def test_models_pickle():
models = (
bb,
bnb,
gp,
nich,
dd(4),
bbnc,
niw(3),
dm(5),
)
for model in models:
bstr = pickle.dumps(model)
model1 = pickle.loads(bstr)
assert_equals(model.name(), model1.name())
if model.name() == 'dd':
assert_equals(len(model.default_hyperparams()['alphas']),
len(model1.default_hyperparams()['alphas']))
elif model.name() == 'niw':
assert_equals(len(model.default_hyperparams()['mu']),
len(model1.default_hyperparams()['mu']))
elif model.name() == 'dm':
assert_equals(model.py_desc().get_np_dtype().shape,
model1.py_desc().get_np_dtype().shape)
def test_models_pickle():
models = (
bb,
bnb,
gp,
nich,
dd(4),
bbnc,
niw(3),
dm(5),
)
for model in models:
bstr = pickle.dumps(model)
model1 = pickle.loads(bstr)
assert_equals(model.name(), model1.name())
if model.name() == 'dd':
assert_equals(len(model.default_hyperparams()['alphas']),
len(model1.default_hyperparams()['alphas']))
elif model.name() == 'niw':
assert_equals(len(model.default_hyperparams()['mu']),
len(model1.default_hyperparams()['mu']))
elif model.name() == 'dm':
assert_equals(model.py_desc().get_np_dtype().shape,
model1.py_desc().get_np_dtype().shape)
def score_dataset(counts):
M, K = counts.shape
Y = np.array([(y, ) for y in counts], dtype=[('', np.int, (K, ))])
view = cxx_numpy_dataview(Y)
r = rng()
defn = model_definition(M, [dm(K)])
prior = {'alphas': [1.] * K}
s = cxx_initialize(defn,
view,
r,
feature_hps=[prior],
assignment=[0] * M)
assert_equals(s.groups(), [0])
return s.score_data(None, None, r)
def score_dataset(counts):
M, K = counts.shape
Y = np.array([(y,) for y in counts], dtype=[('', np.int, (K,))])
view = cxx_numpy_dataview(Y)
r = rng()
defn = model_definition(M, [dm(K)])
prior = {'alphas': [1.] * K}
s = cxx_initialize(
defn,
view,
r,
feature_hps=[prior],
assignment=[0] * M)
assert_equals(s.groups(), [0])
return s.score_data(None, None, r)
def test_dm_cxx():
K = 4
Y = np.array([
([0, 1, 2, 5], ),
([1, 0, 1, 2], ),
([0, 2, 9, 9], ),
],
dtype=[('', np.int, (K, ))])
Y_np = np.vstack(y[0] for y in Y)
cxx_view = cxx_numpy_dataview(Y)
r = rng()
defn = model_definition(Y.shape[0], [dm(K)])
prior = {'alphas': [1.] * K}
cxx_s = cxx_initialize(defn,
cxx_view,
r,
feature_hps=[prior],
assignment=[0] * Y.shape[0])
counts = cxx_s.get_suffstats(0, 0)['counts']
assert_sequence_equal(counts, list(Y_np.sum(axis=0)))
def test_dm_cxx():
K = 4
Y = np.array([
([0, 1, 2, 5],),
([1, 0, 1, 2],),
([0, 2, 9, 9],),
], dtype=[('', np.int, (K,))])
Y_np = np.vstack(y[0] for y in Y)
cxx_view = cxx_numpy_dataview(Y)
r = rng()
defn = model_definition(Y.shape[0], [dm(K)])
prior = {'alphas': [1.] * K}
cxx_s = cxx_initialize(
defn,
cxx_view,
r,
feature_hps=[prior],
assignment=[0] * Y.shape[0])
counts = cxx_s.get_suffstats(0, 0)['counts']
assert_sequence_equal(counts, list(Y_np.sum(axis=0)))
def test_betabin_equiv():
# https://github.com/pymc-devs/pymc/blob/
# a7ab153f2b58d81824a56166747c678d7f421bde/pymc/distributions/discrete.py#L84
def betabin_like(value, alpha, beta, n):
return (gammaln(alpha + beta) - gammaln(alpha) - gammaln(beta) +
gammaln(n + 1) - gammaln(value + 1) - gammaln(n - value + 1) +
gammaln(alpha + value) + gammaln(n + beta - value) -
gammaln(beta + alpha + n))
# this N refers to the number of trials in the binomial distribution
N = 10
# this refers to the dataset size
M = 100
# hyperparams of the beta dist
alpha, beta = 1., 2.
heads = np.random.randint(low=0, high=N + 1, size=M)
tails = N - heads
data = np.vstack((heads, tails)).T
Y = np.array([(y, ) for y in data], dtype=[('', np.int, (2, ))])
view = cxx_numpy_dataview(Y)
r = rng()
defn = model_definition(Y.shape[0], [dm(2)])
prior = {'alphas': [alpha, beta]}
s = cxx_initialize(defn,
view,
r,
feature_hps=[prior],
assignment=[0] * Y.shape[0])
assert_equals(s.groups(), [0])
def all_indices(N):
for i, j in it.product(range(0, N + 1), repeat=2):
if (i + j) == N:
yield i, j
all_data = [(list(ij), ) for ij in all_indices(N)]
Y_test = np.array(all_data, dtype=[('', np.int, (2, ))])
# the actual score is simply a betabin using the updated alpha, beta
alpha1, beta1 = np.array([alpha, beta]) + data.sum(axis=0)
def model_score(Y_value):
_, (score, ) = s.score_value(Y_value, r)
return score
def test_score(Y_value):
score = betabin_like(Y_value[0][0], alpha1, beta1, N)
return score
model_scores = np.array(map(model_score, Y_test))
test_scores = np.array(map(test_score, Y_test))
assert_almost_equals(np.exp(model_scores).sum(), 1., places=2)
assert_almost_equals(np.exp(test_scores).sum(), 1., places=2)
assert_almost_equals(np.abs(model_scores - test_scores).max(),
0.,
places=1)
def test_betabin_equiv():
# https://github.com/pymc-devs/pymc/blob/
# a7ab153f2b58d81824a56166747c678d7f421bde/pymc/distributions/discrete.py#L84
def betabin_like(value, alpha, beta, n):
return (gammaln(alpha + beta) - gammaln(alpha) - gammaln(beta) +
gammaln(n + 1) - gammaln(value + 1) - gammaln(n - value + 1) +
gammaln(alpha + value) + gammaln(n + beta - value) -
gammaln(beta + alpha + n))
# this N refers to the number of trials in the binomial distribution
N = 10
# this refers to the dataset size
M = 100
# hyperparams of the beta dist
alpha, beta = 1., 2.
heads = np.random.randint(low=0, high=N + 1, size=M)
tails = N - heads
data = np.vstack((heads, tails)).T
Y = np.array([(y,) for y in data], dtype=[('', np.int, (2,))])
view = cxx_numpy_dataview(Y)
r = rng()
defn = model_definition(Y.shape[0], [dm(2)])
prior = {'alphas': [alpha, beta]}
s = cxx_initialize(
defn,
view,
r,
feature_hps=[prior],
assignment=[0] * Y.shape[0])
assert_equals(s.groups(), [0])
def all_indices(N):
for i, j in it.product(range(0, N + 1), repeat=2):
if (i + j) == N:
yield i, j
all_data = [(list(ij),) for ij in all_indices(N)]
Y_test = np.array(all_data, dtype=[('', np.int, (2,))])
# the actual score is simply a betabin using the updated alpha, beta
alpha1, beta1 = np.array([alpha, beta]) + data.sum(axis=0)
def model_score(Y_value):
_, (score,) = s.score_value(Y_value, r)
return score
def test_score(Y_value):
score = betabin_like(Y_value[0][0], alpha1, beta1, N)
return score
model_scores = np.array(map(model_score, Y_test))
test_scores = np.array(map(test_score, Y_test))
assert_almost_equals(np.exp(model_scores).sum(), 1., places=2)
assert_almost_equals(np.exp(test_scores).sum(), 1., places=2)
assert_almost_equals(
np.abs(model_scores - test_scores).max(), 0., places=1)
