spaCy/spacy/cli/convert.py
Adriane Boyd 4188beda87
Fix conll converter option (#7071)
Map `conll` to the NER converter, not the `CoNLL-U` converter.
2021-02-18 10:22:41 +01:00

274 lines
9.9 KiB
Python

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