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example_code.py
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# encoding = utf-8
import os
import OpenHHEA
from OpenHHEA.configs.process_configs import *
from OpenHHEA.reasoning.results import SimpleHHEAResult
def customized_reasoning(
configs:OpenHHEA.configs.model_configs.SimpleHHEAConfig,
loader:OpenHHEA.KGDataLoader,
processor:OpenHHEA.KGDataProcessor,
result_path:str
) -> SimpleHHEAResult:
'''
Example code for entity alignment using a flexible customized reasoning workflow.
'''
from OpenHHEA.train import HHEATrainer, SimpleHHEALoss
from OpenHHEA.reasoning import Simple_HHEA, ReasoningEmbeddingBased
from OpenHHEA.reasoning.utils import get_noise_embeddings
### training
MODEL_PATH = os.path.join(configs.model_save_dir, "model.pth")
model = Simple_HHEA(
time_span=1 + 27*13,
ent_name_emb=get_noise_embeddings(processor.name_embeddings, noise_ratio=configs.name_noise_ratio),
ent_time_emb=processor.time_embeddings,
ent_struct_emb=processor.struct_embeddings,
use_structure=configs.use_structure,
use_time=configs.use_time,
emb_size=configs.emb_size,
structure_size=configs.structure_size,
time_size=configs.time_size,
device=configs.device
)
trainer = HHEATrainer(
config=configs,
loss_fn=SimpleHHEALoss(gamma=configs.gamma),
model_save_path=MODEL_PATH
)
trainer.train(
model=model,
train_alignments=loader.sup_pairs,
dev_alignments=loader.ref_pairs,
ent_num=loader.get_num_of_entity()
)
### reasoning
reasoning = ReasoningEmbeddingBased(
config=configs,
dataloader=loader,
dataprocessor=processor,
model=model,
model_path=MODEL_PATH
)
results = reasoning.run(save_result_path=result_path)
return results
def predefined_reasoning(
configs:OpenHHEA.configs.model_configs.SimpleHHEAConfig,
loader:OpenHHEA.KGDataLoader,
processor:OpenHHEA.KGDataProcessor,
result_path:str
) -> SimpleHHEAResult:
'''
Example code for entity alignment using the provided predefined reasoning workflow.
'''
### run SimpleHHEA
pipeline = OpenHHEA.init_model_pipeline(
method_type="SimpleHHEA",
config=configs,
dataloader=loader,
dataprocessor=processor
)
results = pipeline.run(save_result_path=result_path)
return results
if __name__ == "__main__":
'''
Set configurations for model and data processing
'''
configs = OpenHHEA.get_model_config("SimpleHHEA")
configs.set_config(
device=0,
name_noise_ratio=0.1,
emb_size=64,
structure_size=8,
time_size=8,
use_struct=True,
use_time=True,
gamma=1.0,
lr=0.01,
weight_decay=0.001,
epochs=500,
model_save_dir="SimpleHHEA_trained_models"
)
'''
Load knowledge graph data
'''
loader = OpenHHEA.KGDataLoader(data_dir="data/icews_wiki")
'''
Process data to get embeddings
'''
###### process configs of model
name_process_configs = BertProcessConfigs()
### Or you can customize the data processing configs like this
# name_process_configs = ProcessConfigs(method_type="bert", bert_model_path="albert-base-v2", device=0)
struct_process_configs = FualignProcessConfigs(q=0.7)
processor = OpenHHEA.KGDataProcessor(
dataloader=loader,
name_process_configs=name_process_configs,
struct_process_configs=struct_process_configs,
image_process_configs=None,
entity_process_configs=None
)
'''
Entity alignment
'''
RESULT_PATH = "results/results_SimpleHHEA_on_icews_wiki.txt"
### customized
results = customized_reasoning(configs, loader, processor, RESULT_PATH)
### predifined
results = predefined_reasoning(configs, loader, processor, RESULT_PATH)