We provide here the source code for our paper: Dataset2Vec: Learning Dataset Meta-Features.
To train the metafeature extractor, run the d2v.py file.
python d2v.py
To extract metafeatures from new datasets, run the extract_meta_features.py file. Please make sure that your data follows the same format as the existing datasets, i.e. separate predictor and labels .dat files with no headers/indices.
python extract_meta_features.py --file abalone
To cite Dataset2Vec please reference our DAMI paper:
@article{jomaa2021dataset2vec,
title={Dataset2vec: Learning dataset meta-features},
author={Jomaa, Hadi S and Schmidt-Thieme, Lars and Grabocka, Josif},
journal={Data Mining and Knowledge Discovery},
pages={1--22},
year={2021},
publisher={Springer}
}
You can install the ISMLLDataset package directly by
pip install ismlldataset
The following example shows how to read data:
import ismlldataset
dataset_id = 31 # (between 0-119)
dataset = ismlldataset.datasets.get_dataset(dataset_id=dataset_id)
# get data
x,y = dataset.get_data()
# get specific split
x,y = dataset.get_folds(split=1,return_valid=True)
train_x,valid_x,test_x = x
train_y,valid_y,test_y = yWe read metadata similarly:
import ismlldataset
dataset_id = 31 # (between 0-119)
metadataset = ismlldataset.datasets.get_metadataset(dataset_id=dataset_id)
# get configurations and response
x,y = metadataset.get_meta_data()
# normalize response (optional)
metadataset.normalize_response()
# find configuration space
cs = metadataset.get_configuration_space()
# find response of a paricular configuration
sample = cs.sample_configuration()
response,is_valid = metadataset.objective_function(sample)
# get all loss curves
loss = metadataset.get_all_loss_curves()
# get all gradient information
gradients = metadataset.get_gradient_curve(sample)Under development: The tasks of this package so far include hyperparameter optimization:
import ismlldataset
dataset_id = 31
metadataset = ismlldataset.datasets.get_metadataset(dataset_id=dataset_id)
# select HPO approach
task = ismlldataset.tasks.Random(metadataset=metadataset,evaluation='acc')
# run and observe results
task.run(return_results=True)The package is currently under development.
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