def _psi_solve(y, eps=0.00001):
""" Solve psi(c)-log(c)=y by dichotomy
"""
if y > 0:
print "y", y
raise ValueError("y>0, the problem cannot be solved")
u = 1.0
if y > sp.psi(u) - sp.log(u):
while sp.psi(u) - sp.log(u) < y:
u *= 2
u /= 2
else:
while sp.psi(u) - sp.log(u) > y:
u /= 2
return _dichopsi_log(u, 2 * u, y, eps)
def _psi_solve(y, eps=0.00001):
"""
This function solves
solve psi(c)-log(c)=y
by dichotomy
"""
if y>0:
print "y", y
raise ValueError, "y>0, the problem cannot be solved"
u = 1.
if y>sp.psi(u)-sp.log(u):
while sp.psi(u)-sp.log(u)<y:
u = 2*u
u = u/2
else:
while sp.psi(u)-sp.log(u)>y:
u = u/2
return _dichopsi_log(u,2*u,y,eps)
def _dichopsi_log(u, v, y, eps=0.00001):
""" Implements the dichotomic part of the solution of psi(c)-log(c)=y
"""
if u > v:
u, v = v, u
t = (u + v) / 2
if np.absolute(u - v) < eps:
return t
else:
if sp.psi(t) - sp.log(t) > y:
return _dichopsi_log(u, t, y, eps)
else:
return _dichopsi_log(t, v, y, eps)
def learn(A,K,net0={},opts={}):
"""
runs variational bayes for inference of network modules
(i.e. community detection) under a constrained stochastic block
model.
net0 and opts inputs are optional. if provided, length(net0.a0)
must equal K.
inputs:
A: N-by-N undirected (symmetric), binary adjacency matrix w/o
self-edges (note: fastest for sparse and logical A)
K: (maximum) number of modules
net0: initialization/hyperparameter structure for network
net0['Q0']: N-by-K initial mean-field matrix (rows sum to 1)
net0['ap0']: alpha_{+0}, hyperparameter for prior on \theta_+
net0['bp0']: beta_{+0}, hyperparameter for prior on \theta_+
net0['am0']: alpha_{-0}, hyperparameter for prior on \theta_-
net0['bm0']: beta_{-0}, hyperparameter for prior on \theta_-
net0['a0']: alpha_{\mu0}, 1-by-K vector of hyperparameters for
prior on \pi
opts: options
opts['TOL_DF']: tolerance on change in F (outer loop)
opts['MAX_FITER']: maximum number of F iterations (outer loop)
opts['VERBOSE']: verbosity (0=quiet (default),1=print, 2=figures)
outputs:
net: posterior structure for network
net['F']: converged free energy (same as net.F_iter(end))
net['F_iter']: free energy over iterations (learning curve)
net['Q']: N-by-K mean-field matrix (rows sum to 1)
net['K']: K, passed for compatibility with vbmod_restart
net['ap']: alpha_+, hyperparameter for posterior on \theta_+
net['bp']: beta_+, hyperparameter for posterior on \theta_+
net['am']: alpha_-, hyperparameter for posterior on \theta_-
net['bm']: beta_-, hyperparameter for posterior on \theta_-
net['a']: alpha_{\mu}, 1-by-K vector of hyperparameters for
posterior on \pi
"""
# default options
TOL_DF=1e-2
MAX_FITER=30
VERBOSE=0
SAVE_ITER=0
#print "get options from opts struct"
if (type(opts) == type({})) and (len(opts) > 0):
if 'TOL_DF' in opts: TOL_DF=opts['TOL_DF']
if 'MAX_FITER' in opts: MAX_FITER=opts['MAX_FITER']
if 'VERBOSE' in opts: VERBOSE=opts['VERBOSE']
if 'SAVE_ITER' in opts: SAVE_ITER=opts['SAVE_ITER']
N=A.shape[0] # number of nodes
M=0.5*A.sum(0).sum(1) # total number of non-self edges
M=M[0,0]
C=comb(N,2) # total number of possible edges between N nodes
uk=mat(ones([K,1]))
un=mat(ones([N,1]))
#print "default prior hyperparameters"
ap0=2;
bp0=1;
am0=1;
bm0=2;
a0=ones([1,K]);
#print "get initial Q0 matrix and prior hyperparameters from net0 struct"
if (type(net0) == type({})) and (len(net0) > 0):
if 'Q0' in net0: Q=net0['Q0']
if 'ap0' in net0: ap0=net0['ap0']
if 'bp0' in net0: bp0=net0['bp0']
if 'am0' in net0: am0=net0['am0']
if 'bm0' in net0: bm0=net0['bm0']
if 'a0' in net0: a0=net0['a0']
#print "initialize Q if not provided"
try: Q
except NameError: Q=init(N,K)
Qmat=mat(Q)
#print "size of Q=", Q.shape
#Q=init(N,K)
# ensure a0 is a 1-by-K vector
assert(a0.shape == (1,K))
#print "intialize variational distribution hyperparameters to be equal to prior hyperparameters"
ap=ap0
bp=bp0
am=am0
bm=bm0
a=a0
n=Q.sum(0)
# get indices of non-zero row/columns
# to be passed to vbmod_estep_inline
# jntj: must be better way
(rows,cols)=A.nonzero()
# vector to store free energy over iterations
F=[]
for i in range(MAX_FITER):
####################
#VBE-step, to update mean-field Q matrix over module assignments
####################
# compute local and global coupling constants, JL and JG and
# chemical potentials -lnpi
psiap=digamma(ap)
psibp=digamma(bp)
psiam=digamma(am)
psibm=digamma(bm)
psip=digamma(ap+bp)
psim=digamma(am+bm)
JL=psiap-psibp-psiam+psibm
JG=psibm-psim-psibp+psip
lnpi=digamma(a)-digamma(sum(a))
estep_inline(rows,cols,Q,float(JL),float(JG),array(lnpi),array(n))
"""
# local update (technically correct, but slow)
for l in range(N):
# exclude Q[l,:] from contributing to its own update
Q[l,:]=zeros([1,K])
# jntj: doesn't take advantage of sparsity
Al=mat(A.getrow(l).toarray())
AQl=multiply((Al.T*uk.T),Q).sum(0)
nl=Q.sum(0)
lnQl=JL*AQl-JG*nl+lnpi
lnQl=lnQl-lnQl.max()
Q[l,:]=exp(lnQl)
Q[l,:]=Q[l,:]/Q[l,:].sum()
"""
####################
#VBM-step, update distribution over parameters
####################
# compute expected occupation numbers <n*>s
n=Qmat.sum(0)
# compute expected edge counts <n**>s
#QTAQ=mat((Q.T*A*Q).toarray())
#npp=0.5*trace(QTAQ)
npp=0.5*(Qmat.T*A*Qmat).diagonal().sum()
npm=0.5*trace(Qmat.T*(un*n-Qmat))-npp
nmp=M-npp
nmm=C-M-npm
# compute hyperparameters for beta and dirichlet distributions over
# theta_+, theta_-, and pi_mu
ap=npp+ap0
bp=npm+bp0
am=nmp+am0
bm=nmm+bm0
a=n+a0
#print ap, bp, am, bm, a
# evaluate variational free energy, an approximation to the
# negative log-evidence
F.append(betaln(ap,bp)-betaln(ap0,bp0)+betaln(am,bm)-betaln(am0,bm0)+sum(gammaln(a))-gammaln(sum(a))-(sum(gammaln(a0))-gammaln(sum(a0)))-sum(multiply(Qmat,log(Qmat))))
F[i]=-F[i]
print "iteration", i+1 , ": F =", F[i]
# F should always decrease
if (i>1) and F[i] > F[i-1]:
print "\twarning: F increased from", F[i-1] ,"to", F[i]
if (i>1) and (abs(F[i]-F[i-1]) < TOL_DF):
break
return dict(F=F[-1],F_iter=F,Q=Q,K=K,ap=ap,bp=bp,am=am,bm=bm,a=a)
