def test_sia(transition):
sia = actual.sia(transition)
assert sia.alpha == 0.415037
assert sia.cut == ac_cut(Direction.CAUSE, Part((), (1, )),
Part((0, ), (2, )))
assert len(sia.account) == 3
assert len(sia.partitioned_account) == 2
def test_partitioned_repertoire_with_tripartition(s):
tripartition = Tripartition(Part((), (1, )), Part((0, ), ()),
Part((), (2, )))
assert np.array_equal(
s.partitioned_repertoire(Direction.CAUSE, tripartition),
np.array([[[0.25, 0.25], [0.25, 0.25]]]))
def test_transition_apply_cut(transition):
cut = ac_cut(Direction.CAUSE, Part((1, ), (2, )), Part((), (0, )))
cut_transition = transition.apply_cut(cut)
assert cut_transition.before_state == transition.before_state
assert cut_transition.after_state == transition.after_state
assert cut_transition.cause_indices == transition.cause_indices
assert cut_transition.effect_indices == transition.effect_indices
assert cut_transition.cut == cut
assert cut_transition != transition
def test_kcut_equality(kcut_cause, kcut_effect):
other = KCut(Direction.CAUSE, KPartition(
Part((0, 2), (0,)), Part((), (2,)), Part((3,), (3,))))
assert kcut_cause == other
assert hash(kcut_cause) == hash(other)
assert hash(kcut_cause) != hash(kcut_cause.partition)
assert kcut_cause != kcut_effect
assert hash(kcut_cause) != hash(kcut_effect)
def test_actual_cut_matrix():
cut = ac_cut(Direction.CAUSE, Part((0,), (0, 2)), Part((2,), ()))
# fmt: off
answer = np.array([
[0, 0, 1],
[0, 0, 0],
[0, 0, 1]
])
# fmt: on
assert np.array_equal(cut.cut_matrix(3), answer)
def test_actual_apply_cut():
cut = ac_cut(Direction.CAUSE, Part((0,), (0, 2)), Part((2,), ()))
cm = np.ones((3, 3))
# fmt: off
answer = np.array([
[1, 1, 0],
[1, 1, 1],
[1, 1, 0],
])
# fmt: on
assert np.array_equal(cut.apply_cut(cm), answer)
def test_system_accessors(s):
cut_cause = KCut(Direction.CAUSE,
KPartition(Part((0, 2), (0, 1)), Part((1, ), (2, ))))
cs_cause = ConceptStyleSystem(s, Direction.CAUSE, cut_cause)
assert cs_cause.cause_system.cut == cut_cause
assert not cs_cause.effect_system.is_cut
cut_effect = KCut(Direction.EFFECT,
KPartition(Part((0, 2), (0, 1)), Part((1, ), (2, ))))
cs_effect = ConceptStyleSystem(s, Direction.EFFECT, cut_effect)
assert not cs_effect.cause_system.is_cut
assert cs_effect.effect_system.cut == cut_effect
def test_find_mip(direction, subsystem, cut, mechanism, purview, expected):
# Set up testing parameters from scenario
mechanism = tuple(subsystem.nodes[index] for index in mechanism)
purview = tuple(subsystem.nodes[index] for index in purview)
result = subsystem.find_mip(direction, mechanism, purview)
# IMPORTANT: Since several different ways of partitioning the system can
# yield the same phi value, the partition used in finding the MIP is not
# unique. Thus, ``expected['partitions']`` is a dictionary that maps all the
# ways of partitioning the system that yeild the minimal phi value to their
# expected partitioned repertoires.
if expected:
# Construct expected list of possible MIPs
expected = [
Mip(direction=direction,
partition=(Part(
mechanism=tuple(subsystem.nodes[i]
for i in expected_partition[0].mechanism),
purview=tuple(subsystem.nodes[i]
for i in expected_partition[0].purview)),
Part(mechanism=tuple(
subsystem.nodes[i]
for i in expected_partition[1].mechanism),
purview=tuple(
subsystem.nodes[i]
for i in expected_partition[1].purview))),
mechanism=mechanism,
purview=purview,
unpartitioned_repertoire=expected['unpartitioned_repertoire'],
partitioned_repertoire=expected_partitioned_repertoire,
phi=expected['phi']) for expected_partition,
expected_partitioned_repertoire in expected['partitions'].items()
]
print('Result:', '---------', '', result, '', sep='\n')
print('Expected:', '---------', '', sep='\n')
if expected:
print(*[mip for mip in expected], sep='\n')
else:
print(expected)
print('\n')
if expected:
assert result in expected
else:
assert result == expected
def test_concept_style_cuts():
assert list(concept_cuts(Direction.CAUSE, (0,))) == [
KCut(Direction.CAUSE, KPartition(
Part((), ()), Part((), (0,)), Part((0,), ())))]
assert list(concept_cuts(Direction.EFFECT, (0,))) == [
KCut(Direction.EFFECT, KPartition(
Part((), ()), Part((), (0,)), Part((0,), ())))]
def test_mip_bipartitions():
mechanism, purview = (0, ), (1, 2)
answer = set([
Bipartition(Part((), (2, )), Part((0, ), (1, ))),
Bipartition(Part((), (1, )), Part((0, ), (2, ))),
Bipartition(Part((), (1, 2)), Part((0, ), ())),
])
assert set(mip_bipartitions(mechanism, purview)) == answer
def test_mip_bipartition():
mechanism, purview = (0, ), (1, 2)
answer = [
(Part((), (2, )), Part((0, ), (1, ))),
(Part((), (1, )), Part((0, ), (2, ))),
(Part((), (1, 2)), Part((0, ), ())),
]
assert set(mip_bipartitions(mechanism, purview)) == set(answer)
def test_actual_cut_indices():
cut = ac_cut(Direction.CAUSE, Part((0, ), (2, )), Part((4, ), (5, )))
assert cut.indices == (0, 2, 4, 5)
cut = ac_cut(Direction.EFFECT, Part((0, 2), (0, 2)), Part((), ()))
assert cut.indices == (0, 2)
def kcut_cause():
partition = KPartition(Part((0, 2), (0, )), Part((), (2, )),
Part((3, ), (3, )))
return KCut(Direction.CAUSE, partition)
def kcut_effect():
partition = KPartition(Part((0, 2), (0, )), Part((), (2, )),
Part((3, ), (3, )))
return KCut(Direction.EFFECT, partition)
# fmt: on
assert np.array_equal(cut.cut_matrix(3), answer)
def ac_cut(direction, *parts):
return models.ActualCut(direction, KPartition(*parts))
@config.override(PARTITION_TYPE="TRI")
@pytest.mark.parametrize(
"direction,answer",
[
(
Direction.BIDIRECTIONAL,
[
ac_cut(Direction.CAUSE, Part((), ()), Part((), (1, 2)), Part((0,), ())),
ac_cut(
Direction.EFFECT, Part((), ()), Part((1,), (0,)), Part((2,), ())
),
ac_cut(
Direction.EFFECT, Part((), ()), Part((1,), ()), Part((2,), (0,))
),
],
),
(
Direction.CAUSE,
[ac_cut(Direction.CAUSE, Part((), ()), Part((), (1, 2)), Part((0,), ()))],
),
(
Direction.EFFECT,
[
#
# {'partitions':
# {
# (first part, second part): expected partitioned repertoire
# },
# 'phi': expected phi value
# }
scenarios = [
# Past {{{
# ~~~~~~~~
# No cut {{{
# ----------
('past', s, None, (0, ), (0, ), {
'partitions': {
(Part(mechanism=(), purview=(0, )),
Part(mechanism=(0, ), purview=())):
np.array([0.5, 0.5]).reshape(2, 1, 1, order="F")
},
'unpartitioned_repertoire':
np.array([0.5, 0.5]).reshape(2, 1, 1, order="F"),
'phi':
0.0
}),
# }}}
# With cut {{{
# ------------
('past', s, (0, (1, 2)), (1, ), (2, ), {
'partitions': {
(Part(mechanism=(), purview=(2, )),
Part(mechanism=(1, ), purview=())):
# }
scenarios = [
# Previous {{{
# ~~~~~~~~
# No cut {{{
# ----------
(
Direction.CAUSE,
s,
None,
(0, ),
(0, ),
{
"partitions": {
Bipartition(Part(mechanism=(), purview=(0, )),
Part(mechanism=(0, ), purview=())):
np.array([0.5, 0.5]).reshape(2, 1, 1, order="F")
},
"repertoire": np.array([0.5, 0.5]).reshape(2, 1, 1, order="F"),
"phi": 0.0,
},
),
# }}}
# With cut {{{
# ------------
(
Direction.CAUSE,
s,
(0, (1, 2)),
(1, ),
def test_actual_cut_matrix():
cut = ac_cut(Direction.CAUSE, Part((0, ), (0, 2)), Part((2, ), ()))
assert np.array_equal(cut.cut_matrix(3),
np.array([[0, 0, 1], [0, 0, 0], [0, 0, 1]]))
def ac_cut(direction, *parts):
return models.ActualCut(direction, KPartition(*parts))
@config.override(PARTITION_TYPE='TRI')
@pytest.mark.parametrize('direction,answer',
[(Direction.BIDIRECTIONAL, [
ac_cut(Direction.CAUSE, Part(
(), ()), Part((), (1, 2)), Part((0, ), ())),
ac_cut(Direction.EFFECT, Part(
(), ()), Part((1, ), (0, )), Part((2, ), ())),
ac_cut(Direction.EFFECT, Part(
(), ()), Part((1, ), ()), Part((2, ), (0, )))
]),
(Direction.CAUSE, [
ac_cut(Direction.CAUSE, Part(
(), ()), Part((), (1, 2)), Part((0, ), ()))
]),
(Direction.EFFECT, [
ac_cut(Direction.EFFECT, Part(
(), ()), Part((), (0, )), Part((1, 2), ())),
ac_cut(Direction.EFFECT, Part(
(), ()), Part((1, ), (0, )), Part((2, ),
())),
def test_wedge_partitions():
mechanism, purview = (0, ), (1, 2)
assert set(wedge_partitions(mechanism, purview)) == set([
Tripartition(Part((), ()), Part((), (1, 2)), Part((0, ), ())),
])
mechanism, purview = (3, 4), (5, 6)
assert set(wedge_partitions(mechanism, purview)) == set([
Tripartition(Part((), ()), Part((), (5, 6)), Part((3, 4), ())),
Tripartition(Part((), ()), Part((3, ), ()), Part((4, ), (5, 6))),
Tripartition(Part((), ()), Part((3, ), (5, )), Part((4, ), (6, ))),
Tripartition(Part((), ()), Part((3, ), (5, 6)), Part((4, ), ())),
Tripartition(Part((), ()), Part((3, ), (6, )), Part((4, ), (5, ))),
Tripartition(Part((), (5, )), Part((3, ), ()), Part((4, ), (6, ))),
Tripartition(Part((), (5, )), Part((3, ), (6, )), Part((4, ), ())),
Tripartition(Part((), (6, )), Part((3, ), ()), Part((4, ), (5, ))),
Tripartition(Part((), (6, )), Part((3, ), (5, )), Part((4, ), ())),
])
def test_all_partitions():
mechanism, purview = (0, 1), (2, )
assert set(all_partitions(mechanism, purview)) == set([
KPartition(Part((0, 1), ()), Part((), (2, ))),
KPartition(Part((0, ), ()), Part((1, ), ()), Part((), (2, ))),
KPartition(Part((0, ), (2, )), Part((1, ), ()), Part((), ())),
KPartition(Part((0, ), ()), Part((1, ), (2, )), Part((), ())),
])
mechanism, purview = (0, 1), (2, 3)
assert set(all_partitions(mechanism, purview)) == set([
KPartition(Part((0, 1), ()), Part((), (2, 3))),
KPartition(Part((0, ), ()), Part((1, ), (2, 3)), Part((), ())),
KPartition(Part((0, ), (2, 3)), Part((1, ), ()), Part((), ())),
KPartition(Part((0, ), ()), Part((1, ), ()), Part((), (2, 3))),
KPartition(Part((0, ), ()), Part((1, ), (3, )), Part((), (2, ))),
KPartition(Part((0, ), (2, )), Part((1, ), ()), Part((), (3, ))),
KPartition(Part((0, ), ()), Part((1, ), (2, )), Part((), (3, ))),
KPartition(Part((0, ), (3, )), Part((1, ), (2, )), Part((), ())),
KPartition(Part((0, ), (3, )), Part((1, ), ()), Part((), (2, ))),
KPartition(Part((0, ), (2, )), Part((1, ), (3, )), Part((), ())),
])
def test_actual_cut_matrix():
cut = ac_cut(Direction.CAUSE, Part((0, ), (0, 2)), Part((2, ), ()))
assert np.array_equal(cut.cut_matrix(3),
np.array([[0, 0, 1], [0, 0, 0], [0, 0, 1]]))
