def test_metric_pair_metrix():
c1 = np.random.gamma(100, 4, (300, 2))
c2 = np.random.gamma(50, 5, (100, 2))
ms1 = skg.MetricSpace(c1, dist_metric='cityblock')
ms2 = skg.MetricSpace(c2, dist_metric='euclidean')
with pytest.raises(ValueError) as e:
skg.MetricSpacePair(ms1, ms2)
assert 'same distance metric' in e.value
def test_metric_pair_max_dist():
c1 = np.random.gamma(100, 4, (300, 2))
c2 = np.random.gamma(50, 5, (100, 2))
ms1 = skg.MetricSpace(c1, max_dist=50)
ms2 = skg.MetricSpace(c2, max_dist=400)
with pytest.raises(ValueError) as e:
skg.MetricSpacePair(ms1, ms2)
assert 'same max_dist' in e.value
def test_sparse_matrix_no_warning():
# make a really sparse matrix
sparse = skg.MetricSpace(rcoords, max_dist=5)
# call triangular_distance_matrix without warning
V = skg.Variogram(sparse, rvals)
V.triangular_distance_matrix
def test_invalid_dist_func():
# instantiate metrix space
ms = skg.MetricSpace(rcoords, dist_metric='euclidean')
with pytest.raises(AttributeError) as e:
skg.Variogram(ms, rvals, dist_func='cityblock')
assert 'Distance metric' in e.value
def test_dense_matrix_warning():
dense = skg.MetricSpace(rcoords)
# check the warning
with pytest.raises(RuntimeWarning) as w:
V = skg.Variogram(dense, rvals)
V.triangular_distance_matrix
assert 'Only available' in w.value
def test_tree_non_euklidean():
with pytest.raises(ValueError) as e:
ms = skg.MetricSpace(rcoords, 'cityblock')
ms.tree
assert 'can only be constructed' in e.value
def test_unknown_metric():
with pytest.raises(ValueError) as e:
skg.MetricSpace(rcoords, dist_metric='foobar')
assert 'Unknown Distance Metric:' in e.value
