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Changing AutoNormal -> AutoNormalMessenger #114

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Changing AutoNormal -> AutoNormalMessenger #114

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40c538f
Changing AutoNormal -> AutoNormalMessenger (potential memory use impr…
vitkl Jan 25, 2022
5b54a0f
All variables should not be 0-dimensional
vitkl Jan 25, 2022
cfaee32
All variables should not be 0-dimensional (file 2)
vitkl Jan 25, 2022
31e3af9
all variables should not be 0-dimensional (file 3)
vitkl Jan 25, 2022
7027862
All variables should not be 0-dimensional (file 4)
vitkl Jan 25, 2022
2e2346a
temporarily adding AutoNormalMessenger with quantiles from pyro https…
vitkl Jan 25, 2022
70b35c2
black formatting
vitkl Jan 25, 2022
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All variables should not be 0-dimensional (file 2)
@vitkl
vitkl authored Jan 25, 2022
commit cfaee329fbc5ad2fc68605c8d86353261e548885
13 changes: 6 additions & 7 deletions cell2location/models/simplified/_cell2location_v3_no_mg_module.py
View file
Original file line number Diff line number Diff line change
@@ -288,7 +288,9 @@ def forward(self, x_data, idx, batch_index):
# 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),
dist.Gamma(self.gene_add_alpha_hyp_prior_alpha, self.gene_add_alpha_hyp_prior_beta)
.expand([1, 1])
.to_event(2),
)
s_g_gene_add_mean = pyro.sample(
"s_g_gene_add_mean",
@@ -315,7 +317,9 @@ def forward(self, x_data, idx, batch_index):
# =====================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),
dist.Gamma(self.alpha_g_phi_hyp_prior_alpha, self.alpha_g_phi_hyp_prior_beta)
.expand([1, 1])
.to_event(2),
)
alpha_g_inverse = pyro.sample(
"alpha_g_inverse",
@@ -326,18 +330,13 @@ def forward(self, x_data, idx, batch_index):
# expected expression
mu = ((w_sf @ self.cell_state) + (obs2sample @ s_g_gene_add)) * detection_y_s
alpha = obs2sample @ (self.ones / alpha_g_inverse.pow(2))
# convert mean and overdispersion to total count and logits
# total_count, logits = _convert_mean_disp_to_counts_logits(
# mu, alpha, eps=self.eps
# )

# =====================DATA likelihood ======================= #
# Likelihood (sampling distribution) of data_target & add overdispersion via NegativeBinomial
with obs_plate:
pyro.sample(
"data_target",
dist.GammaPoisson(concentration=alpha, rate=alpha / mu),
# dist.NegativeBinomial(total_count=total_count, logits=logits),
obs=x_data,
)