ensemble_modeling2.R

gistfile1.txt

cat("BIOMOD_EnsembleModeling2 loaded", "\n")
BIOMOD_EnsembleModeling2 <- function (modeling.output, chosen.models = "all", em.by = "PA_dataset+repet", 
                                      eval.metric = "all", eval.metric.quality.threshold = NULL, 
                                      models.eval.meth = c("KAPPA", "TSS", "ROC"), prob.mean = TRUE, 
                                      prob.cv = FALSE, prob.ci = FALSE, prob.ci.alpha = 0.05, prob.median = FALSE, 
                                      committee.averaging = FALSE, prob.mean.weight = FALSE, prob.mean.weight.decay = "proportional", 
                                      VarImport = 0) 
{
  biomod2:::.bmCat("Build Ensemble Models")
  args <- biomod2:::.BIOMOD_EnsembleModeling.check.args(modeling.output, 
                                                        chosen.models, eval.metric, eval.metric.quality.threshold, 
                                                        models.eval.meth, prob.mean, prob.cv, prob.ci, prob.ci.alpha, 
                                                        prob.median, committee.averaging, prob.mean.weight, prob.mean.weight.decay, 
                                                        em.by)
  modeling.output <- args$modeling.output
  chosen.models <- args$chosen.models
  eval.metric <- args$eval.metric
  eval.metric.quality.threshold <- args$eval.metric.quality.threshold
  models.eval.meth <- args$models.eval.meth
  prob.mean <- args$prob.mean
  prob.cv <- args$prob.cv
  prob.ci <- args$prob.ci
  prob.ci.alpha <- args$prob.ci.alpha
  prob.median <- args$prob.median
  committee.averaging <- args$committee.averaging
  prob.mean.weight <- args$prob.mean.weight
  prob.mean.weight.decay <- args$prob.mean.weight.decay
  em.by <- args$em.by
  rm("args")
  em.avail <- c("prob.mean", "prob.cv", "prob.ci.inf", "prob.ci.sup", 
                "prob.median", "committee.averaging", "prob.mean.weight")
  em.algo <- em.avail[c(prob.mean, prob.cv, prob.ci, prob.ci, 
                        prob.median, committee.averaging, prob.mean.weight)]
  Options <- list(em.by = em.by)
  expl_var_type = get_var_type(get_formal_data(modeling.output, 
                                               "expl.var"))
  expl_var_range = get_var_range(get_formal_data(modeling.output, 
                                                 "expl.var"))
  EM <- new("BIOMOD.EnsembleModeling.out", sp.name = [email protected], 
            expl.var.names = [email protected], em.by = em.by, 
            modeling.id = [email protected])
  [email protected]@link <- file.path([email protected], 
                                      paste([email protected], ".", [email protected], 
                                            ".models.out", sep = ""))
  em.mod.assemb <- biomod2:::.em.models.assembling(chosen.models, em.by)
  for (assemb in names(em.mod.assemb)) {
    cat("\n\n  >", assemb, "ensemble modeling")
    models.kept <- em.mod.assemb[[assemb]]
    if ([email protected]) {
      eval.obs <- get_formal_data(modeling.output, "eval.resp.var")
      eval.expl <- get_formal_data(modeling.output, "eval.expl.var")
    }
    obs <- get_formal_data(modeling.output, "resp.var")
    expl <- get_formal_data(modeling.output, "expl.var")
    if (em.by %in% c("PA_dataset", "PA_dataset+algo", "PA_dataset+repet")) {
      if (unlist(strsplit(assemb, "_"))[3] != "AllData") {
        if (inherits(get_formal_data(modeling.output),
                     "BIOMOD.formated.data.PA")) {
          kept_cells <- get_formal_data(modeling.output)@PA[, 
                                                            unlist(strsplit(assemb, "_"))[3]]
        }
        else {
          kept_cells <- rep(T, length(obs))
        }
        obs <- obs[kept_cells]
        expl <- expl[kept_cells, , drop = F]
      }
    }
    obs[is.na(obs)] <- 0
    needed_predictions <- biomod2:::.get_needed_predictions(modeling.output, 
                                                            em.by, models.kept, eval.metric, eval.metric.quality.threshold)
    if (!length(needed_predictions)) 
      next
    for (eval.m in eval.metric) {
      models.kept <- needed_predictions$models.kept[[eval.m]]
      models.kept.scores <- needed_predictions$models.kept.scores[[eval.m]]
      for (algo in em.algo) {
        if (algo == "prob.mean") {
          cat("\n   > Mean of probabilities...")
          model_name <- paste([email protected], 
                              "_", "EMmeanBy", eval.m, "_", assemb, sep = "")
          model.bm <- new("EMmean_biomod2_model", model = models.kept, 
                          model_name = model_name, model_class = "EMmean", 
                          model_options = Options, resp_name = [email protected], 
                          expl_var_names = [email protected], 
                          expl_var_type = expl_var_type, expl_var_range = expl_var_range, 
                          modeling.id = [email protected])
        }
        if (algo == "prob.cv") {
          cat("\n   > Coef of variation of probabilities...")
          model_name <- paste([email protected], 
                              "_", "EMcvBy", eval.m, "_", assemb, sep = "")
          model.bm <- new("EMcv_biomod2_model", model = models.kept, 
                          model_name = model_name, model_class = "EMcv", 
                          model_options = Options, resp_name = [email protected], 
                          expl_var_names = [email protected], 
                          expl_var_type = expl_var_type, expl_var_range = expl_var_range, 
                          modeling.id = [email protected])
        }
        if (algo == "prob.median") {
          cat("\n   > Median of probabilities...")
          model_name <- paste([email protected], 
                              "_", "EMmedianBy", eval.m, "_", assemb, sep = "")
          model.bm <- new("EMmedian_biomod2_model", model = models.kept, 
                          model_name = model_name, model_class = "EMmedian", 
                          model_options = Options, resp_name = [email protected], 
                          expl_var_names = [email protected], 
                          expl_var_type = expl_var_type, expl_var_range = expl_var_range, 
                          modeling.id = [email protected])
        }
        if (algo == "prob.ci.inf") {
          cat("\n   > Confidence Interval...")
          model_name <- paste([email protected], 
                              "_", "EMciInfBy", eval.m, "_", assemb, sep = "")
          model.bm <- new("EMci_biomod2_model", model = models.kept, 
                          model_name = model_name, model_class = "EMci", 
                          model_options = Options, resp_name = [email protected], 
                          expl_var_names = [email protected], 
                          expl_var_type = expl_var_type, expl_var_range = expl_var_range, 
                          modeling.id = [email protected], 
                          alpha = prob.ci.alpha, side = "inferior")
        }
        if (algo == "prob.ci.sup") {
          model_name <- paste([email protected], 
                              "_", "EMciSupBy", eval.m, "_", assemb, sep = "")
          model.bm <- new("EMci_biomod2_model", model = models.kept, 
                          model_name = model_name, model_class = "EMci", 
                          model_options = Options, resp_name = [email protected], 
                          expl_var_names = [email protected], 
                          expl_var_type = expl_var_type, expl_var_range = expl_var_range, 
                          modeling.id = [email protected], 
                          alpha = prob.ci.alpha, side = "superior")
        }
        if (algo == "committee.averaging") {
          cat("\n   >  Committee averaging...")
          model_name <- paste([email protected], 
                              "_", "EMcaBy", eval.m, "_", assemb, sep = "")
          models.kept.tresh <- unlist(lapply(models.kept, 
                                             function(x) {
                                               mod <- tail(unlist(strsplit(x, "_")), 3)[3]
                                               run <- tail(unlist(strsplit(x, "_")), 3)[2]
                                               dat <- tail(unlist(strsplit(x, "_")), 3)[1]
                                               return(get_evaluations(modeling.output)[eval.m, 
                                                                                       "Cutoff", mod, run, dat])
                                             }))
          names(models.kept.tresh) <- models.kept
          to_keep <- is.finite(models.kept.tresh)
          model.bm <- new("EMca_biomod2_model", model = models.kept[to_keep], 
                          model_name = model_name, model_class = "EMca", 
                          model_options = Options, resp_name = [email protected], 
                          expl_var_names = [email protected], 
                          expl_var_type = expl_var_type, expl_var_range = expl_var_range, 
                          modeling.id = [email protected], 
                          tresholds = models.kept.tresh[to_keep])
        }
        if (algo == "prob.mean.weight") {
          cat("\n   > Probabilities weighting mean...")
          model_name <- paste([email protected], 
                              "_", "EMwmeanBy", eval.m, "_", assemb, sep = "")
          models.kept.tmp <- models.kept
          models.kept.scores.tmp <- models.kept.scores
          if (eval.m == "ROC") {
            sre.id <- grep("_SRE", models.kept)
            if (length(sre.id) > 0) {
              cat("\n      ! SRE modeling were switched off")
              models.kept.tmp <- models.kept[-sre.id]
              models.kept.scores.tmp <- models.kept.scores[-sre.id]
            }
          }
          models.kept.tmp <- models.kept.tmp[is.finite(models.kept.scores.tmp)]
          models.kept.scores.tmp <- models.kept.scores.tmp[is.finite(models.kept.scores.tmp)]
          models.kept.scores.tmp <- round(models.kept.scores.tmp, 
                                          3)
          cat("\n\t\t", " original models scores = ", 
              models.kept.scores.tmp)
          if (is.numeric(prob.mean.weight.decay)) {
            DecayCount <- sum(models.kept.scores.tmp > 
                                0)
            WOrder <- order(models.kept.scores.tmp, decreasing = T)
            Dweights <- models.kept.scores.tmp
            for (J in 1:DecayCount) Dweights[WOrder[J]] <- I(prob.mean.weight.decay^(DecayCount - 
                                                                                       J + 1))
            for (J in 1:length(models.kept.scores.tmp)) {
              if (sum(models.kept.scores.tmp[J] == models.kept.scores.tmp) > 
                  1) 
                Dweights[which(models.kept.scores.tmp[J] == 
                                 models.kept.scores.tmp)] <- mean(Dweights[which(models.kept.scores.tmp[J] == 
                                                                                   models.kept.scores.tmp)])
            }
            models.kept.scores.tmp <- round(Dweights, 
                                            digits = 3)
            rm(list = c("Dweights", "DecayCount", "WOrder"))
          }
          else if (is.function(prob.mean.weight.decay)) {
            models.kept.scores.tmp <- sapply(models.kept.scores.tmp, 
                                             prob.mean.weight.decay)
          }
          models.kept.scores.tmp <- round(models.kept.scores.tmp/sum(models.kept.scores.tmp, 
                                                                     na.rm = T), digits = 3)
          cat("\n\t\t", " final models weights = ", models.kept.scores.tmp)
          model.bm <- new("EMwmean_biomod2_model", model = models.kept.tmp, 
                          model_name = model_name, model_class = "EMwmean", 
                          model_options = Options, resp_name = [email protected], 
                          expl_var_names = [email protected], 
                          expl_var_type = expl_var_type, expl_var_range = expl_var_range, 
                          modeling.id = [email protected], 
                          penalization_scores = models.kept.scores.tmp)
        }
        pred.bm <- predict(model.bm, expl, formal_predictions = needed_predictions$predictions[, 
                                                                                               model.bm@model, drop = F], on_0_1000 = T)
        pred.bm.name <- paste0(model_name, ".predictions")
        pred.bm.outfile <- file.path(model.bm@resp_name, 
                                     ".BIOMOD_DATA", [email protected], "ensemble.models", 
                                     "ensemble.models.predictions", pred.bm.name)
        dir.create(dirname(pred.bm.outfile), showWarnings = FALSE, 
                   recursive = TRUE)
        assign(pred.bm.name, pred.bm)
        save(list = pred.bm.name, file = pred.bm.outfile, 
             compress = TRUE)
        rm(list = pred.bm.name)
        if (exists("eval.obs") & exists("eval.expl")) {
          eval_pred.bm <- predict(model.bm, eval.expl)
          pred.bm.name <- paste0(model_name, ".predictionsEval")
          assign(pred.bm.name, eval_pred.bm)
          save(list = pred.bm.name, file = pred.bm.outfile, 
               compress = TRUE)
          rm(list = pred.bm.name)
        }
        if (length(models.eval.meth)) {
          cat("\n\t\t\tEvaluating Model stuff...")
          if (algo == "prob.cv") {
            cross.validation <- matrix(NA, 4, length(models.eval.meth), 
                                       dimnames = list(c("Testing.data", "Cutoff", 
                                                         "Sensitivity", "Specificity"), models.eval.meth))
          }
          else {
            if (em.by == "PA_dataset+repet") {
              calib_lines <- get_calib_lines(modeling.output)
              pa_dataset_id <- paste("_", unlist(strsplit(assemb, 
                                                          "_"))[3], sep = "")
              repet_id <- paste("_", unlist(strsplit(assemb, 
                                                     "_"))[2], sep = "")
              if (repet_id == "_Full") {
                eval_lines <- rep(T, length(pred.bm))
              }
              else {
                eval_lines <- !na.omit(calib_lines[, 
                                                   repet_id, pa_dataset_id])
                if (all(!eval_lines)) {
                  eval_lines <- !eval_lines
                }
              }
            }
            else {
              eval_lines <- rep(T, length(pred.bm))
            }
            cross.validation <- sapply(models.eval.meth, 
                                       Find.Optim.Stat, Fit = pred.bm[eval_lines], 
                                       Obs = obs[eval_lines])
            rownames(cross.validation) <- c("Testing.data", 
                                            "Cutoff", "Sensitivity", "Specificity")
          }
          if (exists("eval_pred.bm")) {
            if (algo == "prob.cv") {
              cross.validation <- matrix(NA, 5, length(models.eval.meth), 
                                         dimnames = list(c("Testing.data", "Evaluating.data", 
                                                           "Cutoff", "Sensitivity", "Specificity"), 
                                                         models.eval.meth))
            }
            else {
              true.evaluation <- sapply(models.eval.meth, 
                                        function(x) {
                                          Find.Optim.Stat(Fit = eval_pred.bm * 
                                                            1000, Obs = eval.obs, Fixed.thresh = cross.validation["Cutoff", 
                                                                                                                  x])
                                        })
              cross.validation <- rbind(cross.validation["Testing.data", 
              ], true.evaluation)
              rownames(cross.validation) <- c("Testing.data", 
                                              "Evaluating.data", "Cutoff", "Sensitivity", 
                                              "Specificity")
            }
          }
          cross.validation <- t(round(cross.validation, 
                                      digits = 3))
          model.bm@model_evaluation <- cross.validation
          rm(list = c("cross.validation"))
        }
        if (VarImport > 0) {
          cat("\n\t\t\tEvaluating Predictor Contributions...", 
              "\n")
          variables.importance <- variables_importance(model.bm, 
                                                       expl, nb_rand = VarImport)
          model.bm@model_variables_importance <- variables.importance$mat
          rm(list = c("variables.importance"))
        }
        assign(model_name, model.bm)
        save(list = model_name, file = file.path([email protected], 
                                                 "models", [email protected], model_name))
        [email protected] <- c([email protected], model.bm)
        [email protected] <- c([email protected], model_name)
      }
    }
  }
  names([email protected]) <- [email protected]
  model.name <- paste([email protected], ".", [email protected], "ensemble.models.out", 
                      sep = "")
  assign(x = model.name, value = EM)
  save(list = model.name, file = file.path([email protected], model.name))
  biomod2:::.bmCat("Done")
  return(EM)
}