org.lenskit.eval.traintest

Interface EvalTask

All Known Implementing Classes:

PredictEvalTask, RecommendEvalTask

public interface EvalTask

Interface for evaluation tasks. Each evaluation task performs some task with the trained model and measures the results. Performing a task on a recommender trained over a particular data set results is called a measurement.

See Also:

TrainTestExperiment

Method Summary

Modifier and Type	Method and Description
ConditionEvaluator	createConditionEvaluator(AlgorithmInstance algorithm, DataSet dataSet, RecommenderEngine rec) Set up a measurement of a single recommender.
void	finish() Finalize this eval task.
java.util.List<java.lang.String>	getGlobalColumns() Get columns that will go in the aggregate output file.
java.util.Set<java.lang.Class<?>>	getRequiredRoots() Get the root types required by this evaluation.
java.util.List<java.lang.String>	getUserColumns() Get columns that will go in the per-user output file.
void	start(ExperimentOutputLayout outputLayout) Do initial setup for this eval task.

Method Detail

getGlobalColumns

java.util.List<java.lang.String> getGlobalColumns()

Get columns that will go in the aggregate output file.

Returns:

The list of column names that this task will contribute to the aggregate output file.

getUserColumns

java.util.List<java.lang.String> getUserColumns()

Get columns that will go in the per-user output file.

Returns:

The list of column names that this task will contribute to the per-user output file.

getRequiredRoots

java.util.Set<java.lang.Class<?>> getRequiredRoots()

Get the root types required by this evaluation.

Returns:

The root types required by this evaluation.

start

void start(ExperimentOutputLayout outputLayout)

Do initial setup for this eval task. This should create any per-task output files, etc.

Parameters:

outputLayout - The output layout for experiment results.

finish

void finish()

Finalize this eval task. This should finish writing and close any per-task output files, etc.

createConditionEvaluator

ConditionEvaluator createConditionEvaluator(AlgorithmInstance algorithm,
                                            DataSet dataSet,
                                            RecommenderEngine rec)

Set up a measurement of a single recommender.

Parameters:

algorithm - The algorithm being evaluated.

dataSet - The data set being evaluated.

rec - The recommender engine that will be measured.

Returns:

A condition evaluator that will measure the recommender’s performance on the algorithm and data set.