mirror of
https://github.com/explosion/spaCy.git
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274 lines
9.9 KiB
Python
274 lines
9.9 KiB
Python
from typing import Optional, Any, List, 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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import srsly
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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 ..training import docs_to_json
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from ..tokens import DocBin
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from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs
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from ..training.converters import conllu_to_docs
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# Converters are matched by file extension except for ner/iob, which are
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# matched by file extension and content. To add a converter, add a new
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# entry to this dict with the file extension mapped to the converter function
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# imported from /converters.
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CONVERTERS = {
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"conllubio": conllu_to_docs,
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"conllu": conllu_to_docs,
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"conll": conll_ner_to_docs,
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"ner": conll_ner_to_docs,
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"iob": iob_to_docs,
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"json": json_to_docs,
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}
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# File types that can be written to stdout
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FILE_TYPES_STDOUT = ("json",)
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class FileTypes(str, Enum):
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json = "json"
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spacy = "spacy"
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@app.command("convert")
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def convert_cli(
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# fmt: off
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input_path: str = Arg(..., help="Input file or directory", exists=True),
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output_dir: Path = Arg("-", help="Output directory. '-' for stdout.", allow_dash=True, exists=True),
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file_type: FileTypes = Opt("spacy", "--file-type", "-t", help="Type of data to produce"),
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n_sents: int = Opt(1, "--n-sents", "-n", help="Number of sentences per doc (0 to disable)"),
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seg_sents: bool = Opt(False, "--seg-sents", "-s", help="Segment sentences (for -c ner)"),
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model: Optional[str] = Opt(None, "--model", "--base", "-b", help="Trained spaCy pipeline for sentence segmentation to use as base (for --seg-sents)"),
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morphology: bool = Opt(False, "--morphology", "-m", help="Enable appending morphology to tags"),
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merge_subtokens: bool = Opt(False, "--merge-subtokens", "-T", help="Merge CoNLL-U subtokens"),
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converter: str = Opt("auto", "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
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ner_map: Optional[Path] = Opt(None, "--ner-map", "-nm", help="NER tag mapping (as JSON-encoded dict of entity types)", exists=True),
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lang: Optional[str] = Opt(None, "--lang", "-l", help="Language (if tokenizer required)"),
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concatenate: bool = Opt(None, "--concatenate", "-C", help="Concatenate output to a single file"),
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# fmt: on
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):
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"""
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Convert files into json or DocBin format for training. The resulting .spacy
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file can be used with the train command and other experiment management
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functions.
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If no output_dir is specified and the output format is JSON, the data
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is written to stdout, so you can pipe them forward to a JSON file:
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$ spacy convert some_file.conllu --file-type json > some_file.json
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DOCS: https://spacy.io/api/cli#convert
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"""
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if isinstance(file_type, FileTypes):
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# We get an instance of the FileTypes from the CLI so we need its string value
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file_type = file_type.value
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input_path = Path(input_path)
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output_dir = "-" if output_dir == Path("-") else output_dir
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silent = output_dir == "-"
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msg = Printer(no_print=silent)
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verify_cli_args(msg, input_path, output_dir, file_type, converter, ner_map)
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converter = _get_converter(msg, converter, input_path)
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convert(
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input_path,
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output_dir,
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file_type=file_type,
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n_sents=n_sents,
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seg_sents=seg_sents,
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model=model,
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morphology=morphology,
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merge_subtokens=merge_subtokens,
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converter=converter,
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ner_map=ner_map,
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lang=lang,
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concatenate=concatenate,
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silent=silent,
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msg=msg,
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)
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def convert(
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input_path: Union[str, Path],
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output_dir: Union[str, Path],
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*,
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file_type: str = "json",
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n_sents: int = 1,
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seg_sents: bool = False,
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model: Optional[str] = None,
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morphology: bool = False,
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merge_subtokens: bool = False,
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converter: str = "auto",
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ner_map: Optional[Path] = None,
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lang: Optional[str] = None,
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concatenate: bool = False,
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silent: bool = True,
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msg: Optional[Printer],
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) -> None:
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if not msg:
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msg = Printer(no_print=silent)
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ner_map = srsly.read_json(ner_map) if ner_map is not None else None
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doc_files = []
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for input_loc in walk_directory(Path(input_path), converter):
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input_data = input_loc.open("r", encoding="utf-8").read()
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# Use converter function to convert data
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func = CONVERTERS[converter]
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docs = func(
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input_data,
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n_sents=n_sents,
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seg_sents=seg_sents,
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append_morphology=morphology,
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merge_subtokens=merge_subtokens,
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lang=lang,
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model=model,
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no_print=silent,
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ner_map=ner_map,
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)
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doc_files.append((input_loc, docs))
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if concatenate:
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all_docs = itertools.chain.from_iterable([docs for _, docs in doc_files])
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doc_files = [(input_path, all_docs)]
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for input_loc, docs in doc_files:
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if file_type == "json":
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data = [docs_to_json(docs)]
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len_docs = len(data)
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else:
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db = DocBin(docs=docs, store_user_data=True)
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len_docs = len(db)
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data = db.to_bytes()
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if output_dir == "-":
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_print_docs_to_stdout(data, file_type)
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else:
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if input_loc != input_path:
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subpath = input_loc.relative_to(input_path)
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output_file = Path(output_dir) / subpath.with_suffix(f".{file_type}")
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else:
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output_file = Path(output_dir) / input_loc.parts[-1]
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output_file = output_file.with_suffix(f".{file_type}")
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_write_docs_to_file(data, output_file, file_type)
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msg.good(f"Generated output file ({len_docs} documents): {output_file}")
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def _print_docs_to_stdout(data: Any, output_type: str) -> None:
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if output_type == "json":
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srsly.write_json("-", data)
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else:
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sys.stdout.buffer.write(data)
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def _write_docs_to_file(data: Any, output_file: Path, output_type: str) -> None:
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if not output_file.parent.exists():
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output_file.parent.mkdir(parents=True)
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if output_type == "json":
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srsly.write_json(output_file, data)
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else:
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with output_file.open("wb") as file_:
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file_.write(data)
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def autodetect_ner_format(input_data: str) -> Optional[str]:
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# guess format from the first 20 lines
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lines = input_data.split("\n")[:20]
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format_guesses = {"ner": 0, "iob": 0}
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iob_re = re.compile(r"\S+\|(O|[IB]-\S+)")
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ner_re = re.compile(r"\S+\s+(O|[IB]-\S+)$")
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for line in lines:
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line = line.strip()
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if iob_re.search(line):
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format_guesses["iob"] += 1
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if ner_re.search(line):
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format_guesses["ner"] += 1
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if format_guesses["iob"] == 0 and format_guesses["ner"] > 0:
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return "ner"
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if format_guesses["ner"] == 0 and format_guesses["iob"] > 0:
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return "iob"
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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: Union[str, Path],
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output_dir: Union[str, Path],
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file_type: FileTypes,
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converter: str,
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ner_map: Optional[Path],
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):
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input_path = Path(input_path)
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if file_type not in FILE_TYPES_STDOUT and output_dir == "-":
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msg.fail(
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f"Can't write .{file_type} data to stdout. Please specify an output directory.",
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exits=1,
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)
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if not input_path.exists():
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msg.fail("Input file not found", input_path, exits=1)
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if output_dir != "-" and not Path(output_dir).exists():
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msg.fail("Output directory not found", output_dir, exits=1)
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if ner_map is not None and not Path(ner_map).exists():
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msg.fail("NER map not found", ner_map, exits=1)
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if input_path.is_dir():
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input_locs = walk_directory(input_path, converter)
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if len(input_locs) == 0:
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msg.fail("No input files in directory", input_path, exits=1)
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file_types = list(set([loc.suffix[1:] for loc in input_locs]))
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if converter == "auto" and len(file_types) >= 2:
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file_types = ",".join(file_types)
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msg.fail("All input files must be same type", file_types, exits=1)
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if converter != "auto" and converter not in CONVERTERS:
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msg.fail(f"Can't find converter for {converter}", exits=1)
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def _get_converter(msg, converter, input_path):
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if input_path.is_dir():
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input_path = walk_directory(input_path, converter)[0]
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if converter == "auto":
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converter = input_path.suffix[1:]
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if converter == "ner" or converter == "iob":
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with input_path.open(encoding="utf8") as file_:
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input_data = file_.read()
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converter_autodetect = autodetect_ner_format(input_data)
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if converter_autodetect == "ner":
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msg.info("Auto-detected token-per-line NER format")
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converter = converter_autodetect
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elif converter_autodetect == "iob":
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msg.info("Auto-detected sentence-per-line NER format")
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converter = converter_autodetect
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else:
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msg.warn(
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"Can't automatically detect NER format. "
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"Conversion may not succeed. "
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"See https://spacy.io/api/cli#convert"
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)
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return converter
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