def extract(self, extractor: Extractor, extractable: Extractable = None, tokenizer: Tokenizer = None,
joiner: str = " ", **options) -> List[Extraction]:
"""
Invoke the extractor on the given extractable, accumulating all the extractions in a list.
Args:
extractor (Extractor):
extractable (extractable):
tokenizer: user can pass custom tokenizer if extractor wants token
joiner: user can pass joiner if extractor wants text
options: user can pass arguments as a dict to the extract() function of different extractors
Returns: List of Extraction, containing all the extractions.
"""
if not extractable:
extractable = self
if not tokenizer:
tokenizer = self.etk.default_tokenizer
extracted_results = list()
if extractor.input_type == InputType.TOKENS:
if self.etk.error_policy == ErrorPolicy.PROCESS:
if isinstance(extractable.value, list):
self.etk.log(
"Extractor needs tokens, tokenizer needs string to tokenize, got list, converting to string",
"warning", self.doc_id, self.url)
warnings.warn(
"Extractor needs tokens, tokenizer needs string to tokenize, got list, converting to string")
elif isinstance(extractable.value, dict):
self.etk.log(
"Extractor needs tokens, tokenizer needs string to tokenize, got dict, converting to string",
"warning", self.doc_id, self.url)
warnings.warn(
"Extractor needs tokens, tokenizer needs string to tokenize, got dict, converting to string")
tokens = extractable.get_tokens(tokenizer)
if tokens:
extracted_results = extractor.extract(tokens, **options)
else:
raise ExtractorValueError(
"Extractor needs string, tokenizer needs string to tokenize, got " + str(type(extractable.value)))
elif extractor.input_type == InputType.TEXT:
if self.etk.error_policy == ErrorPolicy.PROCESS:
if isinstance(extractable.value, list):
self.etk.log("Extractor needs string, got extractable value as list, converting to string",
"warning", self.doc_id, self.url)
warnings.warn("Extractor needs string, got extractable value as list, converting to string")
elif isinstance(extractable.value, dict):
self.etk.log("Extractor needs string, got extractable value as dict, converting to string",
"warning", self.doc_id, self.url)
warnings.warn("Extractor needs string, got extractable value as dict, converting to string")
text = extractable.get_string(joiner)
if text:
extracted_results = extractor.extract(text, **options)
else:
# raise ExtractorValueError("Extractor needs string, got " + str(type(extractable.value)))
# TODO: Yixiang - needs to be handled properly
pass
elif extractor.input_type == InputType.OBJECT:
extracted_results = extractor.extract(extractable.value, **options)
elif extractor.input_type == InputType.HTML:
if bool(BeautifulSoup(extractable.value, "html.parser").find()):
extracted_results = extractor.extract(extractable.value, **options)
else:
# raise ExtractorValueError("Extractor needs HTML, got non HTML string")
# TODO: Yixiang - needs to be handled properly
pass
try:
jsonPath = extractable.full_path
except AttributeError:
jsonPath = None
for e in extracted_results:
# for the purpose of provenance hierarrchy tracking, a parent's id for next generation.
e.prov_id = self.provenance_id_index
extraction_provenance_record: ExtractionProvenanceRecord = ExtractionProvenanceRecord(
e.prov_id, jsonPath, e.provenance["extractor_name"],
e.provenance["start_char"], e.provenance["end_char"], e.provenance["confidence"], self,
extractable.prov_id)
self._provenances[e.prov_id] = extraction_provenance_record
# for the purpose of provenance hierarchy tracking
self.provenance_id_index_incrementer()
self.create_provenance(extraction_provenance_record)
return extracted_results
def test_Extractable(self) -> None:
e = Extractable({
'extracted_value': [{
1: 2,
'das': [1, 2, 3]
}],
'confidence': 2.3
})
t = Tokenizer(keep_multi_space=False)
tokens = e.get_tokens(t)
token_attrs = []
for i in tokens:
token_attrs.append({
"orth": i.orth_,
"offset": i.idx,
"full_shape": i._.full_shape
})
expected_token = [{
'orth': 'extracted',
'offset': 0,
'full_shape': 'xxxxxxxxx'
}, {
'orth': '_',
'offset': 9,
'full_shape': '_'
}, {
'orth': 'value',
'offset': 10,
'full_shape': 'xxxxx'
}, {
'orth': ':',
'offset': 16,
'full_shape': ':'
}, {
'orth': '1',
'offset': 18,
'full_shape': 'd'
}, {
'orth': ':',
'offset': 20,
'full_shape': ':'
}, {
'orth': '2',
'offset': 22,
'full_shape': 'd'
}, {
'orth': 'das',
'offset': 24,
'full_shape': 'xxx'
}, {
'orth': ':',
'offset': 28,
'full_shape': ':'
}, {
'orth': '1',
'offset': 30,
'full_shape': 'd'
}, {
'orth': '2',
'offset': 32,
'full_shape': 'd'
}, {
'orth': '3',
'offset': 34,
'full_shape': 'd'
}, {
'orth': 'confidence',
'offset': 36,
'full_shape': 'xxxxxxxxxx'
}, {
'orth': ':',
'offset': 47,
'full_shape': ':'
}, {
'orth': '2.3',
'offset': 49,
'full_shape': 'd.d'
}]
self.assertEqual(token_attrs, expected_token)
text = e.get_string()
expected_str = "extracted_value : 1 : 2 das : 1 2 3 confidence : 2.3 "
self.assertEqual(text, expected_str)