def __init__(
self,
tokenizer: algorithm(Sentence(), List(Word())),
feature_extractor: algorithm(Word(), Flags()),
include_text: Boolean(),
):
self.tokenizer = tokenizer
self.feature_extractor = feature_extractor
self.include_text = include_text
def test_meta_pipeline_graph():
# Test List algorithm generation
build_pipeline_graph(input=List(Word()),
output=List(Word()),
registry=[WordToWordAlgorithm])
# Test Tuple breakdown feature
build_pipeline_graph(input=Tuple(Word(), Matrix()),
output=Text(),
registry=[WordToWordAlgorithm])
# Test Tuple breakdown feature and List algorithm generation
build_pipeline_graph(input=Tuple(List(Word()), Matrix()),
output=List(Word()),
registry=[WordToWordAlgorithm])
def __init__(
self,
lowercase: Boolean(),
stopwords_remove: Boolean(),
binary: Boolean(),
inner_tokenizer: algorithm(Sentence(), List(Word())),
inner_stemmer: algorithm(Word(), Stem()),
inner_stopwords: algorithm(List(Word()), List(Word())),
):
self.stopwords_remove = stopwords_remove
self.inner_tokenizer = inner_tokenizer
self.inner_stemmer = inner_stemmer
self.inner_stopwords = inner_stopwords
SklearnTransformer.__init__(self)
_CountVectorizer.__init__(self, lowercase=lowercase, binary=binary)
def test_simple_pipeline_graph():
graph = build_pipeline_graph(input=MatrixContinuousDense()
,output= MatrixContinuousDense()
,registry=[ExactAlgorithm, HigherInputAlgorithm, LowerOutputAlgorithm]).graph
assert_graph(graph, 3, 3, 6)
graph = build_pipeline_graph(input=List(Text())
,output= Document()
,registry=[WordToWordAlgorithm,
TextToWordAlgorithm,
WordToWordListAlgorithm,
WordListToSentenceAlgorithm,
WordListToSentenceAlgorithm,
SentenceListToDocumentAlgorithm,
TextListToDocumentAlgorithm]).graph
assert_graph(graph, 2, 2, 12)
graph = build_pipeline_graph(input=List(Word())
,output=Document()
,registry=[WordToWordAlgorithm,
TextToWordAlgorithm,
WordToWordListAlgorithm,
WordListToSentenceAlgorithm,
WordListToSentenceAlgorithm,
SentenceListToDocumentAlgorithm,
TextListToDocumentAlgorithm]).graph
assert_graph(graph, 2, 1, 10)
def run(
self, input: Word(domain='general', language='spanish')) -> Summary():
"""This method use Word2Vect of gensim for tranform a word in embedding vector.
"""
try:
return wikipedia.summary(input)
except:
return ""
def run(
self, input: Word(domain="general", language="spanish")
) -> ContinuousVector():
"""This method use Word2Vec of gensim for tranform a word in embedding vector.
"""
try:
return self.model.get_vector(input.lower())
except KeyError:
return np.zeros(400)
def __init__(
self,
extractors: Distinct(
algorithm(Word(), Flags()), exceptions=["MultipleFeatureExtractor"]
),
merger: algorithm(List(Flags()), Flags()),
):
self.extractors = extractors
self.merger = merger
def test_build_pipeline_graph():
test_meta_pipeline_graph()
test_simple_pipeline_graph()
#test failed graph generation
assert_pipeline_graph_failed(Text(), Word(), [])
assert_pipeline_graph_failed(Text(),
Document(),
[WordToWordAlgorithm,
TextToWordAlgorithm,
WordToWordListAlgorithm,
SentenceListToDocumentAlgorithm,
TextListToDocumentAlgorithm])
def run(self, input: Sentence()) -> Tuple(List(Word()), List(Flags())):
tokenized = self.nlp(input)
tokens = []
flags = []
for token in tokenized:
token_flags = {}
if self.extract_lemma:
token_flags["lemma"] = token.lemma_
if self.extract_pos_tag:
token_flags["pos"] = token.pos_
for kv in token.tag_.split("|"):
kv = kv.split("=")
if len(kv) == 2:
token_flags["tag_" + kv[0]] = kv[1]
else:
token_flags["tag_" + kv[0]] = True
if self.extract_dep:
token_flags["dep"] = token.dep_
if self.extract_entity:
token_flags["ent_type"] = token.ent_type_
token_flags["ent_kb_id"] = token.ent_kb_id_
if self.extract_details:
token_flags["is_alpha"] = token.is_alpha
token_flags["is_ascii"] = token.is_ascii
token_flags["is_digit"] = token.is_digit
token_flags["is_lower"] = token.is_lower
token_flags["is_upper"] = token.is_upper
token_flags["is_title"] = token.is_title
token_flags["is_punct"] = token.is_punct
token_flags["is_left_punct"] = token.is_left_punct
token_flags["is_right_punct"] = token.is_right_punct
token_flags["is_space"] = token.is_space
token_flags["is_bracket"] = token.is_bracket
token_flags["is_quote"] = token.is_quote
token_flags["is_currency"] = token.is_currency
token_flags["like_url"] = token.like_url
token_flags["like_num"] = token.like_num
token_flags["like_email"] = token.like_email
token_flags["is_oov"] = token.is_oov
token_flags["is_stop"] = token.is_stop
if self.extract_sentiment:
token_flags["sentiment"] = token.sentiment
tokens.append(token.text)
flags.append(token_flags)
return tokens, flags
def generated_classifier_from_dataset(data: Collection,
number_of_models: int = 5):
models = []
lines, classes = load_training_entities(data)
unique_clases = reduce(lambda x, y: x | y, [set(c) for c in classes])
for _ in range(number_of_models):
classifier = AutoML(
input=ag_List(ag_List(Word())),
output=ag_List(ag_List(Postag())),
)
classifier.fit([[w.text for w in l] for l in lines], classes)
models.append(classifier)
return SentencesAnnotator(models=models,
collection_base=data,
unique_classes=unique_clases)
def __init__(self, tokenizer: algorithm(Sentence(), List(Word()))):
self.tokenizer = tokenizer
def run(self, input: List(Word())) -> List(Vector()):
pass
def run(self, input: Sentence()) -> List(Word()):
pass
def run(self, input: Word()) -> Flags():
r_exp = self._regex()
b = re.fullmatch(r_exp, input) if self.full else re.search(r_exp, input)
return {f"is_{self._name}_regex": bool(b)}
parser.add_argument("--token", default=None)
parser.add_argument("--channel", default=None)
args = parser.parse_args()
print(args)
# ## Experimentation
# Instantiate the classifier.
# Note that the input and output types here are defined to match the problem statement,
# i.e., entity recognition.
classifier = AutoML(
search_algorithm=PESearch,
input=List(List(Word())),
output=List(List(Postag())),
search_iterations=args.iterations,
score_metric=meddocan.F1_beta,
cross_validation_steps=1,
search_kwargs=dict(
pop_size=args.popsize,
search_timeout=args.global_timeout,
evaluation_timeout=args.timeout,
memory_limit=args.memory * 1024 ** 3,
),
)
# This custom logger is used for debugging purposes, to be able later to recover
# the best pipelines and all the errors encountered in the experimentation process.
def run(self, input:Word()) -> List(Word()):
pass
def __init__(self,
tokenizer: algorithm(Sentence(), List(Word())),
token_feature_extractor: algorithm(Word(), Flags()),
# token_sentence_encoder: algorithm(Word(), )
):
pass
def run(
self, input: Tuple(Sentence(), List(Entity()))
) -> Tuple(List(Word()), List(Postag())):
pass
def run(self, input:Text()) -> Word():
pass
def run(self, input:List(Word())) -> Sentence():
pass
def run(self, input: Word()) -> Flags():
flags = [extractor.run(input) for extractor in self.extractors]
return self.merger.run(flags)
def run(
self, input: Word(domain='general', language='spanish')) -> Flags():
"""This method use Word2Vect of gensim for tranform a word in embedding vector.
"""
return dict(in_wikipedia=bool(wikipedia.search(input)))
def run(self, input:Word()) -> Word():
pass
