def loading_data(img_pattern):
loader = DataLoader(configuration_file=conf_file,
loader='mat',
task='fcmri',
event_file=img_pattern[:-4] + ".txt",
img_pattern=img_pattern,
atlas='findlab')
prepro = PreprocessingPipeline(nodes=[
Transformer(),
#Detrender(),
#SampleZNormalizer(),
#FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
return ds
data_path = '/media/robbis/DATA/meg/viviana-hcp/'
conf_file = "/media/robbis/DATA/meg/viviana-hcp/bids.conf"
loader = DataLoader(
configuration_file=conf_file,
data_path=data_path,
subjects="/media/robbis/DATA/meg/viviana-hcp/participants.tsv",
loader='bids-meg',
task='blp',
bids_atlas="complete",
bids_correction="corr",
bids_derivatives='True',
load_fx='hcp-blp')
ds = loader.fetch()
nodes = ds.fa.nodes_1
matrix = np.zeros_like(ds.samples[0])
nanmask = np.logical_not(np.isnan(ds.samples).sum(0))
ds = ds[:, nanmask]
networks = ['AN', 'CON', 'DAN', 'DMN', 'FPN', 'LN', 'MN', 'VAN', 'VFN', 'VPN']
networks = [[n] for n in networks]
kwargs_list = [{'nodes_1': v, 'nodes_2': v} for v in networks]
############################################################################
base_config = dict(prepro=['sample_slicer', 'feature_slicer'],
estimator=[('clf', LinearRegression())],
analysis=TaskPredictionTavor,
analysis__n_jobs=-1,
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from pyitab.analysis.iterator import AnalysisIterator
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.analysis.pipeline import AnalysisPipeline
from sklearn.feature_selection.univariate_selection import SelectKBest
from sklearn.model_selection import *
from sklearn.svm.classes import SVC
import _pickle as pickle
loader = DataLoader(
configuration_file="/home/carlos/fmri/carlo_ofp/ofp_new.conf",
task='OFP_NORES')
ds = loader.fetch()
decoding = RoiDecoding(n_jobs=20, scoring=['accuracy'])
results = dict()
for subject in np.unique(ds.sa.subject):
results[subject] = []
for evidence in [1, 2, 3]:
pipeline = PreprocessingPipeline(nodes=[
TargetTransformer('decision'),
SampleSlicer(**{
'subject': [subject],
'evidence': [evidence]
}),
Balancer(balancer=RandomUnderSampler(return_indices=True),
loader='mat',
task='fcmri',
atlas='findlab',
event_file=m[:-4]+".txt",
img_pattern=m)
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
#Detrender(),
SampleZNormalizer(),
#FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
_default_options = [
{
'prepro':['sample_slicer', 'target_transformer'],
'target_transformer__attr': 'expertise',
'sample_slicer__targets': ['Samatha']
},
{
'prepro':['sample_slicer', 'target_transformer'],
'target_transformer__attr': 'age',
'sample_slicer__targets': ['Samatha']
},
{
'prepro':['sample_slicer', 'target_transformer', 'sample_residual'],
'target_transformer__attr': 'expertise',
from pyitab.preprocessing.math import AbsoluteValueTransformer, SignTransformer
from pyitab.preprocessing.base import Transformer
from pyitab.analysis.states.base import Clustering
from sklearn import cluster, mixture
from joblib import Parallel, delayed
conf_file = "/media/robbis/DATA/fmri/working_memory/working_memory.conf"
conf_file = '/m/home/home9/97/guidotr1/unix/data/simulations/meg/simulations.conf'
loader = DataLoader(configuration_file=conf_file,
loader='simulations',
task='simulations')
ds = loader.fetch(prepro=Transformer())
_default_options = {
'estimator': [
[[('clf1', cluster.MiniBatchKMeans())]],
[[('clf1', cluster.KMeans())]],
[[('clf1', cluster.SpectralClustering())]],
[[('clf1', cluster.AgglomerativeClustering())]],
[[('clf5', mixture.GaussianMixture())]],
],
'sample_slicer__subject': [[trial] for trial in np.unique(ds.sa.subject)],
'estimator__clf1__n_clusters':
range(2, 10),
'estimator__clf5__n_components':
range(2, 10),
}
from imblearn.over_sampling import SMOTE
import numpy as np
from imblearn.under_sampling import *
from imblearn.over_sampling import *
conf_file = "/home/carlos/mount/megmri03/fmri/carlo_ofp/ofp.conf"
conf_file = "/media/robbis/DATA/fmri/carlo_ofp/ofp.conf"
#conf_file = "/home/carlos/fmri/carlo_ofp/ofp_new.conf"
if conf_file[1] == 'h':
from mvpa_itab.utils import enable_logging
root = enable_logging()
loader = DataLoader(configuration_file=conf_file, task='OFP')
ds = loader.fetch()
return_ = True
ratio = 'auto'
_default_options = {
'sample_slicer__evidence': [[1]],
'sample_slicer__subject': [[s] for s in np.unique(ds.sa.subject)],
'balancer__balancer': [
AllKNN(return_indices=return_, ratio=ratio),
CondensedNearestNeighbour(return_indices=return_, ratio=ratio),
EditedNearestNeighbours(return_indices=return_, ratio=ratio),
InstanceHardnessThreshold(return_indices=return_, ratio=ratio),
NearMiss(return_indices=return_, ratio=ratio),
OneSidedSelection(return_indices=return_, ratio=ratio),
RandomUnderSampler(return_indices=return_, ratio=ratio,
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.preprocessing import SampleSlicer, FeatureSlicer
from pyitab.analysis.roi import RoiAnalyzer
import os
import numpy as np
conf_file = "/home/robbis/mount/permut1/sherlock/bids/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids',
task='preproc',
bids_task=['day1'])
subjects = ['marcer', 'matsim', 'simpas']
for s in subjects:
ds = loader.fetch(subject_names=[s],
prepro=[SampleSlicer(trial_type=np.arange(1, 32))])
roi_analyzer = RoiAnalyzer(analysis=GSBS())
roi_analyzer.fit(ds, roi=['aal'], kmax=50)
roi_analyzer.save()
################## Resting state ##########################
conf_file = path = "/home/robbis/mount/permut1/sherlock/bids/bids.conf"
loader = DataLoader(configuration_file=conf_file,
data_path="/home/robbis/mount/permut1/sherlock/bids/",
subjects='participants.tsv',
loader='bids',
task='preproc',
bids_task=['day1'])
conf_file = "/media/robbis/DATA/fmri/carlo_mdm/memory.conf"
loader = DataLoader(
configuration_file=conf_file,
#loader=load_mat_ds,
task='BETA_MVPA')
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
Detrender(),
SampleZNormalizer(),
FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
_default_options = {
#'target_trans__target': ["decision"],
'sample_slicer__accuracy': [[1], [0]],
}
_default_config = {
'prepro': ['sample_slicer', 'target_transformer', 'balancer'],
'sample_slicer__decision': ['NEW', 'OLD'],
'sample_slicer__evidence': [1],
'sample_slicer__accuracy': [0],
'target_transformer__target': "decision",
"balancer__attr": 'subject',
'estimator': [('clf', SVC(C=1, kernel='linear'))],
'estimator__clf__C': 1,
roi_labels=roi_labels,
task='RESIDUALS_MVPA')
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
#Detrender(attr='file'),
Detrender(attr='chunks'),
SampleZNormalizer(),
FeatureZNormalizer(),
SampleSlicer(frame=[1, 2, 3, 4, 5, 6, 7]),
#TargetTransformer(attr='decision'),
MemoryReducer(dtype=np.float16),
#Balancer(attr='frame'),
])
ds = loader.fetch(prepro=prepro, n_subjects=8)
ds = MemoryReducer(dtype=np.float16).transform(ds)
labels = list(roi_labels.keys())[:-1]
import sentry_sdk
sentry_sdk.init("https://[email protected]/1439199")
_default_options = [
{
'target_transformer__attr':
"image_type",
'sample_slicer__attr': {
'image_type': ["I", "O"]
from pyitab.analysis.states.gsbs import GSBS
from pyitab.io.loader import DataLoader
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.preprocessing import SampleSlicer, FeatureSlicer
from pyitab.preprocessing.connectivity import SpeedEstimator
import os
import numpy as np
conf_file = path = "/home/robbis/mount/permut1/sherlock/bids/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids',
task='preproc',
bids_task=['day1'],
bids_run=['01', '02', '03'])
ds = loader.fetch(
subject_names=['matsim'],
prepro=[SampleSlicer(trial_type=np.arange(1, 32)),
FeatureSlicer(aal=[1])])
X = ds.samples
speed = SpeedEstimator().transform(ds)
peaks = speed > np.mean(speed) + 2 * np.std(speed)
peaks_idx = np.nonzero(peaks.flatten())[0]
X_ = np.split(X, peaks_idx, axis=0)
cluster = [i * np.ones(x.shape[0]) for i, x in X_]
cross_val_multiscore, LinearModel, get_coef,
Vectorizer, CSP)
from sklearn.linear_model import LogisticRegression
import warnings
warnings.filterwarnings("ignore")
conf_file = "/media/robbis/DATA/meg/c2b/meeting-december-data/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids-meg',
bids_window='300',
bids_ses='01',
task='power')
ds = loader.fetch(subject_names=['sub-109123'], prepro=[Transformer()])
_default_options = {
'loader__bids_ses': ['01', '02'],
'sample_slicer__targets' : [
['LH', 'RH'],
['LF', 'RF'],
#['LH', 'RH', 'LF', 'RF']
],
'estimator__clf': [
LinearModel(LogisticRegression(C=1, solver='liblinear')),
SVC(C=1, kernel='linear', probability=True),
SVC(C=1, gamma=1, kernel='rbf', probability=True),
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from pyitab.preprocessing.functions import Detrender, SampleSlicer, \
TargetTransformer, Transformer
from pyitab.preprocessing.normalizers import SampleZNormalizer
import warnings
warnings.filterwarnings("ignore")
conf_file = "/media/robbis/DATA/meg/reftep/bids.conf"
loader = DataLoader(configuration_file=conf_file,
task='reftep',
load_fx='reftep-conn',
loader='bids-meg',
bids_pipeline='connectivity+lv')
ds = loader.fetch(n_subjects=9)
_default_options = {
'prepro': [
['sample_slicer', 'target_transformer'],
['sample_slicer', 'feature_znormalizer', 'target_transformer'],
['sample_slicer', 'sample_znormalizer', 'target_transformer'],
],
'sample_slicer__subject': [[s] for s in np.unique(ds.sa.subject)],
'estimator__fsel__k': [50, 100, 150],
'estimator__clf': [
LogisticRegression(penalty='l1', solver='liblinear'),
SVC(C=1, kernel='linear'),
],
}