spaCy/spacy/cli/convert.py
Adriane Boyd ac4297ee39
Minor refactor to conversion of output docs (#5718)
Minor refactor of conversion of docs to output format to avoid
duplicate conversion steps.
2020-07-09 19:42:32 +02:00

257 lines
8.5 KiB
Python

from typing import Optional
from enum import Enum
from pathlib import Path
from wasabi import Printer
import srsly
import re
import sys
from ._app import app, Arg, Opt
from ..gold import docs_to_json
from ..tokens import DocBin
from ..gold.converters import iob2docs, conll_ner2docs, json2docs, conllu2docs
# 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": conllu2docs,
"conllu": conllu2docs,
"conll": conllu2docs,
"ner": conll_ner2docs,
"iob": iob2docs,
"json": json2docs,
}
# 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", "-b", help="Model for sentence segmentation (for -s)"),
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)"),
# fmt: on
):
"""
Convert files into json or DocBin format for use with train command and other
experiment management functions. If no output_dir is specified, the data
is written to stdout, so you can pipe them forward to a JSON file:
$ spacy convert some_file.conllu > some_file.json
"""
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
cli_args = locals()
silent = output_dir == "-"
msg = Printer(no_print=silent)
verify_cli_args(msg, **cli_args)
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,
silent=silent,
msg=msg,
)
def convert(
input_path: Path,
output_dir: 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,
silent: bool = True,
msg: Optional[Path] = None,
) -> None:
if not msg:
msg = Printer(no_print=silent)
ner_map = srsly.read_json(ner_map) if ner_map is not None else None
for input_loc in walk_directory(input_path):
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,
)
if file_type == "json":
data = [docs_to_json(docs)]
else:
data = DocBin(docs=docs, store_user_data=True).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, output_type):
if output_type == "json":
srsly.write_json("-", data)
else:
sys.stdout.buffer.write(data)
def _write_docs_to_file(data, output_file, output_type):
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) -> 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):
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())
else:
locs.append(path)
return locs
def verify_cli_args(
msg,
input_path,
output_dir,
file_type,
n_sents,
seg_sents,
model,
morphology,
merge_subtokens,
converter,
ner_map,
lang,
):
input_path = Path(input_path)
if file_type not in FILE_TYPES_STDOUT and output_dir == "-":
# TODO: support msgpack via stdout in srsly?
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 input_path.is_dir():
input_locs = walk_directory(input_path)
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 len(file_types) >= 2:
file_types = ",".join(file_types)
msg.fail("All input files must be same type", file_types, exits=1)
converter = _get_converter(msg, converter, input_path)
if converter not in CONVERTERS:
msg.fail(f"Can't find converter for {converter}", exits=1)
return converter
def _get_converter(msg, converter, input_path):
if input_path.is_dir():
input_path = walk_directory(input_path)[0]
if converter == "auto":
converter = input_path.suffix[1:]
if converter == "ner" or converter == "iob":
with input_path.open() 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