This page describes Tribuo 4.1. View the documentation for Tribuo 4.3 instead.

Tribuo Frequently Asked Questions

faq graphic


Why is it called Tribuo?

“Tribuo” comes from the Latin for “to assign” or “apportion”, which makes sense since Tribuo is a prediction system for assigning outputs to examples. Plus we know a Latin teacher whom we’d like to keep employed.

When did the project start?

The initial version of Tribuo was written in 2016, and the internal v1.0 was released in the fall of 2016. The first open source release was v4.0, released in August 2020. Tribuo was designed after the Oracle Labs Machine Learning Research Group had written several text classification projects in Java and realised the need for a good single node ML framework on the JVM.

What’s it being used for?

Several internal groups at Oracle are using Tribuo to build ML features, many focused around its text classification and sequence prediction features. We’re releasing it to the wider Java community to help build the ML ecosystem on the Java platform.

What’s the license?

Tribuo is released under the Apache 2.0 license.

How do I contribute?

We welcome bug reports, bug fixes, features, documentation improvements and other feedback on our GitHub repo. The Tribuo mailing list is, archived here. We’re looking at different options for real time chat. Code contributions are accepted under the terms of the Oracle Contributor Agreement. Contributors must have sign the agreement before their PRs can be reviewed or merged.

We’re interested in how the community is using Tribuo. Our users’ feedback and feature requests have always driven Tribuo’s development internally at Oracle, and we want to continue that tradition with the open source project.

What’s the versioning strategy & compatibility plan?

Tribuo approximates semantic versioning. Major version bumps can break the backwards compatibility of both the code and serialized models (though we hope to fix the latter by moving to a new serialization architecture). Provided that it’s an upwards compatible change, minor version bumps can add new features, improve performance (both statistically and in terms of runtime/memory usage), and add new functionality to existing algorithms. Patch releases fix bugs in existing versions and resolve security issues when they are discovered. Patch releases may also add small methods or classes if they are required to fix bugs.

Tribuo’s dependencies may change in each type of release, but patch releases can only bump the versions of existing dependencies (to newer patch releases of those dependencies), and minor releases can only add new dependencies and bump the versions of existing ones.

Anything considered part of the internal API (e.g., the innards of the tree builders and the classes in impl packages outside of Core) can change in any version, but these are usually marked in the javadoc as internal classes and will be closed off in the module system when we adopt it.

Project Overview

Why is the code broken out by prediction task (e.g., Classification, Regression, etc.)?

We designed Tribuo to be as modular as possible. Users are able to depend exclusively on the pieces they need without additional unnecessary components or third party dependencies. If you want to deploy a Tribuo RandomForestClassifier, you only need the tribuo-classification-decision-tree jar and its dependencies; it doesn’t pull in TensorFlow, or anything else. This makes it simpler to package up a production deployment; there is a smaller code surface to test, fewer jars, and less space used up with unnecessary things.

This early design choice has lead to some additional complexity in the development of the core Tribuo library, and we’re interested to see if the community finds this structure useful.

Where’s my fit/predict interface?

Scikit-learn has popularised the fit/predict style in Python Machine Learning libraries, and given Python’s lax approach to typing, those methods are only part of the API by convention rather that being enforced by the type system. In Tribuo, we’ve separated training from prediction. Tribuo’s fit method is called “train” and lives on the Trainer interface, whereas Tribuo’s “predict” method lives on the Model class. Tribuo uses the same predict call to produce both the outputs and the scores for those outputs. Its predict method is the equivalent of both “predict” and “predict_proba” in scikit-learn. We made this separation between training and prediction so as to enable the type system to act as a gate-keeper on prediction; predictions cannot be made using untrained models when it’s impossible to have an untrained model with a predict method. This separation means that integrating new libraries is more complex with Tribuo than with scikit-learn, since to conform to the scikit-learn API it is possible to simply export a small number of methods with specific names. With Tribuo on the other hand, the library needs to depend on Tribuo; however, implementing Tribuo’s interfaces comes with other benefits.

Why are there feature objects? Why not just use arrays?

Primitive arrays in Java are fast, but they imply a dense feature space. One of Tribuo’s design goals is strong support for NLP tasks, which typically have high-dimensional, sparse feature spaces. As a result, every feature space in Tribuo is implicitly sparse, unlike the implicit assumption of density made by most ML libraries. Another consequence of supporting NLP tasks is that Tribuo’s features are named. Each Feature is a tuple of a String and a value. This makes it easy to understand if there is a feature space mismatch (as commonly occurs in NLP when there are out-of-vocabulary terms). Since a Tribuo model knows the names of all the features, it can tell when it encounters an unexpected feature name. This prevents the possibility of loading a model and applying it to data from a completely different domain (i.e., applying an MNIST model to text classification) as the feature spaces will be misaligned: not only will there be a different number of features, but they’ll have different names, too. Since this situation results in an absence of valid features in the supplied Example, Tribuo’s predict methods will throw a RuntimeException.

Why are the model outputs “Predictions”? Why not use arrays?

The model’s Prediction contains a set of named outputs. These names make it easy to understand which score goes with which output. Returning an array means the user has to manually maintain the mapping between the array index and the name of the output (e.g., “hire” = 0, “re-interview” = 1, “reject” = 2) in a separate location from the model file itself. This leads to bugs and mismatches when the user loads the wrong model or uses the wrong mapping. With Tribuo’s approach this can never happen; the model knows what its output domain is, and can describe it to the user in the form the user expects (i.e., Strings).

Why don’t features or outputs have id numbers?

In truth, they do, but feature ids and output ids are managed by Tribuo, and they should never need to be seen by a user of Tribuo. These ids are automatically generated, and should only be necessary for debugging new model implementations or interfaces. Having the ids managed by the library ensures that they can’t be confused when chaining models together, loading data, or featurising inputs.

What’s this about provenance?

Provenance of Models, Datasets and Evaluations is one of the core benefits of Tribuo. It means each model, dataset and evaluation knows exactly how it was created, and moreover, it can generate a configuration file that can reconstruct the object in question from scratch (assuming you still have access to the original training and testing data). The provenance and configuration systems come from OLCUT (Oracle Labs Configuration and Utility Toolkit), a long lived internal library from Oracle Labs which has roots in the configuration system used in Sphinx4. OLCUT provides configuration files in multiple formats and includes ways to operate on provenance in JSON format (other provenance file formats will be added in the future).

What’s the difference between configuration and provenance?

In short, the configuration sets the parameters for an object (e.g., hyperparameters, data scaling, and random seed). The provenance is the configuration plus the information gathered from the specific run that created the model/dataset/evaluation (e.g., the number of features, the number of samples, the timestamp of the data file, and the number of times that the Trainer’s RNG has been used).

The provenance is a superset of the configuration. You can convert a provenance object into a set of configurations, one for each of its constituent parts. In contrast, the configuration cannot be converted into a provenance without executing the code (e.g., loading the dataset or training the model) as otherwise, it won’t know the run-specific information.

How do I know what I can configure in a class?

Tribuo’s configurable classes all implement and classes which implement this can have (possibly private) fields annotated with denoting they can be set by the configuration system. Fields in superclasses can also be set by the configuration provided the superclass also implements Configurable. You can find out what fields are configurable either by inspecting the source on GitHub or by running the utility and handing it a configurable class name. This utility can produce an example configuration snippet in any of OLCUT’s supported configuration languages. For more details see Tribuo’s configuration tutorial.

What’s the difference between a DataSource and a Dataset?

A DataSource performs the inbound ETL step from the source data on disk or from a database. It’s responsible for featurising the data (e.g., converting text into bigram counts), reading the ground truth outputs, and creating the Examples to contain the features and outputs. A DataSource can be lazy; it doesn’t require that all examples be in memory at once (although in practice many of the implementations do load all of the examples). A Dataset, on the other hand, is something suitable for training a model. It has the full feature domain and the full output domain. It keeps every training example in memory and can be split into training and testing chunks in a repeatable way. Datasets can also be transformed using the statistics of the data. For example, Datasets can be rescaled so that the features are constrained between zero and one. These transformations are recorded in the Dataset so that they can be recovered via provenance or incorporated into a TransformedModel that applies the transformations to each input before prediction.

What’s the purpose of Output.fullEquals?

The Output.equals and Output.hashcode methods are constrained to only look at the dimension labels. This means that two Labels can be compared for equality even if they have different confidence scores (as ground truth labels usually have an undefined confidence score, and predicted ones have a defined one). To compare the values including any confidence score the Output.fullEquals method should be used. Note, this implementation of equals and hashcode causes any two Regressors that share the same dimension names to be equal, which is unfortunate. When comparing Regressor, always use Regressor.fullEquals to include both the regressed value, and the variance. As Regressor uses Double.NaN as the sentinel value to indicate that no variance was calculated, NaN variances are considered equal to each other.