Himalaya implements machine learning linear models in Python, focusing on computational efficiency for large numbers of targets.

Use himalaya if you need a library that:

• estimates linear models on large numbers of targets,
• runs on CPU and GPU hardware,
• provides estimators compatible with scikit-learn's API.

import numpy as np
n_samples, n_features, n_targets = 10, 5, 4
np.random.seed(0)
X = np.random.randn(n_samples, n_features)
Y = np.random.randn(n_samples, n_targets)

from himalaya.ridge import RidgeCV
model = RidgeCV(alphas=[1, 10, 100])
model.fit(X, Y)
print(model.best_alphas_)  # [ 10. 100.  10. 100.]

• The model RidgeCV uses the same API as scikit-learn estimators, with methods such as fit, predict, score, etc.
• The model is able to efficiently fit a large number of targets (routinely used with 100k targets).
• The model selects the best hyperparameter alpha for each target independently.

Check more examples of use of himalaya in the gallery of examples.

Himalaya implements the following models:

• Ridge
• RidgeCV
• GroupRidgeCV
• KernelRidge
• KernelRidgeCV
• WeightedKernelRidge
• MultipleKernelRidgeCV
• SparseGroupLassoCV

Himalaya can be used seamlessly with different backends. The available backends are numpy (default), cupy, and pytorch. To change the backend (e.g. to cupy), call:

from himalaya.backend import set_backend
backend = set_backend("cupy")

and give cupy arrays inputs to the himalaya solvers. For convenience, estimators implementing scikit-learn's API can cast arrays to the correct input type.

To run himalaya on a graphics processing unit (GPU), you can use both cupy or pytorch backends.

To use the cupy backend, call:

from himalaya.backend import set_backend
backend = set_backend("cupy")

data = backend.asarray(data)  # cupy arrays are always on GPU

To use the pytorch backend, call:

from himalaya.backend import set_backend
set_backend("torch")

data = backend.asarray(data)  # torch tensors are on CPU by default...
data = data.cuda()  # ...and you can move them to GPU with the `cuda` method.

# or directly use
set_backend("torch_cuda")
data = backend.asarray(data)

Himalaya requires:

• Python 3
• Numpy
• Scikit-learn
• PyTorch (optional GPU backend)
• Cupy (optional GPU backend)
• Matplotlib (optional, for visualization only)
• Pytest (optional, for testing only)

You may install the latest version of himalaya using the package manager pip, which will automatically download himalaya from the Python Package Index (PyPI):

pip install himalaya

To install himalaya from the latest source (main branch), you may call:

pip install git+https://github.com/gallantlab/himalaya.git

Developers can also install himalaya in editable mode via:

git clone https://github.com/gallantlab/himalaya
cd himalaya
pip install --editable .

If you use himalaya in your work, please cite our (future) publication: