def _create_posterior(self, flat_hyperparameter_space: HyperparameterSpace,
trials: Trials) -> HyperparameterSpace:
# Create a list of all hyperparams and their trials.
# Loop through all hyperparams
posterior_distributions: HyperparameterSpace = HyperparameterSpace()
for (hyperparam_key,
hyperparam_distribution) in flat_hyperparameter_space.items():
# Get trial hyperparams
trial_hyperparams: List[HyperparameterSamples] = [
trial.hyperparams.to_flat_as_dict_primitive()[hyperparam_key]
for trial in trials
]
if hyperparam_distribution.is_discrete():
posterior_distribution = self._reweights_categorical(
discrete_distribution=hyperparam_distribution,
trial_hyperparameters=trial_hyperparams)
else:
posterior_distribution = self._create_gaussian_mixture(
continuous_distribution=hyperparam_distribution,
trial_hyperparameters=trial_hyperparams)
posterior_distributions.update(
{hyperparam_key: posterior_distribution})
return posterior_distributions
def plot_distribution_space(hyperparameter_space: HyperparameterSpace,
num_bins=50):
for title, distribution in hyperparameter_space.items():
print(title + ":")
plot_histogram(title, distribution, num_bins=num_bins)
plot_pdf_cdf(title, distribution)