diff --git a/_modules/besskge/dataset.html b/_modules/besskge/dataset.html index f3cc3c7..82652c6 100644 --- a/_modules/besskge/dataset.html +++ b/_modules/besskge/dataset.html @@ -110,6 +110,11 @@
#: {part: int32[n_triple, {h,r,t}]}
triples: Dict[str, NDArray[np.int32]]
+ #: IDs of the triples in KGDataset.triples wrt
+ #: the ordering in the original array/dataframe
+ #: from where the triples originate.
+ original_triple_ids: Dict[str, NDArray[np.int32]]
+
#: Entity labels by ID; str[n_entity]
entity_dict: Optional[List[str]] = None
@@ -164,6 +169,9 @@ Source code for besskge.dataset
and relations have already been assigned. Note that, if entities have
types, entities of the same type need to have contiguous IDs.
Triples are randomly split in train/validation/test sets.
+ The attribute `KGDataset.original_triple_ids` stores the IDs
+ of the triples in each split wrt the original ordering in `data`.
+
If a pre-defined train/validation/test split is wanted, the KGDataset
class should be instantiated manually.
@@ -188,21 +196,27 @@ Source code for besskge.dataset
num_valid = int(num_triples * split[1])
rng = np.random.default_rng(seed=seed)
- rng.shuffle(data, axis=0)
-
- triples = dict()
- triples["train"], triples["valid"], triples["test"] = np.split(
- data, (num_train, num_train + num_valid), axis=0
+ id_shuffle = rng.permutation(np.arange(num_triples))
+ triple_ids = dict()
+ triple_ids["train"], triple_ids["valid"], triple_ids["test"] = np.split(
+ id_shuffle, (num_train, num_train + num_valid), axis=0
)
+ triples = dict()
+ triples["train"] = data[triple_ids["train"]]
+ triples["valid"] = data[triple_ids["valid"]]
+ triples["test"] = data[triple_ids["test"]]
- return cls(
+ ds = cls(
n_entity=data[:, [0, 2]].max() + 1,
n_relation_type=data[:, 1].max() + 1,
entity_dict=entity_dict,
relation_dict=relation_dict,
type_offsets=type_offsets,
triples=triples,
- )
+ original_triple_ids=triple_ids,
+ )
+
+ return ds
[docs] @classmethod
def from_dataframe(
@@ -293,6 +307,9 @@ Source code for besskge.dataset
relation_dict=relation_dict,
type_offsets=type_offsets,
triples=triples,
+ original_triple_ids={
+ k: np.arange(v.shape[0]) for k, v in triples.items()
+ },
)
[docs] @classmethod
@@ -354,6 +371,7 @@ Source code for besskge.dataset
relation_dict=rel_dict,
type_offsets=type_offsets,
triples=triples,
+ original_triple_ids={k: np.arange(v.shape[0]) for k, v in triples.items()},
neg_heads=neg_heads,
neg_tails=neg_tails,
)
@@ -403,6 +421,7 @@ Source code for besskge.dataset
relation_dict=rel_dict,
type_offsets=None,
triples=triples,
+ original_triple_ids={k: np.arange(v.shape[0]) for k, v in triples.items()},
neg_heads=neg_heads,
neg_tails=neg_tails,
)
diff --git a/generated/besskge.dataset.KGDataset.html b/generated/besskge.dataset.KGDataset.html
index 7d5eba9..2c05df2 100644
--- a/generated/besskge.dataset.KGDataset.html
+++ b/generated/besskge.dataset.KGDataset.html
@@ -99,7 +99,7 @@
besskge.dataset.KGDataset
-
-class besskge.dataset.KGDataset(n_entity, n_relation_type, triples, entity_dict=None, relation_dict=None, type_offsets=None, neg_heads=None, neg_tails=None)[source]
+class besskge.dataset.KGDataset(n_entity, n_relation_type, triples, original_triple_ids, entity_dict=None, relation_dict=None, type_offsets=None, neg_heads=None, neg_tails=None)[source]
Represents a complete knowledge graph dataset of (head, relation, tail) triples.
- Parameters:
@@ -107,6 +107,7 @@ besskge.dataset.KGDatasetint) –
n_relation_type (int) –
+original_triple_ids (Dict[str, ndarray[Any, dtype[int32]]]) –
@@ -222,12 +223,12 @@ besskge.dataset.KGDataset
- Parameters:
-df (Union
[DataFrame
, Dict
[str
, DataFrame
]]) – Pandas DataFrame of all triples in the knowledge graph dataset,
+
df (Union
[DataFrame
, Dict
[str
, DataFrame
]]) – Pandas DataFrame of all triples in the knowledge graph dataset,
or dictionary of DataFrames of triples for each part of the dataset split
head_column (Union
[int
, str
]) – Name of the DataFrame column storing head entities
relation_column (Union
[int
, str
]) – Name of the DataFrame column storing relations
tail_column (Union
[int
, str
]) – Name of the DataFrame column storing tail entities
-entity_types (Union
[Series
, Dict
[str
, str
], None
]) – If entities have types, dictionary or pandas Series of mappings
+
entity_types (Union
[Series
, Dict
[str
, str
], None
]) – If entities have types, dictionary or pandas Series of mappings
entity label -> entity type (as strings).
split (Tuple
[float
, float
, float
]) – Tuple to set the train/validation/test split.
Only used if no pre-defined dataset split is specified,
@@ -253,7 +254,9 @@
besskge.dataset.KGDataset
- Parameters:
@@ -326,6 +329,14 @@ besskge.dataset.KGDataset
+-
+original_triple_ids:
Dict
[str
, ndarray
[Any
, dtype
[int32
]]]
+IDs of the triples in KGDataset.triples wrt
+the ordering in the original array/dataframe
+from where the triples originate.
+
+
-
relation_dict:
Optional
[List
[str
]] = None
diff --git a/generated/besskge.dataset.html b/generated/besskge.dataset.html
index 21c2204..17aa00f 100644
--- a/generated/besskge.dataset.html
+++ b/generated/besskge.dataset.html
@@ -98,7 +98,7 @@
Classes
-KGDataset
(n_entity, n_relation_type, triples)
+KGDataset
(n_entity, n_relation_type, ...[, ...])
Represents a complete knowledge graph dataset of (head, relation, tail) triples.
diff --git a/genindex.html b/genindex.html
index 30d510a..8e1a1e6 100644
--- a/genindex.html
+++ b/genindex.html
@@ -87,6 +87,7 @@ Index
| L
| M
| N
+ | O
| P
| R
| S
@@ -680,6 +681,14 @@ N
+O
+
+
+
+
P
diff --git a/objects.inv b/objects.inv
index a7331e8..a3a2de6 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/searchindex.js b/searchindex.js
index 41970ff..2f3a6bd 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file