Class "GenRegRes" and "ThetaRegRes"

Regularisation framework containers.

Objects from the Class

Object of this class are created with the respective regularisation function: knnOptimisation, svmOptimisation, plsdaOptimisation, knntlOptimisation, ...

Slots

algorithm:

Object of class "character" storing the machine learning algorithm name.

hyperparameters:

Object of class "list" with the respective algorithm hyper-parameters tested.

design:

Object of class "numeric" describing the cross-validation design, the test data size and the number of replications.

log:

Object of class "list" with warnings thrown during the hyper-parameters regularisation.

seed:

Object of class "integer" with the random number generation seed.

results:

Object of class "matrix" of dimenstions times (see design) by number of hyperparameters + 1 storing the macro F1 values for the respective best hyper-parameters for each replication.

f1Matrices:

Object of class "list" with respective times cross-validation F1 matrices.

cmMatrices:

Object of class "list" with respective times contingency matrices.

testPartitions:

Object of class "list" with respective times test partitions.

datasize:

Object of class "list" with details about the respective inner and outter training and testing data sizes.

Only in ThetaRegRes:

predictions:

A list of predictions for the optimisation iterations.

otherWeights:

Alternative best theta weigts: a vector per iterations, NULL if no other best weights were found.

Methods

getF1Scores

Returns a matrix of F1 scores for the optimisation parameters.

f1Count

signature(object = "GenRegRes", t = "numeric") and signature(object = "ThetaRegRes", t = "numeric"): Constructs a table of all possible parameter combination and count how many have an F1 scores greater or equal than t. When t is missing (default), the best F1 score is used. This method is useful in conjunctin with plot.

getParams

Returns the best parameters. It is however strongly recommended to inspect the optimisation results. For a ThetaRegRes optimisation result, the method to chose the best parameters can be "median" (default) or "mean" (the median or mean of the best weights is chosen), "max" (the first weights with the highest macro-F1 score, considering that multiple max scoring combinations are possible) or "count" (the observed weight that get the maximum number of observations, see f1Count). The favourP argument can be used to prioritise weights that favour the primary data (i.e. heigh weights). See favourPrimary below.

getSeed

Returns the seed used for the optimisation run.

getWarnings

signature(object = "GenRegRes"): Returns a vector of recorded warnings.

levelPlot

signature(object = "GenRegRes"): Plots a heatmap of of the optimisation results. Only for "GenRegRes" instances.

plot

Plots the optisisation results.

show

Shows the object.

Other functions

Only for ThetaRegRes:

combineThetaRegRes(object)

Takes a list of ThetaRegRes instances to be combined and returnes a new ThetaRegRes instance.

favourPrimary(primary, auxiliary, object, verbose = TRUE)

Takes the primary and auxiliary data sources (two MSnSet instances) and a ThetaRegRes object and returns and updated ThetaRegRes instance containing best parameters/weigths (see the getParams function) favouring the primary data when multiple best theta weights are available.

Author

Laurent Gatto <lg390@cam.ac.uk>

Examples

showClass("GenRegRes")
#> Class "GenRegRes" [package "pRoloc"]
#> 
#> Slots:
#>                                                                       
#> Name:        algorithm hyperparameters          design             log
#> Class:       character            list         numeric            list
#>                                                                       
#> Name:             seed         results      f1Matrices      cmMatrices
#> Class:         integer          matrix            list            list
#>                                       
#> Name:   testPartitions        datasize
#> Class:            list            list
#> 
#> Known Subclasses: "ThetaRegRes"
showClass("ThetaRegRes")
#> Class "ThetaRegRes" [package "pRoloc"]
#> 
#> Slots:
#>                                                                       
#> Name:      predictions    otherWeights       algorithm hyperparameters
#> Class:            list            list       character            list
#>                                                                       
#> Name:           design             log            seed         results
#> Class:         numeric            list         integer          matrix
#>                                                                       
#> Name:       f1Matrices      cmMatrices  testPartitions        datasize
#> Class:            list            list            list            list
#> 
#> Extends: "GenRegRes"