GeometricMeanRelativeAbsoluteError#
- class ignite.metrics.regression.GeometricMeanRelativeAbsoluteError(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#
Calculates the Geometric Mean Relative Absolute Error.
$\text{GMRAE} = \exp(\frac{1}{n}\sum_{j=1}^n \ln\frac{|A_j - P_j|}{|A_j - \bar{A}|})$where $A_j$ is the ground truth, $P_j$ 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).
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.
- Parameters
output_transform (Callable) – a callable that is used to transform the
Engine
’sprocess_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 (Union[str, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
update
arguments ensures theupdate
method is non-blocking. By default, CPU.
Examples
To use with
Engine
andprocess_function
, simply attach the metric instance to the engine. The output of the engine’sprocess_function
needs to be in format of(y_pred, y)
or{'y_pred': y_pred, 'y': y, ...}
.from collections import OrderedDict import torch from torch import nn, optim from ignite.engine import * from ignite.handlers import * from ignite.metrics import * from ignite.metrics.regression import * from ignite.utils import * # create default evaluator for doctests def eval_step(engine, batch): return batch default_evaluator = Engine(eval_step) # create default optimizer for doctests param_tensor = torch.zeros([1], requires_grad=True) default_optimizer = torch.optim.SGD([param_tensor], lr=0.1) # create default trainer for doctests # as handlers could be attached to the trainer, # each test must define his own trainer using `.. testsetup:` def get_default_trainer(): def train_step(engine, batch): return batch return Engine(train_step) # create default model for doctests default_model = nn.Sequential(OrderedDict([ ('base', nn.Linear(4, 2)), ('fc', nn.Linear(2, 1)) ])) manual_seed(666)
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'])
0.0...
Methods
Computes the metric based on its accumulated state.
Resets the metric to its initial state.
- compute()[source]#
Computes the metric based on its accumulated state.
By default, this is called at the end of each epoch.
- Returns
- the actual quantity of interest. However, if a
Mapping
is returned, it will be (shallow) flattened into engine.state.metrics whencompleted()
is called. - Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed.