simulated_annealing: Feature selector that performs simmulated annealing to select...

In UBC-MDS/feature-selection-r: Feature Selection

Description

Algorithm randomly chooses a set of features, trains on them, scores the model. Then the algorithm slightly modifies the chosen features randomly and tests to see if the model improves. If there is improvement, the newer model is kept, if not the algorithm tests to see if the worse model is still kept based on a acceptance probability that decreases as iterations continue and if the model performs worse.

Usage

simulated_annealing(
  scorer,
  X,
  y,
  c = 1,
  iterations = 100,
  bools = FALSE,
  random_state = NULL
)

Arguments

scorer	A custom user-supplied function that accepts X and y (as defined below) as input and returns the error of the datasets.
X	data frame of training features
y	data frame of training targets
c	control rate of feature perturbation
iterations	number of iterations
bools	If true function returns array of boolean values instead of column indicies
random_state	Seed for random number generators

Value

array of selected features

Examples

custom_scorer_fn <- function(data) {
 model <- lm(Y ~ ., data)
 return(mean(model$residuals^2))
}
df <- tgp::friedman.1.data()
data <- dplyr::select(df, -Ytrue)
X <- data[1:(length(data)-1)]
y <- data[length(data)]
features <- featureselection::simulated_annealing(custom_scorer_fn, X, y)

UBC-MDS/feature-selection-r documentation built on April 27, 2020, 7:21 p.m.