def setup_logger(self, verbose=False):
h = pd.read_pickle(self.hist_path) if os.path.exists(
self.hist_path) else None
if h is not None:
print("Resuming from %s..." % self.hist_path)
tasks = [
opt_tools.tasks.DisplayOptimisation(opt_tools.seq_exp_lin(1.1,
20)),
opt_tools.tasks.GPflowLogOptimisation(
opt_tools.seq_exp_lin(1.1, 20)),
# Uncomment to keep track of exact lml bound. This causes problems with memory sometimes.
# exp_tools.GPflowMultiClassificationTrackerLml(
# self.Xt[:, :], self.Yt[:, :], itertools.count(1800, 1800), trigger="time",
# verbose=True, store_x="final_only", store_x_columns=".*(variance|lengthscales)"),
opt_tools.gpflow_tasks.GPflowMultiClassificationTracker(
self.Xt[:, :],
self.Yt[:, :],
itertools.count(300, 1000),
trigger="time",
verbose=True,
store_x="final_only",
store_x_columns=".*(variance|lengthscales)",
old_hist=h),
opt_tools.tasks.StoreOptimisationHistory(self.hist_path,
opt_tools.seq_exp_lin(
1.5,
600,
start_jump=30),
trigger="time",
verbose=False),
opt_tools.tasks.Timeout(self.run_settings.get("timeout", np.inf))
]
tasks[2].pred_batch_size = self.pred_batch_size
self.logger = opt_tools.GPflowOptimisationHelper(self.m, tasks)
def setup_logger(self, verbose=None):
h = pd.read_pickle(self.hist_path) if os.path.exists(self.hist_path) else None
if h is not None:
print("Resuming from %s..." % self.hist_path)
tasks = [
opt_tools.tasks.DisplayOptimisation(opt_tools.seq_exp_lin(1.1, 20)),
opt_tools.tasks.GPflowLogOptimisation(opt_tools.seq_exp_lin(1.1, 20)),
opt_tools.gpflow_tasks.GPflowBinClassTracker(self.Xt[self.test_slice, :], self.Yt[self.test_slice, :],
opt_tools.seq_exp_lin(1.1, 80, 3),
verbose=True, store_x="final_only",
store_x_columns='.*(variance|lengthscales)',
old_hist=h),
opt_tools.tasks.StoreOptimisationHistory(self.hist_path, itertools.count(0, 60), verbose=False)
]
self.logger = opt_tools.GPflowOptimisationHelper(self.m, tasks)
def setUp(self):
X = np.linspace(0, 5, 100)[:, None]
Y = 0.3 * np.sin(2 * X) + 0.05 * rnd.randn(*X.shape)
# model = GPflow.sgpr.SGPR(X, Y, GPflow.kernels.RBF(1), X[rnd.permutation(len(X))[:3], :])
model = GPflow.sgpr.SGPR(X, Y, GPflow.kernels.RBF(1), X[:3, :].copy())
model._compile()
self.optlog = ot.GPflowOptimisationHelper(
model,
[
ot.tasks.DisplayOptimisation(ot.seq_exp_lin(1.0, 1.0)),
ot.tasks.GPflowLogOptimisation(ot.seq_exp_lin(1.0, 1.0), store_fullg=True, store_x=True),
ot.tasks.StoreOptimisationHistory('./opthist.pkl', ot.seq_exp_lin(1.0, np.inf, 5.0, 5.0), verbose=True)
]
)
def setup_logger(self, verbose=False):
h = pd.read_pickle(self.hist_path) if os.path.exists(self.hist_path) else None
if h is not None:
print("Resuming from %s..." % self.hist_path)
tasks = [
opt_tools.tasks.DisplayOptimisation(opt_tools.seq_exp_lin(1.1, 20)),
opt_tools.tasks.GPflowLogOptimisation(opt_tools.seq_exp_lin(1.1, 20)),
exp_tools.GPflowMultiClassificationTrackerLml(
self.Xt[:, :], self.Yt[:, :], itertools.count(1800, 1800), trigger="time",
verbose=True, store_x="final_only", store_x_columns=".*(variance|lengthscales)"),
opt_tools.gpflow_tasks.GPflowMultiClassificationTracker(
self.Xt[:, :], self.Yt[:, :], opt_tools.seq_exp_lin(1.5, 150, start_jump=30), trigger="time",
verbose=True, store_x="final_only", store_x_columns=".*(variance|lengthscales)", old_hist=h),
opt_tools.tasks.StoreOptimisationHistory(self.hist_path, opt_tools.seq_exp_lin(1.5, 600, start_jump=30),
trigger="time", verbose=False),
opt_tools.tasks.Timeout(self.run_settings.get("timeout", np.inf))
]
self.logger = opt_tools.GPflowOptimisationHelper(self.m, tasks)
sys.path.append('..')
import opt_tools as ot
rnd.seed(4)
X = np.linspace(0, 5, 100)[:, None]
Y = 0.3 * np.sin(2 * X) + 0.05 * rnd.randn(*X.shape)
# model = GPflow.sgpr.SGPR(X, Y, GPflow.kernels.RBF(1), X[rnd.permutation(len(X))[:3], :])
model = GPflow.sgpr.SGPR(X, Y, GPflow.kernels.RBF(1), X[:3, :].copy())
model._compile()
optlog = ot.GPflowOptimisationHelper(model, [
ot.tasks.DisplayOptimisation(ot.seq_exp_lin(1.0, 1.0)),
ot.tasks.GPflowLogOptimisation(
ot.seq_exp_lin(1.0, 1.0), store_fullg=True, store_x=True),
ot.tasks.StoreOptimisationHistory(
'./opthist.pkl', ot.seq_exp_lin(1.0, np.inf, 5.0, 5.0), verbose=True)
])
optlog.callback(model.get_free_state())
# Start optimisation
model.optimize(callback=optlog.callback, disp=False, maxiter=10)
optlog.finish(model.get_free_state())
print("Finished first optimisation run. %i iterations." % optlog.hist.i.max())
print("")
# Resume optimisation
hist = pd.read_pickle('./opthist.pkl')
time.sleep(3.0)
model._wrapped_objective = model._objective