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ebecaddb76
Previously the data and width options were one argument in Vectors, which meant you couldn't say vectors = Vectors(strings, width=300). It's better to have two keywords.
337 lines
9.1 KiB
Plaintext
337 lines
9.1 KiB
Plaintext
//- 💫 DOCS > API > VECTORS
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include ../_includes/_mixins
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p
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| Vectors data is kept in the #[code Vectors.data] attribute, which should
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| be an instance of #[code numpy.ndarray] (for CPU vectors) or
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| #[code cupy.ndarray] (for GPU vectors).
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+h(2, "init") Vectors.__init__
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+tag method
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p
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| Create a new vector store. To keep the vector table empty, pass
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| #[code width=0]. You can also create the vector table and add
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| vectors one by one, or set the vector values directly on initialisation.
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+aside-code("Example").
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from spacy.vectors import Vectors
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from spacy.strings import StringStore
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empty_vectors = Vectors(StringStore())
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vectors = Vectors([u'cat'], width=300)
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vectors[u'cat'] = numpy.random.uniform(-1, 1, (300,))
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vector_table = numpy.zeros((3, 300), dtype='f')
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vectors = Vectors(StringStore(), data=vector_table)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code strings]
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+cell #[code StringStore] or list
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+cell
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| List of strings, or a #[+api("stringstore") #[code StringStore]]
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| that maps strings to hash values, and vice versa.
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+row
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+cell #[code data]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+row
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+cell #[code width]
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+cell Number of dimensions.
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+row("foot")
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+cell returns
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+cell #[code Vectors]
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+cell The newly created object.
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+h(2, "getitem") Vectors.__getitem__
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+tag method
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p
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| Get a vector by key. If key is a string, it is hashed to an integer ID
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| using the #[code Vectors.strings] table. If the integer key is not found
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| in the table, a #[code KeyError] is raised.
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+aside-code("Example").
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vectors = Vectors(StringStore(), 300)
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vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
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cat_vector = vectors[u'cat']
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code key]
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+cell unicode / int
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+cell The key to get the vector for.
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+row
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+cell returns
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell The vector for the key.
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+h(2, "setitem") Vectors.__setitem__
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+tag method
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p
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| Set a vector for the given key. If key is a string, it is hashed to an
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| integer ID using the #[code Vectors.strings] table.
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+aside-code("Example").
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vectors = Vectors(StringStore(), 300)
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vectors[u'cat'] = numpy.random.uniform(-1, 1, (300,))
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code key]
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+cell unicode / int
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+cell The key to set the vector for.
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+row
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+cell #[code vector]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell The vector to set.
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+h(2, "iter") Vectors.__iter__
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+tag method
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p Yield vectors from the table.
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+aside-code("Example").
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vector_table = numpy.zeros((3, 300), dtype='f')
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vectors = Vectors(StringStore(), vector_table)
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for vector in vectors:
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print(vector)
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell yields
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A vector from the table.
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+h(2, "len") Vectors.__len__
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+tag method
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p Return the number of vectors that have been assigned.
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+aside-code("Example").
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vector_table = numpy.zeros((3, 300), dtype='f')
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vectors = Vectors(StringStore(), vector_table)
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assert len(vectors) == 3
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell int
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+cell The number of vectors in the data.
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+h(2, "contains") Vectors.__contains__
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+tag method
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p
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| Check whether a key has a vector entry in the table. If key is a string,
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| it is hashed to an integer ID using the #[code Vectors.strings] table.
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+aside-code("Example").
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vectors = Vectors(StringStore(), 300)
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vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
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assert u'cat' in vectors
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code key]
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+cell unicode / int
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+cell The key to check.
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+row("foot")
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+cell returns
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+cell bool
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+cell Whether the key has a vector entry.
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+h(2, "add") Vectors.add
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+tag method
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p
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| Add a key to the table, optionally setting a vector value as well. If
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| key is a string, it is hashed to an integer ID using the
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| #[code Vectors.strings] table.
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+aside-code("Example").
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vectors = Vectors(StringStore(), 300)
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vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code key]
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+cell unicode / int
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+cell The key to add.
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+row
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+cell #[code vector]
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell An optional vector to add.
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+h(2, "items") Vectors.items
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+tag method
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p Iterate over #[code (string key, vector)] pairs, in order.
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+aside-code("Example").
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vectors = Vectors(StringStore(), 300)
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vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
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for key, vector in vectors.items():
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print(key, vector)
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell yields
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+cell tuple
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+cell #[code (string key, vector)] pairs, in order.
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+h(2, "shape") Vectors.shape
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+tag property
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p
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| Get #[code (rows, dims)] tuples of number of rows and number of
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| dimensions in the vector table.
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+aside-code("Example").
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vectors = Vectors(StringStore(), 300)
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vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
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rows, dims = vectors.shape
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assert rows == 1
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assert dims == 300
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell tuple
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+cell #[code (rows, dims)] pairs.
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+h(2, "from_glove") Vectors.from_glove
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+tag method
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p
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| Load #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
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| a directory. Assumes binary format, that the vocab is in a
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| #[code vocab.txt], and that vectors are named
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| #[code vectors.{size}.[fd].bin], e.g. #[code vectors.128.f.bin] for 128d
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| float32 vectors, #[code vectors.300.d.bin] for 300d float64 (double)
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| vectors, etc. By default GloVe outputs 64-bit vectors.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code path]
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+cell unicode / #[code Path]
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+cell The path to load the GloVe vectors from.
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+h(2, "to_disk") Vectors.to_disk
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+tag method
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p Save the current state to a directory.
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+aside-code("Example").
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vectors.to_disk('/path/to/vectors')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code path]
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+cell unicode or #[code Path]
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+cell
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| A path to a directory, which will be created if it doesn't exist.
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| Paths may be either strings or #[code Path]-like objects.
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+h(2, "from_disk") Vectors.from_disk
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+tag method
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p Loads state from a directory. Modifies the object in place and returns it.
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+aside-code("Example").
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vectors = Vectors(StringStore())
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vectors.from_disk('/path/to/vectors')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code path]
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+cell unicode or #[code Path]
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+cell
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| A path to a directory. Paths may be either strings or
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| #[code Path]-like objects.
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+row("foot")
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+cell returns
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+cell #[code Vectors]
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+cell The modified #[code Vectors] object.
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+h(2, "to_bytes") Vectors.to_bytes
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+tag method
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p Serialize the current state to a binary string.
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+aside-code("Example").
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vectors_bytes = vectors.to_bytes()
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code **exclude]
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+cell -
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+cell Named attributes to prevent from being serialized.
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+row("foot")
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+cell returns
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+cell bytes
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+cell The serialized form of the #[code Vectors] object.
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+h(2, "from_bytes") Vectors.from_bytes
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+tag method
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p Load state from a binary string.
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+aside-code("Example").
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fron spacy.vectors import Vectors
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vectors_bytes = vectors.to_bytes()
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new_vectors = Vectors(StringStore())
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new_vectors.from_bytes(vectors_bytes)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code bytes_data]
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+cell bytes
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+cell The data to load from.
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+row
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+cell #[code **exclude]
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+cell -
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+cell Named attributes to prevent from being loaded.
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+row("foot")
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+cell returns
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+cell #[code Vectors]
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+cell The #[code Vectors] object.
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+h(2, "attributes") Attributes
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code data]
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+cell #[code numpy.ndarray] / #[code cupy.ndarray]
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+cell
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| Stored vectors data. #[code numpy] is used for CPU vectors,
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| #[code cupy] for GPU vectors.
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+row
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+cell #[code key2row]
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+cell dict
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+cell
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| Dictionary mapping word hashes to rows in the
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| #[code Vectors.data] table.
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+row
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+cell #[code keys]
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+cell #[code numpy.ndarray]
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+cell
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| Array keeping the keys in order, such that
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| #[code keys[vectors.key2row[key]] == key]
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