def test_correct_vector_mc(self, monkeypatch):
"""Test if function correctly constructs the feature vector. The
``prob_event_mc`` function called within ``feature_vector_mc`` is monkeypatched
to return hard-coded outputs that depend only on the photon number in the event."""
with monkeypatch.context() as m:
m.setattr(
similarity,
"prob_event_mc",
lambda _graph, photons, max_count, n_mean, samples, loss: 1.0 / photons,
)
graph = nx.complete_graph(8)
fv = similarity.feature_vector_mc(graph, [2, 4, 8], 1)
assert fv == [0.5, 0.25, 0.125]
def test_low_count(self):
"""Test if function raises a ``ValueError`` if ``max_count_per_mode`` is negative."""
g = nx.complete_graph(10)
with pytest.raises(ValueError, match="Maximum number of photons"):
similarity.feature_vector_mc(g, [2, 4], -1)
def test_bad_event_photon_numbers_mc(self):
"""Test if function raises a ``ValueError`` when input a minimum photon number that is
below zero."""
with pytest.raises(ValueError, match="Cannot request events with photon number below zero"):
graph = nx.complete_graph(4)
similarity.feature_vector_mc(graph, [-1, 4], 1)
def test_invalid_loss(self):
"""Test if function raises a ``ValueError`` when the loss parameter is specified outside
of range."""
g = nx.complete_graph(10)
with pytest.raises(ValueError, match="Loss parameter must take a value between zero and"):
similarity.feature_vector_mc(g, [2, 4], loss=2)
def test_invalid_n_mean(self):
"""Test if function raises a ``ValueError`` when the mean photon number is specified to
be negative."""
g = nx.complete_graph(10)
with pytest.raises(ValueError, match="Mean photon number must be non-negative"):
similarity.feature_vector_mc(g, [2, 4], n_mean=-1)
def test_invalid_samples(self):
"""Test if function raises a ``ValueError`` when a number of samples less than one is
requested."""
g = nx.complete_graph(10)
with pytest.raises(ValueError, match="Number of samples must be at least one"):
similarity.feature_vector_mc(g, [2, 4], samples=0)