ml_predict process

ml_predict process

.github/workflows/docker-compose-develop.yml

@@ -11,5 +11,5 @@ jobs:
     steps:
       - name: Check out code
         uses: actions/checkout@v4
-      - name: Run docker-compose
-        run: docker-compose up -d
+      - name: Run docker compose
+        run: docker compose up -d

.github/workflows/docker-compose-main.yml

@@ -23,5 +23,5 @@ jobs:
           push: true
           tags: brianpondi/openeocubes:latest
 
-      - name: Run docker-compose
-        run: docker-compose up -d
+      - name: Run docker compose
+        run: docker compose up -d

DESCRIPTION

@@ -26,7 +26,7 @@ Imports:
     gdalcubes,
     rstac
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Collate: 
     'api_process_graphs.R'
     'api_job.R'

R/api.R

@@ -317,6 +317,8 @@ addEndpoint = function() {
   Session$assignProcess(multiply)
   Session$assignProcess(divide)
   Session$assignProcess(evi)
+# assign ml processes
+  Session$assignProcess(ml_predict)
 
 
 }

R/ml-processes.R

@@ -3,4 +3,84 @@
 NULL
 
 
+
+
+#' ml_datacube_schema
+#' @description Return a list with datacube description and schema
+#'
+#' @return datacube list
+ml_datacube_schema <- function() {
+  info <- list(
+    description = "A data cube with the predicted values. It removes the specified dimensions and adds new dimension for the predicted values.",
+    schema = list(type = "object", subtype = "raster-cube")
+  )
+  return(info)
+}
+
+#' ml_model_schema
+#' @description Return a list with datacube description and schema
+#'
+#' @return model list
+ml_model_schema <- function() {
+  info <- list(
+    description = "A ML model that was trained with one of the ML training processes.",
+    schema = list(type = "object", subtype = "mlm-model")
+  )
+  return(info)
+}
+
+#' return objects for the processes
+eo_datacube <- ml_datacube_schema()
+ml_model <- ml_model_schema()
+
+
 #' TO DO : Implement machine learning processes
+#' 
+
+
+#' ml_predict
+ml_predict <- Process$new(
+  id = "ml_predict",
+  description = "Applies a machine learning model to a data cube of input features and returns the predicted values.",
+  categories = as.array("cubes"),
+  summary = "Predict using ML",
+  parameters = list(
+    Parameter$new(
+      name = "data",
+      description = "A data cube with bands.",
+      schema = list(
+        type = "object",
+        subtype = "raster-cube"
+      )
+    ),
+    Parameter$new(
+      name = "model",
+      description = "The current name of the dimension.",
+      schema = ml_model,
+      optional = FALSE
+    ),
+    Parameter$new(
+      name = "dimensions",
+      description = "Zero or more dimensions that will be reduced by the model. Fails with a `DimensionNotAvailable` exception if one of the specified dimensions does not exist.",
+      schema = list(
+        type = list("string", "array")
+      ),
+      optional = FALSE
+    )
+  ),
+  returns = eo_datacube,
+  operation = function(data, model, dimension = NULL) {
+    tryCatch({
+      prediction <- gdalcubes::predict(aoi_cube, model)
+      print(prediction)
+      message("Prediction calculated ....")
+      message(gdalcubes::as_json(prediction))
+
+      return(prediction)
+    },
+    error = function(e){
+      message("Error in prediction: ")
+      message(conditionMessage(e))
+    })
+  }
+)