Add static Interventions Avoided browser rendering

.github/workflows/python-package.yml

@@ -52,14 +52,14 @@ jobs:
           prefix = "rtichoke/_vendor/rtichoke_viz/"
           required = {
               f"{prefix}VENDORED_FROM",
-              f"{prefix}rtichoke-viz-0.6.0.tar.gz",
+              f"{prefix}rtichoke-viz-0.7.0.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-0.5.0.tar.gz" not in names
+          assert f"{prefix}rtichoke-viz-0.6.0.tar.gz" not in names
           PY
 
       - name: Run tests

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 <!--next-version-placeholder-->
 
+- Added opt-in canonical browser rendering for static Interventions Avoided using the verified `rtichoke_viz v0.7.0` release while preserving Plotly as the default.
 - Fixed Interventions Avoided to apply the per-100 scaling to the full model expression, including the false-negative penalty term.
 
 ## v0.1.36 (21/08/2026)
@@ -19,4 +20,4 @@
 
 ## v0.1.0 (27/01/2023)
 
-- First release of `rtichoke`!
+- First release of `rtichoke`!

src/rtichoke/_interventions_avoided_viz_spec_v2.py

@@ -0,0 +1,179 @@
+"""Canonical static Interventions Avoided v2 adapter.
+
+This module translates already-computed production Interventions Avoided
+quantities into the shared rtichoke_viz contract. It deliberately does not
+recompute model statistics or threshold membership.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+
+import polars as pl
+
+from rtichoke.processing.evaluation_semantics import _EvaluationMetadata
+
+_REQUIRED_COLUMNS = {
+    "reference_group",
+    "chosen_cutoff",
+    "net_benefit_interventions_avoided",
+    "real_positives",
+    "n",
+}
+
+
+def _interventions_avoided_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 static Interventions Avoided from production quantities."""
+    missing = _REQUIRED_COLUMNS.difference(performance_data.columns)
+    if missing:
+        raise ValueError(
+            "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",
+            "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(
+            "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)
+    }
+    series_ids = {
+        group: f"series-{index}" for index, group in enumerate(ordered_groups, start=1)
+    }
+
+    evaluations: list[dict[str, object]] = []
+    series: 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)
+
+        display_value = metadata.model or metadata.population
+        series.append(
+            {
+                "id": series_ids[group],
+                "evaluationId": evaluation_ids[group],
+                "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"])],
+            "threshold": float(row["chosen_cutoff"]),
+            "interventionsAvoided": float(row["net_benefit_interventions_avoided"]),
+        }
+        for row in rows
+    ]
+
+    prevalence_values: dict[str, set[float]] = {}
+    population_thresholds: dict[str, list[float]] = {}
+    for row in rows:
+        group = str(row["reference_group"])
+        population = evaluation_metadata[group].population
+        n = float(row["n"])
+        if n <= 0:
+            raise ValueError("Interventions Avoided population size must be positive.")
+        prevalence_values.setdefault(population, set()).add(
+            float(row["real_positives"]) / n
+        )
+        threshold = float(row["chosen_cutoff"])
+        if 0.0 < threshold <= 1.0:
+            population_thresholds.setdefault(population, []).append(threshold)
+
+    populations = list(
+        dict.fromkeys(metadata.population for metadata in evaluation_metadata.values())
+    )
+    references: list[dict[str, object]] = [
+        {
+            "type": "horizontal",
+            "scope": "global",
+            "value": 0.0,
+            "label": "Treat All",
+            "benchmark": "treat_all",
+        }
+    ]
+    for population in populations:
+        values = prevalence_values.get(population, set())
+        if not values:
+            continue
+        if len(values) != 1:
+            raise ValueError(
+                f"Population {population!r} has inconsistent prevalence values."
+            )
+        prevalence = next(iter(values))
+        thresholds = sorted(set(population_thresholds.get(population, [])))
+        references.append(
+            {
+                "type": "path",
+                "scope": "population",
+                "population": population,
+                "label": f"Treat None — {population}",
+                "benchmark": "treat_none",
+                "points": [
+                    {
+                        "x": threshold,
+                        "y": 100.0
+                        * (
+                            1.0
+                            - prevalence
+                            - prevalence * (1.0 - threshold) / threshold
+                        ),
+                    }
+                    for threshold in thresholds
+                ],
+            }
+        )
+
+    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,
+    }

src/rtichoke/_renderers.py

@@ -56,6 +56,7 @@ def write_html(self, path: str | Path) -> Path:
             "gains": "renderGainsV2",
             "lift": "renderLiftV2",
             "decision_curve": "renderDecisionCurveV2",
+            "interventions_avoided": "renderInterventionsAvoidedV2",
         }.get(str(self.spec.get("type")))
         if render_export is None:
             raise ValueError(

src/rtichoke/_vendor/rtichoke_viz/VENDORED_FROM

@@ -1,5 +1,5 @@
 repository=https://github.com/uriahf/rtichoke_viz
-release=v0.6.0
-source_commit=3abb3f07a598c3e22d5362a3f88e52bb6b52b083
-archive=rtichoke-viz-0.6.0.tar.gz
-sha256=625613c7f692ff50b7757a27bb6caf84e311971bde92593141393dbd897af3a2
+release=v0.7.0
+source_commit=b3564d2824ec1791f791fda406c99b3d7865a68f
+archive=rtichoke-viz-0.7.0.tar.gz
+sha256=f09c30e231a8be39c2e89ba6ae39c90ed8cab67021213e17681a475066a9806e