X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=R%2FR6_AgghooCV.R;fp=R%2FR6_AgghooCV.R;h=9cdf19ed5081aec44fc3e4ceb9a72dcb7db306b3;hp=dba42b4edc450a2000c11929504a8b33be12182c;hb=504afaadc783916dc126fb87ab9e067f302eb2c5;hpb=15f48abea9c631d52317ff157c8af0dd4c7a67d3

diff --git a/R/R6_AgghooCV.R b/R/R6_AgghooCV.R
index dba42b4..9cdf19e 100644
--- a/R/R6_AgghooCV.R
+++ b/R/R6_AgghooCV.R
@@ -14,23 +14,15 @@ AgghooCV <- R6::R6Class("AgghooCV",
     #' @param target Vector of targets (generally numeric or factor)
     #' @param task "regression" or "classification"
     #' @param gmodel Generic model returning a predictive function
-    #' @param quality Function assessing the quality of a prediction;
-    #'                quality(y1, y2) --> real number
-    initialize = function(data, target, task, gmodel, quality = NULL) {
+    #' @param loss Function assessing the error of a prediction
+    initialize = function(data, target, task, gmodel, loss = NULL) {
       private$data <- data
       private$target <- target
       private$task <- task
       private$gmodel <- gmodel
-      if (is.null(quality)) {
-        quality <- function(y1, y2) {
-          # NOTE: if classif output is a probability matrix, adapt.
-          if (task == "classification")
-            mean(y1 == y2)
-          else
-            atan(1.0 / (mean(abs(y1 - y2) + 0.01))) #experimental...
-        }
-      }
-      private$quality <- quality
+      if (is.null(loss))
+        loss <- private$defaultLoss
+      private$loss <- loss
     },
     #' @description Fit an agghoo model.
     #' @param CV List describing cross-validation to run. Slots:
@@ -40,113 +32,84 @@ AgghooCV <- R6::R6Class("AgghooCV",
     #'            (irrelevant for V-fold). Default: 0.2.
     #'          - shuffle: wether or not to shuffle data before V-fold.
     #'            Irrelevant for Monte-Carlo; default: TRUE
-    #' @param mode "agghoo" or "standard" (for usual cross-validation)
     fit = function(
       CV = list(type = "MC",
                 V = 10,
                 test_size = 0.2,
-                shuffle = TRUE),
-      mode="agghoo"
+                shuffle = TRUE)
     ) {
       if (!is.list(CV))
         stop("CV: list of type, V, [test_size], [shuffle]")
       n <- nrow(private$data)
-      shuffle_inds <- NA
+      shuffle_inds <- NULL
       if (CV$type == "vfold" && CV$shuffle)
         shuffle_inds <- sample(n, n)
-      if (mode == "agghoo") {
-        vperfs <- list()
-        for (v in 1:CV$V) {
-          test_indices <- private$get_testIndices(CV, v, n, shuffle_inds)
-          vperf <- private$get_modelPerf(test_indices)
-          vperfs[[v]] <- vperf
-        }
-        private$run_res <- vperfs
-      }
-      else {
-        # Standard cross-validation
-        best_index = 0
-        best_perf <- -1
-        for (p in 1:private$gmodel$nmodels) {
-          tot_perf <- 0
-          for (v in 1:CV$V) {
-            test_indices <- private$get_testIndices(CV, v, n, shuffle_inds)
-            perf <- private$get_modelPerf(test_indices, p)
-            tot_perf <- tot_perf + perf / CV$V
-          }
-          if (tot_perf > best_perf) {
-            # TODO: if ex-aequos: models list + choose at random
-            best_index <- p
-            best_perf <- tot_perf
+      # Result: list of V predictive models (+ parameters for info)
+      private$pmodels <- list()
+      for (v in seq_len(CV$V)) {
+        # Prepare train / test data and target, from full dataset.
+        # dataHO: "data Hold-Out" etc.
+        test_indices <- private$get_testIndices(CV, v, n, shuffle_inds)
+        dataHO <- private$data[-test_indices,]
+        testX <- private$data[test_indices,]
+        targetHO <- private$target[-test_indices]
+        testY <- private$target[test_indices]
+        # [HACK] R will cast 1-dim matrices into vectors:
+        if (!is.matrix(dataHO) && !is.data.frame(dataHO))
+          dataHO <- as.matrix(dataHO)
+        if (!is.matrix(testX) && !is.data.frame(testX))
+          testX <- as.matrix(testX)
+        best_model <- NULL
+        best_error <- Inf
+        for (p in seq_len(private$gmodel$nmodels)) {
+          model_pred <- private$gmodel$get(dataHO, targetHO, p)
+          prediction <- model_pred(testX)
+          error <- private$loss(prediction, testY)
+          if (error <= best_error) {
+            newModel <- list(model=model_pred, param=private$gmodel$getParam(p))
+            if (error == best_error)
+              best_model[[length(best_model)+1]] <- newModel
+            else {
+              best_model <- list(newModel)
+              best_error <- error
+            }
           }
         }
-        best_model <- private$gmodel$get(private$data, private$target, best_index)
-        private$run_res <- list( list(model=best_model, perf=best_perf) )
+        # Choose a model at random in case of ex-aequos
+        private$pmodels[[v]] <- best_model[[ sample(length(best_model),1) ]]
       }
     },
     #' @description Predict an agghoo model (after calling fit())
     #' @param X Matrix or data.frame to predict
-    #' @param weight "uniform" (default) or "quality" to weight votes or
-    #'               average models performances (TODO: bad idea?!)
-    predict = function(X, weight="uniform") {
+    predict = function(X) {
       if (!is.matrix(X) && !is.data.frame(X))
         stop("X: matrix or data.frame")
-      if (!is.list(private$run_res)) {
+      if (!is.list(private$pmodels)) {
         print("Please call $fit() method first")
-        return
-      }
-      V <- length(private$run_res)
-      if (V == 1)
-        # Standard CV:
-        return (private$run_res[[1]]$model(X))
-      # Agghoo:
-      if (weight == "uniform")
-        weights <- rep(1 / V, V)
-      else {
-        perfs <- sapply(private$run_res, function(item) item$perf)
-        perfs[perfs < 0] <- 0 #TODO: show a warning (with count of < 0...)
-        total_weight <- sum(perfs) #TODO: error if total_weight == 0
-        weights <- perfs / total_weight
+        return (invisible(NULL))
       }
+      V <- length(private$pmodels)
+      if (length(private$pmodels[[1]]$model(X[1,])) >= 2)
+        # Soft classification:
+        return (Reduce("+", lapply(private$pmodels, function(m) m$model(X))) / V)
       n <- nrow(X)
-      # TODO: detect if output = probs matrix for classif (in this case, adapt?)
-      # prediction agghoo "probabiliste" pour un nouveau x :
-      # argMax({ predict(m_v, x), v in 1..V }) ...
-      if (private$task == "classification") {
-        votes <- as.list(rep(NA, n))
-        parse_numeric <- FALSE
-      }
-      else
-        preds <- matrix(0, nrow=n, ncol=V)
-      for (v in 1:V) {
-        predictions <- private$run_res[[v]]$model(X)
-        if (private$task == "regression")
-          preds <- cbind(preds, weights[v] * predictions)
-        else {
-          if (!parse_numeric && is.numeric(predictions))
-            parse_numeric <- TRUE
-          for (i in 1:n) {
-            if (!is.list(votes[[i]]))
-              votes[[i]] <- list()
-            index <- as.character(predictions[i])
-            if (is.null(votes[[i]][[index]]))
-              votes[[i]][[index]] <- 0
-            votes[[i]][[index]] <- votes[[i]][[index]] + weights[v]
-          }
-        }
-      }
+      all_predictions <- as.data.frame(matrix(nrow=n, ncol=V))
+      for (v in 1:V)
+        all_predictions[,v] <- private$pmodels[[v]]$model(X)
       if (private$task == "regression")
-        return (rowSums(preds))
-      res <- c()
-      for (i in 1:n) {
-        # TODO: if ex-aequos, random choice...
-        ind_max <- which.max(unlist(votes[[i]]))
-        pred_class <- names(votes[[i]])[ind_max]
-        if (parse_numeric)
-          pred_class <- as.numeric(pred_class)
-        res <- c(res, pred_class)
-      }
-      res
+        # Easy case: just average each row
+        rowSums(all_predictions)
+      # "Hard" classification:
+      apply(all_predictions, 1, function(row) {
+        t <- table(row)
+        # Next lines in case of ties (broken at random)
+        tmax <- max(t)
+        sample( names(t)[which(t == tmax)], 1 )
+      })
+    },
+    #' @description Return the list of V best parameters (after calling fit())
+    getParams = function() {
+      lapply(private$pmodels, function(m) m$param)
     }
   ),
   private = list(
@@ -154,51 +117,51 @@ AgghooCV <- R6::R6Class("AgghooCV",
     target = NULL,
     task = NULL,
     gmodel = NULL,
-    quality = NULL,
-    run_res = NULL,
+    loss = NULL,
+    pmodels = NULL,
     get_testIndices = function(CV, v, n, shuffle_inds) {
       if (CV$type == "vfold") {
+        # Slice indices (optionnally shuffled)
         first_index = round((v-1) * n / CV$V) + 1
         last_index = round(v * n / CV$V)
         test_indices = first_index:last_index
-        if (CV$shuffle)
+        if (!is.null(shuffle_inds))
           test_indices <- shuffle_inds[test_indices]
       }
       else
+        # Monte-Carlo cross-validation
         test_indices = sample(n, round(n * CV$test_size))
       test_indices
     },
-    get_modelPerf = function(test_indices, p=0) {
-      getOnePerf <- function(p) {
-        model_pred <- private$gmodel$get(dataHO, targetHO, p)
-        prediction <- model_pred(testX)
-        perf <- private$quality(prediction, testY)
-        list(model=model_pred, perf=perf)
-      }
-      dataHO <- private$data[-test_indices,]
-      testX <- private$data[test_indices,]
-      targetHO <- private$target[-test_indices]
-      testY <- private$target[test_indices]
-      # R will cast 1-dim matrices into vectors:
-      if (!is.matrix(dataHO) && !is.data.frame(dataHO))
-        dataHO <- as.matrix(dataHO)
-      if (!is.matrix(testX) && !is.data.frame(testX))
-        testX <- as.matrix(testX)
-      if (p >= 1)
-        # Standard CV: one model at a time
-        return (getOnePerf(p)$perf)
-      # Agghoo: loop on all models
-      best_model = NULL
-      best_perf <- -1
-      for (p in 1:private$gmodel$nmodels) {
-        model_perf <- getOnePerf(p)
-        if (model_perf$perf > best_perf) {
-          # TODO: if ex-aequos: models list + choose at random
-          best_model <- model_perf$model
-          best_perf <- model_perf$perf
+    defaultLoss = function(y1, y2) {
+      if (private$task == "classification") {
+        if (is.null(dim(y1)))
+          # Standard case: "hard" classification
+          mean(y1 != y2)
+        else {
+          # "Soft" classification: predict() outputs a probability matrix
+          # In this case "target" could be in matrix form.
+          if (!is.null(dim(y2)))
+            mean(rowSums(abs(y1 - y2)))
+          else {
+            # Or not: y2 is a "factor".
+            y2 <- as.character(y2)
+            # NOTE: the user should provide target in matrix form because
+            # matching y2 with columns is rather inefficient!
+            names <- colnames(y1)
+            positions <- list()
+            for (idx in seq_along(names))
+              positions[[ names[idx] ]] <- idx
+            mean(vapply(
+              seq_along(y2),
+              function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])),
+              0))
+          }
         }
       }
-      list(model=best_model, perf=best_perf)
+      else
+        # Regression
+        mean(abs(y1 - y2))
     }
   )
 )