def _initialize(args):
data = args[0]
kwargs = args[1]
t_start = time.time()
state = BCState(data.T, **kwargs) # transpose data to col-major
metadata = state.get_metadata()
diagnostics = {
'log_score': state.log_score(),
'iters': 0,
'time': time.time() - t_start}
return metadata, [diagnostics]
def _initialize(args):
data = args[0]
kwargs = args[1]
t_start = time.time()
state = BCState(data.T, **kwargs) # transpose data to col-major
metadata = state.get_metadata()
diagnostics = {
'log_score': state.log_score(),
'iters': 0,
'time': time.time() - t_start
}
return metadata, [diagnostics]
def _run(args):
data = args[0]
checkpoint = args[1]
t_id = args[2]
verbose = args[3]
init_kwargs = args[4]
trans_kwargs = args[5]
# create copy of trans_kwargs so we don't mutate
trans_kwargs = dict(trans_kwargs)
n_iter = trans_kwargs['N']
if checkpoint is None:
checkpoint = n_iter
n_sweeps = 1
else:
trans_kwargs['N'] = checkpoint
n_sweeps = int(n_iter / checkpoint)
diagnostics = []
state = BCState(data.T, **init_kwargs) # transpose dat to col-major
for i in range(n_sweeps):
t_start = time.time()
state.transition(**trans_kwargs)
t_iter = time.time() - t_start
n_views = state.n_views
log_score = state.log_score()
diagnostic = {
'log_score': log_score,
'n_views': n_views,
'iters': checkpoint,
'time': t_iter
}
diagnostics.append(diagnostic)
if verbose:
msg = "Model {}:\n\t+ sweep {} of {} in {} sec."
msg += "\n\t+ log score: {}"
msg += "\n\t+ n_views: {}\n"
print(msg.format(t_id, i, n_sweeps, t_iter, log_score, n_views))
metadata = state.get_metadata()
return metadata, diagnostics
def _run(args):
data = args[0]
checkpoint = args[1]
t_id = args[2]
verbose = args[3]
init_kwargs = args[4]
trans_kwargs = args[5]
# create copy of trans_kwargs so we don't mutate
trans_kwargs = dict(trans_kwargs)
n_iter = trans_kwargs['N']
if checkpoint is None:
checkpoint = n_iter
n_sweeps = 1
else:
trans_kwargs['N'] = checkpoint
n_sweeps = int(n_iter/checkpoint)
diagnostics = []
state = BCState(data.T, **init_kwargs) # transpose dat to col-major
for i in range(n_sweeps):
t_start = time.time()
state.transition(**trans_kwargs)
t_iter = time.time() - t_start
n_views = state.n_views
log_score = state.log_score()
diagnostic = {
'log_score': log_score,
'n_views': n_views,
'iters': checkpoint,
'time': t_iter}
diagnostics.append(diagnostic)
if verbose:
msg = "Model {}:\n\t+ sweep {} of {} in {} sec."
msg += "\n\t+ log score: {}"
msg += "\n\t+ n_views: {}\n"
print(msg.format(t_id, i, n_sweeps, t_iter, log_score, n_views))
metadata = state.get_metadata()
return metadata, diagnostics
