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tmqa23.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
get_ipython().run_line_magic('autosave', '180')
get_ipython().run_line_magic('matplotlib', 'inline')
# In[2]:
#import convex as cx
import requests
import time
import itertools as it
import re
#import numpy
from copy import copy
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
#from pprint import pprint
import time
import json
import os
import networkx as nx
from math import sqrt
import spacy
from hdt import HDTDocument
import multiprocessing as mp
os.environ['CUDA_VISIBLE_DEVICES'] = "1,2"
from deepcorrect import DeepCorrect
#import deepcorrect
#print(deepcorrect.__file__)
corrector = DeepCorrect('data/deep_punct/deeppunct_params_en', 'data/deep_punct/deeppunct_checkpoint_wikipedia')
# In[3]:
#corrector.correct('of what nationality is ken mcgoogan')
# In[4]:
hdt_wd = HDTDocument("data/kb/wikidata2018_09_11.hdt")
# In[5]:
#nlp = spacy.load("en_core_web_lg")
nlp = spacy.load("/data/users/romain.claret/tm/wiki-kb-linked-entities/nlp_custom_6")
#print(nlp.pipeline)
# In[6]:
# load settings
with open( "settings-graphqa.json", "r") as settings_data:
settings = json.load(settings_data)
use_cache = settings['use_cache']
save_cache = settings['save_cache']
cache_path = settings['cache_path']
#cache_path
# In[425]:
save_cache = True
def save_cache_data():
if save_cache:
with open(os.path.join(cache_path,'wd_local_statements_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_local_statements_dict, separators=(',',':')).encode('utf8'))
with open(os.path.join(cache_path,'wd_labels_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_labels_dict, separators=(',',':')).encode('utf8'))
with open(os.path.join(cache_path,'wd_local_word_ids_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_local_word_ids_dict, separators=(',',':')).encode('utf8'))
with open(os.path.join(cache_path,'wd_online_word_ids_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_online_word_ids_dict, separators=(',',':')).encode('utf8'))
with open(os.path.join(cache_path,'wd_local_predicate_ids_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_local_predicate_ids_dict, separators=(',',':')).encode('utf8'))
with open(os.path.join(cache_path,'wd_online_predicate_ids_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_online_predicate_ids_dict, separators=(',',':')).encode('utf8'))
with open(os.path.join(cache_path,'word_similarities_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(word_similarities_dict, separators=(',',':')).encode('utf8'))
# In[426]:
# Load statements cache
use_cache = True
if use_cache:
path_wd_local_statements_dict = "wd_local_statements_dict.json"
path_wd_labels_dict = 'wd_labels_dict.json'
path_wd_local_word_ids_dict = 'wd_local_word_ids_dict.json'
path_wd_online_word_ids_dict = 'wd_online_word_ids_dict.json'
path_wd_local_predicate_ids_dict = 'wd_local_predicate_ids_dict.json'
path_wd_online_predicate_ids_dict = 'wd_online_predicate_ids_dict.json'
path_word_similarities_dict = 'word_similarities_dict.json'
else:
path_wd_local_statements_dict = "wd_local_statements_dict_empty.json"
path_wd_labels_dict = 'wd_labels_dict_empty.json'
path_wd_local_word_ids_dict = 'wd_local_word_ids_dict_empty.json'
path_wd_online_word_ids_dict = 'wd_online_word_ids_dict_empty.json'
path_wd_local_predicate_ids_dict = 'wd_local_predicate_ids_dict_empty.json'
path_wd_online_predicate_ids_dict = 'wd_online_predicate_ids_dict_empty.json'
path_word_similarities_dict = 'word_similarities_dict_empty.json'
with open(os.path.join(cache_path,path_wd_local_statements_dict), "rb") as data:
wd_local_statements_dict = json.load(data)
with open(os.path.join(cache_path,path_wd_labels_dict), "rb") as data:
wd_labels_dict = json.load(data)
with open(os.path.join(cache_path,path_wd_local_word_ids_dict), "rb") as data:
wd_local_word_ids_dict = json.load(data)
with open(os.path.join(cache_path,path_wd_online_word_ids_dict), "rb") as data:
wd_online_word_ids_dict = json.load(data)
with open(os.path.join(cache_path,path_wd_local_predicate_ids_dict), "rb") as data:
wd_local_predicate_ids_dict = json.load(data)
with open(os.path.join(cache_path,path_wd_online_predicate_ids_dict), "rb") as data:
wd_online_predicate_ids_dict = json.load(data)
with open(os.path.join(cache_path,path_word_similarities_dict), "rb") as data:
word_similarities_dict = json.load(data)
#print("len(wd_local_statements_dict)",len(wd_local_statements_dict))
#print("len(wd_labels_dict)",len(wd_labels_dict))
#print("len(wd_local_word_ids_dict)",len(wd_local_word_ids_dict))
#print("len(wd_online_word_ids_dict)",len(wd_online_word_ids_dict))
#print("len(wd_local_predicate_ids_dict)",len(wd_local_word_ids_dict))
#print("len(wd_online_predicate_ids_dict)",len(wd_online_word_ids_dict))
# In[9]:
def get_kb_ents(text):
#doc = nlp_kb(text)
doc = nlp(text)
#for ent in doc.ents:
# print(" ".join(["ent", ent.text, ent.label_, ent.kb_id_]))
return doc.ents
#ent_text_test = (
# "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, "
# "Douglas reminds us to always bring our towel, even in China or Brazil. "
# "The main character in Doug's novel is the man Arthur Dent, "
# "but Dougledydoug doesn't write about George Washington or Homer Simpson."
#)
#
#en_text_test_2 = ("Which actor voiced the Unicorn in The Last Unicorn?")
#
#print([ent.kb_id_ for ent in get_kb_ents(ent_text_test)])
#[ent.kb_id_ for ent in get_kb_ents(en_text_test_2)]
# In[432]:
def get_nlp(sentence, autocorrect=False):
sentence = sentence.replace("’", "\'")
nlp_sentence = nlp(sentence)
nlp_sentence_list = list(nlp_sentence)
meaningful_punct = []
for i_t, t in enumerate(nlp_sentence_list):
#print(t,t.pos_, t.lemma_)
if t.lemma_ == "year":
nlp_sentence_list[i_t] = "date"
elif t.text == "\'s" or t.text == "s":
if t.lemma_ == "be" or t.lemma_ == "s":
nlp_sentence_list[i_t] = "is"
else: nlp_sentence_list[i_t] = ""
elif t.text == "\'ve" or t.text == "ve":
if t.lemma_ == "have":
nlp_sentence_list[i_t] = "have"
else: nlp_sentence_list[i_t] = ""
elif t.text == "\'re" or t.text == "re":
if t.lemma_ == "be":
nlp_sentence_list[i_t] = "are"
else: nlp_sentence_list[i_t] = ""
elif t.text == "\'ll" or t.text == "ll":
if t.lemma_ == "will":
nlp_sentence_list[i_t] = "will"
else: nlp_sentence_list[i_t] = ""
elif t.text == "\'d" or t.text == "d":
if t.lemma_ == "have":
nlp_sentence_list[i_t] = "had"
elif t.lemma_ == "would":
nlp_sentence_list[i_t] = "would"
else: nlp_sentence_list[i_t] = ""
elif t.is_space:
nlp_sentence_list[i_t] = ""
elif t.pos_ == "PUNCT":
if t.text.count(".") > 2:
meaningful_punct.append((i_t,"..."))
nlp_sentence_list[i_t] = "..."
else:
nlp_sentence_list[i_t] = ""
else: nlp_sentence_list[i_t] = nlp_sentence_list[i_t].text
nlp_sentence_list = [w for w in nlp_sentence_list if w]
#print("nlp_sentence_list",nlp_sentence_list)
if autocorrect:
nlp_sentence = " ".join(nlp_sentence_list)
nlp_sentence = (nlp_sentence.replace("’", "\'").replace("€", "euro").replace("ç", "c")
.replace("à", "a").replace("é","e").replace("ä","a").replace("ö","o")
.replace("ü","u").replace("è","e").replace("¨","").replace("ê","e")
.replace("â","a").replace("ô","o").replace("î","i").replace("û","u")
.replace("_"," ").replace("°","degree").replace("§","section").replace("š","s")
.replace("Š","S").replace("ć","c").replace("Ç", "C")
.replace("À", "A").replace("É","E").replace("Ä","A").replace("Ö","O")
.replace("Ü","U").replace("È","E").replace("Ê","E")
.replace("Â","A").replace("Ô","O").replace("Î","I").replace("Û","U")
.replace("á","a").replace("Á","Á").replace("ó","o").replace("Ó","O")
.replace("ú","u").replace("Ú","U").replace("í","i").replace("Í","I")
)
nlp_sentence = corrector.correct(nlp_sentence)
nlp_sentence = nlp_sentence[0]["sequence"]
nlp_sentence = nlp(nlp_sentence)
nlp_sentence_list = list(nlp_sentence)
for i_t, t in enumerate(nlp_sentence_list):
if t.pos_ == "PUNCT":
if i_t in [mpunct[0] for mpunct in meaningful_punct]:
for mpunct in meaningful_punct:
if i_t == mpunct[0]:
nlp_sentence_list[mpunct[0]] = mpunct[1]
else: nlp_sentence_list[i_t] = ''
else:
nlp_sentence_list[i_t] = nlp_sentence_list[i_t].text
for mpunct in meaningful_punct:
if mpunct[0] < len(nlp_sentence_list):
if nlp_sentence_list[mpunct[0]] != mpunct[1]:
nlp_sentence_list.insert(mpunct[0], mpunct[1])
return nlp(" ".join(nlp_sentence_list).replace(" ", " ").replace(". &",".").replace(". /","."))
#get_nlp("Which genre of album is harder.....faster?", autocorrect=True)
#get_nlp("Which genre of album is Harder ... Faster", autocorrect=True)
#get_nlp("Which home is an example of italianate architecture?", autocorrect=True)
#get_nlp("Your mom's father, were nice in the Years.!?\'\":`’^!$£€\(\)ç*+%&/\\\{\};,àéäöüè¨êâôîû~-_<>°§...@.....", autocorrect=True)
#get_nlp("of what nationality is ken mcgoogan", autocorrect=True)
#get_nlp("you're fun", autocorrect=True)
#get_nlp("where's the fun", autocorrect=True)
#get_nlp("whats the name of the organization that was founded by frei otto", True)
#get_nlp("Hurry! We’re late!",True)
#get_nlp("Who was an influential figure for miško Šuvaković",True)
#get_nlp("what is the second level division of the division crixás do tocantins",True)
# In[11]:
def is_wd_entity(to_check):
pattern = re.compile('^Q[0-9]*$')
if pattern.match(to_check.strip()): return True
else: return False
def is_wd_predicate(to_check):
pattern = re.compile('^P[0-9]*$')
if pattern.match(to_check.strip()): return True
else: return False
def is_valide_wd_id(to_check):
if is_wd_entity(to_check) or is_wd_predicate(to_check): return True
else: return False
#print(is_wd_entity("Q155"))
# In[12]:
# TODO redo the functions and optimize
def is_entity_or_literal(to_check):
if is_wd_entity(to_check.strip()):
return True
pattern = re.compile('^[A-Za-z0-9]*$')
if len(to_check) == 32 and pattern.match(to_check.strip()):
return False
return True
# return if the given string is a literal or a date
def is_literal_or_date(to_check):
return not('www.wikidata.org' in to_check)
# return if the given string describes a year in the format YYYY
def is_year(year):
pattern = re.compile('^[0-9][0-9][0-9][0-9]$')
if not(pattern.match(year.strip())):
return False
else:
return True
# return if the given string is a date
def is_date(date):
pattern = re.compile('^[0-9]+ [A-z]+ [0-9][0-9][0-9][0-9]$')
if not(pattern.match(date.strip())):
return False
else:
return True
# return if the given string is a timestamp
def is_timestamp(timestamp):
pattern = re.compile('^[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]T00:00:00Z')
if not(pattern.match(timestamp.strip())):
return False
else:
return True
# convert the given month to a number
def convert_month_to_number(month):
return{
"january" : "01",
"february" : "02",
"march" : "03",
"april" : "04",
"may" : "05",
"june" : "06",
"july" : "07",
"august" : "08",
"september" : "09",
"october" : "10",
"november" : "11",
"december" : "12"
}[month.lower()]
# convert a date from the wikidata frontendstyle to timestamp style
def convert_date_to_timestamp (date):
sdate = date.split(" ")
# add the leading zero
if (len(sdate[0]) < 2):
sdate[0] = "0" + sdate[0]
return sdate[2] + '-' + convert_month_to_number(sdate[1]) + '-' + sdate[0] + 'T00:00:00Z'
# convert a year to timestamp style
def convert_year_to_timestamp(year):
return year + '-01-01T00:00:00Z'
# get the wikidata id of a wikidata url
def wikidata_url_to_wikidata_id(url):
if not url:
return False
if "XMLSchema#dateTime" in url or "XMLSchema#decimal" in url:
date = url.split("\"", 2)[1]
date = date.replace("+", "")
return date
if(is_literal_or_date(url)):
if is_year(url):
return convert_year_to_timestamp(url)
if is_date(url):
return convert_date_to_timestamp(url)
else:
url = url.replace("\"", "")
return url
else:
url_array = url.split('/')
# the wikidata id is always in the last component of the id
return url_array[len(url_array)-1]
# fetch all statements where the given qualifier statement occurs as subject
def get_all_statements_with_qualifier_as_subject(qualifier):
statements = []
triples, cardinality = hdt_wd.search_triples(qualifier, "", "")
for triple in triples:
sub, pre, obj = triple
# only consider triples with a wikidata-predicate
if pre.startswith("http://www.wikidata.org/"):
statements.append({'entity': sub, 'predicate': pre, 'object': obj})
return statements
# fetch the statement where the given qualifier statement occurs as object
def get_statement_with_qualifier_as_object(qualifier):
triples, cardinality = hdt_wd.search_triples("", "", qualifier)
for triple in triples:
sub, pre, obj = triple
# only consider triples with a wikidata-predicate
if pre.startswith("http://www.wikidata.org/") and sub.startswith("http://www.wikidata.org/entity/Q"):
return (sub, pre, obj)
return False
# returns all statements that involve the given entity
def get_all_statements_of_entity(entity_id):
# check entity pattern
if not is_wd_entity(entity_id.strip()):
return False
if wd_local_statements_dict.get(entity_id) != None:
#print("saved statement")
return wd_local_statements_dict[entity_id]
entity = "http://www.wikidata.org/entity/"+entity_id
statements = []
# entity as subject
triples_sub, cardinality_sub = hdt_wd.search_triples(entity, "", "")
# entity as object
triples_obj, cardinality_obj = hdt_wd.search_triples("", "", entity)
if cardinality_sub + cardinality_obj > 5000:
wd_local_statements_dict[entity_id] = []
return []
# iterate through all triples in which the entity occurs as the subject
for triple in triples_sub:
sub, pre, obj = triple
# only consider triples with a wikidata-predicate or if it is an identifier predicate
if not pre.startswith("http://www.wikidata.org/"):# or (wikidata_url_to_wikidata_id(pre) in identifier_predicates):
continue
# object is statement
if obj.startswith("http://www.wikidata.org/entity/statement/"):
qualifier_statements = get_all_statements_with_qualifier_as_subject(obj)
qualifiers = []
for qualifier_statement in qualifier_statements:
if qualifier_statement['predicate'] == "http://www.wikidata.org/prop/statement/" + wikidata_url_to_wikidata_id(pre):
obj = qualifier_statement['object']
elif is_entity_or_literal(wikidata_url_to_wikidata_id(qualifier_statement['object'])):
qualifiers.append({
"qualifier_predicate":{
"id": wikidata_url_to_wikidata_id(qualifier_statement['predicate'])
},
"qualifier_object":{
"id": wikidata_url_to_wikidata_id(qualifier_statement['object'])
}})
statements.append({'entity': {'id': wikidata_url_to_wikidata_id(sub)}, 'predicate': {'id': wikidata_url_to_wikidata_id(pre)}, 'object': {'id': wikidata_url_to_wikidata_id(obj)}, 'qualifiers': qualifiers})
else:
statements.append({'entity': {'id': wikidata_url_to_wikidata_id(sub)}, 'predicate': {'id': wikidata_url_to_wikidata_id(pre)}, 'object': {'id': wikidata_url_to_wikidata_id(obj)}, 'qualifiers': []})
# iterate through all triples in which the entity occurs as the object
for triple in triples_obj:
sub, pre, obj = triple
# only consider triples with an entity as subject and a wikidata-predicate or if it is an identifier predicate
if not sub.startswith("http://www.wikidata.org/entity/Q"):# or not pre.startswith("http://www.wikidata.org/") or wikidata_url_to_wikidata_id(pre) in identifier_predicates:
continue
if sub.startswith("http://www.wikidata.org/entity/statement/"):
statements_with_qualifier_as_object = get_statement_with_qualifier_as_object(sub, process)
# if no statement was found continue
if not statements_with_qualifier_as_object:
continue
main_sub, main_pred, main_obj = statements_with_qualifier_as_object
qualifier_statements = get_all_statements_with_qualifier_as_subject(sub)
qualifiers = []
for qualifier_statement in qualifier_statements:
if wikidata_url_to_wikidata_id(qualifier_statement['predicate']) == wikidata_url_to_wikidata_id(main_pred):
main_obj = qualifier_statement['object']
elif is_entity_or_literal(wikidata_url_to_wikidata_id(qualifier_statement['object'])):
qualifiers.append({
"qualifier_predicate":{"id": wikidata_url_to_wikidata_id(qualifier_statement['predicate'])},
"qualifier_object":{"id": wikidata_url_to_wikidata_id(qualifier_statement['object'])}
})
statements.append({
'entity': {'id': wikidata_url_to_wikidata_id(main_sub)},
'predicate': {'id': wikidata_url_to_wikidata_id(main_pred)},
'object': {'id': wikidata_url_to_wikidata_id(main_obj)},
'qualifiers': qualifiers
})
else:
statements.append({'entity': {'id': wikidata_url_to_wikidata_id(sub)}, 'predicate': {'id': wikidata_url_to_wikidata_id(pre)}, 'object': {'id': wikidata_url_to_wikidata_id(obj)}, 'qualifiers': []})
# cache the data
wd_local_statements_dict[entity_id] = statements
return statements
#print(len(get_all_statements_of_entity("Q267721")))
#for s in get_all_statements_of_entity("Q267721"):
# print(s)
#save_cache_data()
# In[13]:
def get_wd_ids_online(name, is_predicate=False, top_k=3):
name = name.split('(')[0]
if is_predicate and wd_online_predicate_ids_dict.get(name) != None and use_cache and len(wd_online_predicate_ids_dict)>0:
#print("saved predicate online")
return wd_online_predicate_ids_dict[name][:top_k]
elif not is_predicate and wd_online_word_ids_dict.get(name) != None and use_cache and len(wd_online_word_ids_dict)>0:
#print("saved word online")
return wd_online_word_ids_dict[name][:top_k]
request_successfull = False
entity_ids = ""
while not request_successfull:
try:
if is_predicate:
entity_ids = requests.get('https://www.wikidata.org/w/api.php?action=wbsearchentities&format=json&language=en&type=property&limit=' + str(top_k) + '&search='+name).json()
else:
entity_ids = requests.get('https://www.wikidata.org/w/api.php?action=wbsearchentities&format=json&language=en&limit=' + str(top_k) + '&search='+name).json()
request_successfull = True
except:
time.sleep(5)
results = entity_ids.get("search")
if not results:
if is_predicate: wd_online_predicate_ids_dict[name] = []
else: wd_online_word_ids_dict[name] = []
return []
if not len(results):
if is_predicate: wd_online_predicate_ids_dict[name] = []
else: wd_online_word_ids_dict[name] = []
return []
res = []
for result in results:
res.append(result['id'])
if is_predicate: wd_online_predicate_ids_dict[name] = res
else: wd_online_word_ids_dict[name] = res
if res:
return res[:top_k]
else:
return []
#print(get_wd_ids_online("be", is_predicate=False, top_k=1))
# In[14]:
# very computational
def get_most_similar(word, top_k=3):
print("behold: get_most_similar started with:", word)
word_text = str(word.lower())
if word_similarities_dict.get(word) != None and use_cache and len(word_similarities_dict)>0:
return word_similarities_dict[word][:top_k]
word = nlp.vocab[word_text]
queries = [w for w in word.vocab if w.is_lower == word.is_lower and w.prob >= -15]
by_similarity = sorted(queries, key=lambda w: word.similarity(w), reverse=True)
word_similarities = [(w.text.lower(),float(w.similarity(word))) for w in by_similarity[:10] if w.lower_ != word.lower_]
word_similarities_dict[word_text] = word_similarities
save_cache_data()
return word_similarities[:top_k]
#print(get_most_similar("voiced", top_k=3))
#save_cache_data()
# In[15]:
def get_wd_ids(word, is_predicate=False, top_k=3, limit=6, online=False):
if is_predicate and wd_local_predicate_ids_dict.get(word) != None and use_cache and len(wd_local_predicate_ids_dict)>0:
#print("saved predicate local")
return wd_local_predicate_ids_dict[word][:top_k]
elif not is_predicate and wd_local_word_ids_dict.get(word) != None and use_cache and len(wd_local_word_ids_dict)>0:
#print("saved word local")
return wd_local_word_ids_dict[word][:top_k]
language = "en"
word_formated = str("\""+word+"\""+"@"+language)
to_remove = len("http://www.wikidata.org/entity/")
t_name, card_name = hdt_wd.search_triples("", "http://schema.org/name", word_formated, limit=top_k)
#print("names cardinality of \"" + word+"\": %i" % card_name)
t_alt, card_alt = hdt_wd.search_triples("", 'http://www.w3.org/2004/02/skos/core#altLabel', word_formated, limit=top_k)
#print("alternative names cardinality of \"" + word+"\": %i" % card_alt)
results = list(set(
[t[0][to_remove:] for t in t_name if is_valide_wd_id(t[0][to_remove:])] +
[t[0][to_remove:] for t in t_alt if is_valide_wd_id(t[0][to_remove:])]
))
if is_predicate: results = [r for r in results if is_wd_predicate(r)]
# cache the data
if is_predicate: wd_local_predicate_ids_dict[word] = results
else: wd_local_word_ids_dict[word] = results
return results if limit<=0 else results[:limit-1]
#print(get_wd_ids("", is_predicate=False, top_k=1))
#get_wd_ids("The Last Unicorn", top_k=0, limit=10)
#print(get_wd_ids("wife", is_predicate=False , top_k=0, limit=0))
#print(get_wd_ids("voiced", is_predicate=False , top_k=0, limit=0))
# In[16]:
def get_wd_label(from_id, language="en"):
#print("from_id",from_id)
if is_valide_wd_id(from_id):
if wd_labels_dict.get(from_id) != None and use_cache and len(wd_labels_dict)>0:
#print("saved label local")
return wd_labels_dict[from_id]
id_url = "http://www.wikidata.org/entity/"+from_id
t_name, card_name = hdt_wd.search_triples(id_url, "http://schema.org/name", "")
name = [t[2].split('\"@'+language)[0].replace("\"", "") for t in t_name if "@"+language in t[2]]
#name = [t[2].split('@en')[0] for t in t_name if "@"+language in t[2]]
result = name[0] if name else ''
wd_labels_dict[from_id] = result #caching
return result
else:
return from_id
#print(get_wd_label("P725"))
#get_wd_label("Q20789322")
#get_wd_label("Q267721")
# In[17]:
# Building colors from graph
def get_color(node_type):
if node_type == "entity": return "violet"#"cornflowerblue"
elif node_type == "predicate": return "yellow"
else: return "red"
# Building labels for graph
def get_elements_from_graph(graph):
node_names = nx.get_node_attributes(graph,"name")
node_types = nx.get_node_attributes(graph,"type")
colors = [get_color(node_types[n]) for n in node_names]
return node_names, colors
# Plotting the graph
def plot_graph(graph, name, title="Graph"):
fig = plt.figure(figsize=(14,14))
ax = plt.subplot(111)
ax.set_title(str("answer: "+title), fontsize=10)
#pos = nx.spring_layout(graph)
labels, colors = get_elements_from_graph(graph)
nx.draw(graph, node_size=30, node_color=colors, font_size=10, font_weight='bold', with_labels=True, labels=labels)
plt.tight_layout()
plt.savefig("tmqa1_graphs_imgs/"+str(name)+".png", format="PNG", dpi = 300)
plt.show()
#plot_graph(graph, "file_name_graph", "Graph_title")
# In[112]:
def make_statements_graph_worker(graph, predicate_nodes, turn, indexing_predicates, BANNED_WD_IDS, BANNED_WD_PRED_IDS, BANNED_WD_KEYWORDS, BANNED_WD_PRED_KEYWORDS, in_mp_queue, out_mp_queue, predicate_nodes_lock):
#for statement in statements:
sentinel = None
for statement in iter(in_mp_queue.get, sentinel):
#print("statement",statement)
#if (statement['entity']['id'][0] != "Q"
# or statement['entity']['id'] in BANNED_WD_IDS
# or statement['predicate']['id'][0] != "P"
# or statement['predicate']['id'] in BANNED_WD_PRED_IDS
# or statement['object']['id'][0] != "Q"
# or statement['object']['id'] in BANNED_WD_IDS):
# continue
if (
statement['entity']['id'] in BANNED_WD_IDS
or statement['predicate']['id'][0] != "P"
or statement['predicate']['id'] in BANNED_WD_PRED_IDS
or statement['object']['id'] in BANNED_WD_IDS
):
continue
continue_flag = False
for key in BANNED_WD_PRED_KEYWORDS:
if (get_wd_label(statement['predicate']['id']).find(key) != -1): continue_flag = True
for key in BANNED_WD_KEYWORDS:
if (get_wd_label(statement['entity']['id']).find(key) != -1): continue_flag = True
if (get_wd_label(statement['object']['id']).find(key) != -1): continue_flag = True
if continue_flag: continue
#print(statement)
if not statement['entity']['id'] in graph:
graph.add_node(statement['entity']['id'], name=get_wd_label(statement['entity']['id']), type='entity', turn=turn)
if not statement['object']['id'] in graph:
graph.add_node(statement['object']['id'], name=get_wd_label(statement['object']['id']), type='entity', turn=turn)
with predicate_nodes_lock:
# increment index of predicate or set it at 0
if not statement['predicate']['id'] in predicate_nodes or not indexing_predicates:
predicate_nodes_index = 1
predicate_nodes[statement['predicate']['id']] = 1
else:
predicate_nodes[statement['predicate']['id']] += 1
predicate_nodes_index = predicate_nodes[statement['predicate']['id']]
# add the predicate node
predicate_node_id = (statement['predicate']['id'])
if indexing_predicates: predicate_node_id += "-" + str(predicate_nodes_index)
graph.add_node(predicate_node_id, name=get_wd_label(statement['predicate']['id']), type='predicate', turn=turn)
# add the two edges (entity->predicate->object)
#statement['entity']['id'] in BANNED_WD_IDS
#statement['object']['id'] in BANNED_WD_IDS
#statement['predicate']['id'] in BANNED_WD_PRED_IDS
#if (statement['predicate']['id'] in BANNED_WD_PRED_IDS): break
graph.add_edge(statement['entity']['id'], predicate_node_id)
graph.add_edge(predicate_node_id, statement['object']['id'])
out_mp_queue.put(graph)
# In[196]:
# TODO: handle special literals? which one
def make_statements_graph(statements, indexing_predicates=True, cores=mp.cpu_count()):
BANNED_WD_IDS = [
"Q4167410","Q66087861","Q65932995","Q21281405","Q17442446","Q41770487","Q29548341",
"Q29547399","Q25670"
]
BANNED_WD_PRED_IDS = [
"P1687","P7087","P1889","P646", "P227", "P1256", "P1257", "P1258", "P1260", "P301",
"P18","P1266","P487","P1970","P2529", "P4390", "P4342", "P4213", "P487", "P2624",
"P4953", "P2241", "P345","P703", "P2163", "P18", "P436", "P227", "P646", "P2581",
"P1006", "P244", "P214", "P1051", "P1296","P461", "P2959", "P1657", "P3834","P243",
"P3306","P6932","P356","P1630","P3303","P1921","P1793","P1628","P1184","P1662","P2704",
"P4793","P1921","P2302","P6562","P6127","P4342","P6145","P5786","P5099","P4947","P5032",
"P4933","P4632","P4529","P4277","P4282","P3135","P4276","P3593","P2638","P3804","P3145",
"P2509","P3212","P2704","P480","P3844","P3141","P3808","P3933","P2346","P3077","P3417",
"P2529","P3302","P3143","P2334","P3129","P3138","P3107","P2603","P2631","P2508","P2465",
"P2014", "P1874", "P2518", "P1265", "P1237","P1712", "P1970","P1804","P905","P1562",
"P1258","P646","P345"
]
BANNED_WD_KEYWORDS = []
BANNED_WD_PRED_KEYWORDS = [
"ID", "ISBN","identifier", "IDENTIFIER", "isbn", "ISSN", "issn"
]
graph = nx.Graph()
turn=0
predicate_nodes = mp.Manager().dict()
predicate_nodes_lock = mp.Manager().Lock()
paths_keyword_nodes = []
if cores <= 0: cores = 1
out_mp_queue = mp.Queue()
in_mp_queue = mp.Queue()
sentinel = None
for statement in statements:
in_mp_queue.put(statement)
procs = [mp.Process(target = make_statements_graph_worker, args = (graph, predicate_nodes, turn, indexing_predicates, BANNED_WD_IDS, BANNED_WD_PRED_IDS, BANNED_WD_KEYWORDS, BANNED_WD_PRED_KEYWORDS, in_mp_queue, out_mp_queue, predicate_nodes_lock)) for i in range(cores)]
for proc in procs:
proc.daemon = True
proc.start()
for proc in procs:
in_mp_queue.put(sentinel)
for proc in procs:
local_g = out_mp_queue.get()
graph = nx.compose(graph,local_g)
for proc in procs:
proc.join()
return graph, predicate_nodes
#test_graph = make_statements_graph(test_unduplicate_statements, indexing_predicates=False)
#print(test_graph[1])
#plot_graph(test_graph[0],"test")
#print("len(filtered_statements)",len(filtered_statements))
#start_time = time.time()
#graph, predicate_nodes = make_statements_graph(filtered_statements, indexing_predicates=True, cores=1)
#print(time.time()-start_time)
#print("--> ",len(graph), "nodes and", graph.size(), "edges")
#print(predicate_nodes)
# In[130]:
def merge_lists(list_1, list_2):
if len(list_1) == len(list_2):
return [(list_1[i], list_2[i]) for i in range(0, len(list_1))]
else:
return "Error: lists are not the same lenght"
#print(merge_lists(["author"],['P50']))
# In[421]:
def get_themes_ids_from_chunks(noun_chunks, top_k=3, online=False):
if online:
theme_ids = [get_wd_ids_online(chunk.text, top_k=top_k)+get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks]
else:
theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks]
return theme_ids
def get_themes(nlp_question, raw_question, top_k=3, online=False, max_title_size=5):
#nlp_raw_question = get_nlp(raw_question, autocorrect=False)
#nlp_raw_question_lower = get_nlp(raw_question.lower(), autocorrect=False)
#nlp_raw_question_captialize = get_nlp(" ".join([w.capitalize() for w in raw_question.split(" ")]), autocorrect=False)
#nlp_raw_question_list = list(nlp_raw_question)
#print([e for e in nlp_raw_question])
#print([e for e in nlp_raw_question_lower])
#print([e for e in nlp_raw_question_captialize])
special_words = [w for w in raw_question.lower().split(" ") if w not in nlp_question.text.lower().split()]
special_words_capitalize = [w.capitalize() for w in special_words]
#special_words_capitalize_per_2 = it.permutations(special_words_capitalize,2)
#print("special_words",special_words)
#print("special_words_capitalize",special_words_capitalize)
special_words_titles = []
for per_size in range(2,len(special_words_capitalize)+1):
if per_size > max_title_size:
break
special_words_titles += [" ".join(p) for p in it.permutations(special_words_capitalize,per_size)]
#print("special_words_titles",special_words_titles)
#print(nlp_raw_question_list)
# PART1: finding themes as the user typed it
filter_list = ["PART", "PRON", "NUM"]
nlp_list_src = list(nlp_question)
nlp_list = []
for w in nlp_question:
if w.pos_ not in filter_list:
nlp_list.append(w)
nlp_question = get_nlp(" ".join([e.text for e in nlp_list]))
themes = [(ent, [ent.kb_id_]) for ent in get_kb_ents(nlp_question.text) if ent.kb_id_ != "NIL"]
#print("1 themes",themes)
for w in special_words+special_words_capitalize+special_words_titles:
themes += [(ent, [ent.kb_id_]) for ent in get_kb_ents(w) if ent.kb_id_ != "NIL"]
#print("2 themes",themes)
theme_complements = []
noun_chunks = [chunk for chunk in nlp_question.noun_chunks]
#print("1 noun_chunks",noun_chunks)
for w in special_words+special_words_capitalize+special_words_titles:
noun_chunks += [chunk for chunk in get_nlp(w, autocorrect=False).noun_chunks]
#print("2 noun_chunks",noun_chunks)
#theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks][:top_k]
theme_ids = get_themes_ids_from_chunks(noun_chunks, top_k=3, online=online)
for i, chunk in enumerate(theme_ids):
if chunk: themes.append((noun_chunks[i], chunk))
else: theme_complements.append(noun_chunks[i])
# PART2: finding themes with the question capitalized
#print(nlp_question)
nlp_list_cap = []
nlp_list_low = []
nlp_list_lemma = []
nlp_list_no_det = []
w_filter = ["WDT","WP","WP$","WRB"]
for w in nlp_question:
if w.tag_ not in w_filter:
nlp_list_cap.append(w.text.capitalize())
nlp_list_low.append(w.text.lower())
nlp_list_lemma.append(w.lemma_)
if w.pos_ != "DET":
nlp_list_no_det.append(w.text)
nlp_question_cap = get_nlp(" ".join([e for e in nlp_list_cap]))
nlp_question_low = get_nlp(" ".join([e for e in nlp_list_low]))
nlp_question_lemma = get_nlp(" ".join([e for e in nlp_list_lemma]))
nlp_question_no_det = get_nlp(" ".join([e for e in nlp_list_no_det]))
themes += [(ent, [ent.kb_id_]) for ent in get_kb_ents(nlp_question_cap.text) if ent.kb_id_ != "NIL" and (ent, [ent.kb_id_]) not in themes]
themes += [(ent, [ent.kb_id_]) for ent in get_kb_ents(nlp_question_low.text) if ent.kb_id_ != "NIL" and (ent, [ent.kb_id_]) not in themes]
themes += [(ent, [ent.kb_id_]) for ent in get_kb_ents(nlp_question_lemma.text) if ent.kb_id_ != "NIL" and (ent, [ent.kb_id_]) not in themes]
themes += [(ent, [ent.kb_id_]) for ent in get_kb_ents(nlp_question_no_det.text) if ent.kb_id_ != "NIL" and (ent, [ent.kb_id_]) not in themes]
if online:
themes += [(ent, get_wd_ids_online(ent.text, is_predicate=False, top_k=top_k)) for ent in get_kb_ents(nlp_question_cap.text)]
themes += [(ent, get_wd_ids_online(ent.text, is_predicate=False, top_k=top_k)) for ent in get_kb_ents(nlp_question_low.text)]
themes += [(ent, get_wd_ids_online(ent.text, is_predicate=False, top_k=top_k)) for ent in get_kb_ents(nlp_question_lemma.text)]
themes += [(ent, get_wd_ids_online(ent.text, is_predicate=False, top_k=top_k)) for ent in get_kb_ents(nlp_question_no_det.text)]
noun_chunks = []
previous_title_position = 0
for i_t,t in enumerate(nlp_question):
tmp_row = []
if i_t > previous_title_position:
if t.is_title:
for i_p in range(previous_title_position,i_t+1):
tmp_row.append(nlp_question[i_p])
noun_chunks.append(get_nlp(" ".join([w.text for w in tmp_row])))
if t.is_title:
previous_title_position = i_t
noun_chunks += [chunk for chunk in nlp_question_cap.noun_chunks]
#theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks][:top_k]
theme_ids = get_themes_ids_from_chunks(noun_chunks, top_k=3, online=online)
for i, chunk in enumerate(theme_ids):
if chunk: themes.append((noun_chunks[i], chunk))
else: theme_complements.append(noun_chunks[i])
noun_chunks = [chunk for chunk in nlp_question_low.noun_chunks]
#theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks][:top_k]
theme_ids = get_themes_ids_from_chunks(noun_chunks, top_k=3, online=online)
for i, chunk in enumerate(theme_ids):
if chunk: themes.append((noun_chunks[i], chunk))
else: theme_complements.append(noun_chunks[i])
noun_chunks = [chunk for chunk in nlp_question_lemma.noun_chunks]
#theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks][:top_k]
theme_ids = get_themes_ids_from_chunks(noun_chunks, top_k=3, online=online)
for i, chunk in enumerate(theme_ids):
if chunk: themes.append((noun_chunks[i], chunk))
else: theme_complements.append(noun_chunks[i])
noun_chunks = [chunk for chunk in nlp_question_no_det.noun_chunks]
#theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks][:top_k]
theme_ids = get_themes_ids_from_chunks(noun_chunks, top_k=3, online=online)
for i, chunk in enumerate(theme_ids):
if chunk: themes.append((noun_chunks[i], chunk))
else: theme_complements.append(noun_chunks[i])
themes_filtered = []
for t in themes:
if t[0].text in [tf[0].text for tf in themes_filtered]:
index = [tf[0].text for tf in themes_filtered].index(t[0].text)
tmp = t[1]+[i for j in [tf[1] for index, tf in enumerate(themes_filtered) if tf[0].text == t[0].text] for i in j]
themes_filtered[index] = (t[0],tmp)
else:
themes_filtered.append(t)
# removing the same elments per rows and skipping already existing rows
unique_ids = []
themes_filtered_undupped = []
for tf in themes_filtered:
tmp_ids = []
for tfid in tf[1]:
if tfid not in unique_ids and tfid not in tmp_ids:
tfname = get_wd_label(tfid)
similarity = get_nlp(tfname).similarity(tf[0])
if similarity >= 0.95:
tmp_ids.append(tfid)
unique_ids.append(tfid)
if tmp_ids and tmp_ids not in [tfu[1] for tfu in themes_filtered_undupped]:
themes_filtered_undupped.append((tf[0],tmp_ids))
#for tf in themes_filtered:
# tmp_ids = []
# for tfid in tf[1]:
# if tfid not in tmp_ids:
# tmp_ids.append(tfid)
# if tmp_ids not in [tfu[1] for tfu in themes_filtered_undupped]:
# themes_filtered_undupped.append((tf[0],tmp_ids))
theme_complements_undupped = []
[theme_complements_undupped.append(tc) for tc in theme_complements if tc.text not in [tcu.text for tcu in theme_complements_undupped]]
#print(themes_filtered)
return themes_filtered_undupped, theme_complements_undupped
#q0_themes = get_themes(q0_nlp, top_k=3)
#q0_themes_test = get_themes(q0_nlp_test)
#q0_themes_test_2 = get_themes(q0_nlp_test_2)
#print(q0_themes)
#q_test_3 = get_nlp("the unicorn and the raccoons love obama barack's tacos")
#q_test_3_themes = get_themes(q_test_3, top_k=3)
#print(get_enhanced_themes(q_test_3_themes))
#print(q_test_3_themes)
#q_test_test = get_nlp("What is a tv action show?")
#q_test_test = get_nlp("Who voiced the Unicorn in The Last Unicorn")
#q_test_test = get_nlp("What is the name of the person who created Saved by the Bell?")
#q_test_test = get_nlp("When did the movie Grease come out?")
#q_test_question = "Who was an influential figure for miško Šuvaković"
#q_test_test = get_nlp(q_test_question,True)