# USF prior
#usfnet, usfitems = rw.read_csv('./snet/USF_animal_subset.snet')
#priordict = rw.genGraphPrior([usfnet], [usfitems])
for subj in subs:
print subj
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)
'numnodes': 280,
'numlinks': 6,
'prob_rewire': .3
})
irts = rw.Irts({
'data': [],
'irttype': "exgauss",
'exgauss_lambda': 0.721386887,
'exgauss_sigma': 6.58655566,
'irt_weight': 0.95,
'rcutoff': 20
})
# USF prior
usfnet, usfitems = rw.read_csv('./snet/USF_animal_subset.snet')
priordict = rw.genGraphPrior([usfnet], [usfitems])
for subj in subs:
print subj
category = "animals"
Xs, items, irts.data, numnodes = rw.readX(
subj,
category,
'./Spring2016/results_cleaned.csv',
ignorePerseverations=True)
# uinvite prior
#priorgraphs=[]
#prioritems=[]
#for osub in subs:
# Results of previous simulation were hard to analyze since the graph was changing size as number of lists increased
# This file estimates the number of Misses for each numlists/listlength combo to impute into data
import numpy as np
import rw
import math
import random
import networkx as nx
import pygraphviz
usf,items=rw.read_csv('USF_animal_subset.csv')
usfg=nx.to_networkx_graph(usf)
numnodes=len(items)
usfsize=160
misses=[]
f=open('usf_sims_misses.csv','a', 0) # write/append to file with no buffering
for numlists in range(2,18):
for listlength in [15,30,50,70]:
misses=0
crs=0
for gnum in range(100): # how many samples for each numlists/listlength combo
print numlists, listlength, gnum
# generate toy lists
Xs=[rw.genX(usfg)[0:listlength] for i in range(numlists)]
itemsinXs=np.unique(rw.flatten_list(Xs)) # list of items that appear in toy data
notinx=[] # nodes not in trimmed X
# script to generate USF' network; same as USF network except creates a direct edge between any two animals where
# indirect edge exists (path through exclusively non-animals)
# constraint: path length of 3
import sys
sys.path.append('./rw')
import rw
import numpy as np
import networkx as nx
usf,items=rw.read_csv('USF_full.csv')
usfprime=np.copy(usf)
# animal list
with open('USFanimals.csv','r') as f:
animals = f.read()
animals=animals.split('\n')
animals.pop()
animalidx=[items.values().index(i) for i in animals]
nonanimalidx=sorted([i for i in range(5018) if i not in animalidx],reverse=True)
G = nx.to_networkx_graph(usf)
cutoff=3
for node1 in items:
print node1
for node2 in items:
if node2 > node1: # loop through all pairs (could be optimized)
if (items[node1] in animals) and (items[node2] in animals): # if both nodes are animals
#!/usr/bin/python
import pickle
import numpy as np
import rw
import networkx as nx
import sys
filename = sys.argv[1]
usf_graph, usf_items = rw.read_csv("./snet/USF_animal_subset.snet")
usf_graph_nx = nx.from_numpy_matrix(usf_graph)
usf_numnodes = len(usf_items)
td=rw.Data({
'numx': 3,
'trim': 35})
fh = open(filename,'r')
alldata = pickle.load(fh)
Xs = alldata['datab'][0:10]
graphs = alldata['uinvite_graphs']
items = alldata['items'][0:10]
priordict = alldata['priordict']
# for old pickles. ha, that's kinda funny.
if 'DEFAULTPRIOR' not in priordict.keys():
priordict['DEFAULTPRIOR'] = 0.5
# recompute for halfa... should be identical for a=1
import numpy as np
subs2015=['S101','S102','S103','S104','S105','S106','S107','S108','S109','S110',
'S111','S112','S113','S114','S115','S116','S117','S118','S119','S120']
subs2016=['S1','S2','S3','S4','S5','S7','S8','S9','S10','S11','S12','S13']
fi='s2015.csv'
subs=subs2015
fo=open('pairs.csv','w')
subpairs=open('subpairs.csv','w')
finalpairs=[]
for sub in subs:
# read in graphs
g1, items = rw.read_csv(fi,cols=('node1','node2'),header=1,filters={'subj': sub, 'irt5': '1'},undirected=1)
g2, items2 = rw.read_csv(fi,cols=('node1','node2'),header=1,filters={'subj': sub, 'irt9': '1'},undirected=1)
g3, items3 = rw.read_csv(fi,cols=('node1','node2'),header=1,filters={'subj': sub, 'irt95': '1'},undirected=1)
# re-arrange graphs to have same indices
idx=[items2.values().index(i) for i in items.values()]
g2=g2[idx][:,idx]
idx=[items3.values().index(i) for i in items.values()]
g3=g3[idx][:,idx]
gtotal=(g1+g2+g3)
yes=zip(*np.where(gtotal==4)) # all models agree there is an edge
no=zip(*np.where(gtotal==0)) # all models agree there is no edge
some=zip(*np.where(gtotal>0)) # models disagree on where there is an edge
some=[i for i in some if i not in yes]
# temporary script to take full USF network and generate USF' network
# USF' network includes edges between nodes that are indirectly connected in USF,
# even if nodes are not directly connected
import sys
sys.path.append('./rw')
import rw
usf_full, allitems = rw.read_csv('USF_full.csv')
usf_animal, animals = rw.read_csv('USF_animal_subset.csv')
real_data = {}
real_irts = {}
listlengths = {}
for sub in subs:
data, items, irts, numnodes = rw.readX(sub, "animals", real_lists)
listlengths[sub] = [len(x) for x in data]
data = numToAnimal(data, items)
real_data[sub] = data
real_irts[sub] = irts
## generate fake_data and irts yoked to real_data
numsets = 100 # number of sets of fake data per SS
fake_data = {}
fake_irts = {}
USFnet, USFanimals = rw.read_csv('./snet/USF_animal_subset.snet')
USFnet = nx.to_networkx_graph(USFnet)
# methods: genX, frequency, cbdfs, sa
def genFakeData(td, method="genX", decay=0):
for sub in subs:
fake_data[sub] = []
fake_irts[sub] = []
for setnum in range(numsets):
dataset = []
irtset = []
for listnum, trimval in enumerate(listlengths[sub]):
td.trim = trimval
## SEED START
'S1', 'S2', 'S3', 'S4', 'S5', 'S7', 'S8', 'S9', 'S10', 'S11', 'S12', 'S13'
]
fi = 's2016.csv'
subs = subs2016
fo = open('pairs.csv', 'w')
subpairs = open('subpairs.csv', 'w')
finalpairs = []
for sub in subs:
# read in graphs
g1, items1 = rw.read_csv(fi,
cols=('node1', 'node2'),
header=1,
filters={
'subj': sub,
'uinvite': '1'
},
undirected=1)
g2, items2 = rw.read_csv(fi,
cols=('node1', 'node2'),
header=1,
filters={
'subj': sub,
'priming': '1'
},
undirected=1)
g3, items3 = rw.read_csv(fi,
cols=('node1', 'node2'),
header=1,
filters={
data, items, irts, numnodes = rw.readX(subj, "animals", real_lists)
listlengths[subj] = [len(x) for x in data]
data = numToAnimal(data, items)
real_data[subj] = data
real_irts[subj] = irts
## generate fake_ data and irts yoked to real_ data
numsets = 100 # number of sets of fake data per SS
fake_data = {}
fake_irts = {}
for subj in subs:
graph, items = rw.read_csv(real_graphs,
cols=('node1', 'node2'),
header=1,
filters={
'subj': str(subj),
'uinvite': '1'
})
graph = nx.from_numpy_matrix(graph)
fake_data[subj] = []
fake_irts[subj] = []
for setnum in range(numsets):
dataset = []
irtset = []
for trimval in listlengths[subj]:
toydata.trim = trimval
print str(subj), str(toydata.trim)
[data, irts, alter_graph] = rw.genX(graph, toydata)
data = numToAnimal(data, items)[0]