+from gudhi.representations.preprocessing import DimensionSelector
+import numpy as np
+import pytest
+
+H0_0 = np.array([0.0, 0.0])
+H1_0 = np.array([1.0, 0.0])
+H0_1 = np.array([0.0, 1.0])
+H1_1 = np.array([1.0, 1.0])
+H0_2 = np.array([0.0, 2.0])
+H1_2 = np.array([1.0, 2.0])
+
+
+def test_dimension_selector():
+    X = [[H0_0, H1_0], [H0_1, H1_1], [H0_2, H1_2]]
+    ds = DimensionSelector(index=0)
+    h0 = ds.fit_transform(X)
+    np.testing.assert_array_equal(h0[0], H0_0)
+    np.testing.assert_array_equal(h0[1], H0_1)
+    np.testing.assert_array_equal(h0[2], H0_2)
+
+    ds = DimensionSelector(index=1)
+    h1 = ds.fit_transform(X)
+    np.testing.assert_array_equal(h1[0], H1_0)
+    np.testing.assert_array_equal(h1[1], H1_1)
+    np.testing.assert_array_equal(h1[2], H1_2)
+
+    ds = DimensionSelector(index=2)
+    with pytest.raises(IndexError):
+        h2 = ds.fit_transform([[H0_0, H1_0], [H0_1, H1_1], [H0_2, H1_2]])
diff --git a/src/python/test/test_sklearn_cubical_persistence.py b/src/python/test/test_sklearn_cubical_persistence.py
new file mode 100644
index 00000000..1c05a215
--- /dev/null
+++ b/src/python/test/test_sklearn_cubical_persistence.py
@@ -0,0 +1,59 @@
+""" 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):       Vincent Rouvreau
+
+    Copyright (C) 2021 Inria
+
+    Modification(s):
+      - YYYY/MM Author: Description of the modification
+"""
+
+from gudhi.sklearn.cubical_persistence import CubicalPersistence
+import numpy as np
+from sklearn import datasets
+
+CUBICAL_PERSISTENCE_H0_IMG0 = np.array([[0.0, 6.0], [0.0, 8.0], [0.0, np.inf]])
+
+
+def test_simple_constructor_from_top_cells():
+    cells = datasets.load_digits().images[0]
+    cp = CubicalPersistence(homology_dimensions=0)
+    np.testing.assert_array_equal(cp._CubicalPersistence__transform_only_this_dim(cells), CUBICAL_PERSISTENCE_H0_IMG0)
+    cp = CubicalPersistence(homology_dimensions=[0, 2])
+    diags = cp._CubicalPersistence__transform(cells)
+    assert len(diags) == 2
+    np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
+
+
+def test_simple_constructor_from_top_cells_list():
+    digits = datasets.load_digits().images[:10]
+    cp = CubicalPersistence(homology_dimensions=0, n_jobs=-2)
+
+    diags = cp.fit_transform(digits)
+    assert len(diags) == 10
+    np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
+
+    cp = CubicalPersistence(homology_dimensions=[0, 1], n_jobs=-1)
+    diagsH0H1 = cp.fit_transform(digits)
+    assert len(diagsH0H1) == 10
+    for idx in range(10):
+        np.testing.assert_array_equal(diags[idx], diagsH0H1[idx][0])
+
+def test_simple_constructor_from_flattened_cells():
+    cells = datasets.load_digits().images[0]
+    # Not squared (extended) flatten cells
+    flat_cells = np.hstack((cells, np.zeros((cells.shape[0], 2)))).flatten()
+    
+    cp = CubicalPersistence(homology_dimensions=0, newshape=[-1, 8, 10])
+    diags = cp.fit_transform([flat_cells])
+
+    np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
+
+    # Not squared (extended) non-flatten cells
+    cells = np.hstack((cells, np.zeros((cells.shape[0], 2))))
+    
+    # The aim of this second part of the test is to resize even if not mandatory
+    cp = CubicalPersistence(homology_dimensions=0, newshape=[-1, 8, 10])
+    diags = cp.fit_transform([cells])
+
+    np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)