def test_mice_ordering_by_phi(): phi1 = models.Mice( models.Mip(direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=1.0, partition=())) different_phi1 = models.Mice( models.Mip(direction='different', mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=1.0, partition=())) phi2 = models.Mice( models.Mip(direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=(1.0 + constants.EPSILON * 2), partition=())) assert phi1 < phi2 assert phi2 > phi1 assert phi1 <= phi2 assert phi2 >= phi1 assert phi1 <= different_phi1 assert phi1 >= different_phi1
def test_mice_equality(): phi = 1.0 mice = models.Mice( models.Mip(direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=phi, partition=())) close_enough = models.Mice( models.Mip(direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=(phi - constants.EPSILON / 2), partition=())) not_quite = models.Mice( models.Mip(direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=(phi - constants.EPSILON * 2), partition=())) assert mice == close_enough assert mice != not_quite
def test_mip_ordering(): phi1 = models.Mip(direction=None, mechanism=(), purview=(), partition=None, unpartitioned_repertoire=None, partitioned_repertoire=None, phi=1.0) different_phi1 = models.Mip(direction='different', mechanism=(), purview=(), partition=0, unpartitioned_repertoire=None, partitioned_repertoire=None, phi=1.0) phi2 = models.Mip(direction=0, mechanism=(), purview=(), partition='stilldifferent', unpartitioned_repertoire=None, partitioned_repertoire=None, phi=1.0 + constants.EPSILON * 2) assert phi1 < phi2 assert phi2 > phi1 assert phi1 <= phi2 assert phi2 >= phi1 assert phi1 <= different_phi1 assert phi1 >= different_phi1
def test_null_concept(s, flushcache, restore_fs_cache): flushcache() cause = models.Mice(models.Mip( unpartitioned_repertoire=s.unconstrained_cause_repertoire(()), phi=0, direction=DIRECTIONS[PAST], mechanism=(), purview=(), partition=None, partitioned_repertoire=None)) effect = models.Mice(models.Mip( unpartitioned_repertoire=s.unconstrained_effect_repertoire(()), phi=0, direction=DIRECTIONS[FUTURE], mechanism=(), purview=(), partition=None, partitioned_repertoire=None)) assert (s.null_concept == models.Concept(mechanism=(), phi=0, cause=cause, effect=effect, subsystem=s))
def test_concept_equality_repertoires(s): phi = 1.0 mice1 = models.Mice(models.Mip( direction=None, mechanism=(), purview=(), unpartitioned_repertoire=np.array([1, 2]), partitioned_repertoire=(), phi=0.0, partition=(models.Part((), ()), models.Part((), ())))) mice2 = models.Mice(models.Mip( direction=None, mechanism=(), purview=(), unpartitioned_repertoire=np.array([0, 0]), partitioned_repertoire=None, phi=0.0, partition=(models.Part((), ()), models.Part((), ())))) concept = models.Concept(mechanism=(), cause=mice1, effect=mice2, subsystem=s, phi=phi) another = models.Concept(mechanism=(), cause=mice2, effect=mice1, subsystem=s, phi=phi) assert concept != another
def test_mice_repr_str(): mice = models.Mice(models.Mip( direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=0.0, partition=(models.Part((), ()), models.Part((), ())))) print(repr(mice)) print(str(mice))
def test_constellation_distance_uses_simple_vs_emd(mock_emd_distance, mock_simple_distance, s): """Quick check that we use the correct constellation distance function. If the two constellations differ only in that some concepts have moved to the null concept and all other concepts are the same then we use the simple constellation distance. Otherwise, use the EMD. """ make_mice = lambda: models.Mice(models.Mip( phi=None, direction=None, mechanism=None, purview=None, partition=None, unpartitioned_repertoire=None, partitioned_repertoire=None)) lone_concept = models.Concept(cause=make_mice(), effect=make_mice(), mechanism=(0, 1)) # lone concept -> null concept compute.constellation_distance((lone_concept,), ()) assert mock_emd_distance.called is False assert mock_simple_distance.called is True mock_simple_distance.reset_mock() other_concept = models.Concept(cause=make_mice(), effect=make_mice(), mechanism=(0, 1, 2)) # different concepts in constellation compute.constellation_distance((lone_concept,), (other_concept,)) assert mock_emd_distance.called is True assert mock_simple_distance.called is False
def test_concept_hashing(s): mice = models.Mice(models.Mip( direction=None, mechanism=(0, 1, 2), purview=(0, 1, 2), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=0.0, partition=(models.Part((), ()), models.Part((), ())))) concept = models.Concept( mechanism=(0, 1, 2), cause=mice, effect=mice, subsystem=s, phi=0.0) hash(concept)
def test_concept_repr_str(): mice = models.Mice(models.Mip( direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=0.0, partition=(models.Part((), ()), models.Part((), ())))) concept = models.Concept( mechanism=(), cause=mice, effect=mice, subsystem=None, phi=0.0) print(repr(concept)) print(str(concept))
def test_mip_repr_str(): mip = models.Mip(direction=None, mechanism=(), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=0.0, partition=()) print(repr(mip)) print(str(mip))
def test_concept_equality_one_subsystem_is_subset_of_another(s, subsys_n1n2): phi = 1.0 mice = models.Mice(models.Mip( direction=None, mechanism=(), purview=(1, 2), unpartitioned_repertoire=(), partitioned_repertoire=(), phi=0.0, partition=(models.Part((), ()), models.Part((), ())))) concept = models.Concept(mechanism=(2,), cause=mice, effect=mice, subsystem=s, phi=phi) another = models.Concept(mechanism=(2,), cause=mice, effect=mice, subsystem=subsys_n1n2, phi=phi) assert concept == another
def test_concept_hashing_one_subsystem_is_subset_of_another(s, subsys_n1n2): phi = 1.0 mice = models.Mice(models.Mip( direction=None, mechanism=(), purview=(1, 2), unpartitioned_repertoire=(), partitioned_repertoire=(), phi=0.0, partition=(models.Part((), ()), models.Part((), ())))) concept = models.Concept(mechanism=(2,), cause=mice, effect=mice, subsystem=s, phi=phi) another = models.Concept(mechanism=(2,), cause=mice, effect=mice, subsystem=subsys_n1n2, phi=phi) assert hash(concept) == hash(another) assert(len(set([concept, another])) == 1)
def test_mice_odering_by_mechanism(): small = models.Mice( models.Mip(direction=None, mechanism=(1, 2), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=1.0, partition=())) big = models.Mice( models.Mip(direction=None, mechanism=(1, 2, 3), purview=(), unpartitioned_repertoire=None, partitioned_repertoire=None, phi=1.0, partition=())) assert small < big assert small <= big assert big > small assert big >= small assert big != small
def mip(phi=1.0, dir=None, mech=(), purv=(), partition=None, unpartitioned_repertoire=None, partitioned_repertoire=None): return models.Mip(phi=phi, direction=dir, mechanism=mech, purview=purv, partition=partition, unpartitioned_repertoire=unpartitioned_repertoire, partitioned_repertoire=partitioned_repertoire)