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run.py
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run.py
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# -*- coding: utf-8 -*-
# @Time : 2020-10-19 15:38
# @Author : xiaorui su
# @Email : suxiaorui19@mails.ucas.edu.cn
# @File : run.py
# @Software : PyCharm
# -*- coding: utf-8 -*-
import sys
import random
import os
import numpy as np
from collections import defaultdict
sys.path.append(os.getcwd()) # add the env path
from sklearn.model_selection import train_test_split, StratifiedKFold
from main import train
from config import DRUG_EXAMPLE, RESULT_LOG, PROCESSED_DATA_DIR, LOG_DIR, MODEL_SAVED_DIR, ENTITY2ID_FILE, KG_FILE, \
EXAMPLE_FILE, DRUG_VOCAB_TEMPLATE, ENTITY_VOCAB_TEMPLATE, \
RELATION_VOCAB_TEMPLATE, SEPARATOR, THRESHOLD, TRAIN_DATA_TEMPLATE, DEV_DATA_TEMPLATE, \
TEST_DATA_TEMPLATE, ADJ_ENTITY_TEMPLATE, ADJ_RELATION_TEMPLATE, ModelConfig, NEIGHBOR_SIZE
from utils import pickle_dump, format_filename, write_log, pickle_load
def read_entity2id_file(file_path: str, drug_vocab: dict, entity_vocab: dict):
print(f'Logging Info - Reading entity2id file: {file_path}')
assert len(drug_vocab) == 0 and len(entity_vocab) == 0
with open(file_path, encoding='utf8') as reader:
count = 0
for line in reader:
if (count == 0):
count += 1
continue
#print(line)
#print(line.strip().split(' '))
#kegg '\t'
#ogb " "
drug, entity = line.strip().split('\t')
drug_vocab[entity] = len(drug_vocab)
entity_vocab[entity] = len(entity_vocab)
def read_example_file(file_path: str, separator: str, drug_vocab: dict):
print(f'Logging Info - Reading example file: {file_path}')
assert len(drug_vocab) > 0
examples = []
with open(file_path, encoding='utf8') as reader:
for idx, line in enumerate(reader):
#print(line.strip().split(separator))
d1, d2, flag = line.strip().split(separator)[:3]
if d1 not in drug_vocab or d2 not in drug_vocab:
continue
if d1 in drug_vocab and d2 in drug_vocab:
examples.append([drug_vocab[d1], drug_vocab[d2], int(flag)])
examples_matrix = np.array(examples)
print(f'size of example: {examples_matrix.shape}')
X = examples_matrix[:, :2]
y = examples_matrix[:, 2:3]
train_data_X, valid_data_X, train_y, val_y = train_test_split(X, y, test_size=0.2, stratify=y)
train_data = np.c_[train_data_X, train_y]
valid_data_X, test_data_X, val_y, test_y = train_test_split(valid_data_X, val_y, test_size=0.5)
valid_data = np.c_[valid_data_X, val_y]
test_data = np.c_[test_data_X, test_y]
return examples_matrix
def read_kg(file_path: str, entity_vocab: dict, relation_vocab: dict, neighbor_sample_size: int):
print(f'Logging Info - Reading kg file: {file_path}')
kg = defaultdict(list)
with open(file_path, encoding='utf8') as reader:
count = 0
for line in reader:
if count == 0:
count += 1
continue
head, tail, relation = line.strip().split(' ')
#print(head,tail,relation)
if head not in entity_vocab:
entity_vocab[head] = len(entity_vocab)
if tail not in entity_vocab:
entity_vocab[tail] = len(entity_vocab)
if relation not in relation_vocab:
relation_vocab[relation] = len(relation_vocab)
# undirected graph
kg[entity_vocab[head]].append((entity_vocab[tail], relation_vocab[relation]))
kg[entity_vocab[tail]].append((entity_vocab[head], relation_vocab[relation]))
print(f'Logging Info - num of entities: {len(entity_vocab)}, '
f'num of relations: {len(relation_vocab)}')
#print(kg)
print('Logging Info - Constructing adjacency matrix...')
n_entity = len(entity_vocab)
#neighborsample_size hyperparameter
adj_entity = np.zeros(shape=(n_entity, neighbor_sample_size), dtype=np.int64)
adj_relation = np.zeros(shape=(n_entity, neighbor_sample_size), dtype=np.int64)
##choose neighboor randomly
##revise the select strategy
for entity_id in range(n_entity):
all_neighbors = kg[entity_id]
n_neighbor = len(all_neighbors)
sample_indices = np.random.choice(
n_neighbor,
neighbor_sample_size,
replace=False if n_neighbor >= neighbor_sample_size else True
)
#print(sample_indices)
adj_entity[entity_id] = np.array([all_neighbors[i][0] for i in sample_indices])
adj_relation[entity_id] = np.array([all_neighbors[i][1] for i in sample_indices])
#print(adj_entity)
#print(adj_relation)
return adj_entity, adj_relation
def process_data(dataset: str, neighbor_sample_size: int, K: int):
drug_vocab = {}
entity_vocab = {}
relation_vocab = {}
read_entity2id_file(ENTITY2ID_FILE[dataset], drug_vocab, entity_vocab)
pickle_dump(format_filename(PROCESSED_DATA_DIR, DRUG_VOCAB_TEMPLATE, dataset=dataset), drug_vocab)
pickle_dump(format_filename(PROCESSED_DATA_DIR, ENTITY_VOCAB_TEMPLATE, dataset=dataset), entity_vocab)
examples_file = format_filename(PROCESSED_DATA_DIR, DRUG_EXAMPLE, dataset=dataset)
examples = read_example_file(EXAMPLE_FILE[dataset], SEPARATOR[dataset], drug_vocab)
print(len(examples))
#example contains postive samples and negative samples
#example:[drug1 drug2 interaction]
np.save(examples_file, examples)
adj_entity_file = format_filename(PROCESSED_DATA_DIR, ADJ_ENTITY_TEMPLATE, dataset=dataset)
adj_relation_file = format_filename(PROCESSED_DATA_DIR, ADJ_RELATION_TEMPLATE, dataset=dataset)
adj_entity, adj_relation = read_kg(KG_FILE[dataset], entity_vocab, relation_vocab,
neighbor_sample_size)
pickle_dump(format_filename(PROCESSED_DATA_DIR, DRUG_VOCAB_TEMPLATE, dataset=dataset),
drug_vocab)
pickle_dump(format_filename(PROCESSED_DATA_DIR, ENTITY_VOCAB_TEMPLATE, dataset=dataset),
entity_vocab)
pickle_dump(format_filename(PROCESSED_DATA_DIR, RELATION_VOCAB_TEMPLATE, dataset=dataset),
relation_vocab)
adj_entity_file = format_filename(PROCESSED_DATA_DIR, ADJ_ENTITY_TEMPLATE, dataset=dataset)
np.save(adj_entity_file, adj_entity)
print('Logging Info - Saved:', adj_entity_file)
adj_relation_file = format_filename(PROCESSED_DATA_DIR, ADJ_RELATION_TEMPLATE, dataset=dataset)
np.save(adj_relation_file, adj_relation)
print('Logging Info - Saved:', adj_entity_file)
cross_validation(K, examples, dataset, neighbor_sample_size)
def cross_validation(K_fold, examples, dataset, neighbor_sample_size):
subsets = dict()
n_subsets = int(len(examples) / K_fold)
remain = set(range(0, len(examples) - 1))
for i in reversed(range(0, K_fold - 1)):
subsets[i] = random.sample(remain, n_subsets)
remain = remain.difference(subsets[i])
subsets[K_fold - 1] = remain
#aggregator_types = ['sum', 'concat', 'neigh']
aggregator_types = ['neigh']
for t in aggregator_types:
count = 1
temp = {'dataset': dataset, 'aggregator_type': t, 'avg_auc': 0.0, 'avg_acc': 0.0, 'avg_f1': 0.0,
'avg_aupr': 0.0}
for i in reversed(range(0, K_fold)):
test_d = examples[list(subsets[i])]
print("test_d")
print(test_d)
val_d, test_data = train_test_split(test_d, test_size=0.5)
print("val_d")
print(val_d)
print("test_Data")
print(test_data[0:10])
train_d = []
#train_data [drug1_smile, drug2_smile, interaction]
for j in range(0, K_fold):
if i != j:
train_d.extend(examples[list(subsets[j])])
train_data = np.array(train_d)
train_log = train(
kfold=count,
dataset=dataset,
train_d=train_data,
dev_d=val_d,
test_d=test_data,
neighbor_sample_size=neighbor_sample_size,
embed_dim=32,
n_depth=6, #layer
l2_weight=1e-7,
lr=2e-2,
# lr=5e-3,
optimizer_type='adam',
batch_size=2048,
aggregator_type=t,
n_epoch=50,
callbacks_to_add=['modelcheckpoint', 'earlystopping']
)
count += 1
temp['avg_auc'] = temp['avg_auc'] + train_log['test_auc']
temp['avg_acc'] = temp['avg_acc'] + train_log['test_acc']
temp['avg_f1'] = temp['avg_f1'] + train_log['test_f1']
temp['avg_aupr'] = temp['avg_aupr'] + train_log['test_aupr']
for key in temp:
if key == 'aggregator_type' or key == 'dataset':
continue
temp[key] = temp[key] / K_fold
write_log(format_filename(LOG_DIR, RESULT_LOG[dataset]), temp, 'a')
print(f'Logging Info - {K_fold} fold result: avg_auc: {temp["avg_auc"]}, avg_acc: {temp["avg_acc"]}, avg_f1: {temp["avg_f1"]},avg_aupr: {temp["avg_aupr"]}')
if __name__ == '__main__':
if not os.path.exists(PROCESSED_DATA_DIR):
os.makedirs(PROCESSED_DATA_DIR)
if not os.path.exists(LOG_DIR):
os.makedirs(LOG_DIR)
if not os.path.exists(MODEL_SAVED_DIR):
os.makedirs(MODEL_SAVED_DIR)
model_config = ModelConfig()
#process_data('kegg', NEIGHBOR_SIZE['kegg'], 4)
#process_data('ogb',NEIGHBOR_SIZE['ogb'],4)
##neighbor_number experiment
'''
n = [1,2,3,4,5,6]
for i in n:
process_data('kegg',i,4)'''
process_data('kegg',NEIGHBOR_SIZE['kegg'],4)