modified plotting script to be more agnostic to X
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@ -84,6 +84,10 @@ Two dataset formats are supported:
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- this will shove everything into a single HDF5 file and store some metadata alongside (for now, the symbol map generated, and text/audio lengths)
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- be sure to also define `use_hdf5` in your config YAML.
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### Plotting Metrics
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Included is a helper script to parse the training metrics. Simply invoke it with, for example: `python3 ./scripts/plot.py --log-dir ./training/valle/logs/1693675364 --out-dir ./data/ --xs=ar.engine_step --ys=ar.loss`
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### Notices
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#### Modifying `prom_levels`, `resp_levels`, Or `tasks` For A Model
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@ -26,8 +26,11 @@ def plot(paths, args):
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except Exception as e:
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continue
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if f"{args.model_name}.engine_step" in row:
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for x in args.xs:
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if x not in row:
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continue
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rows.append(row)
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break
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df = pd.DataFrame(rows)
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@ -44,14 +47,16 @@ def plot(paths, args):
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df = pd.concat(dfs)
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if args.max_y is not None:
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df = df[df[f"{args.model_name}.engine_step"] < args.max_x]
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for x in args.xs:
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df = df[df[x] < args.max_x]
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for gtag, gdf in sorted(
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df.groupby("group"),
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key=lambda p: (p[0].split("/")[-1], p[0]),
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):
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for x in args.xs:
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for y in args.ys:
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gdf = gdf.sort_values(f"{args.model_name}.engine_step")
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gdf = gdf.sort_values(x)
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if gdf[y].isna().all():
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continue
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@ -62,7 +67,7 @@ def plot(paths, args):
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gdf[y] = gdf[y].ewm(10).mean()
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gdf.plot(
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x=f"{args.model_name}.engine_step",
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x=x,
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y=y,
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label=f"{y}",
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ax=plt.gca(),
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@ -80,7 +85,8 @@ def plot(paths, args):
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("ys", nargs="+")
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parser.add_argument("--xs", nargs="+", default="ar.engine_step")
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parser.add_argument("--ys", nargs="+", default="ar.loss")
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parser.add_argument("--log-dir", default="logs", type=Path)
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parser.add_argument("--out-dir", default="logs", type=Path)
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parser.add_argument("--filename", default="log.txt")
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