himalaya 0.3.4

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.

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

Himalaya was designed primarily for functional magnetic resonance imaging (fMRI) encoding models. In depth tutorials about using himalaya for fMRI encoding models can be found at gallantlab/voxelwise_tutorials.

Models

Himalaya implements the following models:

• Ridge

• RidgeCV

• GroupRidgeCV

• KernelRidge

• KernelRidgeCV

• WeightedKernelRidge

• MultipleKernelRidgeCV

• 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, 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)

from himalaya.backend import set_backend
backend = set_backend("torch_cuda")
# "torch" uses pytorch on CPU, "torch_cuda" uses pytorch on GPU

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: