WaveHedgesDistance#
- class ignite.metrics.regression.WaveHedgesDistance(output_transform=<function Metric.<lambda>>, device=device(type='cpu'), skip_unrolling=False)[source]#
Calculates the Wave Hedges Distance.
$\text{WHD} = \sum_{j=1}^n\frac{|A_j - P_j|}{max(A_j, P_j)}$where, $A_j$ is the ground truth and $P_j$ is the predicted value.
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__
.- 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.skip_unrolling (bool) –
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 = WaveHedgesDistance() metric.attach(default_evaluator, 'whd') 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['whd'])
1.25...
Changed in version 0.4.5: - Works with DDP.
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.