Rootmeansquarederrorusingslidingwindow

rootmeansquarederrorusingslidingwindow

Metric RootMeanSquaredErrorUsingSlidingWindow from torchmetrics (torchmetrics.RootMeanSquaredErrorUsingSlidingWindow)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with RootMeanSquaredErrorUsingSlidingWindow, or mentions torchmetrics.RootMeanSquaredErrorUsingSlidingWindow directly, or wants the standard torchmetrics implementation.

Reference signature

from torchmetrics import RootMeanSquaredErrorUsingSlidingWindow

# _RootMeanSquaredErrorUsingSlidingWindow(window_size: int = 8, **kwargs: dict[str, typing.Any]) -> None

Library docstring

Wrapper for deprecated import.

>>> from torch import rand
>>> preds = rand(4, 3, 16, 16)
>>> target = rand(4, 3, 16, 16)
>>> rmse_sw = RootMeanSquaredErrorUsingSlidingWindow()
>>> rmse_sw(preds, target)
tensor(0.4158)

Quick recipe

import torchmetrics as _m
score = _m.RootMeanSquaredErrorUsingSlidingWindow(y_true, y_pred)

Don'ts

• Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
• Always check the library version's argument order — sklearn is (y_true, y_pred) while torchmetrics is (preds, target).