mxnet.metric.CompositeEvalMetric¶

class mxnet.metric.CompositeEvalMetric(metrics=None, name='composite', output_names=None, label_names=None)[source]¶

Manages multiple evaluation metrics.

Parameters

metrics (list of EvalMetric) – List of child metrics.
name (str) – 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([[0.3, 0.7], [0, 1.], [0.4, 0.6]])]
>>> labels   = [mx.nd.array([0, 1, 1])]
>>> eval_metrics_1 = mx.metric.Accuracy()
>>> eval_metrics_2 = mx.metric.F1()
>>> eval_metrics = mx.metric.CompositeEvalMetric()
>>> for child_metric in [eval_metrics_1, eval_metrics_2]:
>>>     eval_metrics.add(child_metric)
>>> eval_metrics.update(labels = labels, preds = predicts)
>>> print eval_metrics.get()
(['accuracy', 'f1'], [0.6666666666666666, 0.8])

__init__(metrics=None, name='composite', output_names=None, label_names=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`([metrics, name, output_names, …])	Initialize self.
`add`(metric)	Adds a child metric.
`get`()	Returns the current evaluation result.
`get_config`()	Save configurations of metric.
`get_global`()	Returns the current evaluation result.
`get_global_name_value`()	Returns zipped name and value pairs for global results.
`get_metric`(index)	Returns a child metric.
`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`(labels, preds)	Update the internal evaluation with named label and pred