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Merged
uriahf merged 2 commits into from
Aug 26, 2026
Merged

Adopt time-dependent Interventions Avoided browser rendering #398

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9a2356d
feat: adopt time-dependent interventions avoided browser rendering
uriahf Aug 26, 2026
06b8caa
ci: verify rtichoke viz v0.10.0 in wheels
uriahf Aug 26, 2026
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4 changes: 2 additions & 2 deletions .github/workflows/python-package.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -52,14 +52,14 @@ jobs:
prefix = "rtichoke/_vendor/rtichoke_viz/"
required = {
f"{prefix}VENDORED_FROM",
f"{prefix}rtichoke-viz-0.9.0.tar.gz",
f"{prefix}rtichoke-viz-0.10.0.tar.gz",
f"{prefix}rtichoke-viz.js",
f"{prefix}rtichoke-viz.css",
f"{prefix}rtichoke-viz.schema.json",
f"{prefix}rtichoke-viz-v2.schema.json",
}
assert required <= names
assert f"{prefix}rtichoke-viz-0.7.0.tar.gz" not in names
assert f"{prefix}rtichoke-viz-0.9.0.tar.gz" not in names
PY

- name: Run tests
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201 changes: 201 additions & 0 deletions src/rtichoke/_interventions_avoided_viz_spec_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,12 @@
"n",
}

_REQUIRED_TIMES_COLUMNS = _REQUIRED_COLUMNS | {
"fixed_time_horizon",
"censoring_heuristic",
"competing_heuristic",
}


def _interventions_avoided_v2_spec_from_performance_data(
performance_data: pl.DataFrame,
Expand Down Expand Up @@ -177,3 +183,198 @@ def _interventions_avoided_v2_spec_from_performance_data(
"yAxis": {"label": "Interventions Avoided (per 100)"},
"references": references,
}


def _interventions_avoided_times_v2_spec_from_performance_data(
performance_data: pl.DataFrame,
evaluation_metadata: Mapping[str, _EvaluationMetadata],
*,
min_p_threshold: float = 0.0,
max_p_threshold: float = 1.0,
) -> dict[str, object]:
"""Build canonical time-dependent Interventions Avoided v2.

Model values are copied from the existing production calculation. Only
Treat None reference geometry is derived from the existing AJ event risk.
"""
missing = _REQUIRED_TIMES_COLUMNS.difference(performance_data.columns)
if missing:
raise ValueError(
"Time-dependent Interventions Avoided performance data is missing columns: "
+ ", ".join(sorted(missing))
)

rows = (
performance_data.filter(
pl.col("chosen_cutoff").is_finite()
& pl.col("net_benefit_interventions_avoided").is_finite()
)
.select(
"reference_group",
"fixed_time_horizon",
"censoring_heuristic",
"competing_heuristic",
"chosen_cutoff",
"net_benefit_interventions_avoided",
"real_positives",
"n",
)
.to_dicts()
)
row_groups = {str(row["reference_group"]) for row in rows}
missing_metadata = row_groups.difference(evaluation_metadata)
if missing_metadata:
raise ValueError(
"Time-dependent Interventions Avoided rows are missing evaluation metadata: "
+ ", ".join(sorted(missing_metadata))
)

ordered_groups = [group for group in evaluation_metadata if group in row_groups]
evaluation_ids = {
group: f"evaluation-{index}"
for index, group in enumerate(ordered_groups, start=1)
}
evaluations: list[dict[str, object]] = []
for group in ordered_groups:
metadata = evaluation_metadata[group]
evaluation: dict[str, object] = {
"id": evaluation_ids[group],
"population": metadata.population,
}
if metadata.model is not None:
evaluation["model"] = metadata.model
evaluations.append(evaluation)

series_keys = list(
dict.fromkeys(
(str(row["reference_group"]), float(row["fixed_time_horizon"]))
for row in rows
)
)
series_ids = {
key: f"series-{index}" for index, key in enumerate(series_keys, start=1)
}
series: list[dict[str, object]] = []
for group, horizon in series_keys:
metadata = evaluation_metadata[group]
display_value = metadata.model or metadata.population
series.append(
{
"id": series_ids[(group, horizon)],
"evaluationId": evaluation_ids[group],
"horizon": horizon,
"display": {
"label": display_value,
"group": display_value,
"role": "model" if metadata.model is not None else "population",
},
}
)

data = [
{
"seriesId": series_ids[
(str(row["reference_group"]), float(row["fixed_time_horizon"]))
],
"threshold": float(row["chosen_cutoff"]),
"interventionsAvoided": float(row["net_benefit_interventions_avoided"]),
}
for row in rows
]

risk_rows = (
performance_data.filter(pl.col("chosen_cutoff") == 0)
.select(
"reference_group",
"fixed_time_horizon",
(pl.col("real_positives") / pl.col("n")).alias("event_risk"),
)
.unique()
.to_dicts()
)
risk_values: dict[tuple[str, float], set[float]] = {}
for row in risk_rows:
group = str(row["reference_group"])
metadata = evaluation_metadata.get(group)
if metadata is None:
continue
key = (metadata.population, float(row["fixed_time_horizon"]))
risk_values.setdefault(key, set()).add(float(row["event_risk"]))

thresholds: dict[tuple[str, float], list[float]] = {}
for row in rows:
group = str(row["reference_group"])
key = (
evaluation_metadata[group].population,
float(row["fixed_time_horizon"]),
)
threshold = float(row["chosen_cutoff"])
if 0.0 < threshold <= 1.0:
thresholds.setdefault(key, []).append(threshold)

references: list[dict[str, object]] = [
{
"type": "horizontal",
"scope": "global",
"value": 0.0,
"label": "Treat All",
"benchmark": "treat_all",
}
]
populations = list(
dict.fromkeys(metadata.population for metadata in evaluation_metadata.values())
)
horizons = sorted(
float(value)
for value in performance_data["fixed_time_horizon"].unique().to_list()
)
for population in populations:
for horizon in horizons:
key = (population, horizon)
candidates = risk_values.get(key, set())
if not candidates:
continue
if len(candidates) != 1:
raise ValueError(
"Time-dependent Interventions Avoided must have one calculated "
f"event risk per population and horizon: {population} at {horizon}"
)
event_risk = next(iter(candidates))
references.append(
{
"type": "path",
"scope": "population_horizon",
"population": population,
"horizon": horizon,
"label": f"Treat None — {population}",
"benchmark": "treat_none",
"points": [
{
"x": threshold,
"y": 100.0
* (
1.0
- event_risk
- event_risk * (1.0 - threshold) / threshold
),
}
for threshold in sorted(set(thresholds.get(key, [])))
],
}
)

return {
"schemaVersion": "2.0",
"type": "interventions_avoided",
"evaluations": evaluations,
"series": series,
"data": data,
"x": "threshold",
"y": "interventionsAvoided",
"xAxis": {
"label": "Probability Threshold",
"domain": [min_p_threshold, max_p_threshold],
},
"yAxis": {"label": "Interventions Avoided (per 100)"},
"references": references,
}
8 changes: 4 additions & 4 deletions src/rtichoke/_vendor/rtichoke_viz/VENDORED_FROM
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@@ -1,5 +1,5 @@
repository=https://github.com/uriahf/rtichoke_viz
release=v0.9.0
source_commit=56e2ba95f83c889385c38619571368f74250d428
archive=rtichoke-viz-0.9.0.tar.gz
sha256=6a231c7bc951cdd3f5381e2a0937036a9d9f62b8ea1b36d8dbf81f62c6188ef8
release=v0.10.0
source_commit=b65b903e9456b5eb323dbe0bac823e9ff0c1bd01
archive=rtichoke-viz-0.10.0.tar.gz
sha256=030a9e58c6367147b4904d73cf641d8ef3990d95e70b66022a99d6c8d37b3233
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142 changes: 100 additions & 42 deletions src/rtichoke/_vendor/rtichoke_viz/rtichoke-viz-v2.schema.json
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Original file line number Diff line number Diff line change
Expand Up @@ -4345,53 +4345,111 @@
}
},
{
"type": "object",
"required": [
"type",
"points",
"scope",
"population",
"benchmark"
],
"properties": {
"type": {
"const": "path",
"type": "string"
},
"points": {
"minItems": 2,
"type": "array",
"items": {
"type": "object",
"required": [
"x",
"y"
],
"properties": {
"x": {
"type": "number"
},
"y": {
"type": "number"
"anyOf": [
{
"type": "object",
"required": [
"type",
"points",
"benchmark",
"scope",
"population"
],
"properties": {
"type": {
"const": "path",
"type": "string"
},
"points": {
"minItems": 2,
"type": "array",
"items": {
"type": "object",
"required": [
"x",
"y"
],
"properties": {
"x": {
"type": "number"
},
"y": {
"type": "number"
}
}
}
},
"label": {
"type": "string"
},
"benchmark": {
"const": "treat_none",
"type": "string"
},
"scope": {
"const": "population",
"type": "string"
},
"population": {
"type": "string"
}
}
},
"label": {
"type": "string"
},
"scope": {
"const": "population",
"type": "string"
},
"population": {
"type": "string"
},
"benchmark": {
"const": "treat_none",
"type": "string"
{
"type": "object",
"required": [
"type",
"points",
"benchmark",
"scope",
"population",
"horizon"
],
"properties": {
"type": {
"const": "path",
"type": "string"
},
"points": {
"minItems": 2,
"type": "array",
"items": {
"type": "object",
"required": [
"x",
"y"
],
"properties": {
"x": {
"type": "number"
},
"y": {
"type": "number"
}
}
}
},
"label": {
"type": "string"
},
"benchmark": {
"const": "treat_none",
"type": "string"
},
"scope": {
"const": "population_horizon",
"type": "string"
},
"population": {
"type": "string"
},
"horizon": {
"minimum": 0,
"type": "number"
}
}
}
}
]
}
]
}
Expand Down
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