spaCy/spacy/util.py

130 lines
3.9 KiB
Python

from os import path
import io
import json
import re
from .attrs import TAG, HEAD, DEP, ENT_IOB, ENT_TYPE
DATA_DIR = path.join(path.dirname(__file__), '..', 'data')
def normalize_slice(length, start, stop, step=None):
if not (step is None or step == 1):
raise ValueError("Stepped slices not supported in Span objects."
"Try: list(tokens)[start:stop:step] instead.")
if start is None:
start = 0
elif start < 0:
start += length
start = min(length, max(0, start))
if stop is None:
stop = length
elif stop < 0:
stop += length
stop = min(length, max(start, stop))
assert 0 <= start <= stop <= length
return start, stop
def utf8open(loc, mode='r'):
return io.open(loc, mode, encoding='utf8')
def read_lang_data(data_dir):
with open(path.join(data_dir, 'specials.json')) as file_:
tokenization = json.load(file_)
prefix = read_prefix(data_dir)
suffix = read_suffix(data_dir)
infix = read_infix(data_dir)
return tokenization, prefix, suffix, infix
def read_prefix(data_dir):
with utf8open(path.join(data_dir, 'prefix.txt')) as file_:
entries = file_.read().split('\n')
expression = '|'.join(['^' + re.escape(piece) for piece in entries if piece.strip()])
return expression
def read_suffix(data_dir):
with utf8open(path.join(data_dir, 'suffix.txt')) as file_:
entries = file_.read().split('\n')
expression = '|'.join([piece + '$' for piece in entries if piece.strip()])
return expression
def read_infix(data_dir):
with utf8open(path.join(data_dir, 'infix.txt')) as file_:
entries = file_.read().split('\n')
expression = '|'.join([piece for piece in entries if piece.strip()])
return expression
def read_tokenization(lang):
loc = path.join(DATA_DIR, lang, 'tokenization')
entries = []
seen = set()
with utf8open(loc) as file_:
for line in file_:
line = line.strip()
if line.startswith('#'):
continue
if not line:
continue
pieces = line.split()
chunk = pieces.pop(0)
assert chunk not in seen, chunk
seen.add(chunk)
entries.append((chunk, list(pieces)))
if chunk[0].isalpha() and chunk[0].islower():
chunk = chunk[0].title() + chunk[1:]
pieces[0] = pieces[0][0].title() + pieces[0][1:]
seen.add(chunk)
entries.append((chunk, pieces))
return entries
def read_detoken_rules(lang): # Deprecated?
loc = path.join(DATA_DIR, lang, 'detokenize')
entries = []
with utf8open(loc) as file_:
for line in file_:
entries.append(line.strip())
return entries
def align_tokens(ref, indices): # Deprecated, surely?
start = 0
queue = list(indices)
for token in ref:
end = start + len(token)
emit = []
while queue and queue[0][1] <= end:
emit.append(queue.pop(0))
yield token, emit
start = end
assert not queue
def detokenize(token_rules, words): # Deprecated?
"""To align with treebanks, return a list of "chunks", where a chunk is a
sequence of tokens that are separated by whitespace in actual strings. Each
chunk should be a tuple of token indices, e.g.
>>> detokenize(["ca<SEP>n't", '<SEP>!'], ["I", "ca", "n't", "!"])
[(0,), (1, 2, 3)]
"""
string = ' '.join(words)
for subtoks in token_rules:
# Algorithmically this is dumb, but writing a little list-based match
# machine? Ain't nobody got time for that.
string = string.replace(subtoks.replace('<SEP>', ' '), subtoks)
positions = []
i = 0
for chunk in string.split():
subtoks = chunk.split('<SEP>')
positions.append(tuple(range(i, i+len(subtoks))))
i += len(subtoks)
return positions