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R2Score#

class ignite.contrib.metrics.regression.R2Score(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#

Calculates the R-Squared, the coefficient of determination.

R2=1j=1n(AjPj)2j=1n(AjAˉ)2R^2 = 1 - \frac{\sum_{j=1}^n(A_j - P_j)^2}{\sum_{j=1}^n(A_j - \bar{A})^2}

where AjA_j is the ground truth, PjP_j is the predicted value and Aˉ\bar{A} is the mean of the ground truth.

  • 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) and of type float32.

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, ...}.

metric = R2Score()
metric.attach(default_evaluator, 'r2')
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['r2'])
0.8035...

Changed in version 0.4.3: 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