mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
bc02e86494
Mostly copying from `spacy.tests.util.get_doc`, add additional kwargs to `Doc.__init__` to initialize the most common doc/token values.
117 lines
3.6 KiB
Python
117 lines
3.6 KiB
Python
import numpy
|
|
import tempfile
|
|
import contextlib
|
|
import srsly
|
|
|
|
from spacy import Errors
|
|
from spacy.tokens import Doc, Span
|
|
from spacy.attrs import POS, TAG, HEAD, DEP, LEMMA, MORPH
|
|
|
|
from spacy.vocab import Vocab
|
|
from spacy.util import make_tempdir # noqa: F401
|
|
|
|
|
|
@contextlib.contextmanager
|
|
def make_tempfile(mode="r"):
|
|
f = tempfile.TemporaryFile(mode=mode)
|
|
yield f
|
|
f.close()
|
|
|
|
|
|
def get_doc(
|
|
vocab,
|
|
words=[],
|
|
pos=None,
|
|
heads=None,
|
|
deps=None,
|
|
tags=None,
|
|
ents=None,
|
|
lemmas=None,
|
|
morphs=None,
|
|
):
|
|
"""Create Doc object from given vocab, words and annotations."""
|
|
if heads is not None:
|
|
heads = [i + head for i, head in enumerate(heads)]
|
|
if ents is not None:
|
|
ents = [(vocab.strings[ent_type], start, end) for start, end, ent_type in ents]
|
|
return Doc(vocab, words=words, pos=pos, heads=heads, deps=deps, tags=tags,
|
|
ents=ents, lemmas=lemmas, morphs=morphs)
|
|
|
|
|
|
def get_batch(batch_size):
|
|
vocab = Vocab()
|
|
docs = []
|
|
start = 0
|
|
for size in range(1, batch_size + 1):
|
|
# Make the words numbers, so that they're distinct
|
|
# across the batch, and easy to track.
|
|
numbers = [str(i) for i in range(start, start + size)]
|
|
docs.append(Doc(vocab, words=numbers))
|
|
start += size
|
|
return docs
|
|
|
|
|
|
def get_random_doc(n_words):
|
|
vocab = Vocab()
|
|
# Make the words numbers, so that they're easy to track.
|
|
numbers = [str(i) for i in range(0, n_words)]
|
|
return Doc(vocab, words=numbers)
|
|
|
|
|
|
def apply_transition_sequence(parser, doc, sequence):
|
|
"""Perform a series of pre-specified transitions, to put the parser in a
|
|
desired state."""
|
|
for action_name in sequence:
|
|
if "-" in action_name:
|
|
move, label = action_name.split("-")
|
|
parser.add_label(label)
|
|
with parser.step_through(doc) as stepwise:
|
|
for transition in sequence:
|
|
stepwise.transition(transition)
|
|
|
|
|
|
def add_vecs_to_vocab(vocab, vectors):
|
|
"""Add list of vector tuples to given vocab. All vectors need to have the
|
|
same length. Format: [("text", [1, 2, 3])]"""
|
|
length = len(vectors[0][1])
|
|
vocab.reset_vectors(width=length)
|
|
for word, vec in vectors:
|
|
vocab.set_vector(word, vector=vec)
|
|
return vocab
|
|
|
|
|
|
def get_cosine(vec1, vec2):
|
|
"""Get cosine for two given vectors"""
|
|
return numpy.dot(vec1, vec2) / (numpy.linalg.norm(vec1) * numpy.linalg.norm(vec2))
|
|
|
|
|
|
def assert_docs_equal(doc1, doc2):
|
|
"""Compare two Doc objects and assert that they're equal. Tests for tokens,
|
|
tags, dependencies and entities."""
|
|
assert [t.orth for t in doc1] == [t.orth for t in doc2]
|
|
|
|
assert [t.pos for t in doc1] == [t.pos for t in doc2]
|
|
assert [t.tag for t in doc1] == [t.tag for t in doc2]
|
|
|
|
assert [t.head.i for t in doc1] == [t.head.i for t in doc2]
|
|
assert [t.dep for t in doc1] == [t.dep for t in doc2]
|
|
assert [t.is_sent_start for t in doc1] == [t.is_sent_start for t in doc2]
|
|
|
|
assert [t.ent_type for t in doc1] == [t.ent_type for t in doc2]
|
|
assert [t.ent_iob for t in doc1] == [t.ent_iob for t in doc2]
|
|
for ent1, ent2 in zip(doc1.ents, doc2.ents):
|
|
assert ent1.start == ent2.start
|
|
assert ent1.end == ent2.end
|
|
assert ent1.label == ent2.label
|
|
assert ent1.kb_id == ent2.kb_id
|
|
|
|
|
|
def assert_packed_msg_equal(b1, b2):
|
|
"""Assert that two packed msgpack messages are equal."""
|
|
msg1 = srsly.msgpack_loads(b1)
|
|
msg2 = srsly.msgpack_loads(b2)
|
|
assert sorted(msg1.keys()) == sorted(msg2.keys())
|
|
for (k1, v1), (k2, v2) in zip(sorted(msg1.items()), sorted(msg2.items())):
|
|
assert k1 == k2
|
|
assert v1 == v2
|