def test_score_add_value_matches_score_counts(Model, EXAMPLE, sample_count):
for sample_size in iter_valid_sizes(EXAMPLE, min_size=2, max_size=10):
model = Model()
model.load(EXAMPLE)
samples = set(
canonicalize(model.sample_assignments(sample_size - 1))
for _ in xrange(sample_count))
for sample in samples:
nonempty_group_count = len(sample)
counts = map(len, sample)
actual = numpy.zeros(len(counts) + 1)
expected = numpy.zeros(len(counts) + 1)
# add to existing group
for i, group in enumerate(sample):
group_size = len(sample[i])
expected[i] = model.score_counts(add_to_counts(counts, i))
actual[i] = model.score_add_value(group_size,
nonempty_group_count,
sample_size - 1)
# add to new group
i = len(counts)
group_size = 0
expected[i] = model.score_counts(counts + [1])
actual[i] = model.score_add_value(group_size, nonempty_group_count,
sample_size - 1)
actual = scores_to_probs(actual)
expected = scores_to_probs(expected)
print actual, expected
assert_close(actual, expected, tol=0.05)
def test_score_add_value_matches_score_counts(Model, EXAMPLE, sample_count):
for sample_size in iter_valid_sizes(EXAMPLE, min_size=2, max_size=10):
model = Model()
model.load(EXAMPLE)
samples = set(canonicalize(model.sample_assignments(sample_size - 1)) for _ in xrange(sample_count))
for sample in samples:
nonempty_group_count = len(sample)
counts = map(len, sample)
actual = numpy.zeros(len(counts) + 1)
expected = numpy.zeros(len(counts) + 1)
# add to existing group
for i, group in enumerate(sample):
group_size = len(sample[i])
expected[i] = model.score_counts(add_to_counts(counts, i))
actual[i] = model.score_add_value(group_size, nonempty_group_count, sample_size - 1)
# add to new group
i = len(counts)
group_size = 0
expected[i] = model.score_counts(counts + [1])
actual[i] = model.score_add_value(group_size, nonempty_group_count, sample_size - 1)
actual = scores_to_probs(actual)
expected = scores_to_probs(expected)
print actual, expected
assert_close(actual, expected, tol=0.05)
def test_mixture_score_matches_score_add_value(Model, EXAMPLE, *unused):
sample_count = 200
model = Model()
model.load(EXAMPLE)
if Model.__name__ == 'LowEntropy' and sample_count > model.dataset_size:
raise SkipTest('skipping trivial example')
assignment_vector = model.sample_assignments(sample_count)
assignments = dict(enumerate(assignment_vector))
nonempty_counts = count_assignments(assignments)
nonempty_group_count = len(nonempty_counts)
assert_greater(nonempty_group_count, 1, "test is inaccurate")
def check_counts(mixture, counts, empty_group_count):
# print 'counts =', counts
empty_groupids = frozenset(mixture.empty_groupids)
assert_equal(len(empty_groupids), empty_group_count)
for groupid in empty_groupids:
assert_equal(counts[groupid], 0)
def check_scores(mixture, counts, empty_group_count):
sample_count = sum(counts)
nonempty_group_count = len(counts) - empty_group_count
expected = [
model.score_add_value(
group_size,
nonempty_group_count,
sample_count,
empty_group_count)
for group_size in counts
]
noise = numpy.random.randn(len(counts))
actual = numpy.zeros(len(counts), dtype=numpy.float32)
actual[:] = noise
mixture.score_value(model, actual)
assert_close(actual, expected)
return actual
for empty_group_count in [1, 10]:
print 'empty_group_count =', empty_group_count
counts = nonempty_counts + [0] * empty_group_count
numpy.random.shuffle(counts)
mixture = Model.Mixture()
id_tracker = MixtureIdTracker()
print 'init'
mixture.init(model, counts)
id_tracker.init(len(counts))
check_counts(mixture, counts, empty_group_count)
check_scores(mixture, counts, empty_group_count)
print 'adding'
groupids = []
for _ in xrange(sample_count):
check_counts(mixture, counts, empty_group_count)
scores = check_scores(mixture, counts, empty_group_count)
probs = scores_to_probs(scores)
groupid = sample_discrete(probs)
expected_group_added = (counts[groupid] == 0)
counts[groupid] += 1
actual_group_added = mixture.add_value(model, groupid)
assert_equal(actual_group_added, expected_group_added)
groupids.append(groupid)
if actual_group_added:
id_tracker.add_group()
counts.append(0)
check_counts(mixture, counts, empty_group_count)
check_scores(mixture, counts, empty_group_count)
print 'removing'
for global_groupid in groupids:
groupid = id_tracker.global_to_packed(global_groupid)
counts[groupid] -= 1
expected_group_removed = (counts[groupid] == 0)
actual_group_removed = mixture.remove_value(model, groupid)
assert_equal(actual_group_removed, expected_group_removed)
if expected_group_removed:
id_tracker.remove_group(groupid)
back = counts.pop()
if groupid < len(counts):
counts[groupid] = back
check_counts(mixture, counts, empty_group_count)
check_scores(mixture, counts, empty_group_count)
def test_mixture_score_matches_score_add_value(Model, EXAMPLE, *unused):
sample_count = 200
model = Model()
model.load(EXAMPLE)
if Model.__name__ == 'LowEntropy' and sample_count > model.dataset_size:
raise SkipTest('skipping trivial example')
assignment_vector = model.sample_assignments(sample_count)
assignments = dict(enumerate(assignment_vector))
nonempty_counts = count_assignments(assignments)
nonempty_group_count = len(nonempty_counts)
assert_greater(nonempty_group_count, 1, "test is inaccurate")
def check_counts(mixture, counts, empty_group_count):
# print 'counts =', counts
empty_groupids = frozenset(mixture.empty_groupids)
assert_equal(len(empty_groupids), empty_group_count)
for groupid in empty_groupids:
assert_equal(counts[groupid], 0)
def check_scores(mixture, counts, empty_group_count):
sample_count = sum(counts)
nonempty_group_count = len(counts) - empty_group_count
expected = [
model.score_add_value(group_size, nonempty_group_count,
sample_count, empty_group_count)
for group_size in counts
]
noise = numpy.random.randn(len(counts))
actual = numpy.zeros(len(counts), dtype=numpy.float32)
actual[:] = noise
mixture.score_value(model, actual)
assert_close(actual, expected)
return actual
for empty_group_count in [1, 10]:
print 'empty_group_count =', empty_group_count
counts = nonempty_counts + [0] * empty_group_count
numpy.random.shuffle(counts)
mixture = Model.Mixture()
id_tracker = MixtureIdTracker()
print 'init'
mixture.init(model, counts)
id_tracker.init(len(counts))
check_counts(mixture, counts, empty_group_count)
check_scores(mixture, counts, empty_group_count)
print 'adding'
groupids = []
for _ in xrange(sample_count):
check_counts(mixture, counts, empty_group_count)
scores = check_scores(mixture, counts, empty_group_count)
probs = scores_to_probs(scores)
groupid = sample_discrete(probs)
expected_group_added = (counts[groupid] == 0)
counts[groupid] += 1
actual_group_added = mixture.add_value(model, groupid)
assert_equal(actual_group_added, expected_group_added)
groupids.append(groupid)
if actual_group_added:
id_tracker.add_group()
counts.append(0)
check_counts(mixture, counts, empty_group_count)
check_scores(mixture, counts, empty_group_count)
print 'removing'
for global_groupid in groupids:
groupid = id_tracker.global_to_packed(global_groupid)
counts[groupid] -= 1
expected_group_removed = (counts[groupid] == 0)
actual_group_removed = mixture.remove_value(model, groupid)
assert_equal(actual_group_removed, expected_group_removed)
if expected_group_removed:
id_tracker.remove_group(groupid)
back = counts.pop()
if groupid < len(counts):
counts[groupid] = back
check_counts(mixture, counts, empty_group_count)
check_scores(mixture, counts, empty_group_count)
