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import sys
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import os.path as osp
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this_dir = osp.dirname(__file__)
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lib_path = osp.join(this_dir, '..')
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sys.path.insert(0, lib_path)
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import torch
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from DeepCrossing.trainer import Trainer
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from DeepCrossing.network import DeepCrossing
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from DeepCrossing.criteo_loader import getTestData, getTrainData
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import torch.utils.data as Data
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deepcrossing_config = \
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{
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'embed_dim': 8, # 用于控制稀疏特征经过Embedding层后的稠密特征大小
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'min_dim': 256, # 稀疏特征维度小于min_dim的直接进入stack layer，不用经过embedding层
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'hidden_layers': [512,256,128,64,32],
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'num_epoch': 100,
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'batch_size': 32,
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'lr': 1e-3,
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'l2_regularization': 1e-4,
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'device_id': 0,
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'use_cuda': True,
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'train_file': '../Data/criteo/processed_data/train_set.csv',
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'fea_file': '../Data/criteo/processed_data/fea_col.npy',
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'validate_file': '../Data/criteo/processed_data/val_set.csv',
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'test_file': '../Data/criteo/processed_data/test_set.csv',
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'model_name': '../TrainedModels/DeepCrossing.model'
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}
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if __name__ == "__main__":
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####################################################################################
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# DeepCrossing 模型
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####################################################################################
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training_data, training_label, dense_features_col, sparse_features_col = getTrainData(deepcrossing_config['train_file'], deepcrossing_config['fea_file'])
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train_dataset = Data.TensorDataset(torch.tensor(training_data).float(), torch.tensor(training_label).float())
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test_data = getTestData(deepcrossing_config['test_file'])
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test_dataset = Data.TensorDataset(torch.tensor(test_data).float())
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deepCrossing = DeepCrossing(deepcrossing_config, dense_features_cols=dense_features_col, sparse_features_cols=sparse_features_col)
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####################################################################################
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# 模型训练阶段
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####################################################################################
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# 实例化模型训练器
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trainer = Trainer(model=deepCrossing, config=deepcrossing_config)
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# 训练
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trainer.train(train_dataset)
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# 保存模型
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trainer.save()
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####################################################################################
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# 模型测试阶段
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####################################################################################
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deepCrossing.eval()
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if deepcrossing_config['use_cuda']:
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deepCrossing.loadModel(map_location=lambda storage, loc: storage.cuda(deepcrossing_config['device_id']))
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deepCrossing = deepCrossing.cuda()
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else:
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deepCrossing.loadModel(map_location=torch.device('cpu'))
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y_pred_probs = deepCrossing(torch.tensor(test_data).float().cuda())
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y_pred = torch.where(y_pred_probs>0.5, torch.ones_like(y_pred_probs), torch.zeros_like(y_pred_probs))
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print("Test Data CTR Predict...\n ", y_pred.view(-1))
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