PLE

PLE

| Overview | Configuration | Implementation |

Overview

PLE is a multitasking model with shared and task-specific experts for each task. The model is published in the following paper:

• Hongyan Tang (Tencent PCG), Junning Liu (Tencent PCG), Marco Zhao (Tencent PCG), Xudong Gong (Tencent PCG). Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations, in Recsys 2020 (Best Paper Award).

Model structure:

Configuration

The model_config.yaml file contains all the model hyper-parameters as follows.

Params	Type	Default	Description
model	str	"MMoE"	model name,  which should be same with model class name
dataset_id	str	"TBD"	dataset_id to be determined
loss	list	["binary_crossentropy","binary_crossentropy"]	loss function for each task
metrics	list	['logloss', 'AUC']	a list of metrics for evaluation
task	list	["binary_crossentropy","binary_crossentropy"]	task type supported: "regression", "binary_classification"
num_tasks	int	2	number of tasks
num_layers	int	2	number of cgc layers
num_shared_experts	int	2	number of shared experts
num_specific_experts	int	2	number of task-specific experts
optimizer	str	"adam"	optimizer used for training
learning_rate	float	1.0e-3	learning rate
embedding_regularizer	float/str	1.e-6	regularization weight for embedding matrix: L2 regularization is applied by default. Other optional examples: "l2(1.e-3)", "l1(1.e-3)", "l1_l2(1.e-3, 1.e-3)".
net_regularizer	float/str	0	regularization weight for network parameters: L2 regularization is applied by default. Other optional examples: "l2(1.e-3)", "l1(1.e-3)", "l1_l2(1.e-3, 1.e-3)".
batch_size	int	128	batch size
embedding_dim	int	128	embedding dimension of features. Note that field-wise embedding_dim can be specified in feature_specs.
expert_hidden_units	list	[512, 256, 128]	hidden units in expert networks
gate_hidden_units	list	[128, 64]	hidden units in gating networks
tower_hidden_units	list	[128, 64]	hidden units in tower networks
hidden_activations	str/list	"relu"	activation function in DNN. Particularly, layer-wise activations can be specified as a list, e.g., ["relu", "leakyrelu", "sigmoid"]
net_dropout	float	0	dropout rate in DNN
batch_norm	bool	False	whether using BN in DNN
epochs	int	50	the max number of epochs for training, which can early stop via monitor metrics.
shuffle	bool	True	whether shuffle the data samples for each epoch of training
seed	int	2023	the random seed used for reproducibility
monitor	str/dict	'AUC'	the monitor metrics for early stopping. It supports a single metric, e.g., "AUC". It also supports multiple metrics using a dict, e.g., {"AUC": 2, "logloss": -1} means 2*AUC - logloss.
monitor_mode	str	'max'	"max" means that the higher the better, while "min" denotes that the lower the better.
model_root	str	'./checkpoints/'	the dir to save model checkpoints and running logs
num_workers	int	3	the number of workers for data loader
verbose	int	1	0 for salience while 1 for verbose logging with tqdm
early_stop_patience	int	2	training is stopped when monitor metric fails to become better for early_stop_patience=2consective evaluation intervals.
pickle_feature_encoder	bool	True	whether to pickle the feature encoder during preprocessing. It is used when input data_format="csv".
save_best_only	bool	True	whether to save the best model checkpoint only
eval_steps	int/None	None	evaluate the model on validation data every eval_steps. By default, None means evaluation every epoch.
debug_mode	bool	False	used for code testing. When setting it to True, the experiment_id will be randomly generated to avoid interleaving when running multiple processes for parameter tunning by run_param_tuner.py.
group_id	None (optional)	None	required for metrics like gAUC, NDCG.
use_features	None (optional)	None	used for feature selection, i.e., only selecting an ordered subset of features as model input
feature_specs	dict (optional)	None	used for specifying field-wise configurations, such as embedding_dim, feature_encoder for a specific field.

Implementation

Code structure:

├── config                        # 配置文件夹
│   ├── dataset_config.yaml       # 数据集配置文件
│   └── model_config.yaml         # 模型配置文件
├── src                           # 模型代码文件夹
│   └── MMoE.py                    # 模型代码
├── fuxictr_version.py            # fuxictr加载及版本检查文件
├── README.md                     # 使用说明
└── run_expid.py                  # 执行脚本文件

Requirements:

The model is tested with the following dependencies.

• fuxictr==2.0.3

• pytorch==1.10

Get started:

Running the model on the tiny data:

python run_expid.py --expid PLE_test --gpu 0