Agroforestry/1c_evaluatedModelParameters.R at main · GeospatialCentroid/Agroforestry · GitHub

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###
# functions to
#
#
###
library(terra)
t1 <- rast("C:/Users/carverd/Documents/GitHub/Agroforestry/tests10.tif")
plot(t1)

# parse out the [[]] columns and generate some confusion matrix statistics
generateConfusionMatrixStats <- function(data){
  d2 <- data |>
    dplyr::mutate(allValuesTrim = stringr::str_remove_all(allValues, pattern =  "\\[")) |>
    dplyr::mutate(allValuesTrim = stringr::str_remove_all(allValuesTrim, pattern =  "\\]"))|>
    tidyr::separate(col = allValuesTrim,
                    into = c("trueNegitive", "falsePositive", "falseNegitive", "truePositive"),
                    sep = ",")|>
    dplyr::mutate_at(c("trueNegitive", "falsePositive", "falseNegitive", "truePositive"), as.numeric) |>
    dplyr::mutate(
      truePositiveRate = truePositive/(truePositive+falseNegitive),
      trueNegitiveRate = trueNegitive/(trueNegitive + falsePositive)
    )
  return(d2)
}


relativePath <- "data/processed/parameterTesting"
outputfilename <- paste0("gatheredData_", Sys.Date())

compileCSVS <- function(relativePath, outputfilename){
  library(readr)
  library(dplyr)
  library(purrr)
  # produce the full dataset ------------------------------------------------
  # define path
  wd <- getwd()
  path <- paste0(wd,"/",relativePath)
  # read in files
  files <- list.files(path = path,
                      pattern = "*.csv",
                      full.names = TRUE)

  # read and bind the dataframes
  data <- files |>
    purrr::map(read_csv) |>
    dplyr::bind_rows() |>
    generateConfusionMatrixStats()

  # export file
  readr::write_csv(x = data, file = paste0(wd,"/",relativePath,"/combinedData/", outputfilename))

  return(data)
}


# run the process
data <- compileCSVS(relativePath = relativePath,
                    outputfilename = outputfilename)


# generate plots
parameters <- c("SNIC_SuperPixelSize", "SNIC_SeedShape", "SNIC_Compactness","SNIC_Connectivity")

generatePlots <- function(relativePath, parameters){
  library(readr)
  library(dplyr)
  library(purrr)
  library(plotly)
  # define path
  wd <- getwd()
  path <- paste0(wd,"/",relativePath)

  # read in files
  files <- list.files(path = path,
                      pattern = "*.csv",
                      full.names = TRUE)

  figs <- list()

  for(i in seq_along(parameters)){
    name = parameters[i]

    f2 <- files[grepl(pattern = name, x = files)]
    # read and bind the dataframes
    d2 <- f2 |>
      purrr::map(read_csv) |>
      dplyr::bind_rows() |>
      generateConfusionMatrixStats()

    # set the x value data of interest to a standard column name for ploting
    d2$xVal <- d2[,names(d2)==name] |> pull()

    # plot the false posivity rate, fasle negtive rate, and the overall accuracy for each value
    fig <- plot_ly(data = d2, x = ~xVal) |>
      add_trace(y = ~overallAccuracy, name = 'Overall Accuracy',mode = 'markers', type = "scatter")|>
      add_trace(y = ~truePositiveRate, name = 'True Positive Rate',mode = 'markers', type = "scatter") |>
      add_trace(y = ~trueNegitiveRate, name = 'True Negitive Rate',mode = 'markers', type = "scatter")|>
      layout(title = paste0('Accuracy Plots of ',name),
              plot_bgcolor = "#e5ecf6",
              xaxis = list(title = name),
             yaxis = list(title = 'Accuracy',
                          range=c(0,1.1)),
             legend = list(title=list(text='<b> Measures of Accuracy </b>')))


    figs[[i]]<-fig


  }
  return(figs)
}

plots <- generatePlots(relativePath = relativePath,
              parameters = parameters)