OptFeature

This repository contains the official implementation of our NeurIPS 2023 paper:

• Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network.

Kindly view our preprint here.

Data Preprocessing

You can prepare the Criteo data in the following format. Avazu and KDD12 datasets can be preprocessed by calling its own python file.

python datatransform/criteo2tf.py --store_stat --stats PATH_TO_STORE_STATS
		--dataset RAW_DATASET_FILE --record PATH_TO_PROCESSED_DATASET \
		--threshold YOUR_THRESHOLD --ratio 0.8 0.1 0.1 \

Then you can find a your processed files in the tfrecord format under the PATH_TO_PROCESSED_DATASET folder. You may also need to record the number of feature and field respectively.

Run

To run Baselines:

python trainer.py $DATASET $MODEL \
        --feature $NUM_OF_FEATURE --field $NUM_OF_FIELD \
        --data_dir $PATH_TO_PROCESSED_DATASET

To run OptFeature:

python searcher.py $DATASET $MODEL \
        --feature $NUM_OF_FEATURE --field $NUM_OF_FIELD \
        --data_dir $DATA_DIR

Hyperparameter Settings

Here we list the hyper-parameters we used in the following table. Note that due to the dataset partition, the optimal hyper-parameters over your dataset may differ.

Dataset	LR	L2	FI_LR
Criteo	3e-4	3e-6	3e-5
Avazu	3e-4	3e-6	3e-5
KDD12	3e-5	1e-5	3e-5