HPCC Systems Machine Learning Library
The HPCC Systems Machine Learning Library provides a wide range of Machine Learning algorithms accessible from ECL, and designed to utilize the parallel computing capabilities of HPCC Systems.
If you are new to Machine Learning or would like a refresher on basic concepts and terminology, please see Machine Learning Demystified.
For a tutorial on installing and using the ML bundles, see Using HPCC Systems Machine Learning.
The HPCC Systems Machine Learning library is provided as a set of independently installable HPCC Systems bundles. The HPCC Systems bundle capability provides an easy to use mechanism for packaging and installing ECL feature packages. It also supports prerequisites and will notify you if you are missing the required version of a prerequisite bundle.
There are several core bundles that are utilized by the various ML algorithms and one bundle for each supported family of Machine Learning algorithms.
Each ML algorithm provides a mechanism for learning from provided training data and retrieving a model (GetModel). That model, the encapsulation of the learning, can then be used to predict values for new data (Prediction / Classification). Furthermore, each algorithm provides methods to assess the predictive power of the learned model (Assessment) in ways that are appropriate for that algorithm.
Each bundle supports the ‘myriad’ interface, which is a way to perform many similar actions on different sets of data, with a single invocation. For example, you may want to create a separate model for each city and then use that set of models to predict data for each city using its own unique model. The myriad interface lets you process those activities in parallel. For more detail and a tutorial, please see our Myriad Interface Tutorial.
Provides the core data definitions and attributes for machine learning. ML_Core is a prerequisite for all HPCC Systems production machine learning bundles.
- PBblas – Parallel Block Linear Algebra Subsystem
Provides distributed, scalable matrix operations used by several of the other bundles. Can also be used directly whenever matrix operations are in order. This is a dependency for several of our production machine learning bundles (as shown below).
Supervised Learning Bundles
Ordinary Least Squares Linear Regression for use as a ML algorithm or for other uses such as data analysis.
Random Fourier Features accelerated Gaussian Process Regression.
Classification using Logistic Regression methods, both Binomial (two-classes) and Multinomial (multiple classes). In spite of the name, Logistic Regression is a Classification method, not a Regression method.
General Linear Model. Provides Regression and Classification algorithms for situations in which your data does not match the assumptions of LinearRegression or LogisticRegression. Handles a variety of data distribution assumptions
SVM implementation for Classification and Regression using the popular LibSVM under the hood.
Decision Tree based learning module. Includes Decision Trees, Random Forest, Gradient Boosted Trees, and Boosted Forest capabilities.
- Generalized Neural Networks (GNN)
Parallelized interace to Keras / Tensorflow supporting arbitrarily complex Neural Networks for processing multimedia data types such as Image, Video, and Time-series.
Unsupervised Learning Bundles
Unsupervised Clustering Algorithm. Assigns datapoints to one of K clusters based on Euclidean Distance.
Unsupervised Density-based Clustering Algorithm. Detects cluster boundaries based areas of low density. Produces a variable number of clusters based on density variations.
Natural Language Processing Bundles
Unsupervised vectorization of words, phrases, and sentences. Converts plain text into numeric vectors that can be compared directly or used as features for other Machine Learning algorithms.
Causal Analytics Bundles
Causal Analysis bundle supporting Causal Discovery, Causal Model Validation, Causal Inference, and Causal Metrics. Also includes general purpose Synthetic Dataset Generation and Probability modules.
Note that in order to install or use any of the bundles, you will need to have installed HPCC Systems Client Tools on your local machine.
Content Summary: Common data definitions, Common functions, Data preparation functions.
Tutorial: Using HPCC Systems Machine Learning, Understanding the Myriad Interface
Documentation: ML_Core Documentation
Source code: HPCC Systems ML_Core repository on GitHub
ecl bundle install https://github.com/hpcc-systems/ML_Core.git [PC users see Note 1]
Content Summary: Scalable Linear Algebra / Matrix Operations
Tutorial: Introduction to PBblas
Documentation: PBblas Documentation
Source code: HPCC Systems PBblas repository on GitHub
ecl bundle install https://github.com/hpcc-systems/PBblas.git [PC users see Note 1]
Content Summary: Ordinary Least Squares Linear Regression (multi-variate) with analytics.
Prerequisites: ML_Core, PBblas
Documentation: LinearRegression Documentation
Source code: LinearRegression repository on GitHub
ecl bundle install https://github.com/hpcc-systems/LinearRegression.git [PC users see Note 1]
Content Summary: Random Fourier Features (RFF) accelerated Gaussian Process Regression.
Documentation: GaussianProcessRegression Documentation
Source code: GaussianProcessRegression repository on GitHub
ecl bundle install https://github.com/hpcc-systems/GaussianProcessRegression.git [PC users see Note 1]
Requires Python3 on each cluster server.
Content Summary: Binomial and Multinomial classification with Logistic Regression.
Prerequisites: ML_Core, PBblas
Documentation: LogisticRegression Documentation
Source code: LogisticRegression repository on GitHub
ecl bundle install https://github.com/hpcc-systems/LogisticRegression.git [PC users see Note 1]
Content Summary: General Linear Model for regression and classification
Prerequisites: ML_Core, PBblas
Documentation: GLM Documentation
Source code: GLM Repository on GitHub
ecl bundle install https://github.com/hpcc-systems/GLM.git [PC users see Note 1]
Content Summary: SVM classification and regression with automatic grid search for parameters.
Documentation: SupportVectorMachines Documentation
Source code: SVM Repository on GitHub
ecl bundle install https://github.com/hpcc-systems/SupportVectorMachines.git [PC users see Note 1]
Content Summary: Random Forest, Gradient Boosted Trees, and Gradient Boosted Forest.
Tutorial: LearningTrees Tutorial
Documentation: LearningTrees Documentation
Source code: LearningTrees repository on GitHub
ecl bundle install https://github.com/hpcc-systems/LearningTrees.git [PC users see Note 1]
Content Summary: Generalized Interface to Keras / TensorFlow for Neural Networks
Tutorial: GNN Tutorial
Documentation: GNN Documentation
Source code: GNN repository on GitHub
ecl bundle install https://github.com/hpcc-systems/GNN.git [PC users see Note 1]
Requires TensorFlow to be installed on each cluster server.
Content Summary: K-Means Unsupervised Clustering
Tutorial: KMeans Tutorial
Documentation: KMeans Documentation
Source code: KMeans repository on GitHub
ecl bundle install https://github.com/hpcc-systems/KMeans.git [PC users see Note 1]
Content Summary: DBSCAN Unsupervised Clustering
Tutorial: DBSCAN Tutorial
Documentation: DBSCAN Documentation
Source code: DBSCAN repository on GitHub
ecl bundle install https://github.com/hpcc-systems/dbscan.git [PC users see Note 1]
Content Summary: Text Vectorization for words, phrases, and sentences
Tutorial: Text Vectors Tutorial
Documentation: TextVectors Documentation
Source code: TextVectors repository on GitHub
ecl bundle install https://github.com/hpcc-systems/TextVectors.git [PC users see Note 1]
Content Summary: Causal Analysis Toolkit
Tutorial: Causality Tutorial
Documentation: Causality Documentation
Source code: Causality Toolkit repository on GitHub
ecl bundle install https://github.com/hpcc-systems/HPCC_Causality.git [PC users see Note 1]
Requires Python module “Because” on each cluster server.
 When installing bundles on a PC, the command prompt must be run as Admin. Right click the command icon on the start menu and select “Run as administrator”.