def test_collapser_with_negative_weights():
"""Test that collapser works as expected when some of the vertex and edge
weights are negative."""
n_points = 20
dm = np.random.random((n_points, n_points))
np.fill_diagonal(dm, -np.random.random(n_points))
dm -= 0.2
dm_sparse = coo_matrix(dm)
maxdim = 2
pd_collapse_dense = ripser(dm,
metric='precomputed',
maxdim=maxdim,
collapse_edges=True)['dgms']
pd_collapse_sparse = ripser(dm_sparse,
metric='precomputed',
maxdim=maxdim,
collapse_edges=True)['dgms']
pd_no_collapse = ripser(dm,
metric='precomputed',
maxdim=maxdim,
collapse_edges=False)['dgms']
for i in range(maxdim + 1):
pd_collapse_dense[i] = np.sort(pd_collapse_dense[i], axis=0)
pd_collapse_sparse[i] = np.sort(pd_collapse_dense[i], axis=0)
pd_no_collapse[i] = np.sort(pd_no_collapse[i], axis=0)
assert_almost_equal(pd_collapse_dense[i], pd_no_collapse[i])
assert_almost_equal(pd_collapse_sparse[i], pd_no_collapse[i])
def test_collapse_consistent_with_no_collapse_dense(thresh, coeff, dm):
thresh = np.max(dm) / 2 if thresh else np.inf
maxdim = 3
pd_collapse = ripser(dm, thresh=thresh, maxdim=maxdim, coeff=coeff,
metric='precomputed', collapse_edges=True)['dgms']
pd_no_collapse = ripser(dm, thresh=thresh, maxdim=maxdim, coeff=coeff,
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])
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([], dtype=np.float64).reshape(0, 2)
]
for i in range(2):
assert np.array_equal(diagrams[i], expected[i])
assert np.array_equal(diagrams_csr[i], expected[i])
def test_collapse_consistent_with_no_collapse_coo(thresh, coeff,
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,
coeff=coeff,
metric='precomputed',
collapse_edges=True)['dgms']
pd_no_collapse = ripser(distance_matrix,
thresh=thresh,
maxdim=maxdim,
coeff=coeff,
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])
def test_coo_below_diagonal_and_mixed_same_as_above(dm):
"""Test that if we feed sparse matrices representing the same undirected
weighted graph we obtain the same results regardless of whether all entries
are above the diagonal, all are below the diagonal, or neither.
Furthermore, test that conflicts between stored data in the upper and lower
triangle are resolved in favour of the upper triangle."""
ripser_kwargs = {"maxdim": 2, "metric": "precomputed"}
pd_above = ripser(dm, **ripser_kwargs)['dgms']
pd_below = ripser(dm.T, **ripser_kwargs)['dgms']
_row, _col, _data = dm.row, dm.col, dm.data
coo_shape_kwargs = {} if _data.size else {"shape": (0, 0)}
to_transpose_mask = np.full(len(_row), False)
to_transpose_mask[np.random.choice(np.arange(len(_row)),
size=len(_row) // 2,
replace=False)] = True
row = np.concatenate([_col[to_transpose_mask], _row[~to_transpose_mask]])
col = np.concatenate([_row[to_transpose_mask], _col[~to_transpose_mask]])
dm_mixed = coo_matrix((_data, (row, col)), **coo_shape_kwargs)
pd_mixed = ripser(dm_mixed, **ripser_kwargs)['dgms']
row = np.concatenate([row, _row[to_transpose_mask]])
col = np.concatenate([col, _col[to_transpose_mask]])
data = np.random.random(len(row))
data[:len(_row)] = _data
dm_conflicts = coo_matrix((data, (row, col)), **coo_shape_kwargs)
pd_conflicts = ripser(dm_conflicts, **ripser_kwargs)['dgms']
for i in range(ripser_kwargs["maxdim"] + 1):
pd_above[i] = np.sort(pd_above[i], axis=0)
pd_below[i] = np.sort(pd_below[i], axis=0)
pd_mixed[i] = np.sort(pd_mixed[i], axis=0)
pd_conflicts[i] = np.sort(pd_conflicts[i], axis=0)
assert_almost_equal(pd_above[i], pd_below[i])
