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Recommendation Model
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## Overview
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This is an implementation of WideDeep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper.
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WideDeep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.
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## Dataset
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The Criteo datasets are used for model training and evaluation.
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## Running Code
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### Code Structure
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The entire code structure is as following:
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```
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|--- wide_and_deep/
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train_and_eval.py "Entrance of Wide&Deep model training and evaluation"
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eval.py "Entrance of Wide&Deep model evaluation"
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train.py "Entrance of Wide&Deep model training"
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train_and_eval_multinpu.py "Entrance of Wide&Deep model data parallel training and evaluation"
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train_and_eval_auto_parallel.py
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|--- src/ "Entrance of training and evaluation"
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config.py "Parameters configuration"
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dataset.py "Dataset loader class"
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process_data.py "Process dataset"
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preprocess_data.py "Pre_process dataset"
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wide_and_deep.py "Model structure"
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callbacks.py "Callback class for training and evaluation"
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metrics.py "Metric class"
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|--- script/ "Run shell dir"
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run_multinpu_train.sh "Run data parallel"
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run_auto_parallel_train.sh "Run auto parallel"
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```
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### Train and evaluate model
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To train and evaluate the model, command as follows:
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```
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python train_and_eval.py
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```
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Arguments:
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* `--device_target`: Device where the code will be implemented (Default: Ascend).
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* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
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* `--epochs`: Total train epochs.
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* `--batch_size`: Training batch size.
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* `--eval_batch_size`: Eval batch size.
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* `--field_size`: The number of features.
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* `--vocab_size`： The total features of dataset.
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* `--emb_dim`： The dense embedding dimension of sparse feature.
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* `--deep_layers_dim`： The dimension of all deep layers.
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* `--deep_layers_act`： The activation of all deep layers.
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* `--dropout_flag`： Whether do dropout.
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* `--keep_prob`： The rate to keep in dropout layer.
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* `--ckpt_path`：The location of the checkpoint file.
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* `--eval_file_name` : Eval output file.
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* `--loss_file_name` : Loss output file.
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To train the model in one device, command as follows:
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```
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python train.py
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```
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Arguments:
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* `--device_target`: Device where the code will be implemented (Default: Ascend).
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* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
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* `--epochs`: Total train epochs.
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* `--batch_size`: Training batch size.
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* `--eval_batch_size`: Eval batch size.
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* `--field_size`: The number of features.
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* `--vocab_size`： The total features of dataset.
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* `--emb_dim`： The dense embedding dimension of sparse feature.
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* `--deep_layers_dim`： The dimension of all deep layers.
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* `--deep_layers_act`： The activation of all deep layers.
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* `--dropout_flag`： Whether do dropout.
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* `--keep_prob`： The rate to keep in dropout layer.
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* `--ckpt_path`：The location of the checkpoint file.
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* `--eval_file_name` : Eval output file.
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* `--loss_file_name` : Loss output file.
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To train the model in distributed, command as follows:
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```
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# configure environment path before training
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bash run_multinpu_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE
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```
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```
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# configure environment path before training
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bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE
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```
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To evaluate the model, command as follows:
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```
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python eval.py
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```
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Arguments:
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* `--device_target`: Device where the code will be implemented (Default: Ascend).
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* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
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* `--epochs`: Total train epochs.
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* `--batch_size`: Training batch size.
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* `--eval_batch_size`: Eval batch size.
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* `--field_size`: The number of features.
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* `--vocab_size`： The total features of dataset.
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* `--emb_dim`： The dense embedding dimension of sparse feature.
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* `--deep_layers_dim`： The dimension of all deep layers.
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* `--deep_layers_act`： The activation of all deep layers.
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* `--keep_prob`： The rate to keep in dropout layer.
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* `--ckpt_path`：The location of the checkpoint file.
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* `--eval_file_name` : Eval output file.
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* `--loss_file_name` : Loss output file.
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There are other arguments about models and training process. Use the `--help` or `-h` flag to get a full list of possible arguments with detailed descriptions.
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