% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/instance_hardness_impl.R
\name{instance_hardness}
\alias{instance_hardness}
\title{Remove hard to classify points}
\usage{
instance_hardness(df, var, k = 5, under_ratio = 1, distance = "euclidean")
}
\arguments{
\item{df}{data.frame or tibble. Must have 1 factor variable and remaining
numeric variables.}

\item{var}{Character, name of variable containing factor variable.}

\item{k}{An integer. Number of nearest neighbors used to estimate the
instance hardness of each observation.}

\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{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.}
}
\value{
A data.frame or tibble, depending on type of \code{df}.
}
\description{
Under-samples the majority classes by removing the points that are hardest
to classify.
}
\details{
The instance hardness of each observation is estimated using the
k-Disagreeing Neighbors measure: the proportion of the nearest neighbors
that belong to a different class. Observations that are surrounded by points
of a different class are considered hard to classify. For each majority
class, the hardest observations are removed until the desired \code{under_ratio}
is reached.

All columns used in this function must be numeric with no missing data.
}
\examples{
circle_numeric <- circle_example[, c("x", "y", "class")]

res <- instance_hardness(circle_numeric, var = "class")

res <- instance_hardness(circle_numeric, var = "class", k = 10)

res <- instance_hardness(circle_numeric, var = "class", under_ratio = 1.5)

res <- instance_hardness(circle_numeric, var = "class", distance = "manhattan")
}
\references{
Smith, M. R., Martinez, T., & Giraud-Carrier, C. (2014). An
instance level analysis of data complexity. Machine learning, 95(2),
225-256.
}
\seealso{
\code{\link[=step_instance_hardness]{step_instance_hardness()}} for step function of this method

Other Direct Implementations:
\code{\link[=adasyn]{adasyn()}},
\code{\link[=bsmote]{bsmote()}},
\code{\link[=cluster_centroids]{cluster_centroids()}},
\code{\link[=cnn]{cnn()}},
\code{\link[=enn]{enn()}},
\code{\link[=kmeans_smote]{kmeans_smote()}},
\code{\link[=ncl]{ncl()}},
\code{\link[=nearmiss]{nearmiss()}},
\code{\link[=oss]{oss()}},
\code{\link[=rose]{rose()}},
\code{\link[=smogn]{smogn()}},
\code{\link[=smote]{smote()}},
\code{\link[=smoten]{smoten()}},
\code{\link[=smotenc]{smotenc()}},
\code{\link[=svmsmote]{svmsmote()}},
\code{\link[=tomek]{tomek()}}
}
\concept{Direct Implementations}
