update progress bar, documentation

Author: hbaniecki

NEWS.md

@@ -1,6 +1,7 @@
 # modelStudio 2.1.0
-* **DEFAULTS CHANGES**: if `new_observation = NULL` then choose `new_observation_n = 3` observations, evenly spread by the histogram bins of `y_hat`. This shall always include the observations, which ids are `which.min(y_hat)` and `which.max(y_hat)`. Additionally, improve the observation dropdown text in dashboard. [(#94)](https://github.com/ModelOriented/modelStudio/issues/94)
-* This version requires `DALEX v2.0.1`
+* **DEFAULTS CHANGES**: if `new_observation = NULL` then choose `new_observation_n = 3` observations, evenly spread by the order of `y_hat`. This shall always include the observations, which ids are `which.min(y_hat)` and `which.max(y_hat)`. Additionally, improve the observation dropdown text in dashboard. [(#94)](https://github.com/ModelOriented/modelStudio/issues/94)
+* updated the progress printing
+* this version requires `DALEX v2.0.1`
 * added new options to `ms_options`: `ms_subtitle`, `ms_margin_top` and `ms_margin_bottom`
 * added new parameters to `modelStudio()`: `N_fi = 10*N` and `B_fi = B`
 * added new `license` parameter to `modelStudio()` which allows to specify the connection for `readLines()` (e.g. `'LICENSE'`) which will add file contents into the HTML output as a comment

R/modelStudio.R

@@ -203,11 +203,13 @@ modelStudio.explainer <- function(explainer,
   model_type <- explainer$model_info$type
 
   if (!is.null(max_vars)) max_features <- max_vars
-  if (identical(N_fi, numeric(0))) N_fi <- NULL
+  if (is.null(N)) stop("`N` argument must be an integer")
+  #if (identical(N_fi, numeric(0))) N_fi <- NULL
 
   if (is.null(new_observation)) {
-    if (show_info) message("`new_observation` argument is NULL.\n",
-                           "`new_observation_n` observations needed to calculate local explanations are taken from the data.\n")
+    if (show_info) message(paste0("`new_observation` argument is NULL. ",
+                                  "`new_observation_n` observations needed to ",
+                                  "calculate local explanations are taken from the data.\n"))
     ret <- sample_new_observation(explainer, new_observation_n)
     new_observation <- ret[['no']]
     new_observation_y <- ret[['no_y']]
@@ -263,9 +265,11 @@ modelStudio.explainer <- function(explainer,
 
   ## later update progress bar after all explanation functions
   if (show_info) {
+    increment <- ifelse(eda, 1, 0)
+
     pb <- progress_bar$new(
       format = "  Calculating :what \n    Elapsed time: :elapsedfull ETA::eta ", # :percent  [:bar]
-      total = (3*B + 2 + 1)*obs_count + (2*B_fi + 3*B_fi + B_fi) + 1,
+      total = 1 + increment + (3*B + 2 + 1)*obs_count + (2*B_fi + N/30 + N/10) + 2,
       show_after = 0,
       width = 110
     )
@@ -288,59 +292,65 @@ modelStudio.explainer <- function(explainer,
       ingredients::partial_dependence(
           model, data, predict_function, variable_type = "numerical", N = N,
           variable_splits_type=variable_splits_type),
-      "ingredients::partial_dependence (numerical)", show_info, pb, B)
+      "ingredients::partial_dependence (numerical)", show_info, pb, N/30)
     pd_c <- NULL
     ad_n <- calculate(
       ingredients::accumulated_dependence(
           model, data, predict_function, variable_type = "numerical", N = N,
           variable_splits_type=variable_splits_type),
-      "ingredients::accumulated_dependence (numerical)", show_info, pb, 3*B)
+      "ingredients::accumulated_dependence (numerical)", show_info, pb, N/10)
     ad_c <- NULL
   } else if (all(!which_numerical)) {
     pd_n <- NULL
     pd_c <- calculate(
       ingredients::partial_dependence(
           model, data, predict_function, variable_type = "categorical", N = N,
           variable_splits_type=variable_splits_type),
-      "ingredients::partial_dependence (categorical)", show_info, pb, B)
+      "ingredients::partial_dependence (categorical)", show_info, pb, N/30)
     ad_n <- NULL
     ad_c <- calculate(
       ingredients::accumulated_dependence(
           model, data, predict_function, variable_type = "categorical", N = N,
           variable_splits_type=variable_splits_type),
-      "ingredients::accumulated_dependence (categorical)", show_info, pb, 3*B)
+      "ingredients::accumulated_dependence (categorical)", show_info, pb, N/10)
   } else {
     pd_n <- calculate(
       ingredients::partial_dependence(
         model, data, predict_function, variable_type = "numerical", N = N,
         variable_splits_type=variable_splits_type),
-      "ingredients::partial_dependence (numerical)", show_info, pb, B/2)
+      "ingredients::partial_dependence (numerical)", show_info, pb, N/60)
     pd_c <- calculate(
       ingredients::partial_dependence(
         model, data, predict_function, variable_type = "categorical", N = N,
         variable_splits_type=variable_splits_type),
-      "ingredients::partial_dependence (categorical)", show_info, pb, B/2)
+      "ingredients::partial_dependence (categorical)", show_info, pb, N/60)
     ad_n <- calculate(
       ingredients::accumulated_dependence(
         model, data, predict_function, variable_type = "numerical", N = N,
         variable_splits_type=variable_splits_type),
-      "ingredients::accumulated_dependence (numerical)", show_info, pb, 2*B)
+      "ingredients::accumulated_dependence (numerical)", show_info, pb, 2*N/30)
     ad_c <- calculate(
       ingredients::accumulated_dependence(
         model, data, predict_function, variable_type = "categorical", N = N,
         variable_splits_type=variable_splits_type),
-      "ingredients::accumulated_dependence (categorical)", show_info, pb, B)
+      "ingredients::accumulated_dependence (categorical)", show_info, pb, N/30)
   }
 
   fi_data <- prepare_feature_importance(fi, max_features, options$show_boxplot,
                                         attr(loss_function, "loss_name"), ...)
   pd_data <- prepare_partial_dependence(pd_n, pd_c, variables = variable_names)
   ad_data <- prepare_accumulated_dependence(ad_n, ad_c, variables = variable_names)
-  mp_data <- DALEX::model_performance(explainer)$measures
+  mp_ret <- calculate(
+    DALEX::model_performance(explainer),
+    "DALEX::model_performance", show_info, pb, 1)
+  mp_data <- mp_ret$measures
 
   if (eda) {
     #:# fd_data is used by targetVs and residualsVs plots
-    residuals <- DALEX::model_diagnostics(explainer)$residuals
+    md_ret <- calculate(
+      DALEX::model_diagnostics(explainer),
+      "DALEX::model_diagnostics", show_info, pb, 1)
+    residuals <- md_ret$residuals
     fd_data <- prepare_feature_distribution(data, y, variables = variable_names,
                                             residuals = residuals)
     at_data <- prepare_average_target(data, y, variables = variable_names)
@@ -514,6 +524,8 @@ modelStudio.explainer <- function(explainer,
 
   class(model_studio) <- c(class(model_studio), "modelStudio")
 
+  if (show_info) pb$tick(1, tokens = list(what = "..."))
+
   model_studio
 }
 
@@ -650,7 +662,7 @@ sample_new_observation <- function(explainer, new_observation_n = 3) {
     new_observation_n <- dim(explainer$data)[1]
   }
 
-  ids <- unique(quantile(seq_along(y_hat), seq(0, 1, length.out = new_observation_n), type = 4))
+  ids <- unique(round(seq(1, length(y_hat), length.out = new_observation_n)))
   new_observation_ids <- order(y_hat)[ids]
 
   list(no = explainer$data[new_observation_ids,], no_y = explainer$y[new_observation_ids])

README.md

@@ -73,7 +73,7 @@ install.packages("DALEXtra")
 
 Make a studio for the regression `ranger` model on `apartments` data.
 
-<details>
+<details open>
 <summary><strong><em>code</em></strong></summary>
 
 ```r
@@ -105,8 +105,7 @@ new_observation <- test[1:2,]
 rownames(new_observation) <- c("id1", "id2")
 
 # make a studio for the model
-modelStudio(explainer,
-            new_observation)
+modelStudio(explainer, new_observation)
 ```
 
 </details>
@@ -150,8 +149,7 @@ new_observation <- test_matrix[1:2, , drop=FALSE]
 rownames(new_observation) <- c("id1", "id2")
 
 # make a studio for the model
-modelStudio(explainer,
-            new_observation,
+modelStudio(explainer, new_observation,
             options = ms_options(margin_left = 140))
 ```
 
@@ -319,7 +317,7 @@ or with [`r2d3::save_d3_html()`](https://rstudio.github.io/r2d3/articles/publish
 
 If you use `modelStudio`, please cite our [JOSS article](https://joss.theoj.org/papers/10.21105/joss.01798):
 
-```python
+```
 @Article{modelStudio,
   author = {Hubert Baniecki and Przemyslaw Biecek},
   title = {{modelStudio}: Interactive Studio with Explanations for {ML} Predictive Models},