-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils_csc.py
962 lines (746 loc) · 31.6 KB
/
utils_csc.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
"""
Utils scripts for utils functions
"""
# %%
import seaborn as sns
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors
from codecs import ignore_errors
import scipy.signal as ss
from sklearn import cluster
from sklearn.cluster import AgglomerativeClustering
import numpy as np
import pandas as pd
from pathlib import Path
import pickle
from joblib import Memory, Parallel, delayed
import mne
from mne_bids import BIDSPath, read_raw_bids
from alphacsc import BatchCDL, GreedyCDL
from alphacsc.utils.signal import split_signal
from alphacsc.utils.convolution import construct_X_multi
from config import CDL_PARAMS, SUBJECT_IDS, get_paths, get_cdl_pickle_name
from config import BIDS_ROOT, SSS_CAL_FILE, CT_SPARSE_FILE
from config import RESULTS_DIR, PARTICIPANTS_FILE, N_JOBS, DATA_DIR
def get_raw(subject_id, ch_type='grad', sfreq=150.):
"""
"""
if subject_id[:4] == 'sub-':
subject_id = subject_id.split('-')[1]
# Read raw data from BIDS file
bp = BIDSPath(
root=BIDS_ROOT,
subject=subject_id,
task="smt",
datatype="meg",
extension=".fif",
session="smt",
)
raw = read_raw_bids(bp)
# Preprocess data
raw.load_data()
raw.filter(l_freq=None, h_freq=125)
raw.notch_filter([50, 100])
raw = mne.preprocessing.maxwell_filter(raw, calibration=SSS_CAL_FILE,
cross_talk=CT_SPARSE_FILE,
st_duration=10.0)
# Now deal with Epochs
all_events, all_event_id = mne.events_from_annotations(raw)
# all_event_id = {'audiovis/1200Hz': 1, 'audiovis/300Hz': 2, 'audiovis/600Hz': 3,
# 'button': 4, 'catch/0': 5, 'catch/1': 6}
metadata, events, event_id = mne.epochs.make_metadata(
events=all_events, event_id=all_event_id,
tmin=-3., tmax=0, sfreq=raw.info['sfreq'],
row_events=['button'], keep_last=['audiovis'])
epochs = mne.Epochs(
raw, events, event_id, metadata=metadata,
tmin=-1.7, tmax=1.7,
baseline=(-1.25, -1.0),
preload=True, verbose=False
)
# "good" button events: button event is at most one sec. after an audiovis
# event, and with at least 3 sec. between 2 button events.
epochs = epochs["event_name == 'button' and audiovis > -1. and button == 0."]
# Band-pass filter the data to a range of interest
raw.pick([ch_type, 'stim'])
raw.filter(l_freq=2, h_freq=45)
raw, events = raw.resample(
sfreq, npad='auto', verbose=False, events=epochs.events)
return raw, events
def run_csc(X, **cdl_params):
"""Run a CSC model on a given signal X.
Parameters
----------
X : numpy.ndarray
the data to run the CSC on
cdl_params : dict
dictionary of CSC parameters, such as 'n_atoms', 'n_times_atoms', etc.
Returns
-------
cdl_model
z_hat_
"""
print('Computing CSC')
cdl_params = dict(cdl_params)
n_splits = cdl_params.pop('n_splits', 1)
use_batch_cdl = cdl_params.pop('use_batch_cdl', False)
if use_batch_cdl:
cdl_model = BatchCDL(**cdl_params)
else:
cdl_model = GreedyCDL(**cdl_params)
if n_splits > 1:
X_splits = split_signal(X, n_splits=n_splits, apply_window=True)
X = X[None, :]
else:
X_splits = X.copy()
# Fit the model and learn rank1 atoms
print('Running CSC')
cdl_model.fit(X_splits)
z_hat_ = cdl_model.transform(X)
return cdl_model, z_hat_
def get_subject_info(subject_id, participants_file=PARTICIPANTS_FILE,
verbose=False):
"""For a given subject id, return its age, sex and hand found in the csv
containing all participant info.
Parameters
----------
subject_id : str
the subject id
participants_file : str | Pathlib instance
Path to csv containing all participants info
verbose : bool
if True, will print obtained info
Returns
-------
age : float
the age of the considered participant
sex : str
the sex (MALE | FEMALE) of the considered participant
hand
"""
# get age and sex of the subject
participants = pd.read_csv(participants_file, sep='\t', header=0)
age, sex, hand = participants[participants['participant_id']
== 'sub-' + str(subject_id)][['age', 'sex', 'hand']].iloc[0]
if verbose:
print(f'Subject ID: {subject_id}, {str(age)} year old {sex}')
return age, sex, hand
def get_subject_dipole(subject_id, cdl_model=None, info=None):
"""Compute the atoms' dipoles for a subject for a pre-computed CDL model.
Parameters
----------
subject_id : str
the subject id
cdl_model : alphacsc.ConvolutionalDictionaryLearning instance
info : mne.Info instance
Returns
-------
dip : mne.Dipole instance
"""
epochFif, transFif, bemFif = get_paths(subject_id)
if (cdl_model is None) or (info is None):
# get participant CSC results
file_name = RESULTS_DIR / subject_id / get_cdl_pickle_name()
if not file_name.exists():
print(f"No such file or directory: {file_name}")
return
# load CSC results
cdl_model, info, _, _ = pickle.load(open(file_name, "rb"))
# select only grad channels
meg_indices = mne.pick_types(info, meg='grad')
info = mne.pick_info(info, meg_indices)
# compute noise covariance
cov = mne.make_ad_hoc_cov(info)
u_hat_ = cdl_model.u_hat_
evoked = mne.EvokedArray(u_hat_.T, info)
# compute dipole fit
dip = mne.fit_dipole(evoked, cov, str(bemFif), str(transFif), n_jobs=6,
verbose=False)[0]
# in DAL code
# # Fit a dipole for each atom
# # Read in epochs for task data
# epochs = mne.read_epochs(epochFif)
# epochs.pick_types(meg='grad')
# cov = mne.compute_covariance(epochs)
# info = epochs.info
# # Make an evoked object with all atom topographies for dipole fitting
# evoked = mne.EvokedArray(cdl_model.u_hat_.T, info)
# # Fit dipoles
# dip = mne.fit_dipole(evoked, cov, bemFif, transFif, verbose=False)[0]
return dip
def flip_v(v):
"""Ensure the temporal pattern v peak is positive for each atom.
If necessary, multiply both u and v by -1.
Parameter
---------
v: array (n_atoms, n_times_atom)
temporal pattern
Return
------
v: array (n_atoms, n_times_atom)
"""
index_array = np.argmax(np.absolute(v), axis=1)
val_index = np.take_along_axis(v, np.expand_dims(
index_array, axis=-1), axis=-1).squeeze(axis=-1)
v[val_index < 0] *= -1
return v
def get_atoms_info(subject_id, results_dir=RESULTS_DIR):
"""For a given subject, return a list of dictionary containing all atoms'
informations (subject info, u and v vectors, dipole informations, changes
in activation before and after button press).
Parameters
----------
subject_id : str
the subject id
results_dir : Pathlib instance
Path to all participants CSC pickled results
Returns
-------
new_rows : list of dict
"""
# get participant CSC results
file_name = results_dir / subject_id / get_cdl_pickle_name()
if not file_name.exists():
print(f"No such file or directory: {file_name}")
return
# load CSC results
cdl_model, info, allZ, _ = pickle.load(open(file_name, "rb"))
# get informations about the subject
age, sex, hand = get_subject_info(subject_id, PARTICIPANTS_FILE)
base_row = {'subject_id': subject_id, 'age': age, 'sex': sex, 'hand': hand}
# get informations about atoms
dip = get_subject_dipole(subject_id, cdl_model, info=info)
new_rows = []
for kk, (u, v) in enumerate(zip(cdl_model.u_hat_, flip_v(cdl_model.v_hat_))):
gof, pos, ori = dip.gof[kk], dip.pos[kk], dip.ori[kk]
# calculate the percent change in activation between different phases of movement
# -1.25 to -0.25 sec (150 samples)
pre_sum = np.sum(allZ[:, kk, 68:218])
# -0.25 to 0.25 sec (75 samples)
move_sum = np.sum(allZ[:, kk, 218:293])
# 0.25 to 1.25 sec (150 samples)
post_sum = np.sum(allZ[:, kk, 293:443])
# multiply by 2 for movement phase because there are half as many samples
z1 = (pre_sum - 2 * move_sum) / pre_sum
z2 = (post_sum - 2 * move_sum) / post_sum
z3 = (post_sum - pre_sum) / post_sum
new_rows.append({
**base_row, 'atom_id': kk, 'u_hat': u, 'v_hat': v, 'dipole_gof': gof,
'dipole_pos_x': pos[0], 'dipole_pos_y': pos[1], 'dipole_pos_z': pos[2],
'dipole_ori_x': ori[0], 'dipole_ori_y': ori[1], 'dipole_ori_z': ori[2],
'pre-move_change': z1, 'post-move_change': z2, 'post-pre_change': z3,
'focal': (gof >= 95), 'rebound': (z3 >= 0.1),
'movement_related': (z1 >= 0. and z2 >= 0.6)
})
return new_rows
def get_atom_df(subject_ids=SUBJECT_IDS, results_dir=RESULTS_DIR, save=True):
""" Create a pandas.DataFrame where each row is an atom, and columns are
crutial informations, such a the subject id, its u and v vectors as well
as the participant age and sex.
Parameters
----------
subject_ids : list of str
list of subject ids to which we want to collect their atoms' info
results_dir : Pathlib instance
Path to all participants CSC pickled results
save : bool
if True, save output dataframe as csv
defaults to True
Returns
-------
pandas.DataFrame
"""
new_rows = Parallel(n_jobs=N_JOBS, verbose=1)(
delayed(get_atoms_info)(this_subject_id)
for this_subject_id in subject_ids)
df = pd.DataFrame()
for this_new_row in new_rows:
df = df.append(this_new_row, ignore_index=True)
if save:
df.to_csv(results_dir / 'all_atoms_info.csv')
pickle.dump(df, open(results_dir / 'all_atoms_info.pkl', "wb"))
return df
def double_correlation_clustering(atom_df, u_thresh=0.4, v_thresh=0.4,
exclude_subs=None,
output_dir=RESULTS_DIR):
"""
Parameters
----------
atom_df : pandas DataFrame
each row is an atom, at least the columns 'subject_id', 'atom_id',
'u_hat' and 'v_hat'
"""
if exclude_subs is not None:
atom_df = atom_df[~atom_df['subject_id'].isin(
exclude_subs)].reset_index()
# Calculate the correlation coefficient between all atoms
u_coefs = np.corrcoef(np.stack(atom_df['u_hat']))
v_list = np.stack(atom_df['v_hat'])
v_coefs = np.reshape([np.max(ss.correlate(v1, v2))
for v1 in v_list for v2 in v_list],
(v_list.shape[0], v_list.shape[0]))
group_num = 0
# Make atom groups array to keep track of the group that each atom belongs to
atom_groups = pd.DataFrame(
columns=['subject_id', 'atom_id', 'index', 'group_number'])
for ii, row in atom_df.iterrows():
unique = True
subject_id, atom_id = row.subject_id, row.atom_id
max_corr, max_group = 0, 0
# Loops through the existing groups and calculates the atom's average
# correlation to that group
for group in range(group_num + 1):
indx = atom_groups[atom_groups['group_number']
== group]['index'].tolist()
# Find the u vector and correlation coefficient comparing the
# current atom to each atom in the group
avg_u = np.mean(abs(np.asarray([u_coefs[ii][jj] for jj in indx])))
avg_v = np.mean(abs(np.asarray([v_coefs[ii][jj] for jj in indx])))
# check if this group passes the thresholds
if (avg_u > u_thresh) & (avg_v > v_thresh):
unique = False
# If it does, also check if this is the highest cumulative
# correlation so far
if (avg_u + avg_v) > max_corr:
max_corr = (avg_u + avg_v)
max_group = group
if unique:
# If a similar group is not found, a new group is create and the
# current atom is added to that group
group_num += 1
group_dict = {'subject_id': subject_id, 'atom_id': atom_id,
'index': ii, 'group_number': group_num}
else:
# If the atom was similar to at least one group, sorts it into the
# group that it had the highest cumulative correlation to
group_dict = {'subject_id': subject_id, 'atom_id': atom_id,
'index': ii, 'group_number': max_group}
# Add to group dataframe and reset unique boolean
atom_groups = atom_groups.append(group_dict, ignore_index=True)
if output_dir is not None:
# Save atomGroups to dataframe
csv_dir = output_dir / \
('u_' + str(u_thresh) + '_v_' + str(v_thresh) + '_atom_groups.csv')
atom_groups.to_csv(csv_dir)
group_summary = atom_groups.groupby('group_number')\
.agg({'subject_id': 'nunique', 'atom_id': 'count'})\
.rename(columns={'subject_id': 'nunique_subject_id',
'atom_id': 'count_atom_id'})\
.reset_index()
return atom_groups, group_summary
def single_subject_exclusion(atom_df, u_thresh=0.8, v_thresh=0.8,
n_group_thresh=14, output_dir=RESULTS_DIR):
"""
"""
def procedure(subject_id):
atom_groups = double_correlation_clustering(
atom_df=atom_df[atom_df['subject_id'] == subject_id].reset_index(),
u_thresh=u_thresh, v_thresh=v_thresh, output_dir=None)
new_row = {'subject_id': subject_id,
'exclude': False,
'n_groups': atom_groups['group_number'].nunique()}
if new_row['n_groups'] < n_group_thresh:
new_row['exclude'] = True
return new_row
new_rows = Parallel(n_jobs=N_JOBS, verbose=1)(
delayed(procedure)(this_subject_id)
for this_subject_id in np.unique(atom_df.subject_id))
df = pd.DataFrame()
for this_new_row in new_rows:
df = df.append(this_new_row, ignore_index=True)
if output_dir is not None:
# Save atomGroups to dataframe
df.to_csv(output_dir / 'df_single_subject_exclusion.csv')
return df
def correlation_clustering_atoms(atom_df, threshold=0.4,
output_dir=RESULTS_DIR):
"""
Parameters
----------
threshold : float
threshold to create new groups
Returns
-------
groupSummary, atomGroups (and save them XXX)
"""
# XXX exclude 'bad' subjects (single slustering operation)
# XXX make it read a pre-saved file
exclude_subs = ['CC420061', 'CC121397', 'CC420396', 'CC420348', 'CC320850',
'CC410325', 'CC121428', 'CC110182', 'CC420167', 'CC420261',
'CC322186', 'CC220610', 'CC221209', 'CC220506', 'CC110037',
'CC510043', 'CC621642', 'CC521040', 'CC610052', 'CC520517',
'CC610469', 'CC720497', 'CC610292', 'CC620129', 'CC620490']
atom_df = atom_df[~atom_df['subject_id'].isin(exclude_subs)].reset_index()
# Calculate the correlation coefficient between all atoms
# u_vector_list = np.asarray(atom_df['u_hat'].values)
# v_vector_list = np.asarray(atom_df['v_hat'].values)
# v_coefs = []
# for v in v_vector_list:
# for v2 in v_vector_list:
# coef = np.max(ss.correlate(v, v2))
# v_coefs.append(coef)
# v_coefs = np.asarray(v_coefs)
# v_coefs = np.reshape(v_coefs, (10760, 10760))
# u_coefs = np.corrcoef(u_vector_list, u_vector_list)[0:10760][0:10760]
# Calculate the correlation coefficient between all atoms
u_coefs = np.corrcoef(np.stack(atom_df['u_hat']))
v_list = np.stack(atom_df['v_hat'])
v_coefs = np.reshape([np.max(ss.correlate(v1, v2))
for v1 in v_list for v2 in v_list],
(v_list.shape[0], v_list.shape[0]))
threshold_summary = pd.DataFrame(
columns=['Threshold', 'Number of Groups', 'Number of Top Groups'])
# Set parameters
u_thresh = threshold
v_thresh = threshold
atomNum = 0
groupNum = 0
unique = True
# Make atom groups array to keep track of the group that each atom belongs to
atomGroups = pd.DataFrame(
columns=['subject_id', 'atom_id', 'Index', 'Group number'])
for ii, row in atom_df.iterrows():
# print(row)
subject_id, atom_id = row.subject_id, row.atom_id
max_corr = 0
max_group = 0
# Loops through the existing groups and calculates the atom's average
# correlation to that group
for group in range(0, groupNum + 1):
gr_atoms = atomGroups[atomGroups['Group number'] == group]
inds = gr_atoms['Index'].tolist()
u_groups = []
v_groups = []
# Find the u vector and correlation coefficient comparing the
# current atom to each atom in the group
for ind2 in inds:
u_coef = u_coefs[ii][ind2]
u_groups.append(u_coef)
v_coef = v_coefs[ii][ind2]
v_groups.append(v_coef)
# average across u and psd correlation coefficients in that group
u_groups = abs(np.asarray(u_groups))
avg_u = np.mean(u_groups)
v_groups = abs(np.asarray(v_groups))
avg_v = np.mean(v_groups)
# check if this group passes the thresholds
if (avg_u > u_thresh) & (avg_v > v_thresh):
unique = False
# If it does, also check if this is the highest cumulative
# correlation so far
if (avg_u + avg_v) > max_corr:
max_corr = (avg_u + avg_v)
max_group = group
# If the atom was similar to at least one group, sorts it into the
# group that it had the highest cumulative correlation to
if (unique == False):
groupDict = {'subject_id': subject_id, 'atom_id': atom_id,
'Index': ii, 'Group number': max_group}
# If a similar group is not found, a new group is create and the
# current atom is added to that group
elif (unique == True):
groupNum += 1
print(groupNum)
groupDict = {'subject_id': subject_id, 'atom_id': atom_id,
'Index': ii, 'Group number': groupNum}
# Add to group dataframe and reset unique boolean
atomGroups = atomGroups.append(groupDict, ignore_index=True)
unique = True
# Summary statistics for the current dataframe:
# Number of distinct groups
groups = atomGroups['Group number'].tolist()
groups = np.asarray(groups)
numGroups = len(np.unique(groups))
# Number of atoms and subjects per group
numAtoms_list = []
numSubs_list = []
for un in np.unique(groups):
numAtoms = len(np.where(groups == un)[0])
numAtoms_list.append(numAtoms)
groupRows = atomGroups[atomGroups['Group number'] == un]
sub_list = np.asarray(groupRows['subject_id'].tolist())
numSubs = len(np.unique(sub_list))
numSubs_list.append(numSubs)
numAtoms_list = np.asarray(numAtoms_list)
meanAtoms = np.mean(numAtoms_list)
stdAtoms = np.std(numAtoms_list)
print("Number of groups:")
print(numGroups)
print("Average number of atoms per group:")
print(str(meanAtoms) + " +/- " + str(stdAtoms))
groupSummary = pd.DataFrame(
columns=['Group Number', 'Number of Atoms', 'Number of Subjects'])
groupSummary['Group Number'] = np.unique(groups)
groupSummary['Number of Atoms'] = numAtoms_list
groupSummary['Number of Subjects'] = numSubs_list
numSubs_list = np.asarray(numSubs_list)
topGroups = len(np.where(numSubs_list >= 12)[0])
threshold_dict = {'Threshold': threshold,
'Number of Groups': numGroups,
'Number of Top Groups': topGroups}
threshold_summary = threshold_summary.append(
threshold_dict, ignore_index=True)
if output_dir is not None:
# Save group summary dataframe
csv_dir = output_dir + \
'u_' + str(u_thresh) + '_v_' + str(v_thresh) + '_groupSummary.csv'
groupSummary.to_csv(csv_dir)
# Save atomGroups to dataframe
csv_dir = output_dir + \
'u_' + str(u_thresh) + '_v_' + str(v_thresh) + '_atomGroups.csv'
atomGroups.to_csv(csv_dir)
return groupSummary, atomGroups
def culstering_cah_kmeans(df, data_columns='all', n_clusters=6):
"""Compute a CAH and k-means clustering
"""
if data_columns == 'all':
data = np.array(df)
else:
data = np.array(df[data_columns])
# CAH clustering
clustering = AgglomerativeClustering(n_clusters=n_clusters,
affinity='euclidean',
linkage='ward')
clustering.fit(data)
df['labels_cah'] = clustering.labels_
# k-means clustering
kmeans = cluster.KMeans(n_clusters=n_clusters)
kmeans.fit(data)
df['labels_kmeans'] = kmeans.labels_
return df
def compute_distance_matrix(atom_df):
"""Compute the distance matrix, where
..maths:
M[i,j] = 1 - \frac{\sqrt{corr_u[i,j]^2 + corr_v[i,j]^2}}{\sqrt{2}}
"""
corr_u = np.corrcoef(np.stack(atom_df['u_hat']))
v_list = np.stack(atom_df['v_hat'])
corr_v = np.reshape([np.max(ss.correlate(v1, v2))
for v1 in v_list for v2 in v_list],
(v_list.shape[0], v_list.shape[0]))
D = (1 - np.sqrt(corr_u**2 + corr_v**2) / np.sqrt(2)).clip(min=0)
np.fill_diagonal(D, 0) # enforce the 0 in the diagonal
# ensure that D is symetric while keeping enough precision
D = np.round(D, 6)
return D
def reconstruct_class_signal(df, results_dir):
""" Reonstruct the signal for all atoms in the given dataframe
Parameters
----------
df : pandas.DataFrame
dataframe where each row is an atom, and has at least
the folowing columns :
subject_id : the participant id associated with the atom
atom_id : the atom id
results_dir : Pathlib instance
Path to all participants CSC pickled results
Returns
-------
X : array-like
the reconstructed signal
n_times_atom : int
the minimum number of timestamps per atom, accross all atoms in the
input dataframe
"""
Z_temp = []
D_temp = []
min_n_times_valid = np.inf
for subject_id in set(df['subject_id'].values):
file_name = results_dir / subject_id / get_cdl_pickle_name()
cdl_model, info, _, _ = pickle.load(open(file_name, "rb"))
atom_idx = df[df['subject_id'] ==
subject_id]['atom_id'].values.astype(int)
Z_temp.append(cdl_model.z_hat_[:, atom_idx, :])
min_n_times_valid = min(min_n_times_valid, Z_temp[-1].shape[2])
D_temp.append(cdl_model.D_hat_[atom_idx, :, :])
# combine z and d vectors
Z = Z_temp[0][:, :, :min_n_times_valid]
D = D_temp[0]
for this_z, this_d in zip(Z_temp[1:], D_temp[1:]):
this_z = this_z[:, :, :min_n_times_valid]
Z = np.concatenate((Z, this_z), axis=1)
D = np.concatenate((D, this_d), axis=0)
n_times_atom = D.shape[-1]
X = construct_X_multi(Z, D)
return X, n_times_atom
def get_df_mean(df, col_label='Group number', cdl_params=CDL_PARAMS,
results_dir=RESULTS_DIR, n_jobs=N_JOBS):
"""
Parameters
----------
df : pandas.DataFrame
the clustering dataframe where each row is an atom, and has at least
the folowing columns :
subject_id : the participant id associated with the atom
u_hat : the topomap vector of the atom
v_hat : the temporal pattern of the atom
col_label : the cluster result
col_label : str
the name of the column that contains the cultering result
cdl_params : dict
the CDL parameters to use to compute the mean atom.
By default, use GreedyCDL, to use BatchCDL, ensure that
cdl_params['use_batch_cdl'] = True
results_dir : Pathlib instance
Path to all participants CSC pickled results
n_jobs : int
number of concurrently running jobs
Returns
-------
df_mean : pandas.DataFrame
columns:
col_label : clustering label
u_hat, v_hat : spatial and temporal pattern of the mean atom
z_hat : activation vector of the mean atom
"""
# ensure that only one recurring pattern will be extracted
cdl_params.update(n_atoms=1, n_splits=1)
def procedure(label):
# Reconstruct signal for a given class
X, n_times_atom = reconstruct_class_signal(
df=df[df[col_label] == label], results_dir=results_dir)
cdl_params['n_times_atom'] = n_times_atom
cdl_model, z_hat = run_csc(X, **cdl_params)
# append dataframe
new_row = {col_label: label,
'u_hat': cdl_model.u_hat_[0],
'v_hat': cdl_model.v_hat_[0],
'z_hat': z_hat,
'n_times_atom': n_times_atom}
return new_row
new_rows = Parallel(n_jobs=min(n_jobs, df[col_label].nunique()), verbose=1)(
delayed(procedure)(this_label) for this_label in df[col_label].unique())
df_mean = pd.DataFrame()
for new_row in new_rows:
df_mean = df_mean.append(new_row, ignore_index=True)
df_mean.rename(columns={col_label: 'label'}, inplace=True)
df_mean.to_csv(results_dir / 'df_mean_atom.csv')
return df_mean
def complete_existing_df(atomData, results_dir=RESULTS_DIR):
"""
"""
atomData = pd.read_csv('atomData.csv')
atomData.rename(columns={'Subject ID': 'subject_id',
'Atom number': 'atom_id'},
inplace=True)
# participants = pd.read_csv("participants.tsv", sep='\t', header=0)
participants = pd.read_csv(PARTICIPANTS_FILE, sep='\t', header=0)
participants['subject_id'] = participants['participant_id'].apply(
lambda x: x[4:])
columns = ['subject_id', 'atom_id', 'Dipole GOF',
'Dipole Pos x', 'Dipole Pos y', 'Dipole Pos z',
'Dipole Ori x', 'Dipole Ori y', 'Dipole Ori z', 'Focal',
'Pre-Move Change', 'Post-Move Change',
'Post-Pre Change', 'Movement-related', 'Rebound']
atom_df_temp = pd.merge(atomData[columns], participants[[
'subject_id', 'age', 'sex', 'hand']], how="left", on="subject_id")
subject_dirs = [f for f in results_dir.iterdir() if not f.is_file()]
df = pd.DataFrame()
for subject_dir in subject_dirs:
subject_id = subject_dir.name
base_row = {'subject_id': subject_id}
# get participant CSC results
file_name = results_dir / subject_id / get_cdl_pickle_name()
if not file_name.exists():
print(f"No such file or directory: {file_name}")
continue
# load CSC results
cdl_model, _, _, _ = pickle.load(open(file_name, "rb"))
for kk, (u, v) in enumerate(zip(cdl_model.u_hat_, flip_v(cdl_model.v_hat_))):
new_row = {**base_row, 'atom_id': int(kk), 'u_hat': u, 'v_hat': v}
df = df.append(new_row, ignore_index=True)
atom_df = pd.merge(atom_df_temp, df, how="left",
on=["subject_id", "atom_id"])
atom_df.rename(columns={col: col.lower().replace(' ', '_')
for col in atom_df.columns},
inplace=True)
atom_df.to_csv('all_atoms_info.csv')
pickle.dump(atom_df, open('all_atoms_info.pkl', "wb"))
return atom_df
# %%
if __name__ == '__main__':
atomData = pd.read_csv(RESULTS_DIR / 'atomData.csv')
col_to_drop = [col for col in atomData.columns if 'Unnamed' in col]
atomData = atomData.drop(columns=col_to_drop)
all_atoms_info = complete_existing_df(atomData)
atom_df = all_atoms_info.copy()
BEM_DIR = DATA_DIR / "camcan-mne/freesurfer"
TRANS_DIR = DATA_DIR / "camcan-mne/trans"
TRANS_HALIFAX_DIR == DATA_DIR / "camcan-mne/trans-halifax"
BEM_FILES = [f.name for f in BEM_DIR.iterdir()]
TRANS_FILES = [f.name for f in TRANS_DIR.iterdir()]
TRANS_HALIFAX_FILES = [f.name for f in TRANS_HALIFAX_DIR.iterdir()]
df_trans = pd.DataFrame()
for subject_id in atom_df['subject_id'].unique():
epochFif, transFif, bemFif = get_paths(subject_id)
new_row = {'subject_id': subject_id,
'bem_in_base': bemFif.name.split('-')[0] in BEM_FILES,
'trans_in_base': transFif.name in TRANS_FILES,
'trans_in_halifax': transFif.name in TRANS_HALIFAX_FILES}
df_trans = df_trans.append(new_row, ignore_index=True)
# %%
# all_atoms_info = pd.read_csv('./all_atoms_info.csv')
all_atoms_info = pickle.load(open('all_atoms_info.pkl', "rb"))
exclude_subs = ['CC420061', 'CC121397', 'CC420396', 'CC420348', 'CC320850',
'CC410325', 'CC121428', 'CC110182', 'CC420167', 'CC420261',
'CC322186', 'CC220610', 'CC221209', 'CC220506', 'CC110037',
'CC510043', 'CC621642', 'CC521040', 'CC610052', 'CC520517',
'CC610469', 'CC720497', 'CC610292', 'CC620129', 'CC620490']
atom_groups, group_summuray = double_correlation_clustering(
all_atoms_info, u_thresh=0.4, v_thresh=0.4, exclude_subs=exclude_subs,
output_dir=None)
# %%
# subject_id = list(set(all_atoms_info['subject_id'].values))[0]
subject_id = 'CC110037'
data_cols = ['u_hat', 'v_hat']
sub_df = all_atoms_info[all_atoms_info['subject_id'] == subject_id]
n_sensors = len(all_atoms_info['u_hat'].values[0])
n_times_atom = len(all_atoms_info['v_hat'].values[0])
X = pd.DataFrame()
X[[f'u_{i}' for i in range(n_sensors)]] = pd.DataFrame(
sub_df.u_hat.tolist(), index=sub_df.index)
# X[[f'v_{i}' for i in range(n_times_atom)]] = pd.DataFrame(
# sub_df.v_hat.tolist(), index=sub_df.index)
# %%
neigh = NearestNeighbors(n_neighbors=2, metric='correlation')
nbrs = neigh.fit(X)
distances, indices = nbrs.kneighbors(X)
distances = np.sort(distances, axis=0)
distances = distances[:, 1]
plt.plot(distances)
p = 90
q = int(X.shape[0] * p / 100)
plt.vlines(q, 0, 1, linestyles='--', label=f'{p}%')
plt.legend()
plt.show()
eps = round(distances[q], 2)
print(
f"epsilon choice so that 90% of individuals have their nearest neightbour in less than epsilon: {eps}")
# %%
atom_df = all_atoms_info.copy()
D = compute_distance_matrix(atom_df)
print(D.min(), D.max())
# %%
list_esp = np.linspace(0.1, 0.5, 9)
list_min_samples = [1, 2, 3]
def procedure(eps, min_samples):
y_pred = DBSCAN(eps=eps, min_samples=min_samples,
metric='precomputed').fit_predict(D)
row = {'eps': eps, 'min_samples': min_samples,
'n_groups': len(np.unique(y_pred))}
return row
new_rows = Parallel(n_jobs=N_JOBS, verbose=1)(
delayed(procedure)(eps, min_samples)
for eps in list_esp for min_samples in list_min_samples)
df_dbscan = pd.DataFrame()
for new_row in new_rows:
df_dbscan = df_dbscan.append(new_row, ignore_index=True)
# %%
n_groups = df_dbscan.pivot("eps", "min_samples", "n_groups")
ax = sns.heatmap(n_groups, annot=True)
ax.set_title('Number of groups obtains with DBScan')
ax.set_ylabel(r'$\varepsilon$')
ax.set_xlabel(r"min samples")
plt.show()
# %%