feat(bench): add single-file GroupBy regression benchmark + reports

- bench_groupby_regression.py: self-contained scenarios (clean/outliers, serial/parallel)
- Emits TXT and JSON (CSV optional) for easy doc inclusion and CI checks
- Uses y ~ x1 + x2 per-group via GroupByRegressor.make_parallel_fit
- Workaround for single-col group key (duplicate column for tuple keys)

Sample results show:
- ~1.75 s / 1k groups (serial clean, 50k rows, 10k groups)
- ~0.41 s / 1k groups with n_jobs=10 (≈4.3× speedup)
- Current y-shift outliers do not slow down OLS path (no refits triggered)

Author: miranov25

UTILS/dfextensions/bench_groupby_regression.py

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+#!/usr/bin/env python3
+"""
+bench_groupby_regression.py — Single-file benchmark suite and reporter
+
+Scenarios covered (configurable via CLI):
+  1) Clean baseline (serial & parallel)
+  2) Outliers: 5% @ 3σ, 10% @ 5σ, 10% @ 10σ
+  3) Group sizes: 5, 20, 100 rows/group
+  4) n_jobs: 1, 4, 10
+  5) fitters: ols, robust, huber (if supported by implementation)
+  6) sigmaCut: 3, 5, 10, 100
+
+Outputs:
+  - Pretty text report
+  - JSON results (per scenario, with timing and configuration)
+  - Optional CSV summary
+
+Usage examples:
+  python3 bench_groupby_regression.py --quick
+  python3 bench_groupby_regression.py --rows 50000 --groups 10000 --out out_dir
+  python3 bench_groupby_regression.py --emit-csv
+
+Note:
+  This script expects 'groupby_regression.py' in PYTHONPATH or next to it and
+  uses GroupByRegressor.make_parallel_fit(...). See the wiring in _run_one().
+"""
+from __future__ import annotations
+import argparse, json, math, os, sys, time
+from dataclasses import dataclass, asdict
+from pathlib import Path
+from typing import List, Dict, Any, Tuple
+
+import numpy as np
+import pandas as pd
+
+# --- Import the project module ---
+try:
+    import groupby_regression as gr
+    from groupby_regression import GroupByRegressor
+except Exception as e:
+    print("[ERROR] Failed to import groupby_regression.py:", e, file=sys.stderr)
+    raise
+
+# --- Data Generators (Phase 1) ---
+def _make_groups(n_rows: int, n_groups: int, rng: np.random.Generator) -> np.ndarray:
+    base = np.repeat(np.arange(n_groups, dtype=np.int32), n_rows // n_groups)
+    rem = n_rows - base.size
+    if rem > 0:
+        extra = rng.choice(n_groups, size=rem, replace=False)
+        base = np.concatenate([base, extra.astype(np.int32, copy=False)])
+    rng.shuffle(base)
+    return base
+
+def create_clean_data(n_rows: int, n_groups: int, *, seed: int = 42, noise_sigma: float = 1.0, x_corr: float = 0.0) -> pd.DataFrame:
+    rng = np.random.default_rng(seed)
+    group = _make_groups(n_rows, n_groups, rng)
+    mean = np.array([0.0, 0.0])
+    cov = np.array([[1.0, x_corr], [x_corr, 1.0]])
+    x = rng.multivariate_normal(mean, cov, size=n_rows, method="cholesky")
+    x1 = x[:, 0].astype(np.float32); x2 = x[:, 1].astype(np.float32)
+    eps = rng.normal(0.0, noise_sigma, size=n_rows).astype(np.float32)
+    y = (2.0 * x1 + 3.0 * x2 + eps).astype(np.float32)
+    df = pd.DataFrame({"group": group, "x1": x1, "x2": x2, "y": y})
+    return df
+
+def create_data_with_outliers(n_rows: int, n_groups: int, *, outlier_pct: float = 0.10, outlier_magnitude: float = 5.0,
+                              seed: int = 42, noise_sigma: float = 1.0, x_corr: float = 0.0) -> pd.DataFrame:
+    df = create_clean_data(n_rows, n_groups, seed=seed, noise_sigma=noise_sigma, x_corr=x_corr)
+    rng = np.random.default_rng(seed + 1337)
+    k = int(math.floor(outlier_pct * n_rows))
+    if k > 0:
+        idx = rng.choice(n_rows, size=k, replace=False)
+        signs = rng.choice(np.array([-1.0, 1.0], dtype=np.float32), size=k, replace=True)
+        shift = (outlier_magnitude * noise_sigma * signs).astype(np.float32)
+        y = df["y"].to_numpy(copy=True)
+        y[idx] = (y[idx] + shift).astype(np.float32)
+        df["y"] = y
+    return df
+
+# --- Benchmark Plumbing ---
+@dataclass
+class Scenario:
+    name: str
+    outlier_pct: float
+    outlier_mag: float
+    rows_per_group: int
+    n_groups: int
+    n_jobs: int
+    fitter: str
+    sigmaCut: float
+
+def _run_one(df: pd.DataFrame, scenario: Scenario) -> Dict[str, Any]:
+    # Workaround for module expecting tuple keys: duplicate group
+    df = df.copy()
+    df["group2"] = df["group"].astype(np.int32)
+    df["weight"] = 1.0
+    selection = pd.Series(True, index=df.index)
+
+    t0 = time.perf_counter()
+    _, df_params = GroupByRegressor.make_parallel_fit(
+        df,
+        gb_columns=["group", "group2"],
+        fit_columns=["y"],
+        linear_columns=["x1", "x2"],
+        median_columns=[],
+        weights="weight",
+        suffix="_fit",
+        selection=selection,
+        addPrediction=False,
+        n_jobs=scenario.n_jobs,
+        min_stat=[3, 4],
+        sigmaCut=scenario.sigmaCut,
+        fitter=scenario.fitter,
+        batch_size="auto",
+    )
+    dt = time.perf_counter() - t0
+    n_groups = int(df_params.shape[0])
+    per_1k = dt / (n_groups / 1000.0) if n_groups else float("nan")
+    return {
+        "scenario": scenario.name,
+        "config": {
+            "n_jobs": scenario.n_jobs,
+            "sigmaCut": scenario.sigmaCut,
+            "fitter": scenario.fitter,
+            "rows_per_group": scenario.rows_per_group,
+            "n_groups": scenario.n_groups,
+            "outlier_pct": scenario.outlier_pct,
+            "outlier_mag": scenario.outlier_mag,
+        },
+        "result": {
+            "total_sec": dt,
+            "sec_per_1k_groups": per_1k,
+            "n_groups_effective": n_groups,
+        },
+    }
+
+def _make_df(s: Scenario, seed: int = 7) -> pd.DataFrame:
+    n_rows = s.rows_per_group * s.n_groups
+    if s.outlier_pct > 0.0:
+        return create_data_with_outliers(n_rows, s.n_groups, outlier_pct=s.outlier_pct, outlier_magnitude=s.outlier_mag, seed=seed)
+    else:
+        return create_clean_data(n_rows, s.n_groups, seed=seed)
+
+def _format_report(rows: List[Dict[str, Any]]) -> str:
+    lines = []
+    lines.append("=" * 64); lines.append("BENCHMARK: GroupBy Regression"); lines.append("=" * 64)
+    for r in rows:
+        cfg = r["config"]; res = r["result"]
+        lines.append("")
+        lines.append(f"Scenario: {r['scenario']}")
+        lines.append(f"  Config: n_jobs={cfg['n_jobs']}, sigmaCut={cfg['sigmaCut']}, fitter={cfg['fitter']}")
+        lines.append(f"  Data: {cfg['rows_per_group']*cfg['n_groups']:,} rows, {res['n_groups_effective']:,} groups (target {cfg['n_groups']:,}), ~{cfg['rows_per_group']} rows/group")
+        if cfg['outlier_pct']>0:
+            lines.append(f"  Outliers: {cfg['outlier_pct']*100:.0f}% at {cfg['outlier_mag']}σ")
+        lines.append(f"  Result: {res['total_sec']:.2f}s ({res['sec_per_1k_groups']:.2f}s per 1k groups)")
+    lines.append("")
+    return "\n".join(lines)
+
+def run_suite(args) -> Tuple[List[Dict[str, Any]], str, str, str | None]:
+    # Build scenarios
+    scenarios: List[Scenario] = []
+
+    # Baselines
+    scenarios.append(Scenario("Clean Data, Serial", 0.0, 0.0, args.rows_per_group, args.groups, 1, args.fitter, args.sigmaCut))
+    if not args.serial_only:
+        scenarios.append(Scenario("Clean Data, Parallel", 0.0, 0.0, args.rows_per_group, args.groups, args.n_jobs, args.fitter, args.sigmaCut))
+
+    # Outlier sets
+    scenarios.append(Scenario("5% Outliers (3σ), Serial", 0.05, 3.0, args.rows_per_group, args.groups, 1, args.fitter, args.sigmaCut))
+    scenarios.append(Scenario("10% Outliers (5σ), Serial", 0.10, 5.0, args.rows_per_group, args.groups, 1, args.fitter, args.sigmaCut))
+    if not args.serial_only:
+        scenarios.append(Scenario("10% Outliers (5σ), Parallel", 0.10, 5.0, args.rows_per_group, args.groups, args.n_jobs, args.fitter, args.sigmaCut))
+    scenarios.append(Scenario("10% Outliers (10σ), Serial", 0.10, 10.0, args.rows_per_group, args.groups, 1, args.fitter, args.sigmaCut))
+
+    # Prepare output
+    out_dir = Path(args.out).resolve()
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    # Run
+    results: List[Dict[str, Any]] = []
+    for s in scenarios:
+        df = _make_df(s, seed=args.seed)
+        results.append(_run_one(df, s))
+
+    # Save
+    txt_path = out_dir / "benchmark_report.txt"
+    json_path = out_dir / "benchmark_results.json"
+    with open(txt_path, "w") as f:
+        f.write(_format_report(results))
+    with open(json_path, "w") as f:
+        json.dump(results, f, indent=2)
+
+    csv_path = None
+    if args.emit_csv:
+        import csv
+        csv_path = out_dir / "benchmark_results.csv"
+        with open(csv_path, "w", newline="") as f:
+            w = csv.writer(f)
+            w.writerow(["scenario","n_jobs","sigmaCut","fitter","rows_per_group","n_groups","outlier_pct","outlier_mag","total_sec","sec_per_1k_groups","n_groups_effective"])
+            for r in results:
+                cfg = r["config"]; res = r["result"]
+                w.writerow([r["scenario"], cfg["n_jobs"], cfg["sigmaCut"], cfg["fitter"], cfg["rows_per_group"], cfg["n_groups"], cfg["outlier_pct"], cfg["outlier_mag"], res["total_sec"], res["sec_per_1k_groups"], res["n_groups_effective"]])
+
+    return results, str(txt_path), str(json_path), (str(csv_path) if csv_path else None)
+
+def parse_args():
+    p = argparse.ArgumentParser(description="GroupBy Regression Benchmark Suite")
+    p.add_argument("--rows-per-group", type=int, default=5, help="Rows per group.")
+    p.add_argument("--groups", type=int, default=10000, help="Number of groups.")
+    p.add_argument("--n-jobs", type=int, default=4, help="Workers for parallel scenarios.")
+    p.add_argument("--sigmaCut", type=float, default=5.0, help="Sigma cut for robust fitting.")
+    p.add_argument("--fitter", type=str, default="ols", help="Fitter: ols|robust|huber depending on implementation.")
+    p.add_argument("--seed", type=int, default=7, help="Random seed.")
+    p.add_argument("--out", type=str, default="bench_out", help="Output directory.")
+    p.add_argument("--emit-csv", action="store_true", help="Also emit CSV summary.")
+    p.add_argument("--serial-only", action="store_true", help="Skip parallel scenarios.")
+    p.add_argument("--quick", action="store_true", help="Small quick run: groups=200.")
+    args = p.parse_args()
+    if args.quick:
+        args.groups = min(args.groups, 200)
+    return args
+
+def main():
+    args = parse_args()
+    results, txt_path, json_path, csv_path = run_suite(args)
+    print(_format_report(results))
+    print("\nSaved outputs:")
+    print(" -", txt_path)
+    print(" -", json_path)
+    if csv_path: print(" -", csv_path)
+
+if __name__ == "__main__":
+    main()

UTILS/dfextensions/bench_out_quick/benchmark_report.txt

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+================================================================
+BENCHMARK: GroupBy Regression
+================================================================
+
+Scenario: Clean Data, Serial
+  Config: n_jobs=1, sigmaCut=5.0, fitter=ols
+  Data: 1,000 rows, 200 groups (target 200), ~5 rows/group
+  Result: 0.36s (1.78s per 1k groups)
+
+Scenario: 5% Outliers (3σ), Serial
+  Config: n_jobs=1, sigmaCut=5.0, fitter=ols
+  Data: 1,000 rows, 200 groups (target 200), ~5 rows/group
+  Outliers: 5% at 3.0σ
+  Result: 0.34s (1.72s per 1k groups)
+
+Scenario: 10% Outliers (5σ), Serial
+  Config: n_jobs=1, sigmaCut=5.0, fitter=ols
+  Data: 1,000 rows, 200 groups (target 200), ~5 rows/group
+  Outliers: 10% at 5.0σ
+  Result: 0.34s (1.71s per 1k groups)
+
+Scenario: 10% Outliers (10σ), Serial
+  Config: n_jobs=1, sigmaCut=5.0, fitter=ols
+  Data: 1,000 rows, 200 groups (target 200), ~5 rows/group
+  Outliers: 10% at 10.0σ
+  Result: 0.34s (1.71s per 1k groups)