# CanberraMetric#

class ignite.contrib.metrics.regression.CanberraMetric(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#

Calculates the Canberra Metric.

$\text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{|A_j| + |P_j|}$

where, $A_j$ is the ground truth and $P_j$ is the predicted value.

More details can be found in Botchkarev 2018 or scikit-learn distance metrics

• 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’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 (Union[str, torch.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, ...}.

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.utils import *
from ignite.contrib.metrics.regression import *
from ignite.contrib.metrics import *

# create default evaluator for doctests

def eval_step(engine, batch):
return batch

default_evaluator = Engine(eval_step)

# create default optimizer for doctests

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 = CanberraMetric()
metric.attach(default_evaluator, 'canberra')
y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]])
y_true = y_pred * 1.5
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['canberra'])

0.8000...


Changed in version 0.4.3:

• Fixed implementation: abs in denominator.

• Works with DDP.

Methods

 compute Computes the metric based on it's accumulated state. reset Resets the metric to it's initial state.
compute()[source]#

Computes the metric based on it’s 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 when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()[source]#

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

Return type

None