def sample_tree(data, settings, param, cache, cache_tmp):
p = TreeMCMC(range(data['n_train']), param, settings, cache_tmp)
grow_nodes = [0]
while grow_nodes:
node_id = grow_nodes.pop(0)
p.depth = max(p.depth, get_depth(node_id))
log_psplit = np.log(p.compute_psplit(node_id, param))
train_ids = p.train_ids[node_id]
(do_not_split_node_id, feat_id_chosen, split_chosen, idx_split_global, log_sis_ratio, logprior_nodeid, \
train_ids_left, train_ids_right, cache_tmp, loglik_left, loglik_right) \
= p.precomputed_proposal(data, param, settings, cache, node_id, train_ids, log_psplit)
if do_not_split_node_id:
p.do_not_split[node_id] = True
else:
p.update_left_right_statistics(cache_tmp, node_id, logprior_nodeid, train_ids_left,\
train_ids_right, loglik_left, loglik_right, feat_id_chosen, split_chosen, \
idx_split_global, settings, param, data, cache)
left, right = get_children_id(node_id)
grow_nodes.append(left)
grow_nodes.append(right)
# create mcmc structures
p.both_children_terminal.append(node_id)
parent = get_parent_id(node_id)
if (node_id != 0) and (parent in p.non_leaf_nodes):
p.inner_pc_pairs.append((parent, node_id))
if node_id != 0:
try:
p.both_children_terminal.remove(parent)
except ValueError:
pass
if settings.debug:
print 'sampled new tree:'
p.print_tree()
return p
def sample_tree(data, settings, param, cache, cache_tmp):
p = TreeMCMC(range(data['n_train']), param, settings, cache_tmp)
grow_nodes = [0]
while grow_nodes:
node_id = grow_nodes.pop(0)
p.depth = max(p.depth, get_depth(node_id))
log_psplit = np.log(p.compute_psplit(node_id, param))
train_ids = p.train_ids[node_id]
(do_not_split_node_id, feat_id_chosen, split_chosen, idx_split_global, log_sis_ratio, logprior_nodeid, \
train_ids_left, train_ids_right, cache_tmp, loglik_left, loglik_right) \
= p.precomputed_proposal(data, param, settings, cache, node_id, train_ids, log_psplit)
if do_not_split_node_id:
p.do_not_split[node_id] = True
else:
p.update_left_right_statistics(cache_tmp, node_id, logprior_nodeid, train_ids_left,\
train_ids_right, loglik_left, loglik_right, feat_id_chosen, split_chosen, \
idx_split_global, settings, param, data, cache)
left, right = get_children_id(node_id)
grow_nodes.append(left)
grow_nodes.append(right)
# create mcmc structures
p.both_children_terminal.append(node_id)
parent = get_parent_id(node_id)
if (node_id != 0) and (parent in p.non_leaf_nodes):
p.inner_pc_pairs.append((parent, node_id))
if node_id != 0:
try:
p.both_children_terminal.remove(parent)
except ValueError:
pass
if settings.debug:
print 'sampled new tree:'
p.print_tree()
return p
def sample(self, data, settings, param, cache,databc,cachebc):
if settings.mcmc_type == 'growprune':
step_id = random.randint(0, 1) # only grow and prune moves permitted
elif settings.mcmc_type == 'cgm':
step_id = random_pick([0,1,2,3], [0.25, 0.25, 0.4, 0.1])
else:
raise Exception('invalid mcmc_type')
log_acc = -np.inf
log_r = 0
self.grow_nodes = [n_id for n_id in self.leaf_nodes \
if not stop_split(self.train_ids[n_id], settings, data, cache)]
grow_nodes = self.grow_nodes
if step_id == 0:
if not grow_nodes:
change = False
else:
node_id = random.choice(grow_nodes)
if settings.verbose >= 1:
print 'grow_nodes = %s, chosen node_id = %s' % (grow_nodes, node_id)
do_not_split_node_id, feat_id, split, idx_split_global, logprior_nodeid = \
self.sample_split_prior(data, param, settings, cache, node_id,databc,cachebc)
if do_not_split_node_id==True:
change=False
if do_not_split_node_id==False:
if settings.verbose >= 1:
print 'grow: do_not_split = %s, feat_id = %s, split = %s' \
% (do_not_split_node_id, feat_id, split)
train_ids = self.train_ids[node_id]
t0 = time.time()
if settings.parallelize_compute_statistics==0:
(train_ids_left, train_ids_right, cache_tmp, loglik_left, loglik_right) = \
compute_left_right_statistics(data, param, cache, train_ids, feat_id, split, settings)
else:
(cache_tmp, loglik_left, loglik_right) = \
compute_left_right_statistics_parallel(data, param, cache, train_ids, feat_id, split, settings, databc)
cond = data['x_train'][train_ids, feat_id] <= split
train_ids_left = train_ids[cond]
train_ids_right = train_ids[~cond]
if settings.timer==1:
settings.parallelize_compute_statistics_time += time.time()-t0
loglik = loglik_left + loglik_right
len_both_children_terminal_new = len(self.both_children_terminal)
if get_sibling_id(node_id) not in self.leaf_nodes:
len_both_children_terminal_new += 1
log_acc = self.compute_log_acc_g(node_id, param, len_both_children_terminal_new, \
loglik, train_ids_left, train_ids_right, cache, settings, data, grow_nodes)
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.update_left_right_statistics(cache_tmp, node_id, logprior_nodeid, \
train_ids_left, train_ids_right, loglik_left, loglik_right, \
feat_id, split, idx_split_global, settings, param, data, cache)
# MCMC specific data structure updates
self.both_children_terminal.append(node_id)
parent = get_parent_id(node_id)
if (node_id != 0) and (parent in self.non_leaf_nodes):
self.inner_pc_pairs.append((parent, node_id))
sibling = get_sibling_id(node_id)
if sibling in self.leaf_nodes:
self.both_children_terminal.remove(parent)
change = True
else:
change = False
elif step_id == 1:
if not self.both_children_terminal:
change = False
else:
node_id = random.choice(self.both_children_terminal)
feat_id = self.node_info[node_id][0]
if settings.verbose >= 1:
print 'prune: node_id = %s, feat_id = %s' % (node_id, feat_id)
left, right = get_children_id(node_id)
loglik = self.loglik[left] + self.loglik[right]
len_both_children_new = len(self.both_children_terminal)
grow_nodes_tmp = grow_nodes[:]
grow_nodes_tmp.append(node_id)
try:
grow_nodes_tmp.remove(left)
except ValueError:
pass
try:
grow_nodes_tmp.remove(right)
except ValueError:
pass
log_acc = - self.compute_log_inv_acc_p(node_id, param, len_both_children_new, \
loglik, grow_nodes_tmp, cache, settings, data)
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.remove_leaf_node_statistics(left, settings)
self.remove_leaf_node_statistics(right, settings)
self.leaf_nodes.append(node_id)
self.non_leaf_nodes.remove(node_id)
self.logprior[node_id] = np.log(self.compute_pnosplit(node_id, param))
# OK to set logprior as above since we know that a valid split exists
# MCMC specific data structure updates
self.both_children_terminal.remove(node_id)
parent = get_parent_id(node_id)
if (node_id != 0) and (parent in self.non_leaf_nodes):
self.inner_pc_pairs.remove((parent, node_id))
if node_id != 0:
sibling = get_sibling_id(node_id)
if sibling in self.leaf_nodes:
if settings.debug == 1:
assert(parent not in self.both_children_terminal)
self.both_children_terminal.append(parent)
change = True
else:
change = False
elif step_id == 2:
if not self.non_leaf_nodes:
change = False
else:
node_id = random.choice(self.non_leaf_nodes)
do_not_split_node_id, feat_id, split, idx_split_global, logprior_nodeid = \
self.sample_split_prior(data, param, settings, cache, node_id,databc,cachebc)
if settings.verbose >= 1:
print 'change: node_id = %s, do_not_split = %s, feat_id = %s, split = %s' \
% (node_id, do_not_split_node_id, feat_id, split)
# Note: this just samples a split criterion, not guaranteed to "change"
#assert(not do_not_split_node_id)
if do_not_split_node_id==True:
change = False
if do_not_split_node_id==False:
nodes_subtree = self.get_nodes_subtree(node_id)
nodes_not_in_subtree = self.get_nodes_not_in_subtree(node_id)
if settings.debug == 1:
set1 = set(list(nodes_subtree) + list(nodes_not_in_subtree))
set2 = set(self.leaf_nodes + self.non_leaf_nodes)
assert(sorted(set1) == sorted(set2))
self.create_new_statistics(nodes_subtree, nodes_not_in_subtree, node_id, settings)
self.node_info_new[node_id] = (feat_id, split, idx_split_global)
self.evaluate_new_subtree(data, node_id, param, nodes_subtree, cache, settings, databc, cachebc)
# log_acc will be be modified below
log_acc_tmp, loglik_diff, logprior_diff = self.compute_log_acc_cs(nodes_subtree, node_id)
if settings.debug == 1:
self.check_if_same(log_acc_tmp, loglik_diff, logprior_diff)
log_acc = log_acc_tmp + self.logprior[node_id] - self.logprior_new[node_id]
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.node_info[node_id] = copy(self.node_info_new[node_id])
self.update_subtree(node_id, nodes_subtree, settings)
change = True
else:
change = False
elif step_id == 3:
if not self.inner_pc_pairs:
change = False
else:
node_id, child_id = random.choice(self.inner_pc_pairs)
nodes_subtree = self.get_nodes_subtree(node_id)
nodes_not_in_subtree = self.get_nodes_not_in_subtree(node_id)
if settings.debug == 1:
set1 = set(list(nodes_subtree) + list(nodes_not_in_subtree))
set2 = set(self.leaf_nodes + self.non_leaf_nodes)
assert(sorted(set1) == sorted(set2))
self.create_new_statistics(nodes_subtree, nodes_not_in_subtree, node_id, settings)
self.node_info_new[node_id] = copy(self.node_info[child_id])
self.node_info_new[child_id] = copy(self.node_info[node_id])
if settings.verbose >= 1:
print 'swap: node_id = %s, child_id = %s' % (node_id, child_id)
print 'node_info[node_id] = %s, node_info[child_id] = %s' \
% (self.node_info[node_id], self.node_info[child_id])
self.evaluate_new_subtree(data, node_id, param, nodes_subtree, cache, settings, databc, cachebc)
log_acc, loglik_diff, logprior_diff = self.compute_log_acc_cs(nodes_subtree, node_id)
if settings.debug == 1:
self.check_if_same(log_acc, loglik_diff, logprior_diff)
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.node_info[node_id] = copy(self.node_info_new[node_id])
self.node_info[child_id] = copy(self.node_info_new[child_id])
self.update_subtree(node_id, nodes_subtree, settings)
change = True
else:
change = False
if settings.verbose >= 1:
print 'trying move: step_id = %d, move = %s, log_acc = %s, log_r = %s' \
% (step_id, STEP_NAMES[step_id], log_acc, log_r)
if change:
self.depth = max([get_depth(node_id) for node_id in \
self.leaf_nodes])
self.loglik_current = sum([self.loglik[node_id] for node_id in \
self.leaf_nodes])
if settings.verbose >= 1:
print 'accepted move: step_id = %d, move = %s' % (step_id, STEP_NAMES[step_id])
self.print_stuff()
if settings.debug == 1:
both_children_terminal, inner_pc_pairs = self.recompute_mcmc_data_structures()
print '\nstats from recompute_mcmc_data_structures'
print 'both_children_terminal = %s' % both_children_terminal
print 'inner_pc_pairs = %s' % inner_pc_pairs
assert(sorted(both_children_terminal) == sorted(self.both_children_terminal))
assert(sorted(inner_pc_pairs) == sorted(self.inner_pc_pairs))
grow_nodes_new = [n_id for n_id in self.leaf_nodes \
if not stop_split(self.train_ids[n_id], settings, data, cache)]
if change and (step_id == 1):
print 'grow_nodes_new = %s, grow_nodes_tmp = %s' % (sorted(grow_nodes_new), sorted(grow_nodes_tmp))
assert(sorted(grow_nodes_new) == sorted(grow_nodes_tmp))
return (change, step_id)
def sample(self, data, settings, param, cache):
if settings.mcmc_type == 'growprune':
step_id = random.randint(0, 1) # only grow and prune moves permitted
elif settings.mcmc_type == 'cgm':
step_id = random.randint(0, 3) # all 4 moves equally likely (or think of 50% grow/prune, 25% change, 25% swap)
else:
raise Exception('invalid mcmc_type')
log_acc = -np.inf
log_r = 0
self.grow_nodes = [n_id for n_id in self.leaf_nodes \
if not stop_split(self.train_ids[n_id], settings, data, cache)]
grow_nodes = self.grow_nodes
if step_id == 0: # GROW step
if not grow_nodes:
change = False
else:
node_id = random.choice(grow_nodes)
if settings.verbose >= 1:
print 'grow_nodes = %s, chosen node_id = %s' % (grow_nodes, node_id)
do_not_split_node_id, feat_id, split, idx_split_global, logprior_nodeid = \
self.sample_split_prior(data, param, settings, cache, node_id)
assert not do_not_split_node_id
if settings.verbose >= 1:
print 'grow: do_not_split = %s, feat_id = %s, split = %s' \
% (do_not_split_node_id, feat_id, split)
train_ids = self.train_ids[node_id]
(train_ids_left, train_ids_right, cache_tmp, loglik_left, loglik_right) = \
compute_left_right_statistics(data, param, cache, train_ids, \
feat_id, split, settings)
loglik = loglik_left + loglik_right
len_both_children_terminal_new = len(self.both_children_terminal)
if get_sibling_id(node_id) not in self.leaf_nodes:
len_both_children_terminal_new += 1
log_acc = self.compute_log_acc_g(node_id, param, len_both_children_terminal_new, \
loglik, train_ids_left, train_ids_right, cache, settings, data, grow_nodes)
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.update_left_right_statistics(cache_tmp, node_id, logprior_nodeid, \
train_ids_left, train_ids_right, loglik_left, loglik_right, \
feat_id, split, idx_split_global, settings, param, data, cache)
# MCMC specific data structure updates
self.both_children_terminal.append(node_id)
parent = get_parent_id(node_id)
if (node_id != 0) and (parent in self.non_leaf_nodes):
self.inner_pc_pairs.append((parent, node_id))
sibling = get_sibling_id(node_id)
if sibling in self.leaf_nodes:
self.both_children_terminal.remove(parent)
change = True
else:
change = False
elif step_id == 1: # PRUNE step
if not self.both_children_terminal:
change = False # nothing to prune here
else:
node_id = random.choice(self.both_children_terminal)
feat_id = self.node_info[node_id][0]
if settings.verbose >= 1:
print 'prune: node_id = %s, feat_id = %s' % (node_id, feat_id)
left, right = get_children_id(node_id)
loglik = self.loglik[left] + self.loglik[right]
len_both_children_new = len(self.both_children_terminal)
grow_nodes_tmp = grow_nodes[:]
grow_nodes_tmp.append(node_id)
try:
grow_nodes_tmp.remove(left)
except ValueError:
pass
try:
grow_nodes_tmp.remove(right)
except ValueError:
pass
log_acc = - self.compute_log_inv_acc_p(node_id, param, len_both_children_new, \
loglik, grow_nodes_tmp, cache, settings, data)
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.remove_leaf_node_statistics(left, settings)
self.remove_leaf_node_statistics(right, settings)
self.leaf_nodes.append(node_id)
self.non_leaf_nodes.remove(node_id)
self.logprior[node_id] = np.log(self.compute_pnosplit(node_id, param))
# OK to set logprior as above since we know that a valid split exists
# MCMC specific data structure updates
self.both_children_terminal.remove(node_id)
parent = get_parent_id(node_id)
if (node_id != 0) and (parent in self.non_leaf_nodes):
self.inner_pc_pairs.remove((parent, node_id))
if node_id != 0:
sibling = get_sibling_id(node_id)
if sibling in self.leaf_nodes:
if settings.debug == 1:
assert(parent not in self.both_children_terminal)
self.both_children_terminal.append(parent)
change = True
else:
change = False
elif step_id == 2: # CHANGE
if not self.non_leaf_nodes:
change = False
else:
node_id = random.choice(self.non_leaf_nodes)
do_not_split_node_id, feat_id, split, idx_split_global, logprior_nodeid = \
self.sample_split_prior(data, param, settings, cache, node_id)
if settings.verbose >= 1:
print 'change: node_id = %s, do_not_split = %s, feat_id = %s, split = %s' \
% (node_id, do_not_split_node_id, feat_id, split)
# Note: this just samples a split criterion, not guaranteed to "change"
assert(not do_not_split_node_id)
nodes_subtree = self.get_nodes_subtree(node_id)
nodes_not_in_subtree = self.get_nodes_not_in_subtree(node_id)
if settings.debug == 1:
set1 = set(list(nodes_subtree) + list(nodes_not_in_subtree))
set2 = set(self.leaf_nodes + self.non_leaf_nodes)
assert(sorted(set1) == sorted(set2))
self.create_new_statistics(nodes_subtree, nodes_not_in_subtree, node_id, settings)
self.node_info_new[node_id] = (feat_id, split, idx_split_global)
self.evaluate_new_subtree(data, node_id, param, nodes_subtree, cache, settings)
# log_acc will be be modified below
log_acc_tmp, loglik_diff, logprior_diff = self.compute_log_acc_cs(nodes_subtree, node_id)
if settings.debug == 1:
self.check_if_same(log_acc_tmp, loglik_diff, logprior_diff)
log_acc = log_acc_tmp + self.logprior[node_id] - self.logprior_new[node_id]
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.node_info[node_id] = copy(self.node_info_new[node_id])
self.update_subtree(node_id, nodes_subtree, settings)
change = True
else:
change = False
elif step_id == 3: # SWAP
if not self.inner_pc_pairs:
change = False
else:
node_id, child_id = random.choice(self.inner_pc_pairs)
nodes_subtree = self.get_nodes_subtree(node_id)
nodes_not_in_subtree = self.get_nodes_not_in_subtree(node_id)
if settings.debug == 1:
set1 = set(list(nodes_subtree) + list(nodes_not_in_subtree))
set2 = set(self.leaf_nodes + self.non_leaf_nodes)
assert(sorted(set1) == sorted(set2))
self.create_new_statistics(nodes_subtree, nodes_not_in_subtree, node_id, settings)
self.node_info_new[node_id] = copy(self.node_info[child_id])
self.node_info_new[child_id] = copy(self.node_info[node_id])
if settings.verbose >= 1:
print 'swap: node_id = %s, child_id = %s' % (node_id, child_id)
print 'node_info[node_id] = %s, node_info[child_id] = %s' \
% (self.node_info[node_id], self.node_info[child_id])
self.evaluate_new_subtree(data, node_id, param, nodes_subtree, cache, settings)
log_acc, loglik_diff, logprior_diff = self.compute_log_acc_cs(nodes_subtree, node_id)
if settings.debug == 1:
self.check_if_same(log_acc, loglik_diff, logprior_diff)
log_r = np.log(np.random.rand(1))
if log_r <= log_acc:
self.node_info[node_id] = copy(self.node_info_new[node_id])
self.node_info[child_id] = copy(self.node_info_new[child_id])
self.update_subtree(node_id, nodes_subtree, settings)
change = True
else:
change = False
if settings.verbose >= 1:
print 'trying move: step_id = %d, move = %s, log_acc = %s, log_r = %s' \
% (step_id, STEP_NAMES[step_id], log_acc, log_r)
if change:
self.depth = max([get_depth(node_id) for node_id in \
self.leaf_nodes])
self.loglik_current = sum([self.loglik[node_id] for node_id in \
self.leaf_nodes])
if settings.verbose >= 1:
print 'accepted move: step_id = %d, move = %s' % (step_id, STEP_NAMES[step_id])
self.print_stuff()
if settings.debug == 1:
both_children_terminal, inner_pc_pairs = self.recompute_mcmc_data_structures()
print '\nstats from recompute_mcmc_data_structures'
print 'both_children_terminal = %s' % both_children_terminal
print 'inner_pc_pairs = %s' % inner_pc_pairs
assert(sorted(both_children_terminal) == sorted(self.both_children_terminal))
assert(sorted(inner_pc_pairs) == sorted(self.inner_pc_pairs))
grow_nodes_new = [n_id for n_id in self.leaf_nodes \
if not stop_split(self.train_ids[n_id], settings, data, cache)]
if change and (step_id == 1):
print 'grow_nodes_new = %s, grow_nodes_tmp = %s' % (sorted(grow_nodes_new), sorted(grow_nodes_tmp))
assert(sorted(grow_nodes_new) == sorted(grow_nodes_tmp))
return (change, step_id)
