Adding keras_evaluate function

Author: davidrsch

NAMESPACE

@@ -5,6 +5,7 @@ export(create_keras_sequential_spec)
 export(generic_functional_fit)
 export(generic_sequential_fit)
 export(inp_spec)
+export(keras_evaluate)
 export(keras_losses)
 export(keras_metrics)
 export(keras_optimizers)

R/keras_tools.R

@@ -0,0 +1,34 @@
+#' Evaluate a Kerasnip Model
+#'
+#' This function provides an `kera_evaluate()` method for `model_fit` objects
+#' created by `kerasnip`. It preprocesses the data into the format expected by
+#' Keras and then calls `keras3::evaluate()` on the underlying model.
+#'
+#' @param object A `model_fit` object produced by a `kerasnip` specification.
+#' @param x A data frame or matrix of predictors.
+#' @param y A vector or data frame of outcomes.
+#' @param ... Additional arguments passed on to `keras3::evaluate()`.
+#'
+#' @return A `list` with evaluation results
+#'
+#' @export
+keras_evaluate <- function(object, x, y = NULL, ...) {
+  # 1. Preprocess predictor data (x)
+  x_processed <- process_x(x)
+  x_proc <- x_processed$x_proc
+
+  # 2. Preprocess outcome data (y)
+  y_proc <- NULL
+  if (!is.null(y)) {
+    y_processed <- process_y(
+      y,
+      is_classification = !is.null(object$fit$lvl),
+      class_levels = object$fit$lvl
+    )
+    y_proc <- y_processed$y_proc
+  }
+
+  # 3. Call the underlying Keras evaluate method
+  keras_model <- object$fit$fit
+  keras3::evaluate(keras_model, x = x_proc, y = y_proc, ...)
+}