Pyramid is a Java machine learning library which implements many state-of-the-art machine learning algorithms, including

• Binary and Multi-class classification algorithms:
  • Logistic Regression with L1 regularization (Lasso), L2 regularization (Ridge) and L1+L2 regularization (Elastic-net)
  • Variational Bayesian Logistic Regression
  • Gradient Boosted Trees
  • Naive Bayes
  • Error-Correcting Output Codes (ECOC)
  • Support Vector Machines (SVM)
• Multi-label classification algorithms:
  • Binary Relevance
  • Power Set
  • Probabilistic Classifier Chain (PCC)
  • Conditional Random Field (CRF)
  • Conditional Bernoulli Mixture (CBM)
• Regression algorithms:
  • Linear Regression with L1 regularization (Lasso), L2 regularization (Ridge) and L1+L2 regularization (Elastic-net)
  • Variational Bayesian Linear Regression
  • Regression Tree
  • Gradient Boosted Trees
• Learning to rank algorithms:
  • LambdaMART
• Clustering:
  • K Means
  • Gaussian Mixture
  • Bernoulli Mixture

At the moment, not all algorithms are released. We are actively working on tidying up the source files and adding documentations. We will release a few algorithms at a time when they are ready and hope to have all algorthms released soon!

If you just want to use pyramid as a command line tool (which is very simple), all you need is Java 8.

If you are also a Java developer and wish to call Pyramid Java APIs, you will also need Maven.

Pyramid doesn't require any installation effort. All you need is downloading the latest [pre-compiled package] (https://github.com/cheng-li/pyramid/releases) (with a name like pyramid-x.x.x.zip) and decompressing it. Now you can move into the created folder and type

./pyramid welcome config/welcome.properties

You will see a welcome message and that means everything is working perfectly.

All algorithms implemented in Pyramid can be run though a simple command. All commands have the following format:

./pyramid <app_name> <properties_file>

pyramid is a launcher script which invokes an algorithm/application specified by the user.

The <app_name> is the algorithm/application name and is case-insensitive. The list of available algorithms and applications can be found in the Wiki.

The <properties_file> is the only direct input file for the program. It is used to provide all program configurations, such as input dataset path, program output path, and learning algorithm hyper parameters. The <properties_file> can be specified by either an absolute or a relative path.

Example:

./pyramid welcome config/welcome.properties

or

./pyramid cbm config/cbm.properties

If you are a Java developer who prefer working with the source code or want to contribute to the Pyramid package:

Pyramid uses Maven for its build system.

To compile and package the project from the source code, simply run the mvn clean package -DskipTests command in the cloned directory. The compressed package will be created under the target/releases directory.

We welcome your feedback on the package. To ask questions, request new features or report bugs, please contact Cheng Li via [email protected].