def build_labeled_dataset(self, num_samples=100, seed=0):
"""Builds a problem-specific initial dataset.
By default, samples sequences randomly and evaluates the problem on those
sequences. Can be overwritten by problem-specific dataset generators.
Args:
num_samples: The number of samples to return.
seed: An optional integer seed or np.random.RandomState to be used
for the random number generator.
Returns:
A tf.data.Dataset with data_utils.DatasetSamples.
"""
structures = self.domain.sample_uniformly(num_samples, seed=seed)
rewards = self(structures)
return utils.dataset_from_tensors(
data_utils.DatasetSample(structure=structures, reward=rewards))
def to_dataset(self):
"""Converts the population to a `tf.data.Dataset` with `DatasetSample`s."""
structures, rewards = self.to_structures_and_rewards()
return utils.dataset_from_tensors(
DatasetSample(structure=structures, reward=rewards))