def __init__(self, model):
self.nlp = model
self.duckling_wrapper = DucklingWrapper(parse_datetime=True)
self.stanford_ner = StanfordNERTagger(
'/Users/mac/stanford-tools/stanford-ner-2018-10-16/classifiers/english.all.3class.distsim.crf.ser.gz',
'/Users/mac/stanford-tools/stanford-ner-2018-10-16/stanford-ner.jar',
encoding='utf-8')
def pipeline_init(self, language):
# type: (Text, Text) -> None
from duckling import DucklingWrapper
if self.duckling is None:
try:
self.duckling = DucklingWrapper(language=language) # languages in duckling are eg "de$core"
except ValueError as e:
raise Exception("Duckling error. {}".format(e))
def test():
##
# print(str(DucklingWrapper()))
# result = time_extract(u'received claims on 28th and 27th ')
# print(str(result).replace('[','(').replace(']',')'))
result = time_extract(u'received claims in last two months')
print(str(result).replace('[','(').replace(']',')'))
# # print(str(type(result[0]['value']['value'])))
##
result = DucklingWrapper().parse_time(u'received claims in last two months')
print(str(result))
# test()
# print(str(time_extract("received claims in yesterday")))
# result = DucklingWrapper().parse_time(u'received claims on Jan,28')
# print(str(result))
# result = DucklingWrapper().parse_time(u'received claims on Jan 28')
# print(str(result))
# result = DucklingWrapper().parse_time(u'received claims on January 28')
# print(str(result))
# result = DucklingWrapper().parse_time(u'received claims on January 28')
# print(str(result))
def create_duckling_wrapper(cls, language):
from duckling import DucklingWrapper
try:
# languages in duckling are eg "de$core"
return DucklingWrapper(language=language)
except ValueError as e: # pragma: no cover
raise Exception("Duckling error. {}".format(e))
def init_duck():
"cache duckling parser"
if 'duck' not in d:
logger.info("re/initializing parser, JVMStarted_state = %d" %
jpype.isJVMStarted())
duck = DucklingWrapper()
d['duck'] = duck
logger.info("duck ready to parse....")
def __init__(self,
name: str,
source_key: str = None,
overwrite: bool = False,
source_iter: Callable[[List[str]],
Iterator[IO[AnyStr]]] = file_iter,
output_handler: Callable[[str, Dict[str, Any]], None] = oh):
super().__init__(name, source_key, overwrite)
self.__source_iter = source_iter
self.__output_handler = output_handler
root_path = Path(__file__).parent.parent
entities_path = str(root_path / 'config/entities.csv')
self.entity_reverse_lookup, synonyms, self.regexprs = load_entities(
entities_path)
self.keyword_processor = prepare_keyword_processor(synonyms)
duckling_entities = {ENTITY_DATE, ENTITY_NUMBER}
tagger_entities = {ENTITY_PERSON}
if len(duckling_entities.intersection(ENABLED_SYSTEM_ENTITIES)) > 0:
self.d = DucklingWrapper()
if len(tagger_entities.intersection(ENABLED_SYSTEM_ENTITIES)) > 0:
self.tagger = SequenceTagger.load('ner')
class NERExtractorClass():
def __init__(self , model='en'):
self.nlp = spacy.load(model)
self.duckling_wrapper = DucklingWrapper(parse_datetime=True)
def spacy_parse(self ,text):
# https://spacy.io/api/annotation#named-entities
doc = self.nlp(text)
ner = []
for e in doc.ents:
tmp = {"text":e.text,"label":e.label_}
ner.append(tmp)
return ner
def duckling_parse(self, text):
weekend = 'by the end of the weekend'
asap = 'the end of the day'
text = text.lower()
text += " "
text = text.replace("the end of the week ",weekend).replace("the end of week ",weekend).replace("end of week ",weekend).replace("end of the week ",weekend)
text = text.replace("asap",asap).replace("as soon as possible",asap)
result = self.duckling_wrapper.parse_time(text)
return result
def parse(self, text ,method='spacy'):
if method == 'spacy':
return self.spacy_parse(text)
if method == 'dickling':
return self.duckling_parse(text)
return {}
from nltk.stem.wordnet import WordNetLemmatizer
import sys
import re
from nltk.classify import Senna
from nltk.stem.wordnet import WordNetLemmatizer
pipeline = Senna('/usr/share/senna-v3.0', ['pos', 'chk', 'ner'])
lmtzr = WordNetLemmatizer()
#Stop Words generation
stop_words = []
file_open_stopwords = open('stop_words.txt', 'r')
for each_word in file_open_stopwords:
stop_words.append(each_word.strip())
file_open_stopwords.close()
d = DucklingWrapper()
leave_application_json = {}
context_stack = []
leave_application_previous = {}
def intent_extractor(message):
file_Name = "intent_leave_tfidf.p"
fileObject = open(file_Name, 'rb')
passive_tfidf = pickle.load(fileObject)
fileObject.close()
file_Name_lsa = "intent_leave_lsa.p"
fileObject_lsa = open(file_Name_lsa, 'rb')
passive_lsa = pickle.load(fileObject_lsa)
fileObject_lsa.close()
class DucklingExtractor(Component):
"""Adds entity normalization by analyzing found entities and transforming them into regular formats."""
name = "ner_duckling"
context_provides = {
"process": ["entities"],
}
output_provides = ["entities"]
def __init__(self, duckling_processing_mode, duckling=None):
# type: (Text, Optional[DucklingWrapper]) -> None
self.duckling_processing_mode = duckling_processing_mode
self.duckling = duckling
@classmethod
def required_packages(cls):
# type: () -> List[Text]
return ["duckling"]
@classmethod
def create(cls, duckling_processing_mode):
if duckling_processing_mode not in DUCKLING_PROCESSING_MODES:
raise ValueError(
"Invalid duckling processing mode. Got '{}'. Allowed: {}".
format(duckling_processing_mode,
", ".join(DUCKLING_PROCESSING_MODES)))
return DucklingExtractor(duckling_processing_mode)
@classmethod
def cache_key(cls, model_metadata):
# type: (Metadata) -> Text
return cls.name + "-" + model_metadata.language
def pipeline_init(self, language):
# type: (Text, Text) -> None
from duckling import DucklingWrapper
if self.duckling is None:
try:
self.duckling = DucklingWrapper(
language=language
) # languages in duckling are eg "de$core"
except ValueError as e:
raise Exception("Duckling error. {}".format(e))
def process(self, text, entities):
# type: (Text, List[Dict[Text, Any]], Text) -> Dict[Text, Any]
if self.duckling is not None:
parsed = self.duckling.parse(text)
for duckling_match in parsed:
for entity in entities:
if entity["start"] == duckling_match["start"] and entity[
"end"] == duckling_match["end"]:
entity["value"] = duckling_match["value"]["value"]
entity["duckling"] = duckling_match["dim"]
break
else:
if self.duckling_processing_mode == "append":
# Duckling will retrieve multiple entities, even if they overlap..
# hence the append mode might add some noise to the found entities
entities.append({
"entity":
duckling_match["dim"],
"duckling":
duckling_match["dim"],
"value":
duckling_match["value"]["value"],
"start":
duckling_match["start"],
"end":
duckling_match["end"],
})
return {"entities": entities}
@classmethod
def load(cls, duckling_processing_mode):
# type: (Text) -> DucklingExtractor
return cls.create(duckling_processing_mode)
from flask import Flask, request, jsonify, make_response
from duckling import DucklingWrapper, Language
from dotenv import load_dotenv
import os
import unicodedata
load_dotenv()
app = Flask(__name__)
duck = DucklingWrapper(language=Language.SPANISH, maximum_heap_size='512m')
DUCKLING_HOST = os.getenv("DUCKLING_HOST")
DUCKLING_PORT = os.getenv("DUCKLING_PORT")
def normalize(string: str):
res = unicodedata.normalize('NFKD',
string).encode('ascii',
'ignore').decode('utf-8')
return res
def _parse_date(sent: str):
if sent is None:
return None
ans = duck.parse_time(sent)
precedence = ["year", "month", "day", "hour", "minute", "second"]
if len(ans) > 0:
text = ans[0]["text"]
val = ans[0]["value"]["value"]
if "grain" in ans[0]["value"]:
if normalize(text.lower()) not in [
def duckling_wrapper():
return DucklingWrapper()
def duckling_wrapper_with_datetime():
return DucklingWrapper(parse_datetime=True)
def __init__(self , model='en'):
self.nlp = spacy.load(model)
self.duckling_wrapper = DucklingWrapper(parse_datetime=True)
from duckling import DucklingWrapper
import json
d = DucklingWrapper()
while 1:
try:
sentence = raw_input("Enter: ")
dic = {'out_put': d.parse_time(sentence)}
print d.parse_time(sentence)
file_open = open('duckling_time_new.json', 'w')
json.dump(dic, file_open)
file_open.close()
except Exception, e:
print e
def __init__(self, entities_types):
self.entities_types_list = entities_types
self.ducklingInstance = DucklingWrapper()
def duckling(cls):
return DucklingWrapper()
def test():
print(str(DucklingWrapper()))
result = time_extract(u'received claims on 28th and 27th ')
print(str(result).replace('[', '(').replace(']', ')'))
class ExtractEntitiesStep(AbstractStep):
"""
Extract entities from collected text.
"""
def __init__(self,
name: str,
source_key: str = None,
overwrite: bool = False,
source_iter: Callable[[List[str]],
Iterator[IO[AnyStr]]] = file_iter,
output_handler: Callable[[str, Dict[str, Any]], None] = oh):
super().__init__(name, source_key, overwrite)
self.__source_iter = source_iter
self.__output_handler = output_handler
root_path = Path(__file__).parent.parent
entities_path = str(root_path / 'config/entities.csv')
self.entity_reverse_lookup, synonyms, self.regexprs = load_entities(
entities_path)
self.keyword_processor = prepare_keyword_processor(synonyms)
duckling_entities = {ENTITY_DATE, ENTITY_NUMBER}
tagger_entities = {ENTITY_PERSON}
if len(duckling_entities.intersection(ENABLED_SYSTEM_ENTITIES)) > 0:
self.d = DucklingWrapper()
if len(tagger_entities.intersection(ENABLED_SYSTEM_ENTITIES)) > 0:
self.tagger = SequenceTagger.load('ner')
def process_file(self, file: IO[AnyStr], path: str,
control_data: Dict[str, Any], logger: Logger,
accumulator: Dict[str, Any]) -> None:
logger.debug('process file: {}'.format(file.name))
input_doc = json.load(file)
metadata = input_doc['metadata']
record_id = metadata['record_id']
data = input_doc['data']
text = data['text']
nlp_text = []
for t in text:
entities = []
keywords_found = self.keyword_processor.extract_keywords(
t, span_info=True)
for keyword in keywords_found:
entities.append({
'entity':
self.entity_reverse_lookup[keyword[0]],
'location':
keyword[1:],
'value':
keyword[0],
'confidence':
1.0
})
matches = match_regexprs(t, self.regexprs)
for match in matches:
match['entity'] = self.entity_reverse_lookup[match['value']]
entities.extend(matches)
entities.extend(self.match_system_entities(t))
# is the span of an entity contained within the span
# of another entity
def is_contained(entity):
start, end = entity['location']
for ent in entities:
s, e = ent['location']
# exclude exact span matches
if (start == s and end < e) or (
start > s and end == e) or (start > s and end < e):
return True
return False
def is_valid(entity):
# remove spurious dates
if entity['entity'] == 'sys-date':
start, end = entity['location']
if (end - start) < 8:
return False
value = entity['value']
if isinstance(value, str):
try:
date = parse(value)
except ValueError:
return False
year = date.year
if year < 1990 or year > 2025:
return False
return True
# keep the entity with the longest span where an entity
# is contained within the span of another
pruned_entities = [
ent for ent in entities
if not is_contained(ent) and is_valid(ent)
]
nlp_text.append({'text': t, 'entities': pruned_entities})
now = datetime.utcnow().isoformat()
write_root_dir = control_data['job']['write_root_dir']
step_name = convert_name_to_underscore(self.name)
output_filename = '{}_{}.json'.format(step_name, record_id)
output_path = os.path.join(write_root_dir, step_name, output_filename)
data = {}
data['nlp_text'] = nlp_text
content = {'metadata': metadata, 'data': data}
accumulator['files_output'].append({
'filename': output_filename,
'input': path,
'path': output_path,
'status': 'processed',
'time': now
})
self.__output_handler(output_path, content)
def run(self, control_data: Dict[str, Any], logger: Logger,
accumulator: Dict[str, Any]) -> None:
file_paths = [x['path'] for x in control_data[self.source_key]]
step_name = convert_name_to_underscore(self.name)
processed_file_paths = {}
if step_name in control_data:
for x in control_data[step_name]:
if x['status'] == 'processed':
processed_file_paths[x['input']] = x
for file, path in self.__source_iter(file_paths):
if not self._overwrite and path in processed_file_paths.keys():
accumulator['files_output'].append(processed_file_paths[path])
continue
self.process_file(file, path, control_data, logger, accumulator)
def match_system_entities(self, utter):
matches = []
if ENTITY_DATE in ENABLED_SYSTEM_ENTITIES:
results = self.d.parse_time(utter)
for result in results:
matches.append({
'entity': 'sys-date',
'location': [result['start'], result['end']],
'value': result['value']['value'],
'confidence': 1.0
})
if ENTITY_NUMBER in ENABLED_SYSTEM_ENTITIES:
results = self.d.parse_number(utter)
for result in results:
matches.append({
'entity': 'sys-number',
'location': [result['start'], result['end']],
'value': result['value']['value'],
'confidence': 1.0
})
sentence = None
if ENTITY_PERSON in ENABLED_SYSTEM_ENTITIES:
if sentence is None:
sentence = Sentence(utter)
self.tagger.predict(sentence)
for entity in sentence.get_spans('ner'):
if entity.tag == 'PER':
matches.append({
'entity':
'sys-person',
'location': [entity.start_pos, entity.end_pos],
'value':
entity.text,
'confidence':
entity.score
})
return matches
class DucklingExtractor(EntityExtractor):
"""Adds entity normalization by analyzing found entities and transforming them into regular formats."""
name = "ner_duckling"
context_provides = {
"process": ["entities"],
}
output_provides = ["entities"]
@staticmethod
def available_dimensions():
from duckling.dim import Dim
return [
m[1] for m in getmembers(Dim)
if not m[0].startswith("__") and not m[0].endswith("__")
]
def __init__(self, dimensions=None, duckling=None):
# type: (Text, Optional[DucklingWrapper]) -> None
self.dimensions = dimensions if dimensions else self.available_dimensions(
)
self.duckling = duckling
@classmethod
def required_packages(cls):
# type: () -> List[Text]
return ["duckling"]
@classmethod
def create(cls, duckling_dimensions):
if duckling_dimensions is None:
duckling_dimensions = cls.available_dimensions()
unknown_dimensions = [
dim for dim in duckling_dimensions
if dim not in cls.available_dimensions()
]
if len(unknown_dimensions) > 0:
raise ValueError(
"Invalid duckling dimension. Got '{}'. Allowed: {}".format(
", ".join(unknown_dimensions),
", ".join(cls.available_dimensions())))
return DucklingExtractor(duckling_dimensions)
@classmethod
def cache_key(cls, model_metadata):
# type: (Metadata) -> Text
return cls.name + "-" + model_metadata.language
def pipeline_init(self, language):
# type: (Text, Text) -> None
from duckling import DucklingWrapper
if self.duckling is None:
try:
self.duckling = DucklingWrapper(
language=language
) # languages in duckling are eg "de$core"
except ValueError as e: # pragma: no cover
raise Exception("Duckling error. {}".format(e))
def process(self, text, entities):
# type: (Text, List[Dict[Text, Any]]) -> Dict[Text, Any]
extracted = []
if self.duckling is not None:
matches = self.duckling.parse(text)
relevant_matches = [
match for match in matches if match["dim"] in self.dimensions
]
for match in relevant_matches:
entity = {
"start": match["start"],
"end": match["end"],
"text": match["text"],
"value": match["value"],
"entity": match["dim"]
}
extracted.append(entity)
extracted = self.add_extractor_name(extracted)
entities.extend(extracted)
return {"entities": entities}
def persist(self, model_dir):
# type: (Text) -> Dict[Text, Any]
file_name = self.name + ".json"
full_name = os.path.join(model_dir, file_name)
with io.open(full_name, 'w') as f:
f.write(str(json.dumps({"dimensions": self.dimensions})))
return {"ner_duckling_persisted": file_name}
@classmethod
def load(cls, model_dir, ner_duckling_persisted):
# type: (Text) -> DucklingExtractor
persisted = os.path.join(model_dir, ner_duckling_persisted)
if os.path.isfile(persisted):
with io.open(persisted, encoding='utf-8') as f:
persisted_data = json.loads(f.read())
return cls.create(persisted_data["dimensions"])
class NERExtractorClass():
def __init__(self, model):
self.nlp = model
self.duckling_wrapper = DucklingWrapper(parse_datetime=True)
self.stanford_ner = StanfordNERTagger(
'/Users/mac/stanford-tools/stanford-ner-2018-10-16/classifiers/english.all.3class.distsim.crf.ser.gz',
'/Users/mac/stanford-tools/stanford-ner-2018-10-16/stanford-ner.jar',
encoding='utf-8')
def spacy_extract_persons_from_noun_chunks_and_NNP(self, doc):
persons1 = []
nsubjs = []
for w in doc:
if "nsubj" == w.dep_:
nsubjs.append(w.text)
for w in doc.noun_chunks:
for sub in nsubjs:
if sub in w.text:
persons1.append({"text": w.text, "label": "PERSON"})
persons2 = [{
"text": str(x),
"label": "PERSON"
} for x in doc if x.tag_ == "NNP"]
persons = persons1 + persons2
persons = [dict(t) for t in {tuple(d.items()) for d in persons}]
return persons
def cust_person_spacy_parse(self, text):
# https://spacy.io/api/annotation#named-entities
doc = self.nlp(text)
ner = []
for e in doc.ents:
if e.label_ != "PERSON":
tmp = {"text": e.text, "label": e.label_}
ner.append(tmp)
p_ner = self.spacy_extract_persons_from_noun_chunks_and_NNP(doc)
ner += p_ner
return ner
def spacy_extract_nouns(self, text):
doc = self.nlp(text)
persons = []
for w in doc:
if w.pos_ == "PRON" and w.text.lower() != "it":
tmp = {"text": w.text, "label": "PERSON"}
persons.append(tmp)
return persons
def gazetteer_parse(self, text, ner):
ner = gazetteer_tag(text, ner)
return ner
def cust_person_stanford_parse(self, text):
# https://spacy.io/api/annotation#named-entities
ner = self.stanford_parse(text)
p_ner = self.spacy_extract_nouns(text)
ner += p_ner
g_ner = self.gazetteer_parse(text, ner)
ner += g_ner
tokenized_text = word_tokenize(text)
new_ner = []
last_index = 0
print(ner)
for n in ner:
ner_text = " ".join([n['text'] for n in new_ner])
# print(ner_text)
if n['text'].lower() not in ner_text.lower() and n['label'] != 'O':
# print(n['text'])
if len(n['text'].split()) == 1:
current_index = tokenized_text.index(n['text'])
if abs(current_index - last_index
) == 1 and new_ner[-1]["label"] == n['label']:
if current_index - last_index == 1:
new_ner[-1]["text"] += " " + n['text']
else:
new_ner[-1][
"text"] = n['text'] + " " + new_ner[-1]["text"]
else:
tmp = {"text": n['text'], "label": n['label']}
new_ner.append(tmp)
last_index = current_index
else:
tmp = {"text": n['text'], "label": n['label']}
new_ner.append(tmp)
return new_ner
def spacy_parse(self, text):
# https://spacy.io/api/annotation#named-entities
doc = self.nlp(text)
ner = []
for e in doc.ents:
tmp = {"text": e.text, "label": e.label_}
ner.append(tmp)
return ner
def stanford_parse(self, text):
tokenized_text = word_tokenize(text)
classified_text = self.stanford_ner.tag(tokenized_text)
ner = []
for w, t in classified_text:
tmp = {"text": w, "label": t}
ner.append(tmp)
return ner
def duckling_parse(self, text):
weekend = 'by the end of the weekend'
asap = 'the end of the day'
text = text.lower()
text += " "
text = text.replace("the end of the week ", weekend).replace(
"the end of week ",
weekend).replace("end of week ",
weekend).replace("end of the week ", weekend)
text = text.replace("asap", asap).replace("as soon as possible", asap)
result = self.duckling_wrapper.parse_time(text)
return result
def parse(self, text, method='spacy'):
if method == 'spacy':
return self.spacy_parse(text)
elif method == 'stanford':
return self.stanford_parse(text)
elif method == 'gazetteer':
return self.gazetteer_parse(text)
elif method == 'cust_PERSON_spacy':
return self.cust_person_spacy_parse(text)
elif method == 'cust_PERSON_stanford':
return self.cust_person_stanford_parse(text)
elif method == 'dickling':
return self.duckling_parse(text)
return {}
# import pytest
# from datetime import time, date, timedelta, datetime
# from dateutil import parser
# from dateutil.tz import tzlocal
from datetime import datetime
from duckling import DucklingWrapper, Dim
# from autocorrect import spell
import calendar
import traceback
DW_obj = DucklingWrapper()
def is_future_date(s): ##### future date in respective of year
# print("eneted future")
s = s + " 00:00:00"
# print(s)
date_format = "%Y-%m-%d %H:%M:%S"
start = datetime.strptime(s, date_format)
now = datetime.now()
dt = s
if start > now:
dt = dt.replace(str(int(dt.split('-')[0])),
str(int(str(datetime.now().year)) - 1))
return str(dt).split(' ')[0]
else:
return s
def is_future_date_day(s): ##### future date in respective of month
# print("eneted future")
ent['value'] = txt
ent['entity'] = 'product'
sentence['entities'].append(ent)
sentence['text'] += txt + " "
while random.random() > .5:
m = random.choice(middle)
sentence['text'] += m
txt = df.sample().iloc[0, 0]
ent = dict()
ent['start'] = len(sentence['text'])
ent['end'] = len(sentence['text'] + txt)
ent['value'] = txt
ent['entity'] = 'product'
sentence['entities'].append(ent)
sentence['text'] += txt + " "
sentence['text'] += random.choice(end)
train_data['rasa_nlu_data']["common_examples"].append(sentence)
with open('result.json', 'w+') as fp:
json.dump(train_data, fp)
container = IntentContainer('intent_cache')
d = DucklingWrapper()
d.parse('Bring me 250 ml sugar')
d.parse_
print(d.parse_time(u'Let\'s meet at 11:45am'))
print(d.parse_number(u'Bring me one conserve of ravioli'))
print(d.parse_quantity(u'Bring me 100 g of sugar'))