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Update to pymc 5.5.0 #191

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Merged
merged 4 commits into from
Jun 10, 2023
Merged

Update to pymc 5.5.0 #191

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19d4014
removing not existing dependency
michaelraczycki Jun 10, 2023
477bc6a
removing get_measurable_outputs import
michaelraczycki Jun 10, 2023
9907b39
bumping pymc version to 5.5.0
michaelraczycki Jun 10, 2023
92e08f0
changing factorized_joint_logp to conditional_logp
michaelraczycki Jun 10, 2023
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4 changes: 0 additions & 4 deletions pymc_experimental/distributions/timeseries.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,6 @@
)
from pymc.logprob.abstract import _logprob
from pymc.logprob.basic import logp
from pymc.logprob.utils import ignore_logprob
from pymc.pytensorf import intX
from pymc.util import check_dist_not_registered
from pytensor.graph.basic import Node
Expand Down Expand Up @@ -166,9 +165,6 @@ def dist(cls, P=None, logit_P=None, steps=None, init_dist=None, n_lags=1, **kwar
k = P.shape[-1]
init_dist = pm.Categorical.dist(p=pt.full((k,), 1 / k))

# We can ignore init_dist, as it will be accounted for in the logp term
init_dist = ignore_logprob(init_dist)

return super().dist([P, steps, init_dist], n_lags=n_lags, **kwargs)

@classmethod
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12 changes: 3 additions & 9 deletions pymc_experimental/marginal_model.py
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Expand Up @@ -6,8 +6,8 @@
from pymc import SymbolicRandomVariable
from pymc.distributions.discrete import Bernoulli, Categorical, DiscreteUniform
from pymc.distributions.transforms import Chain
from pymc.logprob.abstract import _get_measurable_outputs, _logprob
from pymc.logprob.basic import factorized_joint_logprob
from pymc.logprob.abstract import _logprob
from pymc.logprob.basic import conditional_logp
from pymc.logprob.transforms import IntervalTransform
from pymc.model import Model
from pymc.pytensorf import constant_fold, inputvars
Expand Down Expand Up @@ -371,12 +371,6 @@ def replace_finite_discrete_marginal_subgraph(fgraph, rv_to_marginalize, all_rvs
return rvs_to_marginalize, marginalized_rvs


@_get_measurable_outputs.register(FiniteDiscreteMarginalRV)
def _get_measurable_outputs_finite_discrete_marginal_rv(op, node):
# Marginalized RVs are not measurable
return node.outputs[1:]


def get_domain_of_finite_discrete_rv(rv: TensorVariable) -> Tuple[int, ...]:
op = rv.owner.op
if isinstance(op, Bernoulli):
Expand All @@ -403,7 +397,7 @@ def finite_discrete_marginal_rv_logp(op, values, *inputs, **kwargs):

# Obtain the joint_logp graph of the inner RV graph
inner_rvs_to_values = {rv: rv.clone() for rv in inner_rvs}
logps_dict = factorized_joint_logprob(rv_values=inner_rvs_to_values, **kwargs)
logps_dict = conditional_logp(rv_values=inner_rvs_to_values, **kwargs)

# Reduce logp dimensions corresponding to broadcasted variables
joint_logp = logps_dict[inner_rvs_to_values[marginalized_rv]]
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2 changes: 1 addition & 1 deletion requirements.txt
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@@ -1,2 +1,2 @@
pymc>=5.4.1
pymc>=5.5.0
scikit-learn