def model_predict(path_finish, path_like):
Config["normal_config"]["pretrain"] = True
Config["normal_config"]["pretrain_model_dir"] = path_finish
engine_finish = ModelEngine(config=Config, model=NFFM)
sample_generator = SampleGenerator()
print()
print("------------start testing finish--------------")
test_loader = sample_generator.instance_a_loader(t="test")
df_finish = engine_finish.predict(test_loader)
print("------------finish testing -------------------")
print("------------start testing like ----------------")
Config["normal_config"]["pretrain_model_dir"] = path_like
engine_like = ModelEngine(config=Config, model=NFFM)
df_like = engine_like.predict(test_loader)
df_finish["like_probability"] = df_like["pred_probability"]
df_finish.columns = [
"uid", "item_id", "finish_probability", "like_probability"
]
df_finish.to_csv(Config["normal_config"]["predict_file"] +
Config["normal_config"]["model_name"],
index=None,
float_format="%.6f")
from data import SampleGenerator
from config import Config
from models_engine import ModelEngine
from model.xDeepFM import xDeepFM
from model.mlp import MLP
# from model.DTFM import DTFM
from model.nffm import NFFM
from model.affm import AFFM
engine = ModelEngine(config=Config, model=AFFM)
sample_generator = SampleGenerator()
for epoch in range(Config["training_config"]['num_epoch']):
print('Epoch {} starts!'.format(epoch))
print('-' * 80)
train_loader = sample_generator.instance_a_loader(t="train")
engine.train_an_epoch(train_loader, epoch_id=epoch)
# evaluation
print()
print("------------start evaluating-----------")
evaluate_loader = sample_generator.instance_a_loader(t="val")
auc = engine.evaluate(evaluate_loader, epoch_id=epoch)
engine.save(epoch, auc=auc)
# close hd5 file