chore: updated figure models and scripts

2026-05-31 16:43:36 +00:00 · 2026-03-23 21:47:04 +01:00
parent e62e842faa
commit 910dba0a7d
16 changed files with 783 additions and 13 deletions
--- a/paper/src/chapters/figures/results/generated/final/final_focus_alpha_deltas.csv
+++ b/paper/src/chapters/figures/results/generated/final/final_focus_alpha_deltas.csv
@@ -3,7 +3,7 @@ alpha,revenue_delta,revenue_delta_pct,reward_delta,reward_delta_pct,volatility_d
 0.1,-14962.041501283413,-4.410637208586118,-14303.760282736213,-4.531344436782669,0.0011858665298920962,0.0,-0.004133727080174038
 0.2,-16153.416666167905,-4.826514761457546,-15398.621298776357,-4.9418165571901715,0.00200624274016295,0.0,-0.0033201883450373615
 0.3,-17294.9275360335,-5.382423616385397,-16544.91845114401,-5.533399709364953,-0.0011022484400295268,0.0,-0.0029151149203366505
-0.4,-19661.294346174283,-6.250307313590199,-18728.35578200908,-6.3953153560217535,3.582812967113658e-05,0.0,-0.0038123361988749577
+0.4,-19543.8750398212,-6.215299839915013,-18613.487687777204,-6.35858461426586,-2.7530592947980215e-05,0.0,-0.0038561140856475523
 0.5,-16411.03168918495,-5.3630681206030015,-15638.77510066732,-5.4888928630525315,0.00015428950526953644,0.0,-0.00439661338956944
 0.6,-14729.668247641937,-5.069964928178309,-13912.22417824401,-5.148827377884945,-0.002735776807082743,0.0,-0.004310129386364658
 0.7,-21160.81910514756,-7.351404104505076,-20171.762105623755,-7.525169314210056,-0.0008903632602569461,0.0,-0.0026198461183787186
--- a/paper/src/chapters/figures/results/generated/final/final_focus_alpha_mode_summary.csv
+++ b/paper/src/chapters/figures/results/generated/final/final_focus_alpha_mode_summary.csv
@@ -7,7 +7,7 @@ alpha,mode,runs,revenue_mean,reward_mean,supra_mean,volatility_mean,coi_leakage_
 0.2,defended,35,318527.35122792586,296199.77820822067,0.0,0.07048630468445288,0.11265850300394666,137.2758153292305
 0.3,baseline,30,321322.30327214615,299000.9636054795,0.0,0.07085669473747759,0.11527347603412934,136.4452630715689
 0.3,defended,44,304027.37573611265,282456.0451543355,0.0,0.06975444629744806,0.11235836111379269,136.4704115371568
-0.4,baseline,33,314565.2423109539,292844.914432166,0.0,0.07031811881503117,0.11300307992768284,136.72547178046122
+0.4,baseline,33,314447.8230046008,292730.04633793415,0.0,0.07038147753765028,0.11304685781445543,136.70817144219887
 0.4,defended,38,294903.9479647796,274116.55865015695,0.0,0.0703539469447023,0.10919074372880788,136.75671002806396
 0.5,baseline,33,306000.80625751516,284916.7489847879,0.0,0.06938663916591635,0.11118137138243217,136.9528780620641
 0.5,defended,35,289589.7745683302,269277.9738841206,0.0,0.06954092867118589,0.10678475799286273,136.65018588845163
--- a/paper/src/chapters/figures/results/generated/final/final_focus_headline_summary.json
+++ b/paper/src/chapters/figures/results/generated/final/final_focus_headline_summary.json
@@ -1,11 +1,14 @@
 {
-  "bundle": "engine/studies/results/wandb_sweep_bundles/bundle_20260317_093826",
+  "bundle": "engine/studies/results/wandb_sweep_bundles/bundle_20260317_122818",
  "focus_cohort": "max_alpha_coverage",
+  "focus_sweep_id": "i88nw811",
+  "focus_run_count": 768,
+  "git_commit": "e62e842faad79b143f5555d187075e85c8926363",
  "alpha_cells": 11,
  "alpha_min": 0.0,
  "alpha_max": 1.0,
-  "mean_revenue_delta_pct": -4.787221975639986,
-  "mean_reward_delta_pct": -4.91730667541704,
+  "mean_revenue_delta_pct": -4.784039478033151,
+  "mean_reward_delta_pct": -4.913967517075595,
  "zone_summary": [
    {
      "zone": "high_alpha_0_7_plus",
@@ -18,10 +21,10 @@
    {
      "zone": "low_alpha_below_0_7",
      "alpha_cells": 7,
-      "revenue_delta_pct_mean": -5.201949225367208,
-      "reward_delta_pct_mean": -5.324947138914036,
-      "coi_leakage_delta_mean": -0.0037041938968711296,
-      "volatility_delta_mean": 0.00011102505536893832
+      "revenue_delta_pct_mean": -5.196948157699325,
+      "reward_delta_pct_mean": -5.319699890091765,
+      "coi_leakage_delta_mean": -0.003710447880695786,
+      "volatility_delta_mean": 0.00010197380928049306
    }
  ]
 }
--- a/paper/src/chapters/figures/results/generated/final/final_focus_zone_summary.csv
+++ b/paper/src/chapters/figures/results/generated/final/final_focus_zone_summary.csv
@@ -1,3 +1,3 @@
 zone,alpha_cells,revenue_delta_pct_mean,reward_delta_pct_mean,coi_leakage_delta_mean,volatility_delta_mean
 high_alpha_0_7_plus,4,-4.0614492886173466,-4.2039358642972955,-0.0018236753956396637,0.00026289072427068336
-low_alpha_below_0_7,7,-5.201949225367208,-5.324947138914036,-0.0037041938968711296,0.00011102505536893832
+low_alpha_below_0_7,7,-5.196948157699325,-5.319699890091765,-0.003710447880695786,0.00010197380928049306
--- a/paper/src/chapters/figures/results/generated/final/plots/final_focus_revenue_by_alpha.pdf
+++ b/paper/src/chapters/figures/results/generated/final/plots/final_focus_revenue_by_alpha.pdf
--- a/paper/src/chapters/figures/results/generated/final/plots/final_focus_revenue_delta.pdf
+++ b/paper/src/chapters/figures/results/generated/final/plots/final_focus_revenue_delta.pdf
--- a/paper/src/chapters/figures/results/generated/final/plots/final_focus_risk_deltas.pdf
+++ b/paper/src/chapters/figures/results/generated/final/plots/final_focus_risk_deltas.pdf
--- a/paper/src/chapters/figures/results/generated/final/revenue_alpha_diagnostics.json
+++ b/paper/src/chapters/figures/results/generated/final/revenue_alpha_diagnostics.json
@@ -0,0 +1,24 @@
+{
+  "normality": {
+    "test": "jarque_bera",
+    "available": true,
+    "statistic": 362.38850707984324,
+    "p_value": 2.0339278125496517e-79
+  },
+  "heteroskedasticity": {
+    "test": "breusch_pagan",
+    "available": true,
+    "lm_stat": 6.0366025380616275,
+    "df": 1,
+    "p_value": 0.014012224810767138
+  },
+  "influence": {
+    "max_leverage": 0.03769234230180875,
+    "mean_leverage": 0.021052631578947392,
+    "high_leverage_threshold": 0.042105263157894736,
+    "high_leverage_count": 0,
+    "max_cooks_distance": 0.29121755538277183,
+    "high_cooks_threshold": 0.042105263157894736,
+    "high_cooks_count": 6
+  }
+}
--- a/paper/src/chapters/figures/results/generated/final/revenue_alpha_filtered.csv
+++ b/paper/src/chapters/figures/results/generated/final/revenue_alpha_filtered.csv
@@ -0,0 +1,96 @@
+sweep_id,sweep_full_id,run_id,run_name,state,run_url,created_at,runtime,downloaded_files,history_rows,selected_for_clone,download_error,alpha,n_products,eta_ux,lambda_coi,baseline_mode,no_robust,study_mode,eval_revenue_mean,eval_reward_mean,eval_stress_revenue_worst,eval_stress_reward_worst,eval_supra_share_mean,eval_supra_penalty_mean,eval_volatility_mean,eval_upward_volatility_mean,eval_coi_level_mean,eval_coi_leakage_mean,objective_score,mode
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+i88nw811,lusiana/capstone_tpu/i88nw811,cvrztiyb,sweep/ppo/sb3/cpu/default/a0.2/baseline/s42,finished,https://wandb.ai/lusiana/capstone_tpu/runs/cvrztiyb,2026-03-16T10:27:26Z,8353.396268583,0,0,0,,0.2,100.0,0.0,0.3,True,True,baseline,333463.32883383776,310606.38883383776,322375.37087837915,300349.6308783791,0.0,0.0,0.0694238399850746,0.0,137.6206723870474,0.1176551945750585,300349.6308783791,baseline
+i88nw811,lusiana/capstone_tpu/i88nw811,7z9spcc6,sweep/ppo/sb3/cpu/default/a0/baseline/s7777,finished,https://wandb.ai/lusiana/capstone_tpu/runs/7z9spcc6,2026-03-16T10:29:46Z,8444.449882423,5,1004,1,,0.0,100.0,0.0,0.3,True,True,baseline,348861.1454509751,324713.0754509751,335967.6160126648,312660.3160126648,0.0,0.0,0.0674835742466741,0.0,136.8813175598437,0.118985751213389,312660.3160126648,baseline
--- a/paper/src/chapters/figures/results/generated/final/revenue_alpha_fixed_effects.json
+++ b/paper/src/chapters/figures/results/generated/final/revenue_alpha_fixed_effects.json
@@ -0,0 +1,31 @@
+{
+  "n": 95,
+  "k": 2,
+  "dof": 93,
+  "df_t": 93,
+  "cov_type": "hc1",
+  "clusters": null,
+  "r2": 0.9759651432807543,
+  "adj_r2": 0.9757067039611925,
+  "sse": 1872600419.7223544,
+  "coefficients": [
+    {
+      "name": "intercept",
+      "coef": 348823.4131652292,
+      "std_error": 1383.3660823209932,
+      "t_stat": 252.15553397115096,
+      "p_value": 0.0,
+      "ci95_low": 346076.3222890517,
+      "ci95_high": 351570.5040414067
+    },
+    {
+      "name": "alpha",
+      "coef": -90140.52744561416,
+      "std_error": 2185.134882447838,
+      "t_stat": -41.25169945785529,
+      "p_value": 0.0,
+      "ci95_low": -94479.77225976942,
+      "ci95_high": -85801.2826314589
+    }
+  ]
+}
--- a/paper/src/chapters/figures/results/generated/final/revenue_alpha_per_sweep.csv
+++ b/paper/src/chapters/figures/results/generated/final/revenue_alpha_per_sweep.csv
@@ -0,0 +1,2 @@
+sweep_id,n,alpha_coef,alpha_std_error,alpha_t_stat,alpha_p_value,alpha_ci95_low,alpha_ci95_high,r2
+i88nw811,95,-90140.52744561416,2185.134882447838,-41.25169945785529,0.0,-94479.77225976942,-85801.2826314589,0.9759651432807543
--- a/paper/src/chapters/figures/results/generated/final/revenue_alpha_sample_accounting.json
+++ b/paper/src/chapters/figures/results/generated/final/revenue_alpha_sample_accounting.json
@@ -0,0 +1,37 @@
+{
+  "bundle_dir": "/home/velocitatem/Documents/Projects/PHANTOM/engine/studies/results/wandb_sweep_bundles/bundle_20260317_122818",
+  "git_commit": "e62e842faad79b143f5555d187075e85c8926363",
+  "cohort_name": "original_n95_baseline_n100",
+  "filters": {
+    "sweep_id": [
+      "i88nw811"
+    ],
+    "mode": "baseline",
+    "n_products": 100.0,
+    "eta_ux": 0.0,
+    "lambda_coi": null,
+    "alpha_min": 0.0,
+    "alpha_max": 1.0
+  },
+  "n_rows": 95,
+  "n_sweeps": 1,
+  "alpha_unique": [
+    0.0,
+    0.1,
+    0.2,
+    0.3,
+    0.4,
+    0.5,
+    0.6,
+    0.7,
+    0.8,
+    0.9,
+    1.0
+  ],
+  "rows_by_sweep": {
+    "i88nw811": 95
+  },
+  "rows_by_mode": {
+    "baseline": 95
+  }
+}
--- a/paper/src/chapters/figures/results/generated/final/revenue_alpha_simple_ols.json
+++ b/paper/src/chapters/figures/results/generated/final/revenue_alpha_simple_ols.json
@@ -0,0 +1,31 @@
+{
+  "n": 95,
+  "k": 2,
+  "dof": 93,
+  "df_t": 93,
+  "cov_type": "iid",
+  "clusters": null,
+  "r2": 0.9759651432807543,
+  "adj_r2": 0.9757067039611925,
+  "sse": 1872600419.7223544,
+  "coefficients": [
+    {
+      "name": "intercept",
+      "coef": 348823.4131652292,
+      "std_error": 860.7176431608721,
+      "t_stat": 405.2704344298337,
+      "p_value": 0.0,
+      "ci95_low": 347114.1985078009,
+      "ci95_high": 350532.6278226575
+    },
+    {
+      "name": "alpha",
+      "coef": -90140.52744561416,
+      "std_error": 1466.838282353916,
+      "t_stat": -61.452259959401054,
+      "p_value": 0.0,
+      "ci95_low": -93053.37756806448,
+      "ci95_high": -87227.67732316385
+    }
+  ]
+}
--- a/paper/src/chapters/figures/results/process_final_sweeps.py
+++ b/paper/src/chapters/figures/results/process_final_sweeps.py
@@ -3,6 +3,7 @@ from __future__ import annotations
 import argparse
 import json
 from pathlib import Path
+import subprocess
 from typing import Any

 import matplotlib
@@ -37,6 +38,20 @@ def _default_plot_dir(output_dir: Path) -> Path:
    return output_dir / "plots"


+def _git_commit() -> str:
+    try:
+        result = subprocess.run(
+            ["git", "rev-parse", "HEAD"],
+            check=True,
+            text=True,
+            capture_output=True,
+            cwd=_project_root(),
+        )
+    except Exception:
+        return "unknown"
+    return result.stdout.strip()
+
+
 def _truthy(value: Any) -> bool:
    if isinstance(value, bool):
        return value
@@ -297,9 +312,16 @@ def _write_include(path: Path, figure_rel_path: str, width: str) -> Path:
    return path


-def run(bundle_dir: Path, output_dir: Path, plot_dir: Path) -> list[Path]:
+def run(
+    bundle_dir: Path,
+    output_dir: Path,
+    plot_dir: Path,
+    focus_sweep_id: str | None = None,
+) -> list[Path]:
    all_runs = _load_runs(bundle_dir)
-    focus_id = _focus_sweep(all_runs)
+    focus_id = str(focus_sweep_id) if focus_sweep_id else _focus_sweep(all_runs)
+    if focus_id not in set(all_runs["sweep_id"].astype(str).unique()):
+        raise ValueError(f"Requested focus sweep_id not found: {focus_id}")
    focus_runs = all_runs[all_runs["sweep_id"] == focus_id].copy()
    alpha_mode = _alpha_mode_summary(focus_runs)
    deltas = _alpha_deltas(alpha_mode)
@@ -324,6 +346,9 @@ def run(bundle_dir: Path, output_dir: Path, plot_dir: Path) -> list[Path]:
    headline = {
        "bundle": str(bundle_dir),
        "focus_cohort": "max_alpha_coverage",
+        "focus_sweep_id": focus_id,
+        "focus_run_count": int(len(focus_runs)),
+        "git_commit": _git_commit(),
        "alpha_cells": int(deltas["alpha"].nunique()) if not deltas.empty else 0,
        "alpha_min": float(deltas["alpha"].min()) if not deltas.empty else None,
        "alpha_max": float(deltas["alpha"].max()) if not deltas.empty else None,
@@ -390,6 +415,7 @@ def main() -> None:
    parser.add_argument("--bundle-dir", type=Path, default=_default_bundle_dir())
    parser.add_argument("--output-dir", type=Path, default=_default_output_dir())
    parser.add_argument("--plot-dir", type=Path, default=None)
+    parser.add_argument("--focus-sweep-id", type=str, default=None)
    args = parser.parse_args()

    _configure_style()
@@ -399,7 +425,10 @@ def main() -> None:
        else _default_plot_dir(args.output_dir)
    )
    outputs = run(
-        bundle_dir=args.bundle_dir, output_dir=args.output_dir, plot_dir=plot_dir
+        bundle_dir=args.bundle_dir,
+        output_dir=args.output_dir,
+        plot_dir=plot_dir,
+        focus_sweep_id=args.focus_sweep_id,
    )
    for path in outputs:
        print(path)
--- a/paper/src/chapters/figures/results/revenue_alpha_analysis.py
+++ b/paper/src/chapters/figures/results/revenue_alpha_analysis.py
@@ -0,0 +1,454 @@
+from __future__ import annotations
+
+import argparse
+import json
+import subprocess
+from pathlib import Path
+from typing import Iterable
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+
+
+def _project_root() -> Path:
+    return Path(__file__).resolve().parents[5]
+
+
+def _default_bundle_dir() -> Path:
+    base = _project_root() / "engine" / "studies" / "results" / "wandb_sweep_bundles"
+    bundles = sorted(
+        [path for path in base.glob("bundle_*") if path.is_dir()],
+        key=lambda path: path.stat().st_mtime,
+        reverse=True,
+    )
+    if not bundles:
+        raise FileNotFoundError(f"No sweep bundle directories found in {base}")
+    return bundles[0]
+
+
+def _bundle_dir_from_id(bundle_id: str) -> Path:
+    token = str(bundle_id).strip()
+    name = token if token.startswith("bundle_") else f"bundle_{token}"
+    path = (
+        _project_root()
+        / "engine"
+        / "studies"
+        / "results"
+        / "wandb_sweep_bundles"
+        / name
+    )
+    if not path.exists():
+        raise FileNotFoundError(f"Bundle not found: {path}")
+    return path
+
+
+def _default_output_dir() -> Path:
+    return Path(__file__).resolve().parent / "generated" / "final"
+
+
+def _truthy(value: object) -> bool:
+    if isinstance(value, bool):
+        return value
+    if value is None:
+        return False
+    return str(value).strip().lower() in {"1", "true", "yes", "on"}
+
+
+def _mode_of(row: pd.Series) -> str:
+    mode_hint = str(row.get("study_mode", "")).strip().lower()
+    if mode_hint in {"baseline", "no_robust"}:
+        return "baseline"
+    if mode_hint in {"defended", "robust"}:
+        return "defended"
+    if _truthy(row.get("baseline_mode")) or _truthy(row.get("no_robust")):
+        return "baseline"
+    return "defended"
+
+
+def _coerce_numeric(frame: pd.DataFrame, columns: Iterable[str]) -> None:
+    for column in columns:
+        if column in frame.columns:
+            frame[column] = pd.to_numeric(frame[column], errors="coerce")
+
+
+def _load_runs(bundle_dir: Path) -> pd.DataFrame:
+    path = bundle_dir / "runs_finished.csv"
+    if not path.exists():
+        raise FileNotFoundError(f"Missing required file: {path}")
+    frame = pd.read_csv(path)
+    frame["mode"] = frame.apply(_mode_of, axis=1)
+    _coerce_numeric(
+        frame,
+        [
+            "alpha",
+            "n_products",
+            "eta_ux",
+            "lambda_coi",
+            "eval_revenue_mean",
+        ],
+    )
+    frame = frame[frame["mode"].isin({"baseline", "defended"})].copy()
+    return frame
+
+
+def _get_git_commit() -> str:
+    try:
+        result = subprocess.run(
+            ["git", "rev-parse", "HEAD"],
+            check=True,
+            text=True,
+            capture_output=True,
+            cwd=_project_root(),
+        )
+    except Exception:
+        return "unknown"
+    return result.stdout.strip()
+
+
+def _apply_filters(frame: pd.DataFrame, args: argparse.Namespace) -> pd.DataFrame:
+    data = frame.copy()
+    if args.sweep_id:
+        allowed = {str(value) for value in args.sweep_id}
+        data = data[data["sweep_id"].astype(str).isin(allowed)]
+    if args.mode != "all":
+        data = data[data["mode"] == args.mode]
+    if args.n_products is not None:
+        data = data[data["n_products"] == float(args.n_products)]
+    if args.eta_ux is not None:
+        data = data[data["eta_ux"] == float(args.eta_ux)]
+    if args.lambda_coi is not None:
+        data = data[data["lambda_coi"] == float(args.lambda_coi)]
+    data = data[data["alpha"].notna() & data["eval_revenue_mean"].notna()]
+    data = data[data["alpha"] >= float(args.alpha_min)]
+    data = data[data["alpha"] <= float(args.alpha_max)]
+    return data.reset_index(drop=True)
+
+
+def _design_matrix(
+    frame: pd.DataFrame,
+    *,
+    include_sweep_fixed_effects: bool,
+) -> tuple[np.ndarray, np.ndarray, list[str]]:
+    y = frame["eval_revenue_mean"].to_numpy(dtype=float)
+    x_alpha = frame["alpha"].to_numpy(dtype=float)
+    columns = ["intercept", "alpha"]
+    blocks = [np.ones_like(x_alpha), x_alpha]
+    if include_sweep_fixed_effects:
+        dummies = pd.get_dummies(
+            frame["sweep_id"].astype(str), prefix="sweep", drop_first=True
+        )
+        if not dummies.empty:
+            blocks.append(dummies.to_numpy(dtype=float).T)
+            columns.extend(dummies.columns.tolist())
+    X = np.vstack(blocks).T
+    return X, y, columns
+
+
+def _covariance_hc1(X: np.ndarray, residuals: np.ndarray) -> np.ndarray:
+    n, k = X.shape
+    xtx_inv = np.linalg.pinv(X.T @ X)
+    xr = X * residuals[:, None]
+    meat = xr.T @ xr
+    scale = float(n) / max(n - k, 1)
+    return scale * (xtx_inv @ meat @ xtx_inv)
+
+
+def _covariance_cluster(
+    X: np.ndarray, residuals: np.ndarray, groups: pd.Series
+) -> tuple[np.ndarray, int]:
+    xtx_inv = np.linalg.pinv(X.T @ X)
+    unique = pd.Series(groups).astype(str).dropna().unique().tolist()
+    g = len(unique)
+    n, k = X.shape
+    if g <= 1:
+        return _covariance_hc1(X, residuals), g
+    meat = np.zeros((k, k), dtype=float)
+    for value in unique:
+        mask = pd.Series(groups).astype(str).to_numpy() == value
+        Xg = X[mask]
+        ug = residuals[mask]
+        xu = Xg.T @ ug
+        meat += np.outer(xu, xu)
+    c = (g / (g - 1.0)) * ((n - 1.0) / max(n - k, 1.0))
+    return c * (xtx_inv @ meat @ xtx_inv), g
+
+
+def _fit_ols(
+    X: np.ndarray,
+    y: np.ndarray,
+    columns: list[str],
+    *,
+    cov_type: str,
+    groups: pd.Series | None = None,
+) -> dict[str, object]:
+    n, k = X.shape
+    beta, _, _, _ = np.linalg.lstsq(X, y, rcond=None)
+    fitted = X @ beta
+    residuals = y - fitted
+    dof = max(n - k, 1)
+    sse = float(np.sum(residuals**2))
+    y_centered = y - float(np.mean(y))
+    sst = float(np.sum(y_centered**2))
+    r2 = float(1.0 - sse / sst) if sst > 0 else 0.0
+    adj_r2 = float(1.0 - (1.0 - r2) * ((n - 1.0) / max(n - k, 1.0)))
+
+    if cov_type == "iid":
+        sigma2 = sse / dof
+        cov = sigma2 * np.linalg.pinv(X.T @ X)
+        df_t = dof
+        clusters = None
+    elif cov_type == "hc1":
+        cov = _covariance_hc1(X, residuals)
+        df_t = dof
+        clusters = None
+    elif cov_type == "cluster":
+        if groups is None:
+            raise ValueError("groups are required when cov_type='cluster'")
+        cov, clusters = _covariance_cluster(X, residuals, groups)
+        df_t = max(clusters - 1, 1)
+    else:
+        raise ValueError(f"Unsupported cov_type: {cov_type}")
+
+    se = np.sqrt(np.clip(np.diag(cov), 0.0, np.inf))
+    t_stats = np.divide(beta, se, out=np.zeros_like(beta), where=se > 0)
+    p_values = 2.0 * (1.0 - stats.t.cdf(np.abs(t_stats), df=df_t))
+    t_crit = float(stats.t.ppf(0.975, df=df_t))
+    ci_low = beta - t_crit * se
+    ci_high = beta + t_crit * se
+
+    coef_rows = []
+    for idx, name in enumerate(columns):
+        coef_rows.append(
+            {
+                "name": name,
+                "coef": float(beta[idx]),
+                "std_error": float(se[idx]),
+                "t_stat": float(t_stats[idx]),
+                "p_value": float(p_values[idx]),
+                "ci95_low": float(ci_low[idx]),
+                "ci95_high": float(ci_high[idx]),
+            }
+        )
+
+    return {
+        "n": int(n),
+        "k": int(k),
+        "dof": int(dof),
+        "df_t": int(df_t),
+        "cov_type": cov_type,
+        "clusters": int(clusters) if clusters is not None else None,
+        "r2": r2,
+        "adj_r2": adj_r2,
+        "sse": sse,
+        "coefficients": coef_rows,
+        "residuals": residuals,
+        "fitted": fitted,
+        "beta": beta,
+    }
+
+
+def _diagnostics(
+    X: np.ndarray, y: np.ndarray, fit: dict[str, object]
+) -> dict[str, object]:
+    residuals = np.asarray(fit["residuals"], dtype=float)
+    n, k = X.shape
+    if residuals.size < 8:
+        normality = {"test": "jarque_bera", "available": False}
+    else:
+        jb_stat, jb_p = stats.jarque_bera(residuals)
+        normality = {
+            "test": "jarque_bera",
+            "available": True,
+            "statistic": float(jb_stat),
+            "p_value": float(jb_p),
+        }
+
+    if k <= 1:
+        hetero = {"test": "breusch_pagan", "available": False}
+    else:
+        u2 = residuals**2
+        aux = _fit_ols(X, u2, [f"x{i}" for i in range(k)], cov_type="iid")
+        lm = float(len(u2) * float(aux["r2"]))
+        df_bp = k - 1
+        p_bp = float(1.0 - stats.chi2.cdf(lm, df_bp))
+        hetero = {
+            "test": "breusch_pagan",
+            "available": True,
+            "lm_stat": lm,
+            "df": int(df_bp),
+            "p_value": p_bp,
+        }
+
+    xtx_inv = np.linalg.pinv(X.T @ X)
+    leverages = np.sum((X @ xtx_inv) * X, axis=1)
+    mse = float(np.sum(residuals**2) / max(n - k, 1))
+    if mse <= 0:
+        cooks = np.zeros(n, dtype=float)
+    else:
+        denom = np.clip((1.0 - leverages) ** 2, 1e-10, np.inf)
+        cooks = ((residuals**2) / (k * mse)) * (leverages / denom)
+
+    return {
+        "normality": normality,
+        "heteroskedasticity": hetero,
+        "influence": {
+            "max_leverage": float(np.max(leverages)) if leverages.size else 0.0,
+            "mean_leverage": float(np.mean(leverages)) if leverages.size else 0.0,
+            "high_leverage_threshold": float(2.0 * k / max(n, 1)),
+            "high_leverage_count": int(np.sum(leverages > (2.0 * k / max(n, 1)))),
+            "max_cooks_distance": float(np.max(cooks)) if cooks.size else 0.0,
+            "high_cooks_threshold": float(4.0 / max(n, 1)),
+            "high_cooks_count": int(np.sum(cooks > (4.0 / max(n, 1)))),
+        },
+    }
+
+
+def run(args: argparse.Namespace) -> list[Path]:
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    runs = _load_runs(Path(args.bundle_dir))
+    filtered = _apply_filters(runs, args)
+    if len(filtered) < 3:
+        raise ValueError("Filtered cohort must contain at least 3 rows")
+    if filtered["alpha"].nunique() < 2:
+        raise ValueError("Filtered cohort must contain at least 2 unique alpha values")
+
+    filtered_csv = output_dir / "revenue_alpha_filtered.csv"
+    filtered.to_csv(filtered_csv, index=False)
+
+    sample_accounting = {
+        "bundle_dir": str(Path(args.bundle_dir)),
+        "git_commit": _get_git_commit(),
+        "cohort_name": str(args.cohort_name),
+        "filters": {
+            "sweep_id": args.sweep_id,
+            "mode": args.mode,
+            "n_products": args.n_products,
+            "eta_ux": args.eta_ux,
+            "lambda_coi": args.lambda_coi,
+            "alpha_min": args.alpha_min,
+            "alpha_max": args.alpha_max,
+        },
+        "n_rows": int(len(filtered)),
+        "n_sweeps": int(filtered["sweep_id"].nunique()),
+        "alpha_unique": sorted(
+            float(v) for v in filtered["alpha"].dropna().unique().tolist()
+        ),
+        "rows_by_sweep": filtered.groupby("sweep_id").size().astype(int).to_dict(),
+        "rows_by_mode": filtered.groupby("mode").size().astype(int).to_dict(),
+    }
+    sample_path = output_dir / "revenue_alpha_sample_accounting.json"
+    sample_path.write_text(json.dumps(sample_accounting, indent=2) + "\n")
+
+    X_simple, y, cols_simple = _design_matrix(
+        filtered, include_sweep_fixed_effects=False
+    )
+    fit_simple = _fit_ols(X_simple, y, cols_simple, cov_type="iid")
+    simple_path = output_dir / "revenue_alpha_simple_ols.json"
+    simple_path.write_text(
+        json.dumps(
+            {
+                k: v
+                for k, v in fit_simple.items()
+                if k not in {"residuals", "fitted", "beta"}
+            },
+            indent=2,
+        )
+        + "\n"
+    )
+
+    X_fe, y_fe, cols_fe = _design_matrix(filtered, include_sweep_fixed_effects=True)
+    cov_type = "cluster" if filtered["sweep_id"].nunique() > 1 else "hc1"
+    fit_fe = _fit_ols(
+        X_fe, y_fe, cols_fe, cov_type=cov_type, groups=filtered["sweep_id"]
+    )
+    fe_path = output_dir / "revenue_alpha_fixed_effects.json"
+    fe_path.write_text(
+        json.dumps(
+            {
+                k: v
+                for k, v in fit_fe.items()
+                if k not in {"residuals", "fitted", "beta"}
+            },
+            indent=2,
+        )
+        + "\n"
+    )
+
+    per_sweep_rows: list[dict[str, float | str | int]] = []
+    for sweep_id, group in filtered.groupby("sweep_id"):
+        if len(group) < 3 or group["alpha"].nunique() < 2:
+            continue
+        X_sw, y_sw, cols_sw = _design_matrix(group, include_sweep_fixed_effects=False)
+        fit_sw = _fit_ols(X_sw, y_sw, cols_sw, cov_type="hc1")
+        alpha_row = next(
+            row for row in fit_sw["coefficients"] if row["name"] == "alpha"
+        )
+        per_sweep_rows.append(
+            {
+                "sweep_id": str(sweep_id),
+                "n": int(fit_sw["n"]),
+                "alpha_coef": float(alpha_row["coef"]),
+                "alpha_std_error": float(alpha_row["std_error"]),
+                "alpha_t_stat": float(alpha_row["t_stat"]),
+                "alpha_p_value": float(alpha_row["p_value"]),
+                "alpha_ci95_low": float(alpha_row["ci95_low"]),
+                "alpha_ci95_high": float(alpha_row["ci95_high"]),
+                "r2": float(fit_sw["r2"]),
+            }
+        )
+    per_sweep_frame = pd.DataFrame(per_sweep_rows)
+    if not per_sweep_frame.empty:
+        per_sweep_frame = per_sweep_frame.sort_values("sweep_id").reset_index(drop=True)
+    per_sweep_path = output_dir / "revenue_alpha_per_sweep.csv"
+    per_sweep_frame.to_csv(per_sweep_path, index=False)
+
+    fit_for_diagnostics = fit_fe if cov_type == "cluster" else fit_simple
+    X_for_diagnostics = X_fe if cov_type == "cluster" else X_simple
+    diagnostics = _diagnostics(X_for_diagnostics, y, fit_for_diagnostics)
+    diagnostics_path = output_dir / "revenue_alpha_diagnostics.json"
+    diagnostics_path.write_text(json.dumps(diagnostics, indent=2) + "\n")
+
+    return [
+        filtered_csv,
+        sample_path,
+        simple_path,
+        fe_path,
+        per_sweep_path,
+        diagnostics_path,
+    ]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Reproducible contamination-vs-revenue analysis from a sweep bundle"
+    )
+    parser.add_argument("--bundle-dir", type=Path, default=None)
+    parser.add_argument("--bundle-id", type=str, default=None)
+    parser.add_argument("--output-dir", type=Path, default=_default_output_dir())
+    parser.add_argument("--cohort-name", type=str, default="custom")
+    parser.add_argument("--sweep-id", action="append", default=[])
+    parser.add_argument(
+        "--mode", choices=["all", "baseline", "defended"], default="all"
+    )
+    parser.add_argument("--n-products", type=float, default=None)
+    parser.add_argument("--eta-ux", type=float, default=None)
+    parser.add_argument("--lambda-coi", type=float, default=None)
+    parser.add_argument("--alpha-min", type=float, default=0.0)
+    parser.add_argument("--alpha-max", type=float, default=1.0)
+    args = parser.parse_args()
+
+    if args.bundle_id:
+        args.bundle_dir = _bundle_dir_from_id(args.bundle_id)
+    elif args.bundle_dir is None:
+        args.bundle_dir = _default_bundle_dir()
+
+    outputs = run(args)
+    for path in outputs:
+        print(path)
+
+
+if __name__ == "__main__":
+    main()
--- a/paper/src/chapters/figures/results/revenue_alpha_classic.py
+++ b/paper/src/chapters/figures/results/revenue_alpha_classic.py
@@ -0,0 +1,63 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+
+
+root = Path(__file__).resolve().parents[5]
+runs = (
+    root
+    / "engine/studies/results/wandb_sweep_bundles/bundle_20260317_122818/runs_finished.csv"
+)
+
+df = pd.read_csv(runs)
+df = df[
+    (df["sweep_id"].astype(str) == "i88nw811")
+    & (df["study_mode"].astype(str) == "baseline")
+    & (pd.to_numeric(df["n_products"], errors="coerce") == 100.0)
+    & (pd.to_numeric(df["eta_ux"], errors="coerce") == 0.0)
+].copy()
+
+alpha = pd.to_numeric(df["alpha"], errors="coerce")
+revenue = pd.to_numeric(df["eval_revenue_mean"], errors="coerce")
+mask = alpha.notna() & revenue.notna()
+alpha = alpha[mask].to_numpy(dtype=float)
+revenue = revenue[mask].to_numpy(dtype=float)
+
+if len(alpha) < 3 or np.unique(alpha).size < 2:
+    raise ValueError("Not enough data for regression")
+
+fit = stats.linregress(alpha, revenue)
+n = len(alpha)
+dof = n - 2
+t_stat = fit.slope / fit.stderr
+p_val = 2.0 * stats.t.sf(abs(t_stat), df=dof)
+r2 = fit.rvalue**2
+t_crit = stats.t.ppf(0.975, dof)
+slope_ci = (fit.slope - t_crit * fit.stderr, fit.slope + t_crit * fit.stderr)
+
+x = np.column_stack([np.ones(n), alpha])
+beta = np.linalg.lstsq(x, revenue, rcond=None)[0]
+resid = revenue - x @ beta
+xtx_inv = np.linalg.pinv(x.T @ x)
+meat = (x * resid[:, None]).T @ (x * resid[:, None])
+cov_hc1 = (n / (n - x.shape[1])) * (xtx_inv @ meat @ xtx_inv)
+se_hc1 = np.sqrt(np.diag(cov_hc1))
+t_hc1 = beta[1] / se_hc1[1]
+p_hc1 = 2.0 * stats.t.sf(abs(t_hc1), df=dof)
+slope_ci_hc1 = (beta[1] - t_crit * se_hc1[1], beta[1] + t_crit * se_hc1[1])
+
+print("Contamination-Revenue Slope")
+print(
+    "cohort: bundle_20260317_122818, sweep=i88nw811, mode=baseline, n_products=100, eta_ux=0.0"
+)
+print(f"n={n}")
+print(f"model: revenue = {fit.intercept:.2f} {fit.slope:+.2f} * alpha")
+print(
+    f"OLS: t({dof})={t_stat:.2f}, p={p_val:.3e}, R^2={r2:.3f}, slope_95CI=[{slope_ci[0]:.2f}, {slope_ci[1]:.2f}]"
+)
+print(
+    f"HC1: t={t_hc1:.2f}, p={p_hc1:.3e}, slope_95CI=[{slope_ci_hc1[0]:.2f}, {slope_ci_hc1[1]:.2f}]"
+)
+print(f"effect: +0.1 alpha -> {0.1 * fit.slope:.2f} revenue units")