def optimize_h_with_bayes_estimator(*args):
"""Assumes that optimal_h calculates gain-optimal value analytically."""
ys = flatten_first_two_dims(
self.sample_predictive_y0(
*args,
nsamples_theta=self.H_NSAMPLES_UTILITY_TERM_THETA,
nsamples_y=self.H_NSAMPLES_UTILITY_TERM_Y))
h = self.optimal_h_bayes_estimator(ys)
if optimize_h_with_bayes_estimator.counter < 3 and not is_valid(h):
print(
"WARNING: your optimal_h_bayes_estimator=%s returns invalid decisions! Are we good here?"
% self.optimal_h_bayes_estimator.__name__)
optimize_h_with_bayes_estimator.counter += 1
return h
def optimal_h_numerically_scipy(ys,
u,
weights=None,
utility_aggregator=gain_weighted,
data_mask=None,
max_niter=10000,
tol=1e-4,
tol_goal=-1,
lr=0.01,
start=None,
optimizer="COBYLA",
verbose=False,
debug=False,
sparse_verbose=True):
""" Using numerical optimization (SciPy) finds optimal h for utility-dependent term expressed by utility_aggregator.
Compatible with optimal_h_numerically.
"""
printv = lambda txt: (print2("[optimal_h_numerically_scipy]" + txt)
if verbose else None)
if sparse_verbose and not verbose:
printv = lambda txt: sparse_print(
"optimal_h_numerically_scipy", "[optimal_h_numerically_scipy]" +
txt, 100)
printd = lambda txt: (print2("[optimal_h_numerically_scipy]" + txt)
if debug else None)
assert len( signature(utility_aggregator).parameters )==3, \
"[optimal_h_numerically_scipy] Your utility_aggregator=%s takes wrong number of params! does not support weights?" % utility_aggregator
env = torch if "cpu" in str(ys.device) else torch.cuda
if data_mask is None:
data_mask = (torch.ones(ys.shape[1:]) if len(ys.shape) > 1 else
torch.tensor(1)).type(env.ByteTensor)
if weights is None: weights = torch.ones_like(ys)
weights /= weights.sum(0) #enforce normalization
weights = torch.tensor(tonumpy(weights), requires_grad=False)
y = torch.tensor(tonumpy(ys), requires_grad=False)
if start is None:
h = (y * weights).sum(0) #start from E(y)
elif start is None or not is_valid(torch.tensor(start)):
printd("start point is invalid. ignoring!")
h = (y * weights).sum(0) #start from E(y)
else:
h = start
start = time.time()
x0 = tonumpy(h).flatten()
fun = lambda h: -utility_aggregator(
u(torch.tensor(h.reshape(y.shape[1:]), dtype=y.dtype), y), weights,
data_mask).item()
result = _scipy_minimize(fun,
x0,
method=optimizer,
max_niter=max_niter,
tol=tol,
tol_goal=tol_goal,
lr=lr,
debug=debug)
if verbose:
printv(
"[%.4f][optimizer=%s ys=%s max_niter=%i tol=%s tol_goal=%s lr=%s u=%s->%s] %s"
% (time.time() - start, optimizer, tuple(ys.shape), max_niter, tol,
tol_goal, lr, u.__name__, utility_aggregator.__name__,
str(result).replace("\n", ";")[:200]))
return torch.tensor(result["x"].reshape(y.shape[1:]), dtype=ys.dtype)
def optimal_h_numerically_ty(ys,
u,
utility_aggregator=gain,
data_mask=None,
max_niter=10000,
tol=1e-4,
tol_goal=-1,
lr=0.01,
start=None,
optimizer=torch.optim.Adam,
verbose=False,
debug=False,
sparse_verbose=True):
""" Using numerical optimization finds optimal h for utility-dependent term expressed by utility_aggregator.
Args:
ys: Samples matrix. The dimensionality should match what utility_aggregator takes:
#y-samples x #theta-samples x data-size.
u: Utility function u(h, ys) -> utilities matrix (the same shape as ys).
utility_aggregator: A function that calculate utility-dependent term.
Should take exactly 2 params: utilites and data_mask.
data_mask: A mask passed to utility_aggregator.
"""
printv = lambda txt: (print2("[optimal_h_numerically_ty] " + txt)
if verbose else None)
if sparse_verbose and not verbose:
printv = lambda txt: sparse_print(
"optimal_h_numerically_ty", "[optimal_h_numerically_ty]" + txt, 100
)
printd = lambda txt: (print2("[optimal_h_numerically_ty] " + txt)
if debug else None)
assert len( signature(utility_aggregator).parameters )==2, \
"[optimal_h_numerically_ty] Your utility_aggregator=%s takes wrong number of params! perhaps requires weights?" % utility_aggregator
env = torch if "cpu" in str(ys.device) else torch.cuda
if data_mask is None: #No data mask provided. Using all data points
data_mask = (torch.ones(ys.shape[2:]) if len(ys.shape) > 2 else
torch.tensor(1)).type(env.ByteTensor)
y = torch.tensor(tonumpy(ys), requires_grad=False)
if start is None:
h = y.mean(0).mean(0).clone().detach().requires_grad_(
True) #start from E(y)
elif start is None or not is_valid(torch.tensor(start)):
printd("start point is invalid. ignoring!")
h = y.mean(0).mean(0).clone().detach().requires_grad_(
True) #start from E(y)
else:
h = torch.tensor(tonumpy(start), requires_grad=True)
optimizer = optimizer([h], lr=lr)
prev_h, prev_goal = torch.tensor(tonumpy(h)), float("inf")
start = time.time()
for i in range(max_niter):
goal = -utility_aggregator(u(h, y), data_mask)
optimizer.zero_grad()
goal.backward(retain_graph=False)
optimizer.step()
#check for convergence:
if (prev_h - h).abs().max() <= tol:
printv(
"[%.2f][ys=%s max_niter=%i tol=%s tol_goal=%s lr=%s u=%s->%s] Finished at %i. iter (tolerance reached): obj=%.4f max-err=%.8f mean-err=%.8f"
% (time.time() - start, tuple(
ys.shape), max_niter, tol, tol_goal, lr, u.__name__,
utility_aggregator.__name__, i + 1, goal.item(),
(prev_h - h).abs().max(), (prev_h - h).abs().mean()))
break
if abs(prev_goal - goal.item()) <= tol_goal:
printv(
"[%.2f][ys=%s max_niter=%i tol=%s tol_goal=%s lr=%s u=%s->%s] Finished at %i. iter (objective tolerance reached): obj=%.4f max-err=%.8f mean-err=%.8f"
% (time.time() - start, tuple(
ys.shape), max_niter, tol, tol_goal, lr, u.__name__,
utility_aggregator.__name__, i + 1, goal.item(),
(prev_h - h).abs().max(), (prev_h - h).abs().mean()))
break
if i >= max_niter - 1:
printv(
"[%.2f][ys=%s max_niter=%i tol=%s tol_goal=%s lr=%s u=%s->%s] Finished at %i. iter (max number reached): obj=%.4f max-err=%.8f mean-err=%.8f"
% (time.time() - start, tuple(
ys.shape), max_niter, tol, tol_goal, lr, u.__name__,
utility_aggregator.__name__, i + 1, goal.item(),
(prev_h - h).abs().max(), (prev_h - h).abs().mean()))
break
if i % (max_niter // 10) == 0:
printd("[%.2f] iter %i: objective=%.4f err=%.6f" %
(time.time() - start, i, goal.item(),
(prev_h - h).abs().max()))
prev_h = torch.tensor(tonumpy(h))
prev_goal = goal.item()
return h