add: custom metrics support,

add: MAVE subspace distance measure

NNSDR/NAMESPACE

@@ -4,6 +4,7 @@ S3method(coef,nnsdr)
 S3method(summary,nnsdr)
 export(dataset)
 export(dist.grassmann)
+export(dist.mave)
 export(dist.subspace)
 export(get.script)
 export(nnsdr)
@@ -12,6 +13,7 @@ export(reinitialize_weights)
 export(reset_optimizer)
 exportClasses(nnsdr)
 import(methods)
+import(reticulate)
 import(stats)
 import(tensorflow)
 importFrom(stats,rbinom)

NNSDR/R/dist_mave.R

@@ -0,0 +1,27 @@
+#' Subspace distance mentioned in [Xia et al, 2002] (first MAVE paper).
+#'
+#' @param A,B Basis matrices (assumed full rank) as representations of elements
+#'  of the Grassmann manifold.
+#' @param is.ortho Boolean to specify if `A` and `B` are semi-orthogonal. If
+#'  false, a QR decomposition is used to orthogonalize both `A` and `B`.
+#'
+#' @seealso
+#'  Y. Xia and H. Tong and W.K. Li and L. Zhu (2002) "An adaptive estimation of
+#'  dimension reduction space" <DOI:10.1111/1467-9868.03411>
+#'
+#' @export
+dist.mave <- function(A, B, is.ortho = FALSE) {
+    if (!is.matrix(A)) A <- as.matrix(A)
+    if (!is.matrix(B)) B <- as.matrix(B)
+
+    if (!is.ortho) {
+        A <- qr.Q(qr(A))
+        B <- qr.Q(qr(B))
+    }
+
+    if (ncol(A) < ncol(B)) {
+        norm((diag(nrow(A)) - tcrossprod(B, B)) %*% A, 'F')
+    } else {
+        norm((diag(nrow(A)) - tcrossprod(A, A)) %*% B, 'F')
+    }
+}

NNSDR/R/nnsdr_class.R

@@ -10,18 +10,20 @@ Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3")
 #' @param add_reduction TODO:
 #' @param hidden_units TODO:
 #' @param activation TODO:
+#' @param output_activation TODO:
 #' @param dropout TODO:
 #' @param loss TODO:
 #' @param optimizer TODO:
 #' @param metrics TODO:
 #' @param trainable_reduction TODO:
 #'
-#' @import tensorflow
+#' @import reticulate tensorflow
 #' @keywords internal
 build.MLP <- function(input_shapes, d, name, add_reduction,
     output_shape        = 1L,
     hidden_units        = 512L,
     activation          = 'relu',
+    output_activation   = NULL,
     dropout             = 0.4,
     loss                = 'MSE',
     optimizer           = 'RMSProp',
@@ -63,9 +65,25 @@ build.MLP <- function(input_shapes, d, name, add_reduction,
         if (dropout > 0)
             out <- K$layers$Dropout(rate = dropout, name = paste0('dropout', i))(out)
     }
-    out <- K$layers$Dense(units = output_shape, name = 'output')(out)
+    out <- K$layers$Dense(units = output_shape, activation = output_activation,
+                          name = 'output')(out)
 
     mlp <- K$models$Model(inputs = inputs, outputs = out, name = name)
+
+    if (!is.null(metrics)) {
+        metrics <- as.list(metrics)
+        for (i in seq_along(metrics)) {
+            metric <- metrics[[i]]
+            if (all(c("nnsdr.metric", name) %in% class(metric))) {
+                metric_fn <- reticulate::py_func(metric(mlp))
+                reticulate::py_set_attr(metric_fn, "__name__", attr(metric, "name"))
+                metrics[[i]] <- metric_fn
+            } else if ("nnsdr.metric" %in% class(metric)) {
+                metrics[[i]] <- NULL    # Drop
+            }
+        }
+    }
+
     mlp$compile(loss = loss, optimizer = optimizer, metrics = metrics)
 
     mlp
@@ -95,18 +113,27 @@ nnsdr <- setRefClass('nnsdr',
             if (is.null(.self$history.opg))
                 return(NULL)
 
-            history <- data.frame(
-                .self$history.opg,
-                model = factor('OPG'),
-                epoch = seq_len(nrow(.self$history.opg))
-            )
-
-            if (!is.null(.self$history.ref))
-                history <- rbind(history, data.frame(
+            if (is.null(.self$history.ref)) {
+                history <- data.frame(
+                    .self$history.opg,
+                    model = factor('OPG')
+                )
+            } else {
+                hist.opg <- data.frame(
+                    .self$history.opg,
+                    model = factor('OPG')
+                )
+                hist.ref <- data.frame(
                     .self$history.ref,
-                    model = factor('Refinement'),
-                    epoch = seq_len(nrow(.self$history.ref))
-                ))
+                    model = factor('Refinement')
+                )
+                # Augment mutualy exclusive columns
+                hist.opg[setdiff(names(hist.ref), names(hist.opg))] <- NA
+                hist.ref[setdiff(names(hist.opg), names(hist.ref))] <- NA
+                # Combine/Bind
+                history <- rbind(hist.opg, hist.ref)
+            }
+            history$epoch <- seq_len(nrow(history))
 
             history
         }