mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
379 lines
14 KiB
Python
379 lines
14 KiB
Python
from pathlib import Path
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from typing import Dict
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from ._util import app, init_cli, Arg, Opt
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from ..vectors import Vectors
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from ..errors import Errors, Warnings
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from ..language import Language
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from ..util import ensure_path, get_lang_class, load_model, OOV_RANK
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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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def must_initialize(init_path: Path, config_path: Path, overrides: Dict) -> bool:
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config = util.load_config(config_path, overrides=overrides)
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if not init_path.exists():
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return True
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elif not (init_path / "config.cfg").exists():
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return True
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else:
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init_cfg = util.load_config(init_path / "config.cfg", interpolate=True)
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if config.to_str() != init_cfg.to_str():
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return True
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else:
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return False
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def init_pipeline(config: Config, use_gpu: int=-1):
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raw_config = config
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config = raw_config.interpolate()
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if config["training"]["seed"] is not None:
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fix_random_seed(config["training"]["seed"])
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allocator = config["training"]["gpu_allocator"]
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if use_gpu >= 0 and allocator:
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set_gpu_allocator(allocator)
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# Use original config here before it's resolved to functions
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sourced_components = get_sourced_components(config)
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with show_validation_error(config_path):
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nlp = util.load_model_from_config(raw_config)
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# Resolve all training-relevant sections using the filled nlp config
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T = registry.resolve(
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config["training"],
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schema=TrainingSchema,
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validate=validate,
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)
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# TODO: It might not be 'corpora'
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corpora = registry.resolve(config["corpora"], validate=True)
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raw_text, tag_map, morph_rules, weights_data = load_from_paths(config)
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util.load_vocab_data_into_model(nlp, lookups=T["lookups"])
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if T["vectors"] is not None:
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add_vectors(nlp, T["vectors"])
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score_weights = T["score_weights"]
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optimizer = T["optimizer"]
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train_corpus = dot_to_object({"corpora": corpora}, T["train_corpus"])
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dev_corpus = dot_to_object({"corpora": corpora}, T["dev_corpus"])
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batcher = T["batcher"]
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train_logger = T["logger"]
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before_to_disk = create_before_to_disk_callback(T["before_to_disk"])
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# Components that shouldn't be updated during training
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frozen_components = T["frozen_components"]
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# Sourced components that require resume_training
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resume_components = [p for p in sourced_components if p not in frozen_components]
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msg.info(f"Pipeline: {nlp.pipe_names}")
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if resume_components:
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with nlp.select_pipes(enable=resume_components):
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msg.info(f"Resuming training for: {resume_components}")
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nlp.resume_training(sgd=optimizer)
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with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
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nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
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# Verify the config after calling 'begin_training' to ensure labels
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# are properly initialized
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verify_config(nlp)
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if tag_map:
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# Replace tag map with provided mapping
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nlp.vocab.morphology.load_tag_map(tag_map)
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if morph_rules:
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# Load morph rules
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nlp.vocab.morphology.load_morph_exceptions(morph_rules)
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# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
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if weights_data is not None:
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tok2vec_component = C["pretraining"]["component"]
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if tok2vec_component is None:
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msg.fail(
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f"To use pretrained tok2vec weights, [pretraining.component] "
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f"needs to specify the component that should load them.",
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exits=1,
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)
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layer = nlp.get_pipe(tok2vec_component).model
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tok2vec_layer = C["pretraining"]["layer"]
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if tok2vec_layer:
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layer = layer.get_ref(tok2vec_layer)
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layer.from_bytes(weights_data)
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msg.info(f"Loaded pretrained weights into component '{tok2vec_component}'")
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return nlp
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def init_vocab(
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lang: str,
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output_dir: Path,
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freqs_loc: Optional[Path] = None,
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clusters_loc: Optional[Path] = None,
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jsonl_loc: Optional[Path] = None,
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vectors_loc: Optional[Path] = None,
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prune_vectors: int = -1,
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truncate_vectors: int = 0,
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vectors_name: Optional[str] = None,
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model_name: Optional[str] = None,
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base_model: Optional[str] = None,
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silent: bool = True,
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) -> Language:
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msg = Printer(no_print=silent, pretty=not silent)
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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(msg, freqs_loc, clusters_loc)
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with msg.loading("Creating blank pipeline..."):
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nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
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msg.good("Successfully created blank pipeline")
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if vectors_loc is not None:
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add_vectors(
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msg, nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name
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)
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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: Union[str, Path]) -> IO:
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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(
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msg: Printer, freqs_loc: Optional[Path], clusters_loc: Optional[Path]
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) -> List[Dict[str, Any]]:
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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(
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lang: str,
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lex_attrs: List[Dict[str, Any]],
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name: Optional[str] = None,
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base_model: Optional[Union[str, Path]] = None,
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) -> Language:
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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(
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msg: Printer,
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nlp: Language,
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vectors_loc: Optional[Path],
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truncate_vectors: int,
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prune_vectors: int,
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name: Optional[str] = None,
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) -> 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(
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msg, vectors_loc, truncate_vectors
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)
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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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# 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_vectors >= 1:
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nlp.vocab.prune_vectors(prune_vectors)
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def read_vectors(msg: Printer, vectors_loc: Path, truncate_vectors: int):
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f = open_file(vectors_loc)
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f = ensure_shape(f)
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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 ensure_shape(lines):
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"""Ensure that the first line of the data is the vectors shape.
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If it's not, we read in the data and output the shape as the first result,
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so that the reader doesn't have to deal with the problem.
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"""
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first_line = next(lines)
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try:
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shape = tuple(int(size) for size in first_line.split())
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except ValueError:
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shape = None
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if shape is not None:
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# All good, give the data
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yield first_line
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yield from lines
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else:
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# Figure out the shape, make it the first value, and then give the
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# rest of the data.
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width = len(first_line.split()) - 1
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captured = [first_line] + list(lines)
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length = len(captured)
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yield f"{length} {width}"
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yield from captured
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def read_freqs(
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freqs_loc: Path, max_length: int = 100, min_doc_freq: int = 5, min_freq: int = 50
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):
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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: Path) -> dict:
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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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