def distance_matrix(self,
recreate: bool = False,
model: str = "ted",
**kwargs: Any) -> np.ndarray:
"""
Compute the distance matrix between each pair of reaction trees
All key-word arguments are passed along to the `route_distance_calculator`
function from the `route_distances` package.
When `model` is "lstm", a key-word argument `model_path` needs to be given
when `model` is "ted", two optional key-word arguments `timeout` and `content`
can be given.
:param recreate: if False, use a cached one if available
:param model: the type of model to use "ted" or "lstm"
:return: the square distance matrix
"""
if model == "lstm" and not kwargs.get("model_path"):
raise KeyError(
"Need to provide 'model_path' argument when using LSTM model for computing distances"
)
content = kwargs.get("content", "both")
cache_key = kwargs.get("model_path",
"") if model == "lstm" else content
if self._distance_matrix.get(cache_key) is not None and not recreate:
return self._distance_matrix[cache_key]
calculator = route_distances_calculator(model, **kwargs)
distances = calculator(self.dicts)
self._distance_matrix[cache_key] = distances
return distances
def main() -> None:
""" Entry-point for CLI tool """
args = _get_args()
tqdm.pandas()
data = _merge_inputs(args.files)
if args.only_clustering:
calculator = None
elif args.model == "ted":
calculator = route_distances_calculator("ted", content="both")
else:
calculator = route_distances_calculator(
"lstm",
model_path=args.model,
fp_size=args.fp_size,
lstm_size=args.lstm_size,
)
if not args.only_clustering:
dist_data = data.progress_apply(_calc_distances, axis=1, calculator=calculator)
data = data.assign(
distance_matrix=dist_data.distance_matrix,
distances_time=dist_data.distances_time,
)
if args.nclusters is not None:
cluster_data = data.progress_apply(
_do_clustering,
axis=1,
nclusters=args.nclusters,
min_density=args.min_density,
)
data = data.assign(
cluster_labels=cluster_data.cluster_labels,
cluster_time=cluster_data.cluster_time,
)
with warnings.catch_warnings(): # This wil suppress a PerformanceWarning
warnings.simplefilter("ignore")
data.to_hdf(args.output, "table")
def distance_to(self,
other: "ReactionTree",
content: str = "both") -> float:
"""
Calculate the distance to another reaction tree
This is a tree edit distance, with unit cost to
insert and deleted nodes, and the Jaccard distance for substituting nodes
:param other: the reaction tree to compare to
:param content: determine what part of the tree to include in the calculation
:return: the distance between the routes
"""
calculator = route_distances_calculator("ted", content=content)
distances = calculator([self.to_dict(), other.to_dict()])
return distances[0, 1]