Official Implementation of ICLR 2024 Spotlight paper SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training.
Paper | Models | Data | Results
Inspired by the great performance of CLIP in vision-language representation learning, we introduce a multi-modal pre-training model for symbolic mathematics, known as SNIP for Symbolic-Numeric Integrated Pre-training, which emphasizes the significance of numeric-augmented representations in math representation learning.
SNIP: A multi-modal transformer model that connects symbolic math equations with numeric data representations using contrastive learning
The code requires dependencies specified in environment.yml. Please follow the relevant libraries to install or run:
conda env create -f environment.yml
This library requires python>3.7
We've released two pretrained SNIP models, each designed for different types of analysis. Download them here. You'll find:
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SNIP-10dmax: This model handles up to 10-dimensional inputs. More info in Section 5 and Appendix D p.3 of paper.
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SNIP-1d-normalized: This model is for 1-dimensional inputs with normalized targets, great for focusing on function patterns. More details in Section 4 and Appendix D of paper.
To use them, create a weights/ folder in your project, download the checkpoints there, and use the --reload_model parameter with the model path, like --reload_model ./weights/snip-1d-normalized.pth."
For pretraining, we generate synthetic data of (symbolic, numeric) pairs for math functions, based on method from SymbolicMathematics. Each pair includes data points generate_datapoints function here for more info. You can also adjust data generation settings here.
The data is generated on-the-fly during training, but if you want to create and analyze it beforehand, use run_export_data.sh:
python train.py --export_data True --dump_path ./dump --max_input_dimension 10
Your exported data will be saved in the data.prefix file.
All training settings for SNIP are in parsers.py. SNIP uses Transformer encoders for both symbolic and numeric heads, which you can find in the encoder_f and encoder_y modules here. For information on contrastive learning and training, look at the trainer file. Here's how you can start the training:
python train.py --loss_type CLIP \
--batch_size 256 \
--dump_path ./dump \
--max_input_dimension 10 \
--exp_id run1-10d \
--lr 4e-5 \
--latent_dim 512 \
--save_periodic 10
Feel free to adjust training and data settings in parsers.py and environment.py under snip/envs/. After running the command, the model trained for every 10 (save_periodic) epochs is saved in dump/ path.
Here we have provided code to test SNIP representations for the cross-modal symbolic-to-numeric property prediction tasks, meaning that in these tasks, the input is the symbolic mathematical equation and the label is the propery defined based on numeric data observations.
To try it out, start by generating data. For instance, to generate 10k training examples for the Non-Convexity Ratio (NCR) prediction task (as explained in paper), use this command:
python train.py --export_data True --is_proppred True --property_type ncr --dump_path ./dump --max_input_dimension 1 --n_steps_per_epoch 625 --exp_name data --exp_id ncr
This saves data for ncr property in dump/data/ncr/. To generate data for other properties, just change the --property_type parameter.
For this task, we use a Transformer encoder architecture (to encode symbolic equation inputs), followed by a regression predictor head (to predict the property). Training is done using Mean Squared Error (MSE) loss. Following are the commands for training different model variants defined in Sec 4 of paper.
Supervised Model (without Pretrining):
python train.py --is_proppred True \
--property_type ncr \
--reload_data functions,dump/data/ncr/train.prefix,dump/data/ncr/train.prefix, \
--normalize_y True \
--batch_size 64 \
--dump_path ./dump \
--max_input_dimension 1 \
--exp_name NCR_pred \
--exp_id run1 \
--lr 1e-5 \
--latent_dim 512 \
--save_periodic 10
SNIP Encoder (frozen):
python train.py --reload_model ./weights/snip-1d-normalized.pth --freeze_encoder True [other parameters]
SNIP Encoder (finetune):
python train.py --reload_model ./weights/snip-1d-normalized.pth --freeze_encoder False [other parameters]
With these commands, the model saves automatically every 10 epochs. To use SNIP's encoder, you should activate --reload_model parameter with the path of model weights. You can also freeze the encoder with --freeze_encoder True.
To test how well your models perform for each property prediction task, use the run_eval_proppred.sh script. For example, if you want to test the NCR property task, use this command:
python eval_proppred.py --is_proppred True \
--property_type ncr \
--reload_model dump/NCR/model.pth \
--reload_data functions,dump/data/ncr/test.prefix,dump/data/ncr/test.prefix,
This command will use the --reload_model parameter to load the weights of your trained model and test it against the dataset specified in the --reload_data path.
To use SNIP for more complex tasks such as Symbolic Regression (uncovering symbolic math equations from data: numeric-to-symbolic generation task), check Multimodal-Symbolic-Regression repository.
Our experimental results of SNIP on SRBench datasets for symbolic regression are provided in the srbench_results/ directory in the Multimodal-Symbolic-Regression repository. These results are shared to help the research community reproduce our paper's findings and serve as reference benchmarks. Each result file contains detailed performance metrics and experimental configurations used in our evaluations.
If you find the paper or the repo helpful, please cite it with
@inproceedings{
meidani2024snip,
title={{SNIP}: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training},
author={Kazem Meidani and Parshin Shojaee and Chandan K. Reddy and Amir Barati Farimani},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=KZSEgJGPxu}
}
This repository is licensed under MIT licence.
This work is built on top of other open source projects, including Deep Learning for Symbolic Mathematics and Contrastive Language-Image Pretraining. We thank the original contributors of these works for open-sourcing their valuable source codes.
For any questions or issues, you are welcome to open an issue in this repo, or contact us at [email protected], and [email protected].