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Calculate number of nodes in the tree estimators #214

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10 changes: 10 additions & 0 deletions sklbench/benchmarks/sklearn_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,6 +134,16 @@ def get_subset_metrics_of_estimator(
and isinstance(iterations[0], Union[Numeric, NumpyNumeric].__args__)
):
metrics.update({"iterations": int(iterations[0])})
if hasattr(estimator_instance, "estimators_"):
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@david-cortes-intel david-cortes-intel May 28, 2026

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This will also be set in other types of estimators - for example:
https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html

Perhaps it could check if the first entry there is of tree type.

Also what about cuML? I guess it could be done by calling as_sklearn():
https://docs.rapids.ai/api/cuml/nightly/pickling_cuml_models/#Converting-Between-cuML-and-scikit-learn-Models

estimators_with_trees = [
t
for t in estimator_instance.estimators_
if hasattr(t, "tree_") and hasattr(t.tree_, "node_count")
]
if estimators_with_trees:
metrics["n_nodes"] = sum(
t.tree_.node_count for t in estimators_with_trees
)
if task == "classification":
y_pred = convert_to_numpy(estimator_instance.predict(x))
metrics.update(
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2 changes: 2 additions & 0 deletions sklbench/report/implementation.py
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Expand Up @@ -71,6 +71,8 @@
# NB: 'n_clusters' is parameter of KMeans while
# 'clusters' is number of computer clusters by DBSCAN
"clusters",
# tree ensembles
"n_nodes",
],
"incomparable": [
"1st-mean run ratio",
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