class Mixture(object):
def __init__(self):
self.clustering = PitmanYor.Mixture()
self.feature_x = nich.Mixture()
self.feature_y = nich.Mixture()
self.id_tracker = MixtureIdTracker()
def __len__(self):
return len(self.clustering)
def init(self, model, empty_group_count=EMPTY_GROUP_COUNT):
assert empty_group_count >= 1
counts = [0] * empty_group_count
self.clustering.init(model.clustering, counts)
assert len(self.clustering) == len(counts)
self.id_tracker.init(len(counts))
self.feature_x.clear()
self.feature_y.clear()
for _ in xrange(empty_group_count):
self.feature_x.add_group(model.feature)
self.feature_y.add_group(model.feature)
self.feature_x.init(model.feature)
self.feature_y.init(model.feature)
def score_value(self, model, xy, scores):
x, y = xy
self.clustering.score_value(model.clustering, scores)
self.feature_x.score_value(model.feature, x, scores)
self.feature_y.score_value(model.feature, y, scores)
def add_value(self, model, groupid, xy):
x, y = xy
group_added = self.clustering.add_value(model.clustering, groupid)
self.feature_x.add_value(model.feature, groupid, x)
self.feature_y.add_value(model.feature, groupid, y)
if group_added:
self.feature_x.add_group(model.feature)
self.feature_y.add_group(model.feature)
self.id_tracker.add_group()
def remove_value(self, model, groupid, xy):
x, y = xy
group_removeed = self.clustering.remove_value(
model.clustering,
groupid)
self.feature_x.remove_value(model.feature, groupid, x)
self.feature_y.remove_value(model.feature, groupid, y)
if group_removeed:
self.feature_x.remove_group(model.feature, groupid)
self.feature_y.remove_group(model.feature, groupid)
self.id_tracker.remove_group(groupid)
class Mixture(object):
def __init__(self):
self.clustering = PitmanYor.Mixture()
self.feature_x = nich.Mixture()
self.feature_y = nich.Mixture()
self.id_tracker = MixtureIdTracker()
def __len__(self):
return len(self.clustering)
def init(self, model, empty_group_count=EMPTY_GROUP_COUNT):
assert empty_group_count >= 1
counts = [0] * empty_group_count
self.clustering.init(model.clustering, counts)
assert len(self.clustering) == len(counts)
self.id_tracker.init(len(counts))
self.feature_x.clear()
self.feature_y.clear()
for _ in xrange(empty_group_count):
self.feature_x.add_group(model.feature)
self.feature_y.add_group(model.feature)
self.feature_x.init(model.feature)
self.feature_y.init(model.feature)
def score_value(self, model, xy, scores):
x, y = xy
self.clustering.score_value(model.clustering, scores)
self.feature_x.score_value(model.feature, x, scores)
self.feature_y.score_value(model.feature, y, scores)
def add_value(self, model, groupid, xy):
x, y = xy
group_added = self.clustering.add_value(model.clustering, groupid)
self.feature_x.add_value(model.feature, groupid, x)
self.feature_y.add_value(model.feature, groupid, y)
if group_added:
self.feature_x.add_group(model.feature)
self.feature_y.add_group(model.feature)
self.id_tracker.add_group()
def remove_value(self, model, groupid, xy):
x, y = xy
group_removeed = self.clustering.remove_value(
model.clustering, groupid)
self.feature_x.remove_value(model.feature, groupid, x)
self.feature_y.remove_value(model.feature, groupid, y)
if group_removeed:
self.feature_x.remove_group(model.feature, groupid)
self.feature_y.remove_group(model.feature, groupid)
self.id_tracker.remove_group(groupid)
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)