#priorgraphs=[]
#prioritems=[]
#for osub in subs:
# if osub != subj:
# g, i = rw.read_csv('s2016.csv',cols=("node1","node2"),header=True,filters={"subj": osub, "uinvite": "1"})
# priorgraphs.append(g)
# prioritems.append(i)
#priordict = rw.genGraphPrior(priorgraphs, prioritems)
toydata.numx = len(Xs)
prior = (priordict, items)
# u-invite
uinvite_prior_graph, bestval = rw.uinvite(Xs,
toydata,
numnodes,
fitinfo=fitinfo,
prior=prior)
# rw
rw_graph = rw.noHidden(Xs, numnodes)
g = nx.to_networkx_graph(uinvite_prior_graph)
g2 = nx.to_networkx_graph(rw_graph)
nx.relabel_nodes(g, items, copy=False)
nx.relabel_nodes(g2, items, copy=False)
rw.write_csv([g, g2], subj + "usf_sparse.csv",
subj) # write multiple graphs
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
# goni
goni_graph=rw.goni(Xs, numnodes, td=toydata, valid=0, fitinfo=fitinfo)
# prior
#toygraphs.numnodes = numnodes
#prior=rw.genSWPrior(toygraphs, fitinfo.prior_samplesize)
#prior_graph, bestval=rw.uinvite(Xs, toydata, numnodes, fitinfo=fitinfo, prior=prior)
# priming
#toydata.priming = 0.5
#priming_graph, bestval=rw.uinvite(Xs, toydata, numnodes, fitinfo=fitinfo)
# complete (irts, prior,, priming)
#complete_graph, bestval=rw.uinvite(Xs, toydata, numnodes, fitinfo=fitinfo, prior=prior, irts=irts)
#toydata.priming = 0.0 # reset priming
g=nx.to_networkx_graph(rw_graph)
g2=nx.to_networkx_graph(goni_graph)
g3=nx.to_networkx_graph(uinvite_graph)
g4=nx.to_networkx_graph(irt5_graph)
g5=nx.to_networkx_graph(irt95_graph)
nx.relabel_nodes(g, items, copy=False)
nx.relabel_nodes(g2, items, copy=False)
nx.relabel_nodes(g3, items, copy=False)
nx.relabel_nodes(g4, items, copy=False)
nx.relabel_nodes(g5, items, copy=False)
rw.write_csv([g, g2, g3, g4, g5],subj+".csv",subj) # write multiple graphs
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: # for i, x in enumerate(Xs): # for item in x: # print>>f, str(i)+","+items[item] # items removed #for i in np.argwhere(graph5-oldgraph==-1): # if i[0]>i[1]: # skip duplicates # print items[i[0]],items[i[1]] # items added #for i in np.argwhere(graph5-oldgraph==1):
# 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): # for item in x: # print>>f, str(i)+","+items[item] # items removed #for i in np.argwhere(graph5-oldgraph==-1): # if i[0]>i[1]: # skip duplicates # print items[i[0]],items[i[1]] # items added #for i in np.argwhere(graph5-oldgraph==1): # if i[0]>i[1]: # skip duplicates
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]]={}
extra_data[items[inum]][items[jnum]] = (result[0], np.mean(rw.flatten_list(result[1])))
extra_data[items[jnum]][items[inum]] = (result[0], np.mean(rw.flatten_list(result[1])))
uinvite_graph[inum,jnum]=1
uinvite_graph[jnum,inum]=1
g=nx.to_networkx_graph(uinvite_graph)
nx.relabel_nodes(g, items, copy=False)
rw.write_csv(g,subj+".csv",subj,extra_data=extra_data) # write multiple graphs
#@joe
#what if we only include edges that are either bidirectional or uni-directional AND in the undirected graph
#there's also a standard procedure for converting directed graphs into undirected graphs
#it's called moralization
# match edge density of USF network (i.e., prior on edges)
})
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)
g2 = nx.to_networkx_graph(uinvite_irt9)
g3 = nx.to_networkx_graph(uinvite_irt95)
nx.relabel_nodes(g, items, copy=False)
nx.relabel_nodes(g2, items, copy=False)
nx.relabel_nodes(g3, items, copy=False)
rw.write_csv([g, g2, g3], subj + "_irt.csv", subj) # write multiple graphs