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