spaCy/spacy/attrs.pyx

168 lines
4.9 KiB
Cython
Raw Normal View History

IDS = {
"": NULL_ATTR,
"IS_ALPHA": IS_ALPHA,
"IS_ASCII": IS_ASCII,
"IS_DIGIT": IS_DIGIT,
"IS_LOWER": IS_LOWER,
"IS_PUNCT": IS_PUNCT,
"IS_SPACE": IS_SPACE,
"IS_TITLE": IS_TITLE,
"IS_UPPER": IS_UPPER,
"LIKE_URL": LIKE_URL,
"LIKE_NUM": LIKE_NUM,
"LIKE_EMAIL": LIKE_EMAIL,
"IS_STOP": IS_STOP,
Reduce stored lexemes data, move feats to lookups (#5238) * Reduce stored lexemes data, move feats to lookups * Move non-derivable lexemes features (`norm / cluster / prob`) to `spacy-lookups-data` as lookups * Get/set `norm` in both lookups and `LexemeC`, serialize in lookups * Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in lookups only * Remove serialization of lexemes data as `vocab/lexemes.bin` * Remove `SerializedLexemeC` * Remove `Lexeme.to_bytes/from_bytes` * Modify normalization exception loading: * Always create `Vocab.lookups` table `lexeme_norm` for normalization exceptions * Load base exceptions from `lang.norm_exceptions`, but load language-specific exceptions from lookups * Set `lex_attr_getter[NORM]` including new lookups table in `BaseDefaults.create_vocab()` and when deserializing `Vocab` * Remove all cached lexemes when deserializing vocab to override existing normalizations with the new normalizations (as a replacement for the previous step that replaced all lexemes data with the deserialized data) * Skip English normalization test Skip English normalization test because the data is now in `spacy-lookups-data`. * Remove norm exceptions Moved to spacy-lookups-data. * Move norm exceptions test to spacy-lookups-data * Load extra lookups from spacy-lookups-data lazily Load extra lookups (currently for cluster and prob) lazily from the entry point `lg_extra` as `Vocab.lookups_extra`. * Skip creating lexeme cache on load To improve model loading times, do not create the full lexeme cache when loading. The lexemes will be created on demand when processing. * Identify numeric values in Lexeme.set_attrs() With the removal of a special case for `PROB`, also identify `float` to avoid trying to convert it with the `StringStore`. * Skip lexeme cache init in from_bytes * Unskip and update lookups tests for python3.6+ * Update vocab pickle to include lookups_extra * Update vocab serialization tests Check strings rather than lexemes since lexemes aren't initialized automatically, account for addition of "_SP". * Re-skip lookups test because of python3.5 * Skip PROB/float values in Lexeme.set_attrs * Convert is_oov from lexeme flag to lex in vectors Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether the lexeme has a vector. Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-05-19 13:59:14 +00:00
"IS_OOV_DEPRECATED": IS_OOV_DEPRECATED,
"IS_BRACKET": IS_BRACKET,
"IS_QUOTE": IS_QUOTE,
"IS_LEFT_PUNCT": IS_LEFT_PUNCT,
"IS_RIGHT_PUNCT": IS_RIGHT_PUNCT,
2018-02-11 17:51:32 +00:00
"IS_CURRENCY": IS_CURRENCY,
"FLAG19": FLAG19,
"FLAG20": FLAG20,
"FLAG21": FLAG21,
"FLAG22": FLAG22,
"FLAG23": FLAG23,
"FLAG24": FLAG24,
"FLAG25": FLAG25,
"FLAG26": FLAG26,
"FLAG27": FLAG27,
"FLAG28": FLAG28,
"FLAG29": FLAG29,
"FLAG30": FLAG30,
"FLAG31": FLAG31,
"FLAG32": FLAG32,
"FLAG33": FLAG33,
"FLAG34": FLAG34,
"FLAG35": FLAG35,
"FLAG36": FLAG36,
"FLAG37": FLAG37,
"FLAG38": FLAG38,
"FLAG39": FLAG39,
"FLAG40": FLAG40,
"FLAG41": FLAG41,
"FLAG42": FLAG42,
"FLAG43": FLAG43,
"FLAG44": FLAG44,
"FLAG45": FLAG45,
"FLAG46": FLAG46,
"FLAG47": FLAG47,
"FLAG48": FLAG48,
"FLAG49": FLAG49,
"FLAG50": FLAG50,
"FLAG51": FLAG51,
"FLAG52": FLAG52,
"FLAG53": FLAG53,
"FLAG54": FLAG54,
"FLAG55": FLAG55,
"FLAG56": FLAG56,
"FLAG57": FLAG57,
"FLAG58": FLAG58,
"FLAG59": FLAG59,
"FLAG60": FLAG60,
"FLAG61": FLAG61,
"FLAG62": FLAG62,
"FLAG63": FLAG63,
"ID": ID,
"ORTH": ORTH,
"LOWER": LOWER,
"NORM": NORM,
"SHAPE": SHAPE,
"PREFIX": PREFIX,
"SUFFIX": SUFFIX,
"LENGTH": LENGTH,
"LEMMA": LEMMA,
"POS": POS,
"TAG": TAG,
"DEP": DEP,
"ENT_IOB": ENT_IOB,
"ENT_TYPE": ENT_TYPE,
"ENT_ID": ENT_ID,
"ENT_KB_ID": ENT_KB_ID,
"HEAD": HEAD,
"SENT_START": SENT_START,
"SPACY": SPACY,
"LANG": LANG,
"MORPH": MORPH,
"IDX": IDX
}
# ATTR IDs, in order of the symbol
NAMES = [key for key, value in sorted(IDS.items(), key=lambda item: item[1])]
2017-09-17 17:28:53 +00:00
locals().update(IDS)
def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
"""
Normalize a dictionary of attributes, converting them to ints.
2017-10-27 19:07:59 +00:00
stringy_attrs (dict): Dictionary keyed by attribute string names. Values
can be ints or strings.
strings_map (StringStore): Defaults to None. If provided, encodes string
values into ints.
RETURNS (dict): Attributes dictionary with keys and optionally values
converted to ints.
"""
inty_attrs = {}
if _do_deprecated:
if 'F' in stringy_attrs:
stringy_attrs["ORTH"] = stringy_attrs.pop("F")
if 'L' in stringy_attrs:
stringy_attrs["LEMMA"] = stringy_attrs.pop("L")
if 'pos' in stringy_attrs:
stringy_attrs["TAG"] = stringy_attrs.pop("pos")
if 'morph' in stringy_attrs:
morphs = stringy_attrs.pop('morph')
if 'number' in stringy_attrs:
stringy_attrs.pop('number')
if 'tenspect' in stringy_attrs:
stringy_attrs.pop('tenspect')
morph_keys = [
'PunctType', 'PunctSide', 'Other', 'Degree', 'AdvType', 'Number',
'VerbForm', 'PronType', 'Aspect', 'Tense', 'PartType', 'Poss',
'Hyph', 'ConjType', 'NumType', 'Foreign', 'VerbType', 'NounType',
2017-03-15 14:26:16 +00:00
'Gender', 'Mood', 'Negative', 'Tense', 'Voice', 'Abbr',
'Derivation', 'Echo', 'Foreign', 'NameType', 'NounType', 'NumForm',
'NumValue', 'PartType', 'Polite', 'StyleVariant',
'PronType', 'AdjType', 'Person', 'Variant', 'AdpType',
'Reflex', 'Negative', 'Mood', 'Aspect', 'Case',
'Polarity', 'PrepCase', 'Animacy' # U20
]
for key in morph_keys:
if key in stringy_attrs:
stringy_attrs.pop(key)
elif key.lower() in stringy_attrs:
stringy_attrs.pop(key.lower())
elif key.upper() in stringy_attrs:
stringy_attrs.pop(key.upper())
for name, value in stringy_attrs.items():
int_key = intify_attr(name)
if int_key is not None:
if strings_map is not None and isinstance(value, str):
if hasattr(strings_map, 'add'):
value = strings_map.add(value)
else:
value = strings_map[value]
inty_attrs[int_key] = value
return inty_attrs
def intify_attr(name):
"""
Normalize an attribute name, converting it to int.
stringy_attr (string): Attribute string name. Can also be int (will then be left unchanged)
RETURNS (int): int representation of the attribute, or None if it couldn't be converted.
"""
if isinstance(name, int):
return name
elif name in IDS:
return IDS[name]
elif name.upper() in IDS:
return IDS[name.upper()]
return None