[REF] Eliminate component sorting from metric calculation (#741)

* Remove sort_df and apply_sort functions.

* Remove sort_by and ascending parameters.

* Update descriptions.

tedana/decomposition/pca.py

@@ -236,7 +236,7 @@ def tedpca(data_cat, data_oc, combmode, mask, adaptive_mask, t2sG,
     comptable, _ = metrics.collect.generate_metrics(
         data_cat, data_oc, comp_ts, adaptive_mask,
         tes, io_generator, 'PCA',
-        metrics=required_metrics, sort_by=None
+        metrics=required_metrics,
     )
 
     # varex_norm from PCA overrides varex_norm from dependence_metrics,

tedana/metrics/_utils.py

@@ -109,44 +109,6 @@ def flip_components(*args, signs):
     return [arg * signs for arg in args]
 
 
-def sort_df(df, by="kappa", ascending=False):
-    """Sort DataFrame and get index.
-
-    Parameters
-    ----------
-    df : :obj:`pandas.DataFrame`
-        DataFrame to sort.
-    by : :obj:`str` or None, optional
-        Column by which to sort the DataFrame. Default is 'kappa'.
-    ascending : :obj:`bool`, optional
-        Whether to sort the DataFrame in ascending (True) or descending (False)
-        order. Default is False.
-
-    Returns
-    -------
-    df : :obj:`pandas.DataFrame`
-        DataFrame after sorting, with index resetted.
-    argsort : array_like
-        Sorting index.
-    """
-    if by is None:
-        return df, df.index.values
-
-    # Order of kwargs is preserved at 3.6+
-    argsort = df[by].argsort()
-    if not ascending:
-        argsort = argsort[::-1]
-    df = df.loc[argsort].reset_index(drop=True)
-    return df, argsort
-
-
-def apply_sort(*args, sort_idx, axis=0):
-    """Apply a sorting index to an arbitrary set of arrays."""
-    for arg in args:
-        assert arg.shape[axis] == len(sort_idx)
-    return [np.take(arg, sort_idx, axis=axis) for arg in args]
-
-
 def check_mask(data, mask):
     """Check that no zero-variance voxels remain in masked data.
 

tedana/metrics/collect.py

@@ -6,13 +6,7 @@
 import pandas as pd
 
 from . import dependence
-from ._utils import (
-    determine_signs,
-    flip_components,
-    sort_df,
-    apply_sort,
-    dependency_resolver,
-)
+from ._utils import determine_signs, flip_components, dependency_resolver
 
 from tedana import io
 from tedana import utils
@@ -33,8 +27,6 @@ def generate_metrics(
     io_generator,
     label,
     metrics=None,
-    sort_by="kappa",
-    ascending=False,
 ):
     """Fit TE-dependence and -independence models to components.
 
@@ -60,19 +52,14 @@ def generate_metrics(
         The label for this metric generation type
     metrics : list
         List of metrics to return
-    sort_by : str, optional
-        Metric to sort component table by. Default is 'kappa'.
-    ascending : bool, optional
-        Whether to sort the table in ascending or descending order.
-        Default is False.
 
     Returns
     -------
     comptable : (C x X) :obj:`pandas.DataFrame`
         Component metric table. One row for each component, with a column for
         each metric. The index is the component number.
     mixing : :obj:`numpy.ndarray`
-        Mixing matrix after sign flipping and sorting.
+        Mixing matrix after sign flipping.
     """
     # Load metric dependency tree from json file
     dependency_config = op.join(utils.get_resource_path(), "config", "metrics.json")
@@ -340,10 +327,6 @@ def generate_metrics(
             comptable["countsigFT2"],
         )
 
-    # Sort the component table and mixing matrix
-    comptable, sort_idx = sort_df(comptable, by=sort_by, ascending=ascending)
-    (mixing,) = apply_sort(mixing, sort_idx=sort_idx, axis=1)
-
     # Write verbose metrics if needed
     if io_generator.verbose:
         write_betas = "map echo betas" in metric_maps

tedana/tests/test_metrics.py

@@ -57,8 +57,6 @@ def test_smoke_generate_metrics(testdata1):
         testdata1["generator"],
         'ICA',
         metrics=metrics,
-        sort_by="kappa",
-        ascending=False,
     )
     assert isinstance(comptable, pd.DataFrame)
 

tedana/workflows/tedana.py

@@ -570,7 +570,7 @@ def tedana_workflow(data, tes, out_dir='.', mask=None,
             comptable, mmix = metrics.collect.generate_metrics(
                 catd, data_oc, mmix_orig, masksum, tes,
                 io_generator, 'ICA',
-                metrics=required_metrics, sort_by='kappa', ascending=False,
+                metrics=required_metrics,
             )
             comptable, metric_metadata = selection.kundu_selection_v2(
                 comptable, n_echos, n_vols
@@ -602,7 +602,7 @@ def tedana_workflow(data, tes, out_dir='.', mask=None,
             comptable, mmix = metrics.collect.generate_metrics(
                 catd, data_oc, mmix_orig, masksum, tes,
                 io_generator, 'ICA',
-                metrics=required_metrics, sort_by='kappa', ascending=False
+                metrics=required_metrics,
             )
             comptable, metric_metadata = selection.kundu_selection_v2(
                     comptable, n_echos, n_vols
@@ -638,8 +638,7 @@ def tedana_workflow(data, tes, out_dir='.', mask=None,
     decomp_metadata = {
         "Method": (
             "Independent components analysis with FastICA "
-            "algorithm implemented by sklearn. Components "
-            "are sorted by Kappa in descending order. "
+            "algorithm implemented by sklearn. "
             "Component signs are flipped to best match the "
             "data."
         ),