Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
Fast Differentiable Sorting and Ranking
Differentiable sorting and ranking operations in O(n log n).
Dependencies
- NumPy
- SciPy
- Numba
- Tensorflow (optional)
- PyTorch (optional)
TensorFlow Example
>>> import tensorflow as tf
>>> from fast_soft_sort.tf_ops import soft_rank, soft_sort
>>> values = tf.convert_to_tensor([[5., 1., 2.], [2., 1., 5.]], dtype=tf.float64)
>>> soft_sort(values, regularization_strength=1.0)
<tf.Tensor: shape=(2, 3), dtype=float64, numpy= array([[1.66666667, 2.66666667, 3.66666667], [1.66666667, 2.66666667, 3.66666667]])>
>>> soft_sort(values, regularization_strength=0.1)
<tf.Tensor: shape=(2, 3), dtype=float64, numpy= array([[1., 2., 5.], [1., 2., 5.]])>
>>> soft_rank(values, regularization_strength=2.0)
<tf.Tensor: shape=(2, 3), dtype=float64, numpy= array([[3. , 1.25, 1.75], [1.75, 1.25, 3. ]])>
>>> soft_rank(values, regularization_strength=1.0)
<tf.Tensor: shape=(2, 3), dtype=float64, numpy= array([[3., 1., 2.], [2., 1., 3.]])>JAX Example
>>> import jax.numpy as jnp
>>> from fast_soft_sort.jax_ops import soft_rank, soft_sort
>>> values = jnp.array([[5., 1., 2.], [2., 1., 5.]], dtype=jnp.float64)
>>> soft_sort(values, regularization_strength=1.0)
[[1.66666667 2.66666667 3.66666667]
[1.66666667 2.66666667 3.66666667]]
>>> soft_sort(values, regularization_strength=0.1)
[[1. 2. 5.]
[1. 2. 5.]]
>>> soft_rank(values, regularization_strength=2.0)
[[3. 1.25 1.75]
[1.75 1.25 3. ]]
>>> soft_rank(values, regularization_strength=1.0)
[[3. 1. 2.]
[2. 1. 3.]]PyTorch Example
>>> import torch
>>> from pytorch_ops import soft_rank, soft_sort
>>> values = fast_soft_sort.torch.tensor([[5., 1., 2.], [2., 1., 5.]], dtype=torch.float64)
>>> soft_sort(values, regularization_strength=1.0)
tensor([[1.6667, 2.6667, 3.6667]
[1.6667, 2.6667, 3.6667]], dtype=torch.float64)
>>> soft_sort(values, regularization_strength=0.1)
tensor([[1., 2., 5.]
[1., 2., 5.]], dtype=torch.float64)
>>> soft_rank(values, regularization_strength=2.0)
tensor([[3.0000, 1.2500, 1.7500],
[1.7500, 1.2500, 3.0000]], dtype=torch.float64)
>>> soft_rank(values, regularization_strength=1.0)
tensor([[3., 1., 2.]
[2., 1., 3.]], dtype=torch.float64)Install
Run python setup.py install or copy the fast_soft_sort/ folder to your
project.
Reference
Fast Differentiable Sorting and Ranking Mathieu Blondel, Olivier Teboul, Quentin Berthet, Josip Djolonga In proceedings of ICML 2020 arXiv:2002.08871
