def validate_user(self, u):
timestamp('START validating user ' + u)
# split the other user's data in half for training/validation
# then just use these halves for all of the user's CV splits
impostors = [x for x in self.data.keys() if x != u]
inner_train = {x:[] for x in impostors}
outer_train = {x:[] for x in impostors}
inner_val = {x:[] for x in impostors}
outer_val = {x:[] for x in impostors}
for x in impostors:
outer_val[x] = self.data[x][:self.p[x]]
outer_train[x] = self.data[x][self.p[x]:]
inner_val[x] = self.data[x][ self.p[x] : self.p[x]+self.k[x] ]
inner_train[x] = self.data[x][ self.p[x]+self.k[x] : ]
timestamp('constructed impostor sample partitions')
print 'outer loop iterations:', int(misc.comb(len(self.data[u]), self.p[u]))
print 'inner loop iterations:', int(misc.comb(len(self.data[u])-self.p[u], self.k[u]))
impostor_params = None
for n_outer, part in enumerate(partition_data(u, self.data[u], self.p[u])):
# set the new partitions for this user
outer_val[part[0]] = part[2]
outer_train[part[0]] = part[1]
for n_inner, inner_part in enumerate(partition_data(u, part[1], self.k[u])):
inner_train[part[0]] = inner_part[1]
inner_val[part[0]] = inner_part[2]
self.auth.update_model(inner_train, [u])
if n_inner == 0 and n_outer == 0:
self.auth.score(inner_val, [u])
inner_ll = self.auth.ll_dict
else:
self.auth.ll_dict = inner_ll
self.auth.score(inner_val, [u], clear_cache=False)
if n_inner % 5 == 0:
timestamp('finished inner loop iteration ' + str(n_inner))
self.auth.compute_threshold()
timestamp('finished threshold computation')
self.auth.update_model(outer_train, [u])
timestamp('finished final model estimation')
#yield self.auth.evaluate(outer_train, user_ls=[u]), self.auth.evaluate(outer_val, user_ls=[u])
yield self.auth.evaluate(outer_val, user_ls=[u])
timestamp('finished outer loop iteration ' + str(n_outer))
self.auth.scores = {}
def validate(self):
'''
takes (username -> [(n-graph -> [latencies])])
returns a list of results from several partitions of the data
'''
for partition in itertools.product(
*[partition_data(u, self.data[u], self.p[u])
for u in self.data.keys()]
):
train = {x[0]:x[1] for x in list(partition)}
val = {x[0]:x[2] for x in list(partition)}
for inner_part in itertools.product(
*[partition_data(u, train[u], self.k[u])
for u in train.keys()]
):
inner_train = {x[0]:x[1] for x in list(inner_part)}
inner_val = {x[0]:x[2] for x in list(inner_part)}
self.auth.estimate_model(inner_train, inner_val)
self.auth.score(inner_val)
self.auth.estimate_model(train,val)
self.auth.compute_threshold()
yield self.auth.evaluate(train), self.auth.evaluate(val)
self.auth.scores = {}
for k,x in pkd.items():
print k, ',', x[0], ',', x[1], ',', int(x[2]/x[0]), ',', int(x[3]/x[1])
print ((pkd['1227981'][2]/pkd['1227981'][0])*
(pkd['1227981'][3]/pkd['1227981'][1])*
(pkd['9999999'][2]/pkd['9999999'][0])*
(pkd['9999999'][3]/pkd['9999999'][1])
)
for u in f.keys():
if not u in ['1227981', '9999999']:
del f[u]
data = f
p = {u:int(pkd[u][0]) for u in ['1227981', '9999999']}
k = {u:int(pkd[u][1]) for u in ['1227981', '9999999']}
i1 = itertools.product(
*[partition_data(u, data[u], p[u])
for u in ['1227981', '9999999']]
)
lli1 = len(list(i1))
i1 = itertools.product(
*[partition_data(u, data[u], p[u])
for u in ['1227981', '9999999']]
)
for i in i1:
train = {x[0]:x[1] for x in list(i)}
i2 = itertools.product(
*[partition_data(u, train[u], k[u])
for u in ['1227981', '9999999']]
)
