xskillscore.mae

xskillscore.mae(a, b, dim=None, weights=None, skipna=False, keep_attrs=False)

Mean Absolute Error.

\[\mathrm{MAE} = \frac{1}{n}\sum_{i=1}^{n}\vert a - b\vert\]
Parameters:

  • a (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.

  • b (xarray.Dataset or xarray.DataArray) – Labeled array(s) over which to apply the function.

  • dim (str, list) – The dimension(s) to apply the mae along. Note that this dimension will be reduced as a result. Defaults to None reducing all dimensions.

  • weights (xarray.Dataset or xarray.DataArray or None) – Weights matching dimensions of dim to apply during the function.

  • skipna (bool) – If True, skip NaNs when computing function.

  • keep_attrs (bool) – If True, the attributes (attrs) will be copied from the first input to the new one. If False (default), the new object will be returned without attributes.

Returns:

Mean Absolute Error.

Return type:

xarray.Dataset or xarray.DataArray

See also

sklearn.metrics.mean_absolute_error

References

https://en.wikipedia.org/wiki/Mean_absolute_error

Examples

>>> a = xr.DataArray(np.random.rand(5, 3, 3), dims=['time', 'x', 'y'])
>>> b = xr.DataArray(np.random.rand(5, 3, 3), dims=['time', 'x', 'y'])
>>> mae(a, b, dim='time')
<xarray.DataArray (x: 3, y: 3)>
array([[0.26014863, 0.40137207, 0.48871634],
       [0.18809417, 0.30197826, 0.2984658 ],
       [0.52934554, 0.19820357, 0.17335851]])
Dimensions without coordinates: x, y