def fit_files_st(self,
files_path_st,
subjects_id_list_st,
confounds_st,
files_path,
subjects_id_list,
confounds,
y,
n_seeds,
extra_var=[]):
'''
Use a list of subject IDs and search for them in the path, grab the results per network.
Same as fit_files() except that you can train and test on different set of data
'''
if self.verbose: start = time.time()
### train subtypes
self.st_crm = []
# for ii in [5,13]:#range(x.shape[1]):
xw = []
for ii in range(n_seeds):
print('Train seed ' + str(ii + 1))
if self.dynamic:
[x_dyn, x_ref] = sbp_util.grab_rmap(subjects_id_list_st,
files_path_st,
ii,
dynamic=self.dynamic)
confounds_dyn = []
for jj in range(len(x_dyn)):
confounds_dyn.append(
(confounds_st[jj], ) * x_dyn[jj].shape[0])
confounds_dyn = np.vstack(confounds_dyn)
x_dyn = np.vstack(x_dyn)
else:
x_ref = sbp_util.grab_rmap(subjects_id_list_st,
files_path_st,
ii,
dynamic=self.dynamic)
x_dyn = x_ref
confounds_dyn = confounds_st
del x_ref
## regress confounds
crm = prediction.ConfoundsRm(confounds_dyn, x_dyn)
## extract subtypes
st = subtypes.clusteringST()
st.fit_network(crm.transform(confounds_dyn, x_dyn),
nSubtypes=self.nSubtypes)
# stage 2
st_s2 = subtypes.clusteringST()
st_s2.fit_network(crm.transform(confounds_dyn, x_dyn),
nSubtypes=self.nSubtypes_stage2)
self.st_crm.append([crm, st, st_s2])
del x_dyn
if self.verbose:
print("Subtype extraction, Time elapsed: {}s)".format(
int(time.time() - start)))
def fit_files_st(self,files_path_st,subjects_id_list_st,confounds_st,files_path,subjects_id_list,confounds,y,n_seeds,extra_var=[]):
'''
Use a list of subject IDs and search for them in the path, grab the results per network.
Same as fit_files() except that you can train and test on different set of data
'''
if self.verbose: start = time.time()
### train subtypes
self.st_crm = []
#for ii in [5,13]:#range(x.shape[1]):
xw = []
for ii in range(n_seeds):
print('Train seed '+str(ii+1))
if self.dynamic:
[x_dyn,x_ref] = sbp_util.grab_rmap(subjects_id_list_st,files_path_st,ii,dynamic=self.dynamic)
confounds_dyn = []
for jj in range(len(x_dyn)):
confounds_dyn.append((confounds_st[jj],)*x_dyn[jj].shape[0])
confounds_dyn = np.vstack(confounds_dyn)
x_dyn = np.vstack(x_dyn)
else:
x_ref = sbp_util.grab_rmap(subjects_id_list_st,files_path_st,ii,dynamic=self.dynamic)
x_dyn = x_ref
confounds_dyn = confounds_st
del x_ref
## regress confounds
crm = prediction.ConfoundsRm(confounds_dyn,x_dyn)
## extract subtypes
st=subtypes.clusteringST()
st.fit_network(crm.transform(confounds_dyn,x_dyn),nSubtypes=self.nSubtypes)
self.st_crm.append([crm,st])
del x_dyn
# compute the W
xw = self.get_w_files(files_path,subjects_id_list,confounds)
if self.verbose: print("Subtype extraction, Time elapsed: {}s)".format(int(time.time() - start)))
### Include extra covariates
if len(extra_var)!=0:
all_var = np.hstack((xw,extra_var))
else:
all_var = xw
### prediction model
if self.verbose: start = time.time()
self.tlp = TwoLevelsPrediction(self.verbose,stage1_model_type=self.stage1_model_type,gamma=self.gamma)
self.tlp.fit(all_var,all_var,y)
if self.verbose: print("Two Levels prediction, Time elapsed: {}s)".format(int(time.time() - start)))
def get_w_files(self,files_path,subjects_id_list,confounds):
### extract w values
W = []
for ii in range(len(self.st_crm)):
x_ref = sbp_util.grab_rmap(subjects_id_list,files_path,ii,dynamic=False)
## compute w values
W.append(self.st_crm[ii][1].compute_weights(self.st_crm[ii][0].transform(confounds,x_ref),mask_part=self.mask_part))
del x_ref
xw = np.hstack(W)
return subtypes.reshapeW(xw)
def get_w_files(self, files_path, subjects_id_list, confounds):
### extract w values
W = []
W2 = []
for ii in range(len(self.st_crm)):
x_ref = sbp_util.grab_rmap(subjects_id_list,
files_path,
ii,
dynamic=False)
## compute w values
W.append(self.st_crm[ii][1].compute_weights(
self.st_crm[ii][0].transform(confounds, x_ref),
mask_part=self.mask_part))
W2.append(self.st_crm[ii][2].compute_weights(
self.st_crm[ii][0].transform(confounds, x_ref),
mask_part=self.mask_part))
del x_ref
xw = np.hstack(W)
xw2 = np.hstack(W2)
return subtypes.reshapeW(xw), subtypes.reshapeW(xw2)
