Created
December 7, 2021 03:59
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import pyro | |
from pyro.infer import SVI, Trace_ELBO, TraceEnum_ELBO, config_enumerate | |
from torch.distributions import constraints | |
data = 2. * torch.Tensor( [-1., -0.5, -0.5, .5, .8, 1.] ) | |
def model(data): | |
guide_efficacy = pyro.sample('guide_efficacy', dist.Beta(1., 1.).expand([len(data)]).to_event(1) ) | |
gene_essentiality = pyro.sample("gene_essentiality", dist.Normal(0., 5.)) | |
mean = gene_essentiality * guide_efficacy | |
with pyro.plate("data", len(data)): | |
obs = pyro.sample("obs", dist.Normal(mean, 1.), obs = data) | |
def guide(data): | |
prob = pyro.param("prob", torch.tensor(0.5), constraint=constraints.unit_interval) | |
z = pyro.sample('assignment', dist.Bernoulli(prob)).long() | |
ge_mean = pyro.param("ge_mean", torch.ones(2)) | |
ge_scale = pyro.param("ge_scale", torch.ones(2), constraint=constraints.positive) | |
gene_essentiality = pyro.sample("gene_essentiality", dist.Normal(ge_mean[z], ge_scale[z])) | |
guide_efficacy_a = pyro.param('guide_efficacy_a', torch.ones([2,len(data)]), constraint=constraints.positive) | |
guide_efficacy_b = pyro.param('guide_efficacy_b', torch.ones([2,len(data)]), constraint=constraints.positive) | |
guide_efficacy = pyro.sample("guide_efficacy", dist.Beta(guide_efficacy_a[z,:], guide_efficacy_b[z,:])) | |
return assignment, gene_essentiality, guide_efficacy | |
TraceEnum_ELBO().loss(model, config_enumerate(guide, "parallel"), data) |
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