mxnet.metric.PearsonCorrelation¶

class mxnet.metric.PearsonCorrelation(name='pearsonr', output_names=None, label_names=None)[source]¶

Computes Pearson correlation.

The pearson correlation is given by

\[\frac{cov(y, \hat{y})}{\sigma{y}\sigma{\hat{y}}}\]

Parameters

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([[1, 0], [0, 1], [0, 1]])]
>>> pr = mx.metric.PearsonCorrelation()
>>> pr.update(labels, predicts)
>>> print pr.get()
('pearson-correlation', 0.42163704544016178)

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

Methods

`__init__`([name, output_names, label_names])	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