spaCy/spacy/tests/util.py
Daniël de Kok e2b70df012
Configure isort to use the Black profile, recursively isort the spacy module (#12721)
* Use isort with Black profile

* isort all the things

* Fix import cycles as a result of import sorting

* Add DOCBIN_ALL_ATTRS type definition

* Add isort to requirements

* Remove isort from build dependencies check

* Typo
2023-06-14 17:48:41 +02:00

105 lines
3.2 KiB
Python

import contextlib
import re
import tempfile
import numpy
import srsly
from thinc.api import get_current_ops
from spacy.tokens import Doc
from spacy.training import split_bilu_label
from spacy.util import make_tempdir # noqa: F401
from spacy.vocab import Vocab
@contextlib.contextmanager
def make_tempfile(mode="r"):
f = tempfile.TemporaryFile(mode=mode)
yield f
f.close()
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 = split_bilu_label(action_name)
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"""
OPS = get_current_ops()
v1 = OPS.to_numpy(OPS.asarray(vec1))
v2 = OPS.to_numpy(OPS.asarray(vec2))
return numpy.dot(v1, v2) / (numpy.linalg.norm(v1) * numpy.linalg.norm(v2))
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
def normalize_whitespace(s):
return re.sub(r"\s+", " ", s)