def test_runner_specify_basic_kernel():
N, V = 10, 20
defn = model_definition(N, V)
data = toy_dataset(defn)
view = data
prng = rng()
latent = model.initialize(defn, view, prng)
r = runner.runner(defn, view, latent, ["crf"])
r.run(prng, 1)
def test_runner_simple():
N, V = 10, 20
defn = model_definition(N, V)
data = toy_dataset(defn)
view = data
prng = rng()
latent = model.initialize(defn, view, prng)
r = runner.runner(defn, view, latent)
r.run(prng, 1)
def test_lda_zero_iter(self):
# compare to model with 0 iterations
prng2 = rng(seed=54321)
latent2 = model.initialize(self.defn, self.docs, prng2)
assert latent2 is not None
r2 = runner.runner(self.defn, self.docs, latent2)
assert r2 is not None
doc_topic2 = latent2.topic_distribution_by_document()
assert doc_topic2 is not None
assert latent2.perplexity() > self.latent.perplexity()
def test_lda_zero_iter(self):
# compare to model with 0 iterations
prng2 = rng(seed=54321)
latent2 = model.initialize(self.defn, self.docs, prng2)
assert latent2 is not None
r2 = runner.runner(self.defn, self.docs, latent2)
assert r2 is not None
doc_topic2 = latent2.topic_distribution_by_document()
assert doc_topic2 is not None
assert latent2.perplexity() > self.latent.perplexity()
def test_runner_simple():
N, V = 10, 100
defn = model_definition(N, V)
data = toy_dataset(defn)
view = numpy_dataview(data)
prng = rng()
latent = model.initialize(defn, view, prng)
kc = runner.default_kernel_config(defn)
r = runner.runner(defn, view, latent, kc)
r.run(prng, 1)
def test_runner_specify_hp_kernels():
N, V = 10, 20
defn = model_definition(N, V)
data = toy_dataset(defn)
view = data
prng = rng()
latent = model.initialize(defn, view, prng)
kernels = ['crf'] + \
runner.second_dp_hp_kernel_config(defn) + \
runner.base_dp_hp_kernel_config(defn)
r = runner.runner(defn, view, latent, kernels)
r.run(prng, 1)
def setup_class(cls):
cls._load_docs()
cls.niters = 100 if os.environ.get('TRAVIS') else 2
cls.defn = model_definition(cls.N, cls.V)
cls.seed = 12345
cls.prng = rng(seed=cls.seed)
cls.latent = model.initialize(cls.defn, cls.docs, cls.prng)
cls.r = runner.runner(cls.defn, cls.docs, cls.latent)
cls.original_perplexity = cls.latent.perplexity()
cls.r.run(cls.prng, cls.niters)
cls.doc_topic = cls.latent.topic_distribution_by_document()
def setup_class(cls):
cls._load_docs()
cls.niters = 100 if os.environ.get('TRAVIS') else 2
cls.defn = model_definition(cls.N, cls.V)
cls.seed = 12345
cls.prng = rng(seed=cls.seed)
cls.latent = model.initialize(cls.defn, cls.docs, cls.prng)
cls.r = runner.runner(cls.defn, cls.docs, cls.latent)
cls.original_perplexity = cls.latent.perplexity()
cls.r.run(cls.prng, cls.niters)
cls.doc_topic = cls.latent.topic_distribution_by_document()
def test_lda_random_seed(self):
# ensure that randomness is contained in rng
# by running model twice with same seed
niters = 10
# model 1
prng1 = rng(seed=54321)
latent1 = model.initialize(self.defn, self.docs, prng1)
runner1 = runner.runner(self.defn, self.docs, latent1)
runner1.run(prng1, niters)
# model2
prng2 = rng(seed=54321)
latent2 = model.initialize(self.defn, self.docs, prng2)
runner2 = runner.runner(self.defn, self.docs, latent2)
runner2.run(prng2, niters)
assert_list_equal(latent1.topic_distribution_by_document(),
latent2.topic_distribution_by_document())
for d1, d2 in zip(latent1.word_distribution_by_topic(),
latent2.word_distribution_by_topic()):
assert_dict_equal(d1, d2)
def test_lda_random_seed(self):
# ensure that randomness is contained in rng
# by running model twice with same seed
niters = 10
# model 1
prng1 = rng(seed=54321)
latent1 = model.initialize(self.defn, self.docs, prng1)
runner1 = runner.runner(self.defn, self.docs, latent1)
runner1.run(prng1, niters)
# model2
prng2 = rng(seed=54321)
latent2 = model.initialize(self.defn, self.docs, prng2)
runner2 = runner.runner(self.defn, self.docs, latent2)
runner2.run(prng2, niters)
assert_list_equal(latent1.topic_distribution_by_document(),
latent2.topic_distribution_by_document())
for d1, d2 in zip(latent1.word_distribution_by_topic(),
latent2.word_distribution_by_topic()):
assert_dict_equal(d1, d2)
def test_runner_second_dp_valid():
N, V = 10, 20
defn = model_definition(N, V)
data = toy_dataset(defn)
prng = rng()
latent = model.initialize(defn, data, prng)
old_beta = latent.beta
old_gamma = latent.gamma
kernels = ['crf'] + \
runner.second_dp_hp_kernel_config(defn)
r = runner.runner(defn, data, latent, kernels)
r.run(prng, 10)
assert_almost_equals(latent.beta, old_beta)
assert_almost_equals(latent.gamma, old_gamma)
assert latent.alpha > 0
