def __init__(self, size=2048, radius=4, dtype=np.int32):
"""An abstract class for managing transformations applied to
model-based optimization datasets when constructing the oracle; for
example, if the oracle learns from molecule fingerprints
Arguments:
size: int
the number of bits in the morgan fingerprint returned by RDKit,
controls the vector size of the molecule embedding
radius: int
the substructure radius passed to RDKit that controls how local
the information encoded in the molecule embedding is
"""
# wrap the deepchem featurizer that relies on rdkit
self.featurizer = feat.CircularFingerprint(size=size, radius=radius)
self.size = size
self.radius = radius
self.dtype = dtype
# download the vocabulary file is not present in the cache
vocab_file = DiskResource(
os.path.join(DATA_DIR, 'smiles_vocab.txt'),
download_method="direct",
download_target=f'{SERVER_URL}/smiles_vocab.txt')
if not vocab_file.is_downloaded:
vocab_file.download()
self.tokenizer = SmilesTokenizer(
os.path.join(DATA_DIR, 'smiles_vocab.txt'))
def __init__(self,
dataset: ContinuousDataset,
rollout_horizon=100,
**kwargs):
"""Initialize the ground truth score function f(x) for a model-based
optimization problem, which involves loading the parameters of an
oracle model and estimating its computational cost
Arguments:
dataset: DiscreteDataset
an instance of a subclass of the DatasetBuilder class which has
a set of design values 'x' and prediction values 'y', and defines
batching and sampling methods for those attributes
noise_std: float
the standard deviation of gaussian noise added to the prediction
values 'y' coming out of the ground truth score function f(x)
in order to make the optimization problem difficult
internal_measurements: int
an integer representing the number of independent measurements of
the prediction made by the oracle, which are subsequently
averaged, and is useful when the oracle is stochastic
"""
# the number of transitions per trajectory to sample
self.rollout_horizon = rollout_horizon
# ensure the trained policy has been downloaded
policy = "dkitty_morphology/dkitty_oracle.pkl"
policy = DiskResource(policy,
is_absolute=False,
download_method="direct",
download_target=f"{SERVER_URL}/{policy}")
if not policy.is_downloaded and not policy.download():
raise ValueError("unable to download trained policy for ant")
# load the weights of the policy
with open(policy.disk_target, "rb") as f:
self.policy = pkl.load(f)
# initialize the oracle using the super class
super(DKittyMorphologyOracle, self).__init__(dataset,
internal_batch_size=1,
is_batched=False,
expect_normalized_y=False,
expect_normalized_x=False,
expect_logits=None,
**kwargs)