FeatureObject: Create a Feature Object

In flacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems

Description

Create a FeatureObject, which will be used as input for all the feature computations.

Usage

createFeatureObject(
  init,
  X,
  y,
  fun,
  minimize,
  lower,
  upper,
  blocks,
  objective,
  force = FALSE
)

Arguments

init	[data.frame] A data.frame, which can be used as initial design. If not provided, it will be created either based on the initial sample X and the objective values y or X and the function definition fun.
X	[data.frame or matrix] A data.frame or matrix containing the initial sample. If not provided, it will be extracted from init.
y	[numeric or integer] A vector containing the objective values of the initial design. If not provided, it will be extracted from init.
fun	[function] A function, which allows the computation of the objective values. If it is not provided, features that require additional function evaluations, can't be computed.
minimize	[logical(1)] Should the objective function be minimized? The default is TRUE.
lower	[numeric or integer] The lower limits per dimension.
upper	[numeric or integer] The upper limits per dimension.
blocks	[integer] The number of blocks per dimension.
objective	[character(1)] The name of the feature, which contains the objective values. The default is "y".
force	[logical(1)] Only change this parameter IF YOU KNOW WHAT YOU ARE DOING! Per default (force = FALSE), the function checks whether the total number of cells that you are trying to generate, is below the (hard-coded) internal maximum of 25,000 cells. If you set this parameter to TRUE, you agree that you want to exceed that internal limit. Note: *Exploratory Landscape Analysis (ELA)* is only useful when you are limited to a small budget (i.e., a small number of function evaluations) and in such scenarios, the number of cells should also be kept low!

Value

[FeatureObject].

Examples

# (1a) create a feature object using X and y:
X = createInitialSample(n.obs = 500, dim = 3,
  control = list(init_sample.lower = -10, init_sample.upper = 10))
y = apply(X, 1, function(x) sum(x^2))
feat.object1 = createFeatureObject(X = X, y = y, 
  lower = -10, upper = 10, blocks = c(5, 10, 4))

# (1b) create a feature object using X and fun:
feat.object2 = createFeatureObject(X = X, 
  fun = function(x) sum(sin(x) * x^2),
  lower = -10, upper = 10, blocks = c(5, 10, 4))

# (1c) create a feature object using a data.frame:
feat.object3 = createFeatureObject(iris[,-5], blocks = 5, 
  objective = "Petal.Length")

# (2) have a look at the feature objects:
feat.object1
feat.object2
feat.object3

# (3) now, one could calculate features
calculateFeatureSet(feat.object1, "ela_meta")
calculateFeatureSet(feat.object2, "cm_grad")
library(plyr)
calculateFeatureSet(feat.object3, "cm_angle", control = list(cm_angle.show_warnings = FALSE))

flacco documentation built on April 1, 2020, 1:06 a.m.