explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-11-15 22:27:12 +03:00
Code Issues Packages Projects Releases Wiki Activity
36d4767aca
spaCy/website/docs/api/vectors.mdx
Adriane Boyd 0fe43f40f1
Support registered vectors (#12492) 
* Support registered vectors

* Format

* Auto-fill [nlp] on load from config and from bytes/disk

* Only auto-fill [nlp]

* Undo all changes to Language.from_disk

* Expand BaseVectors

These methods are needed in various places for training and vector
similarity.

* isort

* More linting

* Only fill [nlp.vectors]

* Update spacy/vocab.pyx

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Revert changes to test related to auto-filling [nlp]

* Add vectors registry

* Rephrase error about vocab methods for vectors

* Switch to dummy implementation for BaseVectors.to_ops

* Add initial draft of docs

* Remove example from BaseVectors docs

* Apply suggestions from code review

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update website/docs/api/basevectors.mdx

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Fix type and lint bpemb example

* Update website/docs/api/basevectors.mdx

---------

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
2023-08-01 15:46:08 +02:00

462 lines
21 KiB
Plaintext
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
title: Vectors
teaser: Store, save and load word vectors
tag: class
source: spacy/vectors.pyx
version: 2
---
Vectors data is kept in the `Vectors.data` attribute, which should be an
instance of `numpy.ndarray` (for CPU vectors) or `cupy.ndarray` (for GPU
vectors).
As of spaCy v3.2, `Vectors` supports two types of vector tables:
- `default`: A standard vector table (as in spaCy v3.1 and earlier) where each
key is mapped to one row in the vector table. Multiple keys can be mapped to
the same vector, and not all of the rows in the table need to be assigned – so
`vectors.n_keys` may be greater or smaller than `vectors.shape[0]`.
- `floret`: Only supports vectors trained with
[floret](https://github.com/explosion/floret), an extended version of
[fastText](https://fasttext.cc) that produces compact vector tables by
combining fastText's subword ngrams with Bloom embeddings. The compact tables
are similar to the [`HashEmbed`](https://thinc.ai/docs/api-layers#hashembed)
embeddings already used in many spaCy components. Each word is represented as
the sum of one or more rows as determined by the settings related to character
ngrams and the hash table.
## Vectors.\_\_init\_\_ {id="init",tag="method"}
Create a new vector store. With the default mode, you can set the vector values
and keys directly on initialization, or supply a `shape` keyword argument to
create an empty table you can add vectors to later. In floret mode, the complete
vector data and settings must be provided on initialization and cannot be
modified later.
> #### Example
>
> ```python
> from spacy.vectors import Vectors
>
> empty_vectors = Vectors(shape=(10000, 300))
>
> data = numpy.zeros((3, 300), dtype='f')
> keys = ["cat", "dog", "rat"]
> vectors = Vectors(data=data, keys=keys)
> ```
| Name | Description |
| ----------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `strings` | The string store. A new string store is created if one is not provided. Defaults to `None`. ~~Optional[StringStore]~~ |
| `shape` | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. ~~Tuple[int, int]~~ |
| `data` | The vector data. ~~numpy.ndarray[ndim=2, dtype=float32]~~ |
| `keys` | A sequence of keys aligned with the data. ~~Iterable[Union[str, int]]~~ |
| `name` | A name to identify the vectors table. ~~str~~ |
| `mode` <Tag variant="new">3.2</Tag> | Vectors mode: `"default"` or [`"floret"`](https://github.com/explosion/floret) (default: `"default"`). ~~str~~ |
| `minn` <Tag variant="new">3.2</Tag> | The floret char ngram minn (default: `0`). ~~int~~ |
| `maxn` <Tag variant="new">3.2</Tag> | The floret char ngram maxn (default: `0`). ~~int~~ |
| `hash_count` <Tag variant="new">3.2</Tag> | The floret hash count. Supported values: 1--4 (default: `1`). ~~int~~ |
| `hash_seed` <Tag variant="new">3.2</Tag> | The floret hash seed (default: `0`). ~~int~~ |
| `bow` <Tag variant="new">3.2</Tag> | The floret BOW string (default: `"<"`). ~~str~~ |
| `eow` <Tag variant="new">3.2</Tag> | The floret EOW string (default: `">"`). ~~str~~ |
| `attr` <Tag variant="new">3.6</Tag> | The token attribute for the vector keys (default: `"ORTH"`). ~~Union[int, str]~~ |
## Vectors.\_\_getitem\_\_ {id="getitem",tag="method"}
Get a vector by key. If the key is not found in the table, a `KeyError` is
raised.
> #### Example
>
> ```python
> cat_id = nlp.vocab.strings["cat"]
> cat_vector = nlp.vocab.vectors[cat_id]
> assert cat_vector == nlp.vocab["cat"].vector
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------- |
| `key` | The key to get the vector for. ~~Union[int, str]~~ |
| **RETURNS** | The vector for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vectors.\_\_setitem\_\_ {id="setitem",tag="method"}
Set a vector for the given key. Not supported for `floret` mode.
> #### Example
>
> ```python
> cat_id = nlp.vocab.strings["cat"]
> vector = numpy.random.uniform(-1, 1, (300,))
> nlp.vocab.vectors[cat_id] = vector
> ```
| Name | Description |
| -------- | ----------------------------------------------------------- |
| `key` | The key to set the vector for. ~~int~~ |
| `vector` | The vector to set. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vectors.\_\_iter\_\_ {id="iter",tag="method"}
Iterate over the keys in the table. In `floret` mode, the keys table is not
used.
> #### Example
>
> ```python
> for key in nlp.vocab.vectors:
> print(key, nlp.vocab.strings[key])
> ```
| Name | Description |
| ---------- | --------------------------- |
| **YIELDS** | A key in the table. ~~int~~ |
## Vectors.\_\_len\_\_ {id="len",tag="method"}
Return the number of vectors in the table.
> #### Example
>
> ```python
> vectors = Vectors(shape=(3, 300))
> assert len(vectors) == 3
> ```
| Name | Description |
| ----------- | ------------------------------------------- |
| **RETURNS** | The number of vectors in the table. ~~int~~ |
## Vectors.\_\_contains\_\_ {id="contains",tag="method"}
Check whether a key has been mapped to a vector entry in the table. In `floret`
mode, returns `True` for all keys.
> #### Example
>
> ```python
> cat_id = nlp.vocab.strings["cat"]
> nlp.vocab.vectors.add(cat_id, numpy.random.uniform(-1, 1, (300,)))
> assert cat_id in vectors
> ```
| Name | Description |
| ----------- | -------------------------------------------- |
| `key` | The key to check. ~~int~~ |
| **RETURNS** | Whether the key has a vector entry. ~~bool~~ |
## Vectors.add {id="add",tag="method"}
Add a key to the table, optionally setting a vector value as well. Keys can be
mapped to an existing vector by setting `row`, or a new vector can be added. Not
supported for `floret` mode.
> #### Example
>
> ```python
> vector = numpy.random.uniform(-1, 1, (300,))
> cat_id = nlp.vocab.strings["cat"]
> nlp.vocab.vectors.add(cat_id, vector=vector)
> nlp.vocab.vectors.add("dog", row=0)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------- |
| `key` | The key to add. ~~Union[str, int]~~ |
| _keyword-only_ | |
| `vector` | An optional vector to add for the key. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
| `row` | An optional row number of a vector to map the key to. ~~int~~ |
| **RETURNS** | The row the vector was added to. ~~int~~ |
## Vectors.resize {id="resize",tag="method"}
Resize the underlying vectors array. If `inplace=True`, the memory is
reallocated. This may cause other references to the data to become invalid, so
only use `inplace=True` if you're sure that's what you want. If the number of
vectors is reduced, keys mapped to rows that have been deleted are removed.
These removed items are returned as a list of `(key, row)` tuples. Not supported
for `floret` mode.
> #### Example
>
> ```python
> removed = nlp.vocab.vectors.resize((10000, 300))
> ```
| Name | Description |
| ----------- | ---------------------------------------------------------------------------------------- |
| `shape` | A `(rows, dims)` tuple describing the number of rows and dimensions. ~~Tuple[int, int]~~ |
| `inplace` | Reallocate the memory. ~~bool~~ |
| **RETURNS** | The removed items as a list of `(key, row)` tuples. ~~List[Tuple[int, int]]~~ |
## Vectors.keys {id="keys",tag="method"}
A sequence of the keys in the table. In `floret` mode, the keys table is not
used.
> #### Example
>
> ```python
> for key in nlp.vocab.vectors.keys():
> print(key, nlp.vocab.strings[key])
> ```
| Name | Description |
| ----------- | --------------------------- |
| **RETURNS** | The keys. ~~Iterable[int]~~ |
## Vectors.values {id="values",tag="method"}
Iterate over vectors that have been assigned to at least one key. Note that some
vectors may be unassigned, so the number of vectors returned may be less than
the length of the vectors table. In `floret` mode, the keys table is not used.
> #### Example
>
> ```python
> for vector in nlp.vocab.vectors.values():
> print(vector)
> ```
| Name | Description |
| ---------- | --------------------------------------------------------------- |
| **YIELDS** | A vector in the table. ~~numpy.ndarray[ndim=1, dtype=float32]~~ |
## Vectors.items {id="items",tag="method"}
Iterate over `(key, vector)` pairs, in order. In `floret` mode, the keys table
is empty.
> #### Example
>
> ```python
> for key, vector in nlp.vocab.vectors.items():
> print(key, nlp.vocab.strings[key], vector)
> ```
| Name | Description |
| ---------- | ------------------------------------------------------------------------------------- |
| **YIELDS** | `(key, vector)` pairs, in order. ~~Tuple[int, numpy.ndarray[ndim=1, dtype=float32]]~~ |
## Vectors.find {id="find",tag="method"}
Look up one or more keys by row, or vice versa. Not supported for `floret` mode.
> #### Example
>
> ```python
> row = nlp.vocab.vectors.find(key="cat")
> rows = nlp.vocab.vectors.find(keys=["cat", "dog"])
> key = nlp.vocab.vectors.find(row=256)
> keys = nlp.vocab.vectors.find(rows=[18, 256, 985])
> ```
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `key` | Find the row that the given key points to. Returns int, `-1` if missing. ~~Union[str, int]~~ |
| `keys` | Find rows that the keys point to. Returns `numpy.ndarray`. ~~Iterable[Union[str, int]]~~ |
| `row` | Find the first key that points to the row. Returns integer. ~~int~~ |
| `rows` | Find the keys that point to the rows. Returns `numpy.ndarray`. ~~Iterable[int]~~ |
| **RETURNS** | The requested key, keys, row or rows. ~~Union[int, numpy.ndarray[ndim=1, dtype=float32]]~~ |
## Vectors.shape {id="shape",tag="property"}
Get `(rows, dims)` tuples of number of rows and number of dimensions in the
vector table.
> #### Example
>
> ```python
> vectors = Vectors(shape(1, 300))
> vectors.add("cat", numpy.random.uniform(-1, 1, (300,)))
> rows, dims = vectors.shape
> assert rows == 1
> assert dims == 300
> ```
| Name | Description |
| ----------- | ------------------------------------------ |
| **RETURNS** | A `(rows, dims)` pair. ~~Tuple[int, int]~~ |
## Vectors.size {id="size",tag="property"}
The vector size, i.e. `rows * dims`.
> #### Example
>
> ```python
> vectors = Vectors(shape=(500, 300))
> assert vectors.size == 150000
> ```
| Name | Description |
| ----------- | ------------------------ |
| **RETURNS** | The vector size. ~~int~~ |
## Vectors.is_full {id="is_full",tag="property"}
Whether the vectors table is full and no slots are available for new keys. If a
table is full, it can be resized using [`Vectors.resize`](/api/vectors#resize).
In `floret` mode, the table is always full and cannot be resized.
> #### Example
>
> ```python
> vectors = Vectors(shape=(1, 300))
> vectors.add("cat", numpy.random.uniform(-1, 1, (300,)))
> assert vectors.is_full
> ```
| Name | Description |
| ----------- | ------------------------------------------- |
| **RETURNS** | Whether the vectors table is full. ~~bool~~ |
## Vectors.n_keys {id="n_keys",tag="property"}
Get the number of keys in the table. Note that this is the number of _all_ keys,
not just unique vectors. If several keys are mapped to the same vectors, they
will be counted individually. In `floret` mode, the keys table is not used.
> #### Example
>
> ```python
> vectors = Vectors(shape=(10, 300))
> assert len(vectors) == 10
> assert vectors.n_keys == 0
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------------------------- |
| **RETURNS** | The number of all keys in the table. Returns `-1` for floret vectors. ~~int~~ |
## Vectors.most_similar {id="most_similar",tag="method"}
For each of the given vectors, find the `n` most similar entries to it by
cosine. Queries are by vector. Results are returned as a
`(keys, best_rows, scores)` tuple. If `queries` is large, the calculations are
performed in chunks to avoid consuming too much memory. You can set the
`batch_size` to control the size/space trade-off during the calculations. Not
supported for `floret` mode.
> #### Example
>
> ```python
> queries = numpy.asarray([numpy.random.uniform(-1, 1, (300,))])
> most_similar = nlp.vocab.vectors.most_similar(queries, n=10)
> ```
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------------------------------- |
| `queries` | An array with one or more vectors. ~~numpy.ndarray~~ |
| _keyword-only_ | |
| `batch_size` | The batch size to use. Default to `1024`. ~~int~~ |
| `n` | The number of entries to return for each query. Defaults to `1`. ~~int~~ |
| `sort` | Whether to sort the entries returned by score. Defaults to `True`. ~~bool~~ |
| **RETURNS** | The most similar entries as a `(keys, best_rows, scores)` tuple. ~~Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]~~ |
## Vectors.get_batch {id="get_batch",tag="method",version="3.2"}
Get the vectors for the provided keys efficiently as a batch.
> #### Example
>
> ```python
> words = ["cat", "dog"]
> vectors = nlp.vocab.vectors.get_batch(words)
> ```
| Name | Description |
| ------ | --------------------------------------- |
| `keys` | The keys. ~~Iterable[Union[int, str]]~~ |
## Vectors.to_ops {id="to_ops",tag="method"}
Change the embedding matrix to use different Thinc ops.
> #### Example
>
> ```python
> from thinc.api import NumpyOps
>
> vectors.to_ops(NumpyOps())
>
> ```
| Name | Description |
| ----- | -------------------------------------------------------- |
| `ops` | The Thinc ops to switch the embedding matrix to. ~~Ops~~ |
## Vectors.to_disk {id="to_disk",tag="method"}
Save the current state to a directory.
> #### Example
>
> ```python
> vectors.to_disk("/path/to/vectors")
>
> ```
| 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]~~ |
## Vectors.from_disk {id="from_disk",tag="method"}
Loads state from a directory. Modifies the object in place and returns it.
> #### Example
>
> ```python
> vectors = Vectors(StringStore())
> vectors.from_disk("/path/to/vectors")
> ```
| Name | Description |
| ----------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| **RETURNS** | The modified `Vectors` object. ~~Vectors~~ |
## Vectors.to_bytes {id="to_bytes",tag="method"}
Serialize the current state to a binary string.
> #### Example
>
> ```python
> vectors_bytes = vectors.to_bytes()
> ```
| Name | Description |
| ----------- | ------------------------------------------------------ |
| **RETURNS** | The serialized form of the `Vectors` object. ~~bytes~~ |
## Vectors.from_bytes {id="from_bytes",tag="method"}
Load state from a binary string.
> #### Example
>
> ```python
> from spacy.vectors import Vectors
> vectors_bytes = vectors.to_bytes()
> new_vectors = Vectors(StringStore())
> new_vectors.from_bytes(vectors_bytes)
> ```
| Name | Description |
| ----------- | --------------------------------- |
| `data` | The data to load from. ~~bytes~~ |
| **RETURNS** | The `Vectors` object. ~~Vectors~~ |
## Attributes {id="attributes"}
| Name | Description |
| ----------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `data` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
| `key2row` | Dictionary mapping word hashes to rows in the `Vectors.data` table. ~~Dict[int, int]~~ |
| `keys` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
| `attr` <Tag variant="new">3.6</Tag> | The token attribute for the vector keys. ~~int~~ |
Reference in New Issue View Git Blame Copy Permalink