IPM

This repo is for paper "Capturing Semantics for Imputation with Pre-trained Language Models." (ICDE 2021).

File Structure

• src: Source code of algorithms
• data: Dataset source files.
  • In the example data, 10 represents the missing rate. An there are total 5 missing datasets generated by the original data

Requirements

• torch>=1.1.0
• pandas>=0.25.3
• numpy>=1.19.5
• pretrained language models can be found in Hugging Face.

Usage

Before running ipm-multi or ipm-binary, the users need to specify the to-impute categorical attributes in ipm_multi.py / ipm_binary.py. For example:

ATTR_DICT = {
    'example':['category', 'brand']
}

where "example" is the dataset name, ['category', 'brand'] is the list of to-impute categorical attributes names.

In our experiments, the to-impute attributes are as follows:

ATTR_DICT = {
    "walmart":["category", "brand"],
    "amazon":["category", "brand"],
    "restaurant":["city"],
    "buy":["manufacturer"],
    "housing":["region", "price", "sqfeet", "beds", "baths", "laundry_options", "parking_options", "lat", "long", "state"],
    "phone":["brand"],
    "zomato":["location"],
    "flipkart":["brand"]
}

To use IPM-Multi in the paper, run this command:

python /IPM/src/ipm_multi.py --model_type=roberta --model_name_or_path=/pretrained_models/roberta-base --data_dir="./data/example" --dataset="example" --train_batch_size=32 --eval_batch_size=32 --max_seq_length=75 --num_epochs=5 --seed=22

To use IPM-Binary in the paper, run this command:

python /IPM/src/ipm_binary.py --model_type=roberta --model_name_or_path=/pretrained_models/roberta-base --data_dir="./data/example" --dataset="example" --train_batch_size=32 --eval_batch_size=32 --max_seq_length=75 --num_epochs=5 --seed=1234

Important Arguments

• --data_dir : Directory of data files.
• --dataset : Name of the dataset.
• --model_name_or_path : Pretrained language model name or path.
• --model_type : Type of the pretrained model. Default is roberta.
• --do_lower_case : Whether tansform inputs into lower case letters. Default True.
• --max_seq_length : Maximum length of the input sequence.
• --train_batch_size : Batch size for training.
• --eval_batch_size : Batch size for evaluation.
• --num_epochs : Number of epochs for training.
• --neg_num : Number of negative examples. Default is 3.

Acknowledgements

We would like to thank the following public repo from which we borrowed various utilites.

• https://github.com/brunnurs/entity-matching-transformer

Citation

If you use this code for your research, please consider citing:

@inproceedings{DBLP:conf/icde/MeiSFYFL21,
  author    = {Yinan Mei and
               Shaoxu Song and
               Chenguang Fang and
               Haifeng Yang and
               Jingyun Fang and
               Jiang Long},
  title     = {Capturing Semantics for Imputation with Pre-trained Language Models},
  booktitle = {{ICDE}},
  pages     = {61--72},
  publisher = {{IEEE}},
  year      = {2021}
}