spaCy/website/docs/api/vectors.md

title	teaser	tag	source	new
Vectors	Store, save and load word vectors	class	spacy/vectors.pyx	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). 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].

Vectors.__init__

Create a new vector store. 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.

Example

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	Type	Description
data	ndarray[ndim=1, dtype='float32']	The vector data.
keys	iterable	A sequence of keys aligned with the data.
shape	tuple	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.
name	str	A name to identify the vectors table.
RETURNS	Vectors	The newly created object.

Vectors.__getitem__

Get a vector by key. If the key is not found in the table, a KeyError is raised.

Example

cat_id = nlp.vocab.strings["cat"]
cat_vector = nlp.vocab.vectors[cat_id]
assert cat_vector == nlp.vocab["cat"].vector

Name	Type	Description
key	int	The key to get the vector for.
returns	ndarray[ndim=1, dtype='float32']	The vector for the key.

Vectors.__setitem__

Set a vector for the given key.

Example

cat_id = nlp.vocab.strings["cat"]
vector = numpy.random.uniform(-1, 1, (300,))
nlp.vocab.vectors[cat_id] = vector

Name	Type	Description
key	int	The key to set the vector for.
vector	ndarray[ndim=1, dtype='float32']	The vector to set.

Vectors.__iter__

Iterate over the keys in the table.

Example

for key in nlp.vocab.vectors:
   print(key, nlp.vocab.strings[key])

Name	Type	Description
YIELDS	int	A key in the table.

Vectors.__len__

Return the number of vectors in the table.

Example

vectors = Vectors(shape=(3, 300))
assert len(vectors) == 3

Name	Type	Description
RETURNS	int	The number of vectors in the table.

Vectors.__contains__

Check whether a key has been mapped to a vector entry in the table.

Example

cat_id = nlp.vocab.strings["cat"]
nlp.vectors.add(cat_id, numpy.random.uniform(-1, 1, (300,)))
assert cat_id in vectors

Name	Type	Description
key	int	The key to check.
RETURNS	bool	Whether the key has a vector entry.

Vectors.add

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. When adding unicode keys, keep in mind that the Vectors class itself has no StringStore, so you have to store the hash-to-string mapping separately. If you need to manage the strings, you should use the Vectors via the Vocab class, e.g. vocab.vectors.

Example

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	Type	Description
key	str / int	The key to add.
vector	ndarray[ndim=1, dtype='float32']	An optional vector to add for the key.
row	int	An optional row number of a vector to map the key to.
RETURNS	int	The row the vector was added to.

Vectors.resize

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.

Example

removed = nlp.vocab.vectors.resize((10000, 300))

Name	Type	Description
shape	tuple	A (rows, dims) tuple describing the number of rows and dimensions.
inplace	bool	Reallocate the memory.
RETURNS	list	The removed items as a list of (key, row) tuples.

Vectors.keys

A sequence of the keys in the table.

Example

for key in nlp.vocab.vectors.keys():
    print(key, nlp.vocab.strings[key])

Name	Type	Description
RETURNS	iterable	The keys.

Vectors.values

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.

Example

for vector in nlp.vocab.vectors.values():
    print(vector)

Name	Type	Description
YIELDS	ndarray[ndim=1, dtype='float32']	A vector in the table.

Vectors.items

Iterate over (key, vector) pairs, in order.

Example

for key, vector in nlp.vocab.vectors.items():
   print(key, nlp.vocab.strings[key], vector)

Name	Type	Description
YIELDS	tuple	(key, vector) pairs, in order.

Vectors.find

Look up one or more keys by row, or vice versa.

Example

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	Type	Description
key	str / int	Find the row that the given key points to. Returns int, -1 if missing.
keys	iterable	Find rows that the keys point to. Returns ndarray.
row	int	Find the first key that points to the row. Returns int.
rows	iterable	Find the keys that point to the rows. Returns ndarray.
RETURNS	The requested key, keys, row or rows.

Vectors.shape

Get (rows, dims) tuples of number of rows and number of dimensions in the vector table.

Example

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	Type	Description
RETURNS	tuple	A (rows, dims) pair.

Vectors.size

The vector size, i.e. rows * dims.

Example

vectors = Vectors(shape=(500, 300))
assert vectors.size == 150000

Name	Type	Description
RETURNS	int	The vector size.

Vectors.is_full

Whether the vectors table is full and has no slots are available for new keys. If a table is full, it can be resized using Vectors.resize.

Example

vectors = Vectors(shape=(1, 300))
vectors.add("cat", numpy.random.uniform(-1, 1, (300,)))
assert vectors.is_full

Name	Type	Description
RETURNS	bool	Whether the vectors table is full.

Vectors.n_keys

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 are mapped to the same vectors, they will be counted individually.

Example

vectors = Vectors(shape=(10, 300))
assert len(vectors) == 10
assert vectors.n_keys == 0

Name	Type	Description
RETURNS	int	The number of all keys in the table.

Vectors.most_similar

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.

Example

queries = numpy.asarray([numpy.random.uniform(-1, 1, (300,))])
most_similar = nlp.vectors.most_similar(queries, n=10)

Name	Type	Description
queries	ndarray	An array with one or more vectors.
batch_size	int	The batch size to use. Default to 1024.
n	int	The number of entries to return for each query. Defaults to 1.
sort	bool	Whether to sort the entries returned by score. Defaults to True.
RETURNS	tuple	The most similar entries as a (keys, best_rows, scores) tuple.

Vectors.to_disk

Save the current state to a directory.

Example

vectors.to_disk("/path/to/vectors")

Name	Type	Description
path	str / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.

Vectors.from_disk

Loads state from a directory. Modifies the object in place and returns it.

Example

vectors = Vectors(StringStore())
vectors.from_disk("/path/to/vectors")

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
RETURNS	Vectors	The modified Vectors object.

Vectors.to_bytes

Serialize the current state to a binary string.

Example

vectors_bytes = vectors.to_bytes()

Name	Type	Description
RETURNS	bytes	The serialized form of the Vectors object.

Vectors.from_bytes

Load state from a binary string.

Example

fron spacy.vectors import Vectors
vectors_bytes = vectors.to_bytes()
new_vectors = Vectors(StringStore())
new_vectors.from_bytes(vectors_bytes)

Name	Type	Description
data	bytes	The data to load from.
RETURNS	Vectors	The Vectors object.

Attributes

Name	Type	Description
data	ndarray[ndim=1, dtype='float32']	Stored vectors data. numpy is used for CPU vectors, cupy for GPU vectors.
key2row	dict	Dictionary mapping word hashes to rows in the Vectors.data table.
keys	ndarray[ndim=1, dtype='float32']	Array keeping the keys in order, such that keys[vectors.key2row[key]] == key.