Trainers

Trainers offer one of the simplest interfaces in Shark, but also form a very diverse group of algorithms for a range of different settings and applications. Each trainer represents a one-step solver for one certain machine learning problem. Usually, it will adapt the parameters of a model such that it represents the solution to some machine learning or data processing problem given a certain data set.

Some trainers are very simple – for example those training a linear model such that it normalizes the components of all examples in a data set to unit variance. Other trainers are quite complex, for example for some multi-class support vector machines. But in most cases, trainers are used to reach analytical solutions to relatively simple problems which are not stated as an iterative optimization problem underneath.

List of Classes

• Supervised Trainers,

• Unsupervised Trainers

The base class ‘AbstractTrainer<ModelT, LabelTypeT>’

AbstractTrainer is the base interface for trainers for supervised learning problems. It is templatized with respect to the type of model it trains and the type of label in the data set. The trainer then defines the following types in its public interface:

Types	Description
ModelType	The type of model the trainer optimizes
InputType	The type of inputs the model takes
LabelType	The type of the labels in the data set
DatasetType	The type of dataset that the trainer expects, this is LabeledData<InputType, LabelType>

A trainer offers the following methods:

Method	Description
train(ModelType&, LabeledData<InputType, LabelType>)	Solves the problem and sets the model parameters
std::string name()	Returns the trainer’s name

Usage of trainers is equally straightforward:

MyModel model;
MyTrainer trainer;
MyDataset data;
trainer.train(model, data);  //model now represents the solution to the problem.

The base class ‘AbstractUnsupervisedTrainer<ModelT>’

AbstractUnsupervisedTrainer is the base interface for trainers for unsupervised learning poblems. It only needs to know about the model type, and offers a typedef for the data format:

Types	Description
ModelType	Type of model which the trainer optimizes
InputType	Type of inputs the model takes
DatasetType	The type of dataset that the trainer expects, this is UnlabeledData<InputType>

These trainers also offer the following methods:

Method	Description
train(ModelType&, UnlabeledData<InputType>)	Solves the problem and stores it in the model.
std::string name()	Returns the name of the trainer

Weighting

Both variants of trainers also come in a version with support for weighting, AbstractWeightedTrainer and AbstractWeightedUnsupervisedTrainer. Both support an additional verion of train which can use a WeightedUnlabeledData or WeightedLabeledData object which adds an additional weighting factor for each element. This factor must be positive, but does not need to sum to one. Using a weighted dataset with all weights equal will lead to the same result as not weighting the dataset.

Both classes declare a new typedef

Types	Description
WeightedDatasetType	The weighted dataset that the trainer expects, this is WeightedUnlabeledData<InputType> or WeightedLabeledData<InputType,LabeledType>

and the following version of train, additionally to the known versions:

Method	Description
train(ModelType&, WeightedDatasetType)	Solves the weighted problem and stores it in the model.