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Place hypothesis tests in test_ripser and remove superseded tests the…
…re. Add regression test for giotto-ai#465
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,79 +1,97 @@ | ||
| import numpy as np | ||
| import scipy as sp | ||
| from .. import ripser | ||
| import pytest | ||
| from hypothesis import given | ||
| from hypothesis.extra.numpy import arrays | ||
| from hypothesis.strategies import floats, integers, composite | ||
| from numpy.testing import assert_almost_equal | ||
| from scipy.sparse import coo_matrix | ||
|
|
||
| X = np.random.random((50, 50)) | ||
| # Without this line, there might be different results because GUDHI | ||
| # assumes zeros in the diagonal | ||
| np.fill_diagonal(X, 0) | ||
| maxdim = 2 | ||
| from gtda.externals import ripser | ||
|
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||
|
|
||
| def test_with_collapser(): | ||
| diags_collapsed = ripser( | ||
| X, | ||
| metric='precomputed', | ||
| maxdim=maxdim, | ||
| collapse_edges=True)['dgms'] | ||
| diags_not_collapsed = ripser( | ||
| X, | ||
| metric='precomputed', | ||
| maxdim=maxdim, | ||
| collapse_edges=False)['dgms'] | ||
| @composite | ||
| def get_dense_distance_matrices(draw): | ||
| """Generate 2d dense square arrays of floats, with zero along the | ||
| diagonal.""" | ||
| shapes = draw(integers(min_value=2, max_value=30)) | ||
| distance_matrix = draw(arrays(dtype=np.float, | ||
| elements=floats(allow_nan=False, | ||
| allow_infinity=True, | ||
| min_value=0), | ||
| shape=(shapes, shapes), unique=False)) | ||
| np.fill_diagonal(distance_matrix, 0) | ||
| return distance_matrix | ||
|
|
||
| for i in range(maxdim): | ||
| assert np.array_equal(diags_collapsed[i], diags_not_collapsed[i]) | ||
|
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||
| @composite | ||
| def get_sparse_distance_matrices(draw): | ||
| """Generate 2d sparse matrices of floats, with zero along the diagonal.""" | ||
| shapes = draw(integers(min_value=2, max_value=40)) | ||
| distance_matrix = draw(arrays(dtype=np.float, | ||
| elements=floats(allow_nan=False, | ||
| allow_infinity=True, | ||
| min_value=0), | ||
| shape=(shapes, shapes), unique=False)) | ||
| distance_matrix = np.triu(distance_matrix, k=1) | ||
| distance_matrix = coo_matrix(distance_matrix) | ||
| row, col, data = \ | ||
| distance_matrix.row, distance_matrix.col, distance_matrix.data | ||
| not_inf_idx = data != np.inf | ||
| row = row[not_inf_idx] | ||
| col = col[not_inf_idx] | ||
| data = data[not_inf_idx] | ||
| shape = (np.max(row) + 1, np.max(col) + 1) if not_inf_idx.any() else (0, 0) | ||
| distance_matrix = coo_matrix((data, (row, col)), shape=shape) | ||
| return distance_matrix | ||
|
|
||
| def test_with_collapser_with_tresh(): | ||
| thresh = 0.1 | ||
| diags_collapsed_thresh = ripser( | ||
| X, | ||
| metric='precomputed', | ||
| maxdim=maxdim, | ||
| thresh=thresh, | ||
| collapse_edges=True)['dgms'] | ||
| diags_not_collapsed_thresh = ripser( | ||
| X, metric='precomputed', maxdim=maxdim, thresh=thresh, | ||
| collapse_edges=False)['dgms'] | ||
|
|
||
| for i in range(maxdim): | ||
| assert np.array_equal(diags_collapsed_thresh[i], | ||
| diags_not_collapsed_thresh[i]) | ||
| @pytest.mark.parametrize('thresh', [False, True]) | ||
| @given(distance_matrix=get_dense_distance_matrices()) | ||
| def test_collapse_consistent_with_no_collapse_dense(thresh, distance_matrix): | ||
| thresh = np.max(distance_matrix) / 2 if thresh else np.inf | ||
| maxdim = 3 | ||
| pd_collapse = ripser(distance_matrix, thresh=thresh, maxdim=maxdim, | ||
| metric='precomputed', collapse_edges=True)['dgms'] | ||
| pd_no_collapse = ripser(distance_matrix, thresh=thresh, maxdim=maxdim, | ||
| metric='precomputed', collapse_edges=False)['dgms'] | ||
| for i in range(maxdim + 1): | ||
| pd_collapse[i] = np.sort(pd_collapse[i], axis=0) | ||
| pd_no_collapse[i] = np.sort(pd_no_collapse[i], axis=0) | ||
| assert_almost_equal(pd_collapse[i], pd_no_collapse[i]) | ||
|
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||
|
|
||
| def test_with_collapser_coo(): | ||
| X_tri = np.triu(X, 1) | ||
| diags_collapsed = ripser( | ||
| sp.sparse.coo_matrix(X_tri), | ||
| metric='precomputed', | ||
| maxdim=maxdim, | ||
| collapse_edges=True)['dgms'] | ||
| diags_not_collapsed = ripser( | ||
| sp.sparse.coo_matrix(X_tri), | ||
| metric='precomputed', | ||
| maxdim=maxdim, | ||
| collapse_edges=False)['dgms'] | ||
| @pytest.mark.parametrize('thresh', [False, True]) | ||
| @given(distance_matrix=get_sparse_distance_matrices()) | ||
| def test_collapse_consistent_with_no_collapse_coo(thresh, distance_matrix): | ||
| if thresh and distance_matrix.nnz: | ||
| thresh = np.max(distance_matrix) / 2 | ||
| else: | ||
| thresh = np.inf | ||
| maxdim = 3 | ||
| pd_collapse = ripser(distance_matrix, thresh=thresh, maxdim=maxdim, | ||
| metric='precomputed', collapse_edges=True)['dgms'] | ||
| pd_no_collapse = ripser(distance_matrix, thresh=thresh, maxdim=maxdim, | ||
| metric='precomputed', collapse_edges=False)['dgms'] | ||
| for i in range(maxdim + 1): | ||
| pd_collapse[i] = np.sort(pd_collapse[i], axis=0) | ||
| pd_no_collapse[i] = np.sort(pd_no_collapse[i], axis=0) | ||
| assert_almost_equal(pd_collapse[i], pd_no_collapse[i]) | ||
|
|
||
| for i in range(maxdim): | ||
| assert np.array_equal(diags_collapsed[i], diags_not_collapsed[i]) | ||
|
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||
|
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||
| def test_with_collapser_coo_thresh(): | ||
| thresh = 0.1 | ||
| X_tri = np.triu(X, 1) | ||
| diags_collapsed = ripser( | ||
| sp.sparse.coo_matrix(X_tri), | ||
| metric='precomputed', | ||
| maxdim=maxdim, | ||
| thresh=thresh, | ||
| collapse_edges=True)['dgms'] | ||
| diags_not_collapsed = ripser( | ||
| sp.sparse.coo_matrix(X_tri), | ||
| metric='precomputed', | ||
| maxdim=maxdim, | ||
| thresh=thresh, | ||
| collapse_edges=False)['dgms'] | ||
|
|
||
| for i in range(maxdim): | ||
| assert np.array_equal(diags_collapsed[i], diags_not_collapsed[i]) | ||
| def test_coo_results_independent_of_order(): | ||
| """Regression test for PR #465""" | ||
| data = np.array([6., 8., 2., 4., 5., 9., 10., 3., 1., 1.]) | ||
| row = np.array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]) | ||
| col = np.array([4, 1, 3, 2, 4, 3, 2, 3, 4, 4]) | ||
| dm = coo_matrix((data, (row, col))) | ||
| diagrams = ripser(dm, metric="precomputed")['dgms'] | ||
| diagrams_csr = ripser(dm.tocsr(), metric="precomputed")['dgms'] | ||
| expected = [np.array([[0., 1.], | ||
| [0., 1.], | ||
| [0., 2.], | ||
| [0., 5.], | ||
| [0., np.inf]]), | ||
| np.array([], shape=(0, 2), dtype=np.float64)] | ||
| for i in range(2): | ||
| assert np.array_equal(diagrams[i], expected[i]) | ||
| assert np.array_equal(diagrams_csr[i], expected[i]) |