mirror of https://github.com/explosion/spaCy.git
274 lines
10 KiB
Python
274 lines
10 KiB
Python
import math
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from tqdm import tqdm
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import numpy
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from ast import literal_eval
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from pathlib import Path
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from preshed.counter import PreshCounter
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import tarfile
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import gzip
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import zipfile
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import srsly
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import warnings
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from wasabi import msg
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from ..vectors import Vectors
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from ..errors import Errors, Warnings
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from ..util import ensure_path, get_lang_class, load_model, OOV_RANK
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from ..lookups import Lookups
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try:
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import ftfy
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except ImportError:
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ftfy = None
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DEFAULT_OOV_PROB = -20
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def init_model(
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# fmt: off
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lang: ("Model language", "positional", None, str),
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output_dir: ("Model output directory", "positional", None, Path),
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freqs_loc: ("Location of words frequencies file", "option", "f", Path) = None,
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clusters_loc: ("Optional location of brown clusters data", "option", "c", str) = None,
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jsonl_loc: ("Location of JSONL-formatted attributes file", "option", "j", Path) = None,
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vectors_loc: ("Optional vectors file in Word2Vec format", "option", "v", str) = None,
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prune_vectors: ("Optional number of vectors to prune to", "option", "V", int) = -1,
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truncate_vectors: ("Optional number of vectors to truncate to when reading in vectors file", "option", "t", int) = 0,
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vectors_name: ("Optional name for the word vectors, e.g. en_core_web_lg.vectors", "option", "vn", str) = None,
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model_name: ("Optional name for the model meta", "option", "mn", str) = None,
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omit_extra_lookups: ("Don't include extra lookups in model", "flag", "OEL", bool) = False,
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base_model: ("Base model (for languages with custom tokenizers)", "option", "b", str) = None
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# fmt: on
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):
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"""
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Create a new model from raw data, like word frequencies, Brown clusters
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and word vectors. If vectors are provided in Word2Vec format, they can
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be either a .txt or zipped as a .zip or .tar.gz.
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"""
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if jsonl_loc is not None:
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if freqs_loc is not None or clusters_loc is not None:
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settings = ["-j"]
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if freqs_loc:
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settings.append("-f")
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if clusters_loc:
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settings.append("-c")
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msg.warn(
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"Incompatible arguments",
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"The -f and -c arguments are deprecated, and not compatible "
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"with the -j argument, which should specify the same "
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"information. Either merge the frequencies and clusters data "
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"into the JSONL-formatted file (recommended), or use only the "
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"-f and -c files, without the other lexical attributes.",
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)
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jsonl_loc = ensure_path(jsonl_loc)
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lex_attrs = srsly.read_jsonl(jsonl_loc)
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else:
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clusters_loc = ensure_path(clusters_loc)
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freqs_loc = ensure_path(freqs_loc)
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if freqs_loc is not None and not freqs_loc.exists():
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msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
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lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)
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with msg.loading("Creating model..."):
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nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
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# Create empty extra lexeme tables so the data from spacy-lookups-data
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# isn't loaded if these features are accessed
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if omit_extra_lookups:
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nlp.vocab.lookups_extra = Lookups()
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nlp.vocab.lookups_extra.add_table("lexeme_cluster")
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nlp.vocab.lookups_extra.add_table("lexeme_prob")
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nlp.vocab.lookups_extra.add_table("lexeme_settings")
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msg.good("Successfully created model")
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if vectors_loc is not None:
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add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name)
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vec_added = len(nlp.vocab.vectors)
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lex_added = len(nlp.vocab)
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msg.good(
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"Sucessfully compiled vocab", f"{lex_added} entries, {vec_added} vectors",
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)
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if not output_dir.exists():
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output_dir.mkdir()
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nlp.to_disk(output_dir)
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return nlp
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def open_file(loc):
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"""Handle .gz, .tar.gz or unzipped files"""
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loc = ensure_path(loc)
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if tarfile.is_tarfile(str(loc)):
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return tarfile.open(str(loc), "r:gz")
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elif loc.parts[-1].endswith("gz"):
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return (line.decode("utf8") for line in gzip.open(str(loc), "r"))
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elif loc.parts[-1].endswith("zip"):
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zip_file = zipfile.ZipFile(str(loc))
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names = zip_file.namelist()
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file_ = zip_file.open(names[0])
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return (line.decode("utf8") for line in file_)
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else:
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return loc.open("r", encoding="utf8")
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def read_attrs_from_deprecated(freqs_loc, clusters_loc):
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if freqs_loc is not None:
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with msg.loading("Counting frequencies..."):
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probs, _ = read_freqs(freqs_loc)
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msg.good("Counted frequencies")
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else:
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probs, _ = ({}, DEFAULT_OOV_PROB) # noqa: F841
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if clusters_loc:
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with msg.loading("Reading clusters..."):
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clusters = read_clusters(clusters_loc)
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msg.good("Read clusters")
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else:
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clusters = {}
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lex_attrs = []
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sorted_probs = sorted(probs.items(), key=lambda item: item[1], reverse=True)
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if len(sorted_probs):
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for i, (word, prob) in tqdm(enumerate(sorted_probs)):
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attrs = {"orth": word, "id": i, "prob": prob}
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# Decode as a little-endian string, so that we can do & 15 to get
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# the first 4 bits. See _parse_features.pyx
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if word in clusters:
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attrs["cluster"] = int(clusters[word][::-1], 2)
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else:
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attrs["cluster"] = 0
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lex_attrs.append(attrs)
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return lex_attrs
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def create_model(lang, lex_attrs, name=None, base_model=None):
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if base_model:
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nlp = load_model(base_model)
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# keep the tokenizer but remove any existing pipeline components due to
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# potentially conflicting vectors
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for pipe in nlp.pipe_names:
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nlp.remove_pipe(pipe)
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else:
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lang_class = get_lang_class(lang)
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nlp = lang_class()
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for lexeme in nlp.vocab:
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lexeme.rank = OOV_RANK
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for attrs in lex_attrs:
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if "settings" in attrs:
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continue
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lexeme = nlp.vocab[attrs["orth"]]
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lexeme.set_attrs(**attrs)
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if len(nlp.vocab):
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oov_prob = min(lex.prob for lex in nlp.vocab) - 1
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else:
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oov_prob = DEFAULT_OOV_PROB
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nlp.vocab.cfg.update({"oov_prob": oov_prob})
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if name:
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nlp.meta["name"] = name
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return nlp
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def add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, name=None):
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vectors_loc = ensure_path(vectors_loc)
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if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
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nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
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for lex in nlp.vocab:
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if lex.rank and lex.rank != OOV_RANK:
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nlp.vocab.vectors.add(lex.orth, row=lex.rank)
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else:
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if vectors_loc:
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with msg.loading(f"Reading vectors from {vectors_loc}"):
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vectors_data, vector_keys = read_vectors(vectors_loc)
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msg.good(f"Loaded vectors from {vectors_loc}")
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else:
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vectors_data, vector_keys = (None, None)
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if vector_keys is not None:
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for word in vector_keys:
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if word not in nlp.vocab:
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nlp.vocab[word]
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if vectors_data is not None:
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nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
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if name is None:
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nlp.vocab.vectors.name = f"{nlp.meta['lang']}_model.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_vectors >= 1:
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nlp.vocab.prune_vectors(prune_vectors)
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def read_vectors(vectors_loc, truncate_vectors=0):
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f = open_file(vectors_loc)
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shape = tuple(int(size) for size in next(f).split())
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if truncate_vectors >= 1:
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shape = (truncate_vectors, shape[1])
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vectors_data = numpy.zeros(shape=shape, dtype="f")
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vectors_keys = []
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for i, line in enumerate(tqdm(f)):
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line = line.rstrip()
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pieces = line.rsplit(" ", vectors_data.shape[1])
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word = pieces.pop(0)
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if len(pieces) != vectors_data.shape[1]:
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msg.fail(Errors.E094.format(line_num=i, loc=vectors_loc), exits=1)
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vectors_data[i] = numpy.asarray(pieces, dtype="f")
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vectors_keys.append(word)
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if i == truncate_vectors - 1:
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break
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return vectors_data, vectors_keys
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def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
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counts = PreshCounter()
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total = 0
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with freqs_loc.open() as f:
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for i, line in enumerate(f):
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freq, doc_freq, key = line.rstrip().split("\t", 2)
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freq = int(freq)
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counts.inc(i + 1, freq)
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total += freq
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counts.smooth()
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log_total = math.log(total)
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probs = {}
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with freqs_loc.open() as f:
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for line in tqdm(f):
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freq, doc_freq, key = line.rstrip().split("\t", 2)
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doc_freq = int(doc_freq)
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freq = int(freq)
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if doc_freq >= min_doc_freq and freq >= min_freq and len(key) < max_length:
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try:
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word = literal_eval(key)
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except SyntaxError:
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# Take odd strings literally.
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word = literal_eval(f"'{key}'")
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smooth_count = counts.smoother(int(freq))
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probs[word] = math.log(smooth_count) - log_total
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oov_prob = math.log(counts.smoother(0)) - log_total
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return probs, oov_prob
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def read_clusters(clusters_loc):
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clusters = {}
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if ftfy is None:
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warnings.warn(Warnings.W004)
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with clusters_loc.open() as f:
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for line in tqdm(f):
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try:
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cluster, word, freq = line.split()
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if ftfy is not None:
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word = ftfy.fix_text(word)
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except ValueError:
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continue
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# If the clusterer has only seen the word a few times, its
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# cluster is unreliable.
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if int(freq) >= 3:
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clusters[word] = cluster
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else:
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clusters[word] = "0"
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# Expand clusters with re-casing
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for word, cluster in list(clusters.items()):
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if word.lower() not in clusters:
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clusters[word.lower()] = cluster
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if word.title() not in clusters:
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clusters[word.title()] = cluster
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if word.upper() not in clusters:
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clusters[word.upper()] = cluster
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return clusters
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