moved_repo_first_update
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rpotter6298
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#!/usr/bin/env python3
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"""
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Scan an analysis directory for HyperTower run folders, extract the best per-fold
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metric/accuracy from the epoch logs, and emit a combined summary.
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Example:
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python scripts/batch_best_metrics.py \
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--analysis-dir analysis_data
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# Holdout ranking (faster, uses summary.json):
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python scripts/batch_best_metrics.py \
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--analysis-dir analysis_data/grid_search \
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--metric holdout_auc_fused \
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--acc-metric holdout_acc_fused \
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--source summary \
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--sort-by mean_auc --desc --top 10
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The script assumes each run directory contains files named `fold{n}_epoch_log.csv`.
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It reports runs that have all five folds (fold0..fold4) present by default.
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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import math
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import sys
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import time
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from pathlib import Path
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from typing import Dict, Iterable, List, Optional, Tuple
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REQUIRED_FOLDS = {f"fold{i}_epoch_log.csv" for i in range(5)}
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def to_float(value: Optional[object]) -> Optional[float]:
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if value is None:
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return None
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if isinstance(value, (int, float)):
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num = float(value)
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if math.isnan(num):
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return None
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return num
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if not isinstance(value, str):
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return None
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value = value.strip()
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if not value:
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return None
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try:
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num = float(value)
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except ValueError:
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return None
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if math.isnan(num):
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return None
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return num
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def best_value_from_csv(csv_path: Path, metric: str) -> Optional[Tuple[float, int]]:
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best: Optional[Tuple[float, int]] = None
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with csv_path.open("r", newline="") as fp:
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reader = csv.DictReader(fp)
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for row in reader:
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val = to_float(row.get(metric))
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if val is None:
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continue
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epoch = int(to_float(row.get("epoch")) or reader.line_num)
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if best is None or val > best[0]:
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best = (val, epoch)
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return best
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def render_progress(current: int, total: Optional[int], matched: int) -> str:
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if total:
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width = 30
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filled = int(width * current / total)
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bar = "#" * filled + "-" * (width - filled)
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return f"[{bar}] {current}/{total} matched {matched}"
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return f"Scanned {current} dirs, matched {matched}"
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def find_run_directories(root: Path,
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shallow: bool,
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required_files: Iterable[str],
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show_progress: bool) -> Iterable[Path]:
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"""
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Yield directories that look like HyperTower runs (contain at least the required fold logs).
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"""
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required_set = set(required_files)
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if shallow:
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entries = [entry for entry in root.iterdir() if entry.is_dir()]
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entries.sort(key=lambda p: p.name)
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total = len(entries)
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matched = 0
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last_update = 0.0
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for idx, entry in enumerate(entries, start=1):
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if show_progress:
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now = time.monotonic()
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if now - last_update >= 0.1 or idx == total:
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msg = render_progress(idx, total, matched)
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print(f"\rScanning {msg}", end="", file=sys.stderr, flush=True)
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last_update = now
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if not entry.is_dir():
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continue
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if all((entry / filename).is_file() for filename in required_set):
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matched += 1
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yield entry
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if show_progress:
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print(file=sys.stderr)
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return
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matched = 0
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scanned = 0
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last_update = 0.0
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for dirpath, dirnames, filenames in os_walk_sorted(root):
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scanned += 1
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if show_progress:
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now = time.monotonic()
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if now - last_update >= 0.2:
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msg = render_progress(scanned, None, matched)
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print(f"\rScanning {msg}", end="", file=sys.stderr, flush=True)
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last_update = now
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files = set(filenames)
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if required_set.issubset(files):
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matched += 1
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yield Path(dirpath)
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if show_progress:
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msg = render_progress(scanned, None, matched)
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print(f"\rScanning {msg}", end="", file=sys.stderr, flush=True)
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print(file=sys.stderr)
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def os_walk_sorted(root: Path):
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"""
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Wrapper around os.walk that yields deterministic, sorted directory order.
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"""
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import os
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for dirpath, dirnames, filenames in os.walk(root):
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dirnames.sort()
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filenames.sort()
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yield dirpath, dirnames, filenames
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def read_summary(run_dir: Path) -> Optional[Dict[str, object]]:
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summary_path = run_dir / "summary.json"
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if not summary_path.exists():
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return None
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try:
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data = json.loads(summary_path.read_text())
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except Exception:
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return None
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if not isinstance(data, dict):
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return None
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return data
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def read_run_id(run_dir: Path, summary: Optional[Dict[str, object]] = None) -> str:
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data = summary if summary is not None else read_summary(run_dir)
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if data:
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rid = data.get("run_id")
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if isinstance(rid, str) and rid:
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return rid
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return run_dir.name
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def mean(values: List[float]) -> Optional[float]:
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return (sum(values) / len(values)) if values else None
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def metric_from_stats(stats: Dict[str, object], metric: str) -> Optional[float]:
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if stats.get("holdout_best_monitor") == metric:
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best_val = to_float(stats.get("holdout_best_so_far"))
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if best_val is not None:
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return best_val
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return to_float(stats.get(metric))
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def task_from_summary(summary: Optional[Dict[str, object]]) -> Optional[str]:
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if not summary:
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return None
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eval_mode = summary.get("eval_mode")
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if isinstance(eval_mode, str):
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mode = eval_mode.strip().lower()
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if mode == "binary":
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return "binary"
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if mode in {"multiclass", "multi", "multi-class"}:
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return "multiclass"
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num_classes = summary.get("num_classes")
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if isinstance(num_classes, (int, float)):
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return "binary" if int(num_classes) <= 2 else "multiclass"
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return None
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def format_table(rows: List[Dict[str, Optional[object]]], columns: List[str]) -> str:
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col_widths = {
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col: max(len(col), max((len(fmt_value(row.get(col))) for row in rows), default=0))
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for col in columns
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}
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header = " | ".join(col.ljust(col_widths[col]) for col in columns)
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divider = "-+-".join("-" * col_widths[col] for col in columns)
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body_lines = [
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" | ".join(fmt_value(row.get(col)).ljust(col_widths[col]) for col in columns)
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for row in rows
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]
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return "\n".join([header, divider, *body_lines])
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def fmt_value(value: Optional[object]) -> str:
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if value is None:
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return ""
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if isinstance(value, str):
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return value
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if isinstance(value, int):
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return str(value)
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return f"{value:.4f}"
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def main() -> None:
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ap = argparse.ArgumentParser(description="Aggregate best per-fold metrics from HyperTower runs.")
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ap.add_argument("--analysis-dir", type=Path, default=Path("analysis_data"),
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help="Directory containing run subdirectories (default: analysis_data)")
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ap.add_argument("--metric", default="auc_fused",
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help="Metric column to maximise (default: auc_fused)")
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ap.add_argument("--acc-metric", default="acc_fused",
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help="Accuracy column to maximise (default: acc_fused)")
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ap.add_argument("--shallow", action="store_true",
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help="Only scan directories directly under analysis-dir")
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ap.add_argument("--source", choices=["epoch_logs", "summary"], default="epoch_logs",
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help="Where to read metrics from (default: epoch_logs)")
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ap.add_argument("--task", choices=["binary", "multiclass", "all"], default="all",
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help="Filter runs by task type (default: all)")
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ap.add_argument("--no-progress", action="store_true",
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help="Disable progress output")
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ap.add_argument("--match", default=None,
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help="Only include run directories whose name contains this substring")
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ap.add_argument("--sort-by", choices=["mean_auc", "mean_acc"], default=None,
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help="Optional column to sort by (default: none)")
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ap.add_argument("--desc", action="store_true",
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help="Sort in descending order (default: ascending)")
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ap.add_argument("--top", type=int, default=None,
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help="Limit output to the top N rows after sorting")
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ap.add_argument("--output-file", type=Path, default=None,
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help="Optional path to write CSV summary")
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args = ap.parse_args()
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root = args.analysis_dir
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if not root.exists():
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raise SystemExit(f"Analysis directory not found: {root}")
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rows: List[Dict[str, Optional[object]]] = []
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missing_summary = 0
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unknown_task = 0
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required_files = REQUIRED_FOLDS if args.source == "epoch_logs" else ["summary.json"]
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for run_dir in find_run_directories(
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root,
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shallow=args.shallow,
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required_files=required_files,
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show_progress=not args.no_progress,
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):
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if args.match and args.match not in run_dir.name:
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continue
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summary = None
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task_label = None
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if args.task != "all" or args.source == "summary":
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summary = read_summary(run_dir)
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if summary is None:
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missing_summary += 1
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continue
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task_label = task_from_summary(summary)
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if args.task != "all":
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if task_label is None:
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unknown_task += 1
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continue
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if task_label != args.task:
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continue
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run_id = read_run_id(run_dir, summary)
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best_metrics: List[float] = []
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best_accs: List[float] = []
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if args.source == "summary":
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folds = summary.get("fold_metrics") if summary else None
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if not folds:
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continue
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for fold in folds:
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stats = fold.get("stats") or {}
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metric_val = metric_from_stats(stats, args.metric)
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acc_val = metric_from_stats(stats, args.acc_metric)
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if metric_val is None or acc_val is None:
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best_metrics = []
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best_accs = []
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break
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best_metrics.append(metric_val)
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best_accs.append(acc_val)
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else:
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for fold_idx in range(5):
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csv_path = run_dir / f"fold{fold_idx}_epoch_log.csv"
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metric_entry = best_value_from_csv(csv_path, args.metric)
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acc_entry = best_value_from_csv(csv_path, args.acc_metric)
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if metric_entry is None or acc_entry is None:
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# Skip this run if any fold is missing data
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best_metrics = []
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best_accs = []
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break
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best_metrics.append(metric_entry[0])
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best_accs.append(acc_entry[0])
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if not best_metrics or not best_accs:
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continue
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rows.append({
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"run_id": run_id,
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"task": task_label,
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"relative_path": str(run_dir.relative_to(root)),
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"mean_auc": mean(best_metrics),
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"mean_acc": mean(best_accs),
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})
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if not rows:
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print("No matching runs found.")
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return
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if args.sort_by:
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def sort_key(row: Dict[str, Optional[float]]) -> float:
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value = row.get(args.sort_by)
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if value is None:
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return float("-inf") if args.desc else float("inf")
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return float(value)
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rows.sort(key=sort_key, reverse=args.desc)
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if args.top is not None:
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rows = rows[:args.top]
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columns = ["run_id", "task", "relative_path", "mean_auc", "mean_acc"]
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if args.task != "all":
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print(f"Task filter: {args.task}")
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if args.match:
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print(f"Name filter: {args.match}")
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print(f"Runs: {len(rows)}\n")
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print(format_table(rows, columns))
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if args.output_file:
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out_path = args.output_file
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out_path.parent.mkdir(parents=True, exist_ok=True)
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with out_path.open("w", newline="") as fp:
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writer = csv.DictWriter(fp, fieldnames=columns)
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writer.writeheader()
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for row in rows:
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writer.writerow(row)
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print(f"\nSummary written to {out_path}")
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if args.task != "all" and (missing_summary or unknown_task):
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print(f"\nSkipped {missing_summary} runs without summary.json and {unknown_task} with unknown task type.")
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if __name__ == "__main__":
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main()
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