% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/adasyn.R
\name{step_adasyn}
\alias{step_adasyn}
\alias{tidy.step_adasyn}
\title{Apply Adaptive Synthetic Algorithm}
\usage{
step_adasyn(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  over_ratio = 1,
  neighbors = 5,
  distance = "euclidean",
  indicator_column = NULL,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("adasyn")
)
}
\arguments{
\item{recipe}{A recipe object. The step will be added to the sequence of
operations for this recipe.}

\item{...}{One or more selector functions to choose which
variable is used to sample the data. See \link[recipes:selections]{recipes::selections}
for more details. The selection should result in \emph{single
factor variable}. For the \code{tidy} method, these are not
currently used.}

\item{role}{Not used by this step since no new variables are created.}

\item{trained}{A logical to indicate if the quantities for preprocessing have
been estimated.}

\item{column}{A character string of the variable name that will
be populated (eventually) by the \code{...} selectors.}

\item{over_ratio}{A numeric value for the ratio of the
minority-to-majority 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 as the majority level.

A named numeric vector can be used instead to give different levels
different targets, for example \code{c(a = 1, b = 0.5)}. The names must be
levels of the outcome and the values are ratios of the majority level,
exactly as in the single-number case. Levels that are not named are left
untouched, as are rows with a missing outcome. Because a vector of targets
is not a single value, supplying one means this argument can no longer be
tuned. See \code{vignette("ratio", package = "themis")} for more details.}

\item{neighbors}{An integer. Number of nearest neighbor that are used
to generate the new examples of the minority class.}

\item{distance}{A character string specifying the distance metric used for
nearest neighbor calculations, defaulting to \code{"euclidean"}. The available
metrics fall into three groups.

\code{"euclidean"}, \code{"cosine"}, and \code{"mahalanobis"} use approximate nearest
neighbors via the RANN package and scale well to large datasets.

\code{"squared_chord"}, \code{"matusita"}, \code{"hellinger"}, and \code{"bhattacharyya"} are
probability-divergence measures that treat each row as a distribution over
the predictors, so they require non-negative values. \code{"hellinger"} and
\code{"bhattacharyya"} further require each row to sum to 1. All four also use
the RANN package and scale well to large datasets.

\code{"manhattan"}, \code{"chebyshev"}, \code{"canberra"}, \code{"soergel"}, \code{"lorentzian"},
\code{"jeffreys"}, \code{"topsoe"}, \code{"jensen-shannon"}, \code{"jensen_difference"},
\code{"taneja"}, and \code{"kumar-johnson"} compute an exact all-pairs distance
matrix. This takes time and memory proportional to the square of the number
of observations in a class, so these are best suited to smaller datasets.
Everything from \code{"canberra"} onwards is a probability divergence requiring
non-negative values, is provided by the philentropy package (which must be
installed separately), and in the case of \code{"jeffreys"}, \code{"taneja"}, and
\code{"kumar-johnson"} requires strictly positive values, since those divide by
individual predictor values.

The probability divergences are meaningful for compositional predictors such
as proportions or counts normalized per observation, and are generally not
appropriate for standardized predictors.}

\item{indicator_column}{A single string or \code{NULL} (the default). If a
string is given, a logical column with that name is added to the output,
marking rows added by the step (\code{TRUE}) vs rows from the original data
(\code{FALSE}).}

\item{skip}{A logical. Should the step be skipped when the recipe is baked by
\code{\link[recipes:bake]{bake()}}? While all operations are baked when \code{\link[recipes:prep]{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 \code{skip = TRUE} as it
may affect the computations for subsequent operations.}

\item{seed}{An integer that will be used as the seed when
applied.}

\item{id}{A character string that is unique to this step to identify it.}
}
\value{
An updated version of \code{recipe} with the new step
added to the sequence of existing steps (if any). For the
\code{tidy} method, a tibble with columns \code{terms} which is
the variable used to sample.
}
\description{
\code{step_adasyn()} creates a \emph{specification} of a recipe step that generates
synthetic positive instances using ADASYN algorithm.
}
\details{
ADASYN is an extension of SMOTE that adaptively generates synthetic minority
class examples based on the local distribution of each minority instance.
Instead of generating the same number of synthetic examples for every
minority instance, ADASYN generates more synthetic examples for instances
that are harder to learn, specifically those surrounded by more majority
class neighbors. This focuses synthetic data generation on the difficult
boundary regions of the minority class, resulting in a more informative
augmentation than standard SMOTE.

All columns in the data are sampled and returned by \code{\link[recipes:juice]{recipes::juice()}}
and \code{\link[recipes:bake]{recipes::bake()}}.

All columns used in this step must be numeric with no missing data.

When used in modeling, users should strongly consider using the
option \code{skip = TRUE} so that the extra sampling is \emph{not}
conducted outside of the training set.
}
\section{Minimum observations}{
Each minority class must have at least \code{neighbors + 1} observations to
perform the ADASYN algorithm.
}

\section{Tidying}{
When you \code{\link[recipes:tidy.recipe]{tidy()}} this step, a tibble is returned with
columns \code{terms} and \code{id}:

\describe{
\item{terms}{character, the selectors or variables selected}
\item{id}{character, id of this step}
}
}

\section{Tuning Parameters}{
This step has 2 tuning parameters:
\itemize{
\item \code{over_ratio}: Over-Sampling Ratio (type: double, default: 1)
\item \code{neighbors}: # Nearest Neighbors (type: integer, default: 5)
}
}

\section{Case weights}{


The underlying operation does not allow for case weights. Supplying data
with a case weights column to this step results in an error.
}

\examples{
\dontshow{if (rlang::is_installed("modeldata")) withAutoprint(\{ # examplesIf}
library(recipes)
library(modeldata)
data(hpc_data)

hpc_data0 <- hpc_data |>
  select(-protocol, -day)

orig <- count(hpc_data0, class, name = "orig")
orig

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

# Since `skip` defaults to TRUE, baking the step has no effect
baked <- up_rec |>
  bake(new_data = hpc_data0) |>
  count(class, name = "baked")
baked

# 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")

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")
\dontshow{\}) # examplesIf}
}
\references{
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.
}
\seealso{
\code{\link[=adasyn]{adasyn()}} for direct implementation

Other Steps for over-sampling:
\code{\link[=step_bsmote]{step_bsmote()}},
\code{\link[=step_kmeans_smote]{step_kmeans_smote()}},
\code{\link[=step_rose]{step_rose()}},
\code{\link[=step_smogn]{step_smogn()}},
\code{\link[=step_smote]{step_smote()}},
\code{\link[=step_smoten]{step_smoten()}},
\code{\link[=step_smotenc]{step_smotenc()}},
\code{\link[=step_svmsmote]{step_svmsmote()}},
\code{\link[=step_upsample]{step_upsample()}}
}
\concept{Steps for over-sampling}
