himalaya 0.3.5

Himalaya: Multiple-target linear models

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.

Himalaya is stable (with particular care for backward compatibility) and open for public use (give it a star!).

Example

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.

Models

Himalaya implements the following models:

• Ridge, RidgeCV

• KernelRidge, KernelRidgeCV

• GroupRidgeCV, MultipleKernelRidgeCV, WeightedKernelRidge

• SparseGroupLassoCV

See the model descriptions in the documentation website.

Himalaya backends

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

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

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

from himalaya.backend import set_backend
backend = set_backend("cupy")  # or "torch_cuda"

data = backend.asarray(data)

Installation

pip install himalaya

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

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

Cite this package

If you use himalaya in your work, please give it a star and cite our (future) publication: