sklearn's random projection with JAX to run on a GPU
Project description
JAX Random Projection Transformers
Using JAX to speed up sklearn's random projection transformers
Installation
Note: Installation with pip will install the CPU-only version of JAX
To use a GPU follow JAX's installation guide before installing jax-random_projections.
pip install jax-random_projections
Usage
from jax_random_projections.sparse import SparseRandomProjectionJAX
transfomer = SparseRandomProjectionJAX()
transfomer.fit_transform(X)
For the API documentation, refer to sklearn's SparseRandomProjection documentation.
The only difference is that jax-random_projections currently only supports xla.DeviceArray and doesn't support dense_output=False and y for fit()
This library currently only includes the SparseRandomProjection but a future release will also include GaussianRandomProjection.
jax-random_projections also includes SparseRandomProjectionJAXCached which uses a lru cache (maxsize=5) to speed up repeated calls by caching the random matrix for data with the same input dimension.
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