Metrics Module¶

UnaryMetric¶

class metrics.UnaryMetric¶

Abstract Base Class from which all unary metrics inherit.

x.__init__(...) initializes x; see help(type(x)) for signature

run(target_dataset)¶

Run the metric for a given target dataset.

Parameters:	target_dataset – The dataset on which the current metric will be run.
Returns:	The result of evaluating the metric on the target_dataset.

class metrics.BinaryMetric¶

Abstract Base Class from which all binary metrics inherit.

x.__init__(...) initializes x; see help(type(x)) for signature

run(ref_dataset, target_dataset)¶

Run the metric for the given reference and target datasets.

Parameters:	ref_dataset (Dataset) – The Dataset to use as the reference dataset when running the evaluation. target_dataset – The Dataset to use as the target dataset when running the evaluation.
Returns:	The result of evaluation the metric on the reference and target dataset.

class metrics.Bias¶

Calculate the bias between a reference and target dataset.

x.__init__(...) initializes x; see help(type(x)) for signature

run(ref_dataset, target_dataset)¶

Calculate the bias between a reference and target dataset.

Note

Overrides BinaryMetric.run()

Parameters:	ref_dataset (Dataset.) – The reference dataset to use in this metric run. target_dataset (Dataset.) – The target dataset to evaluate against the reference dataset in this metric run.
Returns:	The difference between the reference and target datasets.
Return type:	Numpy Array

class metrics.TemporalStdDev¶

Calculate the standard deviation over the time.

x.__init__(...) initializes x; see help(type(x)) for signature

run(target_dataset)¶

Calculate the temporal std. dev. for a datasets.

Note

Overrides UnaryMetric.run()

Parameters:	target_dataset (Dataset) – The target_dataset on which to calculate the temporal standard deviation.
Returns:	The temporal standard deviation of the target dataset
Return type:	Numpy Array