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tmqa1.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
#get_ipython().run_line_magic('autosave', '180')
# In[2]:
#import convex as cx
import requests
import time
import itertools
import numpy
from pprint import pprint
import time
import json
import os
import networkx as nx
from math import sqrt
import spacy
from hdt import HDTDocument
# In[9]:
hdt_wd = HDTDocument("data/kb/wikidata2018_09_11.hdt")
# In[11]:
#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[137]:
# load settings
with open( "settings-tmqa-1.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[53]:
def save_cache_data():
if save_cache:
with open(os.path.join(cache_path,'statements_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(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_word_ids_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_word_ids_dict, separators=(',',':')).encode('utf8'))
with open(os.path.join(cache_path,'wd_predicate_ids_dict.json'), 'wb') as outfile:
outfile.write(json.dumps(wd_predicate_ids_dict, separators=(',',':')).encode('utf8'))
# In[141]:
# Load statements cache
if use_cache:
path_statements_dict = "statements_dict.json"
path_wd_labels_dict = 'wd_labels_dict.json'
path_wd_word_ids_dict = 'wd_word_ids_dict.json'
path_wd_predicate_ids_dict = 'wd_predicate_ids_dict.json'
else:
path_statements_dict = "statements_dict_empty.json"
path_wd_labels_dict = 'wd_labels_dict_empty.json'
path_wd_word_ids_dict = 'wd_word_ids_dict_empty.json'
path_wd_predicate_ids_dict = 'wd_predicate_ids_dict_empty.json'
with open(os.path.join(cache_path,path_statements_dict), "rb") as data:
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_word_ids_dict), "rb") as data:
wd_word_ids_dict = json.load(data)
with open(os.path.join(cache_path,path_wd_predicate_ids_dict), "rb") as data:
wd_predicate_ids_dict = json.load(data)
#print("len(statements_dict)",len(statements_dict))
#print("len(wd_labels_dict)",len(wd_labels_dict))
#print("len(wd_word_ids_dict)",len(wd_word_ids_dict))
#print("len(wd_predicate_ids_dict)",len(wd_predicate_ids_dict))
# In[15]:
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[16]:
def get_nlp(sentence):
return nlp(sentence)
# In[17]:
#questions = [
# "Which actor voiced the Unicorn in The Last Unicorn?",
# "And Alan Arkin was behind...?",
# "And Alan Arkin be behind...? Why How when which was happy make fun",
# "Who is the composer of the soundtrack?",
# "So who performed the songs?",
# "Genre of this band's music?",
# "By the way, who was the director?"
# ]
#
#q_test = str("Which actor voiced the Unicorn in The Last Unicorn? "+
# "And Alan Arkin was behind...? "+
# "And Alan Arkin be behind...? Why How when which was happy make fun. "+
# "Who is the composer of the soundtrack? "+
# "So who performed songs? "+
# "Genre of this band's music? "+
# "By the way, who was the director? ")
#
#q_test_2 = "Who is the wife of Barack Obama?"
# In[18]:
#q0_nlp = get_nlp(questions[0])
#q0_nlp_test = get_nlp(q_test)
#q0_nlp_test_2 = get_nlp(q_test_2)
#print(q0_nlp)
# In[19]:
import re
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_predicate("Q16614390"))
# In[136]:
# TODO redo the functions and optimize
# TODO add cache
def is_entity_or_literal(wd_object):
if is_wd_entity(wd_object.strip()):
return True
pattern = re.compile('^[A-Za-z0-9]*$')
if len(wd_object) == 32 and pattern.match(wd_object.strip()):
return False
return True
# return if the given string is a literal or a date
def is_literal_or_date (answer):
return not('www.wikidata.org' in answer)
# return if the given string describes a year in the format YYYY
def is_year(year):
pattern = re.compile('^[0-9][0-9][0-9][0-9]$')
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 statements_dict.get(entity_id) != None:
#print("saved statement")
return 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:
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
statements_dict[entity_id] = statements
return statements
#print(len(get_all_statements_of_entity("Q16614390")))
#save_cache_data()
# In[124]:
def get_wd_ids_online(name, is_predicate=False, top_k=3):
name = name.split('(')[0]
if is_predicate and wd_predicate_ids_dict.get(name) != None:
#print("saved predicate online")
return wd_predicate_ids_dict[name]
elif not is_predicate and wd_word_ids_dict.get(name) != None:
#print("saved word online")
return wd_word_ids_dict[name]
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_predicate_ids_dict[name] = ""
else: wd_word_ids_dict[name] = ""
return ""
if not len(results):
if is_predicate: wd_predicate_ids_dict[name] = ""
else: wd_word_ids_dict[name] = ""
return ""
res = []
for result in results:
res.append(result['id'])
if is_predicate: wd_predicate_ids_dict[name] = res
else: wd_word_ids_dict[name] = res
return res
#print(get_wd_ids_online("voiced", is_predicate=False, top_k=1))
# In[22]:
import warnings
warnings.filterwarnings('ignore')
# very computational
def get_most_similar(word, topn=5):
word = nlp.vocab[str(word)]
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)
return [(w.lower_,w.similarity(word)) for w in by_similarity[:topn+1] if w.lower_ != word.lower_]
#print(get_most_similar("voiced", topn=3))
# In[128]:
def get_wd_ids(word, is_predicate=False, top_k=3, limit=10):
if is_predicate and wd_predicate_ids_dict.get(word) != None:
#print("saved predicate local")
return wd_predicate_ids_dict[word]
elif not is_predicate and wd_word_ids_dict.get(word) != None:
#print("saved word local")
return wd_word_ids_dict[word]
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_predicate_ids_dict[word] = results
else: wd_word_ids_dict[word] = results
return results if limit<=0 else results[:limit]
#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[127]:
def get_wd_label(from_id):
#print("from_id",from_id)
if is_valide_wd_id(from_id):
if wd_labels_dict.get(from_id) != None:
#print("saved label local")
return wd_labels_dict[from_id]
language = "en"
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('\"@en')[0].replace("\"", "") 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 ''
#print(get_wd_label("P725"))
#get_wd_label("Q20789322")
# In[29]:
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
# 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(str(name)+".png", format="PNG", dpi = 300)
plt.show()
#plot_graph(graph, "file_name_graph", "Graph_title")
# In[30]:
def make_statements_graph(statements, indexing_predicates=True):
graph = nx.Graph()
turn=0
predicate_nodes = {}
for statement in statements:
#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)
# 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)
graph.add_edge(statement['entity']['id'], predicate_node_id)
graph.add_edge(predicate_node_id, statement['object']['id'])
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")
# In[31]:
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[32]:
def get_themes(nlp_question, top_k=3):
filter_list = ["PART", "PRON"]
nlp_list = list(nlp_question)
for i,w in enumerate(nlp_question):
if w.pos_ in filter_list:
del nlp_list[i]
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"]
theme_complements = []
noun_chunks = [chunk for chunk in nlp_question.noun_chunks]
theme_ids = [get_wd_ids(chunk.text, top_k=top_k) for chunk in noun_chunks]
for i, chunk in enumerate(theme_ids):
if chunk: themes.append((noun_chunks[i], chunk))
else: theme_complements.append(noun_chunks[i])
return themes, theme_complements
#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)
# In[33]:
def get_theme_tuples(theme_list, top_k=3):
return [(t, get_wd_ids(t, top_k=top_k)) for t in theme_list]
def get_theme_no_stopwords(theme_list):
return [s for s in theme_list if not s.is_stop]
def get_theme_lemmatized(theme_list):
return [s.lemma_ for s in theme_list]
def get_permutation_tuples(theme_list, start=2):
permutations = []
for i in range(start, len(theme_list)+1):
permutations += itertools.permutations(theme_list,i)
return permutations
def get_lemma_permutation_tuples(theme_list, start=2):
return get_permutation_tuples(get_theme_lemmatized(theme_list), start=2)
def get_non_token_tuples(theme_list):
return [" ".join([e for e in list(l)]) for l in theme_list]
def get_non_token_lower_tuples(theme_list):
return [" ".join([e.lower() for e in list(l)]) for l in theme_list]
def get_non_token_capitalize_tuples(theme_list):
return [" ".join([c.capitalize() for c in [e for e in list(l)]]) for l in theme_list]
def get_text_tuples(theme_list):
return [" ".join([e.text for e in list(l)]) for l in theme_list]
def get_lower_tuples(theme_list):
return [" ".join([e.lower_ for e in list(l)]) for l in theme_list]
def get_capitalized_tuples(theme_list):
return [" ".join([c.capitalize() for c in [e.text for e in list(l)]]) for l in theme_list]
def get_enhanced_themes(themes, top_k=3):
enhanced_themes = []
# permute, capitalize, lowering of the words in the complements
for c in themes[1]:
per_lemma = get_theme_tuples(get_non_token_tuples([n for n in get_permutation_tuples(get_theme_lemmatized(c))]),top_k)
per_nostop = get_theme_tuples(get_text_tuples(get_permutation_tuples(get_theme_no_stopwords(c),start=1)),top_k)
per_lemma_nostop = get_theme_tuples(get_non_token_tuples([get_theme_lemmatized(s) for s in get_permutation_tuples(get_theme_no_stopwords(c),start=1)]),top_k)
per_lemma_lower = get_theme_tuples(get_non_token_lower_tuples([n for n in get_permutation_tuples(get_theme_lemmatized(c))]),top_k)
per_nostop_lower = get_theme_tuples(get_lower_tuples(get_permutation_tuples(get_theme_no_stopwords(c),start=1)),top_k)
per_lemma_nostop_lower = get_theme_tuples(get_non_token_lower_tuples([get_theme_lemmatized(s) for s in get_permutation_tuples(get_theme_no_stopwords(c),start=1)]),top_k)
per_lemma_capitalize = get_theme_tuples(get_non_token_capitalize_tuples([n for n in get_permutation_tuples(get_theme_lemmatized(c))]),top_k)
per_nostop_capitalize = get_theme_tuples(get_capitalized_tuples(get_permutation_tuples(get_theme_no_stopwords(c),start=1)),top_k)
per_lemma_nostop_capitalize = get_theme_tuples(get_non_token_capitalize_tuples([get_theme_lemmatized(s) for s in get_permutation_tuples(get_theme_no_stopwords(c),start=1)]),top_k)
per = get_theme_tuples(get_text_tuples(get_permutation_tuples(c)),top_k)
per_lower = get_theme_tuples(get_lower_tuples(get_permutation_tuples(c)),top_k)
per_capitalize = get_theme_tuples(get_capitalized_tuples(get_permutation_tuples(c)),top_k)
for p in (per + per_lower + per_capitalize +
per_lemma + per_lemma_lower + per_lemma_capitalize +
per_nostop + per_nostop_lower + per_nostop_capitalize +
per_lemma_nostop + per_lemma_nostop_lower + per_lemma_nostop_capitalize):
if p[1] and p not in enhanced_themes: enhanced_themes.append(p)
return enhanced_themes
#q_test_3 = get_nlp("the unicorn and the raccoons love obama barack's tacos")
#q_test_3 = get_nlp("what was the cause of death of yves klein")
#q_test_3_themes = get_themes(q_test_3, top_k=3)
#print(q_test_3_themes[0])
#print(get_enhanced_themes(q_test_3_themes))
# In[77]:
def get_predicates_online(nlp_sentence, top_k=3):
predicates = [p for p in nlp_sentence if p.pos_ == "VERB" or p.pos_ == "AUX"]
predicates_ids = [get_wd_ids_online(p.text, is_predicate=True, top_k=top_k) for p in predicates]
return merge_lists(predicates, predicates_ids)
#q0_predicates = get_predicates_online(q0_nlp, top_k=3)
#q0_predicates_test_2 = get_predicates_online(q0_nlp_test_2, top_k=3)
#print(q0_predicates)
# In[80]:
def get_predicates(nlp_sentence, top_k=3):
predicates = [p for p in nlp_sentence if p.pos_ == "VERB" or p.pos_ == "AUX"]
predicates_ids = [get_wd_ids(p.lemma_, is_predicate=True, top_k=0, limit=0) for p in predicates]
return merge_lists(predicates, predicates_ids)
#q0_nlp_test_0 = get_nlp("Voiced")
#q0_predicates = get_predicates(q0_nlp, top_k=3)
#q0_predicates_test_2 = get_predicates(q0_nlp_test_2, top_k=3)
#print(q0_predicates)
# In[212]:
def extract_ids(to_extract):
return [i for i in itertools.chain.from_iterable([id[1] for id in to_extract])]
#extract_ids([('name', ['id'])]) #q0_themes[0] #q0_focused_parts #q0_predicates
#print(extract_ids([("The Last Unicorn", ['Q16614390']),("Second Theme", ['Q12345'])]))
#extract_ids(q0_focused_parts)
# In[89]:
def get_similarity_by_words(nlp_word_from, nlp_word_to):
if not nlp_word_from or not nlp_word_to:
return 0
elif not nlp_word_from.vector_norm or not nlp_word_to.vector_norm:
return 0
else:
return nlp_word_from.similarity(nlp_word_to)
#print(get_similarity_by_words(get_nlp("character role"), get_nlp("voice actor")))
# In[92]:
def get_similarity_by_ids(word_id_from, word_id_to):
nlp_word_from = get_nlp(get_wd_label(word_id_from))
nlp_word_to = get_nlp(get_wd_label(word_id_to))
return get_similarity_by_words(nlp_word_from, nlp_word_to)
#print(get_similarity_by_ids("P453", "P725"))
# In[93]:
def get_top_similar_statements(statements, from_token_id, similar_to_name, top_k=3, qualifier=False, statement_type="object"):
highest_matching_similarity = -1
top_statements = []
nlp_name = get_nlp(similar_to_name)
if get_wd_label(from_token_id):
for statement in statements:
if qualifier:
if statement.get('qualifiers'):
for qualifier in statement['qualifiers']:
nlp_word_to = get_nlp(get_wd_label(qualifier[statement_type]['id']))
matching_similarity = get_similarity_by_words(nlp_name, nlp_word_to)
if highest_matching_similarity == -1 or matching_similarity > highest_matching_similarity:
highest_matching_similarity = matching_similarity
best_statement = statement
top_statements.append((highest_matching_similarity, best_statement))
else:
nlp_word_to = get_nlp(get_wd_label(statement[statement_type]['id']))
matching_similarity = get_similarity_by_words(nlp_name, nlp_word_to)
if highest_matching_similarity == -1 or matching_similarity > highest_matching_similarity:
highest_matching_similarity = matching_similarity
best_statement = statement
top_statements.append((highest_matching_similarity, best_statement))
return sorted(top_statements, key=lambda x: x[0], reverse=True)[:top_k]
#statements = get_all_statements_of_entity('Q176198')
#top_similar_statements = get_top_similar_statements(statements, 'Q176198', 'voiced')
#print(top_similar_statements[0])
# In[94]:
def get_best_similar_statements_by_word(from_token_ids, similar_to_name, top_k=3, qualifier=False, statement_type="object"):
best_statements = []
for token in from_token_ids:
statements = get_all_statements_of_entity(token)
if statements: best_statements += get_top_similar_statements(statements, token, similar_to_name, top_k=top_k, qualifier=qualifier, statement_type=statement_type)
return sorted(best_statements, key=lambda x: x[0], reverse=True)[:top_k]
#best_similar_statements = get_best_similar_statements_by_word(extract_ids(q0_themes[0]), 'voiced', top_k=3, qualifier=True, statement_type="qualifier_object")
#print(best_similar_statements[0])
# In[95]:
def get_statements_subjects_labels(statements):
return [get_wd_label(t[1]['entity']['id']) for t in statements]
#print(get_statements_subjects_labels(best_similar_statements))
# In[33]:
def get_statements_predicates_labels(statements):
return [get_wd_label(t[1]['predicate']['id']) for t in statements]
#print(get_statements_predicates_labels(best_similar_statements))
# In[96]:
def get_statements_objects_labels(statements):
return [get_wd_label(t[1]['object']['id']) for t in statements]
#print(get_statements_objects_labels(best_similar_statements))
# In[97]:
def get_statements_qualifier_predicates_labels(statements):
return [get_wd_label(t[1]['qualifiers'][0]['qualifier_predicate']['id']) for t in statements]
#print(get_statements_qualifier_predicates_labels(best_similar_statements))
# In[98]:
def get_statements_qualifier_objects_labels(statements):
return [get_wd_label(t[1]['qualifiers'][0]['qualifier_object']['id']) for t in statements]
#print(get_statements_qualifier_objects_labels(best_similar_statements))
# In[100]:
def cluster_extend_by_words(cluster_root_ids, extending_words, top_k=3):
cluster = []
#start_time = time.time()
for name in extending_words:
#start_cluster_time = time.time()
cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=True, statement_type="qualifier_predicate")
cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=True, statement_type="qualifier_object")
cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=False, statement_type="predicate")
cluster += get_best_similar_statements_by_word(cluster_root_ids, name, top_k=top_k, qualifier=False, statement_type="object")
#end_time = time.time()
#print("EXTENDING Cluster with:", name," ->\tRunning time is {}s".format(round(end_time-start_cluster_time,2)))
#end_time = time.time()
#print("EXTENDING Clusters ->\tRunning time is {}s".format(round(end_time-start_time,2)))
return cluster
#test_cluster = cluster_extend_by_words(extract_ids(q0_themes[0]), ['voiced'], top_k=2)
#test_cluster_test_2 = cluster_extend_by_words(extract_ids(q0_themes_test_2[0]), ['birth'], top_k=2)
#print(test_cluster[0])
# In[102]:
# sorts by the similarity value of statements[0]
def sort_statements_by_similarity(statements):
return [s for s in sorted(statements, key=lambda x: x[0], reverse=True)]
#test_sorted_statements = sort_statements_by_similarity(test_cluster)
#test_sorted_statements_test_2 = sort_statements_by_similarity(test_cluster_test_2)
#print(test_sorted_statements[0])
# In[103]:
from copy import copy
# appends spo from qualifiers, removes qualifier tags, and removes similarity scores
def statements_flatter(statements):
best_statements_to_graph = []
for statement in statements:
tmp_statement = copy(statement)
if tmp_statement.get('qualifiers'):
#print("statement", statement)
for q in tmp_statement['qualifiers']:
qualifier_statement = {'entity': {'id': tmp_statement['entity']['id']}}
qualifier_statement['predicate'] = {'id': q['qualifier_predicate']['id']}
qualifier_statement['object'] = {'id': q['qualifier_object']['id']}
best_statements_to_graph.append(qualifier_statement)
del(tmp_statement['qualifiers'])
else:
#print("tmp_statement", tmp_statement)
if ('qualifiers' in tmp_statement): del(tmp_statement['qualifiers'])
if tmp_statement not in best_statements_to_graph:
#print("best_statements_to_graph", tmp_statement)
best_statements_to_graph.append(tmp_statement)
return best_statements_to_graph
#test_flatten_statements = statements_flatter([s[1] for s in test_sorted_statements])
#test_flatten_statements_test_2 = statements_flatter([s[1] for s in test_sorted_statements_test_2])
#print(test_flatten_statements[0])
#test_flatten_statements_test_2
# In[104]:
# remove duplicates from statements
def unduplicate_statements(statements):
filtered_statements = []
[filtered_statements.append(s) for s in statements if s not in [e for e in filtered_statements]]
return filtered_statements
#test_unduplicate_statements = unduplicate_statements(test_flatten_statements)
#print(len(test_flatten_statements))
#print(len(test_unduplicate_statements))
#print(test_unduplicate_statements[0])
# In[105]:
#test_graph = make_statements_graph(test_unduplicate_statements)
#print(test_graph[1])
#plot_graph(test_graph[0], "file_name_graph", "Graph_title")
# In[106]:
def get_statements_by_id(statements, from_token_id, to_id, qualifier=False, statement_type="predicate"):
id_statements = []
if not statements:
return id_statements
if get_wd_label(from_token_id):
for statement in statements:
if qualifier:
if statement.get('qualifiers'):
for s in statement['qualifiers']:
if to_id == s[statement_type]['id']:
id_statements.append(statement)
else:
if to_id == statement[statement_type]['id']:
id_statements.append(statement)
return id_statements
#statements_test = get_all_statements_of_entity('Q176198')
#id_statements_test = get_statements_by_id(statements_test, 'Q176198', 'P725')
#print(id_statements_test[0])
#get_statements_by_id(root_statements, cluster_root_id, predicate_id, qualifier=False, statement_type="predicate")
#statements_test = get_all_statements_of_entity('Q176198')
#id_statements_test = get_statements_by_id(statements_test, 'Q176198', 'P725')
#id_statements_test[0]
# In[107]:
# parameters
# cluster_root_ids: ['Qcode']
# predicates_ids: ['Pcode']
def cluster_extend_by_predicates_ids(cluster_root_ids, predicates_ids):
cluster = []
for cluster_root_id in cluster_root_ids:
root_statements = get_all_statements_of_entity(cluster_root_id)
#print("root_statements", root_statements)
for predicate_id in predicates_ids:
cluster += get_statements_by_id(root_statements, cluster_root_id, predicate_id, qualifier=True, statement_type="qualifier_predicate")
cluster += get_statements_by_id(root_statements, cluster_root_id, predicate_id, qualifier=False, statement_type="predicate")
return cluster
#test_predicate_clusters = cluster_extend_by_predicates_ids(extract_ids(q0_themes[0]), extract_ids(q0_predicates))
#print(len(test_predicate_clusters))
#test_predicate_clusters[0]
#test_predicate_clusters_test_2 = cluster_extend_by_predicates_ids(extract_ids(q0_themes_test_2[0]), extract_ids(q0_predicates_test_2))
#print(len(test_predicate_clusters_test_2))
#print(test_predicate_clusters_test_2[-1])
# In[108]:
def cluster_extractor_from_complements(complements):
for c in complements:
[print(t.pos_) for t in c]
return complements
#print(cluster_extractor_from_complements(q0_themes[1]))
# In[110]: