| """Plot training curves and physical-space validation predictions.""" |
|
|
| import argparse |
| import csv |
| import json |
| from pathlib import Path |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
| from common import load_config, package_path, save_json |
|
|
|
|
| def render(config): |
| output = package_path(config["results"]["directory"]) |
| data = np.load(output/"predictions.npz") |
| metrics = json.loads((output/"metrics.json").read_text()) |
| with open(output/"history.csv") as stream: |
| rows = list(csv.DictReader(stream)) |
| epoch = np.array([int(r["epoch"]) for r in rows]) |
| fig, axes = plt.subplots(1, 2, figsize=(11, 4), constrained_layout=True) |
| for name, label in [("train_loss","Training"),("val_loss","Validation")]: |
| axes[0].semilogy(epoch,[float(r[name]) for r in rows],label=label) |
| axes[0].set(xlabel="Epoch",ylabel="MSE + relative L2 (normalized)") |
| axes[0].legend() |
| axes[1].semilogy(epoch,[float(r["val_relative_l2"]) for r in rows],color="tab:blue",label="Validation") |
| axes[1].axhline(0.0109,color="tab:red",linestyle="--",label="Paper Table 1: 0.0109") |
| axes[1].set(xlabel="Epoch",ylabel="Relative L2 (physical space)") |
| axes[1].legend() |
| for ax in axes: ax.grid(alpha=0.2) |
| fig.savefig(output/"training_curves.png",dpi=200) |
| fig.savefig(output/"training_curves.pdf") |
| plt.close(fig) |
| errors=data["relative_l2"] |
| chosen=[0,int(np.argsort(errors)[len(errors)//2]),int(np.argmax(errors))] |
| fig, axes=plt.subplots(3,2,figsize=(12,10),constrained_layout=True) |
| x=data["x"] |
| for row,index in enumerate(chosen): |
| initial=data["initial"][index,:,0]; target=data["target"][index,:,0]; pred=data["prediction"][index,:,0] |
| axes[row,0].plot(x,initial,color="0.6",linewidth=1,label="Initial u0") |
| axes[row,0].plot(x,target,color="black",linewidth=1.5,label="Reference u(T)") |
| axes[row,0].plot(x,pred,color="tab:blue",linestyle="--",label="LiNO") |
| axes[row,0].set_title(f"Source sample {data['sample_indices'][index]}, relative L2={errors[index]:.4g}") |
| axes[row,0].set(xlabel="x",ylabel="u (dataset units)") |
| axes[row,0].legend() |
| axes[row,1].plot(x,pred-target,color="tab:red") |
| axes[row,1].axhline(0,color="0.5",linewidth=0.5) |
| axes[row,1].set(xlabel="x",ylabel="Prediction - reference") |
| fig.savefig(output/"validation_examples.png",dpi=200) |
| fig.savefig(output/"validation_examples.pdf") |
| plt.close(fig) |
| fig, ax=plt.subplots(figsize=(6,4),constrained_layout=True) |
| ax.hist(errors,bins=35,color="tab:blue",alpha=.85) |
| ax.set(xlabel="Validation relative L2",ylabel="Samples",title=f"All {len(errors)} held-out samples") |
| fig.savefig(output/"error_distribution.png",dpi=200) |
| plt.close(fig) |
| save_json(output/"visualization_manifest.json",dict(figures=["training_curves.png","training_curves.pdf","validation_examples.png","validation_examples.pdf","error_distribution.png"], |
| selected_validation_indices=chosen,sample_count=len(errors),completed_epochs=len(rows),metrics=metrics)) |
| print(f"VISUALIZATION COMPLETED: {output} ({len(rows)} epochs, {len(errors)} validation samples)",flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| parser=argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--config",default="config/config.yaml") |
| args=parser.parse_args() |
| render(load_config(args.config)) |
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|
