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knowledge_graph.py
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knowledge_graph.py
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"""
Copyright (c) 2018, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
Knowledge Graph Environment.
"""
import collections
import os
import pickle
import torch
import torch.nn as nn
from src.data_utils import load_index
from src.data_utils import NO_OP_ENTITY_ID, NO_OP_RELATION_ID
from src.data_utils import DUMMY_ENTITY_ID, DUMMY_RELATION_ID
from src.data_utils import START_RELATION_ID
import src.utils.ops as ops
from src.utils.ops import int_var_cuda, var_cuda
class KnowledgeGraph(nn.Module):
"""
The discrete knowledge graph is stored with an adjacency list.
"""
def __init__(self, args):
super(KnowledgeGraph, self).__init__()
self.entity2id, self.id2entity = {}, {}
self.relation2id, self.id2relation = {}, {}
self.type2id, self.id2type = {}, {}
self.entity2typeid = {}
self.adj_list = None
self.bandwidth = args.bandwidth
self.args = args
self.action_space = None
self.action_space_buckets = None
self.unique_r_space = None
self.train_subjects = None
self.train_objects = None
self.dev_subjects = None
self.dev_objects = None
self.all_subjects = None
self.all_objects = None
self.train_subject_vectors = None
self.train_object_vectors = None
self.dev_subject_vectors = None
self.dev_object_vectors = None
self.all_subject_vectors = None
self.all_object_vectors = None
print('** Create {} knowledge graph **'.format(args.model))
self.load_graph_data(args.data_dir)
self.load_all_answers(args.data_dir)
# Define NN Modules
self.entity_dim = args.entity_dim
self.relation_dim = args.relation_dim
self.emb_dropout_rate = args.emb_dropout_rate
self.num_graph_convolution_layers = args.num_graph_convolution_layers
self.entity_embeddings = None
self.relation_embeddings = None
self.entity_img_embeddings = None
self.relation_img_embeddings = None
self.EDropout = None
self.RDropout = None
self.define_modules()
self.initialize_modules()
def load_graph_data(self, data_dir):
# Load indices
self.entity2id, self.id2entity = load_index(os.path.join(data_dir, 'entity2id.txt'))
print('Sanity check: {} entities loaded'.format(len(self.entity2id)))
self.type2id, self.id2type = load_index(os.path.join(data_dir, 'type2id.txt'))
print('Sanity check: {} types loaded'.format(len(self.type2id)))
with open(os.path.join(data_dir, 'entity2typeid.pkl'), 'rb') as f:
self.entity2typeid = pickle.load(f)
self.relation2id, self.id2relation = load_index(os.path.join(data_dir, 'relation2id.txt'))
print('Sanity check: {} relations loaded'.format(len(self.relation2id)))
# Load graph structures
if self.args.model.startswith('point'):
# Base graph structure used for training and test
adj_list_path = os.path.join(data_dir, 'adj_list.pkl')
with open(adj_list_path, 'rb') as f:
self.adj_list = pickle.load(f)
self.vectorize_action_space(data_dir)
def vectorize_action_space(self, data_dir):
"""
Pre-process and numericalize the knowledge graph structure.
"""
def load_page_rank_scores(input_path):
pgrk_scores = collections.defaultdict(float)
with open(input_path) as f:
for line in f:
e, score = line.strip().split(':')
e_id = self.entity2id[e.strip()]
score = float(score)
pgrk_scores[e_id] = score
return pgrk_scores
# Sanity check
num_facts = 0
out_degrees = collections.defaultdict(int)
for e1 in self.adj_list:
for r in self.adj_list[e1]:
num_facts += len(self.adj_list[e1][r])
out_degrees[e1] += len(self.adj_list[e1][r])
print("Sanity check: maximum out degree: {}".format(max(out_degrees.values())))
print('Sanity check: {} facts in knowledge graph'.format(num_facts))
# load page rank scores
page_rank_scores = load_page_rank_scores(os.path.join(data_dir, 'raw.pgrk'))
def get_action_space(e1):
action_space = []
if e1 in self.adj_list:
for r in self.adj_list[e1]:
targets = self.adj_list[e1][r]
for e2 in targets:
action_space.append((r, e2))
if len(action_space) + 1 >= self.bandwidth:
# Base graph pruning
sorted_action_space = \
sorted(action_space, key=lambda x: page_rank_scores[x[1]], reverse=True)
action_space = sorted_action_space[:self.bandwidth]
action_space.insert(0, (NO_OP_RELATION_ID, e1))
return action_space
def get_unique_r_space(e1):
if e1 in self.adj_list:
return list(self.adj_list[e1].keys())
else:
return []
def vectorize_action_space(action_space_list, action_space_size):
bucket_size = len(action_space_list)
r_space = torch.zeros(bucket_size, action_space_size) + self.dummy_r
e_space = torch.zeros(bucket_size, action_space_size) + self.dummy_e
action_mask = torch.zeros(bucket_size, action_space_size)
for i, action_space in enumerate(action_space_list):
for j, (r, e) in enumerate(action_space):
r_space[i, j] = r
e_space[i, j] = e
action_mask[i, j] = 1
return (int_var_cuda(r_space), int_var_cuda(e_space)), var_cuda(action_mask)
def vectorize_unique_r_space(unique_r_space_list, unique_r_space_size, volatile):
bucket_size = len(unique_r_space_list)
unique_r_space = torch.zeros(bucket_size, unique_r_space_size) + self.dummy_r
for i, u_r_s in enumerate(unique_r_space_list):
for j, r in enumerate(u_r_s):
unique_r_space[i, j] = r
return int_var_cuda(unique_r_space)
if self.args.use_action_space_bucketing:
"""
Store action spaces in buckets.
"""
self.action_space_buckets = {}
action_space_buckets_discrete = collections.defaultdict(list)
self.entity2bucketid = torch.zeros(self.num_entities, 2).long()
num_facts_saved_in_action_table = 0
for e1 in range(self.num_entities):
action_space = get_action_space(e1)
key = int(len(action_space) / self.args.bucket_interval) + 1
self.entity2bucketid[e1, 0] = key
self.entity2bucketid[e1, 1] = len(action_space_buckets_discrete[key])
action_space_buckets_discrete[key].append(action_space)
num_facts_saved_in_action_table += len(action_space)
print('Sanity check: {} facts saved in action table'.format(
num_facts_saved_in_action_table - self.num_entities))
for key in action_space_buckets_discrete:
print('Vectorizing action spaces bucket {}...'.format(key))
self.action_space_buckets[key] = vectorize_action_space(
action_space_buckets_discrete[key], key * self.args.bucket_interval)
else:
action_space_list = []
max_num_actions = 0
for e1 in range(self.num_entities):
action_space = get_action_space(e1)
action_space_list.append(action_space)
if len(action_space) > max_num_actions:
max_num_actions = len(action_space)
print('Vectorizing action spaces...')
self.action_space = vectorize_action_space(action_space_list, max_num_actions)
if self.args.model.startswith('rule'):
unique_r_space_list = []
max_num_unique_rs = 0
for e1 in sorted(self.adj_list.keys()):
unique_r_space = get_unique_r_space(e1)
unique_r_space_list.append(unique_r_space)
if len(unique_r_space) > max_num_unique_rs:
max_num_unique_rs = len(unique_r_space)
self.unique_r_space = vectorize_unique_r_space(unique_r_space_list, max_num_unique_rs)
def load_all_answers(self, data_dir, add_reversed_edges=False):
def add_subject(e1, e2, r, d):
if not e2 in d:
d[e2] = {}
if not r in d[e2]:
d[e2][r] = set()
d[e2][r].add(e1)
def add_object(e1, e2, r, d):
if not e1 in d:
d[e1] = {}
if not r in d[e1]:
d[e1][r] = set()
d[e1][r].add(e2)
# store subjects for all (rel, object) queries and
# objects for all (subject, rel) queries
train_subjects, train_objects = {}, {}
dev_subjects, dev_objects = {}, {}
all_subjects, all_objects = {}, {}
# include dummy examples
add_subject(self.dummy_e, self.dummy_e, self.dummy_r, train_subjects)
add_subject(self.dummy_e, self.dummy_e, self.dummy_r, dev_subjects)
add_subject(self.dummy_e, self.dummy_e, self.dummy_r, all_subjects)
add_object(self.dummy_e, self.dummy_e, self.dummy_r, train_objects)
add_object(self.dummy_e, self.dummy_e, self.dummy_r, dev_objects)
add_object(self.dummy_e, self.dummy_e, self.dummy_r, all_objects)
for file_name in ['raw.kb', 'train.triples', 'dev.triples', 'test.triples']:
if 'NELL' in self.args.data_dir and self.args.test and file_name == 'train.triples':
continue
with open(os.path.join(data_dir, file_name)) as f:
for line in f:
e1, e2, r = line.strip().split()
e1, e2, r = self.triple2ids((e1, e2, r))
if file_name in ['raw.kb', 'train.triples']:
add_subject(e1, e2, r, train_subjects)
add_object(e1, e2, r, train_objects)
if add_reversed_edges:
add_subject(e2, e1, self.get_inv_relation_id(r), train_subjects)
add_object(e2, e1, self.get_inv_relation_id(r), train_objects)
if file_name in ['raw.kb', 'train.triples', 'dev.triples']:
add_subject(e1, e2, r, dev_subjects)
add_object(e1, e2, r, dev_objects)
if add_reversed_edges:
add_subject(e2, e1, self.get_inv_relation_id(r), dev_subjects)
add_object(e2, e1, self.get_inv_relation_id(r), dev_objects)
add_subject(e1, e2, r, all_subjects)
add_object(e1, e2, r, all_objects)
if add_reversed_edges:
add_subject(e2, e1, self.get_inv_relation_id(r), all_subjects)
add_object(e2, e1, self.get_inv_relation_id(r), all_objects)
self.train_subjects = train_subjects
self.train_objects = train_objects
self.dev_subjects = dev_subjects
self.dev_objects = dev_objects
self.all_subjects = all_subjects
self.all_objects = all_objects
# change the answer set into a variable
def answers_to_var(d_l):
d_v = collections.defaultdict(collections.defaultdict)
for x in d_l:
for y in d_l[x]:
v = torch.LongTensor(list(d_l[x][y])).unsqueeze(1)
d_v[x][y] = int_var_cuda(v)
return d_v
self.train_subject_vectors = answers_to_var(train_subjects)
self.train_object_vectors = answers_to_var(train_objects)
self.dev_subject_vectors = answers_to_var(dev_subjects)
self.dev_object_vectors = answers_to_var(dev_objects)
self.all_subject_vectors = answers_to_var(all_subjects)
self.all_object_vectors = answers_to_var(all_objects)
def load_fuzzy_facts(self):
# extend current adjacency list with fuzzy facts
dev_path = os.path.join(self.args.data_dir, 'dev.triples')
test_path = os.path.join(self.args.data_dir, 'test.triples')
with open(dev_path) as f:
dev_triples = [l.strip() for l in f.readlines()]
with open(test_path) as f:
test_triples = [l.strip() for l in f.readlines()]
removed_triples = set(dev_triples + test_triples)
theta = 0.5
fuzzy_fact_path = os.path.join(self.args.data_dir, 'train.fuzzy.triples')
count = 0
with open(fuzzy_fact_path) as f:
for line in f:
e1, e2, r, score = line.strip().split()
score = float(score)
if score < theta:
continue
print(line)
if '{}\t{}\t{}'.format(e1, e2, r) in removed_triples:
continue
e1_id = self.entity2id[e1]
e2_id = self.entity2id[e2]
r_id = self.relation2id[r]
if not r_id in self.adj_list[e1_id]:
self.adj_list[e1_id][r_id] = set()
if not e2_id in self.adj_list[e1_id][r_id]:
self.adj_list[e1_id][r_id].add(e2_id)
count += 1
if count > 0 and count % 1000 == 0:
print('{} fuzzy facts added'.format(count))
self.vectorize_action_space(self.args.data_dir)
def get_inv_relation_id(self, r_id):
return r_id + 1
def get_all_entity_embeddings(self):
return self.EDropout(self.entity_embeddings.weight)
def get_entity_embeddings(self, e):
return self.EDropout(self.entity_embeddings(e))
def get_all_relation_embeddings(self):
return self.RDropout(self.relation_embeddings.weight)
def get_relation_embeddings(self, r):
return self.RDropout(self.relation_embeddings(r))
def get_all_entity_img_embeddings(self):
return self.EDropout(self.entity_img_embeddings.weight)
def get_entity_img_embeddings(self, e):
return self.EDropout(self.entity_img_embeddings(e))
def get_relation_img_embeddings(self, r):
return self.RDropout(self.relation_img_embeddings(r))
def virtual_step(self, e_set, r):
"""
Given a set of entities (e_set), find the set of entities (e_set_out) which has at least one incoming edge
labeled r and the source entity is in e_set.
"""
batch_size = len(e_set)
e_set_1D = e_set.view(-1)
r_space = self.action_space[0][0][e_set_1D]
e_space = self.action_space[0][1][e_set_1D]
e_space = (r_space.view(batch_size, -1) == r.unsqueeze(1)).long() * e_space.view(batch_size, -1)
e_set_out = []
for i in range(len(e_space)):
e_set_out_b = var_cuda(unique(e_space[i].data))
e_set_out.append(e_set_out_b.unsqueeze(0))
e_set_out = ops.pad_and_cat(e_set_out, padding_value=self.dummy_e)
return e_set_out
def id2triples(self, triple):
e1, e2, r = triple
return self.id2entity[e1], self.id2entity[e2], self.id2relation[r]
def triple2ids(self, triple):
e1, e2, r = triple
return self.entity2id[e1], self.entity2id[e2], self.relation2id[r]
def define_modules(self):
if not self.args.relation_only:
self.entity_embeddings = nn.Embedding(self.num_entities, self.entity_dim)
if self.args.model == 'complex':
self.entity_img_embeddings = nn.Embedding(self.num_entities, self.entity_dim)
self.EDropout = nn.Dropout(self.emb_dropout_rate)
self.relation_embeddings = nn.Embedding(self.num_relations, self.relation_dim)
if self.args.model == 'complex':
self.relation_img_embeddings = nn.Embedding(self.num_relations, self.relation_dim)
self.RDropout = nn.Dropout(self.emb_dropout_rate)
def initialize_modules(self):
if not self.args.relation_only:
nn.init.xavier_normal_(self.entity_embeddings.weight)
nn.init.xavier_normal_(self.relation_embeddings.weight)
@property
def num_entities(self):
return len(self.entity2id)
@property
def num_relations(self):
return len(self.relation2id)
@property
def self_edge(self):
return NO_OP_RELATION_ID
@property
def self_e(self):
return NO_OP_ENTITY_ID
@property
def dummy_r(self):
return DUMMY_RELATION_ID
@property
def dummy_e(self):
return DUMMY_ENTITY_ID
@property
def dummy_start_r(self):
return START_RELATION_ID