HyperLearn is a Statsmodel, a result of the collaboration of languages such as PyTorch, NoGil Numba, Numpy, Pandas, Scipy & LAPACK, and has similarities to Scikit Learn.
This project started last month by Daniel Hanchen and still has some unstable packages. He aims to make Linear Regression, Ridge, PCA, LDA/QDA faster, which then flows onto other algorithms being faster.
This Statsmodels combo incorporates novel algorithms to make it 50% more faster and enables it to use 50% lesser RAM along with a leaner GPU Sklearn.
HyperLearn also has an embedded statistical inference measures, and can be called similar to a Scikit Learn’s syntax (model.confidence_interval_)
There is a 50%+ improvement on Quadratic Discriminant Analysis (similar improvements for other models) as can be seen below:
Time(s) is Fit + Predict. RAM(mb) = max( RAM(Fit), RAM(Predict) )
Daniel further went on to publish some prelim algorithm timing results on a range of algos from MKL Scipy, PyTorch, MKL Numpy, HyperLearn’s methods + Numba JIT compiled algorithms
Here are his key findings on the HyperLearn statsmodel:
You can find all the details of the test on reddit.com
For more insights on HyperLearn, check out the release notes on Github.
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