“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.
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.
Several internal groups at Oracle are using Tribuo to build ML features, many focused around it’s 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.
Tribuo is released under the Apache 2.0 license.
We welcome bug reports, bug fixes, features, documentation improvements and other feedback on our GitHub repo. The Tribuo mailing list is email@example.com, 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.
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.
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 it’s 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
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.
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.
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.
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 it’s output domain is,
and can describe it to the user in the form the user expects (i.e. Strings).
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.
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
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 configurations. 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.
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
Datasets can be rescaled so that the features are constrained
between zero and one. These transformations are recorded in the
that they can be recovered via provenance or incorporated into a
TransformedModel that applies the transformations to each input before
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 a score of 1.0, and predicted ones do not). 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
that share the same dimension names to be equal, which is unfortunate. When
Regressor, always use
Regressor.fullEquals to include both the
regressed value, and the variance. As
Double.NaN as the
sentinel value to indicate that no variance was calculated, NaN variances are
considered equal to each other.