Allow customizing style of model_graph nodes (#7302)

Co-authored-by: Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>

Author: williambdean

pymc/model_graph.py

@@ -14,8 +14,10 @@
 import warnings
 
 from collections import defaultdict
-from collections.abc import Iterable, Sequence
+from collections.abc import Callable, Iterable, Sequence
+from enum import Enum
 from os import path
+from typing import Any
 
 from pytensor import function
 from pytensor.graph import Apply
@@ -41,6 +43,119 @@ def fast_eval(var):
     return function([], var, mode="FAST_COMPILE")()
 
 
+class NodeType(str, Enum):
+    """Enum for the types of nodes in the graph."""
+
+    POTENTIAL = "Potential"
+    FREE_RV = "Free Random Variable"
+    OBSERVED_RV = "Observed Random Variable"
+    DETERMINISTIC = "Deterministic"
+    DATA = "Data"
+
+
+GraphvizNodeKwargs = dict[str, Any]
+NodeFormatter = Callable[[TensorVariable], GraphvizNodeKwargs]
+
+
+def default_potential(var: TensorVariable) -> GraphvizNodeKwargs:
+    """Default data for potential in the graph."""
+    return {
+        "shape": "octagon",
+        "style": "filled",
+        "label": f"{var.name}\n~\nPotential",
+    }
+
+
+def random_variable_symbol(var: TensorVariable) -> str:
+    """Get the symbol of the random variable."""
+    symbol = var.owner.op.__class__.__name__
+
+    if symbol.endswith("RV"):
+        symbol = symbol[:-2]
+
+    return symbol
+
+
+def default_free_rv(var: TensorVariable) -> GraphvizNodeKwargs:
+    """Default data for free RV in the graph."""
+    symbol = random_variable_symbol(var)
+
+    return {
+        "shape": "ellipse",
+        "style": None,
+        "label": f"{var.name}\n~\n{symbol}",
+    }
+
+
+def default_observed_rv(var: TensorVariable) -> GraphvizNodeKwargs:
+    """Default data for observed RV in the graph."""
+    symbol = random_variable_symbol(var)
+
+    return {
+        "shape": "ellipse",
+        "style": "filled",
+        "label": f"{var.name}\n~\n{symbol}",
+    }
+
+
+def default_deterministic(var: TensorVariable) -> GraphvizNodeKwargs:
+    """Default data for the deterministic in the graph."""
+    return {
+        "shape": "box",
+        "style": None,
+        "label": f"{var.name}\n~\nDeterministic",
+    }
+
+
+def default_data(var: TensorVariable) -> GraphvizNodeKwargs:
+    """Default data for the data in the graph."""
+    return {
+        "shape": "box",
+        "style": "rounded, filled",
+        "label": f"{var.name}\n~\nData",
+    }
+
+
+def get_node_type(var_name: VarName, model) -> NodeType:
+    """Return the node type of the variable in the model."""
+    v = model[var_name]
+
+    if v in model.deterministics:
+        return NodeType.DETERMINISTIC
+    elif v in model.free_RVs:
+        return NodeType.FREE_RV
+    elif v in model.observed_RVs:
+        return NodeType.OBSERVED_RV
+    elif v in model.data_vars:
+        return NodeType.DATA
+    else:
+        return NodeType.POTENTIAL
+
+
+NodeTypeFormatterMapping = dict[NodeType, NodeFormatter]
+
+DEFAULT_NODE_FORMATTERS: NodeTypeFormatterMapping = {
+    NodeType.POTENTIAL: default_potential,
+    NodeType.FREE_RV: default_free_rv,
+    NodeType.OBSERVED_RV: default_observed_rv,
+    NodeType.DETERMINISTIC: default_deterministic,
+    NodeType.DATA: default_data,
+}
+
+
+def update_node_formatters(node_formatters: NodeTypeFormatterMapping) -> NodeTypeFormatterMapping:
+    node_formatters = {**DEFAULT_NODE_FORMATTERS, **node_formatters}
+
+    unknown_keys = set(node_formatters.keys()) - set(NodeType)
+    if unknown_keys:
+        raise ValueError(
+            f"Node formatters must be of type NodeType. Found: {list(unknown_keys)}."
+            f" Please use one of {[node_type.value for node_type in NodeType]}."
+        )
+
+    return node_formatters
+
+
 class ModelGraph:
     def __init__(self, model):
         self.model = model
@@ -148,42 +263,23 @@ def make_compute_graph(
 
         return input_map
 
-    def _make_node(self, var_name, graph, *, nx=False, cluster=False, formatting: str = "plain"):
+    def _make_node(
+        self,
+        var_name,
+        graph,
+        *,
+        node_formatters: NodeTypeFormatterMapping,
+        nx=False,
+        cluster=False,
+        formatting: str = "plain",
+    ):
         """Attaches the given variable to a graphviz or networkx Digraph"""
         v = self.model[var_name]
 
-        shape = None
-        style = None
-        label = str(v)
-
-        if v in self.model.potentials:
-            shape = "octagon"
-            style = "filled"
-            label = f"{var_name}\n~\nPotential"
-        elif v in self.model.basic_RVs:
-            shape = "ellipse"
-            if v in self.model.observed_RVs:
-                style = "filled"
-            else:
-                style = None
-            symbol = v.owner.op.__class__.__name__
-            if symbol.endswith("RV"):
-                symbol = symbol[:-2]
-            label = f"{var_name}\n~\n{symbol}"
-        elif v in self.model.deterministics:
-            shape = "box"
-            style = None
-            label = f"{var_name}\n~\nDeterministic"
-        else:
-            shape = "box"
-            style = "rounded, filled"
-            label = f"{var_name}\n~\nData"
-
-        kwargs = {
-            "shape": shape,
-            "style": style,
-            "label": label,
-        }
+        node_type = get_node_type(var_name, self.model)
+        node_formatter = node_formatters[node_type]
+
+        kwargs = node_formatter(v)
 
         if cluster:
             kwargs["cluster"] = cluster
@@ -240,6 +336,7 @@ def make_graph(
         save=None,
         figsize=None,
         dpi=300,
+        node_formatters: NodeTypeFormatterMapping | None = None,
     ):
         """Make graphviz Digraph of PyMC model
 
@@ -255,18 +352,26 @@ def make_graph(
                 "The easiest way to install all of this is by running\n\n"
                 "\tconda install -c conda-forge python-graphviz"
             )
+
+        node_formatters = node_formatters or {}
+        node_formatters = update_node_formatters(node_formatters)
+
         graph = graphviz.Digraph(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
                 with graph.subgraph(name="cluster" + plate_label) as sub:
                     for var_name in all_var_names:
-                        self._make_node(var_name, sub, formatting=formatting)
+                        self._make_node(
+                            var_name, sub, formatting=formatting, node_formatters=node_formatters
+                        )
                     # plate label goes bottom right
                     sub.attr(label=plate_label, labeljust="r", labelloc="b", style="rounded")
             else:
                 for var_name in all_var_names:
-                    self._make_node(var_name, graph, formatting=formatting)
+                    self._make_node(
+                        var_name, graph, formatting=formatting, node_formatters=node_formatters
+                    )
 
         for child, parents in self.make_compute_graph(var_names=var_names).items():
             # parents is a set of rv names that precede child rv nodes
@@ -287,7 +392,12 @@ def make_graph(
 
         return graph
 
-    def make_networkx(self, var_names: Iterable[VarName] | None = None, formatting: str = "plain"):
+    def make_networkx(
+        self,
+        var_names: Iterable[VarName] | None = None,
+        formatting: str = "plain",
+        node_formatters: NodeTypeFormatterMapping | None = None,
+    ):
         """Make networkx Digraph of PyMC model
 
         Returns
@@ -302,6 +412,10 @@ def make_networkx(self, var_names: Iterable[VarName] | None = None, formatting:
                 "The easiest way to install all of this is by running\n\n"
                 "\tconda install networkx"
             )
+
+        node_formatters = node_formatters or {}
+        node_formatters = update_node_formatters(node_formatters)
+
         graphnetwork = networkx.DiGraph(name=self.model.name)
         for plate_label, all_var_names in self.get_plates(var_names).items():
             if plate_label:
@@ -314,6 +428,7 @@ def make_networkx(self, var_names: Iterable[VarName] | None = None, formatting:
                         var_name,
                         subgraphnetwork,
                         nx=True,
+                        node_formatters=node_formatters,
                         cluster="cluster" + plate_label,
                         formatting=formatting,
                     )
@@ -332,7 +447,13 @@ def make_networkx(self, var_names: Iterable[VarName] | None = None, formatting:
                 graphnetwork.graph["name"] = self.model.name
             else:
                 for var_name in all_var_names:
-                    self._make_node(var_name, graphnetwork, nx=True, formatting=formatting)
+                    self._make_node(
+                        var_name,
+                        graphnetwork,
+                        nx=True,
+                        formatting=formatting,
+                        node_formatters=node_formatters,
+                    )
 
         for child, parents in self.make_compute_graph(var_names=var_names).items():
             # parents is a set of rv names that precede child rv nodes
@@ -346,6 +467,7 @@ def model_to_networkx(
     *,
     var_names: Iterable[VarName] | None = None,
     formatting: str = "plain",
+    node_formatters: NodeTypeFormatterMapping | None = None,
 ):
     """Produce a networkx Digraph from a PyMC model.
 
@@ -367,6 +489,10 @@ def model_to_networkx(
         Subset of variables to be plotted that identify a subgraph with respect to the entire model graph
     formatting : str, optional
         one of { "plain" }
+    node_formatters : dict, optional
+        A dictionary mapping node types to functions that return a dictionary of node attributes.
+        Check out the networkx documentation for more information
+        how attributes are added to nodes: https://networkx.org/documentation/stable/reference/classes/generated/networkx.Graph.add_node.html
 
     Examples
     --------
@@ -392,6 +518,17 @@ def model_to_networkx(
             obs = Normal("obs", theta, sigma=sigma, observed=y)
 
         model_to_networkx(schools)
+
+    Add custom attributes to Free Random Variables and Observed Random Variables nodes.
+
+    .. code-block:: python
+
+        node_formatters = {
+            "Free Random Variable": lambda var: {"shape": "circle", "label": var.name},
+            "Observed Random Variable": lambda var: {"shape": "square", "label": var.name},
+        }
+        model_to_networkx(schools, node_formatters=node_formatters)
+
     """
     if "plain" not in formatting:
         raise ValueError(f"Unsupported formatting for graph nodes: '{formatting}'. See docstring.")
@@ -403,7 +540,9 @@ def model_to_networkx(
             stacklevel=2,
         )
     model = pm.modelcontext(model)
-    return ModelGraph(model).make_networkx(var_names=var_names, formatting=formatting)
+    return ModelGraph(model).make_networkx(
+        var_names=var_names, formatting=formatting, node_formatters=node_formatters
+    )
 
 
 def model_to_graphviz(
@@ -414,6 +553,7 @@ def model_to_graphviz(
     save: str | None = None,
     figsize: tuple[int, int] | None = None,
     dpi: int = 300,
+    node_formatters: NodeTypeFormatterMapping | None = None,
 ):
     """Produce a graphviz Digraph from a PyMC model.
 
@@ -441,6 +581,10 @@ def model_to_graphviz(
         the size of the saved figure.
     dpi : int, optional
         Dots per inch. It only affects the resolution of the saved figure. The default is 300.
+    node_formatters : dict, optional
+        A dictionary mapping node types to functions that return a dictionary of node attributes.
+        Check out graphviz documentation for more information on available
+        attributes. https://graphviz.org/docs/nodes/
 
     Examples
     --------
@@ -475,6 +619,16 @@ def model_to_graphviz(
 
         # creates the file `schools.pdf`
         model_to_graphviz(schools).render("schools")
+
+    Display Free Random Variables and Observed Random Variables nodes with custom formatting.
+
+    .. code-block:: python
+
+        node_formatters = {
+            "Free Random Variable": lambda var: {"shape": "circle", "label": var.name},
+            "Observed Random Variable": lambda var: {"shape": "square", "label": var.name},
+        }
+        model_to_graphviz(schools, node_formatters=node_formatters)
     """
     if "plain" not in formatting:
         raise ValueError(f"Unsupported formatting for graph nodes: '{formatting}'. See docstring.")
@@ -491,4 +645,5 @@ def model_to_graphviz(
         save=save,
         figsize=figsize,
         dpi=dpi,
+        node_formatters=node_formatters,
     )