mxnet.metric.CustomMetric¶

class mxnet.metric.CustomMetric(feval, name=None, allow_extra_outputs=False, output_names=None, label_names=None)[source]¶

Computes a customized evaluation metric.

The feval function can return a tuple of (sum_metric, num_inst) or return an int sum_metric.

Parameters

feval (callable(label, pred)) – Customized evaluation function.
name (str) – The name of the metric. (the default is None).
allow_extra_outputs (bool, optional) – If true, the prediction outputs can have extra outputs. This is useful in RNN, where the states are also produced in outputs for forwarding. (the default is False).
name – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.

Examples

>>> predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))]
>>> labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))]
>>> feval = lambda x, y : (x + y).mean()
>>> eval_metrics = mx.metric.CustomMetric(feval=feval)
>>> eval_metrics.update(labels, predicts)
>>> print eval_metrics.get()
('custom(<lambda>)', 6.0)

__init__(feval, name=None, allow_extra_outputs=False, output_names=None, label_names=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(feval[, name, allow_extra_outputs, …])	Initialize self.
`get`()	Gets the current evaluation result.
`get_config`()	Save configurations of metric.
`get_global`()	Gets the current global evaluation result.
`get_global_name_value`()	Returns zipped name and value pairs for global results.
`get_name_value`()	Returns zipped name and value pairs.
`reset`()	Resets the internal evaluation result to initial state.
`reset_local`()	Resets the local portion of the internal evaluation results to initial state.
`update`(labels, preds)	Updates the internal evaluation result.
`update_dict`(label, pred)	Update the internal evaluation with named label and pred