rzen
4947556c02
- scripts/plot_loss.py: reads reports/loss-<config>.csv, renders train/val loss as a terminal ASCII chart, saves it to reports/loss-<config>.txt. Verified on a synthetic curve. Keeps deps at torch + numpy. - README: documented plotting choice and the Phase 4 run commands.
91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
"""Render the training loss curve as an ASCII chart — no plotting dependency.
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Reads reports/loss-<config>.csv (written by src/train.py), prints a terminal
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chart of train and val loss, and saves the same rendering to
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reports/loss-<config>.txt as a committable artifact.
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Usage:
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python scripts/plot_loss.py [--config v1]
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"""
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import argparse
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import csv
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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TRAIN_CH = "."
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VAL_CH = "*"
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def read_csv(path: Path):
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iters, train, val = [], [], []
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with open(path, newline="") as f:
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for row in csv.DictReader(f):
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iters.append(int(row["iter"]))
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train.append(float(row["train_loss"]))
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val.append(float(row["val_loss"]))
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return iters, train, val
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def render(iters, series, width=64, height=18) -> str:
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"""series: list of (name, values, char). Returns a multi-line chart string."""
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all_vals = [v for _, vals, _ in series for v in vals]
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lo, hi = min(all_vals), max(all_vals)
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if hi == lo:
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hi = lo + 1.0
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imin, imax = min(iters), max(iters)
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if imax == imin:
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imax = imin + 1
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def col(it: int) -> int:
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return round((it - imin) / (imax - imin) * (width - 1))
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def row(v: float) -> int:
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return round((hi - v) / (hi - lo) * (height - 1)) # row 0 = top = hi
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grid = [[" "] * width for _ in range(height)]
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for _, vals, ch in series:
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for it, v in zip(iters, vals):
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grid[row(v)][col(it)] = ch
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lines = []
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for r in range(height):
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yval = hi - r / (height - 1) * (hi - lo)
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lines.append(f"{yval:6.3f} |" + "".join(grid[r]))
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lines.append(" " * 7 + "+" + "-" * width)
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axis = f"{imin:<{width // 2}}{imax:>{width - width // 2}}"
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lines.append(" " * 8 + axis)
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legend = f" legend: '{TRAIN_CH}' train '{VAL_CH}' val (x: iter, y: loss)"
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lines.append(legend)
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return "\n".join(lines)
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--config", default="v1")
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args = ap.parse_args()
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csv_path = ROOT / "reports" / f"loss-{args.config}.csv"
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if not csv_path.exists():
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sys.exit(f"no loss log at {csv_path} — run training first")
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iters, train, val = read_csv(csv_path)
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if len(iters) < 2:
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sys.exit(f"only {len(iters)} data point(s) logged so far — need at least 2")
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chart = render(iters, [("train", train, TRAIN_CH), ("val", val, VAL_CH)])
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header = (f"loss curve — {args.config} "
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f"(final train {train[-1]:.4f}, val {val[-1]:.4f} @ iter {iters[-1]})")
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out = header + "\n" + chart
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print(out)
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txt_path = ROOT / "reports" / f"loss-{args.config}.txt"
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txt_path.write_text(out + "\n")
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print(f"\nsaved {txt_path}")
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if __name__ == "__main__":
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main()
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