clean docs

R/modelStudio.R

@@ -60,7 +60,7 @@
 #' library("DALEX")
 #' library("modelStudio")
 #'
-#' #:# ex1 classification on 'titanic_imputed' dataset
+#' #:# ex1 classification on 'titanic' data
 #'
 #' # fit a model
 #' model_titanic <- glm(survived ~., data = titanic_imputed, family = "binomial")
@@ -82,7 +82,7 @@
 #'
 #' \donttest{
 #'
-#' #:# ex2 regression on 'apartments' dataset
+#' #:# ex2 regression on 'apartments' data
 #' library("ranger")
 #'
 #' model_apartments <- ranger(m2.price ~. ,data = apartments)
@@ -113,13 +113,11 @@
 #' #:# ex3 xgboost model on 'HR' dataset
 #' library("xgboost")
 #'
-#' # fit a model
 #' HR_matrix <- model.matrix(status == "fired" ~ . -1, HR)
 #'
+#' # fit a model
 #' xgb_matrix <- xgb.DMatrix(HR_matrix, label = HR$status == "fired")
-#'
-#' params <- list(max_depth = 7, objective = "binary:logistic", eval_metric = "auc")
-#'
+#' params <- list(max_depth = 3, objective = "binary:logistic", eval_metric = "auc")
 #' model_HR <- xgb.train(params, xgb_matrix, nrounds = 300)
 #'
 #' # create an explainer for the model
@@ -206,7 +204,7 @@ modelStudio.explainer <- function(explainer,
   if (show_info) {
     pb <- progress_bar$new(
       format = "  Calculating :what \n    Elapsed time: :elapsedfull ETA::eta", # :percent  [:bar]
-      total = (3*B + 2 + 1)*obs_count + (B + 3*B + B) + 1,
+      total = (3*B + 2 + 1)*obs_count + (2*B + 3*B + B) + 1,
       show_after = 0
     )
     pb$tick(0, tokens = list(what = "..."))
@@ -216,7 +214,7 @@ modelStudio.explainer <- function(explainer,
   fi <- calculate(
     ingredients::feature_importance(
         model, data, y, predict_function, variables = variable_names, B = B, N = 10*N),
-    "ingredients::feature_importance", show_info, pb, B)
+    "ingredients::feature_importance", show_info, pb, 2*B)
 
   which_numerical <- which_variables_are_numeric(data)
 

README.md

@@ -130,7 +130,7 @@ test_matrix <- model.matrix(survived ~.-1, test)
 
 # fit a model
 xgb_matrix <- xgb.DMatrix(train_matrix, label = train$survived)
-params <- list(max_depth = 7, objective = "binary:logistic", eval_metric = "auc")
+params <- list(max_depth = 3, objective = "binary:logistic", eval_metric = "auc")
 model <- xgb.train(params, xgb_matrix, nrounds = 500)
 
 # create an explainer for the model

vignettes/ms-perks-features.Rmd

@@ -27,7 +27,7 @@ Let's use `HR` dataset to explore `modelStudio` parameters:
 
 ```{r results="hide"}
 train <- DALEX::HR
-train$fired <- ifelse(train$status == "fired", 1, 0)
+train$fired <- as.factor(ifelse(train$status == "fired", 1, 0))
 train$status <- NULL
 
 head(train)
@@ -42,7 +42,7 @@ Prepare `HR_test` data and a `ranger` model for the explainer:
 ```{r results="hide", eval = FALSE}
 # fit a ranger model
 library("ranger")
-model <- ranger(fired ~., data = train)
+model <- ranger(fired ~., data = train, probability = TRUE)
 
 # prepare validation dataset
 test <- DALEX::HR_test[1:1000,]
@@ -189,7 +189,7 @@ modelStudio(explainer,
 
 --------------------------------------------------------------------
 
-## plot options
+## additional options
 
 Customize some of `modelStudio` looks by overwriting default options returned
 by the `ms_options()` function. Full list of options:
@@ -212,8 +212,44 @@ modelStudio(explainer,
             options = new_options)
 ```
 
+All visual options can be changed after the calculations using `ms_update_options()`.
+
+```{r eval = FALSE}
+old_ms <- modelStudio(explainer)
+old_ms
+
+# update the options
+new_ms <- ms_update_options(old_ms,
+                            time = 0,
+                            facet_dim = c(1,2),
+                            margin_left = 150)
+new_ms
+```
+
 -------------------------------------------------------------------
 
+## update observations
+
+Use `ms_update_observations()` to add more observations with their local explanations to the `modelStudio`.
+
+```{r eval = FALSE}
+old_ms <- modelStudio(explainer)
+old_ms
+
+# add new observations
+plus_ms <- ms_update_observations(old_ms,
+                                  explainer,
+                                  new_observation = test[101:102,])
+plus_ms
+
+# overwrite old observations
+new_ms <- ms_update_observations(old_ms,
+                                 explainer,
+                                 new_observation = test[103:104,],
+                                 overwrite = TRUE)
+new_ms
+```
+
 ## DALEXtra
 
 Use `explain_*()` functions from the [DALEXtra](https://github.com/ModelOriented/DALEXtra)
@@ -225,10 +261,8 @@ library(DALEXtra)
 library(mlr)
 
 # fit a model
-task <- makeRegrTask(id = "task", data = train, target = "fired")
-
-learner <- makeLearner("regr.ranger", predict.type = "response")
-
+task <- makeClassifTask(id = "task", data = train, target = "fired")
+learner <- makeLearner("classif.ranger", predict.type = "prob")
 model <- train(learner, task)
 
 # create an explainer for the model
@@ -238,8 +272,7 @@ explainer_mlr <- explain_mlr(model,
                              label = "mlr")
 
 # make a studio for the model
-modelStudio(explainer_mlr,
-            B = 10)
+modelStudio(explainer_mlr)
 ```
 
 ## References

vignettes/ms-r-python-examples.Rmd

@@ -95,9 +95,7 @@ train$survived <- as.factor(train$survived)
 
 # fit a model
 task <- TaskClassif$new(id = "titanic", backend = train, target = "survived")
-
 learner <- lrn("classif.ranger", predict_type = "prob")
-
 learner$train(task)
 
 # create an explainer for the model
@@ -137,9 +135,7 @@ test_matrix <- model.matrix(survived ~.-1, test)
 
 # fit a model
 xgb_matrix <- xgb.DMatrix(train_matrix, label = train$survived)
-
-params <- list(max_depth = 7, objective = "binary:logistic", eval_metric = "auc")
-
+params <- list(max_depth = 3, objective = "binary:logistic", eval_metric = "auc")
 model <- xgb.train(params, xgb_matrix, nrounds = 500)
 
 # create an explainer for the model
@@ -179,9 +175,8 @@ test <- data[-index,]
 train$survived <- as.factor(train$survived)
 
 # fit a model
-cv <- trainControl(method = "repeatedcv", number = 3, repeats = 10)
-
-model <- train(survived ~ ., data = train, method = "gbm", trControl = cv)
+cv <- trainControl(method = "repeatedcv", number = 3, repeats = 3)
+model <- train(survived ~ ., data = train, method = "gbm", trControl = cv, verbose = FALSE)
 
 # create an explainer for the model
 explainer <- explain(model,
@@ -212,6 +207,7 @@ data <- DALEX::titanic_imputed
 
 # init h2o
 h2o.init()
+h2o.no_progress()
 
 # split the data
 h2o_split <- h2o.splitFrame(as.h2o(data))
@@ -223,12 +219,8 @@ train$survived <- as.factor(train$survived)
 
 # fit a model
 automl <- h2o.automl(y = "survived", training_frame = train, max_runtime_secs = 30)
-
 model <- automl@leader
 
-# stop h2o progress printing
-h2o.no_progress()
-
 # create an explainer for the model
 explainer <- explain_h2o(model,
                          data = test,
@@ -266,7 +258,7 @@ train <- data[index,]
 test <- data[-index,]
 
 # fit a model
-rand_forest() %>%
+model <- rand_forest() %>%
   set_engine("ranger", importance = "impurity") %>%
   set_mode("regression") %>%
   fit(m2.price ~ ., data = train)