% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nearmiss.R
\name{step_nearmiss}
\alias{step_nearmiss}
\alias{tidy.step_nearmiss}
\title{Remove Points Near Other Classes}
\usage{
step_nearmiss(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  column = NULL,
  under_ratio = 1,
  neighbors = 5,
  distance = "euclidean",
  version = 1,
  n_neighbors_ver3 = 3,
  skip = TRUE,
  seed = sample.int(10^5, 1),
  distance_with = recipes::all_predictors(),
  id = rand_id("nearmiss")
)
}
\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{under_ratio}{A numeric value for the ratio of the
majority-to-minority frequencies. The default value (1) means
that all other levels are sampled down to have the same
frequency as the least occurring level. A value of 2 would mean
that the majority levels will have (at most) (approximately)
twice as many rows than the minority level.

A named numeric vector can be used instead to give different levels
different targets, for example \code{c(a = 2, b = 3)}. The names must be levels
of the outcome and the values are ratios of the minority 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{version}{An integer. Which of the three NearMiss variants to use,
\code{1}, \code{2}, or \code{3}. Defaults to \code{1}. See the details section.}

\item{n_neighbors_ver3}{An integer. The number of nearest neighbors used
to build the candidate pool of the NearMiss-3 variant. Only used when
\code{version = 3}. Defaults to \code{3}.}

\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{distance_with}{A call to a selector function to choose
which variables are used for distance calculations. Defaults to
\code{\link[recipes:all_predictors]{recipes::all_predictors()}}. The variable selected by \code{...} is
always excluded from the distance calculations.}

\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_nearmiss()} creates a \emph{specification} of a recipe step that removes
majority class instances by undersampling points in the majority class based
on their distance to points in the minority class.
}
\details{
The \code{version} argument selects between the three NearMiss variants:

\describe{
\item{\code{version = 1}}{Retains the points from the majority class which
have the smallest mean distance to their nearest points in the minority
class.}
\item{\code{version = 2}}{Retains the points from the majority class which
have the smallest mean distance to their farthest points in the minority
class.}
\item{\code{version = 3}}{Works in two stages. First, the \code{n_neighbors_ver3}
nearest majority class neighbors of each minority class point form a
candidate pool, and all other majority class points are removed. Then the
points of that pool which have the largest mean distance to their nearest
minority class points are retained.}
}

Since the size of the NearMiss-3 candidate pool is governed by
\code{n_neighbors_ver3} rather than by \code{under_ratio}, the pool can be smaller
than the target set by \code{under_ratio}. The whole pool is then retained and
the target is not reached.

With more than two classes, the mean distance is computed to the nearest
points across all other classes, not only the minority class. This differs
from imbalanced-learn, which measures distance to the minority class only.
The binary case, the primary intended use, is unaffected.

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 selected by \code{distance_with} 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 NearMiss 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{under_ratio}: Under-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 majority levels down to about 1000 each
  # 1000/259 is approx 3.862
  step_nearmiss(class, under_ratio = 3.862) |>
  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 fewer rows than the
# target n (= ratio * minority_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 NEARMISS") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))

recipe(class ~ x + y, data = circle_example) |>
  step_nearmiss(class) |>
  prep() |>
  bake(new_data = NULL) |>
  ggplot(aes(x, y, color = class)) +
  geom_point() +
  labs(title = "With NEARMISS") +
  xlim(c(1, 15)) +
  ylim(c(1, 15))
\dontshow{\}) # examplesIf}
}
\references{
Inderjeet Mani and I Zhang. knn approach to unbalanced data
distributions: a case study involving information extraction. In Proceedings
of workshop on learning from imbalanced datasets, 2003.
}
\seealso{
\code{\link[=nearmiss]{nearmiss()}} for direct implementation

Other Steps for under-sampling:
\code{\link[=step_cluster_centroids]{step_cluster_centroids()}},
\code{\link[=step_cnn]{step_cnn()}},
\code{\link[=step_downsample]{step_downsample()}},
\code{\link[=step_enn]{step_enn()}},
\code{\link[=step_instance_hardness]{step_instance_hardness()}},
\code{\link[=step_ncl]{step_ncl()}},
\code{\link[=step_oss]{step_oss()}},
\code{\link[=step_tomek]{step_tomek()}}
}
\concept{Steps for under-sampling}
