Rescale data with emergent constraints
Rescale label data using a single emergent constraint.
Description
This diagnostic uses an emergent relationship between data marked as
var_type=label
(Y axis) and var_type=feature
(X axis) together with an
observation of the X axis (var_type=prediction_input
and
var_type=prediction_input_error
) to calculate factors that are necessary to
rescale each input point so that it matches the constraint. The rescaling is
applied to data marked as var_type=label_to_rescale
. All data needs the
attribute tag
which needs to be identical for label
,
prediction_input
, prediction_input_error
and label_to_rescale
. Only
a single tag
for feature
is possible.
Project
CRESCENDO
Configuration options in recipe
- group_by_attributes: list of str, optional (default: [‘dataset’])
List of attributes used to separate different input points.
- ignore: list of dict, optional
Ignore specific datasets by specifying multiple
dict
s of metadata.- legend_kwargs: dict, optional
Optional keyword arguments of
matplotlib.pyplot.legend()
(affects only plots with legends).- pattern: str, optional
Pattern matched against ancestor file names.
- plot_emergent_relationship: dict, optional
If given, plot emergent relationship between X and Y data. Specify additional keyword arguments by
plot_kwargs
and plot appearance options bypyplot_kwargs
(processed as functions ofmatplotlib.pyplot
). Use{}
to plot with default settings.- plot_kwargs_for_groups: dict, optional
Specify additional keyword arguments (values) for the different points defined by
group_by_attributes
(keys) used in plots.- savefig_kwargs: dict, optional
Keyword arguments for
matplotlib.pyplot.savefig()
.- seaborn_settings: dict, optional
Options for
seaborn.set()
(affects all plots).