def M_step(self, anneal, model_params, my_suff_stat, my_data):
""" MCA M_step
my_data variables used:
my_data['y'] Datapoints
my_data['candidates'] Candidate H's according to selection func.
Annealing variables used:
anneal['T'] Temperature for det. annealing AND softmax
anneal['N_cut_factor'] 0.: no truncation; 1. trunc. according to model
"""
comm = self.comm
H, Hprime = self.H, self.Hprime
gamma = self.gamma
W = model_params['W']
pies = model_params['pi']
sigma = model_params['sigma']
# Read in data:
my_y = my_data['y']
my_cand = my_data['candidates']
my_logpj = my_suff_stat['logpj']
my_N, D = my_y.shape
N = comm.allreduce(my_N)
state_mtx = self.state_matrix # shape: (no_states, Hprime)
state_abs = self.state_abs # shape: (no_states,)
no_states = len(state_abs)
# Disable some warnings
old_seterr = np.seterr(divide='ignore', under='ignore')
# To compute et_loglike:
my_ldenom_sum = 0.0
ldenom_sum = 0.0
# Precompute
T = anneal['T']
T_rho = np.maximum(T, self.rho_T_bound)
rho = 1. / (1. - 1. / T_rho)
rho = np.maximum(np.minimum(rho, self.rho_ubound), self.rho_lbound)
beta = 1. / T
pre1 = -1. / 2. / sigma / sigma
pil_bar = np.log(pies / (1. - pies))
Wl = accel.log(np.abs(W))
Wrho = accel.exp(rho * Wl)
Wrhos = np.sign(W) * Wrho
Wsquared = W * W
# Some asserts
assert np.isfinite(pil_bar).all()
assert np.isfinite(Wl).all()
assert np.isfinite(Wrho).all()
assert (Wrho > 1e-86).all()
my_corr = beta * ((my_logpj).max(axis=1)) # shape: (my_N,)
my_logpjb = beta * my_logpj - my_corr[:,
None] # shape: (my_N, no_states)
my_pj = accel.exp(my_logpj) # shape: (my_N, no_states)
my_pjb = accel.exp(my_logpjb) # shape: (my_N, no_states)
# Precompute factor for pi update and ET cutting
A_pi_gamma = 0.
B_pi_gamma = 0.
for gp in xrange(0, self.gamma + 1):
a = comb(H, gp) * pies**gp * (1. - pies)**(H - gp)
A_pi_gamma += a
B_pi_gamma += gp * a
# Truncate data
if anneal['Ncut_factor'] > 0.0:
tracing.tracepoint("M_step:truncating")
my_logdenoms = accel.log(my_pjb.sum(axis=1)) + my_corr
N_use = int(N * (1 - (1 - A_pi_gamma) * anneal['Ncut_factor']))
cut_denom = parallel.allsort(my_logdenoms)[-N_use]
my_sel, = np.where(my_logdenoms >= cut_denom)
my_N, = my_sel.shape
N_use = comm.allreduce(my_N)
else:
my_N, _ = my_y.shape
my_sel = np.arange(my_N)
N_use = N
# Allocate suff-stat arrays
my_Wp = np.zeros_like(W) # shape (H, D)
my_Wq = np.zeros_like(W) # shape (H, D)
my_pi = 0.0 #
my_sigma = 0.0 #
# Do reverse correlation if requested
if self.rev_corr:
my_y_rc = my_data['y_rc']
D_rev_corr = my_y_rc.shape[1]
my_rev_corr = np.zeros((H, D_rev_corr))
my_rev_corr_count = np.zeros(H)
# Iterate over all datapoints
tracing.tracepoint("M_step:iterating...")
dlog.append('N_use', N_use)
for n in my_sel:
y = my_y[n, :] # shape (D,)
cand = my_cand[n, :] # shape (Hprime,)
logpj = my_logpj[n, :] # shape (no_states,)
logpjb = my_logpjb[n, :] # shape (no_states,)
pj = my_pj[n, :] # shape (no_states,)
pjb = my_pjb[n, :] # shape (no_states,)
this_Wp = np.zeros_like(
W) # numerator for W (current datapoint) (H, D)
this_Wq = np.zeros_like(
W) # denominator for W (current datapoint) (H, D)
this_pi = 0.0 # numerator for pi update (current datapoint)
this_sigma = 0.0 # numerator for gamma update (current datapoint)
# Zero active hidden causes
# this_Wp += 0. # nothing to do
# this_Wq += 0. # nothing to do
# this_pi += 0. # nothing to do
this_sigma += pjb[0] * (y**2).sum()
# One active hidden cause
this_Wp += (pjb[1:(H + 1), None]) * y[None, :]
this_Wq += (pjb[1:(H + 1), None])
this_pi += pjb[1:(H + 1)].sum()
this_sigma += (pjb[1:(H + 1)] * ((W - y)**2).sum(axis=1)).sum()
# Handle hidden states with more than 1 active cause
W_ = W[cand] # is (Hprime, D)
Wl_ = Wl[cand] # is ( " ")
Wrho_ = Wrho[cand] # is ( " ")
Wrhos_ = Wrhos[cand] # is ( " ")
#Wbar = calc_Wbar(state_mtx, W_)
#Wlbar = np.log(np.abs(Wbar))
t0 = np.dot(state_mtx, Wrhos_)
Wlbar = accel.log(np.abs(t0)) / rho # is (no_states, D)
#Wlbar = np.maximum(Wlbar, -9.21)
Wbar = np.sign(t0) * accel.exp(Wlbar) # is (no_states, D)
t = Wlbar[:, None, :] - Wl_[None, :, :]
t = np.maximum(t, 0.)
Aid = state_mtx[:, :,
None] * accel.exp(logpjb[H + 1:, None, None] -
(rho - 1) * t)
Aid = Aid.sum(axis=0)
#Aid = calc_Aid(logpjb[H+1:], W_, Wl_, state_mtx, Wbar, Wlbar, rho)
#assert np.isfinite(Wlbar).all()
#assert np.isfinite(Wbar).all()
#assert np.isfinite(pjb).all()
#assert np.isfinite(Aid).all()
this_Wp[cand] += Aid * y[None, :]
this_Wq[cand] += Aid
this_pi += (pjb[1 + H:] * state_abs).sum()
this_sigma += (pjb[1 + H:] * ((Wbar - y)**2).sum(axis=1)).sum()
denom = pjb.sum()
my_Wp += this_Wp / denom
my_Wq += this_Wq / denom
my_pi += this_pi / denom
my_sigma += this_sigma / denom
#self.tbl.append("logpj", logpj)
#self.tbl.append("corr", my_corr[n])
#self.tbl.append("denom", denom)
#self.tbl.append("cand", cand)
#self.tbl.append("Aid", Aid)
my_ldenom_sum += accel.log(np.sum(
accel.exp(logpj))) #For loglike computation
# Estimate reverse correlation
if self.rev_corr:
pys = pjb / denom
if np.isfinite(pys).all():
my_rev_corr += pys[1:H + 1, None] * my_y_rc[n, None, :]
my_rev_corr_count += pys[1:H + 1]
my_rev_corr[cand] += np.sum(state_mtx[:, :, None] *
pys[H + 1:, None, None] *
my_y_rc[n, None, :],
axis=0)
my_rev_corr_count[cand] += np.sum(state_mtx[:, :] *
pys[H + 1, None],
axis=0)
else:
print "Not all finite rev_corr %d" % n
# Calculate updated W
if 'W' in self.to_learn:
tracing.tracepoint("M_step:update W")
Wp = np.empty_like(my_Wp)
Wq = np.empty_like(my_Wq)
assert np.isfinite(my_Wp).all()
assert np.isfinite(my_Wq).all()
comm.Allreduce([my_Wp, MPI.DOUBLE], [Wp, MPI.DOUBLE])
comm.Allreduce([my_Wq, MPI.DOUBLE], [Wq, MPI.DOUBLE])
# Make sure wo do not devide by zero
tiny = self.tol
Wq[Wq < tiny] = tiny
# Calculate updated W
W_new = Wp / Wq
# Add inertia depending on Wq
alpha = 2.5
inertia = np.maximum(1. - accel.exp(-Wq / alpha), 0.2)
W_new = inertia * W_new + (1 - inertia) * W
else:
W_new = W
# Calculate updated pi
if 'pi' in self.to_learn:
tracing.tracepoint("M_step:update pi")
assert np.isfinite(my_pi).all()
pi_new = A_pi_gamma / B_pi_gamma * pies * comm.allreduce(
my_pi) / N_use
else:
pi_new = pies
# Calculate updated sigma
if 'sigma' in self.to_learn: # TODO: XXX see LinCA XXX (merge!)
tracing.tracepoint("M_step:update sigma")
assert np.isfinite(my_sigma).all()
sigma_new = np.sqrt(comm.allreduce(my_sigma) / D / N_use)
else:
sigma_new = sigma
# Put all together and compute (always) et_approx_likelihood
ldenom_sum = comm.allreduce(my_ldenom_sum)
lAi = (H * np.log(1. - pi_new)) - (
(D / 2) * np.log(2 * pi)) - (D * np.log(sigma_new))
# For practical and et approx reasons we use: sum of restected respons=1
loglike_et = (lAi * N_use) + ldenom_sum
if self.rev_corr:
rev_corr = np.empty_like(my_rev_corr)
rev_corr_count = np.empty_like(my_rev_corr_count)
comm.Allreduce([my_rev_corr, MPI.DOUBLE], [rev_corr, MPI.DOUBLE])
comm.Allreduce([my_rev_corr_count, MPI.DOUBLE],
[rev_corr_count, MPI.DOUBLE])
rev_corr /= (1e-16 + rev_corr_count[:, None])
else:
rev_corr = np.zeros((H, D))
# Restore np.seterr
np.seterr(**old_seterr)
return {
'W': W_new,
'pi': pi_new,
'sigma': sigma_new,
'rev_corr': rev_corr,
'Q': loglike_et
}
def M_step(self, anneal, model_params, my_suff_stat, my_data):
""" MCA M_step
my_data variables used:
my_data['y'] Datapoints
my_data['candidates'] Candidate H's according to selection func.
Annealing variables used:
anneal['T'] Temperature for det. annealing AND softmax
anneal['N_cut_factor'] 0.: no truncation; 1. trunc. according to model
"""
comm = self.comm
H, Hprime = self.H, self.Hprime
gamma = self.gamma
W = model_params['W']
pies = model_params['pi']
sigma = model_params['sigma']
# Read in data:
my_y = my_data['y']
my_cand = my_data['candidates']
my_logpj = my_suff_stat['logpj']
my_N, D = my_y.shape
N = comm.allreduce(my_N)
state_mtx = self.state_matrix # shape: (no_states, Hprime)
state_abs = self.state_abs # shape: (no_states,)
no_states = len(state_abs)
# Disable some warnings
old_seterr = np.seterr(divide='ignore', under='ignore')
# To compute et_loglike:
my_ldenom_sum = 0.0
ldenom_sum = 0.0
# Precompute
T = anneal['T']
T_rho = np.maximum(T, self.rho_T_bound)
rho = 1./(1.-1./T_rho)
rho = np.maximum(np.minimum(rho, self.rho_ubound), self.rho_lbound)
beta = 1./T
pre1 = -1./2./sigma/sigma
pil_bar = np.log( pies/(1.-pies) )
Wl = accel.log(np.abs(W))
Wrho = accel.exp(rho * Wl)
Wrhos = np.sign(W) * Wrho
Wsquared = W*W
# Some asserts
assert np.isfinite(pil_bar).all()
assert np.isfinite(Wl).all()
assert np.isfinite(Wrho).all()
assert (Wrho > 1e-86).all()
my_corr = beta*((my_logpj).max(axis=1)) # shape: (my_N,)
my_logpjb = beta*my_logpj - my_corr[:, None] # shape: (my_N, no_states)
my_pj = accel.exp(my_logpj) # shape: (my_N, no_states)
my_pjb = accel.exp(my_logpjb) # shape: (my_N, no_states)
# Precompute factor for pi update and ET cutting
A_pi_gamma = 0.; B_pi_gamma = 0.
for gp in xrange(0, self.gamma+1):
a = comb(H, gp) * pies**gp * (1.-pies)**(H-gp)
A_pi_gamma += a
B_pi_gamma += gp * a
# Truncate data
if anneal['Ncut_factor'] > 0.0:
tracing.tracepoint("M_step:truncating")
my_logdenoms = accel.log(my_pjb.sum(axis=1)) + my_corr
N_use = int(N * (1 - (1 - A_pi_gamma) * anneal['Ncut_factor']))
cut_denom = parallel.allsort(my_logdenoms)[-N_use]
my_sel, = np.where(my_logdenoms >= cut_denom)
my_N, = my_sel.shape
N_use = comm.allreduce(my_N)
else:
my_N,_ = my_y.shape
my_sel = np.arange(my_N)
N_use = N
# Allocate suff-stat arrays
my_Wp = np.zeros_like(W) # shape (H, D)
my_Wq = np.zeros_like(W) # shape (H, D)
my_pi = 0.0 #
my_sigma = 0.0 #
# Do reverse correlation if requested
if self.rev_corr:
my_y_rc = my_data['y_rc']
D_rev_corr = my_y_rc.shape[1]
my_rev_corr = np.zeros( (H,D_rev_corr) )
my_rev_corr_count = np.zeros(H)
# Iterate over all datapoints
tracing.tracepoint("M_step:iterating...")
dlog.append('N_use', N_use)
for n in my_sel:
y = my_y[n,:] # shape (D,)
cand = my_cand[n,:] # shape (Hprime,)
logpj = my_logpj[n,:] # shape (no_states,)
logpjb = my_logpjb[n,:] # shape (no_states,)
pj = my_pj[n,:] # shape (no_states,)
pjb = my_pjb[n,:] # shape (no_states,)
this_Wp = np.zeros_like(W) # numerator for W (current datapoint) (H, D)
this_Wq = np.zeros_like(W) # denominator for W (current datapoint) (H, D)
this_pi = 0.0 # numerator for pi update (current datapoint)
this_sigma = 0.0 # numerator for gamma update (current datapoint)
# Zero active hidden causes
# this_Wp += 0. # nothing to do
# this_Wq += 0. # nothing to do
# this_pi += 0. # nothing to do
this_sigma += pjb[0] * (y**2).sum()
# One active hidden cause
this_Wp += (pjb[1:(H+1),None]) * y[None, :]
this_Wq += (pjb[1:(H+1),None])
this_pi += pjb[1:(H+1)].sum()
this_sigma += (pjb[1:(H+1)] * ((W-y)**2).sum(axis=1)).sum()
# Handle hidden states with more than 1 active cause
W_ = W[cand] # is (Hprime, D)
Wl_ = Wl[cand] # is ( " ")
Wrho_ = Wrho[cand] # is ( " ")
Wrhos_ = Wrhos[cand] # is ( " ")
#Wbar = calc_Wbar(state_mtx, W_)
#Wlbar = np.log(np.abs(Wbar))
t0 = np.dot(state_mtx, Wrhos_)
Wlbar = accel.log(np.abs(t0)) / rho # is (no_states, D)
#Wlbar = np.maximum(Wlbar, -9.21)
Wbar = np.sign(t0)*accel.exp(Wlbar) # is (no_states, D)
t = Wlbar[:, None, :]-Wl_[None, :, :]
t = np.maximum(t, 0.)
Aid = state_mtx[:,:, None] * accel.exp(logpjb[H+1:,None,None] - (rho-1)*t)
Aid = Aid.sum(axis=0)
#Aid = calc_Aid(logpjb[H+1:], W_, Wl_, state_mtx, Wbar, Wlbar, rho)
#assert np.isfinite(Wlbar).all()
#assert np.isfinite(Wbar).all()
#assert np.isfinite(pjb).all()
#assert np.isfinite(Aid).all()
this_Wp[cand] += Aid * y[None, :]
this_Wq[cand] += Aid
this_pi += (pjb[1+H:] * state_abs).sum()
this_sigma += (pjb[1+H:] * ((Wbar-y)**2).sum(axis=1)).sum()
denom = pjb.sum()
my_Wp += this_Wp / denom
my_Wq += this_Wq / denom
my_pi += this_pi / denom
my_sigma += this_sigma / denom
#self.tbl.append("logpj", logpj)
#self.tbl.append("corr", my_corr[n])
#self.tbl.append("denom", denom)
#self.tbl.append("cand", cand)
#self.tbl.append("Aid", Aid)
my_ldenom_sum += accel.log(np.sum(accel.exp(logpj))) #For loglike computation
# Estimate reverse correlation
if self.rev_corr:
pys = pjb / denom
if np.isfinite(pys).all():
my_rev_corr += pys[1:H+1, None]*my_y_rc[n,None,:]
my_rev_corr_count += pys[1:H+1]
my_rev_corr[cand] += np.sum(state_mtx[:,:,None]*pys[H+1:,None,None]*my_y_rc[n,None,:], axis=0)
my_rev_corr_count[cand] += np.sum(state_mtx[:,:]*pys[H+1,None], axis=0)
else:
print "Not all finite rev_corr %d" % n
# Calculate updated W
if 'W' in self.to_learn:
tracing.tracepoint("M_step:update W")
Wp = np.empty_like(my_Wp)
Wq = np.empty_like(my_Wq)
assert np.isfinite(my_Wp).all()
assert np.isfinite(my_Wq).all()
comm.Allreduce( [my_Wp, MPI.DOUBLE], [Wp, MPI.DOUBLE] )
comm.Allreduce( [my_Wq, MPI.DOUBLE], [Wq, MPI.DOUBLE] )
# Make sure wo do not devide by zero
tiny = self.tol
Wq[Wq < tiny] = tiny
# Calculate updated W
W_new = Wp / Wq
# Add inertia depending on Wq
alpha = 2.5
inertia = np.maximum(1. - accel.exp(-Wq / alpha), 0.2)
W_new = inertia*W_new + (1-inertia)*W
else:
W_new = W
# Calculate updated pi
if 'pi' in self.to_learn:
tracing.tracepoint("M_step:update pi")
assert np.isfinite(my_pi).all()
pi_new = A_pi_gamma / B_pi_gamma * pies * comm.allreduce(my_pi) / N_use
else:
pi_new = pies
# Calculate updated sigma
if 'sigma' in self.to_learn: # TODO: XXX see LinCA XXX (merge!)
tracing.tracepoint("M_step:update sigma")
assert np.isfinite(my_sigma).all()
sigma_new = np.sqrt(comm.allreduce(my_sigma) / D / N_use)
else:
sigma_new = sigma
# Put all together and compute (always) et_approx_likelihood
ldenom_sum = comm.allreduce(my_ldenom_sum)
lAi = (H * np.log(1. - pi_new)) - ((D/2) * np.log(2*pi)) -( D * np.log(sigma_new))
# For practical and et approx reasons we use: sum of restected respons=1
loglike_et = (lAi * N_use) + ldenom_sum
if self.rev_corr:
rev_corr = np.empty_like(my_rev_corr)
rev_corr_count = np.empty_like(my_rev_corr_count)
comm.Allreduce( [my_rev_corr, MPI.DOUBLE], [rev_corr, MPI.DOUBLE])
comm.Allreduce( [my_rev_corr_count, MPI.DOUBLE], [rev_corr_count, MPI.DOUBLE])
rev_corr /= (1e-16+rev_corr_count[:,None])
else:
rev_corr = np.zeros( (H, D) )
# Restore np.seterr
np.seterr(**old_seterr)
return { 'W': W_new, 'pi': pi_new, 'sigma': sigma_new , 'rev_corr': rev_corr, 'Q':loglike_et}
def E_step(self, anneal, model_params, my_data):
""" MCA E_step
my_data variables used:
my_data['y'] Datapoints
my_data['can'] Candidate H's according to selection func.
Annealing variables used:
anneal['T'] Temperature for det. annealing AND softmax
anneal['N_cut_factor'] 0.: no truncation; 1. trunc. according to model
"""
comm = self.comm
my_y = my_data['y']
my_cand = my_data['candidates']
my_N, D = my_data['y'].shape
H = self.H
state_mtx = self.state_matrix # shape: (no_states, Hprime)
state_abs = self.state_abs # shape: (no_states,)
no_states = len(state_abs)
W = model_params['W']
pies = model_params['pi']
sigma = model_params['sigma']
# Disable some warnings
old_seterr = np.seterr(divide='ignore', under='ignore')
# Precompute
T = anneal['T']
T_rho = np.maximum(T, self.rho_T_bound)
rho = 1. / (1. - 1. / T_rho)
rho = np.maximum(np.minimum(rho, self.rho_ubound), self.rho_lbound)
beta = 1. / T
pre1 = -1. / 2. / sigma / sigma
pil_bar = np.log(pies / (1. - pies))
Wl = accel.log(np.abs(W))
Wrho = accel.exp(rho * Wl)
Wrhos = np.sign(W) * Wrho
# Allocate return structures
F = np.empty([my_N, 1 + H + no_states])
# Iterate over all datapoints
tracing.tracepoint("E_step:iterating...")
for n in xrange(my_N):
y = my_y[n, :]
cand = my_cand[n, :]
# Zero active hidden causes
log_prod_joint = pre1 * (y**2).sum()
F[n, 0] = log_prod_joint
# Hidden states with one active cause
log_prod_joint = pil_bar + pre1 * ((W - y)**2).sum(axis=1)
F[n, 1:H + 1] = log_prod_joint
# Handle hidden states with more than 1 active cause
log_prior = pil_bar * state_abs # is (no_states,)
Wrhos_ = Wrhos[cand] # is (Hprime, D)
t0 = np.dot(state_mtx, Wrhos_)
Wbar = np.sign(t0) * accel.exp(accel.log(np.abs(t0)) / rho)
log_prod_joint = log_prior + pre1 * ((Wbar - y)**2).sum(axis=1)
F[n, 1 + H:] = log_prod_joint
assert np.isfinite(F).all()
# Restore np.seterr
np.seterr(**old_seterr)
return {'logpj': F}
def E_step(self, anneal, model_params, my_data):
""" MCA E_step
my_data variables used:
my_data['y'] Datapoints
my_data['can'] Candidate H's according to selection func.
Annealing variables used:
anneal['T'] Temperature for det. annealing AND softmax
anneal['N_cut_factor'] 0.: no truncation; 1. trunc. according to model
"""
comm = self.comm
my_y = my_data['y']
my_cand = my_data['candidates']
my_N, D = my_data['y'].shape
H = self.H
state_mtx = self.state_matrix # shape: (no_states, Hprime)
state_abs = self.state_abs # shape: (no_states,)
no_states = len(state_abs)
W = model_params['W']
pies = model_params['pi']
sigma = model_params['sigma']
# Disable some warnings
old_seterr = np.seterr(divide='ignore', under='ignore')
# Precompute
T = anneal['T']
T_rho = np.maximum(T, self.rho_T_bound)
rho = 1./(1.-1./T_rho)
rho = np.maximum(np.minimum(rho, self.rho_ubound), self.rho_lbound)
beta = 1./T
pre1 = -1./2./sigma/sigma
pil_bar = np.log( pies/(1.-pies) )
Wl = accel.log(np.abs(W))
Wrho = accel.exp(rho * Wl)
Wrhos = np.sign(W) * Wrho
# Allocate return structures
F = np.empty( [my_N, 1+H+no_states] )
# Iterate over all datapoints
tracing.tracepoint("E_step:iterating...")
for n in xrange(my_N):
y = my_y[n,:]
cand = my_cand[n,:]
# Zero active hidden causes
log_prod_joint = pre1 * (y**2).sum()
F[n,0] = log_prod_joint
# Hidden states with one active cause
log_prod_joint = pil_bar + pre1 * ((W-y)**2).sum(axis=1)
F[n,1:H+1] = log_prod_joint
# Handle hidden states with more than 1 active cause
log_prior = pil_bar * state_abs # is (no_states,)
Wrhos_ = Wrhos[cand] # is (Hprime, D)
t0 = np.dot(state_mtx, Wrhos_)
Wbar = np.sign(t0) * accel.exp(accel.log(np.abs(t0))/rho)
log_prod_joint = log_prior + pre1 * ((Wbar-y)**2).sum(axis=1)
F[n,1+H:] = log_prod_joint
assert np.isfinite(F).all()
# Restore np.seterr
np.seterr(**old_seterr)
return { 'logpj': F }
