Apply Adaptive Synthetic AlgorithmSource:
step_adasyn creates a specification of a recipe
step that generates synthetic positive instances using ADASYN algorithm.
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose which variable is used to sample the data. See
selections()for more details. The selection should result in single factor variable. For the
tidymethod, these are not currently used.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of the variable name that will be populated (eventually) by the
A numeric value for the ratio of the majority-to-minority frequencies. The default value (1) means that all other levels are sampled up to have the same frequency as the most occurring level. A value of 0.5 would mean that the minority levels will have (at most) (approximately) half as many rows than the majority level.
An integer. Number of nearest neighbor that are used to generate the new examples of the minority class.
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when
prep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using
skip = TRUEas it may affect the computations for subsequent operations.
An integer that will be used as the seed when applied.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step
added to the sequence of existing steps (if any). For the
tidy method, a tibble with columns
terms which is
the variable used to sample.
All columns used in this step must be numeric with no missing data.
When used in modeling, users should strongly consider using the
skip = TRUE so that the extra sampling is not
conducted outside of the training set.
tidy() this step, a tibble with columns
(the selectors or variables selected) will be returned.
He, H., Bai, Y., Garcia, E. and Li, S. 2008. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference. pp.1322-1328.
library(recipes) library(modeldata) data(hpc_data) hpc_data0 <- hpc_data %>% select(-protocol, -day) orig <- count(hpc_data0, class, name = "orig") orig #> # A tibble: 4 × 2 #> class orig #> <fct> <int> #> 1 VF 2211 #> 2 F 1347 #> 3 M 514 #> 4 L 259 up_rec <- recipe(class ~ ., data = hpc_data0) %>% # Bring the minority levels up to about 1000 each # 1000/2211 is approx 0.4523 step_adasyn(class, over_ratio = 0.4523) %>% prep() training <- up_rec %>% bake(new_data = NULL) %>% count(class, name = "training") training #> # A tibble: 4 × 2 #> class training #> <fct> <int> #> 1 VF 2211 #> 2 F 1347 #> 3 M 1000 #> 4 L 1000 # Since `skip` defaults to TRUE, baking the step has no effect baked <- up_rec %>% bake(new_data = hpc_data0) %>% count(class, name = "baked") baked #> # A tibble: 4 × 2 #> class baked #> <fct> <int> #> 1 VF 2211 #> 2 F 1347 #> 3 M 514 #> 4 L 259 # Note that if the original data contained more rows than the # target n (= ratio * majority_n), the data are left alone: orig %>% left_join(training, by = "class") %>% left_join(baked, by = "class") #> # A tibble: 4 × 4 #> class orig training baked #> <fct> <int> <int> <int> #> 1 VF 2211 2211 2211 #> 2 F 1347 1347 1347 #> 3 M 514 1000 514 #> 4 L 259 1000 259 library(ggplot2) ggplot(circle_example, aes(x, y, color = class)) + geom_point() + labs(title = "Without ADASYN") recipe(class ~ x + y, data = circle_example) %>% step_adasyn(class) %>% prep() %>% bake(new_data = NULL) %>% ggplot(aes(x, y, color = class)) + geom_point() + labs(title = "With ADASYN")