def upaintbox_sample(log_res, hold, Y, held_out, ext, sig, sig_w, iterate, K,
data_run):
print("Trial Number: " + str(data_run))
N, T = Y.shape
res = 1
tree = gen_tree(K, res)
ctree, ptree = tree
Z = draw_Z_tree(tree, N)
#W = sample_W(Y,Z,sig,sig_w)
W = np.reshape(np.random.normal(0, sig_w, K * T), (K, T))
ll_list = []
iter_time = []
f_count = []
lapse_data = []
pred_ll = []
pred = 0
for it in range(iterate):
if it % hold == 0:
if res < 2**log_res:
res = res * 2
start = time.time()
N, K = Z.shape
#sample Z
Z, prob_matrix = sample_Z(Y, Z, W, sig, sig_w, tree)
# if it%10 == 0:
# print("iteration: " + str(it))
# print("Sparsity: " + str(np.sum(Z,axis=0)))
# print('predictive log likelihood: ' + str(pred))
#sample paintbox
tree, lapse = sample_pb(Z, tree, res)
#sample W
W = sample_W(Y, Z, sig, sig_w)
#add new features
ll_list.append(log_data_zw(Y, Z, W, sig))
F, D = get_FD(tree)
f_count.append(F)
#predictive log likelihood
# if it%500 == 0:
# pred = pred_ll_paintbox(held_out, W, tree, sig)
# pred_ll.append(pred)
# if it%1000 == 0 and it > 0:
# display_W(W,3,3,3,3,'four')
#handling last iteration edge case
drop = 0
if it == iterate - 1:
drop = 1
Z, W, tree = new_feature(Y, Z, W, tree, ext, K, res, sig, sig_w, drop)
end = time.time()
iter_time.append(end - start)
lapse_data.append(lapse)
iter_time = np.cumsum(iter_time)
return (ll_list, iter_time, f_count, lapse_data, Z, W, prob_matrix,
pred_ll, tree)
def sample_recover(held,observe,W,tree,sig,obs_indices):
N,obs = observe.shape
R,T = held.shape
K,T = W.shape
log_recover = 0
vec = get_vec(tree)
W_obs = W[:,obs_indices]
#initialize Z
Z = draw_Z_tree(tree,R)
indices = [i for i in range(T)]
hidden = [x for x in indices if x not in obs_indices]
#print(hidden)
iterate = 100
for it in range(iterate):
N,K = Z.shape
#sample Z
Z,prob_matrix = sample_Z(held[:,obs_indices],Z,W_obs,sig,tree)
#print("sampled Z")
#print(Z)
for row in Z:
log_recover += np.log(Z_paintbox(row,vec))
log_recover += log_data_zw(held[:,hidden],Z,W[:,hidden],sig)
return log_recover
def ugibbs_sample(Y, ext, sig, sig_w, iterate, K, data_run):
print("Trial Number: " + str(data_run))
N, T = Y.shape
tree = gen_tree(K, res)
ctree, ptree = tree
Z = draw_Z_tree(tree, N)
#W = sample_W(Y,Z,sig,sig_w)
W = np.reshape(np.random.normal(0, sig_w, K * T), (K, T))
ll_list = []
iter_time = []
f_count = []
for it in range(iterate):
start = time.time()
N, K = Z.shape
#sample Z
Z = sample_Z(Y, Z, W, sig, sig_w, tree)
if it % 10 == 0:
print("iteration: " + str(it))
print("Sparsity: " + str(np.sum(Z, axis=0)))
#sample paintbox
tree = sample_pb(Z, tree, res)
#ctree,ptree = tree
#sample W
W = sample_W(Y, Z, sig, sig_w)
#add new features
vec = get_vec(tree)
#ll_list.append(log_data_zw(Y,Z,W,sig) + Z_vec(Z,vec) + log_w_sig(W,sig))
ll_list.append(log_data_zw(Y, Z, W, sig))
F, D = get_FD(tree)
f_count.append(F)
Z, W, tree = new_feature(Y, Z, W, tree, ext, K, res, sig, sig_w)
end = time.time()
iter_time.append(end - start)
iter_time = np.cumsum(iter_time)
return (ll_list, iter_time, f_count, Z, W)
def upaintbox_sample(log_res,hold,Y,held_out,ext,sig,sig_w,iterate,K,truncate,obs_indices,limit,Z_init=[],W_init=[],data_dim = [3,3,2,2],init=False,display=False):
#print('time limit')
#print(limit)
small_x,small_y,big_x,big_y = data_dim
N,T = Y.shape
#technically this is a bug
#generating tree with res = 1 is wrong.
#but you fixed it in tree paintbox hard coded 0.5
res = 1
tree = gen_tree(K,res)
ctree,ptree = tree
if init:
Z = draw_Z_tree(tree,N)
#Z = Z_init
W = W_init
else:
Z = draw_Z_tree(tree,N)
W = np.reshape(np.random.normal(0,sig_w,K*T),(K,T))
#Z = np.loadtxt('assignments.txt')
#print(Z)
#W = sample_W(Y,Z,sig,sig_w)
#W = np.loadtxt('features.txt')
# full = generate_gg_blocks()
# W = np.zeros((3,T))
# W[0,:] = full[0,:]
# W[1,:] = full[2,:]
# W[2,:] = full[0,:] + full[2,:]
# display_W(W,'four')
ll_list = []
iter_time = []
f_count = []
lapse_data = []
pred_ll = []
pred = 0
rec_ll = []
rec = 0
observe = held_out[:,obs_indices]
for redo in range(1):
if redo == 1:
res = 1
N,K = Z.shape
tree = gen_tree(K,res)
ctree,ptree = tree
for it in range(iterate):
if it == 0:
start = time.time()
if it > 0:
#print(np.sum(iter_time))
#print(limit)
if np.sum(iter_time) > limit:
break
if it%hold == 0:
if res < 2**log_res:
res = res*2
start = time.time()
N,K = Z.shape
#sample Z
Z,prob_matrix = sample_Z(Y,Z,W,sig,tree)
if it%10 == 0 and display:
print("iteration: " + str(it))
print("Sparsity: " + str(np.sum(Z,axis=0)))
#print('predictive log likelihood: ' + str(pred))
print('recover log likelihood: ' + str(rec))
#sample paintbox
tree,lapse = sample_pb(Z,tree,res)
#sample W
W = sample_W(Y,Z,sig,sig_w)
#add new features
ll_list.append(log_data_zw(Y,Z,W,sig))
F,D = get_FD(tree)
f_count.append(F)
#recovered log likelihood
if it%50 == 49 and it > 0:
#pred = pred_ll_paintbox(held_out, W, tree, sig)
pred = 0
pred_ll.append(pred)
#rec = sample_recover(held_out,observe,W,tree,sig,obs_indices)
rec = recover_paintbox(held_out,observe,W,tree,sig,obs_indices)
rec_ll.append(rec)
end = time.time()
iter_time.append(end - start)
start = time.time()
#Auxiliary printouts
#if it%500 == 0 and it > 0:
# print_paintbox(tree,W,data_dim,'four')
#if it%200 == 0 and it > 0:
# display_W(W,data_dim,'nine')
#handling last iteration edge case
drop = 0
if it == iterate - 1:
drop = 1
Z,W,tree = new_feature(Y,Z,W,tree,ext,K,res,sig,sig_w,drop,truncate)
lapse_data.append(lapse)
iter_time = np.cumsum(iter_time)
return (ll_list,iter_time,f_count,lapse_data,Z,W,prob_matrix,pred_ll,rec_ll,tree)