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
for itemnum, item in enumerate(itemset):
ss_items[itemnum] = usf_items[item]
convertX[item] = itemnum
items.append(ss_items)
Xs = [[convertX[i] for i in x] for x in Xs]
datab.append(Xs)
seednum += numlists
for listnum in range(1, len(data) + 1):
print simnum, listnum
flatdata = rw.flatten_list(data[:listnum])
if 'rw' in methods:
rw_graph = rw.noHidden(flatdata, usf_numnodes)
if 'goni' in methods:
goni_graph = rw.goni(flatdata,
usf_numnodes,
td=toydata,
valid=0,
fitinfo=fitinfo)
if 'chan' in methods:
chan_graph = rw.chan(flatdata, usf_numnodes)
if 'kenett' in methods:
kenett_graph = rw.kenett(flatdata, usf_numnodes)
if 'fe' in methods:
fe_graph = rw.firstEdge(flatdata, usf_numnodes)
if 'uinvite' in methods:
uinvite_graphs, priordict = rw.hierarchicalUinvite(
datab[:listnum],
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
}
import random
allsubs=["S101","S102","S103","S104","S105","S106","S107","S108","S109","S110",
"S111","S112","S113","S114","S115","S116","S117","S118","S119","S120"]
# free parameters
jeff=0.9 # 1-IRT weight
beta=1.1 # for gamma distribution when generating IRTs from hidden nodes
subj="S105"
category="animals"
Xs, items, irts, numnodes=rw.readX(subj,category,'exp/results_cleaned.csv')
# Find best graph!
best_graph, bestval=rw.findBestGraph(Xs, irts, jeff, beta)
best_rw=rw.noHidden(Xs, numnodes)
# convert best graph to networkX graph, add labels, write to file
g=nx.to_networkx_graph(best_graph)
g2=nx.to_networkx_graph(best_rw)
nx.relabel_nodes(g, items, copy=False)
nx.relabel_nodes(g2, items, copy=False)
#nx.write_dot(g,subj+".dot") # write to DOT
#rw.write_csv(g,subj+".csv",subj) # write single graph
rw.write_csv([g, g2],subj+".csv",subj) # write multiple graphs
# write lists to file
#with open(subj+'_lists.csv','w') as f:
import random
allsubs=["S101","S102","S103","S104","S105","S106","S107","S108","S109","S110",
"S111","S112","S113","S114","S115","S116","S117","S118","S119","S120"]
# free parameters
beta=1.1 # for gamma distribution when generating IRTs from hidden nodes
subj="S120"
category="vegetables"
for subj in allsubs:
Xs, items, irts, numnodes=rw.readX(subj,category,'exp/results_cleaned.csv')
# Find best graphs!
rw_graph = rw.noHidden(Xs,numnodes)
rw_g=nx.to_networkx_graph(rw_graph) # use rw.drawDot instead
nx.relabel_nodes(rw_g, items, copy=False)
nx.write_dot(rw_g,subj+"_rw.dot")
invite_graph, val=rw.findBestGraph(Xs)
invite_g=nx.to_networkx_graph(invite_graph)
nx.relabel_nodes(invite_g, items, copy=False)
nx.write_dot(invite_g,subj+"_invite.dot")
jeff=0.9 # IRT weight
irt_graph, val=rw.findBestGraph(Xs, irts, jeff, beta)
irt_g=nx.to_networkx_graph(irt_graph)
nx.relabel_nodes(irt_g, items, copy=False)
nx.write_dot(irt_g,subj+"_irt9.dot")
"S110", "S111", "S112", "S113", "S114", "S115", "S116", "S117", "S118",
"S119", "S120"
]
# free parameters
jeff = 0.9 # 1-IRT weight
beta = 1.1 # for gamma distribution when generating IRTs from hidden nodes
subj = "S001"
category = "animals"
Xs, items, irts, numnodes = rw.readX(subj, category, 's1_data.csv')
# Find best graph!
#best_graph, bestval=rw.findBestGraph(Xs, irts, jeff, beta)
best_graph, bestval = rw.findBestGraph(Xs, numnodes=numnodes)
best_rw = rw.noHidden(Xs, numnodes)
# convert best graph to networkX graph, add labels, write to file
g = nx.to_networkx_graph(best_graph)
g2 = nx.to_networkx_graph(best_rw)
nx.relabel_nodes(g, items, copy=False)
nx.relabel_nodes(g2, items, copy=False)
#nx.write_dot(g,subj+".dot") # write to DOT
#rw.write_csv(g,subj+".csv",subj) # write single graph
rw.write_csv([g, g2], subj + ".csv", subj) # write multiple graphs
# write lists to file
#with open(subj+'_lists.csv','w') as f:
# for i, x in enumerate(Xs):
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
'irttype': "exgauss",
'exgauss_lambda': 0.721386887,
'exgauss_sigma': 6.58655566,
'irt_weight': 0.95,
'rcutoff': 20
})
ss_irts.data = ss_irtdata
irts.append(ss_irts)
items.append(ss_items)
Xs.append(ss_Xs)
numnodes.append(ss_numnodes)
graphs, priordict = rw.hierarchicalUinvite(Xs,
items,
numnodes,
toydata,
fitinfo=fitinfo,
seed=None,
irts=irts)
for subnum, subj in enumerate(subs):
g = nx.to_networkx_graph(graphs[subnum])
g2 = nx.to_networkx_graph(rw.noHidden(Xs[subnum], numnodes[subnum]))
nx.relabel_nodes(g, items[subnum], copy=False)
nx.relabel_nodes(g2, items[subnum], copy=False)
rw.write_csv([g, g2], subj + "_irt.csv", subj) # write multiple graphs
with open('prior_irt.pickle', 'w') as fh:
pickle.dump(priordict, fh)