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This page describes Tribuo 4.0. View the documentation for Tribuo 4.3 instead.

Architecture

Tribuo is a library for creating Machine Learning (ML) models and for using those models to make predictions on previously unseen data.

A ML model is the result of applying some training algorithm to a dataset. Most commonly, such algorithms produce output in the form of a large number of floating point values; however, this output may take one of many different forms, such as a tree-structured if/else statement. In Tribuo, a model includes not only this output, but also the necessary feature and output statistics to map from the named feature space into Tribuo’s ids, and from Tribuo’s output ids into the named output space.

A Tribuo Model can also be thought of as a learned mapping from a sparse feature space of doubles to a dense output space (e.g., of class label probabilities, or regressed outputs etc). Every dimension of the input and output are named. This naming system makes it possible to check that the input and model agree on the feature space they are using.

Data flow overview

Tribuo architecture diagram

Tribuo loads data using a DataSource implementation, which might load from a location like a DB or a file on disk. This DataSource processes the input data, converting it into Tribuo’s storage format, an Example. An Example is a tuple of an Output (i.e., what you want to predict) and a list of Features, where each Feature is a tuple of a String feature name and a double feature value. The DataSource is then read into a Dataset, which accumulates statistics about the data for future use in model construction. Datasets can be split into chunks to separate out training and testing data, or to filter out examples according to some criterion. As Examples are fed into a Dataset, the Features are observed and have their statistics recorded in a FeatureMap. Similarly the Outputs are recorded in the appropriate OutputInfo subclass for the specified Output subclass. Once the Dataset has been processed, it’s passed to a Trainer, which contains the training algorithm along with any necessary parameter values (in ML these are called hyperparameters to differentiate them from the learned model parameters), and the Trainer performs some iterations of the training algorithm before producing the Model. A Model contains the necessary learned parameters to make predictions along with a Provenance object which records how the Model was constructed (e.g., data file name, data hash, trainer hyperparameters, time stamp, etc). Both Models and Datasets can be serialized out to disk using Java Serialization. Once a model has been trained, it can be fed previously unseen Examples to produce Predictions of their Outputs. If the new Examples have known Outputs, then the Predictions can be passed to an Evaluator, which calculates statistics like the accuracy (i.e., the number of times the predicted output was the same as the provided output).

Structure

Tribuo includes several top level modules:

  • Core provides Tribuo’s core classes and interfaces.
  • Data provides loaders for text, sql and csv data, along with the columnar package which provides infrastructure for working with columnar data.
  • Math provides Tribuo’s linear algebra library, along with kernels and gradient optimizers.
  • JSON provides a JSON data loader and a tool to strip provenance from trained models.

Tribuo has separate modules for each prediction task:

  • Classification contains an Output implementation called Label, which represents a multi-class classification. A Label is a tuple of a String name, and a double precision score value. For each of OutputFactory, OutputInfo, Evaluator and Evaluation, the Classification package includes a classification-specific implementation, namely LabelFactory, LabelInfo, LabelEvaluator andLabelEvaluation, respectively.
  • Regression contains an Output implementation called Regressor, which represents multidimensional regression. Each Regressor is a tuple of dimension names, double precision dimension values, and double precision dimension variances. It has companion implementations of OutputFactory, OutputInfo, Evaluator and Evaluation called RegressionFactory, RegressionInfo, RegressionEvaluator and RegressionEvaluation, respectively. By default, the dimensions are named “DIM-x” where x is a non-negative integer.
  • AnomalyDetection contains an Output implementation called Event, which represents the detection of an anomalous or expected event (represented by the EventType enum containing ANOMALY and EXPECTED). Each Event is a tuple of an EventType instance and a double precision score value, representing the score of the event type. The AnomalyDetection package has companion implementations of OutputFactory, OutputInfo, Evaluator and Evaluation called AnomalyFactory, AnomalyInfo, AnomalyEvaluator and AnomalyEvaluation, respectively.
  • Clustering contains an Output implementation called ClusterID, which represents the cluster id number assigned. Each ClusterID is a non-negative integer id number and a double precision score representing the strength of association. The Clustering package has companion implementations of OutputFactory, OutputInfo, Evaluator and Evaluation called ClusteringFactory, ClusteringInfo, ClusteringEvaluator and ClusteringEvaluation, respectively.
  • MultiLabel contains an Output implementation called MultiLabel, which represents a multi-label classification. Each MultiLabel is a possibly empty set of Label instances with their associated scores. The MultiLabel package has companion implementations of OutputFactory, OutputInfo, Evaluator and Evaluation called MultiLabelFactory, MultiLabelInfo, MultiLabelEvaluator and MultiLabelEvaluation, respectively. It also has a Trainer<MultiLabel> which accepts a Trainer<Label> and generates a Model<MultiLabel> by using the inner trainer to make independent predictions for each Label. This is a reasonable baseline strategy to use for multi-label problems.

Finally, there are cross-cutting module collections:

  • Common provides shared infrastructure for the prediction tasks.
  • Interop provides infrastructure for working with large external libraries like TensorFlow and ONNX Runtime.
  • Util provides independent libraries that Tribuo uses for specific tasks. For example, InformationTheory is a library of information theoretic functions, and Tokens provides the interface Tribuo uses for tokenization along with implementations of several tokenizers.

Configuration, Options and Provenance

Many of Tribuo’s trainers, datasources and other classes implement the Configurable interface. This is provided by OLCUT, and allows for runtime configuration of classes based on configuration files written in a variety of formats. The default format is xml. Other available formats include JSON & edn.

The configuration system is integrated into the command line arguments Options system build into OLCUT’s ConfigurationManager. Values in configuration files can be overridden on the command line by supplying --@<object-name>.<field-name> <value> in the arguments. The configuration system provides the basis of Tribuo’s model tracking Provenance system, which records all hyperparameters, dataset parameters (e.g., file location, train/test split, etc.), and any user-supplied instance information, along with run specific information such as the file hash, number of training examples, etc. A model provenance can be converted into a list of configurations for each Configurable object involved in the model training. Similarly, an evaluation provenance can be converted into the configurations for the model as well as the configurations for the test dataset. These configurations can be loaded into a fresh ConfigurationManager and optionally saved to disk. The evaluation or model training can then be repeated or rerun with tweaks like new data or a hyperparameter change.

Configurable classes have @Config annotations on their fields, and such fields have the value from the configuration file inserted into them upon construction in the configuration system. A snippet from the classification SGD trainer is given below to illustrate this:

public class LinearSGDTrainer implements Trainer<Label>, WeightedExamples {
    @Config(description="The classification objective function to use.")
    private LabelObjective objective = new LogMulticlass();

    @Config(description="The gradient optimiser to use.")
    private StochasticGradientOptimiser optimiser = new AdaGrad(1.0,0.1);

    @Config(description="The number of gradient descent epochs.")
    private int epochs = 5;

    @Config(description="Log values after this many updates.")
    private int loggingInterval = -1;

    @Config(description="Minibatch size in SGD.")
    private int minibatchSize = 1;

    @Config(description="Seed for the RNG used to shuffle elements.")
    private long seed = Trainer.DEFAULT_SEED;

    @Config(description="Shuffle the data before each epoch. Only turn off for debugging.")
    private boolean shuffle = true;

    private SplittableRandom rng;

    private int trainInvocationCounter;
}

Only fields which are configured need to be annotated @Config. Other fields can be set in the appropriate constructor. OLCUT requires that all classes which implement Configurable have a no-args constructor. The Configurable interface allows for a postConfig method, which is called after the object has been constructed and the appropriate field values inserted, but before it is published or returned from the ConfigurationManager. This postConfig method is used to perform the validation that would normally be performed in a constructor, and it can be called from regular constructors. Default values for the configurable parameters can be specified in the same way default fields are usually specified. The @Config annotation has optional parameters for supplying the description, declaring whether the field is mandatory, and determining whether the field value should be redacted from any configuration or provenance based on this object. More details about OLCUT can be found in it’s documentation.

The LinearSGDTrainer class above is configured by the xml snippet below:

<config> 
   <component name="logistic" type="org.tribuo.classification.sgd.linear.LinearSGDTrainer">
        <property name="objective" value="log"/>
        <property name="optimiser" value="adam"/>
        <property name="epochs" value="10"/>
        <property name="loggingInterval" value="100"/>
        <property name="minibatchSize" value="1"/>
        <property name="seed" value="1"/>
    </component>
    <component name="log" type="org.tribuo.classification.sgd.objectives.LogMulticlass"/>
    <component name="adam" type="org.tribuo.math.optimisers.Adam">
        <property name="initialLearningRate" value="3e-4"/>
        <property name="betaOne" value="0.95"/>
    </component>
</config>

This instantiates a LinearSGDTrainer with a logistic regression objective and an Adam gradient optimiser, using Andrei Karpathy’s preferred learning rate and an adjusted beta one parameter (note these parameters are just demonstration values, we’re not recommending these specific values).

As configuration is part of the class file rather than the public documented API (because it operates on private fields), OLCUT ships with a CLI utility for inspecting a configurable class and generating an example configuration in any supported configuration format. To use this utility from the command line you can run:

$ java -cp <path-to-jars-including-olcut-core> com.oracle.labs.mlrg.olcut.config.DescribeConfigurable -n <class-name> -o -e xml

where the -n argument denotes what class to describe, -o denotes that an example configuration should be generated, and -e gives the file format to emit the example configuration in.

For example, running DescribeConfigurable on LinearSGDTrainer gives:

$ java -cp <path-to-tribuo-jars> com.oracle.labs.mlrg.olcut.config.DescribeConfigurable -n org.tribuo.classification.sgd.linear.LinearSGDTrainer -o -e xml

Class: org.tribuo.classification.sgd.linear.LinearSGDTrainer

Field Name      Type                                         Mandatory Redact Default                                                       Description
epochs          int                                          false     false  5                                                             The number of gradient descent epochs.
loggingInterval int                                          false     false  -1                                                            Log values after this many updates.
minibatchSize   int                                          false     false  1                                                             Minibatch size in SGD.
objective       org.tribuo.classification.sgd.LabelObjective false     false  LogMulticlass                                                 The classification objective function to use.
optimiser       org.tribuo.math.StochasticGradientOptimiser  false     false  AdaGrad(initialLearningRate=1.0,epsilon=0.1,initialValue=0.0) The gradient optimiser to use.
seed            long                                         false     false  12345                                                         Seed for the RNG used to shuffle elements.
shuffle         boolean                                      false     false  true                                                          Shuffle the data before each epoch. Only turn off for debugging.

Example :
<?xml version="1.0" encoding="utf-8"?>
<!--OLCUT configuration file-->
<config>
<component name="example" type="org.tribuo.classification.sgd.linear.LinearSGDTrainer" export="false" import="false">
	<property name="seed" value="0"/>
	<property name="minibatchSize" value="0"/>
	<property name="shuffle" value="false"/>
	<property name="epochs" value="0"/>
	<property name="optimiser" value="StochasticGradientOptimiser-instance"/>
	<property name="loggingInterval" value="0"/>
	<property name="objective" value="LabelObjective-instance"/>
</component>
</config>

It’s also possible to access this information programmatically, but there are several ways of doing that in OLCUT each appropriate to different use cases.

Data Loading

Built-in formats

Tribuo supports several common input formats for loading in data:

  • libsvm/svmlight - a sparse numerical format for classification and regression tasks.
  • CSV - a plain text delimited format (using an RFC4180 compliant parser).
  • JSON - JavaScript Object Notation. Tribuo natively reads JSON objects, which are a map from String to primitive value. The whole file is an array of such objects.
  • SQL - Tribuo has a JDBC loader, which can query a database and convert the result set into Tribuo Examples.
  • text - a one document per line format in which with the response variable before the text is delimited by ` ## `.

There are two CSV loaders: A simple one for reading a CSV file (with or without a header) where all the columns are either features or responses, and a complex loader based on Tribuo’s RowProcessor. The RowProcessor also underlies the SQL and JSON loaders, and is extremely configurable. For more details see the Columnar Inputs section below. If there are other common formats of interest, let us know by filing an issue.

Tribuo’s interfaces are extensible, and implementing another format simply requires implementing the DataSource interface. We recommend using LibSVMDataSource or TextDataSource as examples of how to implement a flat file format. For columnar data, Tribuo has specialised processing infrastructure. This is used for the CSV, JSON and SQL loaders, and it provides a large amount of flexibility.

Columnar Inputs

Columnar data sources require a configurable extraction step to map the columns into Tribuo Example and Feature objects. A single column may contain multiple features, may be extraneous, or may contain Example-level metadata. In addition, the user must specify which column(s) contain the output variable. To support this usecase, Tribuo provides the RowProcessor, a configurable mechanism for converting a ColumnarIterator.Row (which is a tuple of a Map<String,String> and a row number) into an Example. The RowProcessor uses four interfaces to process the input map:

  • FieldExtractor - processes the whole row at once, extracting metadata fields. These extracted fields, such as an Example’s id number, are then written into the Example. As described in the javadoc, the Example’s weight is handled as a special case of the metadata processing.
  • FieldProcessor - processes a single field to produce a (possibly empty) list of Features.
  • FeatureProcessor - processes all the features after they have been generated by a FieldProcessor. This allows for the generation of features that depend upon multiple other features, such as conjunctions. It also facilitates the filtering out of irrelevant or unnecessary features.
  • ResponseProcessor - processes the designated response fields using the supplied OutputFactory to convert the field text into an Output instance.

These interfaces are supplied to the RowProcessor on construction (or configuration). By default, FieldProcessors are bound to a single column, but there is an optional system which generates new FieldProcessors based on supplied regexes. This system can be used if the data is drawn from a schema-less format where the presence of fields in particular documents is not known in advance by the user. The regex system is also useful when the set of fields is large and the number of unique FieldProcessors is small. For example, the same field processor can be applied to all columns whose name begins with “A”, thus avoiding the need to write a large configuration or code file to describe all such columns. Although these regexes are usually instantiated once, before any rows are processed, RowProcessor is intentionally subclass-able so that developers can trigger expansion whenever necessary. In the current implementation, there is at most one FieldProcessor per field; we’ll reconsider this restriction if there is sufficient interest.

Internally, the RowProcessor operates on ColumnarFeature, which is a feature subclass that tracks both the feature name and the column name. It’s used to allow additional flexibility in the FeatureProcessors when generating conjunction or other cross-cutting features. ColumnarFeatures should not be depended on when outside the columnar processing infrastructure since the Example contract does not guarantee that feature objects are preserved after being stored in an Example.

If your columnar data is not in a format currently supported by Tribuo, you can subclass ColumnarDataSource, provide an implementation of ColumnarIterator, which converts from your input format into ColumnarIterator.Row, and then configure the RowProcessor to extract Examples from your data.

Splitting up Datasets

DataSources are not designed for splitting data into chunks; however, Tribuo provides several other mechanisms for splitting data into training and test sets, subsampling data based on it’s properties, and creating cross-validation folds. The train/test and cross-validation splits are self-explanatory, though it’s worth noting that the cross-validation splits use the feature domain of the entire, underlying dataset. The DatasetView underlies the cross-validation splits and can also be constructed using a predicate function (or a list of indices). The predicate function accepts an Example and thus can depend on the features, outputs or metadata encoded in an Example.

Transforming datasets

Tribuo supports independent, feature-based transformations including the rescaling or binning of features. These feature transformations can be found in the org.tribuo.transform package, which provides the mechanisms for fitting and applying transformations. Transformations can be chained to create pipelines that are applied in the supplied sequence to the specified feature(s). Local transformation pipelines are those that are applied only to the given named feature(s), whereas global transformations pipelines are applied after local pipelines and apply to every feature. Local transformations pipelines can also be applied to features which match a regular expression (regex). Specifically, every feature name which matches the regex receives a copy of that transformation pipeline, and that pipeline is then applied to the feature. An exception will be thrown if an attempt is made to apply a regex transformation pipeline to a feature that has already received a local transformation pipeline. Additionally, all transformations are applied to the feature domain to ensure it maintains the proper statistics.

Currently, transformations must be based on only a single feature at a time, but we plan to introduce global feature transformations in some future release to allow operations over the whole feature space such as PCA.

Weights and Metadata

Examples can have metadata attached to them, and this metadata can be used to filter out Examples or otherwise tag them for special processing. The metadata takes the form of a Map<String,String>, which can only be appended to; the values cannot be modified after insertion. In addition, each Example has a float-valued weight field, which can be used to denote the importance of an Example in a training or evaluation setting. Only training algorithms that implement the WeightedExamples tag interface support weighted examples; otherwise the example weights are ignored. The weight field is currently supported in the RegressionEvaluator if the weighted evaluation flag is turned on. We’ll consider adding this support to the other evaluators, although it may require breaking API changes since the return types of some accessor methods could change from integer to floating point values.

Obfuscation

One of Tribuo’s benefits is it’s extensive tracking of model metadata and provenance; however, we realise this metadata isn’t necessarily something that should live in third-party accessible, deployed models. As a result, Tribuo provides a few transformation mechanisms to remove metadata from a trained model.

Provenance

Provenance can be removed from Model objects using the StripProvenance program located in the JSON module. There are three kinds of stored provenance: trainer provenance, data provenance, and instance provenance. Each type of provenance can be removed separately. It is also possible to insert a SHA-256 hash of the full provenance object into the model as a tracking mechanism. We intend for the user to store the hash as a key for the original provenance JSON in an external storage mechanism. Alternatively, @Config fields can be marked redact=True which will prevent those values from being stored in the provenance or any configuration.

Feature Hashing

In addition to its use as a dimensionality reduction technique, feature hashing also obfuscates the original feature names in cases where the forward mapping from original names to hashed names has not been stored by the system. So as to avoid the storage of such a forward mapping, Tribuo provides an implementation of feature hashing that lives entirely in the feature domain object. This means that Tribuo has no knowledge of the true feature names, and the system transparently hashes the inputs. The feature names tend to be particularly sensitive when working with NLP problems. For example, without such hashing, bigrams would appear in the feature domains.