himalaya 0.3.1

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.

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

Models

Himalaya implements the following models:

• Ridge

• RidgeCV

• GroupRidgeCV

• KernelRidge

• KernelRidgeCV

• WeightedKernelRidge

• MultipleKernelRidgeCV

• SparseGroupLassoCV

Himalaya backends

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.

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

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

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)

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 cite our (future) publication: