def test_sampling_zero_length_intervals():
f = hp_module.Float("f", 2, 2)
rand_sample = f.random_sample()
assert rand_sample == 2
val = 2
prob = hp_module.value_to_cumulative_prob(val, f)
assert prob == 1
def test_reverse_log_sampling_random_state():
f = hp_module.Float("f", 1e-3, 1e3, sampling="reverse_log")
rand_sample = f.random_sample()
assert rand_sample >= f.min_value
assert rand_sample <= f.max_value
val = 1e-3
prob = hp_module.value_to_cumulative_prob(val, f)
assert prob == 0
new_val = hp_module.cumulative_prob_to_value(prob, f)
assert np.isclose(val, new_val)
val = 1
prob = hp_module.value_to_cumulative_prob(val, f)
assert prob > 0 and prob < 1
new_val = hp_module.cumulative_prob_to_value(prob, f)
assert np.isclose(val, new_val)
def _vectorize_trials(self):
x = []
y = []
ongoing_trials = set(self.ongoing_trials.values())
for trial in self.trials.values():
# Create a vector representation of each Trial's hyperparameters.
trial_hps = trial.hyperparameters
vector = []
for hp in self._nonfixed_space():
# For hyperparameters not present in the trial (either added after
# the trial or inactive in the trial), set to default value.
if (trial_hps.is_active(hp) # inactive
and hp.name
in trial_hps.values # added after the trial
):
trial_value = trial_hps.values[hp.name]
else:
trial_value = hp.default
# Embed an HP value into the continuous space [0, 1].
prob = hp_module.value_to_cumulative_prob(trial_value, hp)
vector.append(prob)
if trial in ongoing_trials:
# "Hallucinate" the results of ongoing trials. This ensures that
# repeat trials are not selected when running distributed.
x_h = np.array(vector).reshape((1, -1))
y_h_mean, y_h_std = self.gpr.predict(x_h)
# Give a pessimistic estimate of the ongoing trial.
score = y_h_mean[0] + y_h_std[0]
elif trial.status == "COMPLETED":
score = trial.score
# Always frame the optimization as a minimization for scipy.minimize.
if self.objective.direction == "max":
score = -1 * score
else:
continue
x.append(vector)
y.append(score)
x = np.array(x)
y = np.array(y)
return x, y