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Source code for ignite.metrics.regression.geometric_mean_relative_absolute_error

from typing import cast, List, Tuple

import torch

import ignite.distributed as idist
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import reinit__is_reduced
from ignite.metrics.regression._base import _BaseRegression

[docs]class GeometricMeanRelativeAbsoluteError(_BaseRegression): r"""Calculates the Geometric Mean Relative Absolute Error. .. math:: \text{GMRAE} = \exp(\frac{1}{n}\sum_{j=1}^n \ln\frac{|A_j - P_j|}{|A_j - \bar{A}|}) where :math:A_j is the ground truth, :math:P_j is the predicted value and :math: bar{A} is the mean of the ground truth. More details can be found in Botchkarev 2018__. - update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}. - y and y_pred must be of same shape (N, ) or (N, 1). __ https://arxiv.org/abs/1809.03006 Parameters are inherited from Metric.__init__. .. warning:: Current implementation of GMRAE stores all input data (output and target) as tensors before computing the metric. This can potentially lead to a memory error if the input data is larger than available RAM. In distributed configuration, all stored data (output and target) is mutually collected across all processes using all gather collective operation. This can potentially lead to a memory error. Compute method compute the metric on zero rank process only and final result is broadcasted to all processes. Args: output_transform: a callable that is used to transform the :class:~ignite.engine.engine.Engine's process_function's output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as (y_pred, y) or {'y_pred': y_pred, 'y': y}. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your update arguments ensures the update method is non-blocking. By default, CPU. Examples: To use with Engine and process_function, simply attach the metric instance to the engine. The output of the engine's process_function needs to be in format of (y_pred, y) or {'y_pred': y_pred, 'y': y, ...}. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = GeometricMeanRelativeAbsoluteError() metric.attach(default_evaluator, 'gmare') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['gmare']) .. testoutput:: 0.0... """ _state_dict_all_req_keys = ("_predictions", "_targets")
[docs] @reinit__is_reduced def reset(self) -> None: self._predictions: List[torch.Tensor] = [] self._targets: List[torch.Tensor] = []
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() y_pred = y_pred.clone().to(self._device) y = y.clone().to(self._device) self._predictions.append(y_pred) self._targets.append(y)
[docs] def compute(self) -> float: if len(self._predictions) < 1 or len(self._targets) < 1: raise NotComputableError( "GeometricMeanRelativeAbsoluteError must have at least one example before it can be computed." ) _prediction_tensor = torch.cat(self._predictions, dim=0) _target_tensor = torch.cat(self._targets, dim=0) # All gather across all processes _prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor)) _target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor)) result = torch.exp( torch.log( torch.abs(_target_tensor - _prediction_tensor) / torch.abs(_target_tensor - _target_tensor.mean()) ).mean() ).item() return result

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