def run_perm_test(row, network_index):
network_index = int(network_index)
network_names = {
0: "visual",
1: "somatomotor",
2: "dorsal_attention",
3: "ventral_attention",
4: "limbic",
5: "fronto_parietal",
6: "default_mode",
7: "subcortical",
8: "cerebellum"
}
row_array = row.split(';')
perm_ind = row_array[0]
perm_y = row_array[1]
perm_y = np.asarray(perm_y.split(',')).astype(int)
analysis_name = network_names[network_index] + '_perm_' + str(perm_ind)
project_folder = './perms/'
# get data
data = IQData()
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_single_networks(use_cached=True)
X = data.networks[network_index]
y = data.fsiq
del data
# run analysis
pipe = construct_hyperpipe(analysis_name, project_folder)
pipe.groups = y
pipe.fit(X, perm_y, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)
def run_network(network_index):
network_index = int(network_index)
network_names = {
0: "visual",
1: "somatomotor",
2: "dorsal_attention",
3: "ventral_attention",
4: "limbic",
5: "fronto_parietal",
6: "default_mode",
7: "subcortical",
8: "cerebellum"
}
analysis_name = network_names[network_index]
project_folder = '.'
# get data
data = IQData()
covariates = np.asarray([data.age, data.gender, data.handedness]).T
data.load_single_networks(use_cached=False)
X = data.networks[network_index]
y = data.fsiq
del data
# run analysis
pipe = construct_hyperpipe(analysis_name, project_folder)
pipe.fit(X, y, **{'covariates': covariates})
os.remove(pipe.output_settings.pretrained_model_filename)