mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
3b8918e166
* API docs: Rename kb_in_memory to inmemorylookupkb, add to sidebar * adjust to mdx * linkout to InMemoryLookupKB at first occurrence in kb.mdx * fix links to docs * revert Azure trigger setting (I'll make a separate PR) Co-authored-by: svlandeg <svlandeg@github.com>
304 lines
12 KiB
Plaintext
304 lines
12 KiB
Plaintext
---
|
|
title: InMemoryLookupKB
|
|
teaser:
|
|
The default implementation of the KnowledgeBase interface. Stores all
|
|
information in-memory.
|
|
tag: class
|
|
source: spacy/kb/kb_in_memory.pyx
|
|
version: 3.5
|
|
---
|
|
|
|
The `InMemoryLookupKB` class inherits from [`KnowledgeBase`](/api/kb) and
|
|
implements all of its methods. It stores all KB data in-memory and generates
|
|
[`Candidate`](/api/kb#candidate) objects by exactly matching mentions with
|
|
entity names. It's highly optimized for both a low memory footprint and speed of
|
|
retrieval.
|
|
|
|
## InMemoryLookupKB.\_\_init\_\_ {id="init",tag="method"}
|
|
|
|
Create the knowledge base.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.kb import InMemoryLookupKB
|
|
> vocab = nlp.vocab
|
|
> kb = InMemoryLookupKB(vocab=vocab, entity_vector_length=64)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ---------------------- | ------------------------------------------------ |
|
|
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
|
| `entity_vector_length` | Length of the fixed-size entity vectors. ~~int~~ |
|
|
|
|
## InMemoryLookupKB.entity_vector_length {id="entity_vector_length",tag="property"}
|
|
|
|
The length of the fixed-size entity vectors in the knowledge base.
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------ |
|
|
| **RETURNS** | Length of the fixed-size entity vectors. ~~int~~ |
|
|
|
|
## InMemoryLookupKB.add_entity {id="add_entity",tag="method"}
|
|
|
|
Add an entity to the knowledge base, specifying its corpus frequency and entity
|
|
vector, which should be of length
|
|
[`entity_vector_length`](/api/inmemorylookupkb#entity_vector_length).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> kb.add_entity(entity="Q42", freq=32, entity_vector=vector1)
|
|
> kb.add_entity(entity="Q463035", freq=111, entity_vector=vector2)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------------- | ---------------------------------------------------------- |
|
|
| `entity` | The unique entity identifier. ~~str~~ |
|
|
| `freq` | The frequency of the entity in a typical corpus. ~~float~~ |
|
|
| `entity_vector` | The pretrained vector of the entity. ~~numpy.ndarray~~ |
|
|
|
|
## InMemoryLookupKB.set_entities {id="set_entities",tag="method"}
|
|
|
|
Define the full list of entities in the knowledge base, specifying the corpus
|
|
frequency and entity vector for each entity.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> kb.set_entities(entity_list=["Q42", "Q463035"], freq_list=[32, 111], vector_list=[vector1, vector2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ------------- | ---------------------------------------------------------------- |
|
|
| `entity_list` | List of unique entity identifiers. ~~Iterable[Union[str, int]]~~ |
|
|
| `freq_list` | List of entity frequencies. ~~Iterable[int]~~ |
|
|
| `vector_list` | List of entity vectors. ~~Iterable[numpy.ndarray]~~ |
|
|
|
|
## InMemoryLookupKB.add_alias {id="add_alias",tag="method"}
|
|
|
|
Add an alias or mention to the knowledge base, specifying its potential KB
|
|
identifiers and their prior probabilities. The entity identifiers should refer
|
|
to entities previously added with
|
|
[`add_entity`](/api/inmemorylookupkb#add_entity) or
|
|
[`set_entities`](/api/inmemorylookupkb#set_entities). The sum of the prior
|
|
probabilities should not exceed 1. Note that an empty string can not be used as
|
|
alias.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> kb.add_alias(alias="Douglas", entities=["Q42", "Q463035"], probabilities=[0.6, 0.3])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------------- | --------------------------------------------------------------------------------- |
|
|
| `alias` | The textual mention or alias. Can not be the empty string. ~~str~~ |
|
|
| `entities` | The potential entities that the alias may refer to. ~~Iterable[Union[str, int]]~~ |
|
|
| `probabilities` | The prior probabilities of each entity. ~~Iterable[float]~~ |
|
|
|
|
## InMemoryLookupKB.\_\_len\_\_ {id="len",tag="method"}
|
|
|
|
Get the total number of entities in the knowledge base.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> total_entities = len(kb)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------------- |
|
|
| **RETURNS** | The number of entities in the knowledge base. ~~int~~ |
|
|
|
|
## InMemoryLookupKB.get_entity_strings {id="get_entity_strings",tag="method"}
|
|
|
|
Get a list of all entity IDs in the knowledge base.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> all_entities = kb.get_entity_strings()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------------- |
|
|
| **RETURNS** | The list of entities in the knowledge base. ~~List[str]~~ |
|
|
|
|
## InMemoryLookupKB.get_size_aliases {id="get_size_aliases",tag="method"}
|
|
|
|
Get the total number of aliases in the knowledge base.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> total_aliases = kb.get_size_aliases()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------------------------------- |
|
|
| **RETURNS** | The number of aliases in the knowledge base. ~~int~~ |
|
|
|
|
## InMemoryLookupKB.get_alias_strings {id="get_alias_strings",tag="method"}
|
|
|
|
Get a list of all aliases in the knowledge base.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> all_aliases = kb.get_alias_strings()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------- |
|
|
| **RETURNS** | The list of aliases in the knowledge base. ~~List[str]~~ |
|
|
|
|
## InMemoryLookupKB.get_candidates {id="get_candidates",tag="method"}
|
|
|
|
Given a certain textual mention as input, retrieve a list of candidate entities
|
|
of type [`Candidate`](/api/kb#candidate). Wraps
|
|
[`get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.lang.en import English
|
|
> nlp = English()
|
|
> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
|
|
> candidates = kb.get_candidates(doc[0:2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------------------- |
|
|
| `mention` | The textual mention or alias. ~~Span~~ |
|
|
| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[Candidate]~~ |
|
|
|
|
## InMemoryLookupKB.get_candidates_batch {id="get_candidates_batch",tag="method"}
|
|
|
|
Same as [`get_candidates()`](/api/inmemorylookupkb#get_candidates), but for an
|
|
arbitrary number of mentions. The [`EntityLinker`](/api/entitylinker) component
|
|
will call `get_candidates_batch()` instead of `get_candidates()`, if the config
|
|
parameter `candidates_batch_size` is greater or equal than 1.
|
|
|
|
The default implementation of `get_candidates_batch()` executes
|
|
`get_candidates()` in a loop. We recommend implementing a more efficient way to
|
|
retrieve candidates for multiple mentions at once, if performance is of concern
|
|
to you.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.lang.en import English
|
|
> nlp = English()
|
|
> doc = nlp("Douglas Adams wrote 'The Hitchhiker's Guide to the Galaxy'.")
|
|
> candidates = kb.get_candidates((doc[0:2], doc[3:]))
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------------------------------------------- |
|
|
| `mentions` | The textual mention or alias. ~~Iterable[Span]~~ |
|
|
| **RETURNS** | An iterable of iterable with relevant `Candidate` objects. ~~Iterable[Iterable[Candidate]]~~ |
|
|
|
|
## InMemoryLookupKB.get_alias_candidates {id="get_alias_candidates",tag="method"}
|
|
|
|
Given a certain textual mention as input, retrieve a list of candidate entities
|
|
of type [`Candidate`](/api/kb#candidate).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> candidates = kb.get_alias_candidates("Douglas")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------------------- |
|
|
| `alias` | The textual mention or alias. ~~str~~ |
|
|
| **RETURNS** | The list of relevant `Candidate` objects. ~~List[Candidate]~~ |
|
|
|
|
## InMemoryLookupKB.get_vector {id="get_vector",tag="method"}
|
|
|
|
Given a certain entity ID, retrieve its pretrained entity vector.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> vector = kb.get_vector("Q42")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------ |
|
|
| `entity` | The entity ID. ~~str~~ |
|
|
| **RETURNS** | The entity vector. ~~numpy.ndarray~~ |
|
|
|
|
## InMemoryLookupKB.get_vectors {id="get_vectors",tag="method"}
|
|
|
|
Same as [`get_vector()`](/api/inmemorylookupkb#get_vector), but for an arbitrary
|
|
number of entity IDs.
|
|
|
|
The default implementation of `get_vectors()` executes `get_vector()` in a loop.
|
|
We recommend implementing a more efficient way to retrieve vectors for multiple
|
|
entities at once, if performance is of concern to you.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> vectors = kb.get_vectors(("Q42", "Q3107329"))
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------------- |
|
|
| `entities` | The entity IDs. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The entity vectors. ~~Iterable[Iterable[numpy.ndarray]]~~ |
|
|
|
|
## InMemoryLookupKB.get_prior_prob {id="get_prior_prob",tag="method"}
|
|
|
|
Given a certain entity ID and a certain textual mention, retrieve the prior
|
|
probability of the fact that the mention links to the entity ID.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> probability = kb.get_prior_prob("Q42", "Douglas")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------------------------------- |
|
|
| `entity` | The entity ID. ~~str~~ |
|
|
| `alias` | The textual mention or alias. ~~str~~ |
|
|
| **RETURNS** | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
|
|
|
|
## InMemoryLookupKB.to_disk {id="to_disk",tag="method"}
|
|
|
|
Save the current state of the knowledge base to a directory.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> kb.to_disk(path)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
|
| `exclude` | List of components to exclude. ~~Iterable[str]~~ |
|
|
|
|
## InMemoryLookupKB.from_disk {id="from_disk",tag="method"}
|
|
|
|
Restore the state of the knowledge base from a given directory. Note that the
|
|
[`Vocab`](/api/vocab) should also be the same as the one used to create the KB.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.vocab import Vocab
|
|
> vocab = Vocab().from_disk("/path/to/vocab")
|
|
> kb = FullyImplementedKB(vocab=vocab, entity_vector_length=64)
|
|
> kb.from_disk("/path/to/kb")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------------------------------------------------------- |
|
|
| `loc` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
|
| `exclude` | List of components to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The modified `KnowledgeBase` object. ~~KnowledgeBase~~ |
|