knntlOptimisation: theta parameter optimisation

In lgatto/pRoloc: A unifying bioinformatics framework for spatial proteomics

theta parameter optimisation

Description

Classification parameter optimisation for the KNN implementation of Wu and Dietterich's transfer learning schema

Usage

knntlOptimisation(
  primary,
  auxiliary,
  fcol = "markers",
  k,
  times = 50,
  test.size = 0.2,
  xval = 5,
  by = 0.5,
  length.out,
  th,
  xfolds,
  BPPARAM = BiocParallel::bpparam(),
  method = "Breckels",
  log = FALSE,
  seed
)

Arguments

primary	An instance of class "MSnSet".
auxiliary	An instance of class "MSnSet".
fcol	The feature meta-data containing marker definitions. Default is markers.
k	Numeric vector of length 2, containing the best k parameters to use for the primary (k[1]) and auxiliary (k[2]) datasets. See knnOptimisation for generating best k.
times	The number of times cross-validation is performed. Default is 50.
test.size	The size of test (validation) data. Default is 0.2 (20 percent).
xval	The number of rounds of cross-validation to perform.
by	The increment for theta, must be one of c(1, 0.5, 0.25, 0.2, 0.15, 0.1, 0.05)
length.out	Alternative to using by parameter. Specifies the desired length of the sequence of theta to test.
th	A matrix of theta values to test for each class as generated from the function thetas, the number of columns should be equal to the number of classes contained in fcol. Note: columns will be ordered according to getMarkerClasses(primary, fcol). This argument is only valid if the default method 'Breckels' is used.
xfolds	Option to pass specific folds for the cross validation.
BPPARAM	Required for parallelisation. If not specified selects a default BiocParallelParam, from global options or, if that fails, the most recently registered() back-end.
method	The k-NN transfer learning method to use. The default is 'Breckels' as described in the Breckels et al (2016). If 'Wu' is specificed then the original method implemented Wu and Dietterich (2004) is implemented.
log	A logical defining whether logging should be enabled. Default is FALSE. Note that logging produes considerably bigger objects.
seed	The optional random number generator seed.

Details

knntlOptimisation implements a variation of Wu and Dietterich's transfer learning schema: P. Wu and T. G. Dietterich. Improving SVM accuracy by training on auxiliary data sources. In Proceedings of the Twenty-First International Conference on Machine Learning, pages 871 - 878. Morgan Kaufmann, 2004. A grid search for the best theta is performed.

Value

A list of containing the theta combinations tested, associated macro F1 score and accuracy for each combination over each round (specified by times).

Author(s)

Lisa Breckels

References

Breckels LM, Holden S, Wonjar D, Mulvey CM, Christoforou A, Groen AJ, Kohlbacher O, Lilley KS, Gatto L. Learning from heterogeneous data sources: an application in spatial proteomics. bioRxiv. doi: http://dx.doi.org/10.1101/022152

Wu P, Dietterich TG. Improving SVM Accuracy by Training on Auxiliary Data Sources. Proceedings of the 21st International Conference on Machine Learning (ICML); 2004.

See Also

knntlClassification and example therein.

lgatto/pRoloc documentation built on Oct. 23, 2024, 12:51 a.m.