mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-10 19:57:17 +03:00
Remove names for vectors (#12243)
* Remove names for vectors Named vectors are basically a carry-over from v2 and aren't used for anything. * Format
This commit is contained in:
Adriane Boyd
GitHub
parent
5089efa2d0
commit
cf85b81f34
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@ -21,7 +21,6 @@ def init_vectors_cli(
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prune: int = Opt(-1, "--prune", "-p", help="Optional number of vectors to prune to"),
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truncate: int = Opt(0, "--truncate", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
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mode: str = Opt("default", "--mode", "-m", help="Vectors mode: default or floret"),
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name: Optional[str] = Opt(None, "--name", "-n", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
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verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
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jsonl_loc: Optional[Path] = Opt(None, "--lexemes-jsonl", "-j", help="Location of JSONL-formatted attributes file", hidden=True),
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# fmt: on
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@ -44,7 +43,6 @@ def init_vectors_cli(
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vectors_loc,
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truncate=truncate,
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prune=prune,
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name=name,
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mode=mode,
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)
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msg.good(f"Successfully converted {len(nlp.vocab.vectors)} vectors")
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@ -174,8 +174,7 @@ class Language:
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if not isinstance(vocab, Vocab) and vocab is not True:
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raise ValueError(Errors.E918.format(vocab=vocab, vocab_type=type(Vocab)))
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if vocab is True:
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vectors_name = meta.get("vectors", {}).get("name")
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vocab = create_vocab(self.lang, self.Defaults, vectors_name=vectors_name)
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vocab = create_vocab(self.lang, self.Defaults)
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else:
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if (self.lang and vocab.lang) and (self.lang != vocab.lang):
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raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang))
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@ -229,7 +228,6 @@ class Language:
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"width": self.vocab.vectors_length,
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"vectors": len(self.vocab.vectors),
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"keys": self.vocab.vectors.n_keys,
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"name": self.vocab.vectors.name,
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"mode": self.vocab.vectors.mode,
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}
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self._meta["labels"] = dict(self.pipe_labels)
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@ -2197,9 +2195,6 @@ class Language:
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if path.exists():
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data = srsly.read_json(path)
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self.meta.update(data)
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# self.meta always overrides meta["vectors"] with the metadata
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# from self.vocab.vectors, so set the name directly
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self.vocab.vectors.name = data.get("vectors", {}).get("name")
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def deserialize_vocab(path: Path) -> None:
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if path.exists():
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@ -2268,9 +2263,6 @@ class Language:
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def deserialize_meta(b):
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data = srsly.json_loads(b)
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self.meta.update(data)
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# self.meta always overrides meta["vectors"] with the metadata
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# from self.vocab.vectors, so set the name directly
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self.vocab.vectors.name = data.get("vectors", {}).get("name")
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deserializers: Dict[str, Callable[[bytes], Any]] = {}
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deserializers["config.cfg"] = lambda b: self.config.from_bytes(
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@ -181,7 +181,7 @@ def test_issue4042_bug2():
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@pytest.mark.issue(4725)
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def test_issue4725_1():
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"""Ensure the pickling of the NER goes well"""
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vocab = Vocab(vectors_name="test_vocab_add_vector")
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vocab = Vocab()
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nlp = English(vocab=vocab)
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config = {
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"update_with_oracle_cut_size": 111,
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@ -84,7 +84,7 @@ def test_issue1539():
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@pytest.mark.issue(1807)
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def test_issue1807():
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"""Test vocab.set_vector also adds the word to the vocab."""
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vocab = Vocab(vectors_name="test_issue1807")
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vocab = Vocab()
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assert "hello" not in vocab
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vocab.set_vector("hello", numpy.ones((50,), dtype="f"))
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assert "hello" in vocab
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@ -94,13 +94,12 @@ def test_issue1807():
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def test_issue2871():
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"""Test that vectors recover the correct key for spaCy reserved words."""
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words = ["dog", "cat", "SUFFIX"]
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vocab = Vocab(vectors_name="test_issue2871")
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vocab = Vocab()
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vocab.vectors.resize(shape=(3, 10))
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vector_data = numpy.zeros((3, 10), dtype="f")
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for word in words:
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_ = vocab[word] # noqa: F841
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vocab.set_vector(word, vector_data[0])
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vocab.vectors.name = "dummy_vectors"
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assert vocab["dog"].rank == 0
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assert vocab["cat"].rank == 1
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assert vocab["SUFFIX"].rank == 2
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@ -125,7 +124,7 @@ def test_issue4725_2():
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# ensures that this runs correctly and doesn't hang or crash because of the global vectors
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# if it does crash, it's usually because of calling 'spawn' for multiprocessing (e.g. on Windows),
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# or because of issues with pickling the NER (cf test_issue4725_1)
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vocab = Vocab(vectors_name="test_vocab_add_vector")
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vocab = Vocab()
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data = numpy.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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@ -340,7 +339,7 @@ def test_vectors_doc_doc_similarity(vocab, text1, text2):
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def test_vocab_add_vector():
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vocab = Vocab(vectors_name="test_vocab_add_vector")
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vocab = Vocab()
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data = OPS.xp.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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@ -356,7 +355,7 @@ def test_vocab_add_vector():
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def test_vocab_prune_vectors():
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vocab = Vocab(vectors_name="test_vocab_prune_vectors")
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vocab = Vocab()
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_ = vocab["cat"] # noqa: F841
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_ = vocab["dog"] # noqa: F841
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_ = vocab["kitten"] # noqa: F841
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@ -405,7 +404,7 @@ def test_vectors_serialize():
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def test_vector_is_oov():
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vocab = Vocab(vectors_name="test_vocab_is_oov")
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vocab = Vocab()
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data = OPS.xp.ndarray((5, 3), dtype="f")
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data[0] = 1.0
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data[1] = 2.0
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@ -202,7 +202,6 @@ def convert_vectors(
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*,
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truncate: int,
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prune: int,
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name: Optional[str] = None,
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mode: str = VectorsMode.default,
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) -> None:
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vectors_loc = ensure_path(vectors_loc)
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@ -241,12 +240,6 @@ def convert_vectors(
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strings=nlp.vocab.strings, data=vectors_data, keys=vector_keys
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)
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nlp.vocab.deduplicate_vectors()
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if name is None:
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# TODO: Is this correct? Does this matter?
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nlp.vocab.vectors.name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
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else:
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nlp.vocab.vectors.name = name
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nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
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if prune >= 1 and mode != VectorsMode.floret:
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nlp.vocab.prune_vectors(prune)
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@ -52,7 +52,6 @@ cdef class Vectors:
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DOCS: https://spacy.io/api/vectors
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"""
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cdef public object strings
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cdef public object name
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cdef readonly object mode
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cdef public object data
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cdef public object key2row
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@ -64,14 +63,13 @@ cdef class Vectors:
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cdef readonly unicode bow
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cdef readonly unicode eow
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def __init__(self, *, strings=None, shape=None, data=None, keys=None, name=None, mode=Mode.default, minn=0, maxn=0, hash_count=1, hash_seed=0, bow="<", eow=">"):
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def __init__(self, *, strings=None, shape=None, data=None, keys=None, mode=Mode.default, minn=0, maxn=0, hash_count=1, hash_seed=0, bow="<", eow=">"):
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"""Create a new vector store.
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strings (StringStore): The string store.
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shape (tuple): Size of the table, as (# entries, # columns)
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data (numpy.ndarray or cupy.ndarray): The vector data.
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keys (iterable): A sequence of keys, aligned with the data.
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name (str): A name to identify the vectors table.
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mode (str): Vectors mode: "default" or "floret" (default: "default").
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minn (int): The floret char ngram minn (default: 0).
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maxn (int): The floret char ngram maxn (default: 0).
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@ -85,7 +83,6 @@ cdef class Vectors:
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self.strings = strings
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if self.strings is None:
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self.strings = StringStore()
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self.name = name
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if mode not in Mode.values():
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raise ValueError(
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Errors.E202.format(
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@ -11,7 +11,8 @@ from .vectors import Vectors
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from pathlib import Path
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def create_vocab(
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lang: Optional[str], defaults: Any, vectors_name: Optional[str] = ...
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lang: Optional[str],
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defaults: Any,
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) -> Vocab: ...
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class Vocab:
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@ -28,7 +29,6 @@ class Vocab:
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strings: Optional[Union[List[str], StringStore]] = ...,
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lookups: Optional[Lookups] = ...,
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oov_prob: float = ...,
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vectors_name: Optional[str] = ...,
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writing_system: Dict[str, Any] = ...,
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get_noun_chunks: Optional[Callable[[Union[Doc, Span]], Iterator[Span]]] = ...,
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) -> None: ...
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@ -23,7 +23,7 @@ from .lang.norm_exceptions import BASE_NORMS
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from .lang.lex_attrs import LEX_ATTRS, is_stop, get_lang
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def create_vocab(lang, defaults, vectors_name=None):
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def create_vocab(lang, defaults):
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# If the spacy-lookups-data package is installed, we pre-populate the lookups
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# with lexeme data, if available
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lex_attrs = {**LEX_ATTRS, **defaults.lex_attr_getters}
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@ -39,7 +39,6 @@ def create_vocab(lang, defaults, vectors_name=None):
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lex_attr_getters=lex_attrs,
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writing_system=defaults.writing_system,
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get_noun_chunks=defaults.syntax_iterators.get("noun_chunks"),
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vectors_name=vectors_name,
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)
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@ -51,8 +50,8 @@ cdef class Vocab:
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DOCS: https://spacy.io/api/vocab
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"""
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def __init__(self, lex_attr_getters=None, strings=tuple(), lookups=None,
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oov_prob=-20., vectors_name=None, writing_system={},
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get_noun_chunks=None, **deprecated_kwargs):
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oov_prob=-20., writing_system={}, get_noun_chunks=None,
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**deprecated_kwargs):
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"""Create the vocabulary.
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lex_attr_getters (dict): A dictionary mapping attribute IDs to
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@ -61,7 +60,6 @@ cdef class Vocab:
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vice versa.
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lookups (Lookups): Container for large lookup tables and dictionaries.
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oov_prob (float): Default OOV probability.
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vectors_name (str): Optional name to identify the vectors table.
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get_noun_chunks (Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]):
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A function that yields base noun phrases used for Doc.noun_chunks.
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"""
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@ -78,7 +76,7 @@ cdef class Vocab:
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_ = self[string]
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self.lex_attr_getters = lex_attr_getters
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self.morphology = Morphology(self.strings)
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self.vectors = Vectors(strings=self.strings, name=vectors_name)
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self.vectors = Vectors(strings=self.strings)
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self.lookups = lookups
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self.writing_system = writing_system
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self.get_noun_chunks = get_noun_chunks
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@ -308,7 +306,7 @@ cdef class Vocab:
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for key, row in self.vectors.key2row.items()
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}
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# replace vectors with deduplicated version
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self.vectors = Vectors(strings=self.strings, data=data, name=self.vectors.name)
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self.vectors = Vectors(strings=self.strings, data=data)
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for key, row in key2row.items():
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self.vectors.add(key, row=row)
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@ -358,7 +356,7 @@ cdef class Vocab:
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keys = xp.asarray([key for (prob, i, key) in priority], dtype="uint64")
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keep = xp.ascontiguousarray(self.vectors.data[indices[:nr_row]])
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toss = xp.ascontiguousarray(self.vectors.data[indices[nr_row:]])
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self.vectors = Vectors(strings=self.strings, data=keep, keys=keys[:nr_row], name=self.vectors.name)
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self.vectors = Vectors(strings=self.strings, data=keep, keys=keys[:nr_row])
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syn_keys, syn_rows, scores = self.vectors.most_similar(toss, batch_size=batch_size)
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syn_keys = ops.to_numpy(syn_keys)
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remap = {}
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@ -201,7 +201,7 @@ This functionality was previously available as part of the command `init-model`.
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</Infobox>
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```bash
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$ python -m spacy init vectors [lang] [vectors_loc] [output_dir] [--prune] [--truncate] [--name] [--verbose]
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$ python -m spacy init vectors [lang] [vectors_loc] [output_dir] [--prune] [--truncate] [--verbose]
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```
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| Name | Description |
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@ -212,7 +212,6 @@ $ python -m spacy init vectors [lang] [vectors_loc] [output_dir] [--prune] [--tr
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| `--truncate`, `-t` | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. ~~int (option)~~ |
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| `--prune`, `-p` | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. ~~int (option)~~ |
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| `--mode`, `-m` | Vectors mode: `default` or [`floret`](https://github.com/explosion/floret). Defaults to `default`. ~~Optional[str] \(option)~~ |
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| `--name`, `-n` | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. ~~Optional[str] \(option)~~ |
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| `--verbose`, `-V` | Print additional information and explanations. ~~bool (flag)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| **CREATES** | A spaCy pipeline directory containing the vocab and vectors. |
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@ -52,7 +52,6 @@ modified later.
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| `shape` | Size of the table as `(n_entries, n_columns)`, the number of entries and number of columns. Not required if you're initializing the object with `data` and `keys`. ~~Tuple[int, int]~~ |
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| `data` | The vector data. ~~numpy.ndarray[ndim=2, dtype=float32]~~ |
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| `keys` | A sequence of keys aligned with the data. ~~Iterable[Union[str, int]]~~ |
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| `name` | A name to identify the vectors table. ~~str~~ |
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| `mode` <Tag variant="new">3.2</Tag> | Vectors mode: `"default"` or [`"floret"`](https://github.com/explosion/floret) (default: `"default"`). ~~str~~ |
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| `minn` <Tag variant="new">3.2</Tag> | The floret char ngram minn (default: `0`). ~~int~~ |
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| `maxn` <Tag variant="new">3.2</Tag> | The floret char ngram maxn (default: `0`). ~~int~~ |
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@ -27,7 +27,6 @@ Create the vocabulary.
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| `strings` | A [`StringStore`](/api/stringstore) that maps strings to hash values, and vice versa, or a list of strings. ~~Union[List[str], StringStore]~~ |
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| `lookups` | A [`Lookups`](/api/lookups) that stores the `lexeme_norm` and other large lookup tables. Defaults to `None`. ~~Optional[Lookups]~~ |
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| `oov_prob` | The default OOV probability. Defaults to `-20.0`. ~~float~~ |
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| `vectors_name` | A name to identify the vectors table. ~~str~~ |
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| `writing_system` | A dictionary describing the language's writing system. Typically provided by [`Language.Defaults`](/api/language#defaults). ~~Dict[str, Any]~~ |
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| `get_noun_chunks` | A function that yields base noun phrases used for [`Doc.noun_chunks`](/api/doc#noun_chunks). ~~Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]~~ |
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