Adjust base cube classification to extract time series using block strategy

NAMESPACE

@@ -325,7 +325,6 @@ S3method(sits_apply,raster_cube)
 S3method(sits_apply,sits)
 S3method(sits_as_sf,raster_cube)
 S3method(sits_as_sf,sits)
-S3method(sits_bands,base_raster_cube)
 S3method(sits_bands,default)
 S3method(sits_bands,patterns)
 S3method(sits_bands,raster_cube)

R/api_check.R

@@ -1766,7 +1766,7 @@
     n_bands <- length(.samples_bands.sits(samples))
     n_times <- length(.samples_timeline(samples))
     if(inherits(samples, "sits_base"))
-        n_bands_base <- length(.samples_bands_base(samples))
+        n_bands_base <- length(.samples_base_bands(samples))
     else
         n_bands_base <- 0
     .check_that(ncol(pred) == 2 + n_bands * n_times + n_bands_base)

R/api_classify.R

@@ -17,7 +17,9 @@
 #' in the classified images for each corresponding year.
 #'
 #' @param  tile            Single tile of a data cube.
-#' @param  band            Band to be produced.
+#' @param  out_band        Band to be produced.
+#' @param  bands           Bands to extract time series
+#' @param  base_bands      Base bands to extract values
 #' @param  ml_model        Model trained by \code{\link[sits]{sits_train}}.
 #' @param  block           Optimized block to be read into memory.
 #' @param  roi             Region of interest.
@@ -29,7 +31,9 @@
 #' @param  progress        Show progress bar?
 #' @return List of the classified raster layers.
 .classify_tile <- function(tile,
-                           band,
+                           out_band,
+                           bands,
+                           base_bands,
                            ml_model,
                            block,
                            roi,
@@ -42,7 +46,7 @@
     # Output file
     out_file <- .file_derived_name(
         tile = tile,
-        band = band,
+        band = out_band,
         version = version,
         output_dir = output_dir
     )
@@ -53,7 +57,7 @@
         }
         probs_tile <- .tile_derived_from_file(
             file = out_file,
-            band = band,
+            band = out_band,
             base_tile = tile,
             labels = .ml_labels_code(ml_model),
             derived_class = "probs_cube",
@@ -105,7 +109,8 @@
         values <- .classify_data_read(
             tile = tile,
             block = block,
-            bands = .ml_bands(ml_model),
+            bands = bands,
+            base_bands = base_bands,
             ml_model = ml_model,
             impute_fn = impute_fn,
             filter_fn = filter_fn
@@ -138,7 +143,7 @@
         # Prepare probability to be saved
         band_conf <- .conf_derived_band(
             derived_class = "probs_cube",
-            band = band
+            band = out_band
         )
         offset <- .offset(band_conf)
         if (.has(offset) && offset != 0) {
@@ -181,7 +186,7 @@
     # Merge blocks into a new probs_cube tile
     probs_tile <- .tile_derived_merge_blocks(
         file = out_file,
-        band = band,
+        band = out_band,
         labels = .ml_labels_code(ml_model),
         base_tile = tile,
         block_files = block_files,
@@ -374,11 +379,12 @@
 #' @param  tile            Input tile to read data.
 #' @param  block           Bounding box in (col, row, ncols, nrows).
 #' @param  bands           Bands to extract time series
+#' @param  base_bands      Base bands to extract values
 #' @param  ml_model        Model trained by \code{\link[sits]{sits_train}}.
 #' @param  impute_fn       Imputation function
 #' @param  filter_fn       Smoothing filter function to be applied to the data.
 #' @return A matrix with values for classification.
-.classify_data_read <- function(tile, block, bands,
+.classify_data_read <- function(tile, block, bands, base_bands,
                                 ml_model, impute_fn, filter_fn) {
     # For cubes that have a time limit to expire (MPC cubes only)
     tile <- .cube_token_generator(tile)
@@ -388,7 +394,7 @@
         tile = tile,
         block = block
     )
-    # Read and preprocess values of each band
+    # Read and preprocess values of each eo band
     values <- purrr::map(bands, function(band) {
         # Get band values (stops if band not found)
         values <- .tile_read_block(
@@ -436,9 +442,23 @@
         # Return values
         return(as.data.frame(values))
     })
+    # Read and preprocess values of each base band
+    values_base <- purrr::map(base_bands, function(band) {
+        # Read and preprocess values of each base band
+        values_base <- .tile_read_block(
+            tile = .tile_base_info(tile),
+            band = band,
+            block = block
+        )
+        # Return values
+        return(as.data.frame(values_base))
+    })
+    # Combine two lists
+    values <- c(values, values_base)
     # collapse list to get data.frame
-    values <- suppressMessages(purrr::list_cbind(values,
-                                                 name_repair = "universal"))
+    values <- suppressMessages(
+        purrr::list_cbind(values, name_repair = "universal")
+    )
     # Compose final values
     values <- as.matrix(values)
     # Set values features name

R/api_cube.R

@@ -131,11 +131,29 @@ NULL
     }
     .cube_set_class(cube)
 }
+#' @title Identity function for data cubes
+#' @keywords internal
+#' @noRd
+#' @name .cube
+#' @param x  cube
+#'
+#' @return data cube object.
 .cube <- function(x) {
     # return the cube
     x
 }
-#' @title Return areas of classes of a class_cue
+#' @title Get base info from a data cube
+#' @keywords internal
+#' @noRd
+#' @name .cube
+#' @param x  cube
+#'
+#' @return data cube from base_info
+.cube_base_info <- function(x) {
+    # return base info data cube
+    dplyr::bind_rows(x[["base_info"]])
+}
+#' @title Return areas of classes of a class_cube
 #' @keywords internal
 #' @noRd
 #' @name .cube_class_areas
@@ -192,7 +210,7 @@ NULL
         class(cube) <- c("raster_cube", class(cube))
         bands <- .cube_bands(cube)
     } else {
-        stop(.conf("messages", "cube_bands"))
+        stop(.conf("messages", ".cube_bands"))
     }
     return(bands)
 }
@@ -203,7 +221,7 @@ NULL
         cube <- tibble::as_tibble(cube)
         bands <- .cube_bands(cube, add_cloud, dissolve)
     } else {
-        stop(.conf("messages", "cube_bands"))
+        stop(.conf("messages", ".cube_bands"))
     }
     return(bands)
 }
@@ -544,6 +562,17 @@ NULL
     }
     return(is_regular)
 }
+
+#' @title Check that cube is a base cube
+#' @name .cube_is_base
+#' @keywords internal
+#' @noRd
+#' @param cube  datacube
+#' @return Called for side effects.
+.cube_is_base <- function(cube) {
+    inherits(cube, "base_raster_cube")
+}
+
 #' @title Find out how many images are in cube during a period
 #' @noRd
 #' @param cube  A data cube.

R/api_data.R

@@ -49,6 +49,10 @@
     } else {
         cld_band <- NULL
     }
+    if (.cube_is_base(cube)) {
+        bands <- setdiff(bands, .cube_bands(.cube_base_info(cube)))
+    }
+
     # define parallelization strategy
     # find block size
     rast <- .raster_open_rast(.tile_path(cube))
@@ -81,9 +85,7 @@
     }
     if (.has(cube[["base_info"]])) {
         # get base info
-        cube_base <- cube[["base_info"]]
-        # bind all base info
-        cube_base <- dplyr::bind_rows(cube_base)
+        cube_base <- .cube_base_info(cube)
         # get bands
         bands_base <- .cube_bands(cube_base)
         # extract data
@@ -97,6 +99,7 @@
         )
         # save base data
         ts_tbl[["base_data"]] <- base_tbl[["time_series"]]
+        # add base class
         class(ts_tbl) <- c("sits_base", class(ts_tbl))
     }
     return(ts_tbl)

R/api_plot_time_series.R

@@ -74,7 +74,7 @@
             # how many time series are to be plotted?
             number <- nrow(data2)
             # what are the band names?
-            bands <- .samples_bands(data2)
+            bands <- .samples_bands(data2, include_base = FALSE)
             # what are the reference dates?
             ref_dates <- .samples_timeline(data2)
             # align all time series to the same dates

R/api_plot_vector.R

@@ -87,7 +87,7 @@
     # verifies if stars package is installed
     .check_require_packages("stars")
     # verifies if tmap package is installed
-    .check_require_packages("plot")
+    .check_require_packages("tmap")
     # precondition - check color palette
     .check_palette(palette)
     # revert the palette

R/api_predictors.R

@@ -19,11 +19,12 @@
     # Get samples time series
     pred <- .ts(samples)
     # By default get bands as the same of first sample
-    bands <- .samples_bands(samples)
+    bands <- .samples_bands(samples, include_base = FALSE)
     # Preprocess time series
     if (.has(ml_model)) {
         # If a model is informed, get predictors from model bands
-        bands <- .ml_bands(ml_model)
+        bands <- intersect(.ml_bands(ml_model), bands)
+
         # Normalize values for old version model classifiers that
         #   do not normalize values itself
         # Models trained after version 1.2 do this automatically before
@@ -49,7 +50,7 @@
             })
         }
     }
-    # Create predictors...
+    # Create predictors
     pred <- pred[c(.pred_cols, bands)]
     # Add sequence 'index' column grouped by 'sample_id'
     pred <- pred |>
@@ -68,36 +69,22 @@
 }
 #' @export
 .predictors.sits_base <- function(samples, ml_model = NULL) {
-    # Get predictors for time series
     # Prune samples time series
     samples <- .samples_prune(samples)
     # Get samples time series
-    pred <- .ts(samples)
-    # By default get bands as the same of first sample
-    bands <- .samples_bands.sits(samples)
-    # Create predictors...
-    pred <- pred[c(.pred_cols, bands)]
-    # Add sequence 'index' column grouped by 'sample_id'
-    pred <- pred |>
-        dplyr::select("sample_id", "label", dplyr::all_of(bands)) |>
-        dplyr::group_by(.data[["sample_id"]]) |>
-        dplyr::mutate(index = seq_len(dplyr::n())) |>
-        dplyr::ungroup()
-    # Rearrange data to create predictors
-    pred <- tidyr::pivot_wider(
-        data = pred, names_from = "index", values_from = dplyr::all_of(bands),
-        names_prefix = if (length(bands) == 1) bands else "",
-        names_sep = ""
-    )
-    # get predictors for base data
-    base <- dplyr::bind_rows(samples$base_data)
-    base <- base[,-1]
-    # join time series predictors with base data predictors
-    pred <- dplyr::bind_cols(pred, base)
+    pred <- .predictors.sits(samples, ml_model)
+    # Get predictors for base data
+    pred_base <- samples |>
+                 dplyr::rename(
+                     "_" = "time_series", "time_series" = "base_data"
+                 ) |>
+                 .predictors.sits() |>
+                 dplyr::select(-.data[["label"]])
+    # Merge predictors
+    pred <- dplyr::inner_join(pred, pred_base, by = "sample_id")
     # Return predictors
     pred
 }
-
 #' @title Get predictors names with timeline
 #' @keywords internal
 #' @noRd
@@ -113,7 +100,6 @@
         USE.NAMES = FALSE
     ))
 }
-
 #' @title Get features from predictors
 #' @keywords internal
 #' @noRd