In pokyah/agrometeoR.extras: complementary functions for agrometeoR destined to dataviz

knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(mlr)
library(purrr)
devtools::load_all()

Using the benchmark results to compute bmr analysis

hourly_tsa_Pameseb_bmrsResult_2019_04_15 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/2019-04-15_hourly_tsa_Pameseb_bmrsResult.rds")

hourly_tsa_Pameseb_bmrsResult_2019_04_15_perfs = mlr::getBMRPerformances(hourly_tsa_Pameseb_bmrsResult_2019_04_15)

hourly_tsa_Pameseb_bmrsResult_2019_04_15_preds = mlr::getBMRPredictions(hourly_tsa_Pameseb_bmrsResult_2019_04_15)

rm(hourly_tsa_Pameseb_bmrsResult_2019_04_15)

hourly_tsa_Pameseb_tasks_2019_04_15 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/tasksPamesebForBmrs.rds")

PamesebAnalysis = makeBmrsExtraction(
  benchmark_perfs = hourly_tsa_Pameseb_bmrsResult_2019_04_15_perfs,
  benchmark_preds = hourly_tsa_Pameseb_bmrsResult_2019_04_15_preds,
  tasks = hourly_tsa_Pameseb_tasks_2019_04_15, as.df = TRUE)

PamesebBmrExtractionDataset = PamesebAnalysis$output$value$dataset
PamesebBmrPlots = makeBmrsPLots(PamesebBmrExtractionDataset)

# Q1, Q2, median, ymin, ymax
# https://www.r-bloggers.com/exploring-ggplot2-boxplots-defining-limits-and-adjusting-style/
pameseb.stats.rmse = boxplot.stats(PamesebBmrExtractionDataset$rmse)$stats
pameseb.stats.residuals = boxplot.stats(PamesebBmrExtractionDataset$residuals)$stats

pameseb.stats.residuals.df = PamesebBmrPlots$output$value$stats_residuals$learner %>% purrr::map_df(.,c, .id = "learner")

pameseb.stats.rmse.df = PamesebBmrPlots$output$value$stats_rmse$learner %>% purrr::map_df(.,c, .id = "learner")

hourly_tsa_PamIrm_bmrsResult_2019_04_11 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/2019-04-11_hourly_tsa_PamIrm_bmrsResult.rds")

hourly_tsa_PamIrm_bmrsResult_2019_04_11 = hourly_tsa_PamIrm_bmrsResult_2019_04_11$output$value

hourly_tsa_PamIrm_bmrsResult_2019_04_11_perfs = mlr::getBMRPerformances(hourly_tsa_PamIrm_bmrsResult_2019_04_11)

hourly_tsa_PamIrm_bmrsResult_2019_04_11_preds = mlr::getBMRPredictions(hourly_tsa_PamIrm_bmrsResult_2019_04_11)

rm(hourly_tsa_PamIrm_bmrsResult_2019_04_11)

hourly_tsa_PamIrm_tasks_2019_04_11 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/tasksForBmrs.rds")

PamesebIrmAnalysis = makeBmrsExtraction(
  benchmark_perfs = hourly_tsa_PamIrm_bmrsResult_2019_04_11_perfs,
  benchmark_preds = hourly_tsa_PamIrm_bmrsResult_2019_04_11_preds,
  tasks = hourly_tsa_PamIrm_tasks_2019_04_11, as.df = TRUE)

PamesebIrmBmrExtractionDataset = PamesebIrmAnalysis$output$value$dataset
PamesebIrmBmrPlots = makeBmrsPLots(PamesebIrmBmrExtractionDataset)

# Q1, Q2, median, ymin, ymax
# https://www.r-bloggers.com/exploring-ggplot2-boxplots-defining-limits-and-adjusting-style/
# PamesebIrm.stats.rmse = boxplot.stats(PamesebBmrExtractionDataset$rmse)$stats
# PamesebIrm.stats.residuals = boxplot.stats(PamesebBmrExtractionDataset$residuals)$stats

PamesebIrmstats.residuals.df = PamesebIrmBmrPlots$output$value$stats_residuals$learner %>% purrr::map_df(.,c, .id = "learner")

PamesebIrm.stats.rmse.df = PamesebIrmBmrPlots$output$value$stats_rmse$learner %>% purrr::map_df(.,c, .id = "learner")

# 

daily_tsamax_Pameseb_bmrsResult_2019_04_19 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/daily_tsa_Pameseb2019_04_18_bmrsResult.rds")

daily_tsa_Pameseb_bmrsResult_2019_04_19_perfs = mlr::getBMRPerformances(daily_tsa_Pameseb_bmrsResult_2019_04_19$output$value)

daily_tsa_Pameseb_bmrsResult_2019_04_19_preds = mlr::getBMRPredictions(daily_tsa_Pameseb_bmrsResult_2019_04_19$output$value)

rm(daily_tsamax_Pameseb_bmrsResult_2019_04_19)

daily_tsa_Pameseb_tasks_2019_04_19 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/tasksdataPamesebDailyForBmrs.rds")

dailyPamesebAnalysis = makeBmrsExtraction(
  benchmark_perfs = daily_tsa_Pameseb_bmrsResult_2019_04_19_perfs,
  benchmark_preds = daily_tsa_Pameseb_bmrsResult_2019_04_19_preds,
  tasks = daily_tsa_Pameseb_tasks_2019_04_19, as.df = TRUE)

dailyPamesebBmrExtractionDataset = dailyPamesebAnalysis$output$value$dataset
dailyPamesebBmrPlots = makeBmrsPLots(dailyPamesebBmrExtractionDataset)

# Q1, Q2, median, ymin, ymax
# https://www.r-bloggers.com/exploring-ggplot2-boxplots-defining-limits-and-adjusting-style/
#pameseb.stats.rmse = boxplot.stats(PamesebBmrExtractionDataset$rmse)$stats
#pameseb.stats.residuals = boxplot.stats(PamesebBmrExtractionDataset$residuals)$stats

dailyPameseb.stats.residuals.df = dailyPamesebBmrPlots$output$value$stats_residuals$learner %>% purrr::map_df(.,c, .id = "learner")

dailyPameseb.stats.rmse.df = dailyPamesebBmrPlots$output$value$stats_rmse$learner %>% purrr::map_df(.,c, .id = "learner")

intdailyPamesebBmrPlots = dailyPamesebBmrPlots$output$value[3:6]
intdailyPamesebBmrPlots = intdailyPamesebBmrPlots %>% purrr::map(ggplotly)
names(intdailyPamesebBmrPlots) %>% purrr::map(function(x){
  htmlwidgets::saveWidget(intdailyPamesebBmrPlots[x], title = paste0("~/Desktop/", x))}
  )

daily_tsamax_PamesebIrm_bmrsResult_2019_04_19 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/daily_tsa_PamesebIRm2019-04-18_bmrsResult.rds")

daily_tsamax_PamesebIrm_bmrsResult_2019_04_19_perfs = mlr::getBMRPerformances(daily_tsamax_PamesebIrm_bmrsResult_2019_04_19$output$value)

daily_tsamax_PamesebIrm_bmrsResult_2019_04_19_preds = mlr::getBMRPredictions(daily_tsamax_PamesebIrm_bmrsResult_2019_04_19$output$value)

rm(daily_tsamax_PamesebIrm_bmrsResult_2019_04_19)

daily_tsamax_PamesebIrm_tasks_2019_04_19 = readRDS("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/tasksdataPamesebIrmDailyForBmrs.rds")

dailyPamesebIrmAnalysis = makeBmrsExtraction(
  benchmark_perfs = daily_tsamax_PamesebIrm_bmrsResult_2019_04_19_perfs,
  benchmark_preds = daily_tsamax_PamesebIrm_bmrsResult_2019_04_19_preds,
  tasks = daily_tsamax_PamesebIrm_tasks_2019_04_19, as.df = TRUE)

dailyPamesebIrmBmrExtractionDataset = dailyPamesebIrmAnalysis$output$value$dataset
dailyPamesebIrmBmrPlots = makeBmrsPLots(bmrsExtraction = dailyPamesebIrmBmrExtractionDataset, param = "tsa max", timeRes = "daily", networks = "Pameseb & IRM")

# Q1, Q2, median, ymin, ymax
# https://www.r-bloggers.com/exploring-ggplot2-boxplots-defining-limits-and-adjusting-style/
#pameseb.stats.rmse = boxplot.stats(PamesebBmrExtractionDataset$rmse)$stats
#pameseb.stats.residuals = boxplot.stats(PamesebBmrExtractionDataset$residuals)$stats

dailyPamesebIrm.stats.residuals.df = dailyPamesebIrmBmrPlots$output$value$stats_residuals$learner %>% purrr::map_df(.,c, .id = "learner")

dailyPamesebIrm.stats.rmse.df = dailyPamesebIrmBmrPlots$output$value$stats_rmse$learner %>% purrr::map_df(.,c, .id = "learner")

bmrsNames = list.files(path = "~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/")


analyzeResults = function(bmrsName){

  params = strsplit(bmrsName, "_")[[1]]
  param = params[1]
  timeRes = params[2]
  if (length(params) == 4) {
    networks = paste0(params[3], "_", params[4])
  }else{
    networks = params[3]
  }

    bmrsResult = readRDS(paste0("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/", bmrsName, "/", bmrsName, "_bmrsResults.rds"))

  if (!is.null(bmrsResult$output$value)) {
    perfs = mlr::getBMRPerformances(bmrsResult$output$value)
    preds = mlr::getBMRPredictions(bmrsResult$output$value)
  }else{
    perfs = mlr::getBMRPerformances(bmrsResult)
    preds = mlr::getBMRPredictions(bmrsResult)
  }

  bmrsResult = NULL

  tasks = readRDS(paste0("~/Documents/code/pokyah/agrometeoR/data-raw/extdata/AGROMET/bmr_results/", bmrsName, "/", bmrsName, "_tasks.rds"))

    if (!is.null(tasks$output$value)) {
      tasks = tasks$output$value
  }else{
      tasks = tasks
  }

  extraction = makeBmrsExtraction(
    benchmark_perfs = perfs,
    benchmark_preds = preds,
    tasks = tasks, as.df = TRUE)$output$value$dataset

  perfs = NULL
  preds = NULL

  plotsAndTables = makeBmrsPLots(
    bmrsExtraction = extraction,
    path = "~/Desktop/sc_april_2019/",
    param = param,
    timeRes = timeRes,
    networks = networks)$output$value

  extraction = NULL
  tasks = NULL
  perfs = NULL
  preds = NULL


  # write.csv(plotsAndTables$stats_residuals, paste0("~/Desktop/sc_april_2019/", bmrsName, "_stats_residuals.csv"))
  # 
  # lapply(names(plotsAndTables)[3:7], function(x){
  #   browser()
  #   htmlwidgets::saveWidget(plotsAndTables[[x]],
  #     file = paste0("~/Desktop/sc_april_2019/", bmrsName, x, ".html"))
  # })

  #saveRDS(plotsAndTables, paste0("~/Desktop/sc_april_2019/", bmrsName, ".rds"))
  #return(plotsAndTables)
}

plotsAndTables = bmrsNames[1] %>% purrr::map(analyzeResults)
names(plotsAndTables) = bmrsNames[1:2]

pokyah/agrometeoR.extras documentation built on May 27, 2019, 2:07 p.m.