def repos(
page: PositiveInt = PositiveInt(1),
per_page: PositiveInt = PositiveInt(10),
db: Session = Depends(get_db),
):
controller: SearchController = get_search_controller(db)
return controller.get_searches(int(page), int(per_page))
def repos(
page: PositiveInt = PositiveInt(1),
per_page: PositiveInt = PositiveInt(10),
search_id: Optional[UUID] = None,
db: Session = Depends(get_db),
):
controller: RepoController = get_repo_controller(db)
if search_id:
search_id = str(search_id) # type: ignore
return controller.get_repos(int(page), int(per_page), search_id=search_id) # type: ignore
def maximum_likelihood_estimate_sgd(
distr_output: DistributionOutput,
samples: mx.ndarray,
init_biases: List[mx.ndarray.NDArray] = None,
num_epochs: PositiveInt = PositiveInt(5),
learning_rate: PositiveFloat = PositiveFloat(1e-2),
hybridize: bool = True,
) -> Iterable[float]:
model_ctx = mx.cpu()
arg_proj = distr_output.get_args_proj()
arg_proj.initialize()
if hybridize:
arg_proj.hybridize()
if init_biases is not None:
for param, bias in zip(arg_proj.proj, init_biases):
param.params[param.prefix + "bias"].initialize(
mx.initializer.Constant(bias), force_reinit=True
)
trainer = mx.gluon.Trainer(
arg_proj.collect_params(),
"sgd",
{"learning_rate": learning_rate, "clip_gradient": 10.0},
)
# The input data to our model is one-dimensional
dummy_data = mx.nd.array(np.ones((len(samples), 1)))
train_data = mx.gluon.data.DataLoader(
mx.gluon.data.ArrayDataset(dummy_data, samples),
batch_size=BATCH_SIZE,
shuffle=True,
)
for e in range(num_epochs):
cumulative_loss = 0
num_batches = 0
# inner loop
for i, (data, sample_label) in enumerate(train_data):
data = data.as_in_context(model_ctx)
sample_label = sample_label.as_in_context(model_ctx)
with mx.autograd.record():
distr_args = arg_proj(data)
distr = distr_output.distribution(distr_args)
loss = distr.loss(sample_label)
if not hybridize:
assert loss.shape == distr.batch_shape
loss.backward()
trainer.step(BATCH_SIZE)
num_batches += 1
cumulative_loss += mx.nd.mean(loss).asscalar()
print("Epoch %s, loss: %s" % (e, cumulative_loss / num_batches))
return [
param[0].asnumpy() for param in arg_proj(mx.nd.array(np.ones((1, 1))))
]
def test_dirichlet_multinomial(hybridize: bool) -> None:
num_samples = 8000
dim = 3
n_trials = 500
alpha = np.array([1.0, 2.0, 3.0])
distr = DirichletMultinomial(dim=3,
n_trials=n_trials,
alpha=mx.nd.array(alpha))
cov = distr.variance.asnumpy()
samples = distr.sample(num_samples)
alpha_hat = maximum_likelihood_estimate_sgd(
DirichletMultinomialOutput(dim=dim, n_trials=n_trials),
samples,
init_biases=None,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
distr = DirichletMultinomial(dim=3,
n_trials=n_trials,
alpha=mx.nd.array(alpha_hat))
cov_hat = distr.variance.asnumpy()
assert np.allclose(
alpha_hat, alpha, atol=0.1, rtol=0.1
), f"alpha did not match: alpha = {alpha}, alpha_hat = {alpha_hat}"
assert np.allclose(
cov_hat, cov, atol=0.1, rtol=0.1
), f"Covariance did not match: cov = {cov}, cov_hat = {cov_hat}"
def test_gamma_likelihood(alpha: float, beta: float, hybridize: bool) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
alphas = mx.nd.zeros((NUM_SAMPLES, )) + alpha
betas = mx.nd.zeros((NUM_SAMPLES, )) + beta
distr = Gamma(alphas, betas)
samples = distr.sample()
init_biases = [
inv_softplus(alpha - START_TOL_MULTIPLE * TOL * alpha),
inv_softplus(beta - START_TOL_MULTIPLE * TOL * beta),
]
alpha_hat, beta_hat = maximum_likelihood_estimate_sgd(
GammaOutput(),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(5),
)
assert (np.abs(alpha_hat - alpha) < TOL * alpha
), f"alpha did not match: alpha = {alpha}, alpha_hat = {alpha_hat}"
assert (np.abs(beta_hat - beta) < TOL *
beta), f"beta did not match: beta = {beta}, beta_hat = {beta_hat}"
def test_categorical_likelihood(num_cats: int, cat_probs: np.ndarray,
hybridize: bool):
"""
Test to check that maximizing the likelihood recovers the parameters
"""
cat_prob = mx.nd.array(cat_probs)
cat_probs = mx.nd.zeros((NUM_SAMPLES, num_cats)) + cat_prob
distr = Categorical(cat_probs.log())
samples = distr.sample()
cat_prob_init = mx.nd.random_uniform(1 - TOL, 1 + TOL, num_cats) * cat_prob
cat_prob_init = cat_prob_init / cat_prob_init.sum()
init_biases = [cat_prob_init]
cat_log_prob_hat = maximum_likelihood_estimate_sgd(
CategoricalOutput(num_cats),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(25),
)
cat_prob_hat = np.exp(cat_log_prob_hat)
prob_deviation = np.abs(cat_prob_hat - cat_prob.asnumpy()).flatten()
tolerance = (TOL * cat_prob.asnumpy()).flatten()
assert np.all(
np.less(prob_deviation, tolerance)
), f"cat_prob did not match: cat_prob = {cat_prob}, cat_prob_hat = {cat_prob_hat}"
def test_poisson_likelihood(rate: float, hybridize: bool) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
rates = mx.nd.zeros(NUM_SAMPLES) + rate
distr = Poisson(rates)
samples = distr.sample()
init_biases = [inv_softplus(rate - START_TOL_MULTIPLE * TOL * rate)]
rate_hat = maximum_likelihood_estimate_sgd(
PoissonOutput(),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(20),
)
print("rate:", rate_hat)
assert (np.abs(rate_hat[0] - rate) < TOL *
rate), f"mu did not match: rate = {rate}, rate_hat = {rate_hat}"
def test_beta_likelihood(concentration1: float, concentration0: float) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
concentration1s = torch.zeros((NUM_SAMPLES, )) + concentration1
concentration0s = torch.zeros((NUM_SAMPLES, )) + concentration0
distr = Beta(concentration1s, concentration0s)
samples = distr.sample()
init_biases = [
inv_softplus(concentration1 -
START_TOL_MULTIPLE * TOL * concentration1),
inv_softplus(concentration0 -
START_TOL_MULTIPLE * TOL * concentration0),
]
concentration1_hat, concentration0_hat = maximum_likelihood_estimate_sgd(
BetaOutput(),
samples,
init_biases=init_biases,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
assert (
np.abs(concentration1_hat - concentration1) < TOL * concentration1
), f"concentration1 did not match: concentration1 = {concentration1}, concentration1_hat = {concentration1_hat}"
assert (
np.abs(concentration0_hat - concentration0) < TOL * concentration0
), f"concentration0 did not match: concentration0 = {concentration0}, concentration0_hat = {concentration0_hat}"
def maximum_likelihood_estimate_sgd(
distr_output: DistributionOutput,
samples: torch.Tensor,
init_biases: List[np.ndarray] = None,
num_epochs: PositiveInt = PositiveInt(5),
learning_rate: PositiveFloat = PositiveFloat(1e-2),
):
arg_proj = distr_output.get_args_proj(in_features=1)
if init_biases is not None:
for param, bias in zip(arg_proj.proj, init_biases):
nn.init.constant_(param.bias, bias)
dummy_data = torch.ones((len(samples), 1))
dataset = TensorDataset(dummy_data, samples)
train_data = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
optimizer = SGD(arg_proj.parameters(), lr=learning_rate)
for e in range(num_epochs):
cumulative_loss = 0
num_batches = 0
for i, (data, sample_label) in enumerate(train_data):
optimizer.zero_grad()
distr_args = arg_proj(data)
distr = distr_output.distribution(distr_args)
loss = -distr.log_prob(sample_label).mean()
loss.backward()
clip_grad_norm_(arg_proj.parameters(), 10.0)
optimizer.step()
num_batches += 1
cumulative_loss += loss.item()
if len(distr_args[0].shape) == 1:
return [
param.detach().numpy() for param in arg_proj(torch.ones((1, 1)))
]
return [
param[0].detach().numpy() for param in arg_proj(torch.ones((1, 1)))
]
def test_gaussian_likelihood(mu: float, sigma: float, hybridize: bool):
'''
Test to check that maximizing the likelihood recovers the parameters
'''
# generate samples
mus = mx.nd.zeros((NUM_SAMPLES, )) + mu
sigmas = mx.nd.zeros((NUM_SAMPLES, )) + sigma
distr = Gaussian(mus, sigmas)
samples = distr.sample()
init_biases = [
mu - START_TOL_MULTIPLE * TOL * mu,
inv_softplus(sigma - START_TOL_MULTIPLE * TOL * sigma),
]
mu_hat, sigma_hat = maximum_likelihood_estimate_sgd(
GaussianOutput(),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.001),
num_epochs=PositiveInt(5),
)
assert (np.abs(mu_hat - mu) <
TOL * mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (np.abs(sigma_hat - sigma) < TOL * sigma
), f"alpha did not match: sigma = {sigma}, sigma_hat = {sigma_hat}"
def repos_search(
search_options: SearchOptions = Body(
...,
example={
"qualifiers": [
{"language": "Elixir", "created": ">2020-01-01"},
{"language": "Python"},
]
},
),
page: PositiveInt = PositiveInt(1),
per_page: PositiveInt = PositiveInt(10),
db: Session = Depends(get_db),
):
controller: RepoController = get_repo_controller(db)
return controller.search_repos(search_options, int(page), int(per_page))
def test_neg_binomial(mu_alpha: Tuple[float, float], hybridize: bool) -> None:
'''
Test to check that maximizing the likelihood recovers the parameters
'''
# test instance
mu, alpha = mu_alpha
# generate samples
mus = mx.nd.zeros((NUM_SAMPLES, )) + mu
alphas = mx.nd.zeros((NUM_SAMPLES, )) + alpha
neg_bin_distr = NegativeBinomial(mu=mus, alpha=alphas)
samples = neg_bin_distr.sample()
init_biases = [
inv_softplus(mu - START_TOL_MULTIPLE * TOL * mu),
inv_softplus(alpha + START_TOL_MULTIPLE * TOL * alpha),
]
mu_hat, alpha_hat = maximum_likelihood_estimate_sgd(
NegativeBinomialOutput(),
samples,
hybridize=hybridize,
init_biases=init_biases,
num_epochs=PositiveInt(15),
)
assert (np.abs(mu_hat - mu) <
TOL * mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (np.abs(alpha_hat - alpha) < TOL * alpha
), f"alpha did not match: alpha = {alpha}, alpha_hat = {alpha_hat}"
def test_deterministic_l2(mu: float, hybridize: bool) -> None:
'''
Test to check that maximizing the likelihood recovers the parameters.
This tests uses the Gaussian distribution with fixed variance and sample mean.
This essentially reduces to determistic L2.
'''
# generate samples
mu = mu
mus = mx.nd.zeros(NUM_SAMPLES) + mu
deterministic_distr = Gaussian(mu=mus, sigma=0.1 * mx.nd.ones_like(mus))
samples = deterministic_distr.sample()
class GaussianFixedVarianceOutput(GaussianOutput):
@classmethod
def domain_map(cls, F, mu, sigma):
sigma = 0.1 * F.ones_like(sigma)
return mu.squeeze(axis=-1), sigma.squeeze(axis=-1)
mu_hat, _ = maximum_likelihood_estimate_sgd(
GaussianFixedVarianceOutput(),
samples,
init_biases=[3 * mu, 0.1],
hybridize=hybridize,
num_epochs=PositiveInt(1),
)
assert (np.abs(mu_hat - mu) <
TOL * mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
def test_weibull_likelihood(rate: float, shape: float,
hybridize: bool) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
rates = mx.nd.zeros((NUM_SAMPLES, )) + rate
shapes = mx.nd.zeros((NUM_SAMPLES, )) + shape
distr = Weibull(rates, shapes)
samples = distr.sample()
init_biases = [
inv_softplus(rate - START_TOL_MULTIPLE * TOL * rate),
inv_softplus(shape - START_TOL_MULTIPLE * TOL * shape),
]
rate_hat, shape_hat = maximum_likelihood_estimate_sgd(
WeibullOutput(),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
print("rate:", rate_hat, "shape:", shape_hat)
assert (np.abs(rate_hat - rate) < TOL *
rate), f"rate did not match: rate = {rate}, rate_hat = {rate_hat}"
assert (np.abs(shape_hat - shape) < TOL * shape
), f"shape did not match: shape = {shape}, shape_hat = {shape_hat}"
def test_genpareto_likelihood(xi: float, beta: float, hybridize: bool) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
xis = mx.nd.zeros((NUM_SAMPLES, )) + xi
betas = mx.nd.zeros((NUM_SAMPLES, )) + beta
distr = GenPareto(xis, betas)
samples = distr.sample()
init_biases = [
inv_softplus(xi - START_TOL_MULTIPLE * TOL * xi),
inv_softplus(beta - START_TOL_MULTIPLE * TOL * beta),
]
xi_hat, beta_hat = maximum_likelihood_estimate_sgd(
GenParetoOutput(),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
print("XI:", xi_hat, "BETA:", beta_hat)
assert (np.abs(xi_hat - xi) <
TOL * xi), f"alpha did not match: xi = {xi}, xi_hat = {xi_hat}"
assert (np.abs(beta_hat - beta) < TOL *
beta), f"beta did not match: beta = {beta}, beta_hat = {beta_hat}"
def test_loglogistic_likelihood(mu: float, sigma: float,
hybridize: bool) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
mus = mx.nd.zeros((NUM_SAMPLES, )) + mu
sigmas = mx.nd.zeros((NUM_SAMPLES, )) + sigma
distr = Loglogistic(mus, sigmas)
samples = distr.sample()
init_biases = [
mu - START_TOL_MULTIPLE * TOL * mu,
inv_softplus(sigma - START_TOL_MULTIPLE * TOL * sigma),
]
mu_hat, sigma_hat = maximum_likelihood_estimate_sgd(
LoglogisticOutput(),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
print("mu:", mu_hat, "sigma:", sigma_hat)
assert (np.abs(mu_hat - mu) <
TOL * mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (np.abs(sigma_hat - sigma) < TOL * sigma
), f"sigma did not match: sigma = {sigma}, sigma_hat = {sigma_hat}"
def test_gamma_likelihood(concentration: float, rate: float) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
concentrations = torch.zeros((NUM_SAMPLES,)) + concentration
rates = torch.zeros((NUM_SAMPLES,)) + rate
distr = Gamma(concentrations, rates)
samples = distr.sample()
init_biases = [
inv_softplus(concentration - START_TOL_MULTIPLE * TOL * concentration),
inv_softplus(rate - START_TOL_MULTIPLE * TOL * rate),
]
concentration_hat, rate_hat = maximum_likelihood_estimate_sgd(
GammaOutput(),
samples,
init_biases=init_biases,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
assert (
np.abs(concentration_hat - concentration) < TOL * concentration
), f"concentration did not match: concentration = {concentration}, concentration_hat = {concentration_hat}"
assert (
np.abs(rate_hat - rate) < TOL * rate
), f"rate did not match: rate = {rate}, rate_hat = {rate_hat}"
def test_binned_likelihood(num_bins: float, bin_probabilites: np.ndarray,
hybridize: bool):
'''
Test to check that maximizing the likelihood recovers the parameters
'''
bin_prob = mx.nd.array(bin_probabilites)
bin_center = mx.nd.array(np.logspace(-1, 1, num_bins))
# generate samples
bin_probs = mx.nd.zeros((NUM_SAMPLES, num_bins)) + bin_prob
bin_centers = mx.nd.zeros((NUM_SAMPLES, num_bins)) + bin_center
distr = Binned(bin_probs, bin_centers)
samples = distr.sample()
# add some jitter to the uniform initialization and normalize
bin_prob_init = mx.nd.random_uniform(1 - TOL, 1 + TOL, num_bins) * bin_prob
bin_prob_init = bin_prob_init / bin_prob_init.sum()
init_biases = [bin_prob_init]
bin_prob_hat, = maximum_likelihood_estimate_sgd(
BinnedOutput(list(bin_center.asnumpy())),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(25),
)
assert all(
mx.nd.abs(mx.nd.array(bin_prob_hat) - bin_prob) < TOL * bin_prob
), f"bin_prob did not match: bin_prob = {bin_prob}, bin_prob_hat = {bin_prob_hat}"
def test_crud_list_query(mocker, loop, mock_sqlalchemy_filters):
def __query(*_args, **__kwargs):
return mock_query
apply_filters, apply_sort = mock_sqlalchemy_filters
mock_query = mocker.Mock()
mock_query.options = mocker.Mock(side_effect=__query)
mock_query.offset = mocker.Mock(side_effect=__query)
mock_query.limit = mocker.Mock(side_effect=__query)
mock_query.all = mocker.Mock(return_value=[])
session = mocker.Mock()
session.query = mocker.Mock(return_value=mock_query)
filter_spec = [{"filter1": "value1"}]
sort_spec = [{"sort1": "value1"}]
options = ("option1", "option2")
offset = NonNegativeInt(10)
limit = PositiveInt(50)
loop.run_until_complete(
crud.list_instances(Person, session, filter_spec, sort_spec, offset,
limit, options))
assert apply_filters.call_args == mocker.call(mock_query, filter_spec)
assert apply_sort.call_args == mocker.call(mock_query, sort_spec)
assert mock_query.options.call_args == mocker.call(options)
assert mock_query.offset.call_args == mocker.call(offset)
assert mock_query.limit.call_args == mocker.call(limit)
def test_dirichlet(hybridize: bool) -> None:
num_samples = 2000
dim = 3
alpha = np.random.gamma(shape=1, size=dim) + 0.5
distr = Dirichlet(alpha=mx.nd.array(alpha))
cov = distr.variance.asnumpy()
samples = distr.sample(num_samples)
alpha_hat = maximum_likelihood_estimate_sgd(
DirichletOutput(dim=dim),
samples,
init_biases=None,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
distr = Dirichlet(alpha=mx.nd.array(alpha_hat))
cov_hat = distr.variance.asnumpy()
assert np.allclose(
alpha_hat, alpha, atol=0.1, rtol=0.1
), f"alpha did not match: alpha = {alpha}, alpha_hat = {alpha_hat}"
assert np.allclose(
cov_hat, cov, atol=0.1, rtol=0.1
), f"Covariance did not match: cov = {cov}, cov_hat = {cov_hat}"
def test_box_cox_tranform(
lambdas: Tuple[float, float],
mu_sigma: Tuple[float, float],
hybridize: bool,
):
'''
Test to check that maximizing the likelihood recovers the parameters
'''
# test instance
lam_1, lam_2 = lambdas
mu, sigma = mu_sigma
# generate samples
lamdas_1 = mx.nd.zeros((NUM_SAMPLES, )) + lam_1
lamdas_2 = mx.nd.zeros((NUM_SAMPLES, )) + lam_2
transform = InverseBoxCoxTransform(lamdas_1, lamdas_2)
mus = mx.nd.zeros((NUM_SAMPLES, )) + mu
sigmas = mx.nd.zeros((NUM_SAMPLES, )) + sigma
gausian_distr = Gaussian(mus, sigmas)
# Here the base distribution is Guassian which is transformed to
# non-Gaussian via the inverse Box-Cox transform.
# Sampling from `trans_distr` gives non-Gaussian samples
trans_distr = TransformedDistribution(gausian_distr, transform)
# Given the non-Gaussian samples find the true parameters
# of the Box-Cox transformation as well as the underlying Gaussian distribution.
samples = trans_distr.sample()
init_biases = [
mu - START_TOL_MULTIPLE * TOL * mu,
inv_softplus(sigma - START_TOL_MULTIPLE * TOL * sigma),
lam_1 - START_TOL_MULTIPLE * TOL * lam_1,
inv_softplus(lam_2 - START_TOL_MULTIPLE * TOL * lam_2),
]
mu_hat, sigma_hat, lam_1_hat, lam_2_hat = maximum_likelihood_estimate_sgd(
TransformedDistributionOutput(
GaussianOutput(),
InverseBoxCoxTransformOutput(lb_obs=lam_2, fix_lambda_2=True),
),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.01),
num_epochs=PositiveInt(18),
)
assert (np.abs(lam_1_hat - lam_1) < TOL * lam_1
), f"lam_1 did not match: lam_1 = {lam_1}, lam_1_hat = {lam_1_hat}"
# assert (
# np.abs(lam_2_hat - lam_2) < TOL * lam_2
# ), f"lam_2 did not match: lam_2 = {lam_2}, lam_2_hat = {lam_2_hat}"
assert np.abs(mu_hat - mu) < TOL * np.abs(
mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (np.abs(sigma_hat - sigma) < TOL * sigma
), f"sigma did not match: sigma = {sigma}, sigma_hat = {sigma_hat}"
def test_lowrank_multivariate_gaussian(hybridize: bool) -> None:
num_samples = 2000
dim = 2
rank = 1
mu = np.arange(0, dim) / float(dim)
D = np.eye(dim) * (np.arange(dim) / dim + 0.5)
W = np.sqrt(np.ones((dim, rank)) * 0.2)
Sigma = D + W.dot(W.transpose())
distr = LowrankMultivariateGaussian(
mu=mx.nd.array([mu]),
D=mx.nd.array([np.diag(D)]),
W=mx.nd.array([W]),
dim=dim,
rank=rank,
)
assert np.allclose(
distr.variance[0].asnumpy(), Sigma, atol=0.1, rtol=0.1
), f"did not match: sigma = {Sigma}, sigma_hat = {distr.variance[0]}"
samples = distr.sample(num_samples).squeeze().asnumpy()
mu_hat, D_hat, W_hat = maximum_likelihood_estimate_sgd(
LowrankMultivariateGaussianOutput(dim=dim,
rank=rank,
sigma_init=0.2,
sigma_minimum=0.0),
samples,
learning_rate=PositiveFloat(0.01),
num_epochs=PositiveInt(25),
init_biases=
None, # todo we would need to rework biases a bit to use it in the multivariate case
hybridize=hybridize,
)
distr = LowrankMultivariateGaussian(
dim=dim,
rank=rank,
mu=mx.nd.array([mu_hat]),
D=mx.nd.array([D_hat]),
W=mx.nd.array([W_hat]),
)
Sigma_hat = distr.variance.asnumpy()
assert np.allclose(
mu_hat, mu, atol=0.2,
rtol=0.1), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert np.allclose(
Sigma_hat, Sigma, atol=0.1, rtol=0.1
), f"sigma did not match: sigma = {Sigma}, sigma_hat = {Sigma_hat}"
def test_inflated_neg_binomial_likelihood(
mu: float,
alpha: float,
zero_probability: float,
hybridize: bool,
) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
num_samples = 2000 # Required for convergence
distr = ZeroInflatedNegativeBinomialOutput().distribution(distr_args=[
mx.nd.array([[1 -
zero_probability, zero_probability]]), # mixture probs
mx.nd.array([mu, alpha]), # loc, shape of Neg Bin
mx.nd.array([0.0]),
])
distr_output = ZeroInflatedNegativeBinomialOutput()
samples = distr.sample(num_samples).squeeze()
init_biases = None
(
(_, zero_probability_hat),
mu_hat,
alpha_hat,
_,
) = maximum_likelihood_estimate_sgd(
distr_output=distr_output,
samples=samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.1),
num_epochs=PositiveInt(20),
)
assert (
np.abs(zero_probability_hat - zero_probability) <
TOL * zero_probability
), f"zero_probability did not match: zero_probability = {zero_probability}, zero_probability_hat = {zero_probability_hat}"
assert (np.abs(mu_hat - mu) <
TOL * mu), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (np.abs(alpha_hat - alpha) < TOL * alpha
), f"alpha did not match: alpha = {alpha}, alpha_hat = {alpha_hat}"
def test_studentT_likelihood(
mu_sigma_nu: Tuple[float, float, float], hybridize: bool
) -> None:
'''
Test to check that maximizing the likelihood recovers the parameters
'''
# test instance
mu, sigma, nu = mu_sigma_nu
# generate samples
mus = mx.nd.zeros((NUM_SAMPLES, 1)) + mu
sigmas = mx.nd.zeros((NUM_SAMPLES, 1)) + sigma
nus = mx.nd.zeros((NUM_SAMPLES, 1)) + nu
distr = StudentT(mus, sigmas, nus)
samples = distr.sample()
# nu takes very long to learn, so we initialize it at the true value.
# transform used is softplus(x) + 2
init_bias = [
mu - START_TOL_MULTIPLE * TOL * mu,
inv_softplus(sigma - START_TOL_MULTIPLE * TOL * sigma),
inv_softplus(nu - 2),
]
mu_hat, sigma_hat, nu_hat = maximum_likelihood_estimate_sgd(
StudentTOutput(),
samples,
init_biases=init_bias,
hybridize=hybridize,
num_epochs=PositiveInt(10),
learning_rate=1e-2,
)
assert (
np.abs(mu_hat - mu) < TOL * mu
), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert (
np.abs(sigma_hat - sigma) < TOL * sigma
), f"sigma did not match: sigma = {sigma}, sigma_hat = {sigma_hat}"
assert (
np.abs(nu_hat - nu) < TOL * nu
), "nu0 did not match: nu0 = %s, nu_hat = %s" % (nu, nu_hat)
def test_inflated_poisson_likelihood(
rate: float,
hybridize: bool,
zero_probability: float,
) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
num_samples = 2000 # Required for convergence
distr = ZeroInflatedPoissonOutput().distribution(distr_args=[
mx.nd.array([[1 - zero_probability, zero_probability]]),
mx.nd.array([rate]),
mx.nd.array([0.0]),
])
distr_output = ZeroInflatedPoissonOutput()
samples = distr.sample(num_samples).squeeze()
init_biases = None
(_, zero_probability_hat), rate_hat, _ = maximum_likelihood_estimate_sgd(
distr_output=distr_output,
samples=samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.15),
num_epochs=PositiveInt(25),
)
assert (
np.abs(zero_probability_hat - zero_probability) <
TOL * zero_probability
), f"zero_probability did not match: zero_probability = {zero_probability}, zero_probability_hat = {zero_probability_hat}"
assert (np.abs(rate_hat - rate) < TOL *
rate), f"rate did not match: rate = {rate}, rate_hat = {rate_hat}"
def test_multivariate_gaussian() -> None:
num_samples = 2000
dim = 2
mu = np.arange(0, dim) / float(dim)
L_diag = np.ones((dim, ))
L_low = 0.1 * np.ones((dim, dim)) * np.tri(dim, k=-1)
L = np.diag(L_diag) + L_low
Sigma = L.dot(L.transpose())
distr = MultivariateGaussian(mu=mx.nd.array(mu), L=mx.nd.array(L))
samples = distr.sample(num_samples)
mu_hat, L_hat = maximum_likelihood_estimate_sgd(
MultivariateGaussianOutput(dim=dim),
samples,
init_biases=
None, # todo we would need to rework biases a bit to use it in the multivariate case
hybridize=False,
learning_rate=PositiveFloat(0.01),
num_epochs=PositiveInt(10),
)
distr = MultivariateGaussian(mu=mx.nd.array([mu_hat]),
L=mx.nd.array([L_hat]))
Sigma_hat = distr.variance[0].asnumpy()
assert np.allclose(
mu_hat, mu, atol=0.1,
rtol=0.1), f"mu did not match: mu = {mu}, mu_hat = {mu_hat}"
assert np.allclose(
Sigma_hat, Sigma, atol=0.1, rtol=0.1
), f"Sigma did not match: sigma = {Sigma}, sigma_hat = {Sigma_hat}"
from gluonts.dataset.common import Dataset
from gluonts.support.pandas import forecast_start
def generate_random_dataset(
num_ts: int, start_time: str, freq: str, min_length: int, max_length: int
) -> Dataset:
start_timestamp = pd.Timestamp(start_time, freq=freq)
for _ in range(num_ts):
ts_length = np.random.randint(low=min_length, high=max_length)
target = np.random.uniform(size=(ts_length,))
data = {"target": target, "start": start_timestamp}
yield data
PREDICTION_LENGTH = PositiveInt(30)
SEASON_LENGTH = PositiveInt(210)
START_TIME = "2018-01-03 14:37:12" # That's a Wednesday
MIN_LENGTH = 300
MAX_LENGTH = 400
NUM_TS = 10
@pytest.mark.parametrize(
"predictor_cls", [SeasonalNaivePredictor, Naive2Predictor]
)
@pytest.mark.parametrize(
"freq", ["1min", "15min", "30min", "1H", "2H", "12H", "7D", "1W", "1M"]
)
def test_seasonal_naive(predictor_cls, freq: str):
predictor = predictor_cls(
class BenchFunctests(Bench):
num_jobs: Op[PositiveInt] = PositiveInt(DEFAULT_NPROC)
def test_piecewise_linear(
gamma: float,
slopes: np.ndarray,
knot_spacings: np.ndarray,
hybridize: bool,
) -> None:
'''
Test to check that minimizing the CRPS recovers the quantile function
'''
num_samples = 500 # use a few samples for timeout failure
gammas = mx.nd.zeros((num_samples, )) + gamma
slopess = mx.nd.zeros((num_samples, len(slopes))) + mx.nd.array(slopes)
knot_spacingss = mx.nd.zeros(
(num_samples, len(knot_spacings))) + mx.nd.array(knot_spacings)
pwl_sqf = PiecewiseLinear(gammas, slopess, knot_spacingss)
samples = pwl_sqf.sample()
# Parameter initialization
gamma_init = gamma - START_TOL_MULTIPLE * TOL * gamma
slopes_init = slopes - START_TOL_MULTIPLE * TOL * slopes
knot_spacings_init = knot_spacings
# We perturb knot spacings such that even after the perturbation they sum to 1.
mid = len(slopes) // 2
knot_spacings_init[:mid] = (knot_spacings[:mid] -
START_TOL_MULTIPLE * TOL * knot_spacings[:mid])
knot_spacings_init[mid:] = (knot_spacings[mid:] +
START_TOL_MULTIPLE * TOL * knot_spacings[mid:])
init_biases = [gamma_init, slopes_init, knot_spacings_init]
# check if it returns original parameters of mapped
gamma_hat, slopes_hat, knot_spacings_hat = maximum_likelihood_estimate_sgd(
PiecewiseLinearOutput(len(slopes)),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.01),
num_epochs=PositiveInt(20),
)
# Since the problem is highly non-convex we may not be able to recover the exact parameters
# Here we check if the estimated parameters yield similar function evaluations at different quantile levels.
quantile_levels = np.arange(0.1, 1.0, 0.1)
# create a LinearSplines instance with the estimated parameters to have access to .quantile
pwl_sqf_hat = PiecewiseLinear(
mx.nd.array(gamma_hat),
mx.nd.array(slopes_hat).expand_dims(axis=0),
mx.nd.array(knot_spacings_hat).expand_dims(axis=0),
)
# Compute quantiles with the estimated parameters
quantiles_hat = np.squeeze(
pwl_sqf_hat.quantile(mx.nd.array(quantile_levels).expand_dims(axis=0),
axis=1).asnumpy())
# Compute quantiles with the original parameters
# Since params is replicated across samples we take only the first entry
quantiles = np.squeeze(
pwl_sqf.quantile(
mx.nd.array(quantile_levels).expand_dims(axis=0).repeat(
axis=0, repeats=num_samples),
axis=1,
).asnumpy()[0, :])
for ix, (quantile, quantile_hat) in enumerate(zip(quantiles,
quantiles_hat)):
assert np.abs(quantile_hat - quantile) < TOL * quantile, (
f"quantile level {quantile_levels[ix]} didn't match:"
f" "
f"q = {quantile}, q_hat = {quantile_hat}")
def test_inflated_beta_likelihood(
alpha: float,
beta: float,
hybridize: bool,
inflated_at: str,
zero_probability: float,
one_probability: float,
) -> None:
"""
Test to check that maximizing the likelihood recovers the parameters
"""
# generate samples
alphas = mx.nd.zeros((NUM_SAMPLES, )) + alpha
betas = mx.nd.zeros((NUM_SAMPLES, )) + beta
zero_probabilities = mx.nd.zeros((NUM_SAMPLES, )) + zero_probability
one_probabilities = mx.nd.zeros((NUM_SAMPLES, )) + one_probability
if inflated_at == "zero":
distr = ZeroInflatedBeta(alphas,
betas,
zero_probability=zero_probabilities)
distr_output = ZeroInflatedBetaOutput()
elif inflated_at == "one":
distr = OneInflatedBeta(alphas,
betas,
one_probability=one_probabilities)
distr_output = OneInflatedBetaOutput()
else:
distr = ZeroAndOneInflatedBeta(
alphas,
betas,
zero_probability=zero_probabilities,
one_probability=one_probabilities,
)
distr_output = ZeroAndOneInflatedBetaOutput()
samples = distr.sample()
init_biases = [
inv_softplus(alpha - START_TOL_MULTIPLE * TOL * alpha),
inv_softplus(beta - START_TOL_MULTIPLE * TOL * beta),
]
parameters = maximum_likelihood_estimate_sgd(
distr_output,
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(10),
)
if inflated_at == "zero":
alpha_hat, beta_hat, zero_probability_hat = parameters
assert (
np.abs(zero_probability_hat[0] - zero_probability) <
TOL * zero_probability
), f"zero_probability did not match: zero_probability = {zero_probability}, zero_probability_hat = {zero_probability_hat}"
elif inflated_at == "one":
alpha_hat, beta_hat, one_probability_hat = parameters
assert (
np.abs(one_probability_hat - one_probability) <
TOL * one_probability
), f"one_probability did not match: one_probability = {one_probability}, one_probability_hat = {one_probability_hat}"
else:
(
alpha_hat,
beta_hat,
zero_probability_hat,
one_probability_hat,
) = parameters
assert (
np.abs(zero_probability_hat - zero_probability) <
TOL * zero_probability
), f"zero_probability did not match: zero_probability = {zero_probability}, zero_probability_hat = {zero_probability_hat}"
assert (
np.abs(one_probability_hat - one_probability) <
TOL * one_probability
), f"one_probability did not match: one_probability = {one_probability}, one_probability_hat = {one_probability_hat}"
assert (np.abs(alpha_hat - alpha) < TOL * alpha
), f"alpha did not match: alpha = {alpha}, alpha_hat = {alpha_hat}"
assert (np.abs(beta_hat - beta) < TOL *
beta), f"beta did not match: beta = {beta}, beta_hat = {beta_hat}"
