def analysis(conf, name):
#print(conf._default_options)
kwargs = conf._get_kwargs()
#print(kwargs)
a = AnalysisPipeline(conf, name=name).fit(**kwargs)
a.save(path="/media/robbis/DATA/fmri/c2b/")
return
def analysis(conf, name):
#print(conf._default_options)
kwargs = conf._get_kwargs()
#print(kwargs)
a = AnalysisPipeline(conf, name=name).fit(**kwargs)
a.save(path="/media/guidotr1/Seagate_Pt1/data/simulations/")
return
{
'task': ['task5']
},
])
iterator = AnalysisIterator(options,
AnalysisConfigurator,
config_kwargs=base_config)
errs = []
for conf in iterator:
kwargs = conf._get_kwargs()
try:
a = AnalysisPipeline(conf, name='fingerprint+tavor').fit(ds, **kwargs)
a.save()
except Exception as err:
errs.append([conf._default_options, err])
#######################################################################################
base_config = dict(prepro=['sample_slicer', 'feature_slicer'],
estimator=[('clf', LinearRegression())],
analysis=TaskPredictionTavor,
analysis__n_jobs=-1,
analysis__permutation=0,
analysis__verbose=0,
kwargs__x_attr={'task': ['rest']},
kwargs__prepro=[SampleZNormalizer()])
options = dict(sample_slicer__band=[
def run(n_jobs):
path = "/media/robbis/Seagate_Pt1/data/working_memory/"
conf_file = "%s/data/working_memory.conf" % (path)
### Load datasets ###
iterator_kwargs = {
"loader__img_pattern": [
#'power_parcel.mat',
'power_normalized.mat',
#'connectivity_matrix.mat'
'mpsi_normalized.mat'
],
"fetch__prepro": [['none'], ['none']],
"loader__task": ["POWER", "CONN"]
}
config_kwargs = {
'loader': DataLoader,
'loader__configuration_file': conf_file,
'loader__loader': 'mat',
'loader__task': 'POWER',
#'fetch__n_subjects': 57,
"loader__data_path": "%s/data/" % (path),
"loader__subjects": "%s/data/participants.csv" % (path),
}
iterator = AnalysisIterator(iterator_kwargs,
AnalysisConfigurator,
config_kwargs=config_kwargs,
kind='list')
ds_list = [generate(configurator) for configurator in iterator]
for i, ds in enumerate(ds_list):
ds_ = ds.copy()
if i == 0:
k = np.arange(1, 88, 10)
ds_ = DatasetFxNormalizer(ds_fx=np.mean).transform(ds_)
else:
k = np.arange(1, 400, 50)
#ds_ = DatasetFxNormalizer(ds_fx=np.mean).transform(ds_)
_default_options = {
#'sample_slicer__targets' : [['0back', '2back'], ['0back', 'rest'], ['rest', '2back']],
#'kwargs__ds': ds_list,
'sample_slicer__targets': [['0back'], ['2back']],
'target_transformer__attr': [
'accuracy_0back_both', 'accuracy_2back_both', 'rt_0back_both',
'rt_2back_both'
],
'sample_slicer__band': [['alpha'], ['beta'], ['theta'], ['gamma']],
'estimator__fsel__k':
k,
'clf__C': [1, 10, 100],
'clf__kernel': ['linear', 'rbf']
}
_default_config = {
'prepro': ['sample_slicer', 'target_transformer'],
'sample_slicer__band': ['gamma'],
'sample_slicer__targets': ['0back', '2back'],
'estimator': [('fsel', SelectKBest(score_func=f_regression, k=5)),
('clf', SVR(C=10, kernel='linear'))],
'estimator__clf__C':
1,
'estimator__clf__kernel':
'linear',
'cv':
GroupShuffleSplit,
'cv__n_splits':
75,
'cv__test_size':
0.25,
'analysis_scoring': ['r2', 'neg_mean_squared_error'],
'analysis':
RoiRegression,
'analysis__n_jobs':
n_jobs,
'analysis__permutation':
0,
'analysis__verbose':
0,
'kwargs__roi': ['matrix_values'],
'kwargs__cv_attr':
'subjects',
}
iterator = AnalysisIterator(_default_options,
AnalysisConfigurator,
config_kwargs=_default_config)
for conf in iterator:
kwargs = conf._get_kwargs()
a = AnalysisPipeline(conf,
name="triton+behavioural").fit(ds_, **kwargs)
a.save()
del a
'roi': ['lateral_ips'],
'kwargs__prepro': ['featurenorm', 'samplenorm'],
'analysis__n_jobs': 2
}
_default_options = {
'sampleslicer__evidence': [[1], [2], [3]],
'cv__n_splits': [3, 5],
}
iterator = AnalysisIterator(_default_options,
AnalysisConfigurator(**_default_config))
for conf in iterator:
kwargs = conf._get_kwargs()
a = AnalysisPipeline(conf, name="permut1_wm").fit(ds, **kwargs)
a.save()
##################################################
# Review
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from pyitab.analysis.iterator import AnalysisIterator
from pyitab.analysis.configurator import AnalysisConfigurator
loader = DataLoader(
configuration_file="/home/carlos/fmri/carlo_ofp/ofp_new.conf",
task='OFP_NORES')
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
Detrender(),
Detrender(chunks_attr='file'),
import sentry_sdk
sentry_sdk.init(
"https://[email protected]/1439199",
traces_sample_rate=1.0,
)
errors = []
iterator = AnalysisIterator(_default_options,
AnalysisConfigurator,
config_kwargs=_default_config)
for conf in iterator:
kwargs = conf._get_kwargs()
try:
a = AnalysisPipeline(conf, name="reftep+nine+subjects").fit(**kwargs)
a.save(path="/media/robbis/DATA/meg/reftep/derivatives/results/")
except Exception as err:
capture_exception(err)
errors.append([conf, err])
######## Permutations ################
conf_file = "/media/robbis/DATA/meg/reftep/bids.conf"
_default_options = {
'loader__bids_band': [
['alphalow'],
],
'loader__bids_pipeline':
['aal+connectivity', 'seed+connectivity', 'sensor+connectivity'],
'fetch__subject_names': [['sub-003']],
def analysis(ds, conf):
kwargs = conf._get_kwargs()
a = AnalysisPipeline(conf, name="c2b_01").fit(ds, **kwargs)
a.save()
del a
'kwargs__cv_attr': 'subject',
}
errs = []
filtered_options = [_default_options[2]]
iterator = AnalysisIterator(filtered_options,
AnalysisConfigurator(**_default_config),
kind='configuration')
for conf in iterator:
kwargs = conf._get_kwargs()
try:
a = AnalysisPipeline(conf, name="fingerprint").fit(ds, **kwargs)
a.save(path="/home/carlos/fmri/carlo_mdm/0_results/")
except Exception as err:
errs.append([conf._default_options, err])
capture_exception(err)
##### Results #####
from pyitab.results.base import filter_dataframe
from pyitab.results.bids import get_searchlight_results_bids
from scipy.stats import zscore
dataframe = get_searchlight_results_bids('/media/robbis/DATA/fmri/carlo_mdm/0_results/derivatives/')
df = filter_dataframe(dataframe, id=['uv5oyc6s'], filetype=['full'])
mask = ni.load("/media/robbis/DATA/fmri/carlo_mdm/1_single_ROIs/mask_intersection.nii.gz").get_data()
'analysis': RoiDecoding,
'analysis__n_jobs': 5,
'analysis__permutation': 0,
'analysis__verbose': 0,
'kwargs__roi': ['matrix_values'],
'kwargs__cv_attr':'subjects',
}
iterator = AnalysisIterator(_default_options, AnalysisConfigurator(**_default_config))
for conf in iterator:
kwargs = conf._get_kwargs()
a = AnalysisPipeline(conf, name="wm_mpsi_norm_sign").fit(ds, **kwargs)
a.save()
del a
###############################################################
# 2019 #
conf_file = "/media/robbis/DATA/fmri/working_memory/working_memory.conf"
loader = DataLoader(configuration_file=conf_file,
loader=load_mat_ds,
task='MPSI_NORM')
prepro = PreprocessingPipeline(nodes=[
Transformer(),
#SignTransformer(),
'analysis': TemporalDecoding,
'analysis__n_jobs': 8,
'analysis__permutation': 0,
'analysis__verbose': 0,
'kwargs__roi': ['matrix_values'],
#'kwargs__cv_attr':'subjects',
}
estimators = []
iterator = AnalysisIterator(_default_options, AnalysisConfigurator, config_kwargs=_default_config)
for conf in iterator:
kwargs = conf._get_kwargs()
a = AnalysisPipeline(conf, name="cross+session").fit(ds=None, **kwargs)
a.save(save_estimator=True)
est = a._estimator.scores['mask-matrix_values_value-1.0'][0]['estimator']
estimators.append(est)
del a
#########################
# Prediction timecourse #
# Plot results
# See results.py
colormap = {'LH':'navy', 'RH':'firebrick', 'LF':'cornflowerblue', 'RF':'salmon'}
colors = [colormap[t] for t in y]
# Load second session