# -*- coding: utf-8 -*-
"""Instance creation utilities."""
import pathlib
from typing import Callable, List, Mapping, Optional, Sequence, Set, TextIO, Tuple, Union
import numpy as np
import pandas
import torch
from pkg_resources import iter_entry_points
from ..typing import LabeledTriples, MappedTriples
__all__ = [
"compute_compressed_adjacency_list",
"load_triples",
"get_entities",
"get_relations",
"tensor_to_df",
]
TRIPLES_DF_COLUMNS = ("head_id", "head_label", "relation_id", "relation_label", "tail_id", "tail_label")
def _load_importers(group_subname: str) -> Mapping[str, Callable[[str], LabeledTriples]]:
return {
entry_point.name: entry_point.load()
for entry_point in iter_entry_points(group=f"pykeen.triples.{group_subname}")
}
#: Functions for specifying exotic resources with a given prefix
PREFIX_IMPORTERS: Mapping[str, Callable[[str], LabeledTriples]] = _load_importers("prefix_importer")
#: Functions for specifying exotic resources based on their file extension
EXTENSION_IMPORTERS: Mapping[str, Callable[[str], LabeledTriples]] = _load_importers("extension_importer")
[docs]def load_triples(
path: Union[str, pathlib.Path, TextIO],
delimiter: str = "\t",
encoding: Optional[str] = None,
column_remapping: Optional[Sequence[int]] = None,
) -> LabeledTriples:
"""Load triples saved as tab separated values.
:param path: The key for the data to be loaded. Typically, this will be a file path ending in ``.tsv``
that points to a file with three columns - the head, relation, and tail. This can also be used to
invoke PyKEEN data importer entrypoints (see below).
:param delimiter: The delimiter between the columns in the file
:param encoding: The encoding for the file. Defaults to utf-8.
:param column_remapping: A remapping if the three columns do not follow the order head-relation-tail.
For example, if the order is head-tail-relation, pass ``(0, 2, 1)``
:returns: A numpy array representing "labeled" triples.
:raises ValueError: if a column remapping was passed but it was not a length 3 sequence
Besides TSV handling, PyKEEN does not come with any importers pre-installed. A few can be found at:
- :mod:`pybel.io.pykeen`
- :mod:`bio2bel.io.pykeen`
"""
if isinstance(path, (str, pathlib.Path)):
path = str(path)
for extension, handler in EXTENSION_IMPORTERS.items():
if path.endswith(f".{extension}"):
return handler(path)
for prefix, handler in PREFIX_IMPORTERS.items():
if path.startswith(f"{prefix}:"):
return handler(path[len(f"{prefix}:") :])
if encoding is None:
encoding = "utf-8"
if column_remapping is not None:
if len(column_remapping) != 3:
raise ValueError("remapping must have length of three")
df = pandas.read_csv(
path,
sep=delimiter,
encoding=encoding,
dtype=str,
header=None,
usecols=column_remapping,
keep_default_na=False,
)
if column_remapping is not None:
df = df[[df.columns[c] for c in column_remapping]]
return df.to_numpy()
[docs]def get_entities(triples: torch.LongTensor) -> Set[int]:
"""Get all entities from the triples."""
return set(triples[:, [0, 2]].flatten().tolist())
[docs]def get_relations(triples: torch.LongTensor) -> Set[int]:
"""Get all relations from the triples."""
return set(triples[:, 1].tolist())
[docs]def tensor_to_df(
tensor: torch.LongTensor,
**kwargs: Union[torch.Tensor, np.ndarray, Sequence],
) -> pandas.DataFrame:
"""Take a tensor of triples and make a pandas dataframe with labels.
:param tensor: shape: (n, 3)
The triples, ID-based and in format (head_id, relation_id, tail_id).
:param kwargs:
Any additional number of columns. Each column needs to be of shape (n,). Reserved column names:
{"head_id", "head_label", "relation_id", "relation_label", "tail_id", "tail_label"}.
:return:
A dataframe with n rows, and 3 + len(kwargs) columns.
:raises ValueError:
If a reserved column name appears in kwargs.
"""
# Input validation
additional_columns = set(kwargs.keys())
forbidden = additional_columns.intersection(TRIPLES_DF_COLUMNS)
if len(forbidden) > 0:
raise ValueError(
f"The key-words for additional arguments must not be in {TRIPLES_DF_COLUMNS}, but {forbidden} were "
f"used.",
)
# convert to numpy
tensor = tensor.cpu().numpy()
data = dict(zip(["head_id", "relation_id", "tail_id"], tensor.T))
# Additional columns
for key, values in kwargs.items():
# convert PyTorch tensors to numpy
if isinstance(values, torch.Tensor):
values = values.cpu().numpy()
data[key] = values
# convert to dataframe
rv = pandas.DataFrame(data=data)
# Re-order columns
columns = list(TRIPLES_DF_COLUMNS[::2]) + sorted(set(rv.columns).difference(TRIPLES_DF_COLUMNS))
return rv.loc[:, columns]
[docs]def compute_compressed_adjacency_list(
mapped_triples: MappedTriples,
num_entities: Optional[int] = None,
) -> Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor]:
"""Compute compressed undirected adjacency list representation for efficient sampling.
The compressed adjacency list format is inspired by CSR sparse matrix format.
:param mapped_triples:
the ID-based triples
:param num_entities:
the number of entities.
:return: a tuple `(degrees, offsets, compressed_adj_lists)` where
- degrees: shape: `(num_entities,)`
- offsets: shape: `(num_entities,)`
- compressed_adj_list: shape: `(2 * num_triples, 2)`
with
.. code::
adj_list[i] = compressed_adj_list[offsets[i]:offsets[i+1]]
"""
num_entities = num_entities or mapped_triples[:, [0, 2]].max().item() + 1
num_triples = mapped_triples.shape[0]
adj_lists: List[List[Tuple[int, float]]] = [[] for _ in range(num_entities)]
for i, (s, _, o) in enumerate(mapped_triples):
adj_lists[s].append((i, o.item()))
adj_lists[o].append((i, s.item()))
degrees = torch.tensor([len(a) for a in adj_lists], dtype=torch.long)
assert torch.sum(degrees) == 2 * num_triples
offset = torch.empty(num_entities, dtype=torch.long)
offset[0] = 0
offset[1:] = torch.cumsum(degrees, dim=0)[:-1]
compressed_adj_lists = torch.cat([torch.as_tensor(adj_list, dtype=torch.long) for adj_list in adj_lists], dim=0)
return degrees, offset, compressed_adj_lists