Package org.tribuo.transform

Provides infrastructure for applying transformations to a Dataset.

See: Description

Interface Summary

Interface	Description
Transformation	An interface representing a class of transformations which can be applied to a feature.
TransformationProvenance	A tag interface for provenances in the transformation system.
Transformer	A fitted Transformation which can apply a transform to the input value.
TransformStatistics	An interface for the statistics that need to be collected for a specific Transformation on a single feature.

Class Summary

Class	Description
TransformationMap	A carrier type for a set of transformations to be applied to a Dataset.
TransformationMap.TransformationList	A carrier type as OLCUT does not support nested generics.
TransformedModel<T extends Output<T>>	Wraps a Model with it's TransformerMap so all Examples are transformed appropriately before the model makes predictions.
TransformerMap	A collection of Transformers which can be applied to a Dataset or Example.
TransformerMap.TransformerMapProvenance	Provenance for TransformerMap.
TransformTrainer<T extends Output<T>>	A Trainer which encapsulates another trainer plus a TransformationMap object to apply to each Dataset before training each Model.

Package org.tribuo.transform Description

Provides infrastructure for applying transformations to a Dataset.

This package is the necessary infrastructure for transformations. The workflow is first to build a TransformationMap which represents the Transformations and the order that they should be applied to the specified Features. This can be applied to a Dataset to produce a TransformerMap which contains a fitted set of Transformers which can be used to apply the transformation to any other Dataset (e.g., to apply the same transformation to training and test sets), or to be used at prediction time to stream data through.

It also provides a TransformTrainer which accepts a TransformationMap and an inner Trainer and produces a TransformedModel which automatically transforms it's input data at prediction time.

Transformations don't produce new Features - they only modify the values of existing ones. When doing so they can be instructed to treat Features that are absent due to sparsity as zero or as not existing at all. Independently, we can explicitly add zero-valued Features by densifying the dataset before the transformation is fit or before it is applied. Once they exist these Features can be altered by Transformers and are visible to Transformations which are being fit.

The transformation fitting methods have two parameters which alter their behaviour: includeImplicitZeroFeatures and densify. includeImplicitZeroFeatures controls if the transformation incorporates the implicit zero valued features (i.e., the ones not present in the example but are present in the dataset's FeatureMap) when building the transformation statistics. This is important when working with, e.g. IDFTransformation as it allows correct computation of the inverse document frequency, but can be detrimental to features which are one-hot encodings of categoricals (as they have many more implicit zeros). densify controls if the example or dataset should have its implicit zero valued features converted into explicit zero valued features (i.e., it makes a sparse example into a dense one which contains an explicit value for every feature known to the dataset) before the transformation is applied, and transformations are only applied to feature values which are present.

These parameters interact to form 4 possibilities:

• Both false: transformations are only fit on explicit feature values, and only applied to explicit feature values
• Both true: transformations include explicit features and implicit zeros, and implicit zeros are converted into explicit zeros and transformed
• includeImplicitZeroFeatures is true, densify is false: the implicit zeroes are used to fit the transformation, but not modified when the transformation is applied. This is most useful when working with text data where you want to compute IDF style statistics
• includeImplicitZeroFeatures is false, densify is true: the implicit zeros are not used to fit the transformation, but are converted to explicit zeros and transformed. This is less useful than the other three combinations, but could be used to move the minimum value, or when zero is not appropriate for a missing value and needs to be transformed.

One further option is to call MutableDataset.densify() before passing the data to TransformTrainer.train(org.tribuo.Dataset<T>, java.util.Map<java.lang.String, com.oracle.labs.mlrg.olcut.provenance.Provenance>), which is equivalent to setting includeImplicitZeroFeatures to true and densify to true. To sum up, in the context of transformations includeImplicitZeroFeatures determines whether (implicit) zero-values features are measured and densify determines whether they can be altered.