The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

torchMAUM

This R package provides a torch efficient extension of the AUM for multi-class classification, which was created to be used as a surrogate loss for optimizing Area Under the ROC Curve (AUC) in supervised binary classification and changepoint detection problems. # Installation

if(!requireNamespace("remotes"))install.packages("remotes")
  remotes::install_github("tdhock/aum")

Usage

To extend AUM for multi-class classification , the OvR(One-Versus-Rest) approach was used , to average results , both macro and micro averages were used and there is one funtion for each averaging method( see Scikit-learn for more details and equations) Given a labels tensor:

four_labels = torch::torch_tensor(c(1,3,2,2),dtype=torch::torch_long())

and a prediction tensor:

four_pred = torch::torch_tensor(matrix(c(0.4, 0.3, 0.3,
                          0.2, 0.1, 0.7,
                          0.5,0.2,0.3,
                          0.3,0.4,0.3),
                          ncol=3,byrow=TRUE))

We could : - Plot ROC curves using either macro or micro averaging - Compute AUC either macro or micro - Compute the AUM either macro or micro, micro AUM supports weighting . ### ROC curves ROC macro:

(torchMAUM::Draw_ROC_curve_macro(four_pred,four_labels))

ROC micro:

(torchMAUM::Draw_ROC_curve_micro(four_pred,four_labels))

### AUC value AUC macro:

(torchMAUM::ROC_AUC_macro(four_pred,four_labels))

torch_tensor
0.805556
[ CPUFloatType{} ]

AUC micro:

(torchMAUM::ROC_AUC_micro(four_pred,four_labels))

torch_tensor
0.734375
[ CPUFloatType{} ]

AUM values

AUM macro:

(torchMAUM::ROC_AUM_macro(four_pred,four_labels))

torch_tensor
0.0277778
[ CPUFloatType{} ]

AUM micro: The AUM micro has two variants: either weighted or unweighted . To use the weighted version we need to pass the counts of each class in the dataset to the function :

unweighted:

(torchMAUM::ROC_AUM_micro(four_pred,four_labels,counts=NULL))

torch_tensor
0.05
[ CPUFloatType{} ]

weighted:

counts=torch::torch_tensor(c(1,2,1))
(torchMAUM::ROC_AUM_micro(four_pred,four_labels,counts=counts))

torch_tensor
0.0388889
[ CPUFloatType{} ]

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.