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server_interface.py
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server_interface.py
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import sched, time
import json
import math
from os import mkdir, path as os_path
base_path = os_path.dirname(os_path.abspath(__file__))
cache_path = os_path.join(base_path,'cache','edu')
document_path = os_path.join(base_path,'documents','edu')
# from knowpy.models.knowledge_extraction.ontology_builder import OntologyBuilder
from knowpy.models.knowledge_extraction.knowledge_graph_builder import KnowledgeGraphBuilder
from knowpy.models.reasoning.question_answerer import QuestionAnswerer
from knowpy.models.reasoning.question_answerer_naive import QuestionAnswererNaive
from knowpy.models.reasoning.adaptive_question_answerer import AdaptiveQuestionAnswerer
from knowpy.models.knowledge_extraction.knowledge_graph_manager import KnowledgeGraphManager
from knowpy.models.knowledge_extraction.couple_extractor import filter_invalid_sentences
from knowpy.models.knowledge_extraction.question_answer_extractor import QuestionAnswerExtractor
from knowpy.misc.doc_reader import load_or_create_cache, DocParser, get_document_list
from knowpy.misc.graph_builder import get_concept_description_dict, get_betweenness_centrality, save_graphml
from knowpy.misc.levenshtein_lib import labels_are_contained, remove_similar_labels
from knowpy.misc.utils import *
from more_itertools import unique_everseen
from tqdm import tqdm
from pathos.multiprocessing import ProcessingPool as Pool
import sys
import logging
logger = logging.getLogger('knowpy')
# logger.setLevel(logging.INFO)
logger.setLevel(logging.ERROR)
logger.addHandler(logging.StreamHandler(sys.stdout))
# import sys
# _,information_units,model_family = sys.argv
# information_units = information_units.casefold()
# model_family = model_family.casefold()
MAX_HISTORY_LEN = 0
WITH_ANNOTATIONS = False
ADAPTIVE_QA = True
DOCUMENTS_PER_CHUNK = 2**9
PARAGRAPHS_PER_CHUNK = 2**15
EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES = False # set it to True to reduce the number of considered Q/A and memory footprint
AVOID_JUMPS = True
AVOID_COREFERENCING = False
MIN_EXPLAINABILITY= 0.55
ADAPTIVITY_OPTIONS= {
'remove_old_content': False,
'old_content_confidence_scaling_factor': 0.66,
}
OPTIMAL_ARCHETYPE_IDENTIFICATION_OPTIONS = {
'max_question_length': 50,
'valid_question_type_set': set([
'disco', # elementary discourse units
'qaamr', # abstract meaning representations
]),
'question_to_question_max_similarity_threshold': 1,
'explanatory_sentence_horizon': 10,
}
OVERVIEW_OPTIONS = {
'answer_horizon': 2,
'question_horizon': 4,
######################
## QuestionAnswerer stuff
'tfidf_importance': 0,
'answer_pertinence_threshold': 0.57,
'answer_to_question_max_similarity_threshold': None,
'answer_to_answer_max_similarity_threshold': 0.85,
'use_weak_pointers': False,
'top_k': 2,
######################
'keep_the_n_most_similar_concepts': 1,
'query_concept_similarity_threshold': 0.55,
'include_super_concepts_graph': False,
'include_sub_concepts_graph': True,
'consider_incoming_relations': True,
######################
'sort_archetypes_by_relevance': True,
'minimise': True,
}
DOX_OPTIONS = dict(OVERVIEW_OPTIONS)
DOX_OPTIONS.update({
######################
## ExplainabilityEstimator stuff
'set_of_archetypes_to_consider': None, # set(['why','how'])
'answer_horizon': None,
'remove_duplicate_answer_sentences': True,
######################
## QuestionAnswerer stuff
'tfidf_importance': 0,
'use_weak_pointers': False,
'answer_pertinence_threshold': None,
'answer_to_question_max_similarity_threshold': None,
'answer_to_answer_max_similarity_threshold': None,
'top_k': 100,
######################
})
ARCHETYPE_FITNESS_OPTIONS = {
'only_overview_exploration': False,
'answer_pertinence_threshold': 0.15,
'answer_to_question_max_similarity_threshold': None,
'answer_to_answer_max_similarity_threshold': 0.85,
}
OQA_OPTIONS = {
'answer_horizon': 20,
######################
## QuestionAnswerer stuff
'answer_pertinence_threshold': 0.15,
'tfidf_importance': 1/2,
'answer_to_question_max_similarity_threshold': None,
'answer_to_answer_max_similarity_threshold': 0.85,
'use_weak_pointers': False,
'top_k': 100,
'keep_the_n_most_similar_concepts': 2,
'query_concept_similarity_threshold': 0.55,
'add_external_definitions': False,
'include_super_concepts_graph': True,
'include_sub_concepts_graph': True,
'consider_incoming_relations': True,
}
QA_EXTRACTOR_OPTIONS = {
'models_dir': 'question_extractor/data/models',
# 'sbert_model': {
# 'url': 'facebook-dpr-question_encoder-multiset-base', # model for paraphrase identification
# 'use_cuda': True,
# },
'tf_model': {
# 'url': 'https://tfhub.dev/google/universal-sentence-encoder-qa2/3', # English QA
'url': 'https://tfhub.dev/google/universal-sentence-encoder-multilingual-qa/3', # Multilingual QA # 16 languages (Arabic, Chinese-simplified, Chinese-traditional, English, French, German, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Spanish, Thai, Turkish, Russian)
# 'url': 'https://tfhub.dev/google/LAReQA/mBERT_En_En/1',
# 'cache_dir': '/Users/toor/Documents/Software/DLModels/tf_cache_dir/',
# 'use_cuda': True,
},
# 'with_cache': False,
'with_tqdm': True,
'use_cuda': True,
'default_batch_size': 10,
'default_cache_dir': cache_path,
'generate_kwargs': {
"max_length": 128,
"num_beams": 10,
# "num_return_sequences": 1,
# "length_penalty": 1.5,
# "no_repeat_ngram_size": 3, # do not set it when answer2question=False, questions always start with the same ngrams
"early_stopping": True,
},
'e2e_generate_kwargs': {
"max_length": 128,
"num_beams": 10,
# "num_beam_groups": 1,
"num_return_sequences": 10,
# "length_penalty": 1.5,
# "no_repeat_ngram_size": 3, # do not set it when answer2question=False, questions always start with the same ngrams
"early_stopping": True,
# "return_dict_in_generate": True,
# "forced_eos_token_id": True
},
'task_list': [
'answer2question',
'question2answer'
],
}
QA_CLEANING_OPTIONS = {
# 'sorted_template_list': None,
'min_qa_pertinence': 0,
'max_qa_similarity': 1,
# 'max_answer_to_question_similarity': 0.9502,
'min_answer_to_sentence_overlap': 0.75,
'min_question_to_sentence_overlap': 0.5,
'max_answer_to_question_overlap': 0.75,
'coreference_resolution': False,
}
KG_MANAGER_OPTIONS = {
# 'spacy_model': 'en_core_web_trf',
# 'n_threads': 1,
# 'use_cuda': True,
'with_cache': False,
'with_tqdm': False,
'min_sentence_len': 150,
'max_paragraph_len': 1000,
}
GRAPH_BUILDER_OPTIONS = {
# 'spacy_model': 'en_core_web_trf',
# 'n_threads': 1,
# 'use_cuda': True,
'with_cache': False,
'with_tqdm': True,
'max_syntagma_length': None,
'add_source': True,
'add_label': True,
'lemmatize_label': False,
# 'default_similarity_threshold': 0.75,
'default_similarity_threshold': 0,
'tf_model': {
'url': 'https://tfhub.dev/google/universal-sentence-encoder-large/5', # Transformer
# 'url': 'https://tfhub.dev/google/universal-sentence-encoder/4', # DAN
# 'cache_dir': '/Users/toor/Documents/Software/DLModels/tf_cache_dir/',
# 'use_cuda': True,
# 'with_cache': True,
# 'batch_size': 100,
},
'with_centered_similarity': True,
}
CONCEPT_CLASSIFIER_OPTIONS = {
# 'spacy_model': 'en_core_web_trf',
# 'n_threads': 1,
# 'use_cuda': True,
'default_batch_size': 20,
'with_tqdm':True,
'tf_model': {
'url': 'https://tfhub.dev/google/universal-sentence-encoder-large/5', # Transformer
# 'url': 'https://tfhub.dev/google/universal-sentence-encoder/4', # DAN
# 'cache_dir': '/Users/toor/Documents/Software/DLModels/tf_cache_dir/',
},
# 'sbert_model': {
# 'url': 'all-MiniLM-L12-v2',
# 'use_cuda': True,
# },
'with_centered_similarity': True,
'default_similarity_threshold': 0.75,
# 'default_tfidf_importance': 3/4,
'default_tfidf_importance': 0,
}
SENTENCE_CLASSIFIER_OPTIONS = {
# 'spacy_model': 'en_core_web_trf',
# 'n_threads': 1,
# 'use_cuda': True,
# 'default_batch_size': 100,
'with_tqdm':True,
'with_cache': False,
'with_centered_similarity': False,
'with_topic_scaling': False,
'with_stemmed_tfidf': False,
'default_tfidf_importance': 0,
}
SUMMARISER_OPTIONS = {
# 'spacy_model': 'en_core_web_trf',
# 'n_threads': 1,
# 'use_cuda': True,
'hf_model': {
# 'url': 't5-base',
'url': 'facebook/bart-large-cnn', # baseline
# 'url': 'google/pegasus-billsum',
# 'url': 'sshleifer/distilbart-cnn-12-6', # speedup (over the baseline): 1.24
# 'url': 'sshleifer/distilbart-cnn-12-3', # speedup (over the baseline): 1.78
# 'url': 'sshleifer/distilbart-cnn-6-6', # speedup (over the baseline): 2.09
# 'cache_dir': '/Users/toor/Documents/Software/DLModels/hf_cache_dir/',
'framework': 'pt',
# 'use_cuda': False,
},
}
################ Initialise data structures ################
def init(model_family, information_units):
using_special_graph = 'edu' in information_units or 'amr' in information_units
normal_graph_without_jumps = using_special_graph or AVOID_JUMPS
with_qa_dict_list = using_special_graph or ADAPTIVE_QA
print(f'server_interface {model_family} {information_units}, with with_qa_dict_list: {with_qa_dict_list}')
graph_cache = os_path.join(cache_path,f"graph_clauses_lemma-{GRAPH_BUILDER_OPTIONS['lemmatize_label']}_jumps-{not normal_graph_without_jumps}.pkl")
edu_graph_cache = os_path.join(cache_path,f"graph_edu_lemma-{GRAPH_BUILDER_OPTIONS['lemmatize_label']}_paragraphs-{EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES}_jumps-{not AVOID_JUMPS}.pkl")
edu_disco_only_graph_cache = os_path.join(cache_path,f"graph_edu_disco_only_lemma-{GRAPH_BUILDER_OPTIONS['lemmatize_label']}_paragraphs-{EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES}_jumps-{not AVOID_JUMPS}.pkl")
edu_amr_only_graph_cache = os_path.join(cache_path,f"graph_edu_amr_only_lemma-{GRAPH_BUILDER_OPTIONS['lemmatize_label']}_paragraphs-{EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES}_jumps-{not AVOID_JUMPS}.pkl")
# betweenness_centrality_cache = os_path.join(cache_path,'betweenness_centrality.pkl')
qa_dict_list_cache = os_path.join(cache_path,f'qa_dict_list_{EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES}.pkl')
cleaned_qa_dict_list_cache = os_path.join(cache_path,f'cleaned_qa_dict_list_{EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES}.pkl')
filtered_qa_dict_list_cache = os_path.join(cache_path,f'filtered_qa_dict_list_{EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES}.pkl')
qa_cache = os_path.join(cache_path,f'{"adaptive_" if ADAPTIVE_QA else ""}qa_embedder-{"clause_edu"}.pkl')
################ Configuration ################
if model_family == 'tf':
SENTENCE_CLASSIFIER_OPTIONS['tf_model'] = {
# 'url': 'https://tfhub.dev/google/universal-sentence-encoder-qa/3', # English QA
'url': 'https://tfhub.dev/google/universal-sentence-encoder-multilingual-qa/3', # Multilingual QA # 16 languages (Arabic, Chinese-simplified, Chinese-traditional, English, French, German, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Spanish, Thai, Turkish, Russian)
# 'url': 'https://tfhub.dev/google/LAReQA/mBERT_En_En/1',
# 'cache_dir': '/Users/toor/Documents/Software/DLModels/tf_cache_dir/',
'use_cuda': True,
'with_cache': True,
}
elif model_family == 'fb':
SENTENCE_CLASSIFIER_OPTIONS['sbert_model'] = {
'url': 'multi-qa-MiniLM-L6-cos-v1', # model for paraphrase identification
'use_cuda': True,
'with_cache': True,
}
# Model Performance Semantic Search (6 Datasets) Queries (GPU / CPU) per sec.
# multi-qa-MiniLM-L6-cos-v1 51.83 18,000 / 750
# multi-qa-distilbert-cos-v1 52.83 7,000 / 350
# multi-qa-mpnet-base-cos-v1 57.46 4,000 / 170
########################################################################
def extract_graph():
print('Building Graph..')
document_list = get_document_list(document_path)
chunks = tuple(get_chunks(document_list, elements_per_chunk=DOCUMENTS_PER_CHUNK))
num_chunks = math.ceil(len(document_list)/DOCUMENTS_PER_CHUNK)
del document_list
kg_builder = KnowledgeGraphBuilder(GRAPH_BUILDER_OPTIONS)
# kg_builder = OntologyBuilder(GRAPH_BUILDER_OPTIONS)
for i,docs in enumerate(tqdm(chunks)):
load_or_create_cache(graph_cache+f'.{i}_{num_chunks}_{normal_graph_without_jumps}.pkl', lambda: kg_builder.set_document_list(docs, avoid_jumps=not normal_graph_without_jumps).build(add_verbs=False, add_predicates_label=False))
graph = []
for i,docs in enumerate(tqdm(chunks)):
graph += load_or_create_cache(graph_cache+f'.{i}_{num_chunks}_{normal_graph_without_jumps}.pkl', lambda: kg_builder.set_document_list(docs, avoid_jumps=not normal_graph_without_jumps).build(add_verbs=False, add_predicates_label=False))
graph = list(unique_everseen(graph))
return graph
graph = load_or_create_cache(graph_cache, extract_graph)
# graph = load_or_create_cache(
# graph_cache,
# lambda: OntologyBuilder(GRAPH_BUILDER_OPTIONS).set_documents_path(document_path).build()
# )
# save_graphml(graph, 'knowledge_graph')
print('Graph size:', len(graph))
print('Grammatical Clauses:', len(list(filter(lambda x: '{obj}' in x[1], graph))))
########################################################################
print('Building Question Answerer..')
# betweenness_centrality = load_or_create_cache(
# betweenness_centrality_cache,
# lambda: get_betweenness_centrality(filter(lambda x: '{obj}' in x[1], graph))
# )
if with_qa_dict_list:
qa_dict_list = load_or_create_cache(qa_dict_list_cache, lambda: QuestionAnswerExtractor(QA_EXTRACTOR_OPTIONS).extract(graph, cache_path=qa_dict_list_cache, use_paragraph_text=EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES))
print(f'qa_dict_list now has len {len(qa_dict_list)}')
qa_dict_list = load_or_create_cache(cleaned_qa_dict_list_cache, lambda: QuestionAnswerExtractor(QA_EXTRACTOR_OPTIONS).clean_qa_dict_list(qa_dict_list, cache_path=cleaned_qa_dict_list_cache, **QA_CLEANING_OPTIONS))
print(f'qa_dict_list now has len {len(qa_dict_list)}')
qa_dict_list = load_or_create_cache(filtered_qa_dict_list_cache, lambda: filter_invalid_sentences(QuestionAnswerExtractor(QA_EXTRACTOR_OPTIONS), qa_dict_list, key=lambda x: x['sentence'], avoid_coreferencing=AVOID_COREFERENCING))
print(f'qa_dict_list now has len {len(qa_dict_list)}')
edu_graph = None
if 'edu_amr' in information_units or 'amr_edu' in information_units:
edu_graph = load_or_create_cache(
edu_graph_cache,
lambda: QuestionAnswerExtractor(QA_EXTRACTOR_OPTIONS).extract_aligned_graph_from_qa_dict_list(
graph,
GRAPH_BUILDER_OPTIONS,
qa_dict_list=qa_dict_list,
elements_per_chunk=PARAGRAPHS_PER_CHUNK,
avoid_jumps=AVOID_JUMPS,
cache_path=edu_graph_cache,
add_verbs=False,
add_predicates_label=False,
use_paragraph_text=EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES,
)
)
elif 'edu' in information_units:
edu_graph = load_or_create_cache(
edu_disco_only_graph_cache,
lambda: QuestionAnswerExtractor(QA_EXTRACTOR_OPTIONS).extract_aligned_graph_from_qa_dict_list(
graph,
GRAPH_BUILDER_OPTIONS,
qa_dict_list=qa_dict_list,
qa_type_to_use= [
'disco', # elementary discourse units
# 'qaamr', # abstract meaning representations
],
elements_per_chunk=PARAGRAPHS_PER_CHUNK,
avoid_jumps=AVOID_JUMPS,
cache_path=edu_disco_only_graph_cache,
add_verbs=False,
add_predicates_label=False,
use_paragraph_text=EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES,
)
)
elif 'amr' in information_units:
edu_graph = load_or_create_cache(
edu_amr_only_graph_cache,
lambda: QuestionAnswerExtractor(QA_EXTRACTOR_OPTIONS).extract_aligned_graph_from_qa_dict_list(
graph,
GRAPH_BUILDER_OPTIONS,
qa_dict_list=qa_dict_list,
qa_type_to_use= [
# 'disco', # elementary discourse units
'qaamr', # abstract meaning representations
],
elements_per_chunk=PARAGRAPHS_PER_CHUNK,
avoid_jumps=AVOID_JUMPS,
cache_path=edu_amr_only_graph_cache,
add_verbs=False,
add_predicates_label=False,
use_paragraph_text=EXTRACT_QA_USING_PARAGRAPHS_INSTEAD_OF_SENTENCES,
)
)
if edu_graph is None:
kg_manager = KnowledgeGraphManager(KG_MANAGER_OPTIONS, graph)
else:
kg_manager = KnowledgeGraphManager.build_from_edus_n_clauses(
KG_MANAGER_OPTIONS,
graph= graph,
# qa_dict_list= qa_dict_list,
# kg_builder_options = GRAPH_BUILDER_OPTIONS,
# qa_extractor_options= QA_EXTRACTOR_OPTIONS,
use_only_elementary_discourse_units= ('clause' not in information_units),
edu_graph= edu_graph,
# qa_type_to_use= [
# 'disco', # elementary discourse units
# 'qaamr', # abstract meaning representations
# ],
)
del edu_graph
del graph
if ADAPTIVE_QA:
qa = AdaptiveQuestionAnswerer(
kg_manager= kg_manager,
qa_dict_list= qa_dict_list,
concept_classifier_options= CONCEPT_CLASSIFIER_OPTIONS,
sentence_classifier_options= SENTENCE_CLASSIFIER_OPTIONS,
answer_summariser_options= SUMMARISER_OPTIONS,
# betweenness_centrality=None,
min_explainability= MIN_EXPLAINABILITY,
dox_options= DOX_OPTIONS,
archetype_fitness_options= ARCHETYPE_FITNESS_OPTIONS,
archetype_weight_dict= None,
)
else:
if with_qa_dict_list: del qa_dict_list
qa = QuestionAnswerer(
kg_manager= kg_manager,
concept_classifier_options= CONCEPT_CLASSIFIER_OPTIONS,
sentence_classifier_options= SENTENCE_CLASSIFIER_OPTIONS,
answer_summariser_options= SUMMARISER_OPTIONS,
# betweenness_centrality=None,
)
qa.load_cache(qa_cache, save_if_init=True)
if ADAPTIVE_QA:
print('aspect_explainability_dict:', json.dumps(dict(sorted(qa.aspect_explainability_dict.items(), key=lambda x: x[-1], reverse=True)), indent=4))
return qa, qa_cache
################ Define methods ################
# already_stored = False
def get_question_answer_dict(qa, question_list, options=None):
if not options:
options = {}
return qa.ask(question_list, **options)
def get_question_answer_dict_quality(qa, question_answer_dict, top=5):
return qa.get_question_answer_dict_quality(question_answer_dict, top=top)
def get_summarised_question_answer_dict(qa, question_answer_dict, options=None):
if not options:
options = {}
return qa.summarise_question_answer_dict(question_answer_dict, **options)
def get_concept_overview(qa, query_template_list=None, concept_uri=None, concept_label= None, options=None, is_new_content_fn=None):
if not options:
options = {}
question_answer_dict = qa.get_concept_overview(
query_template_list= query_template_list,
concept_uri= concept_uri,
concept_label= concept_label,
adaptivity_options= ADAPTIVITY_OPTIONS,
**options
)
# remove unanswered questions
return dict(filter(lambda x: x[-1], question_answer_dict.items()))
def annotate_text(qa, sentence, similarity_threshold=None, max_concepts_per_alignment=1, tfidf_importance=None, is_preprocessed_content=True):
return qa.concept_classifier.annotate(
DocParser().set_content_list([sentence]),
similarity_threshold= similarity_threshold,
max_concepts_per_alignment= max_concepts_per_alignment,
tfidf_importance= tfidf_importance,
concept_id_filter= lambda x: x in qa.overview_aspect_set,
is_preprocessed_content= is_preprocessed_content,
)
def annotate_question_summary_tree(qa, question_summary_tree, similarity_threshold=None, max_concepts_per_alignment=1, tfidf_importance=None, is_preprocessed_content=True):
return qa.annotate_question_summary_tree(question_summary_tree, similarity_threshold=similarity_threshold, max_concepts_per_alignment=max_concepts_per_alignment, tfidf_importance=tfidf_importance, is_preprocessed_content=is_preprocessed_content)
def get_taxonomical_view(qa, concept_uri, depth=0):
return qa.get_taxonomical_view(concept_uri, depth=depth)
def annotate_taxonomical_view(qa, taxonomical_view, similarity_threshold=None, max_concepts_per_alignment=1, tfidf_importance=None, is_preprocessed_content=True):
return qa.annotate_taxonomical_view(taxonomical_view, similarity_threshold=similarity_threshold, max_concepts_per_alignment=max_concepts_per_alignment, tfidf_importance=tfidf_importance, is_preprocessed_content=is_preprocessed_content)
def get_equivalent_concepts(qa, concept_uri):
return qa.adjacency_list.get_equivalent_concepts(concept_uri)
def get_label_list(qa, concept_uri):
return qa.kg_manager.get_label_list(concept_uri)
def store_cache(qa, qa_cache):
qa.store_cache(qa_cache)
# ############### Cache scheduler ###############
# SCHEDULING_TIMER = 15*60 # 15 minutes
# from threading import Timer
# def my_task(is_first=False):
# if not is_first:
# store_cache()
# Timer(SCHEDULING_TIMER, my_task).start()
# # start your scheduler
# my_task(is_first=True)