% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/svmsmote.R
\name{step_svmsmote}
\alias{step_svmsmote}
\alias{tidy.step_svmsmote}
\title{Apply SVM-SMOTE Algorithm}
\usage{
step_svmsmote(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  over_ratio = 1,
  neighbors = 5,
  distance = "euclidean",
  m_neighbors = NULL,
  out_step = 0.5,
  indicator_column = NULL,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  id = rand_id("svmsmote")
)
}
\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{m_neighbors}{An integer or \code{NULL}. Number of nearest neighbors, among
all classes, that are used to label each minority support vector as noise,
danger, or safe. Defaults to \code{NULL}, which means \code{2 * neighbors}.}

\item{out_step}{A number. Step size used when extrapolating new examples
away from safe support vectors. Defaults to 0.5.}

\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_svmsmote()} creates a \emph{specification} of a recipe step that generate
new examples of the minority class near the decision boundary using the
support vectors of a fitted support vector machine.
}
\details{
SVM-SMOTE (Support Vector Machine SMOTE) works the same way as SMOTE, except
that instead of generating points around every point of the minority class,
it focuses generation near the decision boundary. A support vector machine is
fitted to the data and the support vectors that belong to the minority class
are used as the base points for generating new examples.

For each minority support vector its nearest neighbors among all classes are
calculated. If all of the neighbors come from a different class the support
vector is labeled noise and is discarded. If more than half of the neighbors
come from a different class the support vector is labeled "danger" and new
points are interpolated between it and its minority-class neighbors. The
remaining support vectors are considered to be in a safe region and new points
are extrapolated away from their minority-class neighbors.

The number of neighbors used for this labeling is controlled by
\code{m_neighbors}, and how far the extrapolated points are placed is controlled by
\code{out_step}.

The support vector machine is always fitted with \code{\link[kernlab:ksvm]{kernlab::ksvm()}} using a
radial basis function kernel (\code{kernel = "rbfdot"}) and a cost of \code{C = 1},
matching the reference implementations of the method. These are not exposed
as arguments because the fitted model is only used to identify which minority
observations are support vectors, and not for prediction, so the sampling
results are relatively insensitive to them.

SMOTE generates new examples of the minority class using nearest neighbors
of these cases. For each existing minority class example, new examples are
created by interpolating between the example and its nearest neighbors. The
number of nearest neighbors used is controlled by the number of neighbors
argument (\code{k} in \code{\link[=smote]{smote()}}, \code{neighbors} in \code{\link[=step_smote]{step_smote()}}), and the number
of new examples generated is controlled by \code{over_ratio}.

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 SVM-SMOTE 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 3 tuning parameters:
\itemize{
\item \code{over_ratio}: Over-Sampling Ratio (type: double, default: 1)
\item \code{neighbors}: # Nearest Neighbors (type: integer, default: 5)
\item \code{m_neighbors}: # Nearest Neighbors (type: integer, default: NULL)
}
}

\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(c("modeldata", "kernlab"))) 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_svmsmote(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

library(ggplot2)

ggplot(circle_example, aes(x, y, color = class)) +
  geom_point() +
  labs(title = "Without SMOTE")

recipe(class ~ x + y, data = circle_example) |>
  step_svmsmote(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With SVM-SMOTE")
\dontshow{\}) # examplesIf}
}
\references{
Nguyen, H. M., Cooper, E. W., and Kamei, K. (2011). Borderline
over-sampling for imbalanced data classification. International Journal of
Knowledge Engineering and Soft Data Paradigms, 3(1), 4-21.
}
\seealso{
\code{\link[=svmsmote]{svmsmote()}} for direct implementation

Other Steps for over-sampling:
\code{\link[=step_adasyn]{step_adasyn()}},
\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_upsample]{step_upsample()}}
}
\concept{Steps for over-sampling}
