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pymc_experimental/model/marginal/distributions.py

@@ -1,21 +1,18 @@
-from typing import Sequence
+from collections.abc import Sequence
 
 import numpy as np
 import pytensor.tensor as pt
-from pymc.distributions import (
-    Bernoulli,
-    Categorical,
-    DiscreteUniform,
-    SymbolicRandomVariable
-)
-from pymc.logprob.basic import conditional_logp, logp
+
+from pymc.distributions import Bernoulli, Categorical, DiscreteUniform, SymbolicRandomVariable
 from pymc.logprob.abstract import _logprob
+from pymc.logprob.basic import conditional_logp, logp
 from pymc.pytensorf import constant_fold
-from pytensor.graph.replace import clone_replace, graph_replace
-from pytensor.scan import scan, map as scan_map
 from pytensor.compile.mode import Mode
 from pytensor.graph import vectorize_graph
-from pytensor.tensor import TensorVariable, TensorType
+from pytensor.graph.replace import clone_replace, graph_replace
+from pytensor.scan import map as scan_map
+from pytensor.scan import scan
+from pytensor.tensor import TensorType, TensorVariable
 
 from pymc_experimental.distributions import DiscreteMarkovChain
 

pymc_experimental/model/marginal/graph_analysis.py

@@ -1,17 +1,17 @@
-from itertools import zip_longest, chain
-from typing import Sequence
+from collections.abc import Sequence
+from itertools import chain, zip_longest
 
 from pymc import SymbolicRandomVariable
 from pytensor.compile import SharedVariable
-from pytensor.graph import ancestors, Constant, graph_inputs, Variable
+from pytensor.graph import Constant, Variable, ancestors, graph_inputs
 from pytensor.graph.basic import io_toposort
-from pytensor.tensor import TensorVariable, TensorType
+from pytensor.tensor import TensorType, TensorVariable
 from pytensor.tensor.blockwise import Blockwise
-from pytensor.tensor.elemwise import DimShuffle, Elemwise, CAReduce
+from pytensor.tensor.elemwise import CAReduce, DimShuffle, Elemwise
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.rewriting.subtensor import is_full_slice
 from pytensor.tensor.shape import Shape
-from pytensor.tensor.subtensor import Subtensor, get_idx_list, AdvancedSubtensor
+from pytensor.tensor.subtensor import AdvancedSubtensor, Subtensor, get_idx_list
 from pytensor.tensor.type_other import NoneTypeT
 
 
@@ -58,7 +58,6 @@ def find_conditional_dependent_rvs(dependable_rv, all_rvs):
     ]
 
 
-
 def collect_shared_vars(outputs, blockers):
     return [
         inp for inp in graph_inputs(outputs, blockers=blockers) if isinstance(inp, SharedVariable)
@@ -86,18 +85,22 @@ def _advanced_indexing_axis_and_ndim(idxs) -> tuple[int, int]:
     return adv_group_axis, adv_group_ndim
 
 
-def _broadcast_dims(inputs_dims: Sequence[tuple[tuple[int, ...], ...]]) -> tuple[tuple[int, ...], ...]:
+def _broadcast_dims(
+    inputs_dims: Sequence[tuple[tuple[int, ...], ...]],
+) -> tuple[tuple[int, ...], ...]:
     output_ndim = max((len(input_dim) for input_dim in inputs_dims), default=0)
     # Add missing dims
-    inputs_dims = [
-        ((),) * (output_ndim - len(input_dim)) + input_dim for input_dim in inputs_dims
-    ]
+    inputs_dims = [((),) * (output_ndim - len(input_dim)) + input_dim for input_dim in inputs_dims]
     # Combine aligned dims
-    output_dims = tuple(tuple(sorted(set(chain.from_iterable(inputs_dim)))) for inputs_dim in zip(*inputs_dims))
+    output_dims = tuple(
+        tuple(sorted(set(chain.from_iterable(inputs_dim)))) for inputs_dim in zip(*inputs_dims)
+    )
     return output_dims
 
 
-def subgraph_dim_connection(input_var, other_inputs, output_vars) -> list[tuple[tuple[int, ...], ...]]:
+def subgraph_dim_connection(
+    input_var, other_inputs, output_vars
+) -> list[tuple[tuple[int, ...], ...]]:
     """Identify how the dims of rv_to_marginalize are consumed by the dims of the output_rvs.
 
     Raises
@@ -135,13 +138,16 @@ def subgraph_dim_connection(input_var, other_inputs, output_vars) -> list[tuple[
                 op_batch_ndim = node.op.batch_ndim(node)
 
             # Collapse all core_dims
-            core_dims = tuple(sorted(chain.from_iterable([i for input_dim in inputs_dims for i in input_dim[op_batch_ndim:]])))
-            batch_dims = _broadcast_dims(
-                tuple(
-                    input_dims[:op_batch_ndim]
-                    for input_dims in inputs_dims
+            core_dims = tuple(
+                sorted(
+                    chain.from_iterable(
+                        [i for input_dim in inputs_dims for i in input_dim[op_batch_ndim:]]
+                    )
                 )
             )
+            batch_dims = _broadcast_dims(
+                tuple(input_dims[:op_batch_ndim] for input_dims in inputs_dims)
+            )
             # Add batch dims to each output_dims
             batch_dims = tuple(batch_dim + core_dims for batch_dim in batch_dims)
             for out in node.outputs:
@@ -221,7 +227,7 @@ def subgraph_dim_connection(input_var, other_inputs, output_vars) -> list[tuple[
                     elif value_dim:
                         # We are trying to partially slice or index a known dimension
                         raise NotImplementedError(
-                            f"Partial slicing or advanced integer indexing of known dimensions not supported"
+                            "Partial slicing or advanced integer indexing of known dimensions not supported"
                         )
                     elif isinstance(idx, slice):
                         # Unknown dimensions kept by partial slice.

pymc_experimental/model/marginal/marginal_model.py

@@ -23,10 +23,19 @@
 __all__ = ["MarginalModel", "marginalize"]
 
 from pymc_experimental.distributions import DiscreteMarkovChain
-from pymc_experimental.model.marginal.distributions import FiniteDiscreteMarginalRV, DiscreteMarginalMarkovChainRV, \
-    get_domain_of_finite_discrete_rv, _add_reduce_batch_dependent_logps
-from pymc_experimental.model.marginal.graph_analysis import find_conditional_input_rvs, is_conditional_dependent, \
-    find_conditional_dependent_rvs, subgraph_dim_connection, collect_shared_vars
+from pymc_experimental.model.marginal.distributions import (
+    DiscreteMarginalMarkovChainRV,
+    FiniteDiscreteMarginalRV,
+    _add_reduce_batch_dependent_logps,
+    get_domain_of_finite_discrete_rv,
+)
+from pymc_experimental.model.marginal.graph_analysis import (
+    collect_shared_vars,
+    find_conditional_dependent_rvs,
+    find_conditional_input_rvs,
+    is_conditional_dependent,
+    subgraph_dim_connection,
+)
 
 ModelRVs = TensorVariable | Sequence[TensorVariable] | str | Sequence[str]
 
@@ -613,4 +622,3 @@ def replace_finite_discrete_marginal_subgraph(fgraph, rv_to_marginalize, all_rvs
     marginalized_rvs = marginalization_op(*inputs)
     fgraph.replace_all(tuple(zip(rvs_to_marginalize, marginalized_rvs)))
     return rvs_to_marginalize, marginalized_rvs
-

tests/model/marginal/test_distributions.py

@@ -1,13 +1,13 @@
 import numpy as np
 import pymc as pm
 import pytest
+
 from pymc.logprob.abstract import _logprob
 from pytensor import tensor as pt
 from scipy.stats import norm
 
 from pymc_experimental import MarginalModel
 from pymc_experimental.distributions import DiscreteMarkovChain
-
 from pymc_experimental.model.marginal.distributions import FiniteDiscreteMarginalRV
 
 

tests/model/marginal/test_graph_analysis.py

@@ -1,12 +1,12 @@
 import pytensor.tensor as pt
 import pytest
+
 from pymc.distributions import CustomDist
 
 from pymc_experimental.model.marginal.graph_analysis import subgraph_dim_connection
 
 
 class TestSubgraphDimConnection:
-
     def test_dimshuffle(self):
         inp = pt.zeros(shape=(5, 1, 4, 3))
         out1 = pt.matrix_transpose(inp)
@@ -31,11 +31,16 @@ def test_subtensor(self):
         assert dims == ((1,),)
 
         invalid_out = inp[0, :1]
-        with pytest.raises(NotImplementedError, match="Partial slicing of known dimensions not supported"):
+        with pytest.raises(
+            NotImplementedError, match="Partial slicing of known dimensions not supported"
+        ):
             subgraph_dim_connection(inp, [], [invalid_out])
 
         # If we are slicing a dummy / unknown dimension that's fine
-        valid_out = pt.expand_dims(inp[:, 0], 1)[0, :1,]
+        valid_out = pt.expand_dims(inp[:, 0], 1)[
+            0,
+            :1,
+        ]
         [dims] = subgraph_dim_connection(inp, [], [valid_out])
         assert dims == ((), (2,))
 
@@ -53,11 +58,23 @@ def test_elemwise(self):
         # By removing the last dimension, we align the first and the last in the addition
         out = inp + inp[:, 0]
         [dims] = subgraph_dim_connection(inp, [], [out])
-        assert dims == ((0,), (0, 1,))
+        assert dims == (
+            (0,),
+            (
+                0,
+                1,
+            ),
+        )
 
         out = inp + inp.T
         [dims] = subgraph_dim_connection(inp, [], [out])
-        assert dims == ((0, 1), (0, 1,))
+        assert dims == (
+            (0, 1),
+            (
+                0,
+                1,
+            ),
+        )
 
     def test_blockwise(self):
         inp = pt.zeros(shape=(5, 4, 3, 2))

tests/model/marginal/test_marginal_model.py

@@ -17,11 +17,11 @@
 from scipy.special import log_softmax, logsumexp
 from scipy.stats import halfnorm, norm
 
+from pymc_experimental.model.marginal.graph_analysis import is_conditional_dependent
 from pymc_experimental.model.marginal.marginal_model import (
     MarginalModel,
     marginalize,
 )
-from pymc_experimental.model.marginal.graph_analysis import is_conditional_dependent
 from tests.utils import equal_computations_up_to_root