###############################################################################
from pymri.dataset.datasets import DatasetManager
mvpa_directory = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa/'
print('Loading database from %s' % mvpa_directory)
dataset = DatasetManager(
mvpa_directory=mvpa_directory,
# conditions has to be tuples
contrast=(('PlanTool_0', 'PlanTool_5'), ('PlanCtrl_0', 'PlanCtrl_5')),
)
dataset_reduced = dataset.feature_reduction(
k_features=784,
reduction_method='SelectKBest (SKB)',
normalize=True,
nnadl=True)
from pymri.model import FNN
# create Classifier
cls = FNN(type='FNN simple',
input_layer_size=784,
hidden_layer_size=46,
output_layer_size=2,
epochs=100,
mini_batch_size=11,
learning_rate=3.0,
verbose=True)
from pymri.dataset.datasets import DatasetManager
mvpa_directory = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa'
roi_path = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa/ROIs/pSMG.nii.gz'
runs = 5
volumes = 145
n_time = 0
# Load the dataset
print('Loading database from %s' % mvpa_directory)
dataset = DatasetManager(
mvpa_directory=mvpa_directory,
# conditions has to be tuples
contrast=(('PlanTool_0', 'PlanTool_5'), ('PlanCtrl_0', 'PlanCtrl_5')))
dataset.feature_reduction(roi_path=roi_path,
k_features=784,
reduction_method='SKB')
training_data, test_data, validation_data = dataset.leave_one_run_out(
runs=runs, volumes=volumes, n_time=n_time)
print('Saving file to /tmp/fmri.pkl')
import pickle
pickle.dump((training_data, test_data), open("/tmp/fmri.pkl", "wb"))
path_mask_brain=path_base + 'mask.nii.gz',
contrast=(('ExeTool_0', 'ExeTool_5'), ('ExeCtrl_0',
'ExeCtrl_5')),
nnadl=True)
# load data
ds.load_data()
###############################################################################
#
# CHOOSE ROIs
#
###############################################################################
# select feature reduction method
ds.feature_reduction(roi_selection='SelectKBest',
k_features=k_features,
normalize=True)
# ds.feature_reduction(
# roi_selection='/amu/master/nifti/bold/roi_mask_plan.nii.gz',
# normalize=True
# )
k_features = ds.X_processed.shape[1]
print(k_features)
'''
roi - 'SelectKBest' xor 'PCA' xor 'RBM' xor 'path_to_mask.nii.gz'
NOTICE: the same number of features has to be used for each mask.
So if number of features in mask differ then smaller number of feautres is
chosen (k). From the more numerous masks only k-greatest is taken.
n_time = 0
# Load the dataset
print('Loading database from %s' % mvpa_directory)
dataset = DatasetManager(
mvpa_directory=mvpa_directory,
# conditions has to be tuples
contrast=(
('PlanTool_0', 'PlanTool_5'),
('PlanCtrl_0', 'PlanCtrl_5')
)
)
dataset.feature_reduction(
roi_path=roi_path,
k_features=784,
reduction_method='SKB'
)
training_data, test_data, validation_data = dataset.leave_one_run_out(
runs=runs,
volumes=volumes,
n_time=n_time
)
print('Saving file to /tmp/fmri.pkl')
import pickle
pickle.dump(
(training_data, test_data),
open("/tmp/fmri.pkl", "wb")
)
path_mask_brain=path_base + 'mask.nii.gz',
contrast=(('ExeTool_0', 'ExeTool_5'), ('ExeCtrl_0', 'ExeCtrl_5')),
nnadl = True
)
# load data
ds.load_data()
###############################################################################
#
# CHOOSE ROIs
#
###############################################################################
# select feature reduction method
ds.feature_reduction(
roi_selection='SelectKBest', k_features=k_features,
normalize=True
)
# ds.feature_reduction(
# roi_selection='/amu/master/nifti/bold/roi_mask_plan.nii.gz',
# normalize=True
# )
k_features = ds.X_processed.shape[1]
print(k_features)
'''
roi - 'SelectKBest' xor 'PCA' xor 'RBM' xor 'path_to_mask.nii.gz'
NOTICE: the same number of features has to be used for each mask.
So if number of features in mask differ then smaller number of feautres is
# mvpa_directory = '/tmp/Maestro_Project1/GK011RZJA/Right_Hand/mvpa/'
mvpa_directory = \
'/amu/master/Maestro_Project1.preprocessed/GK011RZJA/Right_Hand/mvpa/'
print('Loading database from %s' % mvpa_directory)
dataset = DatasetManager(
mvpa_directory=mvpa_directory,
# conditions has to be tuples
contrast=(('PlanTool_0', 'PlanTool_5'), ('PlanCtrl_0', 'PlanCtrl_5')),
)
dataset_reduced = dataset.feature_reduction(
k_features=k_features,
reduction_method='SelectKBest (SKB)',
normalize=True,
nnadl=True
)
from pymri.model import FNN
# create Classifier
cls = FNN(
type='FNN simple',
input_layer_size=k_features,
hidden_layer_size=46,
output_layer_size=2,
epochs=100,
# epochs=10,
mini_batch_size=11,
learning_rate=3.0,
