geodl (version 0.3.1)

luz_metric_precision: luz_metric_precision

Description

luz_metric function to calculate macro-averaged, class aggregated precision

Usage

luz_metric_precision(
  nCls = 3,
  cropFactorMsk = 0,
  cropFactorPred = 0,
  smooth = 1,
  mode = "multiclass",
  biThresh = 0.5,
  zeroStart = TRUE,
  clsWghts = rep(1, nCls),
  usedDS = FALSE
)

Value

Calculated metric returned as a base-R vector as opposed to tensor.

Arguments

nCls

Number of classes being differentiated.

cropFactorMsk

Number of rows and columns of cells to not include for mask in assessment to minimize edge effects. Default is 0 or no cropping.

cropFactorPred

Number of rows and columns of cells to not include for prediction in assessment to minimize edge effects. Default is 0 or no cropping.

smooth

A smoothing factor to avoid divide by zero errors. Default is 1.

mode

Either "binary" or "multiclass". If "binary", only the logit for the positive class prediction should be provided. If both the positive and negative or background class probability is provided for a binary classification, use the "multiclass" mode. Note that this package is designed to treat all predictions as multiclass. The "binary" mode is only provided for use outside of the standard geodl workflow.

biThresh

Probability threshold to define postive case prediction. Default is 0.5.

zeroStart

TRUE or FALSE. If class indices start at 0 as opposed to 1, this should be set to TRUE. This is required to implement one-hot encoding since R starts indexing at 1. Default is TRUE.

clsWghts

Vector of class weightings loss calculatokn. Default is equal weightings.

usedDS

TRUE or FALSE. If deep supervision was implemented and masks are produced at varying scales using the defineSegDataSetDS() function, this should be set to TRUE. Only the original resolution is used to calculate assessment metrics. Default is FALSE.

Details

Calculates precision based on luz_metric() for use within training and validation loops.

Examples

# \donttest{
library(terra)
library(torch)
#Generate example data as SpatRasters
ref <- terra::rast(matrix(sample(c(1, 2, 3), 625, replace=TRUE), nrow=25, ncol=25))
pred1 <- terra::rast(matrix(sample(c(1:150), 625, replace=TRUE), nrow=25, ncol=25))
pred2 <- terra::rast(matrix(sample(c(1:150), 625, replace=TRUE), nrow=25, ncol=25))
pred3 <- terra::rast(matrix(sample(c(1:150), 625, replace=TRUE), nrow=25, ncol=25))
pred <- c(pred2, pred2, pred3)

#Convert SpatRaster to array
ref <- terra::as.array(ref)
pred <- terra::as.array(pred)

#Convert arrays to tensors and reshape
ref <- torch::torch_tensor(ref, dtype=torch::torch_long())
pred <- torch::torch_tensor(pred, dtype=torch::torch_float32())
ref <- ref$permute(c(3,1,2))
pred <- pred$permute(c(3,1,2))

#Add mini-batch dimension
ref <- ref$unsqueeze(1)
pred <- pred$unsqueeze(1)

#Duplicate tensors to have a batch of two
ref <- torch::torch_cat(list(ref, ref), dim=1)
pred <- torch::torch_cat(list(pred, pred), dim=1)
#Calculate Macro-Averaged, Class Aggregated Precision
metric<-luz_metric_precision(nCls=3,
                             smooth=1e-8,
                             mode = "multiclass",
                             zeroStart=FALSE,
                             usedDS=FALSE)
metric<-metric$new()
metric$update(pred,ref)
metric$compute()
# }