def manual_selection(comptable, acc=None, rej=None):
"""
Perform manual selection of components.
Parameters
----------
comptable : (C x M) :obj:`pandas.DataFrame`
Component metric table, where `C` is components and `M` is metrics
acc : :obj:`list`, optional
List of accepted components. Default is None.
rej : :obj:`list`, optional
List of rejected components. Default is None.
Returns
-------
comptable : (C x M) :obj:`pandas.DataFrame`
Component metric table with classification.
"""
LGR.info('Performing manual ICA component selection')
RepLGR.info("Next, components were manually classified as "
"BOLD (TE-dependent), non-BOLD (TE-independent), or "
"uncertain (low-variance).")
if ('classification' in comptable.columns
and 'original_classification' not in comptable.columns):
comptable['original_classification'] = comptable['classification']
comptable['original_rationale'] = comptable['rationale']
comptable['classification'] = 'accepted'
comptable['rationale'] = ''
all_comps = comptable.index.values
if acc is not None:
acc = [int(comp) for comp in acc]
if rej is not None:
rej = [int(comp) for comp in rej]
if acc is not None and rej is None:
rej = sorted(np.setdiff1d(all_comps, acc))
elif acc is None and rej is not None:
acc = sorted(np.setdiff1d(all_comps, rej))
elif acc is None and rej is None:
LGR.info('No manually accepted or rejected components supplied. '
'Accepting all components.')
# Accept all components if no manual selection provided
acc = all_comps[:]
rej = []
ign = np.setdiff1d(all_comps, np.union1d(acc, rej))
comptable.loc[acc, 'classification'] = 'accepted'
comptable.loc[rej, 'classification'] = 'rejected'
comptable.loc[rej, 'rationale'] += 'I001;'
comptable.loc[ign, 'classification'] = 'ignored'
comptable.loc[ign, 'rationale'] += 'I001;'
# Move decision columns to end
comptable = clean_dataframe(comptable)
return comptable
def kundu_tedpca(comptable, n_echos, kdaw=10., rdaw=1., stabilize=False):
"""
Select PCA components using Kundu's decision tree approach.
Parameters
----------
comptable : :obj:`pandas.DataFrame`
Component table with relevant metrics: kappa, rho, and normalized
variance explained. Component number should be the index.
n_echos : :obj:`int`
Number of echoes in dataset.
kdaw : :obj:`float`, optional
Kappa dimensionality augmentation weight. Must be a non-negative float,
or -1 (a special value). Default is 10.
rdaw : :obj:`float`, optional
Rho dimensionality augmentation weight. Must be a non-negative float,
or -1 (a special value). Default is 1.
stabilize : :obj:`bool`, optional
Whether to stabilize convergence by reducing dimensionality, for low
quality data. Default is False.
Returns
-------
comptable : :obj:`pandas.DataFrame`
Component table with components classified as 'accepted', 'rejected',
or 'ignored'.
metric_metadata : :obj:`dict`
Dictionary with metadata about calculated metrics.
Each entry corresponds to a column in ``comptable``.
"""
LGR.info('Performing PCA component selection with Kundu decision tree')
comptable['classification'] = 'accepted'
comptable['rationale'] = ''
eigenvalue_elbow = getelbow(comptable['normalized variance explained'],
return_val=True)
diff_varex_norm = np.abs(
np.diff(comptable['normalized variance explained']))
lower_diff_varex_norm = diff_varex_norm[(len(diff_varex_norm) // 2):]
varex_norm_thr = np.mean(
[lower_diff_varex_norm.max(),
diff_varex_norm.min()])
varex_norm_min = comptable['normalized variance explained'][
(len(diff_varex_norm) // 2) + np.arange(len(lower_diff_varex_norm))[
lower_diff_varex_norm >= varex_norm_thr][0] + 1]
varex_norm_cum = np.cumsum(comptable['normalized variance explained'])
fmin, fmid, fmax = getfbounds(n_echos)
if int(kdaw) == -1:
lim_idx = utils.andb(
[comptable['kappa'] < fmid, comptable['kappa'] > fmin]) == 2
kappa_lim = comptable.loc[lim_idx, 'kappa'].values
kappa_thr = kappa_lim[getelbow(kappa_lim)]
lim_idx = utils.andb(
[comptable['rho'] < fmid, comptable['rho'] > fmin]) == 2
rho_lim = comptable.loc[lim_idx, 'rho'].values
rho_thr = rho_lim[getelbow(rho_lim)]
stabilize = True
LGR.info('kdaw set to -1. Switching TEDPCA algorithm to '
'kundu-stabilize')
elif int(rdaw) == -1:
lim_idx = utils.andb(
[comptable['rho'] < fmid, comptable['rho'] > fmin]) == 2
rho_lim = comptable.loc[lim_idx, 'rho'].values
rho_thr = rho_lim[getelbow(rho_lim)]
else:
kappa_thr = np.average(sorted(
[fmin, (getelbow(comptable['kappa'], return_val=True) / 2), fmid]),
weights=[kdaw, 1, 1])
rho_thr = np.average(sorted([
fmin, (getelbow_cons(comptable['rho'], return_val=True) / 2), fmid
]),
weights=[rdaw, 1, 1])
# Reject if low Kappa, Rho, and variance explained
is_lowk = comptable['kappa'] <= kappa_thr
is_lowr = comptable['rho'] <= rho_thr
is_lowe = comptable['normalized variance explained'] <= eigenvalue_elbow
is_lowkre = is_lowk & is_lowr & is_lowe
comptable.loc[is_lowkre, 'classification'] = 'rejected'
comptable.loc[is_lowkre, 'rationale'] += 'P001;'
# Reject if low variance explained
is_lows = comptable['normalized variance explained'] <= varex_norm_min
comptable.loc[is_lows, 'classification'] = 'rejected'
comptable.loc[is_lows, 'rationale'] += 'P002;'
# Reject if Kappa over limit
is_fmax1 = comptable['kappa'] == F_MAX
comptable.loc[is_fmax1, 'classification'] = 'rejected'
comptable.loc[is_fmax1, 'rationale'] += 'P003;'
# Reject if Rho over limit
is_fmax2 = comptable['rho'] == F_MAX
comptable.loc[is_fmax2, 'classification'] = 'rejected'
comptable.loc[is_fmax2, 'rationale'] += 'P004;'
if stabilize:
temp7 = varex_norm_cum >= 0.95
comptable.loc[temp7, 'classification'] = 'rejected'
comptable.loc[temp7, 'rationale'] += 'P005;'
under_fmin1 = comptable['kappa'] <= fmin
comptable.loc[under_fmin1, 'classification'] = 'rejected'
comptable.loc[under_fmin1, 'rationale'] += 'P006;'
under_fmin2 = comptable['rho'] <= fmin
comptable.loc[under_fmin2, 'classification'] = 'rejected'
comptable.loc[under_fmin2, 'rationale'] += 'P007;'
n_components = comptable.loc[comptable['classification'] ==
'accepted'].shape[0]
LGR.info('Selected {0} components with Kappa threshold: {1:.02f}, Rho '
'threshold: {2:.02f}'.format(n_components, kappa_thr, rho_thr))
# Move decision columns to end
comptable = clean_dataframe(comptable)
metric_metadata = collect.get_metadata(comptable)
return comptable, metric_metadata
def kundu_selection_v2(comptable, n_echos, n_vols):
"""
Classify components as "accepted," "rejected," or "ignored" based on
relevant metrics.
The selection process uses previously calculated parameters listed in
comptable for each ICA component such as Kappa (a T2* weighting metric),
Rho (an S0 weighting metric), and variance explained.
See `Notes` for additional calculated metrics used to classify each
component into one of the listed groups.
Parameters
----------
comptable : (C x M) :obj:`pandas.DataFrame`
Component metric table. One row for each component, with a column for
each metric. The index should be the component number.
n_echos : :obj:`int`
Number of echos in original data
n_vols : :obj:`int`
Number of volumes in dataset
Returns
-------
comptable : :obj:`pandas.DataFrame`
Updated component table with additional metrics and with
classification (accepted, rejected, or ignored)
Notes
-----
The selection algorithm used in this function was originated in ME-ICA
by Prantik Kundu, and his original implementation is available at:
https://github.com/ME-ICA/me-ica/blob/b2781dd087ab9de99a2ec3925f04f02ce84f0adc/meica.libs/select_model.py
This component selection process uses multiple, previously calculated
metrics that include kappa, rho, variance explained, noise and spatial
frequency metrics, and measures of spatial overlap across metrics.
Prantik began to update these selection criteria to use SVMs to distinguish
components, a hypercommented version of this attempt is available at:
https://gist.github.com/emdupre/ca92d52d345d08ee85e104093b81482e
References
----------
* Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y.,
Vértes, P. E., Inati, S. J., ... & Bullmore, E. T.
(2013). Integrated strategy for improving functional
connectivity mapping using multiecho fMRI. Proceedings
of the National Academy of Sciences, 110(40),
16187-16192.
"""
LGR.info(
'Performing ICA component selection with Kundu decision tree v2.5')
RepLGR.info("Next, component selection was performed to identify "
"BOLD (TE-dependent), non-BOLD (TE-independent), and "
"uncertain (low-variance) components using the Kundu "
"decision tree (v2.5; Kundu et al., 2013).")
RefLGR.info("Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., "
"Vértes, P. E., Inati, S. J., ... & Bullmore, E. T. "
"(2013). Integrated strategy for improving functional "
"connectivity mapping using multiecho fMRI. Proceedings "
"of the National Academy of Sciences, 110(40), "
"16187-16192.")
comptable['classification'] = 'accepted'
comptable['rationale'] = ''
# Set knobs
LOW_PERC = 25
HIGH_PERC = 90
if n_vols < 100:
EXTEND_FACTOR = 3
else:
EXTEND_FACTOR = 2
RESTRICT_FACTOR = 2
# Lists of components
all_comps = np.arange(comptable.shape[0])
# unclf is a full list that is whittled down over criteria
# since the default classification is "accepted", at the end of the tree
# the remaining elements in unclf are classified as accepted
unclf = all_comps.copy()
"""
Step 1: Reject anything that's obviously an artifact
a. Estimate a null variance
"""
# Rho is higher than Kappa
temp_rej0a = all_comps[(comptable['rho'] > comptable['kappa'])]
comptable.loc[temp_rej0a, 'classification'] = 'rejected'
comptable.loc[temp_rej0a, 'rationale'] += 'I002;'
# Number of significant voxels for S0 model is higher than number for R2
# model *and* number for R2 model is greater than zero.
temp_rej0b = all_comps[(
(comptable['countsigFS0'] > comptable['countsigFR2']) &
(comptable['countsigFR2'] > 0))]
comptable.loc[temp_rej0b, 'classification'] = 'rejected'
comptable.loc[temp_rej0b, 'rationale'] += 'I003;'
rej = np.union1d(temp_rej0a, temp_rej0b)
# Dice score for S0 maps is higher than Dice score for R2 maps and variance
# explained is higher than the median across components.
temp_rej1 = all_comps[(comptable['dice_FS0'] > comptable['dice_FR2'])
& (comptable['variance explained'] > np.median(
comptable['variance explained']))]
comptable.loc[temp_rej1, 'classification'] = 'rejected'
comptable.loc[temp_rej1, 'rationale'] += 'I004;'
rej = np.union1d(temp_rej1, rej)
# T-value is less than zero (noise has higher F-statistics than signal in
# map) and variance explained is higher than the median across components.
temp_rej2 = unclf[(comptable.loc[unclf, 'signal-noise_t'] < 0)
& (comptable.loc[unclf, 'variance explained'] >
np.median(comptable['variance explained']))]
comptable.loc[temp_rej2, 'classification'] = 'rejected'
comptable.loc[temp_rej2, 'rationale'] += 'I005;'
rej = np.union1d(temp_rej2, rej)
unclf = np.setdiff1d(unclf, rej)
# Quit early if no potentially accepted components remain
if len(unclf) == 0:
LGR.warning('No BOLD-like components detected. Ignoring all remaining '
'components.')
ign = sorted(np.setdiff1d(all_comps, rej))
comptable.loc[ign, 'classification'] = 'ignored'
comptable.loc[ign, 'rationale'] += 'I006;'
# Move decision columns to end
comptable = clean_dataframe(comptable)
return comptable
"""
Step 2: Make a guess for what the good components are, in order to
estimate good component properties
a. Not outlier variance
b. Kappa>kappa_elbow
c. Rho<Rho_elbow
d. High R2* dice compared to S0 dice
e. Gain of F_R2 in clusters vs noise
f. Estimate a low and high variance
"""
# Step 2a
# Upper limit for variance explained is median across components with high
# Kappa values. High Kappa is defined as Kappa above Kappa elbow.
varex_upper_p = np.median(comptable.loc[
comptable['kappa'] > getelbow(comptable['kappa'], return_val=True),
'variance explained'])
# Sort component table by variance explained and find outlier components by
# change in variance explained from one component to the next.
# Remove variance-explained outliers from list of components to consider
# for acceptance. These components will have another chance to be accepted
# later on.
# NOTE: We're not sure why this is done this way, nor why it's specifically
# done three times.
ncls = unclf.copy()
for i_loop in range(3):
temp_comptable = comptable.loc[ncls].sort_values(
by=['variance explained'], ascending=False)
diff_vals = temp_comptable['variance explained'].diff(-1)
diff_vals = diff_vals.fillna(0)
ncls = temp_comptable.loc[diff_vals < varex_upper_p].index.values
# Compute elbows from other elbows
f05, _, f01 = getfbounds(n_echos)
kappas_nonsig = comptable.loc[comptable['kappa'] < f01, 'kappa']
# NOTE: Would an elbow from all Kappa values *ever* be lower than one from
# a subset of lower values?
kappa_elbow = np.min((getelbow(kappas_nonsig, return_val=True),
getelbow(comptable['kappa'], return_val=True)))
rho_elbow = np.mean((getelbow(comptable.loc[ncls, 'rho'], return_val=True),
getelbow(comptable['rho'], return_val=True), f05))
# Provisionally accept components based on Kappa and Rho elbows
acc_prov = ncls[(comptable.loc[ncls, 'kappa'] >= kappa_elbow)
& (comptable.loc[ncls, 'rho'] < rho_elbow)]
# Quit early if no potentially accepted components remain
if len(acc_prov) <= 1:
LGR.warning('Too few BOLD-like components detected. '
'Ignoring all remaining.')
ign = sorted(np.setdiff1d(all_comps, rej))
comptable.loc[ign, 'classification'] = 'ignored'
comptable.loc[ign, 'rationale'] += 'I006;'
# Move decision columns to end
comptable = clean_dataframe(comptable)
return comptable
# Calculate "rate" for kappa: kappa range divided by variance explained
# range, for potentially accepted components
# NOTE: What is the logic behind this?
kappa_rate = ((np.max(comptable.loc[acc_prov, 'kappa']) -
np.min(comptable.loc[acc_prov, 'kappa'])) /
(np.max(comptable.loc[acc_prov, 'variance explained']) -
np.min(comptable.loc[acc_prov, 'variance explained'])))
comptable['kappa ratio'] = kappa_rate * comptable[
'variance explained'] / comptable['kappa']
# Calculate bounds for variance explained
varex_lower = stats.scoreatpercentile(
comptable.loc[acc_prov, 'variance explained'], LOW_PERC)
varex_upper = stats.scoreatpercentile(
comptable.loc[acc_prov, 'variance explained'], HIGH_PERC)
"""
Step 3: Get rid of midk components; i.e., those with higher than
max decision score and high variance
"""
max_good_d_score = EXTEND_FACTOR * len(acc_prov)
midk = unclf[(comptable.loc[unclf, 'd_table_score'] > max_good_d_score) & (
comptable.loc[unclf,
'variance explained'] > EXTEND_FACTOR * varex_upper)]
comptable.loc[midk, 'classification'] = 'rejected'
comptable.loc[midk, 'rationale'] += 'I007;'
unclf = np.setdiff1d(unclf, midk)
acc_prov = np.setdiff1d(acc_prov, midk)
"""
Step 4: Find components to ignore
"""
# collect high variance unclassified components
# and mix of high/low provisionally accepted
high_varex = np.union1d(
acc_prov,
unclf[comptable.loc[unclf, 'variance explained'] > varex_lower])
# ignore low variance components
ign = np.setdiff1d(unclf, high_varex)
# but only if they have bad decision scores
ign = np.setdiff1d(
ign, ign[comptable.loc[ign, 'd_table_score'] < max_good_d_score])
# and low kappa
ign = np.setdiff1d(ign, ign[comptable.loc[ign, 'kappa'] > kappa_elbow])
comptable.loc[ign, 'classification'] = 'ignored'
comptable.loc[ign, 'rationale'] += 'I008;'
unclf = np.setdiff1d(unclf, ign)
"""
Step 5: Scrub the set if there are components that haven't been rejected or
ignored, but are still not listed in the provisionally accepted group.
"""
if len(unclf) > len(acc_prov):
comptable['d_table_score_scrub'] = np.nan
# Recompute the midk steps on the limited set to clean up the tail
d_table_rank = np.vstack([
len(unclf) - stats.rankdata(comptable.loc[unclf, 'kappa']),
len(unclf) - stats.rankdata(comptable.loc[unclf, 'dice_FR2']),
len(unclf) -
stats.rankdata(comptable.loc[unclf, 'signal-noise_t']),
stats.rankdata(comptable.loc[unclf, 'countnoise']),
len(unclf) - stats.rankdata(comptable.loc[unclf, 'countsigFR2'])
]).T
comptable.loc[unclf, 'd_table_score_scrub'] = d_table_rank.mean(1)
num_acc_guess = int(
np.mean([
np.sum((comptable.loc[unclf, 'kappa'] > kappa_elbow)
& (comptable.loc[unclf, 'rho'] < rho_elbow)),
np.sum(comptable.loc[unclf, 'kappa'] > kappa_elbow)
]))
# Rejection candidate based on artifact type A: candartA
conservative_guess = num_acc_guess / RESTRICT_FACTOR
candartA = np.intersect1d(
unclf[comptable.loc[unclf,
'd_table_score_scrub'] > conservative_guess],
unclf[comptable.loc[unclf, 'kappa ratio'] > EXTEND_FACTOR * 2])
candartA = (candartA[comptable.loc[candartA, 'variance explained'] >
varex_upper * EXTEND_FACTOR])
comptable.loc[candartA, 'classification'] = 'rejected'
comptable.loc[candartA, 'rationale'] += 'I009;'
midk = np.union1d(midk, candartA)
unclf = np.setdiff1d(unclf, midk)
# Rejection candidate based on artifact type B: candartB
conservative_guess2 = num_acc_guess * HIGH_PERC / 100.
candartB = unclf[
comptable.loc[unclf, 'd_table_score_scrub'] > conservative_guess2]
candartB = (candartB[comptable.loc[candartB, 'variance explained'] >
varex_lower * EXTEND_FACTOR])
comptable.loc[candartB, 'classification'] = 'rejected'
comptable.loc[candartB, 'rationale'] += 'I010;'
midk = np.union1d(midk, candartB)
unclf = np.setdiff1d(unclf, midk)
# Find components to ignore
# Ignore high variance explained, poor decision tree scored components
new_varex_lower = stats.scoreatpercentile(
comptable.loc[unclf[:num_acc_guess], 'variance explained'],
LOW_PERC)
candart = unclf[comptable.loc[unclf,
'd_table_score_scrub'] > num_acc_guess]
ign_add0 = candart[
comptable.loc[candart, 'variance explained'] > new_varex_lower]
ign_add0 = np.setdiff1d(ign_add0, midk)
comptable.loc[ign_add0, 'classification'] = 'ignored'
comptable.loc[ign_add0, 'rationale'] += 'I011;'
ign = np.union1d(ign, ign_add0)
unclf = np.setdiff1d(unclf, ign)
# Ignore low Kappa, high variance explained components
ign_add1 = np.intersect1d(
unclf[comptable.loc[unclf, 'kappa'] <= kappa_elbow],
unclf[comptable.loc[unclf,
'variance explained'] > new_varex_lower])
ign_add1 = np.setdiff1d(ign_add1, midk)
comptable.loc[ign_add1, 'classification'] = 'ignored'
comptable.loc[ign_add1, 'rationale'] += 'I012;'
# at this point, unclf is equivalent to accepted
# Move decision columns to end
comptable = clean_dataframe(comptable)
return comptable
def manual_selection(comptable, acc=None, rej=None):
"""
Perform manual selection of components.
Parameters
----------
comptable : (C x M) :obj:`pandas.DataFrame`
Component metric table, where `C` is components and `M` is metrics
acc : :obj:`list`, optional
List of accepted components. Default is None.
rej : :obj:`list`, optional
List of rejected components. Default is None.
Returns
-------
comptable : (C x M) :obj:`pandas.DataFrame`
Component metric table with classification.
metric_metadata : :obj:`dict`
Dictionary with metadata about calculated metrics.
Each entry corresponds to a column in ``comptable``.
"""
LGR.info("Performing manual ICA component selection")
RepLGR.info("Next, components were manually classified as "
"BOLD (TE-dependent), non-BOLD (TE-independent), or "
"uncertain (low-variance).")
if ("classification" in comptable.columns
and "original_classification" not in comptable.columns):
comptable["original_classification"] = comptable["classification"]
comptable["original_rationale"] = comptable["rationale"]
comptable["classification"] = "accepted"
comptable["rationale"] = ""
all_comps = comptable.index.values
if acc is not None:
acc = [int(comp) for comp in acc]
if rej is not None:
rej = [int(comp) for comp in rej]
if acc is not None and rej is None:
rej = sorted(np.setdiff1d(all_comps, acc))
elif acc is None and rej is not None:
acc = sorted(np.setdiff1d(all_comps, rej))
elif acc is None and rej is None:
LGR.info(
"No manually accepted or rejected components supplied. Accepting all components."
)
# Accept all components if no manual selection provided
acc = all_comps[:]
rej = []
ign = np.setdiff1d(all_comps, np.union1d(acc, rej))
comptable.loc[acc, "classification"] = "accepted"
comptable.loc[rej, "classification"] = "rejected"
comptable.loc[rej, "rationale"] += "I001;"
comptable.loc[ign, "classification"] = "ignored"
comptable.loc[ign, "rationale"] += "I001;"
# Move decision columns to end
comptable = clean_dataframe(comptable)
metric_metadata = collect.get_metadata(comptable)
return comptable, metric_metadata
