tedana.selection.selection_nodes.calc_max_good_meanmetricrank

calc_max_good_meanmetricrank(selector, decide_comps, metric_suffix=None, log_extra_info='', custom_node_label='', only_used_metrics=False)

Calculate the metric “max_good_meanmetricrank”.

Calculates the max_good_meanmetricrank to use in the kundu decision tree. This is the number of components selected with decide_comps * the extend_factor calculated in calc_extend_factor

Parameters:

• selector (ComponentSelector) – If only_used_metrics is False, the updated selector is returned.

• decide_comps (str or list[str]) – What classification(s) to operate on. using default or intermediate_classification labels. For example: decide_comps=’unclassified’ means to operate only on unclassified components. Use ‘all’ to include all components.

• metric_suffix (str) – By default, this will output a value called “max_good_meanmetricrank” If this variable is not None or “” then it will output: “max_good_meanmetricrank_[metric_suffix]”. Default=None

• log_extra_info (str) – Additional text to the information log. Default=””.

• custom_node_label (str) – A short label to describe what happens in this step. If “” then a label is automatically generated. Default=””.

• only_used_metrics (bool) – If True, only return the component_table metrics that would be used. Default=False.

Returns:

• selector (ComponentSelector) – If only_used_metrics is False, the updated selector is returned.

• used_metrics (set(str)) – If only_used_metrics is True, the names of the metrics used in the function are returned.

Note

“meanmetricrank” is the same as “d_table_score” and is used to set a threshold for the “d_table” values in the component table. This metric ranks the components based on 5 metrics and then outputs the mean rank across the 5 metrics. Thus “meanmetricrank” is a slightly more descriptive name but d_table was used in earlier versions of this code. It might be worth consistently using the same term, but this note will hopefully suffice for now.