Added networkx export functionality (#6046)

* Added networkx export functionality

Apologies for delay but finally got around to updating the latest version of model_graph.py to include networkx export function
as discussed in #5677

* Update model_graph.py

restoring whitespace #5677 (comment)

* Corrected cluster(subgraph) behaviour

subgraph(cluster) attributes are now stored on each node that is a member of that subgraph(cluster).

* Update pymc/model_graph.py

Co-authored-by: Oriol Abril-Pla <[email protected]>

* Correcting docstring spacing

* Updated to include new function model_to_networkx

* linted and checked by pre-commit

* added test for model_to_networkx function

* added function import

* added model_to_networkx

* corrected formatting

* more linting

* Update test_model_graph.py

* Update __init__.py

* Redid changes

* added function to models.rst

* corrected indent issue

indent issue cause by merge resolution

* corrected spelling error

* added networkx to requirements

* redid pre-commit and ran it twice

* Update pymc/model_graph.py

Co-authored-by: Oriol Abril-Pla <[email protected]>

* Update pymc/model_graph.py

Co-authored-by: Oriol Abril-Pla <[email protected]>

* Update pymc/model_graph.py

Co-authored-by: Oriol Abril-Pla <[email protected]>

* Update pymc/model_graph.py

Co-authored-by: Oriol Abril-Pla <[email protected]>

* Update pymc/model_graph.py

Co-authored-by: Oriol Abril-Pla <[email protected]>

* Removed trailing whitespace

Removed trailing whitespace from lines 312, 314,

* removing more trailing whitespace

Co-authored-by: Oriol Abril-Pla <[email protected]>
Co-authored-by: Joni Pelham <[email protected]>

Author: 3 people

conda-envs/environment-dev.yml

@@ -17,6 +17,7 @@ dependencies:
 - pandas>=0.24.0
 - pip
 - python-graphviz
+- networkx
 - scipy>=1.4.1
 - typing-extensions>=3.7.4
 # Extra dependencies for dev, testing and docs build

conda-envs/environment-test.yml

@@ -19,6 +19,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - python-graphviz
+- networkx
 - scipy>=1.4.1
 - typing-extensions>=3.7.4
 # Extra dependencies for testing

conda-envs/windows-environment-dev.yml

@@ -17,6 +17,7 @@ dependencies:
 - pandas>=0.24.0
 - pip
 - python-graphviz
+- networkx
 - scipy>=1.4.1
 - typing-extensions>=3.7.4
 # Extra dependencies for dev, testing and docs build

conda-envs/windows-environment-test.yml

@@ -20,6 +20,7 @@ dependencies:
 - pandas>=0.24.0
 - pip
 - python-graphviz
+- networkx
 - scipy>=1.4.1
 - typing-extensions>=3.7.4
 # Extra dependencies for testing

docs/source/api/model.rst

@@ -10,6 +10,7 @@ Model creation and inspection
 
    Model
    model_to_graphviz
+   model_to_networkx
    modelcontext
 
 Others

pymc/__init__.py

@@ -65,7 +65,7 @@ def __set_compiler_flags():
     probit,
 )
 from pymc.model import *
-from pymc.model_graph import model_to_graphviz
+from pymc.model_graph import model_to_graphviz, model_to_networkx
 from pymc.plots import *
 from pymc.printing import *
 from pymc.sampling import *

pymc/model_graph.py

@@ -125,8 +125,8 @@ def make_compute_graph(
 
         return input_map
 
-    def _make_node(self, var_name, graph, *, formatting: str = "plain"):
-        """Attaches the given variable to a graphviz Digraph"""
+    def _make_node(self, var_name, graph, *, nx=False, cluster=False, formatting: str = "plain"):
+        """Attaches the given variable to a graphviz or networkx Digraph"""
         v = self.model[var_name]
 
         shape = None
@@ -168,7 +168,13 @@ def _make_node(self, var_name, graph, *, formatting: str = "plain"):
             "label": label,
         }
 
-        graph.node(var_name.replace(":", "&"), **kwargs)
+        if cluster:
+            kwargs["cluster"] = cluster
+
+        if nx:
+            graph.add_node(var_name.replace(":", "&"), **kwargs)
+        else:
+            graph.node(var_name.replace(":", "&"), **kwargs)
 
     def _eval(self, var):
         return function([], var, mode="FAST_COMPILE")()
@@ -178,7 +184,6 @@ def get_plates(self, var_names: Optional[Iterable[VarName]] = None) -> Dict[str,
 
         Just groups by the shape of the underlying distribution.  Will be wrong
         if there are two plates with the same shape.
-
         Returns
         -------
         dict
@@ -234,9 +239,134 @@ def make_graph(self, var_names: Optional[Iterable[VarName]] = None, formatting:
 
         return graph
 
+    def make_networkx(
+        self, var_names: Optional[Iterable[VarName]] = None, formatting: str = "plain"
+    ):
+        """Make networkx Digraph of PyMC model
+
+        Returns
+        -------
+        networkx.Digraph
+        """
+        try:
+            import networkx
+        except ImportError:
+            raise ImportError(
+                "This function requires the python library networkx, along with binaries. "
+                "The easiest way to install all of this is by running\n\n"
+                "\tconda install networkx"
+            )
+        graphnetwork = networkx.DiGraph(name=self.model.name)
+        for plate_label, all_var_names in self.get_plates(var_names).items():
+            if plate_label:
+                # # must be preceded by 'cluster' to get a box around it
+
+                subgraphnetwork = networkx.DiGraph(name="cluster" + plate_label, label=plate_label)
+
+                for var_name in all_var_names:
+                    self._make_node(
+                        var_name,
+                        subgraphnetwork,
+                        nx=True,
+                        cluster="cluster" + plate_label,
+                        formatting=formatting,
+                    )
+                for sgn in subgraphnetwork.nodes:
+                    networkx.set_node_attributes(
+                        subgraphnetwork,
+                        {sgn: {"labeljust": "r", "labelloc": "b", "style": "rounded"}},
+                    )
+                node_data = {
+                    e[0]: e[1]
+                    for e in graphnetwork.nodes(data=True) & subgraphnetwork.nodes(data=True)
+                }
+
+                graphnetwork = networkx.compose(graphnetwork, subgraphnetwork)
+                networkx.set_node_attributes(graphnetwork, node_data)
+                graphnetwork.graph["name"] = self.model.name
+            else:
+                for var_name in all_var_names:
+
+                    self._make_node(var_name, graphnetwork, nx=True, formatting=formatting)
+
+        for child, parents in self.make_compute_graph(var_names=var_names).items():
+            # parents is a set of rv names that preceed child rv nodes
+            for parent in parents:
+                graphnetwork.add_edge(parent.replace(":", "&"), child.replace(":", "&"))
+        return graphnetwork
+
+
+def model_to_networkx(
+    model=None,
+    *,
+    var_names: Optional[Iterable[VarName]] = None,
+    formatting: str = "plain",
+):
+    """Produce a networkx Digraph from a PyMC model.
+
+    Requires networkx, which may be installed most easily with::
+
+        conda install networkx
+
+    Alternatively, you may install using pip with::
+
+        pip install networkx
+
+    See https://networkx.org/documentation/stable/ for more information.
+
+    Parameters
+    ----------
+    model : Model
+        The model to plot. Not required when called from inside a modelcontext.
+    var_names : iterable of str, optional
+        Subset of variables to be plotted that identify a subgraph with respect to the entire model graph
+    formatting : str, optional
+        one of { "plain" }
+
+    Examples
+    --------
+    How to plot the graph of the model.
+
+    .. code-block:: python
+
+        import numpy as np
+        from pymc import HalfCauchy, Model, Normal, model_to_networkx
+
+        J = 8
+        y = np.array([28, 8, -3, 7, -1, 1, 18, 12])
+        sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])
+
+        with Model() as schools:
+
+            eta = Normal("eta", 0, 1, shape=J)
+            mu = Normal("mu", 0, sigma=1e6)
+            tau = HalfCauchy("tau", 25)
+
+            theta = mu + tau * eta
+
+            obs = Normal("obs", theta, sigma=sigma, observed=y)
+
+        model_to_networkx(schools)
+    """
+    if not "plain" in formatting:
+
+        raise ValueError(f"Unsupported formatting for graph nodes: '{formatting}'. See docstring.")
+
+    if formatting != "plain":
+        warnings.warn(
+            "Formattings other than 'plain' are currently not supported.",
+            UserWarning,
+            stacklevel=2,
+        )
+    model = pm.modelcontext(model)
+    return ModelGraph(model).make_networkx(var_names=var_names, formatting=formatting)
+
 
 def model_to_graphviz(
-    model=None, *, var_names: Optional[Iterable[VarName]] = None, formatting: str = "plain"
+    model=None,
+    *,
+    var_names: Optional[Iterable[VarName]] = None,
+    formatting: str = "plain",
 ):
     """Produce a graphviz Digraph from a PyMC model.
 
@@ -286,7 +416,9 @@ def model_to_graphviz(
         raise ValueError(f"Unsupported formatting for graph nodes: '{formatting}'. See docstring.")
     if formatting != "plain":
         warnings.warn(
-            "Formattings other than 'plain' are currently not supported.", UserWarning, stacklevel=2
+            "Formattings other than 'plain' are currently not supported.",
+            UserWarning,
+            stacklevel=2,
         )
     model = pm.modelcontext(model)
     return ModelGraph(model).make_graph(var_names=var_names, formatting=formatting)

pymc/tests/test_model_graph.py

@@ -20,10 +20,64 @@
 
 import pymc as pm
 
-from pymc.model_graph import ModelGraph, model_to_graphviz
+from pymc.model_graph import ModelGraph, model_to_graphviz, model_to_networkx
 from pymc.tests.helpers import SeededTest
 
 
+def school_model():
+    """
+    Schools model to use in testing model_to_networkx function
+    """
+    J = 8
+    y = np.array([28, 8, -3, 7, -1, 1, 18, 12])
+    sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])
+    with pm.Model() as schools:
+        eta = pm.Normal("eta", 0, 1, shape=J)
+        mu = pm.Normal("mu", 0, sigma=1e6)
+        tau = pm.HalfCauchy("tau", 25)
+        theta = mu + tau * eta
+        obs = pm.Normal("obs", theta, sigma=sigma, observed=y)
+    return schools
+
+
+class BaseModelNXTest(SeededTest):
+    network_model = {
+        "graph_attr_dict_factory": dict,
+        "node_dict_factory": dict,
+        "node_attr_dict_factory": dict,
+        "adjlist_outer_dict_factory": dict,
+        "adjlist_inner_dict_factory": dict,
+        "edge_attr_dict_factory": dict,
+        "graph": {"name": "", "label": "8"},
+        "_node": {
+            "eta": {
+                "shape": "ellipse",
+                "style": "rounded",
+                "label": "eta\n~\nNormal",
+                "cluster": "cluster8",
+                "labeljust": "r",
+                "labelloc": "b",
+            },
+            "obs": {
+                "shape": "ellipse",
+                "style": "rounded",
+                "label": "obs\n~\nNormal",
+                "cluster": "cluster8",
+                "labeljust": "r",
+                "labelloc": "b",
+            },
+            "tau": {"shape": "ellipse", "style": None, "label": "tau\n~\nHalfCauchy"},
+            "mu": {"shape": "ellipse", "style": None, "label": "mu\n~\nNormal"},
+        },
+        "_adj": {"eta": {"obs": {}}, "obs": {}, "tau": {"obs": {}}, "mu": {"obs": {}}},
+        "_pred": {"eta": {}, "obs": {"tau": {}, "eta": {}, "mu": {}}, "tau": {}, "mu": {}},
+        "_succ": {"eta": {"obs": {}}, "obs": {}, "tau": {"obs": {}}, "mu": {"obs": {}}},
+    }
+
+    def test_networkx(self):
+        assert self.network_model == model_to_networkx(school_model()).__dict__
+
+
 def radon_model():
     """Similar in shape to the Radon model"""
     n_homes = 919

scripts/generate_pip_deps_from_conda.py

@@ -51,6 +51,7 @@
     "mkl-service",
     "numba",
     "python-graphviz",
+    "networkx",
     "blas",
     "jax",
 }