mirror of
https://github.com/explosion/spaCy.git
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Add apply CLI (#11376)
* annotate cli first try * add batch-size and n_process * rename to apply * typing fix * handle file suffixes * walk directories * support jsonl * typing fix * remove debug * make suffix optional for walk * revert unrelated * don't warn but raise * better error message * minor touch up * Update spacy/tests/test_cli.py Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/cli/apply.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/cli/apply.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * update tests and bugfix * add force_overwrite * typo * fix adding .spacy suffix * Update spacy/cli/apply.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/cli/apply.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/cli/apply.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * store user data and rename cmd arg * include test for user attr * rename cmd arg * better help message * documentation * prettier * black * link fix * Update spacy/cli/apply.py Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com> * Update website/docs/api/cli.md Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com> * Update website/docs/api/cli.md Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com> * Update website/docs/api/cli.md Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com> * addressing reviews * dont quit but warn * prettier Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: Paul O'Leary McCann <polm@dampfkraft.com>
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@ -16,6 +16,7 @@ from .debug_config import debug_config # noqa: F401
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from .debug_model import debug_model # noqa: F401
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from .debug_diff import debug_diff # noqa: F401
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from .evaluate import evaluate # noqa: F401
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from .apply import apply # noqa: F401
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from .convert import convert # noqa: F401
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from .init_pipeline import init_pipeline_cli # noqa: F401
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from .init_config import init_config, fill_config # noqa: F401
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@ -582,6 +582,29 @@ def setup_gpu(use_gpu: int, silent=None) -> None:
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local_msg.info("To switch to GPU 0, use the option: --gpu-id 0")
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def walk_directory(path: Path, suffix: Optional[str] = None) -> List[Path]:
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if not path.is_dir():
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return [path]
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paths = [path]
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locs = []
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seen = set()
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for path in paths:
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if str(path) in seen:
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continue
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seen.add(str(path))
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if path.parts[-1].startswith("."):
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continue
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elif path.is_dir():
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paths.extend(path.iterdir())
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elif suffix is not None and not path.parts[-1].endswith(suffix):
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continue
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else:
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locs.append(path)
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# It's good to sort these, in case the ordering messes up cache.
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locs.sort()
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return locs
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def _format_number(number: Union[int, float], ndigits: int = 2) -> str:
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"""Formats a number (float or int) rounding to `ndigits`, without truncating trailing 0s,
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as happens with `round(number, ndigits)`"""
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143
spacy/cli/apply.py
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143
spacy/cli/apply.py
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@ -0,0 +1,143 @@
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import tqdm
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import srsly
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from itertools import chain
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from pathlib import Path
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from typing import Optional, List, Iterable, cast, Union
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from wasabi import msg
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from ._util import app, Arg, Opt, setup_gpu, import_code, walk_directory
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from ..tokens import Doc, DocBin
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from ..vocab import Vocab
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from ..util import ensure_path, load_model
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path_help = """Location of the documents to predict on.
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Can be a single file in .spacy format or a .jsonl file.
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Files with other extensions are treated as single plain text documents.
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If a directory is provided it is traversed recursively to grab
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all files to be processed.
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The files can be a mixture of .spacy, .jsonl and text files.
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If .jsonl is provided the specified field is going
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to be grabbed ("text" by default)."""
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out_help = "Path to save the resulting .spacy file"
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code_help = (
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"Path to Python file with additional " "code (registered functions) to be imported"
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)
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gold_help = "Use gold preprocessing provided in the .spacy files"
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force_msg = (
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"The provided output file already exists. "
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"To force overwriting the output file, set the --force or -F flag."
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)
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DocOrStrStream = Union[Iterable[str], Iterable[Doc]]
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def _stream_docbin(path: Path, vocab: Vocab) -> Iterable[Doc]:
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"""
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Stream Doc objects from DocBin.
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"""
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docbin = DocBin().from_disk(path)
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for doc in docbin.get_docs(vocab):
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yield doc
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def _stream_jsonl(path: Path, field: str) -> Iterable[str]:
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"""
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Stream "text" field from JSONL. If the field "text" is
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not found it raises error.
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"""
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for entry in srsly.read_jsonl(path):
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if field not in entry:
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msg.fail(
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f"{path} does not contain the required '{field}' field.", exits=1
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)
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else:
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yield entry[field]
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def _stream_texts(paths: Iterable[Path]) -> Iterable[str]:
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"""
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Yields strings from text files in paths.
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"""
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for path in paths:
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with open(path, "r") as fin:
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text = fin.read()
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yield text
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@app.command("apply")
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def apply_cli(
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# fmt: off
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model: str = Arg(..., help="Model name or path"),
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data_path: Path = Arg(..., help=path_help, exists=True),
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output_file: Path = Arg(..., help=out_help, dir_okay=False),
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code_path: Optional[Path] = Opt(None, "--code", "-c", help=code_help),
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text_key: str = Opt("text", "--text-key", "-tk", help="Key containing text string for JSONL"),
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force_overwrite: bool = Opt(False, "--force", "-F", help="Force overwriting the output file"),
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU."),
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batch_size: int = Opt(1, "--batch-size", "-b", help="Batch size."),
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n_process: int = Opt(1, "--n-process", "-n", help="number of processors to use.")
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):
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"""
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Apply a trained pipeline to documents to get predictions.
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Expects a loadable spaCy pipeline and path to the data, which
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can be a directory or a file.
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The data files can be provided in multiple formats:
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1. .spacy files
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2. .jsonl files with a specified "field" to read the text from.
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3. Files with any other extension are assumed to be containing
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a single document.
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DOCS: https://spacy.io/api/cli#apply
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"""
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data_path = ensure_path(data_path)
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output_file = ensure_path(output_file)
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code_path = ensure_path(code_path)
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if output_file.exists() and not force_overwrite:
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msg.fail(force_msg, exits=1)
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if not data_path.exists():
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msg.fail(f"Couldn't find data path: {data_path}", exits=1)
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import_code(code_path)
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setup_gpu(use_gpu)
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apply(data_path, output_file, model, text_key, batch_size, n_process)
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def apply(
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data_path: Path,
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output_file: Path,
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model: str,
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json_field: str,
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batch_size: int,
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n_process: int,
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):
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docbin = DocBin(store_user_data=True)
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paths = walk_directory(data_path)
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if len(paths) == 0:
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docbin.to_disk(output_file)
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msg.warn("Did not find data to process,"
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f" {data_path} seems to be an empty directory.")
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return
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nlp = load_model(model)
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msg.good(f"Loaded model {model}")
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vocab = nlp.vocab
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streams: List[DocOrStrStream] = []
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text_files = []
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for path in paths:
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if path.suffix == ".spacy":
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streams.append(_stream_docbin(path, vocab))
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elif path.suffix == ".jsonl":
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streams.append(_stream_jsonl(path, json_field))
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else:
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text_files.append(path)
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if len(text_files) > 0:
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streams.append(_stream_texts(text_files))
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datagen = cast(DocOrStrStream, chain(*streams))
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for doc in tqdm.tqdm(nlp.pipe(datagen, batch_size=batch_size, n_process=n_process)):
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docbin.add(doc)
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if output_file.suffix == "":
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output_file = output_file.with_suffix(".spacy")
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docbin.to_disk(output_file)
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@ -1,4 +1,4 @@
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from typing import Callable, Iterable, Mapping, Optional, Any, List, Union
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from typing import Callable, Iterable, Mapping, Optional, Any, Union
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from enum import Enum
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from pathlib import Path
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from wasabi import Printer
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@ -7,7 +7,7 @@ import re
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import sys
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import itertools
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from ._util import app, Arg, Opt
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from ._util import app, Arg, Opt, walk_directory
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from ..training import docs_to_json
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from ..tokens import Doc, DocBin
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from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs
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@ -189,33 +189,6 @@ def autodetect_ner_format(input_data: str) -> Optional[str]:
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return None
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def walk_directory(path: Path, converter: str) -> List[Path]:
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if not path.is_dir():
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return [path]
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paths = [path]
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locs = []
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seen = set()
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for path in paths:
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if str(path) in seen:
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continue
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seen.add(str(path))
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if path.parts[-1].startswith("."):
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continue
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elif path.is_dir():
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paths.extend(path.iterdir())
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elif converter == "json" and not path.parts[-1].endswith("json"):
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continue
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elif converter == "conll" and not path.parts[-1].endswith("conll"):
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continue
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elif converter == "iob" and not path.parts[-1].endswith("iob"):
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continue
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else:
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locs.append(path)
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# It's good to sort these, in case the ordering messes up cache.
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locs.sort()
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return locs
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def verify_cli_args(
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msg: Printer,
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input_path: Path,
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@ -5,6 +5,7 @@ from typing import Tuple, List, Dict, Any
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import pkg_resources
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import time
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import spacy
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import numpy
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import pytest
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import srsly
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@ -32,6 +33,7 @@ from spacy.cli.package import _is_permitted_package_name
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from spacy.cli.project.remote_storage import RemoteStorage
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from spacy.cli.project.run import _check_requirements
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from spacy.cli.validate import get_model_pkgs
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from spacy.cli.apply import apply
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from spacy.cli.find_threshold import find_threshold
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from spacy.lang.en import English
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from spacy.lang.nl import Dutch
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@ -885,6 +887,82 @@ def test_span_length_freq_dist_output_must_be_correct():
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assert list(span_freqs.keys()) == [3, 1, 4, 5, 2]
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def test_applycli_empty_dir():
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with make_tempdir() as data_path:
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output = data_path / "test.spacy"
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apply(data_path, output, "blank:en", "text", 1, 1)
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def test_applycli_docbin():
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with make_tempdir() as data_path:
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output = data_path / "testout.spacy"
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nlp = spacy.blank("en")
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doc = nlp("testing apply cli.")
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# test empty DocBin case
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docbin = DocBin()
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docbin.to_disk(data_path / "testin.spacy")
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apply(data_path, output, "blank:en", "text", 1, 1)
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docbin.add(doc)
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docbin.to_disk(data_path / "testin.spacy")
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apply(data_path, output, "blank:en", "text", 1, 1)
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def test_applycli_jsonl():
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with make_tempdir() as data_path:
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output = data_path / "testout.spacy"
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data = [{"field": "Testing apply cli.", "key": 234}]
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data2 = [{"field": "234"}]
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srsly.write_jsonl(data_path / "test.jsonl", data)
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apply(data_path, output, "blank:en", "field", 1, 1)
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srsly.write_jsonl(data_path / "test2.jsonl", data2)
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apply(data_path, output, "blank:en", "field", 1, 1)
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def test_applycli_txt():
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with make_tempdir() as data_path:
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output = data_path / "testout.spacy"
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with open(data_path / "test.foo", "w") as ftest:
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ftest.write("Testing apply cli.")
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apply(data_path, output, "blank:en", "text", 1, 1)
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def test_applycli_mixed():
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with make_tempdir() as data_path:
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output = data_path / "testout.spacy"
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text = "Testing apply cli"
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nlp = spacy.blank("en")
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doc = nlp(text)
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jsonl_data = [{"text": text}]
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srsly.write_jsonl(data_path / "test.jsonl", jsonl_data)
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docbin = DocBin()
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docbin.add(doc)
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docbin.to_disk(data_path / "testin.spacy")
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with open(data_path / "test.txt", "w") as ftest:
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ftest.write(text)
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apply(data_path, output, "blank:en", "text", 1, 1)
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# Check whether it worked
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result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
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assert len(result) == 3
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for doc in result:
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assert doc.text == text
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def test_applycli_user_data():
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Doc.set_extension("ext", default=0)
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val = ("ext", 0)
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with make_tempdir() as data_path:
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output = data_path / "testout.spacy"
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nlp = spacy.blank("en")
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doc = nlp("testing apply cli.")
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doc._.ext = val
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docbin = DocBin(store_user_data=True)
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docbin.add(doc)
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docbin.to_disk(data_path / "testin.spacy")
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apply(data_path, output, "blank:en", "", 1, 1)
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result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
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assert result[0]._.ext == val
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def test_local_remote_storage():
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with make_tempdir() as d:
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filename = "a.txt"
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@ -12,6 +12,7 @@ menu:
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- ['train', 'train']
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- ['pretrain', 'pretrain']
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- ['evaluate', 'evaluate']
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- ['apply', 'apply']
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- ['find-threshold', 'find-threshold']
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- ['assemble', 'assemble']
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- ['package', 'package']
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@ -474,7 +475,7 @@ report span characteristics such as the average span length and the span (or
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span boundary) distinctiveness. The distinctiveness measure shows how different
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the tokens are with respect to the rest of the corpus using the KL-divergence of
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the token distributions. To learn more, you can check out Papay et al.'s work on
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[*Dissecting Span Identification Tasks with Performance Prediction* (EMNLP 2020)](https://aclanthology.org/2020.emnlp-main.396/).
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[_Dissecting Span Identification Tasks with Performance Prediction_ (EMNLP 2020)](https://aclanthology.org/2020.emnlp-main.396/).
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</Infobox>
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@ -1162,6 +1163,37 @@ $ python -m spacy evaluate [model] [data_path] [--output] [--code] [--gold-prepr
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| **CREATES** | Training results and optional metrics and visualizations. |
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## apply {#apply new="3.5" tag="command"}
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Applies a trained pipeline to data and stores the resulting annotated documents
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in a `DocBin`. The input can be a single file or a directory. The recognized
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input formats are:
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1. `.spacy`
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2. `.jsonl` containing a user specified `text_key`
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3. Files with any other extension are assumed to be plain text files containing
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a single document.
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When a directory is provided it is traversed recursively to collect all files.
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```cli
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$ python -m spacy apply [model] [data-path] [output-file] [--code] [--text-key] [--force-overwrite] [--gpu-id] [--batch-size] [--n-process]
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```
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| Name | Description |
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| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `model` | Pipeline to apply to the data. Can be a package or a path to a data directory. ~~str (positional)~~ |
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| `data_path` | Location of data to be evaluated in spaCy's [binary format](/api/data-formats#training), jsonl, or plain text. ~~Path (positional)~~ |
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| `output-file`, `-o` | Output `DocBin` path. ~~str (positional)~~ |
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| `--code`, `-c` <Tag variant="new">3</Tag> | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--text-key`, `-tk` | The key for `.jsonl` files to use to grab the texts from. Defaults to `text`. ~~Optional[str] \(option)~~ |
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| `--force-overwrite`, `-F` | If the provided `output-file` already exists, then force `apply` to overwrite it. If this is `False` (default) then quits with a warning instead. ~~bool (flag)~~ |
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| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
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| `--batch-size`, `-b` | Batch size to use for prediction. Defaults to `1`. ~~int (option)~~ |
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| `--n-process`, `-n` | Number of processes to use for prediction. Defaults to `1`. ~~int (option)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| **CREATES** | A `DocBin` with the annotations from the `model` for all the files found in `data-path`. |
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## find-threshold {#find-threshold new="3.5" tag="command"}
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Runs prediction trials for a trained model with varying tresholds to maximize
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@ -1187,7 +1219,6 @@ be provided.
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> $ python -m spacy find-threshold my_nlp data.spacy spancat threshold spans_sc_f
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> ```
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|
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| Name | Description |
|
||||
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `model` | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~ |
|
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|
|
|
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Loading…
Reference in New Issue
Block a user