mirror of
https://github.com/explosion/spaCy.git
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36ecba224e
* Set up CI for tests with GPU agent * Update tests for enabled GPU * Fix steps filename * Add parallel build jobs as a setting * Fix test requirements * Fix install test requirements condition * Fix pipeline models test * Reset current ops in prefer/require testing * Fix more tests * Remove separate test_models test * Fix regression 5551 * fix StaticVectors for GPU use * fix vocab tests * Fix regression test 5082 * Move azure steps to .github and reenable default pool jobs * Consolidate/rename azure steps Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
import numpy
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import tempfile
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import contextlib
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import srsly
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from spacy.tokens import Doc
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from spacy.vocab import Vocab
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from spacy.util import make_tempdir # noqa: F401
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from thinc.api import get_current_ops
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@contextlib.contextmanager
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def make_tempfile(mode="r"):
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f = tempfile.TemporaryFile(mode=mode)
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yield f
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f.close()
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def get_batch(batch_size):
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vocab = Vocab()
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docs = []
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start = 0
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for size in range(1, batch_size + 1):
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# Make the words numbers, so that they're distinct
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# across the batch, and easy to track.
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numbers = [str(i) for i in range(start, start + size)]
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docs.append(Doc(vocab, words=numbers))
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start += size
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return docs
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def get_random_doc(n_words):
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vocab = Vocab()
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# Make the words numbers, so that they're easy to track.
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numbers = [str(i) for i in range(0, n_words)]
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return Doc(vocab, words=numbers)
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def apply_transition_sequence(parser, doc, sequence):
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"""Perform a series of pre-specified transitions, to put the parser in a
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desired state."""
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for action_name in sequence:
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if "-" in action_name:
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move, label = action_name.split("-")
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parser.add_label(label)
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with parser.step_through(doc) as stepwise:
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for transition in sequence:
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stepwise.transition(transition)
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def add_vecs_to_vocab(vocab, vectors):
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"""Add list of vector tuples to given vocab. All vectors need to have the
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same length. Format: [("text", [1, 2, 3])]"""
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length = len(vectors[0][1])
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vocab.reset_vectors(width=length)
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for word, vec in vectors:
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vocab.set_vector(word, vector=vec)
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return vocab
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def get_cosine(vec1, vec2):
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"""Get cosine for two given vectors"""
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OPS = get_current_ops()
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v1 = OPS.to_numpy(OPS.asarray(vec1))
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v2 = OPS.to_numpy(OPS.asarray(vec2))
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return numpy.dot(v1, v2) / (numpy.linalg.norm(v1) * numpy.linalg.norm(v2))
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def assert_docs_equal(doc1, doc2):
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"""Compare two Doc objects and assert that they're equal. Tests for tokens,
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tags, dependencies and entities."""
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assert [t.orth for t in doc1] == [t.orth for t in doc2]
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assert [t.pos for t in doc1] == [t.pos for t in doc2]
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assert [t.tag for t in doc1] == [t.tag for t in doc2]
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assert [t.head.i for t in doc1] == [t.head.i for t in doc2]
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assert [t.dep for t in doc1] == [t.dep for t in doc2]
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assert [t.is_sent_start for t in doc1] == [t.is_sent_start for t in doc2]
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assert [t.ent_type for t in doc1] == [t.ent_type for t in doc2]
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assert [t.ent_iob for t in doc1] == [t.ent_iob for t in doc2]
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for ent1, ent2 in zip(doc1.ents, doc2.ents):
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assert ent1.start == ent2.start
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assert ent1.end == ent2.end
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assert ent1.label == ent2.label
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assert ent1.kb_id == ent2.kb_id
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def assert_packed_msg_equal(b1, b2):
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"""Assert that two packed msgpack messages are equal."""
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msg1 = srsly.msgpack_loads(b1)
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msg2 = srsly.msgpack_loads(b2)
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assert sorted(msg1.keys()) == sorted(msg2.keys())
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for (k1, v1), (k2, v2) in zip(sorted(msg1.items()), sorted(msg2.items())):
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assert k1 == k2
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assert v1 == v2
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