50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

Overview

[Due to the time taken @ uni, work + hell breaking loose in my life, since things have calmed down a bit, will continue commiting!!!] [By the way, I'm still looking for new contributors! Please help make HyperLearn no1!!]

drawing

HyperLearn is what drives Umbra's AI engines. It is open source to everyone, everywhere, and we hope humanity can rise to the stars.

[Notice - I will be updating the package monthly or bi-weekly due to other commitments]


drawing https://hyperlearn.readthedocs.io/en/latest/index.html

Faster, Leaner GPU Sklearn, Statsmodels written in PyTorch

GitHub issues Github All Releases

50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels combo with new novel algorithms.

HyperLearn is written completely in PyTorch, NoGil Numba, Numpy, Pandas, Scipy & LAPACK, and mirrors (mostly) Scikit Learn. HyperLearn also has statistical inference measures embedded, and can be called just like Scikit Learn's syntax (model.confidence_interval_) Ongoing documentation: https://hyperlearn.readthedocs.io/en/latest/index.html

I'm also writing a mini book! A sneak peak: drawing

drawing

Comparison of Speed / Memory

Algorithm n p Time(s) RAM(mb) Notes
Sklearn Hyperlearn Sklearn Hyperlearn
QDA (Quad Dis A) 1000000 100 54.2 22.25 2,700 1,200 Now parallelized
LinearRegression 1000000 100 5.81 0.381 700 10 Guaranteed stable & fast

Time(s) is Fit + Predict. RAM(mb) = max( RAM(Fit), RAM(Predict) )

I've also added some preliminary results for N = 5000, P = 6000 drawing

Since timings are not good, I have submitted 2 bug reports to Scipy + PyTorch:

  1. EIGH very very slow --> suggesting an easy fix #9212 https://github.com/scipy/scipy/issues/9212
  2. SVD very very slow and GELS gives nans, -inf #11174 https://github.com/pytorch/pytorch/issues/11174

Help is really needed! Message me!


Key Methodologies and Aims

1. Embarrassingly Parallel For Loops

2. 50%+ Faster, 50%+ Leaner

3. Why is Statsmodels sometimes unbearably slow?

4. Deep Learning Drop In Modules with PyTorch

5. 20%+ Less Code, Cleaner Clearer Code

6. Accessing Old and Exciting New Algorithms


1. Embarrassingly Parallel For Loops

  • Including Memory Sharing, Memory Management
  • CUDA Parallelism through PyTorch & Numba

2. 50%+ Faster, 50%+ Leaner

3. Why is Statsmodels sometimes unbearably slow?

  • Confidence, Prediction Intervals, Hypothesis Tests & Goodness of Fit tests for linear models are optimized.
  • Using Einstein Notation & Hadamard Products where possible.
  • Computing only what is necessary to compute (Diagonal of matrix and not entire matrix).
  • Fixing the flaws of Statsmodels on notation, speed, memory issues and storage of variables.

4. Deep Learning Drop In Modules with PyTorch

  • Using PyTorch to create Scikit-Learn like drop in replacements.

5. 20%+ Less Code, Cleaner Clearer Code

  • Using Decorators & Functions where possible.
  • Intuitive Middle Level Function names like (isTensor, isIterable).
  • Handles Parallelism easily through hyperlearn.multiprocessing

6. Accessing Old and Exciting New Algorithms

  • Matrix Completion algorithms - Non Negative Least Squares, NNMF
  • Batch Similarity Latent Dirichelt Allocation (BS-LDA)
  • Correlation Regression
  • Feasible Generalized Least Squares FGLS
  • Outlier Tolerant Regression
  • Multidimensional Spline Regression
  • Generalized MICE (any model drop in replacement)
  • Using Uber's Pyro for Bayesian Deep Learning

Goals & Development Schedule

Will Focus on & why:

1. Singular Value Decomposition & QR Decomposition

* SVD/QR is the backbone for many algorithms including:
    * Linear & Ridge Regression (Regression)
    * Statistical Inference for Regression methods (Inference)
    * Principal Component Analysis (Dimensionality Reduction)
    * Linear & Quadratic Discriminant Analysis (Classification & Dimensionality Reduction)
    * Pseudoinverse, Truncated SVD (Linear Algebra)
    * Latent Semantic Indexing LSI (NLP)
    * (new methods) Correlation Regression, FGLS, Outlier Tolerant Regression, Generalized MICE, Splines (Regression)

On Licensing: HyperLearn is under a GNU v3 License. This means:

  1. Commercial use is restricted. Only software with 0 cost can be released. Ie: no closed source versions are allowed.
  2. Using HyperLearn must entail all of the code being avaliable to everyone who uses your public software.
  3. HyperLearn is intended for academic, research and personal purposes. Any explicit commercialisation of the algorithms and anything inside HyperLearn is strictly prohibited.

HyperLearn promotes a free and just world. Hence, it is free to everyone, except for those who wish to commercialise on top of HyperLearn. Ongoing documentation: https://hyperlearn.readthedocs.io/en/latest/index.html [As of 2020, HyperLearn's license has been changed to BSD 3]

Owner
Daniel Han-Chen
Fast energy efficient machine learning algorithms
Daniel Han-Chen
Repository for DCA0305, an undergraduate course about Machine Learning Workflows and Pipelines

Federal University of Rio Grande do Norte Technology Center Department of Computer Engineering and Automation Machine Learning Based Systems Design Re

Ivanovitch Silva 81 Oct 18, 2022
MiniTorch - a diy teaching library for machine learning engineers

This repo is the full student code for minitorch. It is designed as a single repo that can be completed part by part following the guide book. It uses

1.1k Jan 07, 2023
Fit interpretable models. Explain blackbox machine learning.

InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig

InterpretML 5.2k Jan 09, 2023
Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)

Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)

141 Dec 27, 2022
MLOps pipeline project using Amazon SageMaker Pipelines

This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drif

AWS Samples 3 Sep 16, 2022
Machine-learning-dell - Repositório com as atividades desenvolvidas no curso de Machine Learning

📚 Descrição Neste curso da Dell aprofundamos nossos conhecimentos em Machine Learning. 🖥️ Aulas (Em curso) 1.1 - Python aplicado a Data Science 1.2

Claudia dos Anjos 1 Jan 05, 2022
Hypernets: A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

DataCanvas 216 Dec 23, 2022
ETNA – time series forecasting framework

ETNA Time Series Library Predict your time series the easiest way Homepage | Documentation | Tutorials | Contribution Guide | Release Notes ETNA is an

Tinkoff.AI 675 Jan 08, 2023
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python

Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end

Najibulloh Asror 3 Feb 10, 2022
Distributed scikit-learn meta-estimators in PySpark

sk-dist: Distributed scikit-learn meta-estimators in PySpark What is it? sk-dist is a Python package for machine learning built on top of scikit-learn

Ibotta 282 Dec 09, 2022
Neighbourhood Retrieval (Nearest Neighbours) with Distance Correlation.

Neighbourhood Retrieval with Distance Correlation Assign Pseudo class labels to datapoints in the latent space. NNDC is a slim wrapper around FAISS. N

The Learning Machines 1 Jan 16, 2022
Optimal Randomized Canonical Correlation Analysis

ORCCA Optimal Randomized Canonical Correlation Analysis This project is for the python version of ORCCA algorithm. It depends on Numpy for matrix calc

Yinsong Wang 1 Nov 21, 2021
[HELP REQUESTED] Generalized Additive Models in Python

pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized

daniel servén 747 Jan 05, 2023
Model factory is a ML training platform to help engineers to build ML models at scale

Model Factory Machine learning today is powering many businesses today, e.g., search engine, e-commerce, news or feed recommendation. Training high qu

16 Sep 23, 2022
A Python Module That Uses ANN To Predict A Stocks Price And Also Provides Accurate Technical Analysis With Many High Potential Implementations!

Stox A Module to predict the "close price" for the next day and give "technical analysis". It uses a Neural Network and the LSTM algorithm to predict

Stox 31 Dec 16, 2022
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification Introduction. This package includes the pyth

5 Dec 06, 2022
Test symmetries with sklearn decision tree models

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Rupert Tombs 2 Jul 19, 2022
机器学习检测webshell

ai-webshell-detect 机器学习检测webshell,利用textcnn+简单二分类网络,基于keras,花了七天 检测原理: 从文件熵 文件长度 文件语句提取出特征,然后文件熵与长度送入二分类网络,文件语句送入textcnn 项目原理,介绍,怎么做出来的

Huoji's 56 Dec 14, 2022
Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft 366 Jan 03, 2023
Nevergrad - A gradient-free optimization platform

Nevergrad - A gradient-free optimization platform nevergrad is a Python 3.6+ library. It can be installed with: pip install nevergrad More installati

Meta Research 3.4k Jan 08, 2023