The videos which demonstrate the entire path of decision region of the (subsampled) Convex Lasso method with respect to the regularization parameter
- Download IMDB dataset from here and Amazon dataset from here and store them under the folder
./data/
. - Download GLUE-QQ, GLUE-COLA, MNIST and CIFAR10 datasets based on the notebook
dl_dataset_huggingface.ipynb
. - The ECG signals are stored in the WFDB format at a sampling rate of 100Hz. The signals are unpacked using the python package -https://wfdb.readthedocs.io/en/latest/. Additionally, text features are extracted using OpenAI’s embedding model.
pip install -r requirements.txt
Run the following lines to obtain OpenAI embedding data.
python3 preprocess-dataset.py --data_path ./data --export_num full --embedding OpenAI --data_name IMDB
python3 preprocess-dataset.py --data_path ./data --export_num 30K --embedding OpenAI --data_name Amazon
python3 preprocess-dataset.py --data_path ./data --export_num 50K --embedding OpenAI --data_name glue-qqp
python3 preprocess-dataset.py --data_path ./data --export_num full --embedding OpenAI --data_name glue-cola
Open the notebook ecg_signal_extraction_from_wfdb.ipynb
.
For the result on 2D spiral dataset, simply open the notebook Illustration_spiral.ipynb
.
For the results on Feature-based transfer learning, run the following lines:
for data in IMDB Amazon cola qqp ECG-signal ECG-report mnist cifar10
do
for tm in cvx noncvx
do
python3 main_FT_input_num.py --data_path ./data/ --data_name $data --seed 1 --train_method $tm --Epochs 20 --train_choice f1 --embed OpenAI --Hidden 50 --train_num f1 --shuffle --add_eps --aug_sym
done
done
Open the notebook FT_plot_input_num.ipynb