def network_properties(command):
subj_props = command['data_parameters']
command = command['network_parameters']
Xs, items, irts, numnodes = rw.readX(subj_props['subject'],
subj_props['category'],
subj_props['fullpath'])
def no_persev(x):
seen = set()
seen_add = seen.add
return [i for i in x if not (i in seen or seen_add(i))]
toydata = rw.Data({
'numx': len(Xs),
'trim': 1,
'jump': 0.0,
'jumptype': "stationary",
'priming': 0.0,
'startX': "stationary"
})
fitinfo = rw.Fitinfo({
'startGraph': "goni_valid",
'goni_size': 2,
'goni_threshold': 2,
'followtype': "avg",
'prior_samplesize': 10000,
'recorddir': "records/",
'prune_limit': 100,
'triangle_limit': 100,
'other_limit': 100
})
if command['network_method'] == "RW":
bestgraph = rw.noHidden(Xs, numnodes)
elif command['network_method'] == "Goni":
bestgraph = rw.goni(Xs, numnodes, td=toydata, valid=0, fitinfo=fitinfo)
elif command['network_method'] == "U-INVITE":
no_persev_Xs = [no_persev(x) for x in Xs
] # U-INVITE doesn't work with perseverations
bestgraph, ll = rw.uinvite(no_persev_Xs,
toydata,
numnodes,
fitinfo=fitinfo,
debug=False)
nxg = nx.to_networkx_graph(bestgraph)
node_degree = np.mean(nxg.degree().values())
nxg_json = jsonGraph(nxg, items)
clustering_coefficient = nx.average_clustering(nxg)
try:
aspl = nx.average_shortest_path_length(nxg)
except:
aspl = "disjointed graph"
return {
"type": "network_properties",
"node_degree": node_degree,
"clustering_coefficient": clustering_coefficient,
"aspl": aspl,
"graph": nxg_json
}
category="animals"
Xs, items, irts.data, numnodes=rw.readX(subj,category,'./Spring2015/results_cleaned.csv',ignorePerseverations=True)
# uinvite prior
priorgraphs=[]
prioritems=[]
for osub in subs:
if osub != subj:
g, i = rw.read_csv('Sdirected2015.csv',cols=("node1","node2"),header=True,filters={"subj": osub, "uinvite": "1"},undirected=False)
priorgraphs.append(g)
prioritems.append(i)
priordict = rw.genGraphPrior(priorgraphs, prioritems)
toydata.numx = len(Xs)
prior = (priordict, items)
# u-invite
directed_prior_graph, bestval=rw.uinvite(Xs, toydata, numnodes, fitinfo=fitinfo, prior=prior)
# rw
rw_graph=rw.noHidden(Xs, numnodes)
g=nx.DiGraph(directed_prior_graph)
g2=nx.DiGraph(rw_graph)
nx.relabel_nodes(g, items, copy=False)
nx.relabel_nodes(g2, items, copy=False)
rw.write_csv([g, g2],subj+"_directed_prior_nosparse.csv",subj,directed=True) # write multiple graphs
'exgauss_sigma': 6.58655566,
'irt_weight': 0.95,
'rcutoff': 20})
for subj in subs:
print subj
category="animals"
Xs, items, irts.data, numnodes=rw.readX(subj,category,'./Spring2015/results_cleaned.csv',ignorePerseverations=True)
toydata.numx = len(Xs)
[mu, sig, lambd] = rw.mexgauss(rw.flatten_list(irts.data))
irts.exgauss_sigma = sig
irts.exgauss_lambda = lambd
# u-invite
uinvite_graph, bestval=rw.uinvite(Xs, toydata, numnodes, fitinfo=fitinfo)
# irt95
irts.irt_weight=0.95
irt95_graph, bestval=rw.uinvite(Xs, toydata, numnodes, irts=irts, fitinfo=fitinfo)
# irt5
irts.irt_weight=0.5
irt5_graph, bestval=rw.uinvite(Xs, toydata, numnodes, irts=irts, fitinfo=fitinfo)
# rw
rw_graph=rw.noHidden(Xs, numnodes)
# goni
goni_graph=rw.goni(Xs, numnodes, td=toydata, valid=0, fitinfo=fitinfo)
'irttype': "exgauss",
'lambda': 0.721386887,
'sigma': 6.58655566,
'irt_weight': 0.5,
'rcutoff': 20})
subs=['S102','S103','S104','S105','S106','S107','S108','S109','S110',
'S111','S112','S113','S114','S115','S116','S117','S118','S119','S120']
# u-invite
for subj in subs:
category="animals"
Xs, items, irts.data, numnodes=rw.readX(subj,category,'./Spring2015/results_cleaned.csv',ignorePerseverations=True)
toydata.numx = len(Xs)
uinvite_graph, bestval=rw.uinvite(Xs, toydata, numnodes, fitinfo=fitinfo)
#orig=rw.probX(Xs, uinvite_graph, toydata)
extra_data={}
for inum, i in enumerate(uinvite_graph):
for jnum, j in enumerate(i):
if uinvite_graph[inum,jnum]==1:
uinvite_graph[inum,jnum]=0
uinvite_graph[jnum,inum]=0
result=rw.probX(Xs, uinvite_graph, toydata, forceCompute=True)
if items[inum] not in extra_data:
extra_data[items[inum]]={}
if items[jnum] not in extra_data:
extra_data[items[jnum]]={}
'numnodes': 50,
'numlinks': 6
})
fh = open('priming_test.csv', 'w')
seed = 15
for td in toydata:
print "numx: ", td.numx
# generate data with priming and fit best graph
g, a = rw.genG(toygraph, seed=seed)
[Xs, irts, alter_graph] = rw.genX(g, td, seed=seed)
bestgraph_priming, ll = rw.uinvite(Xs,
td,
toygraph.numnodes,
fitinfo=fitinfo,
seed=seed,
debug=True)
priming_cost = rw.cost(bestgraph_priming, a)
print priming_cost
td.priming = 0.0
# fit best graph assuming no priming
bestgraph_nopriming, ll = rw.uinvite(Xs,
td,
toygraph.numnodes,
fitinfo=fitinfo,
seed=seed,
debug=True)
nopriming_cost = rw.cost(bestgraph_nopriming, a)
print nopriming_cost
irts = rw.Irts({
'data': [],
'irttype': "exgauss",
'lambda': 0.721386887,
'sigma': 6.58655566,
'irt_weight': 0.9,
'rcutoff': 20
})
for subj in subs:
category = "animals"
Xs, items, irts.data, numnodes = rw.readX(
subj, category, './Spring2015/results_cleaned.csv')
uinvite_irt9, bestval = rw.uinvite(Xs,
toydata,
numnodes,
fitinfo=fitinfo,
irts=irts)
irts.irt_weight = 0.95
uinvite_irt95, bestval = rw.uinvite(Xs,
toydata,
numnodes,
fitinfo=fitinfo,
irts=irts)
irts.irt_weight = 0.5
uinvite_irt5, bestval = rw.uinvite(Xs,
toydata,
numnodes,
fitinfo=fitinfo,
irts=irts)
g = nx.to_networkx_graph(uinvite_irt5)
rnd += 1
changesmade = 0
np.random.shuffle(subs)
for subj in subs:
print "ss: ", subj
subj_idx = original_order.index(subj)
toydata.numx = len(ss_Xs[subj_idx])
fitinfo.startGraph = prior_graphs[subj_idx]
prior = (priordict, prior_items[subj_idx])
# find best graph
uinvite_graph, bestval = rw.uinvite(ss_Xs[subj_idx],
toydata,
ss_numnodes[subj_idx],
fitinfo=fitinfo,
prior=prior)
## update prior if graph has changed
if not np.array_equal(uinvite_graph, prior_graphs[subj_idx]):
changesmade += 1
prior_graphs[subj_idx] = uinvite_graph
priordict = rw.genGraphPrior(prior_graphs, prior_items)
for subj in range(len(subs)):
g = nx.to_networkx_graph(prior_graphs[subj])
g2 = nx.to_networkx_graph(rw.noHidden(ss_Xs[subj], ss_numnodes[subj]))
nx.relabel_nodes(g, prior_items[subj], copy=False)
nx.relabel_nodes(g2, prior_items[subj], copy=False)
rw.write_csv([g, g2], subj + ".csv", subj) # write multiple graphs