def test_speed(self):
model1 = OnlineLDA(num_words=1000,
num_topics=100,
num_documents=10000,
alpha=.1,
eta=.3)
# random vocabulary
vocab = [
''.join(
choice(ascii_letters) for _ in range(5 + random.randint(10)))
for _ in range(model1.num_words)
]
model0 = ReferenceLDA(vocab,
D=model1.num_documents,
K=model1.num_topics,
alpha=model1.alpha[0, 0],
eta=model1.eta,
kappa=.9,
tau0=1024)
# generate D random documents of length up to N
D = 110
N = 600
docs1 = []
for _ in range(D):
wordids = random.permutation(model1.num_words)[:1 +
random.randint(N)]
docs1.append([(w, random.randint(10)) for w in wordids])
docs0 = [zip(*doc) for doc in docs1]
initial_gamma = random.gamma(100., 1. / 100., [model1.num_topics, D])
start = time()
gamma0, _ = model0.do_e_step(docs0,
max_steps=100,
gamma=initial_gamma.T)
time0 = time() - start
start = time()
gamma1, _ = model1.do_e_step(docs1,
max_iter=100,
latents=initial_gamma)
time1 = time() - start
# make sure that C++ implementation is actually faster than Python version
self.assertLess(
time1,
time0,
msg=
'Inference step took longer ({0:.2f} s) than reference implementation ({1:.2f})'
.format(time1, time0))
def test_vi(self):
W = 100
K = 20
D = 10
N = 100
# generate random vocabulary
vocab = [
''.join(
choice(ascii_letters) for _ in range(5 + random.randint(10)))
for _ in range(W)
]
model0 = ReferenceLDA(vocab, K, D, 0.1, 0.3, 1024., 0.9)
model1 = OnlineLDA(num_words=W, num_topics=K, num_documents=D)
model1.alpha = model0._alpha
model1.lambdas = model0._lambda
# generate D random documents of length up to N
docs1 = []
for _ in range(D):
docs1.append([
(w, random.randint(10))
for w in random.permutation(W)[:1 + random.randint(N)]
])
docs0 = [zip(*doc) for doc in docs1]
# use the same initialization of gamma
initial_gamma = random.gamma(100., 1. / 100., [K, D])
gamma0, sstats0 = model0.do_e_step(docs0,
max_steps=50,
gamma=initial_gamma.T)
gamma1, sstats1 = model1.do_e_step(docs1,
max_iter=50,
latents=initial_gamma)
# make sure e-Step gives the same results
self.assertGreater(
corrcoef(gamma0.T.ravel(), gamma1.ravel())[0, 1], 0.99)
self.assertGreater(
corrcoef(sstats0.ravel(), sstats1.ravel())[0, 1], 0.99)