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spaCy/website/docs/api/doc.jade
Ramanan Balakrishnan fbccc8c87d
Update documentation on doc.to_array
2017-10-20 14:23:48 +05:30

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//- 💫 DOCS > API > DOC
include ../../_includes/_mixins
p A container for accessing linguistic annotations.
+h(2, "attributes") Attributes
+table(["Name", "Type", "Description"])
+row
+cell #[code mem]
+cell #[code Pool]
+cell The document's local memory heap, for all C data it owns.
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell The store of lexical types.
+row
+cell #[code user_data]
+cell -
+cell A generic storage area, for user custom data.
+row
+cell #[code is_tagged]
+cell bool
+cell
| A flag indicating that the document has been part-of-speech
| tagged.
+row
+cell #[code is_parsed]
+cell bool
+cell A flag indicating that the document has been syntactically parsed.
+row
+cell #[code sentiment]
+cell float
+cell The document's positivity/negativity score, if available.
+row
+cell #[code user_hooks]
+cell dict
+cell
| A dictionary that allows customisation of the #[code Doc]'s
| properties.
+row
+cell #[code user_token_hooks]
+cell dict
+cell
| A dictionary that allows customisation of properties of
| #[code Token] children.
+row
+cell #[code user_span_hooks]
+cell dict
+cell
| A dictionary that allows customisation of properties of
| #[code Span] children.
+h(2, "init") Doc.__init__
+tag method
p Construct a #[code Doc] object.
+aside("Note")
| The most common way to get a #[code Doc] object is via the #[code nlp]
| object. This method is usually only used for deserialization or preset
| tokenization.
+table(["Name", "Type", "Description"])
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell A storage container for lexical types.
+row
+cell #[code words]
+cell -
+cell A list of strings to add to the container.
+row
+cell #[code spaces]
+cell -
+cell
| A list of boolean values indicating whether each word has a
| subsequent space. Must have the same length as #[code words], if
| specified. Defaults to a sequence of #[code True].
+footrow
+cell return
+cell #[code Doc]
+cell The newly constructed object.
+h(2, "getitem") Doc.__getitem__
+tag method
p Get a #[code Token] object.
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
assert doc[0].text == 'Give'
assert doc[-1].text == '.'
span = doc[1:3]
assert span.text == 'it back'
+table(["Name", "Type", "Description"])
+row
+cell #[code i]
+cell int
+cell The index of the token.
+footrow
+cell return
+cell #[code Token]
+cell The token at #[code doc[i]].
p Get a #[code Span] object.
+table(["Name", "Type", "Description"])
+row
+cell #[code start_end]
+cell tuple
+cell The slice of the document to get.
+footrow
+cell return
+cell #[code Span]
+cell The span at #[code doc[start : end]].
+h(2, "iter") Doc.__iter__
+tag method
p Iterate over #[code Token] objects.
+table(["Name", "Type", "Description"])
+footrow
+cell yield
+cell #[code Token]
+cell A #[code Token] object.
+h(2, "len") Doc.__len__
+tag method
p Get the number of tokens in the document.
+table(["Name", "Type", "Description"])
+footrow
+cell return
+cell int
+cell The number of tokens in the document.
+h(2, "similarity") Doc.similarity
+tag method
p
| Make a semantic similarity estimate. The default estimate is cosine
| similarity using an average of word vectors.
+table(["Name", "Type", "Description"])
+row
+cell #[code other]
+cell -
+cell
| The object to compare with. By default, accepts #[code Doc],
| #[code Span], #[code Token] and #[code Lexeme] objects.
+footrow
+cell return
+cell float
+cell A scalar similarity score. Higher is more similar.
+h(2, "to_array") Doc.to_array
+tag method
p
| Export given token attributes to a numpy #[code ndarray].
| If #[code attr_ids] is a sequence of #[code M] attributes,
| the output array will be of shape #[code (N, M)], where #[code N]
| is the length of the #[code Doc] (in tokens). If #[code attr_ids] is
| a single attribute, the output shape will be #[code (N,)]. You can
| specify attributes by integer ID (e.g. #[code spacy.attrs.LEMMA])
| or string name (e.g. 'LEMMA' or 'lemma'). The values will be 32-bit
| integers.
+aside-code("Example").
from spacy import attrs
doc = nlp(text)
# All strings mapped to integers, for easy export to numpy
np_array = doc.to_array([attrs.LOWER, attrs.POS,
attrs.ENT_TYPE, attrs.IS_ALPHA])
np_array = doc.to_array("POS")
+table(["Name", "Type", "Description"])
+row
+cell #[code attr_ids]
+cell int or string
+cell
| A list of attributes (int IDs or string names) or
| a single attribute (int ID or string name)
+footrow
+cell return
+cell
| #[code numpy.ndarray[ndim=2, dtype='int32']] or
| #[code numpy.ndarray[ndim=1, dtype='int32']]
+cell
| The exported attributes as a 2D numpy array, with one row per
| token and one column per attribute (when #[code attr_ids] is a
| list), or as a 1D numpy array, with one item per attribute (when
| #[code attr_ids] is a single value).
+h(2, "count_by") Doc.count_by
+tag method
p Count the frequencies of a given attribute.
+table(["Name", "Type", "Description"])
+row
+cell #[code attr_id]
+cell int
+cell The attribute ID
+footrow
+cell return
+cell dict
+cell A dictionary mapping attributes to integer counts.
+h(2, "from_array") Doc.from_array
+tag method
p Load attributes from a numpy array.
+table(["Name", "Type", "Description"])
+row
+cell #[code attr_ids]
+cell ints
+cell A list of attribute ID ints.
+row
+cell #[code values]
+cell #[code numpy.ndarray[ndim=2, dtype='int32']]
+cell The attribute values to load.
+footrow
+cell return
+cell #[code None]
+cell -
+h(2, "to_bytes") Doc.to_bytes
+tag method
p Export the document contents to a binary string.
+table(["Name", "Type", "Description"])
+footrow
+cell return
+cell bytes
+cell
| A losslessly serialized copy of the #[code Doc] including all
| annotations.
+h(2, "from_bytes") Doc.from_bytes
+tag method
p Import the document contents from a binary string.
+table(["Name", "Type", "Description"])
+row
+cell #[code byte_string]
+cell bytes
+cell The string to load from.
+footrow
+cell return
+cell #[code Doc]
+cell The #[code self] variable.
+h(2, "merge") Doc.merge
+tag method
p
| Retokenize the document, such that the span at
| #[code doc.text[start_idx : end_idx]] is merged into a single token. If
| #[code start_idx] and #[code end_idx] do not mark start and end token
| boundaries, the document remains unchanged.
+table(["Name", "Type", "Description"])
+row
+cell #[code start_idx]
+cell int
+cell The character index of the start of the slice to merge.
+row
+cell #[code end_idx]
+cell int
+cell The character index after the end of the slice to merge.
+row
+cell #[code **attributes]
+cell -
+cell
| Attributes to assign to the merged token. By default,
| attributes are inherited from the syntactic root token of
| the span.
+footrow
+cell return
+cell #[code Token]
+cell
| The newly merged token, or None if the start and end
| indices did not fall at token boundaries
+h(2, "read_bytes") Doc.read_bytes
+tag staticmethod
p A static method, used to read serialized #[code Doc] objects from a file.
+aside-code("Example").
from spacy.tokens.doc import Doc
loc = 'test_serialize.bin'
with open(loc, 'wb') as file_:
file_.write(nlp(u'This is a document.').to_bytes())
file_.write(nlp(u'This is another.').to_bytes())
docs = []
with open(loc, 'rb') as file_:
for byte_string in Doc.read_bytes(file_):
docs.append(Doc(nlp.vocab).from_bytes(byte_string))
assert len(docs) == 2
+table(["Name", "Type", "Description"])
+row
+cell file
+cell buffer
+cell A binary buffer to read the serialized annotations from.
+footrow
+cell yield
+cell bytes
+cell Binary strings from with documents can be loaded.
+h(2, "text") Doc.text
+tag property
p A unicode representation of the document text.
+table(["Name", "Type", "Description"])
+footrow
+cell return
+cell unicode
+cell The original verbatim text of the document.
+h(2, "text_with_ws") Doc.text_with_ws
+tag property
p
| An alias of #[code Doc.text], provided for duck-type compatibility with
| #[code Span] and #[code Token].
+table(["Name", "Type", "Description"])
+footrow
+cell return
+cell unicode
+cell The original verbatim text of the document.
+h(2, "sents") Doc.sents
+tag property
p Iterate over the sentences in the document.
+table(["Name", "Type", "Description"])
+footrow
+cell yield
+cell #[code Span]
+cell Sentences in the document.
+h(2, "ents") Doc.ents
+tag property
p Iterate over the entities in the document.
+table(["Name", "Type", "Description"])
+footrow
+cell yield
+cell #[code Span]
+cell Entities in the document.
+h(2, "noun_chunks") Doc.noun_chunks
+tag property
p
| Iterate over the base noun phrases in the document. A base noun phrase,
| or "NP chunk", is a noun phrase that does not permit other NPs to be
| nested within it.
+table(["Name", "Type", "Description"])
+footrow
+cell yield
+cell #[code Span]
+cell Noun chunks in the document
+h(2, "vector") Doc.vector
+tag property
p
| A real-valued meaning representation. Defaults to an average of the
| token vectors.
+table(["Name", "Type", "Description"])
+footrow
+cell return
+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
+cell A 1D numpy array representing the document's semantics.
+h(2, "has_vector") Doc.has_vector
+tag property
p
| A boolean value indicating whether a word vector is associated with the
| object.
+table(["Name", "Type", "Description"])
+footrow
+cell return
+cell bool
+cell Whether the document has a vector data attached.
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