def build_embedded_connectome(est_path_iterlist, ID, multimodal, embed):
"""
Embeds ensemble population of graphs into an embedded ensemble feature vector.
Parameters
----------
est_path_iterlist : list
List of file paths to .npy file containing graph with thresholding applied.
ID : str
A subject id or other unique identifier.
multimodal : list
List of booleans indicating whether multiple modalities of input data have been specified.
embed : str
Specifies which type of ensemble embedding will be performed. Options include `omni` and `mase`.
"""
import yaml
from pathlib import Path
from pynets.stats.embeddings import _mase_embed, _omni_embed
# Available functional and structural connectivity models
with open("%s%s" % (str(Path(__file__).parent.parent), '/runconfig.yaml'),
'r') as stream:
hardcoded_params = yaml.load(stream)
try:
func_models = hardcoded_params['available_models']['func_models']
except KeyError:
print(
'ERROR: available functional models not sucessfully extracted from runconfig.yaml'
)
try:
struct_models = hardcoded_params['available_models'][
'struct_models']
except KeyError:
print(
'ERROR: available structural models not sucessfully extracted from runconfig.yaml'
)
if multimodal is True:
out_path = None
atlases = list(
set([x.split('/')[-3].split('/')[0] for x in est_path_iterlist]))
parcel_dict_func = dict.fromkeys(atlases)
parcel_dict_dwi = dict.fromkeys(atlases)
est_path_iterlist_dwi = list(
set([
i for i in est_path_iterlist
if i.split('est-')[1].split('_')[0] in struct_models
]))
est_path_iterlist_func = list(
set([
i for i in est_path_iterlist
if i.split('est-')[1].split('_')[0] in func_models
]))
func_subnets = list(
set([
i.split('_est')[0].split('/')[-1]
for i in est_path_iterlist_func
]))
dwi_subnets = list(
set([
i.split('_est')[0].split('/')[-1]
for i in est_path_iterlist_dwi
]))
for atlas in atlases:
if len(func_subnets) > 1:
parcel_dict_func[atlas] = {}
for sub_net in func_subnets:
parcel_dict_func[atlas][sub_net] = []
else:
parcel_dict_func[atlas] = []
if len(dwi_subnets) > 1:
parcel_dict_dwi[atlas] = {}
for sub_net in dwi_subnets:
parcel_dict_dwi[atlas][sub_net] = []
else:
parcel_dict_dwi[atlas] = []
for graph_path in est_path_iterlist_dwi:
if atlas in graph_path:
if len(dwi_subnets) > 1:
for sub_net in dwi_subnets:
if sub_net in graph_path:
parcel_dict_dwi[atlas][sub_net].append(
graph_path)
else:
parcel_dict_dwi[atlas].append(graph_path)
for graph_path in est_path_iterlist_func:
if atlas in graph_path:
if len(func_subnets) > 1:
for sub_net in func_subnets:
if sub_net in graph_path:
parcel_dict_func[atlas][sub_net].append(
graph_path)
else:
parcel_dict_func[atlas].append(graph_path)
pop_list = []
for pop_ref in parcel_dict_func[atlas]:
# RSN case
if isinstance(pop_ref, dict):
rsns = [i.split('_')[1] for i in list(pop_ref.keys())]
i = 0
for rsn in rsns:
pop_rsn_list = []
for graph in pop_ref[rsn]:
pop_list.append(np.load(graph))
if len(pop_rsn_list) > 1:
if len(list(set([i.shape
for i in pop_rsn_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population '
'that precludes embedding')
if embed == 'omni':
out_path = _omni_embed(pop_list, atlas,
graph_path, ID, rsns[i])
elif embed == 'mase':
out_path = _mase_embed(pop_list, atlas,
graph_path, ID, rsns[i])
else:
raise ValueError(
'Embedding type not recognized. Presently supported options include: '
'omni or mase')
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
i = i + 1
else:
pop_list.append(np.load(pop_ref))
if len(pop_list) > 1:
if len(list(set([i.shape for i in pop_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population that '
'precludes embedding')
if embed == 'omni':
out_path = _omni_embed(pop_list, atlas, graph_path, ID)
elif embed == 'mase':
out_path = _mase_embed(pop_list, atlas, graph_path, ID)
else:
raise ValueError(
'Embedding type not recognized. Presently supported options include: '
'omni or mase')
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
pop_list = []
for pop_ref in parcel_dict_dwi[atlas]:
# RSN case
if isinstance(pop_ref, dict):
rsns = [i.split('_')[1] for i in list(pop_ref.keys())]
i = 0
for rsn in rsns:
pop_rsn_list = []
for graph in pop_ref[rsn]:
pop_list.append(np.load(graph))
if len(pop_rsn_list) > 1:
if len(list(set([i.shape
for i in pop_rsn_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population '
'that precludes embedding')
if embed == 'omni':
out_path = _omni_embed(pop_list, atlas,
graph_path, ID, rsns[i])
elif embed == 'mase':
out_path = _mase_embed(pop_list, atlas,
graph_path, ID, rsns[i])
else:
raise ValueError(
'Embedding type not recognized. Presently supported options include: '
'omni or mase')
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
i = i + 1
else:
pop_list.append(np.load(pop_ref))
if len(pop_list) > 1:
if len(list(set([i.shape for i in pop_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population that '
'precludes embedding')
if embed == 'omni':
out_path = _omni_embed(pop_list, atlas, graph_path, ID)
elif embed == 'mase':
out_path = _mase_embed(pop_list, atlas, graph_path, ID)
else:
raise ValueError(
'Embedding type not recognized. Presently supported options include: '
'omni or mase')
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
elif (multimodal is False) and (len(est_path_iterlist) > 1):
atlases = list(
set([x.split('/')[-3].split('/')[0] for x in est_path_iterlist]))
parcel_dict = dict.fromkeys(atlases)
subnets = list(
set([
i.split('_est')[0].split('/')[-1] for i in est_path_iterlist
if i.split('_est')[0].split('/')[-1] != ID
]))
out_path = None
for atlas in atlases:
if len(subnets) > 1:
parcel_dict[atlas] = {}
for sub_net in subnets:
parcel_dict[atlas][sub_net] = []
else:
parcel_dict[atlas] = []
for graph_path in est_path_iterlist:
if atlas in graph_path:
if len(subnets) > 1:
for sub_net in subnets:
if sub_net in graph_path:
parcel_dict[atlas][sub_net].append(graph_path)
else:
parcel_dict[atlas].append(graph_path)
pop_list = []
for pop_ref in parcel_dict[atlas]:
# RSN case
if isinstance(pop_ref, dict):
rsns = [i.split('_')[1] for i in list(pop_ref.keys())]
i = 0
for rsn in rsns:
pop_rsn_list = []
for graph in pop_ref[rsn]:
pop_list.append(np.load(graph))
if len(pop_rsn_list) > 1:
if len(list(set([i.shape
for i in pop_rsn_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population '
'that precludes embedding')
out_path = _omni_embed(pop_rsn_list, rsns[i])
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
i = i + 1
else:
pop_list.append(np.load(pop_ref))
if len(pop_list) > 1:
if len(list(set([i.shape for i in pop_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population that '
'precludes embedding')
if embed == 'omni':
out_path = _omni_embed(pop_list, atlas, graph_path, ID)
elif embed == 'mase':
out_path = _mase_embed(pop_list, atlas, graph_path, ID)
else:
raise ValueError(
'Embedding type not recognized. Presently supported options include: '
'omni or mase')
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
else:
raise RuntimeWarning(
'ERROR: Only one graph sampled, omnibus embedding not appropriate.'
)
return out_path
def build_omnetome(est_path_iterlist, ID):
"""
Embeds ensemble population of graphs into an embedded ensemble feature
vector.
Parameters
----------
est_path_iterlist : list
List of file paths to .npy file containing graph.
ID : str
A subject id or other unique identifier.
References
----------
.. [1] Liu, Y., He, L., Cao, B., Yu, P. S., Ragin, A. B., & Leow, A. D.
(2018). Multi-view multi-graph embedding for brain network clustering
analysis. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018.
.. [2] Levin, K., Athreya, A., Tang, M., Lyzinski, V., & Priebe, C. E.
(2017, November). A central limit theorem for an omnibus embedding of
multiple random dot product graphs. In Data Mining Workshops (ICDMW),
2017 IEEE International Conference on (pp. 964-967). IEEE.
"""
from pathlib import Path
import sys
import numpy as np
from pynets.core.utils import flatten
from pynets.stats.embeddings import _omni_embed
from pynets.core.utils import load_runconfig
# Available functional and structural connectivity models
hardcoded_params = load_runconfig()
try:
func_models = hardcoded_params["available_models"]["func_models"]
except KeyError:
print(
"ERROR: available functional models not sucessfully extracted"
" from runconfig.yaml"
)
sys.exit(1)
try:
struct_models = hardcoded_params["available_models"][
"struct_models"]
except KeyError:
print(
"ERROR: available structural models not sucessfully extracted"
" from runconfig.yaml"
)
sys.exit(1)
try:
n_components = hardcoded_params["gradients"][
"n_components"][0]
except KeyError:
print(
"ERROR: available gradient dimensionality presets not "
"sucessfully extracted from runconfig.yaml"
)
sys.exit(1)
if isinstance(est_path_iterlist, list):
est_path_iterlist = list(flatten(est_path_iterlist))
else:
est_path_iterlist = [est_path_iterlist]
if len(est_path_iterlist) > 1:
atlases = list(set([x.split("/")[-3].split("/")[0]
for x in est_path_iterlist]))
parcel_dict_func = dict.fromkeys(atlases)
parcel_dict_dwi = dict.fromkeys(atlases)
est_path_iterlist_dwi = list(
set(
[
i
for i in est_path_iterlist
if i.split("model-")[1].split("_")[0] in struct_models
]
)
)
est_path_iterlist_func = list(
set(
[
i
for i in est_path_iterlist
if i.split("model-")[1].split("_")[0] in func_models
]
)
)
if "_rsn" in ";".join(est_path_iterlist_func):
func_subnets = list(
set([i.split("_rsn-")[1].split("_")[0] for i in
est_path_iterlist_func])
)
else:
func_subnets = []
if "_rsn" in ";".join(est_path_iterlist_dwi):
dwi_subnets = list(
set([i.split("_rsn-")[1].split("_")[0] for i in
est_path_iterlist_dwi])
)
else:
dwi_subnets = []
out_paths_func = []
out_paths_dwi = []
for atlas in atlases:
if len(func_subnets) >= 1:
parcel_dict_func[atlas] = {}
for sub_net in func_subnets:
parcel_dict_func[atlas][sub_net] = []
else:
parcel_dict_func[atlas] = []
if len(dwi_subnets) >= 1:
parcel_dict_dwi[atlas] = {}
for sub_net in dwi_subnets:
parcel_dict_dwi[atlas][sub_net] = []
else:
parcel_dict_dwi[atlas] = []
for graph_path in est_path_iterlist_dwi:
if atlas in graph_path:
if len(dwi_subnets) >= 1:
for sub_net in dwi_subnets:
if sub_net in graph_path:
parcel_dict_dwi[atlas][sub_net].append(
graph_path)
else:
parcel_dict_dwi[atlas].append(graph_path)
for graph_path in est_path_iterlist_func:
if atlas in graph_path:
if len(func_subnets) >= 1:
for sub_net in func_subnets:
if sub_net in graph_path:
parcel_dict_func[atlas][sub_net].append(
graph_path)
else:
parcel_dict_func[atlas].append(graph_path)
if len(parcel_dict_func[atlas]) > 0:
if isinstance(parcel_dict_func[atlas], dict):
# RSN case
for rsn in parcel_dict_func[atlas]:
pop_rsn_list = []
graph_path_list = []
for graph in parcel_dict_func[atlas][rsn]:
pop_rsn_list.append(np.load(graph))
graph_path_list.append(graph)
if len(pop_rsn_list) > 1:
if len(
list(set([i.shape for i in
pop_rsn_list]))) > 1:
raise RuntimeWarning(
"Inconsistent number of"
" vertices in graph population "
"that precludes embedding...")
out_path = _omni_embed(
pop_rsn_list, atlas, graph_path_list, ID, rsn,
n_components
)
out_paths_func.append(out_path)
else:
print(
"WARNING: Only one graph sampled, omnibus"
" embedding not appropriate."
)
pass
else:
pop_list = []
graph_path_list = []
for pop_ref in parcel_dict_func[atlas]:
pop_list.append(np.load(pop_ref))
graph_path_list.append(pop_ref)
if len(pop_list) > 1:
if len(list(set([i.shape for i in pop_list]))) > 1:
raise RuntimeWarning(
"Inconsistent number of vertices in "
"graph population that precludes embedding")
out_path = _omni_embed(pop_list, atlas,
graph_path_list, ID,
n_components=n_components)
out_paths_func.append(out_path)
else:
print(
"WARNING: Only one graph sampled, omnibus "
"embedding not appropriate."
)
pass
if len(parcel_dict_dwi[atlas]) > 0:
if isinstance(parcel_dict_dwi[atlas], dict):
# RSN case
graph_path_list = []
for rsn in parcel_dict_dwi[atlas]:
pop_rsn_list = []
for graph in parcel_dict_dwi[atlas][rsn]:
pop_rsn_list.append(np.load(graph))
graph_path_list.append(graph)
if len(pop_rsn_list) > 1:
if len(
list(set([i.shape for i in
pop_rsn_list]))) > 1:
raise RuntimeWarning(
"Inconsistent number of"
" vertices in graph population "
"that precludes embedding")
out_path = _omni_embed(
pop_rsn_list, atlas, graph_path_list, ID,
rsn, n_components
)
out_paths_dwi.append(out_path)
else:
print(
"WARNING: Only one graph sampled, omnibus"
" embedding not appropriate."
)
pass
else:
pop_list = []
graph_path_list = []
for pop_ref in parcel_dict_dwi[atlas]:
pop_list.append(np.load(pop_ref))
graph_path_list.append(pop_ref)
if len(pop_list) > 1:
if len(list(set([i.shape for i in pop_list]))) > 1:
raise RuntimeWarning(
"Inconsistent number of vertices in graph"
" population that precludes embedding")
out_path = _omni_embed(pop_list, atlas,
graph_path_list,
ID, n_components=n_components)
out_paths_dwi.append(out_path)
else:
print(
"WARNING: Only one graph sampled, omnibus "
"embedding not appropriate."
)
pass
else:
print("At least two graphs required to build an omnetome...")
out_paths_func = []
out_paths_dwi = []
pass
return out_paths_dwi, out_paths_func
def build_omnetome(est_path_iterlist, ID):
"""
Embeds ensemble population of graphs into an embedded ensemble feature vector.
Parameters
----------
est_path_iterlist : list
List of file paths to .npy file containing graph.
ID : str
A subject id or other unique identifier.
References
----------
.. [1] Liu, Y., He, L., Cao, B., Yu, P. S., Ragin, A. B., & Leow, A. D. (2018).
Multi-view multi-graph embedding for brain network clustering analysis.
32nd AAAI Conference on Artificial Intelligence, AAAI 2018.
.. [2] Levin, K., Athreya, A., Tang, M., Lyzinski, V., & Priebe, C. E. (2017,
November). A central limit theorem for an omnibus embedding of multiple random
dot product graphs. In Data Mining Workshops (ICDMW), 2017 IEEE International
Conference on (pp. 964-967). IEEE.
"""
import yaml
import pkg_resources
from pynets.stats.embeddings import _omni_embed
# Available functional and structural connectivity models
with open(pkg_resources.resource_filename("pynets", "runconfig.yaml"),
'r') as stream:
hardcoded_params = yaml.load(stream)
try:
func_models = hardcoded_params['available_models']['func_models']
except KeyError:
print(
'ERROR: available functional models not sucessfully extracted from runconfig.yaml'
)
try:
struct_models = hardcoded_params['available_models'][
'struct_models']
except KeyError:
print(
'ERROR: available structural models not sucessfully extracted from runconfig.yaml'
)
stream.close()
atlases = list(
set([x.split('/')[-3].split('/')[0] for x in est_path_iterlist]))
parcel_dict_func = dict.fromkeys(atlases)
parcel_dict_dwi = dict.fromkeys(atlases)
est_path_iterlist_dwi = list(
set([
i for i in est_path_iterlist
if i.split('est-')[1].split('_')[0] in struct_models
]))
est_path_iterlist_func = list(
set([
i for i in est_path_iterlist
if i.split('est-')[1].split('_')[0] in func_models
]))
func_subnets = list(
set([
i.split('_est')[0].split('/')[-1] for i in est_path_iterlist_func
]))
dwi_subnets = list(
set([i.split('_est')[0].split('/')[-1]
for i in est_path_iterlist_dwi]))
out_paths_func = []
out_paths_dwi = []
for atlas in atlases:
if len(func_subnets) >= 1:
parcel_dict_func[atlas] = {}
for sub_net in func_subnets:
parcel_dict_func[atlas][sub_net] = []
else:
parcel_dict_func[atlas] = []
if len(dwi_subnets) >= 1:
parcel_dict_dwi[atlas] = {}
for sub_net in dwi_subnets:
parcel_dict_dwi[atlas][sub_net] = []
else:
parcel_dict_dwi[atlas] = []
for graph_path in est_path_iterlist_dwi:
if atlas in graph_path:
if len(dwi_subnets) >= 1:
for sub_net in dwi_subnets:
if sub_net in graph_path:
parcel_dict_dwi[atlas][sub_net].append(graph_path)
else:
parcel_dict_dwi[atlas].append(graph_path)
for graph_path in est_path_iterlist_func:
if atlas in graph_path:
if len(func_subnets) >= 1:
for sub_net in func_subnets:
if sub_net in graph_path:
parcel_dict_func[atlas][sub_net].append(graph_path)
else:
parcel_dict_func[atlas].append(graph_path)
pop_list = []
for pop_ref in parcel_dict_func[atlas]:
# RSN case
if isinstance(pop_ref, dict):
rsns = [i.split('_')[1] for i in list(pop_ref.keys())]
i = 0
for rsn in rsns:
pop_rsn_list = []
for graph in pop_ref[rsn]:
pop_list.append(np.load(graph))
if len(pop_rsn_list) > 1:
if len(list(set([i.shape for i in pop_rsn_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population '
'that precludes embedding')
out_path = _omni_embed(pop_list, atlas, graph_path, ID,
rsns[i])
out_paths_func.append(out_path)
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
i = i + 1
else:
pop_list.append(np.load(pop_ref))
if len(pop_list) > 1:
if len(list(set([i.shape for i in pop_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population that '
'precludes embedding')
out_path = _omni_embed(pop_list, atlas, graph_path, ID)
out_paths_func.append(out_path)
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
pop_list = []
for pop_ref in parcel_dict_dwi[atlas]:
# RSN case
if isinstance(pop_ref, dict):
rsns = [i.split('_')[1] for i in list(pop_ref.keys())]
i = 0
for rsn in rsns:
pop_rsn_list = []
for graph in pop_ref[rsn]:
pop_list.append(np.load(graph))
if len(pop_rsn_list) > 1:
if len(list(set([i.shape for i in pop_rsn_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population '
'that precludes embedding')
out_path = _omni_embed(pop_list, atlas, graph_path, ID,
rsns[i])
out_paths_dwi.append(out_path)
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
i = i + 1
else:
pop_list.append(np.load(pop_ref))
if len(pop_list) > 1:
if len(list(set([i.shape for i in pop_list]))) > 1:
raise RuntimeWarning(
'ERROR: Inconsistent number of vertices in graph population that '
'precludes embedding')
out_path = _omni_embed(pop_list, atlas, graph_path, ID)
out_paths_dwi.append(out_path)
else:
print(
'WARNING: Only one graph sampled, omnibus embedding not appropriate.'
)
pass
return out_paths_dwi, out_paths_func
