core.modules.evaluator

Copyright (c) Meta, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Attributes

NONE

Classes

Evaluator

Functions

forcesx_mae(prediction, target[, key])
forcesx_mse(prediction, target[, key])
forcesy_mae(prediction, target[, key])
forcesy_mse(prediction, target[, key])
forcesz_mae(prediction, target[, key])
forcesz_mse(prediction, target[, key])
energy_forces_within_threshold(→ dict[str, float | int])
energy_within_threshold(→ dict[str, float | int])
average_distance_within_threshold(→ dict[str, float | int])
min_diff(pred_pos, dft_pos, cell, pbc)
cosine_similarity(prediction, target[, key])
mae(→ dict[str, float | int])
mse(→ dict[str, float | int])
magnitude_error(→ dict[str, float | int])

Module Contents

core.modules.evaluator.NONE

class core.modules.evaluator.Evaluator(task: str | None = None, eval_metrics: dict | None = None)

task_metrics: ClassVar[dict[str, str]]

task_primary_metric: ClassVar[dict[str, str | None]]

task

target_metrics

eval(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], prev_metrics=None)

update(key, stat, metrics)

core.modules.evaluator.forcesx_mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE)

core.modules.evaluator.forcesx_mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE)

core.modules.evaluator.forcesy_mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)

core.modules.evaluator.forcesy_mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)

core.modules.evaluator.forcesz_mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)

core.modules.evaluator.forcesz_mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None)

core.modules.evaluator.energy_forces_within_threshold(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None) → dict[str, float | int]

core.modules.evaluator.energy_within_threshold(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None) → dict[str, float | int]

core.modules.evaluator.average_distance_within_threshold(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = None) → dict[str, float | int]

core.modules.evaluator.min_diff(pred_pos: torch.Tensor, dft_pos: torch.Tensor, cell: torch.Tensor, pbc: torch.Tensor)

core.modules.evaluator.cosine_similarity(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE)

core.modules.evaluator.mae(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE) → dict[str, float | int]

core.modules.evaluator.mse(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE) → dict[str, float | int]

core.modules.evaluator.magnitude_error(prediction: dict[str, torch.Tensor], target: dict[str, torch.Tensor], key: collections.abc.Hashable = NONE, p: int = 2) → dict[str, float | int]