Plotters

A group of trainer callbacks that accumulate model predictions over the validation and test splits and render diagnostic plots to the run’s output directory. Each is registered with a CallbackSpec like any other callback, and they accept optional keyword arguments (such as a map of class indices to display names for the classification plots).

Regression plots:

• Parity (ParityPlotter): predicted value against true value, where points on the diagonal are perfect predictions.

• Bias (BiasPlotter): prediction residual as a function of the true value, revealing systematic over- or under-prediction.

Classification plots:

• Confusion Matrix (CMPlotter): counts of predicted versus true classes.

• P(true) (PTruePlotter): distribution of the probability the model assigns to the correct class.

• P(positive) (BinaryPPositivePlotter): distribution of the predicted positive-class probability for binary tasks.

• ROC (ROCPlotter): receiver operating characteristic curve.

• Precision-Recall (PrecisionRecallPlotter): precision against recall across thresholds.

from icegraph.trainer.callbacks import CallbackSpec, ParityPlotter, ROCPlotter

trainer.register_callback(CallbackSpec(callback=ParityPlotter, kwargs={}))
trainer.register_callback(CallbackSpec(callback=ROCPlotter, kwargs={}))