up param names

marcozanotti · Jan 17, 2024 · 2c642b3 · 2c642b3
1 parent 3259576
commit 2c642b3
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diff --git a/dashboard/R/fit_model.R b/dashboard/R/fit_model.R
@@ -227,7 +227,7 @@ generate_model_spec <- function(method, params) {
 
   } else if (method == "SVM") {
 
-    if (params$boundary == "linear") {
+    if (params$boundary == "Linear") {
       model_spec <- svm_linear(
         mode = "regression",
         cost = !!params$cost,
@@ -238,7 +238,8 @@ generate_model_spec <- function(method, params) {
       model_spec <- svm_rbf(
         mode = "regression",
         cost = !!params$cost,
-        margin = !!params$margin
+        margin = !!params$margin,
+        rbf_sigma = !!params$rbf_sigma
       ) |>
         set_engine("kernlab")
     }
@@ -520,7 +521,7 @@ fit_model_tuning <- function(
   # grid_spec <- generate_grid_spec(method, model_spec, grid_size, seed)
 
   # tuning
-  if (n_folds > 20 | grid_size > 25) {
+  if (n_folds > 10 | grid_size > 25) {
     doFuture::registerDoFuture()
     future::plan(strategy = "multisession", workers = parallelly::availableCores() - 1)
     message("Number of parallel workers: ", future::nbrOfWorkers())
@@ -543,4 +544,3 @@ fit_model_tuning <- function(
   return(wkfl_fit)
 
 }
-
diff --git a/dashboard/R/utils.R b/dashboard/R/utils.R
@@ -9,56 +9,83 @@ set_options <- function() {
       "dl" = c("Feed-Forward", "COMING SOON!"),
       "mix" = c("Feed-Forward AR", "ARIMA-Boost", "Prophet-Boost"),
       "ens" = c("Average", "Weighted Average", "Median"),
-      "stk" = c("Linear Regression", "Elastic Net")
+      "stk" = c("Linear Regression", "Elastic Net"),
+      "tune" = c(
+        "Elastic Net", "MARS", "KNN", "SVM", "Random Forest", "Boosted Trees", "Cubist",
+        "Feed-Forward", "Feed-Forward AR", "ARIMA-Boost", "Prophet-Boost"
+      )
     ),
     tsf.dashboard.methods_params = list(
       "Naive" = NULL,
       "Seasonal Naive" = NULL,
-      "Rolling Average" = c("window_size"),
+      "Rolling Average" = c("window_size") |> purrr::set_names(c("Window Size")),
       "ETS" = c(
         "auto_ets", "error", "trend", "season", "damping",
         "smooth_level", "smooth_trend", "smooth_season"
-      ),
+      ) |> purrr::set_names(c("Auto-ETS", "Error", "Trend", "Seasonality", "Damped Trend", "Alpha", "Beta", "Gamma")),
       "Theta" = NULL,
       "SARIMA" = c(
         "auto_arima", "non_seasonal_ar", "non_seasonal_differences", "non_seasonal_ma",
         "seasonal_ar", "seasonal_differences", "seasonal_ma"
-      ),
-      "TBATS" = c("auto_tbats", "tbats_seasonal_period_1", "tbats_seasonal_period_2", "tbats_seasonal_period_3"),
-      "STLM" = c("auto_stlm", "trend_model", "stlm_seasonal_period_1", "stlm_seasonal_period_2", "stlm_seasonal_period_3"),
+      ) |> purrr::set_names(c("Auto-ARIMA", "p", "d", "q", "P", "D", "Q")),
+      "TBATS" = c("auto_tbats", "tbats_seasonal_period_1", "tbats_seasonal_period_2", "tbats_seasonal_period_3") |>
+        purrr::set_names(c("Auto-TBATS", "Seasonal Period 1", "Seasonal Period 2", "Seasonal Period 3")),
+      "STLM" = c("auto_stlm", "trend_model", "stlm_seasonal_period_1", "stlm_seasonal_period_2", "stlm_seasonal_period_3") |>
+        purrr::set_names(c("Auto-STLM", "Trend Model", "Seasonal Period 1", "Seasonal Period 2", "Seasonal Period 3")),
       "Prophet" = c(
         "auto_prophet", "growth", "logistic_cap", "logistic_floor",
         "changepoint_num", "changepoint_range", "prophet_season",
         "seasonality_yearly", "seasonality_weekly", "seasonality_daily",
         "prior_scale_changepoints", "prior_scale_seasonality", "prior_scale_holidays"
-      ),
+      ) |>
+        purrr::set_names(c(
+          "Auto-Prophet", "Growth", "Logistic Cap", "Logistic Floor",
+          "Changepoints Num", "Changepoints Range", "Seasonality",
+          "Yearly Seasonality", "Weekly Seasonality", "Daily Seasonality",
+          "Changepoint Flexibility", "Seasonality Stength", "Holidays Strength"
+        )),
       "Linear Regression" = NULL,
-      "Elastic Net" = c("penalty", "mixture"),
-      "MARS" = c("num_terms", "prod_degree", "prune_method"),
-      "KNN" = c("neighbors"),
-      "SVM" = c("boundary", "cost", "margin"),
-      "Random Forest" = c("rf_mtry", "rf_trees", "rf_min_n"),
+      "Elastic Net" = c("penalty", "mixture") |> purrr::set_names(c("Penalty", "Mixture")),
+      "MARS" = c("num_terms", "prod_degree", "prune_method") |>
+        purrr::set_names(c("Num Terms", "Interactions Degree", "Prune Method")),
+      "KNN" = c("neighbors") |> purrr::set_names(c("K-neighbors")),
+      "SVM" = c("boundary", "cost", "margin", "rbf_sigma") |>
+        purrr::set_names(c("Boundary Type", "Cost", "Margin", "Sigma")),
+      "Random Forest" = c("rf_mtry", "rf_trees", "rf_min_n") |>
+        purrr::set_names(c("Random Predictors", "Trees", "Min Node Size")),
       "Boosted Trees" = c(
         "boost_method",
         "boost_mtry", "boost_trees", "boost_min_n", "boost_tree_depth",
         "boost_learn_rate", "boost_loss_reduction", "boost_sample_size"
-      ),
-      "Cubist" = c("committees", "cub_neighbors", "max_rules"),
-      "Feed-Forward" = c("ff_hidden_units", "ff_penalty", "ff_epochs", "ff_dropout", "ff_learn_rate"),
+      ) |>
+        purrr::set_names(c(
+          "Boosting Method", "Random Predictors", "Trees", "Min Node Size",
+          "Tree Depth", "Learning Rate", "Min Loss Reduction", "Sample"
+        )),
+      "Cubist" = c("committees", "cub_neighbors", "max_rules") |>
+        purrr::set_names(c("Num Members", "Neighbors", "Max Rules")),
+      "Feed-Forward" = c("ff_hidden_units", "ff_penalty", "ff_epochs", "ff_dropout", "ff_learn_rate") |>
+        purrr::set_names(c("Hidden Units", "Decay", "Epochs", "Dropout", "Learning Rate")),
       "Feed-Forward AR" = c(
         "ffar_non_seasonal_ar", "ffar_seasonal_ar",
         "ffar_hidden_units", "ffar_penalty", "ffar_epochs", "ffar_num_networks"
-      ),
+      ) |> purrr::set_names(c("p", "P", "Hidden Units", "Decay", "Epochs", "Num Networks")),
       "ARIMA-Boost" = c(
         "arima_boost_mtry", "arima_boost_trees", "arima_boost_min_n",
         "arima_boost_tree_depth", "arima_boost_learn_rate", "arima_boost_loss_reduction",
         "arima_boost_sample_size"
-      ),
+      ) |> purrr::set_names(c(
+        "Random Predictors", "Trees", "Min Node Size", "Tree Depth",
+        "Learning Rate", "Min Loss Reduction", "Sample"
+      )),
       "Prophet-Boost" = c(
         "prophet_boost_mtry", "prophet_boost_trees", "prophet_boost_min_n",
         "prophet_boost_tree_depth", "prophet_boost_learn_rate", "prophet_boost_loss_reduction",
         "prophet_boost_sample_size"
-      )
+      ) |> purrr::set_names(c(
+        "Random Predictors", "Trees", "Min Node Size", "Tree Depth",
+        "Learning Rate", "Min Loss Reduction", "Sample"
+      ))
     ),
     tsf.dashboard.transfs = c("log", "boxcox", "norm", "stand", "diff", "sdiff"),
     tsf.dashboard.test_transfs = c("test_log", "test_diff", "test_sdiff"),
@@ -159,7 +186,7 @@ get_default <- function(parameter, return_value = TRUE) {
     "penalty" = 1, "mixture" = 0.5, # Elastic Net
     "num_terms" = 20, "prod_degree" = 1, "prune_method" = "backward", # MARS
     "neighbors" = 5, # KNN
-    "boundary" = "linear", "cost" = 1, "margin" = 0.1, # SVM
+    "boundary" = "Linear", "cost" = 1, "margin" = 0.1, "rbf_sigma" = 0.02, # SVM
     "rf_mtry" = 5, "rf_trees" = 500, "rf_min_n" = 5, # Random Forest
     "boost_method" = "XGBoost", # Boosted Trees
     "boost_mtry" = 5, "boost_trees" = 100, "boost_min_n" = 1, "boost_tree_depth" = 6,

diff --git a/dashboard/test.R b/dashboard/test.R
@@ -209,7 +209,7 @@ input <- list(
   n_folds = 5,
   metric = "RMSE",
   grid_size = 10,
-  tune_xx_rf = c("rf_mtry")
+  tune_xx_rf = c("mtry")
 )
 
 data = data_selected

diff --git a/dashboard/todo.txt b/dashboard/todo.txt
@@ -11,11 +11,10 @@ Next steps:
 - documentazione in alto a destra
 
 To Do:
-- aggiungere rbf_sigma conditional su svm_rbf
 - finire update_tune_model_parameters con mapping parametri (occhio, non mtry ma rf_mtry)
 - testare flusso optimize fino al forecast
-- change split in generate_forecast
 - fare la funzione parse_parameters per ottenere i nomi da mostrare in UI dal parametro
+- cambiare assegnazione nomi ai parametri in UI
 
 - aggiungere metodi di automl (h2o)
 - pensare e aggiungere la sezione di stacking (LM + Elastic Net)

diff --git a/dashboard/tsf_dashboard.Rmd b/dashboard/tsf_dashboard.Rmd
@@ -51,10 +51,11 @@ dl_methods <- methods$dl
 mix_methods <- methods$mix
 ens_methods <- methods$ens
 stk_methods <- methods$stk
+tune_methods <- methods$tune
 methods <- c(ts_methods, ml_methods, dl_methods, mix_methods)
 
-methods_params <- getOption("tsf.dashboard.methods_params")
-methods_params_cl <- methods_params |> map(clean_chr)
+mtd_prm <- getOption("tsf.dashboard.methods_params")
+mtd_prm_names <- purrr::map(mtd_prm, names)
 
 metrics <- toupper(getOption("tsf.dashboard.metrics"))
 ```
@@ -526,7 +527,7 @@ pickerInput(
     `Deep Learning` = dl_methods,
     `Mixed Algorithms` = mix_methods
   ), 
-  selected = ts_methods[1]
+  selected = "Naive"
 )
 
 actionButton(inputId = "apply_forecast", label = "Forecast!", icon = icon("play"))
@@ -537,9 +538,8 @@ observeEvent(
     updateNumericInput(session = session, inputId = "n_future", value = 12)
     updateNumericInput(session = session, inputId = "n_assess", value = 24)
     updatePrettyRadioButtons(session = session, inputId = "assess_type", selected = "Rolling")
-    updatePickerInput(session = session, inputId = "method", selected = ts_methods[1])
-    updateNumericInput(session = session, inputId = "window_size", value = 12)
-    shinyjs::delay(ms = 300, expr = {shinyjs::click(id = "apply_forecast")})
+    updatePickerInput(session = session, inputId = "method", selected = "Naive")
+    # shinyjs::delay(ms = 300, expr = {shinyjs::click(id = "apply_forecast")})
   }
 )
 
@@ -634,8 +634,8 @@ conditionalPanel(
     numericInput(inputId = "logistic_cap", label = "Logistic Cap", value = get_default("logistic_cap"), min = -Inf, max = Inf, step = 1),
     numericInput(inputId = "logistic_floor", label = "Logistic Floor", value = get_default("logistic_floor"), min = -Inf, max = Inf, step = 1)
   ),
-  numericInput(inputId = "changepoint_num", label = "Changepoints (num)", value = get_default("changepoint_num"), min = 0, max = Inf, step = 1),
-  numericInput(inputId = "changepoint_range", label = "Changepoints (range)", value = get_default("changepoint_range"), min = 0, max = 1),
+  numericInput(inputId = "changepoint_num", label = "Changepoints Num", value = get_default("changepoint_num"), min = 0, max = Inf, step = 1),
+  numericInput(inputId = "changepoint_range", label = "Changepoints Range", value = get_default("changepoint_range"), min = 0, max = 1),
   prettyRadioButtons(inputId = "prophet_season", label = "Seasonality", choices = c("additive", "multiplicative"), inline = TRUE, selected = get_default("prophet_season")),
   awesomeCheckbox(inputId = "seasonality_yearly", label = "Yearly Seasonality?", value = get_default("seasonality_yearly")),
   awesomeCheckbox(inputId = "seasonality_weekly", label = "Weekly Seasonality?", value = get_default("seasonality_weekly")),
@@ -680,9 +680,13 @@ conditionalPanel(
 conditionalPanel(
   condition = "input.method == 'SVM'",
   h5("Algorithm hyperparameters: "),
-  prettyRadioButtons(inputId = "boundary", label = "Boundary Type", choices = c("linear", "radial"), inline = TRUE, selected = get_default("boundary")),
+  prettyRadioButtons(inputId = "boundary", label = "Boundary Type", choices = c("Linear", "Radial"), inline = TRUE, selected = get_default("boundary")),
   numericInput(inputId = "cost", label = "Cost", value = get_default("cost"), min = 0, max = Inf),
-  numericInput(inputId = "margin", label = "Margin", value = get_default("margin"), min = 0, max = Inf)
+  numericInput(inputId = "margin", label = "Margin", value = get_default("margin"), min = 0, max = Inf),
+  conditionalPanel(
+    condition = "input.boundary == 'Radial'",
+    numericInput(inputId = "rbf_sigma", label = "Sigma", value = get_default("rbf_sigma"), min = 0, max = Inf)
+  )
 )
 
 # Random Forest
@@ -928,7 +932,7 @@ pickerInput(
     `Deep Learning` = dl_methods,
     `Mixed Algorithms` = mix_methods
   ), 
-  selected = ts_methods[c(4, 6)], options = list("actions-box" = TRUE)
+  selected = c("ETS", "SARIMA"), options = list("actions-box" = TRUE)
 )
 
 br()
@@ -943,7 +947,7 @@ observeEvent(
     updateNumericInput(session = session, inputId = "comp_n_future", value = 12)
     updateNumericInput(session = session, inputId = "comp_n_assess", value = 24)
     updatePrettyRadioButtons(session = session, inputId = "comp_assess_type", selected = "Rolling")
-    updatePickerInput(session = session, inputId = "comp_method", selected = ts_methods[c(4, 6)])
+    updatePickerInput(session = session, inputId = "comp_method", selected = c("ETS", "SARIMA"))
     updateMaterialSwitch(session = session, inputId = "comp_tune", value = FALSE)
     # shinyjs::delay(ms = 300, expr = {shinyjs::click(id = "tune_apply_forecast")})
   }
@@ -1073,12 +1077,11 @@ dropdownButton(
 pickerInput(
   inputId = "tune_method", label = h3("Forecast Algorithm"), multiple = FALSE,
   choices = list(
-    `Time Series` = ts_methods, 
     `Machine Learning` = ml_methods,
     `Deep Learning` = dl_methods,
     `Mixed Algorithms` = mix_methods
   ), 
-  selected = ml_methods[2]
+  selected = "Elastic Net"
 )
 
 actionButton(inputId = "tune_apply_forecast", label = "Forecast!", icon = icon("play"))
@@ -1093,8 +1096,8 @@ observeEvent(
     updateSliderInput(session = session, inputId = "tune_n_folds", value = 5)
     updateSelectInput(session = session, inputId = "tune_metric", selected = "RMSE")
     updateSliderInput(session = session, inputId = "tune_grid_size", value = 10)
-    updateSelectInput(session = session, inputId = "tune_method", selected = ml_methods[2])
-    updatePickerInput(session = session, inputId = "tune_elanet", selected = methods_params_cl[["Elastic Net"]][2])
+    updateSelectInput(session = session, inputId = "tune_method", selected = "Elastic Net")
+    updatePickerInput(session = session, inputId = "tune_elanet", selected = mtd_prm_names[["Elastic Net"]])
     # shinyjs::delay(ms = 300, expr = {shinyjs::click(id = "tune_apply_forecast")})
   }
 )
@@ -1108,8 +1111,20 @@ conditionalPanel(
   h5("Algorithm hyperparameters to optimize: "),
   pickerInput(
     inputId = "tune_xx_elanet", label = NULL, # _xx_ need to recognize it easily in update_tune_model_parameters()
-    choices = methods_params_cl[["Elastic Net"]], multiple = TRUE, 
-    selected = methods_params_cl[["Elastic Net"]][2],
+    choices = mtd_prm_names[["Elastic Net"]], multiple = TRUE, 
+    selected = mtd_prm_names[["Elastic Net"]],
+    options = list("actions-box" = TRUE)
+  )
+)
+
+# Random Forest
+conditionalPanel(
+  condition = "input.tune_method == 'Random Forest'",
+  h5("Algorithm hyperparameters to optimize: "),
+  pickerInput(
+    inputId = "tune_xx_elanet", label = NULL, # _xx_ need to recognize it easily in update_tune_model_parameters()
+    choices = mtd_prm_names[["Random Forest"]], multiple = TRUE, 
+    selected = mtd_prm_names[["Random Forest"]],
     options = list("actions-box" = TRUE)
   )
 )
@@ -1159,7 +1174,7 @@ pickerInput(
     `Deep Learning` = dl_methods,
     `Mixed Algorithms` = mix_methods
   ), 
-  selected = ts_methods[c(4, 6)], multiple = TRUE,
+  selected = c("ETS", "SARIMA"), multiple = TRUE,
   options =  list(
     "actions-box" = FALSE,
     "max-options" = 5,
@@ -1169,13 +1184,13 @@ pickerInput(
 
 pickerInput(
   inputId = "ens_type", label = h3("Ensemble Method"), 
-  choices = ens_methods, selected = ens_methods[1], multiple = TRUE,
+  choices = ens_methods, selected = "Average", multiple = TRUE,
   options =  list("actions-box" = TRUE)
 )
 
 pickerInput(
   inputId = "stk_type", label = h3("Stacking Method"), 
-  choices = stk_methods, selected = stk_methods[1], multiple = TRUE,
+  choices = stk_methods, selected = "Linear Regression", multiple = TRUE,
   options =  list("actions-box" = TRUE)
 )
 
@@ -1191,8 +1206,9 @@ observeEvent(
     updateNumericInput(session = session, inputId = "ens_n_future", value = 12)
     updateNumericInput(session = session, inputId = "ens_n_assess", value = 24)
     updatePrettyRadioButtons(session = session, inputId = "ens_assess_type", selected = "Rolling")
-    updatePickerInput(session = session, inputId = "ens_method", selected = ts_methods[c(4, 6)])
-    updatePickerInput(session = session, inputId = "ens_type", selected = ens_methods[1])
+    updatePickerInput(session = session, inputId = "ens_method", selected = c("ETS", "SARIMA"))
+    updatePickerInput(session = session, inputId = "ens_type", selected = "Average")
+    updatePickerInput(session = session, inputId = "stk_type", selected = "Linear Regression")
     updateMaterialSwitch(session = session, inputId = "ens_tune", value = FALSE)
     # shinyjs::delay(ms = 300, expr = {shinyjs::click(id = "tune_apply_forecast")})
   }
@@ -1311,7 +1327,7 @@ pickerInput(
     `Deep Learning` = dl_methods,
     `Mixed Algorithms` = mix_methods
   ), 
-  selected = ts_methods[4]
+  selected = "ETS"
 )
 
 actionButton(inputId = "scn_apply_forecast", label = "Forecast!", icon = icon("play"))
@@ -1322,7 +1338,7 @@ observeEvent(
     updateNumericInput(session = session, inputId = "scn_n_future", value = 12)
     updateNumericInput(session = session, inputId = "scn_n_assess", value = 24)
     updatePrettyRadioButtons(session = session, inputId = "scn_assess_type", selected = "Rolling")
-    updateSelectInput(session = session, inputId = "scn_method", selected = ts_methods[4])
+    updateSelectInput(session = session, inputId = "scn_method", selected = "ETS")
     # shinyjs::delay(ms = 300, expr = {shinyjs::click(id = "tune_apply_forecast")})
   }
 )