def model(X: DeviceArray) -> DeviceArray:
"""Gamma-Poisson hierarchical model for daily sales forecasting
Args:
X: input data
Returns:
output data
"""
n_stores, n_days, n_features = X.shape
n_features -= 1 # remove one dim for target
eps = 1e-12 # epsilon
plate_features = numpyro.plate(Plate.features, n_features, dim=-1)
plate_stores = numpyro.plate(Plate.stores, n_stores, dim=-2)
plate_days = numpyro.plate(Plate.days, n_days, dim=-1)
disp_param_mu = numpyro.sample(Site.disp_param_mu,
dist.Normal(loc=4., scale=1.))
disp_param_sigma = numpyro.sample(Site.disp_param_sigma,
dist.HalfNormal(scale=1.))
with plate_stores:
disp_param_offsets = numpyro.sample(
Site.disp_param_offsets,
dist.Normal(loc=jnp.zeros((n_stores, 1)), scale=0.1))
disp_params = disp_param_mu + disp_param_offsets * disp_param_sigma
disp_params = numpyro.sample(Site.disp_params,
dist.Delta(disp_params),
obs=disp_params)
with plate_features:
coef_mus = numpyro.sample(
Site.coef_mus,
dist.Normal(loc=jnp.zeros(n_features), scale=jnp.ones(n_features)))
coef_sigmas = numpyro.sample(
Site.coef_sigmas, dist.HalfNormal(scale=2. * jnp.ones(n_features)))
with plate_stores:
coef_offsets = numpyro.sample(
Site.coef_offsets,
dist.Normal(loc=jnp.zeros((n_stores, n_features)), scale=1.))
coefs = coef_mus + coef_offsets * coef_sigmas
coefs = numpyro.sample(Site.coefs, dist.Delta(coefs), obs=coefs)
with plate_days, plate_stores:
targets = X[..., -1]
features = jnp.nan_to_num(X[..., :-1]) # padded features to 0
is_observed = jnp.where(jnp.isnan(targets), jnp.zeros_like(targets),
jnp.ones_like(targets))
not_observed = 1 - is_observed
means = (is_observed * jnp.exp(
jnp.sum(jnp.expand_dims(coefs, axis=1) * features, axis=2)) +
not_observed * eps)
betas = is_observed * jnp.exp(-disp_params) + not_observed
alphas = means * betas
return numpyro.sample(Site.days,
dist.GammaPoisson(alphas, betas),
obs=jnp.nan_to_num(targets))
def test_gamma_poisson_log_prob(shape):
gamma_conc = onp.exp(onp.random.normal(size=shape))
gamma_rate = onp.exp(onp.random.normal(size=shape))
value = np.arange(15)
num_samples = 300000
poisson_rate = onp.random.gamma(gamma_conc, 1 / gamma_rate, size=(num_samples,) + shape)
log_probs = dist.Poisson(poisson_rate).log_prob(value)
expected = logsumexp(log_probs, 0) - np.log(num_samples)
actual = dist.GammaPoisson(gamma_conc, gamma_rate).log_prob(value)
assert_allclose(actual, expected, rtol=0.05)
def NB2(mu=None, k=None):
conc = 1. / k
rate = conc / mu
return dist.GammaPoisson(conc, rate)
def model(X: DeviceArray) -> DeviceArray:
"""Gamma-Poisson hierarchical model for daily sales forecasting
Args:
X: input data
Returns:
output data
"""
n_stores, n_days, n_features = X.shape
n_features -= 1 # remove one dim for target
eps = 1e-12 # epsilon
plate_features = numpyro.plate(Plate.features, n_features, dim=-1)
plate_stores = numpyro.plate(Plate.stores, n_stores, dim=-2)
plate_days = numpyro.plate(Plate.days, n_days, dim=-1)
disp_param_mu = numpyro.sample(Site.disp_param_mu,
dist.Normal(loc=4.0, scale=1.0))
disp_param_sigma = numpyro.sample(Site.disp_param_sigma,
dist.HalfNormal(scale=1.0))
with plate_stores:
with numpyro.handlers.reparam(
config={Site.disp_params: TransformReparam()}):
disp_params = numpyro.sample(
Site.disp_params,
dist.TransformedDistribution(
dist.Normal(loc=jnp.zeros((n_stores, 1)), scale=0.1),
dist.transforms.AffineTransform(disp_param_mu,
disp_param_sigma),
),
)
with plate_features:
coef_mus = numpyro.sample(
Site.coef_mus,
dist.Normal(loc=jnp.zeros(n_features), scale=jnp.ones(n_features)),
)
coef_sigmas = numpyro.sample(
Site.coef_sigmas,
dist.HalfNormal(scale=2.0 * jnp.ones(n_features)))
with plate_stores:
with numpyro.handlers.reparam(
config={Site.coefs: TransformReparam()}):
coefs = numpyro.sample(
Site.coefs,
dist.TransformedDistribution(
dist.Normal(loc=jnp.zeros((n_stores, n_features)),
scale=1.0),
dist.transforms.AffineTransform(coef_mus, coef_sigmas),
),
)
with plate_days, plate_stores:
targets = X[..., -1]
features = jnp.nan_to_num(X[..., :-1]) # padded features to 0
is_observed = jnp.where(jnp.isnan(targets), jnp.zeros_like(targets),
jnp.ones_like(targets))
not_observed = 1 - is_observed
means = (is_observed * jnp.exp(
jnp.sum(jnp.expand_dims(coefs, axis=1) * features, axis=2)) +
not_observed * eps)
betas = is_observed * jnp.exp(-disp_params) + not_observed
alphas = means * betas
return numpyro.sample(Site.days,
dist.GammaPoisson(alphas, betas),
obs=jnp.nan_to_num(targets))
def model(X: DeviceArray):
n_stores, n_days, n_features = X.shape
n_features -= 1 # remove one dim for target
plate_features = numpyro.plate(Plate.features, n_features, dim=-1)
plate_stores = numpyro.plate(Plate.stores, n_stores, dim=-2)
plate_days = numpyro.plate(Plate.days, n_days, dim=-1)
disp_param_mu = numpyro.sample(
Site.disp_param_mu,
dist.Normal(
loc=model_params[Param.loc_disp_param_mu],
scale=model_params[Param.scale_disp_param_mu],
),
)
disp_param_sigma = numpyro.sample(
Site.disp_param_sigma,
dist.TransformedDistribution(
dist.Normal(
loc=model_params[Param.loc_disp_param_logsigma],
scale=model_params[Param.scale_disp_param_logsigma],
),
transforms=dist.transforms.ExpTransform(),
),
)
with plate_stores:
with numpyro.handlers.reparam(
config={Site.disp_params: TransformReparam()}):
disp_params = numpyro.sample(
Site.disp_params,
dist.TransformedDistribution(
dist.Normal(
loc=model_params[Param.loc_disp_params],
scale=model_params[Param.scale_disp_params],
),
dist.transforms.AffineTransform(
disp_param_mu, disp_param_sigma),
),
)
with plate_features:
coef_mus = numpyro.sample(
Site.coef_mus,
dist.Normal(
loc=model_params[Param.loc_coef_mus],
scale=model_params[Param.scale_coef_mus],
),
)
coef_sigmas = numpyro.sample(
Site.coef_sigmas,
dist.TransformedDistribution(
dist.Normal(
loc=model_params[Param.loc_coef_logsigmas],
scale=model_params[Param.scale_coef_logsigmas],
),
transforms=dist.transforms.ExpTransform(),
),
)
with plate_stores:
with numpyro.handlers.reparam(
config={Site.coefs: TransformReparam()}):
coefs = numpyro.sample(
Site.coefs,
dist.TransformedDistribution(
dist.Normal(
loc=model_params[Param.loc_coefs],
scale=model_params[Param.scale_coefs],
),
dist.transforms.AffineTransform(
coef_mus, coef_sigmas),
),
)
with plate_days, plate_stores:
features = jnp.nan_to_num(X[..., :-1])
means = jnp.exp(
jnp.sum(jnp.expand_dims(coefs, axis=1) * features, axis=2))
betas = jnp.exp(-disp_params)
alphas = means * betas
return numpyro.sample(Site.days, dist.GammaPoisson(alphas, betas))
def model(X: DeviceArray):
n_stores, n_days, n_features = X.shape
n_features -= 1 # remove one dim for target
plate_features = numpyro.plate(Plate.features, n_features, dim=-1)
plate_stores = numpyro.plate(Plate.stores, n_stores, dim=-2)
plate_days = numpyro.plate(Plate.days, n_days, dim=-1)
disp_param_mu = numpyro.sample(
Site.disp_param_mu,
dist.Normal(loc=model_params[Param.loc_disp_param_mu],
scale=model_params[Param.scale_disp_param_mu]))
disp_param_sigma = numpyro.sample(
Site.disp_param_sigma,
dist.TransformedDistribution(
dist.Normal(
loc=model_params[Param.loc_disp_param_logsigma],
scale=model_params[Param.scale_disp_param_logsigma]),
transforms=dist.transforms.ExpTransform()))
with plate_stores:
disp_param_offsets = numpyro.sample(
Site.disp_param_offsets,
dist.Normal(
loc=model_params[Param.loc_disp_param_offsets],
scale=model_params[Param.scale_disp_param_offsets]),
)
disp_params = disp_param_mu + disp_param_offsets * disp_param_sigma
disp_params = numpyro.sample(Site.disp_params,
dist.Delta(disp_params),
obs=disp_params)
with plate_features:
coef_mus = numpyro.sample(
Site.coef_mus,
dist.Normal(loc=model_params[Param.loc_coef_mus],
scale=model_params[Param.scale_coef_mus]))
coef_sigmas = numpyro.sample(
Site.coef_sigmas,
dist.TransformedDistribution(
dist.Normal(
loc=model_params[Param.loc_coef_logsigmas],
scale=model_params[Param.scale_coef_logsigmas]),
transforms=dist.transforms.ExpTransform()))
with plate_stores:
coef_offsets = numpyro.sample(
Site.coef_offsets,
dist.Normal(loc=model_params[Param.loc_coef_offsets],
scale=model_params[Param.scale_coef_offsets]))
coefs = coef_mus + coef_offsets * coef_sigmas
coefs = numpyro.sample(Site.coefs,
dist.Delta(coefs),
obs=coefs)
with plate_days, plate_stores:
features = jnp.nan_to_num(X[..., :-1])
means = jnp.exp(
jnp.sum(jnp.expand_dims(coefs, axis=1) * features, axis=2))
betas = jnp.exp(-disp_params)
alphas = means * betas
return numpyro.sample(Site.days, dist.GammaPoisson(alphas, betas))
def forward(self, x_data, idx, obs2sample):
# obs2sample = batch_index # one_hot(batch_index, self.n_exper)
(obs_axis, ) = self.create_plates(x_data, idx, obs2sample)
# =====================Gene expression level scaling m_g======================= #
# Explains difference in sensitivity for each gene between single cell and spatial technology
m_g_alpha_hyp = pyro.sample(
"m_g_alpha_hyp",
dist.Gamma(self.m_g_shape * self.m_g_mean_var, self.m_g_mean_var),
)
m_g_beta_hyp = pyro.sample(
"m_g_beta_hyp",
dist.Gamma(self.m_g_rate * self.m_g_mean_var, self.m_g_mean_var),
)
m_g = pyro.sample(
"m_g",
dist.Gamma(m_g_alpha_hyp,
m_g_beta_hyp).expand([1, self.n_vars]).to_event(2))
# =====================Cell abundances w_sf======================= #
# factorisation prior on w_sf models similarity in locations
# across cell types f and reflects the absolute scale of w_sf
with obs_axis:
n_s_cells_per_location = pyro.sample(
"n_s_cells_per_location",
dist.Gamma(
self.N_cells_per_location * self.N_cells_mean_var_ratio,
self.N_cells_mean_var_ratio,
))
y_s_groups_per_location = pyro.sample(
"y_s_groups_per_location",
dist.Gamma(self.Y_groups_per_location, self.ones))
# cell group loadings
shape = self.ones_1_n_groups * y_s_groups_per_location / self.n_groups_tensor
rate = self.ones_1_n_groups / (n_s_cells_per_location /
y_s_groups_per_location)
with obs_axis:
z_sr_groups_factors = pyro.sample(
"z_sr_groups_factors",
dist.Gamma(
shape,
rate) # .to_event(1)#.expand([self.n_groups]).to_event(1)
) # (n_obs, n_groups)
k_r_factors_per_groups = pyro.sample(
"k_r_factors_per_groups",
dist.Gamma(self.factors_per_groups,
self.ones).expand([self.n_groups, 1
]).to_event(2)) # (self.n_groups, 1)
c2f_shape = k_r_factors_per_groups / self.n_factors_tensor
x_fr_group2fact = pyro.sample(
"x_fr_group2fact",
dist.Gamma(c2f_shape, k_r_factors_per_groups).expand([
self.n_groups, self.n_factors
]).to_event(2)) # (self.n_groups, self.n_factors)
with obs_axis:
w_sf_mu = z_sr_groups_factors @ x_fr_group2fact
w_sf = pyro.sample("w_sf",
dist.Gamma(
w_sf_mu * self.w_sf_mean_var_ratio_tensor,
self.w_sf_mean_var_ratio_tensor,
)) # (self.n_obs, self.n_factors)
# =====================Location-specific additive component======================= #
l_s_add_alpha = pyro.sample("l_s_add_alpha",
dist.Gamma(self.ones, self.ones))
l_s_add_beta = pyro.sample("l_s_add_beta",
dist.Gamma(self.ones, self.ones))
with obs_axis:
l_s_add = pyro.sample("l_s_add",
dist.Gamma(l_s_add_alpha,
l_s_add_beta)) # (self.n_obs, 1)
# =====================Gene-specific additive component ======================= #
# per gene molecule contribution that cannot be explained by
# cell state signatures (e.g. background, free-floating RNA)
s_g_gene_add_alpha_hyp = pyro.sample(
"s_g_gene_add_alpha_hyp",
dist.Gamma(self.gene_add_alpha_hyp_prior_alpha,
self.gene_add_alpha_hyp_prior_beta))
s_g_gene_add_mean = pyro.sample(
"s_g_gene_add_mean",
dist.Gamma(
self.gene_add_mean_hyp_prior_alpha,
self.gene_add_mean_hyp_prior_beta,
).expand([self.n_exper, 1]).to_event(2)) # (self.n_exper)
s_g_gene_add_alpha_e_inv = pyro.sample(
"s_g_gene_add_alpha_e_inv",
dist.Exponential(s_g_gene_add_alpha_hyp).expand(
[self.n_exper, 1]).to_event(2)) # (self.n_exper)
s_g_gene_add_alpha_e = self.ones / jnp.power(s_g_gene_add_alpha_e_inv,
2) # (self.n_exper)
s_g_gene_add = pyro.sample(
"s_g_gene_add",
dist.Gamma(s_g_gene_add_alpha_e,
s_g_gene_add_alpha_e / s_g_gene_add_mean).expand([
self.n_exper, self.n_vars
]).to_event(2)) # (self.n_exper, n_vars)
# =====================Gene-specific overdispersion ======================= #
alpha_g_phi_hyp = pyro.sample(
"alpha_g_phi_hyp",
dist.Gamma(self.alpha_g_phi_hyp_prior_alpha,
self.alpha_g_phi_hyp_prior_beta))
alpha_g_inverse = pyro.sample(
"alpha_g_inverse",
dist.Exponential(alpha_g_phi_hyp).expand(
[self.n_exper,
self.n_vars]).to_event(2)) # (self.n_exper, self.n_vars)
# =====================Expected expression ======================= #
# expected expression
mu = (w_sf @ self.cell_state) * m_g + (
obs2sample @ s_g_gene_add) + l_s_add
theta = obs2sample @ (self.ones / jnp.power(alpha_g_inverse, 2))
# =====================DATA likelihood ======================= #
# Likelihood (sampling distribution) of data_target & add overdispersion via NegativeBinomial
with obs_axis:
pyro.sample(
"data_target",
dist.GammaPoisson(concentration=theta, rate=theta / mu),
obs=x_data,
)
# =====================Compute mRNA count from each factor in locations ======================= #
mRNA = w_sf * (self.cell_state * m_g).sum(-1)
pyro.deterministic("u_sf_mRNA_factors", mRNA)
