ai.libs.mlplan.metamining.similaritymeasures

Interface IHeterogenousSimilarityMeasureComputer

All Known Implementing Classes:

F1Optimizer, F3Optimizer

public interface IHeterogenousSimilarityMeasureComputer

Encapsulates a model that is trained to compute the similarity between two multidimensional measures, e.g. data set meta features, algorithm meta features and algorithm performance on a data set.

Method Summary

Modifier and Type	Method and Description
void	build(org.nd4j.linalg.api.ndarray.INDArray X, org.nd4j.linalg.api.ndarray.INDArray W, org.nd4j.linalg.api.ndarray.INDArray R) Build a model based on training data that can then be used to estimate the similarity of two measures for a new problem.
double	computeSimilarity(org.nd4j.linalg.api.ndarray.INDArray x, org.nd4j.linalg.api.ndarray.INDArray w) Compute the 'quality of the match' of given feature values for a new problem instance based on the training.

Method Detail

build

void build(org.nd4j.linalg.api.ndarray.INDArray X,
           org.nd4j.linalg.api.ndarray.INDArray W,
           org.nd4j.linalg.api.ndarray.INDArray R)

Build a model based on training data that can then be used to estimate the similarity of two measures for a new problem.

Parameters:

X - Feature values for instances of the first measure (One row = features of one instance, e.g. meta features of a data set)

W - Feature values for instances of the second measure (One row = features of one instance, e.g. a characterization of a machine learning pipeline)

R - A matrix giving an indication of how good of a match a specific instance of the first measure is to a specific instance of the second measure, i.e. how well a pipeline performs on a data set

computeSimilarity

double computeSimilarity(org.nd4j.linalg.api.ndarray.INDArray x,
                         org.nd4j.linalg.api.ndarray.INDArray w)

Compute the 'quality of the match' of given feature values for a new problem instance based on the training.

Parameters:

x - Feature values for the instance for the first measure (e.g. meta data of a new data set)

w - Feature values for the instance for the second measure (e.g. a characterization of machine learning pipeline)

Returns:

The quality of the match, or similarity for the given vectors