Add export_graphviz with plot_tree-style node panels

ROADMAP.md

@@ -11,7 +11,7 @@
 ## v0.2.0
 - [x] Superset branching on categorical features
 - [x] More plotting options
-- [ ] Exporting to Graphviz, planned for v0.4.0 ([#69](https://github.com/optimal-uoft/sgtlearn/issues/69))
+- [x] Exporting to Graphviz with `export_graphviz` ([#69](https://github.com/optimal-uoft/sgtlearn/issues/69))
 - [x] Feature importances matching scikit-learn's API for SGTs and SGT$_K$
 - [x] Adding TAO Refinement
 - [x] **Sklearn-style NaN support**: split search uses finite values only, and each univariate node learns which child receives missing values.

docs/api/index.rst

@@ -15,6 +15,7 @@ The public API is re-exported from the top-level ``sgtlearn`` package.
    plot_tree
    configure_feature_dict
    export_text
+   export_graphviz
    make_plus
    tao.TAO_refine
 

docs/api/plotting.rst

@@ -3,7 +3,7 @@ Plotting & Export
 
 .. currentmodule:: sgtlearn
 
-Visualize a fitted SGT, or export it as indented text rules.
+Visualize a fitted SGT, or export it as indented text rules or Graphviz DOT.
 
 plot_tree
 ---------
@@ -36,3 +36,37 @@ apply to ``X`` rounded to float32: ``1.5 + 1e-9`` rounds to ``1.5`` and follows
    |   |--- value: 0.00
    |--- x > 1.50
    |   |--- value: 1.00
+
+export_graphviz
+---------------
+
+.. autofunction:: export_graphviz
+
+``export_graphviz`` draws the same panels as ``plot_tree`` with Graphviz
+HTML-like labels. Each internal node shows its shape function, shaded by the
+child each region routes to, and with ``X`` a histogram of the samples that
+reach it (a heatmap with marginal histograms for pair nodes). Edges are colored
+by child; hover over an edge in a rendered SVG to see the ``export_text`` rule
+for that child. The output is plain DOT text and needs no extra package. To
+render it, install the ``graphviz`` Python package and the Graphviz binaries:
+
+.. code-block:: python
+
+   import graphviz
+   from sklearn.datasets import load_iris
+   from sgtlearn import SGTClassifier, export_graphviz
+
+   iris = load_iris()
+   model = SGTClassifier(num_partitions=3, max_depth=2, random_state=0)
+   model.fit(iris.data, iris.target)
+   dot = export_graphviz(
+       model,
+       X=iris.data,
+       feature_names=[n.replace(" (cm)", "") for n in iris.feature_names],
+       class_names=iris.target_names,
+   )
+   graphviz.Source(dot).render("tree", format="svg")
+
+.. image:: ../_static/export_graphviz_iris.svg
+   :alt: An SGT on iris rendered from export_graphviz, with a shape-function
+         histogram in each internal node.

docs/roadmap.rst

@@ -18,7 +18,7 @@ v0.2.0
 
 - ✅ Superset branching on categorical features
 - ✅ More plotting options
-- ⬜ Exporting to Graphviz, planned for v0.4.0 (see
+- ✅ Exporting to Graphviz with ``export_graphviz`` (see
   `issue #69 <https://github.com/optimal-uoft/sgtlearn/issues/69>`_)
 - ✅ Feature importances matching scikit-learn's API for SGTs and :math:`\mathrm{SGT}_K`
 - ✅ TAO refinement

sgtlearn/__init__.py

@@ -6,7 +6,7 @@
 """
 
 from sgtlearn import tao
-from sgtlearn._export import export_text, plot_tree
+from sgtlearn._export import export_graphviz, export_text, plot_tree
 from sgtlearn.base import (
     BaseShapeCART,
     ProcessedFeatures,
@@ -25,6 +25,7 @@
     "SGTClassifier",
     "SGTRegressor",
     "configure_feature_dict",
+    "export_graphviz",
     "export_text",
     "make_plus",
     "plot_tree",