update 3-19
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rpotter6298
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#!/usr/bin/env python3
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"""
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Per-epoch learning curves for mdonly runs.
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Reads epoch_log.csv from each fold dir and plots val_auc, val_acc,
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hld_auc, hld_acc — one figure per metric, all folds as individual lines.
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Usage:
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python scripts/output_analysis/visualizations/plot_mdonly_curves.py \
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--run-dirs analysis_data/pipeline_mdonly_50ep \
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analysis_data/pipeline_mdonly_200ep \
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analysis_data/pipeline_mdonly_500ep \
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--eval-mode binary
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"""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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METRICS = [
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("val_auc", "Val AUC"),
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("val_acc", "Val Accuracy"),
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("hld_auc", "Holdout AUC"),
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("hld_acc", "Holdout Accuracy"),
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]
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PHASE_SHADING = {
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"tower_warmup": "#d0e8ff",
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"fused_warmup": "#d0ffe8",
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}
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def _load_folds(mode_dir: Path) -> list[pd.DataFrame]:
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fold_dirs = sorted(
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[p for p in mode_dir.glob("fold*") if p.is_dir()],
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key=lambda p: int(p.name.replace("fold", "")),
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)
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frames = []
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for fd in fold_dirs:
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csv = fd / "epoch_log.csv"
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if not csv.exists():
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print(f" [warn] {csv} not found, skipping")
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continue
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df = pd.read_csv(csv)
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df["_fold"] = int(fd.name.replace("fold", ""))
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frames.append(df)
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return frames
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def _shade_warmup(ax: plt.Axes, df: pd.DataFrame) -> None:
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"""Shade warmup phase regions based on first fold's phase column."""
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if "phase" not in df.columns:
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return
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prev_phase = None
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start = None
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for _, row in df.iterrows():
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phase = row["phase"]
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ep = row["epoch"]
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if phase != prev_phase:
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if prev_phase in PHASE_SHADING and start is not None:
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ax.axvspan(start - 0.5, ep - 0.5, color=PHASE_SHADING[prev_phase],
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alpha=0.35, zorder=0, label=f"{prev_phase.replace('_', ' ')}")
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start = ep
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prev_phase = phase
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# close last span
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if prev_phase in PHASE_SHADING and start is not None:
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ax.axvspan(start - 0.5, df["epoch"].max() + 0.5,
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color=PHASE_SHADING[prev_phase], alpha=0.35, zorder=0)
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def plot_curves(
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run_dirs: list[Path],
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eval_mode: str,
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tower_mode: str,
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out_dir: Path | None,
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) -> None:
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# Collect (label, frames) pairs
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datasets: list[tuple[str, list[pd.DataFrame]]] = []
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for rd in run_dirs:
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mode_dir = rd / eval_mode / tower_mode
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if not mode_dir.exists():
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print(f" [skip] {mode_dir} not found")
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continue
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frames = _load_folds(mode_dir)
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if not frames:
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print(f" [skip] no epoch_log.csv found under {mode_dir}")
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continue
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datasets.append((rd.name, frames))
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if not datasets:
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print("No data found — nothing to plot.")
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return
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# One figure per metric
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for metric_key, metric_label in METRICS:
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# Check any fold actually has this metric with non-nan values
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has_data = any(
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not frames[0][metric_key].isna().all()
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for _, frames in datasets
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if frames and metric_key in frames[0].columns
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)
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if not has_data:
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continue
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n_runs = len(datasets)
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fig, axes = plt.subplots(1, n_runs, figsize=(5 * n_runs, 4.5), squeeze=False)
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for col_idx, (run_label, frames) in enumerate(datasets):
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ax = axes[0][col_idx]
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if frames and metric_key in frames[0].columns:
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_shade_warmup(ax, frames[0])
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colours = plt.cm.tab10(np.linspace(0, 0.9, len(frames)))
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for frame, colour in zip(frames, colours):
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if metric_key not in frame.columns:
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continue
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vals = frame[metric_key].values
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epochs = frame["epoch"].values
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mask = ~np.isnan(vals.astype(float))
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if mask.sum() == 0:
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continue
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ax.plot(epochs[mask], vals[mask],
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linewidth=1.4, color=colour,
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label=f"fold {frame['_fold'].iloc[0]}")
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ax.set_title(run_label, fontsize=10)
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ax.set_xlabel("Epoch")
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if col_idx == 0:
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ax.set_ylabel(metric_label)
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ax.legend(fontsize=7, loc="lower right")
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ax.grid(True, linewidth=0.4, alpha=0.5)
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fig.suptitle(f"{metric_label} [{eval_mode} / {tower_mode}]", fontsize=12)
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fig.tight_layout()
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dest = out_dir or (run_dirs[0].parent / "mdonly_plots")
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dest.mkdir(parents=True, exist_ok=True)
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fname = f"mdonly_{metric_key}_{eval_mode}_{tower_mode}.png"
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fig.savefig(dest / fname, dpi=150, bbox_inches="tight")
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plt.close(fig)
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print(f"Saved: {dest / fname}")
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def main() -> None:
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--run-dirs", nargs="+", required=True,
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help="One or more run directories (e.g. analysis_data/pipeline_mdonly_50ep).")
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ap.add_argument("--eval-mode", default="binary", choices=["binary", "multiclass"])
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ap.add_argument("--tower-mode", default="single", choices=["single", "ensemble"])
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ap.add_argument("--out", default=None,
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help="Output directory for plots (default: {first_run_dir}/../mdonly_plots).")
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args = ap.parse_args()
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run_dirs = [Path(d) for d in args.run_dirs]
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out_dir = Path(args.out) if args.out else None
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plot_curves(run_dirs, args.eval_mode, args.tower_mode, out_dir)
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if __name__ == "__main__":
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main()
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