mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-29 11:26:28 +03:00
144 lines
4.7 KiB
Python
144 lines
4.7 KiB
Python
import tqdm
|
|
import srsly
|
|
|
|
from itertools import chain
|
|
from pathlib import Path
|
|
from typing import Optional, List, Iterable, cast, Union
|
|
|
|
from wasabi import msg
|
|
|
|
from ._util import app, Arg, Opt, setup_gpu, import_code, walk_directory
|
|
|
|
from ..tokens import Doc, DocBin
|
|
from ..vocab import Vocab
|
|
from ..util import ensure_path, load_model
|
|
|
|
|
|
path_help = """Location of the documents to predict on.
|
|
Can be a single file in .spacy format or a .jsonl file.
|
|
Files with other extensions are treated as single plain text documents.
|
|
If a directory is provided it is traversed recursively to grab
|
|
all files to be processed.
|
|
The files can be a mixture of .spacy, .jsonl and text files.
|
|
If .jsonl is provided the specified field is going
|
|
to be grabbed ("text" by default)."""
|
|
|
|
out_help = "Path to save the resulting .spacy file"
|
|
code_help = (
|
|
"Path to Python file with additional " "code (registered functions) to be imported"
|
|
)
|
|
gold_help = "Use gold preprocessing provided in the .spacy files"
|
|
force_msg = (
|
|
"The provided output file already exists. "
|
|
"To force overwriting the output file, set the --force or -F flag."
|
|
)
|
|
|
|
|
|
DocOrStrStream = Union[Iterable[str], Iterable[Doc]]
|
|
|
|
|
|
def _stream_docbin(path: Path, vocab: Vocab) -> Iterable[Doc]:
|
|
"""
|
|
Stream Doc objects from DocBin.
|
|
"""
|
|
docbin = DocBin().from_disk(path)
|
|
for doc in docbin.get_docs(vocab):
|
|
yield doc
|
|
|
|
|
|
def _stream_jsonl(path: Path, field: str) -> Iterable[str]:
|
|
"""
|
|
Stream "text" field from JSONL. If the field "text" is
|
|
not found it raises error.
|
|
"""
|
|
for entry in srsly.read_jsonl(path):
|
|
if field not in entry:
|
|
msg.fail(f"{path} does not contain the required '{field}' field.", exits=1)
|
|
else:
|
|
yield entry[field]
|
|
|
|
|
|
def _stream_texts(paths: Iterable[Path]) -> Iterable[str]:
|
|
"""
|
|
Yields strings from text files in paths.
|
|
"""
|
|
for path in paths:
|
|
with open(path, "r") as fin:
|
|
text = fin.read()
|
|
yield text
|
|
|
|
|
|
@app.command("apply")
|
|
def apply_cli(
|
|
# fmt: off
|
|
model: str = Arg(..., help="Model name or path"),
|
|
data_path: Path = Arg(..., help=path_help, exists=True),
|
|
output_file: Path = Arg(..., help=out_help, dir_okay=False),
|
|
code_path: Optional[Path] = Opt(None, "--code", "-c", help=code_help),
|
|
text_key: str = Opt("text", "--text-key", "-tk", help="Key containing text string for JSONL"),
|
|
force_overwrite: bool = Opt(False, "--force", "-F", help="Force overwriting the output file"),
|
|
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU."),
|
|
batch_size: int = Opt(1, "--batch-size", "-b", help="Batch size."),
|
|
n_process: int = Opt(1, "--n-process", "-n", help="number of processors to use.")
|
|
):
|
|
"""
|
|
Apply a trained pipeline to documents to get predictions.
|
|
Expects a loadable spaCy pipeline and path to the data, which
|
|
can be a directory or a file.
|
|
The data files can be provided in multiple formats:
|
|
1. .spacy files
|
|
2. .jsonl files with a specified "field" to read the text from.
|
|
3. Files with any other extension are assumed to be containing
|
|
a single document.
|
|
DOCS: https://spacy.io/api/cli#apply
|
|
"""
|
|
data_path = ensure_path(data_path)
|
|
output_file = ensure_path(output_file)
|
|
code_path = ensure_path(code_path)
|
|
if output_file.exists() and not force_overwrite:
|
|
msg.fail(force_msg, exits=1)
|
|
if not data_path.exists():
|
|
msg.fail(f"Couldn't find data path: {data_path}", exits=1)
|
|
import_code(code_path)
|
|
setup_gpu(use_gpu)
|
|
apply(data_path, output_file, model, text_key, batch_size, n_process)
|
|
|
|
|
|
def apply(
|
|
data_path: Path,
|
|
output_file: Path,
|
|
model: str,
|
|
json_field: str,
|
|
batch_size: int,
|
|
n_process: int,
|
|
):
|
|
docbin = DocBin(store_user_data=True)
|
|
paths = walk_directory(data_path)
|
|
if len(paths) == 0:
|
|
docbin.to_disk(output_file)
|
|
msg.warn(
|
|
"Did not find data to process,"
|
|
f" {data_path} seems to be an empty directory."
|
|
)
|
|
return
|
|
nlp = load_model(model)
|
|
msg.good(f"Loaded model {model}")
|
|
vocab = nlp.vocab
|
|
streams: List[DocOrStrStream] = []
|
|
text_files = []
|
|
for path in paths:
|
|
if path.suffix == ".spacy":
|
|
streams.append(_stream_docbin(path, vocab))
|
|
elif path.suffix == ".jsonl":
|
|
streams.append(_stream_jsonl(path, json_field))
|
|
else:
|
|
text_files.append(path)
|
|
if len(text_files) > 0:
|
|
streams.append(_stream_texts(text_files))
|
|
datagen = cast(DocOrStrStream, chain(*streams))
|
|
for doc in tqdm.tqdm(nlp.pipe(datagen, batch_size=batch_size, n_process=n_process)):
|
|
docbin.add(doc)
|
|
if output_file.suffix == "":
|
|
output_file = output_file.with_suffix(".spacy")
|
|
docbin.to_disk(output_file)
|