def _add_name_tokens_features(feature_extractor):
name_tokens_column = keys.NAME_TOKENS
# Levenshtein distance on name tokens
feature_extractor.add(
features.SimilarStrings(
name_tokens_column,
name_tokens_column,
label=f'{name_tokens_column}_levenshtein',
))
# String kernel similarity on name tokens
feature_extractor.add(
features.SimilarStrings(
name_tokens_column,
name_tokens_column,
algorithm='cosine',
analyzer='char_wb',
label=f'{name_tokens_column}_string_kernel_cosine',
))
# Weighted intersection of name tokens
feature_extractor.add(
features.SharedTokens(
name_tokens_column,
name_tokens_column,
label=f'{name_tokens_column}_shared',
))
def extract_features(
candidate_pairs: pd.MultiIndex,
wikidata: pd.DataFrame,
target: pd.DataFrame,
path_io: str,
) -> pd.DataFrame:
"""Extract feature vectors by comparing pairs of
*(Wikidata, target catalog)* records.
**Main features:**
- exact match on full names and URLs
- match on tokenized names, URLs, and genres
- `Levenshtein distance <https://en.wikipedia.org/wiki/Levenshtein_distance>`_
on name tokens
- `string kernel <https://en.wikipedia.org/wiki/String_kernel>`_
similarity on name tokens
- weighted intersection on name tokens
- match on dates by maximum shared precision
- `cosine similarity <https://en.wikipedia.org/wiki/Cosine_similarity>`_
on textual descriptions
- match on occupation QIDs
See :mod:`features` for more details.
This function uses multithreaded parallel processing.
:param candidate_pairs: an index of *(QID, target ID)* pairs
that should undergo comparison
:param wikidata: a preprocessed Wikidata dataset (typically a chunk)
:param target: a preprocessed target catalog dataset (typically a chunk)
:param path_io: input/output path to an extracted feature file
:return: the feature vectors dataset
"""
LOGGER.info('Extracting features ...')
# Early return cached features, for development purposes
if os.path.isfile(path_io):
LOGGER.info("Will reuse existing features: '%s'", path_io)
return pd.read_pickle(path_io)
def in_both_datasets(col: str) -> bool:
return (col in wikidata.columns) and (col in target.columns)
feature_extractor = rl.Compare(n_jobs=cpu_count())
# Exact match on full name
name_column = keys.NAME
if in_both_datasets(name_column):
feature_extractor.add(
features.ExactMatch(name_column,
name_column,
label=f'{name_column}_exact'))
# URL features
if in_both_datasets(keys.URL):
_add_url_features(feature_extractor)
# Date features
_add_date_features(feature_extractor, in_both_datasets)
# Name tokens features
if in_both_datasets(keys.NAME_TOKENS):
_add_name_tokens_features(feature_extractor)
# Cosine similarity on description
description_column = keys.DESCRIPTION
if in_both_datasets(description_column):
feature_extractor.add(
features.SimilarStrings(
description_column,
description_column,
algorithm='cosine',
analyzer='soweego',
label=f'{description_column}_cosine',
))
# Match on occupation QIDs
occupations_column = keys.OCCUPATIONS
if in_both_datasets(occupations_column):
feature_extractor.add(
features.SharedOccupations(
occupations_column,
occupations_column,
label=f'{occupations_column}_shared',
))
# Match on tokenized genres
genres_column = keys.GENRES
if in_both_datasets(genres_column):
feature_extractor.add(
features.SharedTokens(
genres_column,
genres_column,
label=f'{genres_column}_tokens_shared',
))
feature_vectors = feature_extractor.compute(candidate_pairs, wikidata,
target)
feature_vectors = feature_vectors[~feature_vectors.index.duplicated(
) # Drop duplicates
]
os.makedirs(os.path.dirname(path_io), exist_ok=True)
pd.to_pickle(feature_vectors, path_io)
LOGGER.info("Features dumped to '%s'", path_io)
LOGGER.info('Feature extraction done')
return feature_vectors