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25003: Adds support for new "value_robust_contributions_min_cases" parameter to react_aggregate, MINOR #584

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Merged
cademack merged 2 commits into from
Jan 29, 2026
Merged

25003: Adds support for new "value_robust_contributions_min_cases" parameter to react_aggregate, MINOR #584

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933b417
new param pass thrus
cademack Jan 29, 2026
7e4c3c9
new version
cademack Jan 29, 2026
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9 changes: 8 additions & 1 deletion howso/client/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3694,6 +3694,7 @@ def react_aggregate( # noqa: C901
value_robust_contributions_features: t.Optional[Collection[str]] = None,
value_robust_contributions_num_buckets: int = 30,
value_robust_contributions_min_samples: int = 15,
value_robust_contributions_min_cases: int = 15,
weight_feature: t.Optional[str] = None,
) -> dict[str, dict[str, t.Any]]:
"""
Expand Down Expand Up @@ -4018,10 +4019,15 @@ def react_aggregate( # noqa: C901
The maximum number of buckets to bin continuous values into when computing the
"value_robust_accuracy_contributions", "value_robust_prediction_contributions" or
"value_robust_surprisal_asymmetry" details.
value_robust_contributions_num_samples: int, default 15
value_robust_contributions_min_samples: int, default 15
The minumum number of samples required for a combination of feature values for its
aggregated measure to be returned when computing the "value_robust_accuracy_contributions",
"value_robust_prediction_contributions" or "value_robust_surprisal_asymmetry" details.
value_robust_contributions_min_cases: int, default 15
The minimum number of unique cases for a given nominal class or continuous bucket to be
used as a possible feature value when collecting all combinations of feature values in
the data to report metrics over. If unspecified, there is no filtering based on number
of unique cases.
weight_feature : str, optional
The name of feature whose values to use as case weights.
When left unspecified uses the internally managed case weight.
Expand Down Expand Up @@ -4100,6 +4106,7 @@ def react_aggregate( # noqa: C901
"value_robust_contributions_features": value_robust_contributions_features,
"value_robust_contributions_num_buckets": value_robust_contributions_num_buckets,
"value_robust_contributions_min_samples": value_robust_contributions_min_samples,
"value_robust_contributions_min_cases": value_robust_contributions_min_cases,
"weight_feature": weight_feature,
})
if stats is None:
Expand Down
9 changes: 8 additions & 1 deletion howso/engine/trainee.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3592,6 +3592,7 @@ def react_aggregate(
value_robust_contributions_features: t.Optional[Collection[str]] = None,
value_robust_contributions_num_buckets: int = 30,
value_robust_contributions_min_samples: int = 15,
value_robust_contributions_min_cases: int = 15,
weight_feature: t.Optional[str] = None,
) -> AggregateReaction:
"""
Expand Down Expand Up @@ -3920,10 +3921,15 @@ def react_aggregate(
The maximum number of buckets to bin continuous values into when computing the
"value_robust_accuracy_contributions", "value_robust_prediction_contributions" or
"value_robust_surprisal_asymmetry" details.
value_robust_contributions_num_samples: int, default 15
value_robust_contributions_min_samples: int, default 15
The minumum number of samples required for a combination of feature values for its
aggregated measure to be returned when computing the "value_robust_accuracy_contributions",
"value_robust_prediction_contributions" or "value_robust_surprisal_asymmetry" details.
value_robust_contributions_min_cases: int, default 15
The minimum number of unique cases for a given nominal class or continuous bucket to be
used as a possible feature value when collecting all combinations of feature values in
the data to report metrics over. If unspecified, there is no filtering based on number
of unique cases.
weight_feature : str, optional
The name of feature whose values to use as case weights.
When left unspecified uses the internally managed case weight.
Expand Down Expand Up @@ -3967,6 +3973,7 @@ def react_aggregate(
value_robust_contributions_buckets=value_robust_contributions_buckets,
value_robust_contributions_num_buckets=value_robust_contributions_num_buckets,
value_robust_contributions_min_samples=value_robust_contributions_min_samples,
value_robust_contributions_min_cases=value_robust_contributions_min_cases,
weight_feature=weight_feature,
)
else:
Expand Down
2 changes: 1 addition & 1 deletion version.json
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
{
"dependencies": {
"howso-engine": "110.0.1"
"howso-engine": "110.1.0"
}
}
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