Please note that the Github username that we are using for this project is "yzhou63".
To perform feature selection in regression problems using genetic algorithm, we conduct the following steps sequentially:
Initialization: create the first generation -- generation_t0 (with function "generate_founders")
Evaluatation: assess the fitness of each chromosomes or models, and rank the values of fitness in the population (with function "evaluate_fitness")
Selection, crossover, and mutation: select the more fit "parents" and generate a new generation using possible crossover and mutation (with function "create_next_generation")
Looping and termination: repeat step 2 and 3 until either the maximum number of iterations is reached or the algorithm converges (with the primary function--"select")
We randomly create the first generation with the function "generate_founders". As a generation size of P ranging from C and 2C is recommended for binary codings of chromosomes/predictive models (where C is the length of the chromosome or the number of predictors), we calculate P as 2C. Also, since P is less than 200 in most real applications, we set 200 as the upper bounds of P in this case (if P exceeds 200, we would print a warning to the user). We then randomly sample 1.2*C*P 0's and 1's, splitting them up into the P chromosomes/models (note that we sample 1.2*C*P instead of C*P as we want to have enough data so that each chromosomes/model could be unique). We also add two additional samples to our data, where the first sample has the last design variable as 1 and the other design variables as 0, and the second sample has the last design variable as 0 while the others being 1. In this way, we ensure that each gene/predictor appears in at least one chromosome/model, but not all chromosomes/models. After selecting chromosomes with unique genes or models with unique design variables and ensuring that we have at least one 1's in each choromosome or model, we obtain our first generation--generation_t0.
############## # auxiliary function I ############## # This function will initiative the founding chromosomes # the output is a randomly created first generation # the inputs are: # X is a matrix that contains the predictors in the input dataset # start_chrom is the user-defined size of a generation generate_founders <- function(X, start_chrom) { # number of predictors --------------- C <- dim(X)[2] # number of founders ---------------- if (is.null(start_chrom)) { P <- 2 * C if (P > 200) { #check for the maximum chrom P <- 200 } if (P %% 2 != 0) { #check for even number of parents P <- P - 1 } } else { if (start_chrom > 200) cat("Warning: P > 200, algorithm may require lots of running time") P <- start_chrom #start_chrom is the number of chroms defined by the user } #randomly generate founders ---------------- geneSample <- sample(c(0, 1), replace = TRUE, size = ceiling(1.2 * C * P)) #update geneSample to make sure that each gene/variable #will exist in at least one chrome, but not all. geneSample <- c(rep(0, C - 1), rep(1, C), 0, geneSample) #create a first generation indices <- seq_along(geneSample) first_gen_raw <- split(geneSample, ceiling(indices / C)) generation_t0 <- matrix(unlist(unique(first_gen_raw)[1:P]), ncol = C, byrow = TRUE) generation_t0 <- generation_t0[apply(generation_t0[,], 1, function(x) !all(x == 0)), ] return(generation_t0) }
We then evaluate the fitness of each chromosome/model using the function "evaluate_fitness". We first define an auxiliary function named "rank_objective_function", which takes its inputs as the values of the fitness for each model and whether the user requires minimization. It will return each model's fitness and its corresponding rank as the output. Note that if the user requires the fitness function to be those such as AIC or BIC, then the ranking will be based on the negative of these values, and if functions such as log-likehood is the objective criterion, then their positive values will be used.
############## # auxiliary functions II ############## # This function will give the rank of the fitness of a model # based on the targeted objective function and whether a minimization # or maximization is desired for the objective function # obj_fun_output is a numeric vector containg the objective function output # for each chromosome for ranking # minimize a logical value indicating whether to rank according to # minimization or maximaziation optimization, default is minimize rank_objective_function <- function(obj_fun_output, minimize) { P <- length(obj_fun_output) if (isTRUE(minimize)) { r <- base::rank(-obj_fun_output, na.last = TRUE, ties.method = "first") } else { r <- base::rank(obj_fun_output, na.last = TRUE, ties.method = "first") } return(cbind(chr = 1:P, parent_rank = r, obj_fun_output)) }
We then evaluate the fitness of each model through using the function "evaluate_fitness". We consider several variations depending on the users' inputs: whether our variable selection is based on linear regression or GLM, what we use for our fitness function (AIC, BIC, log-likelihood, a user-defined function, etc.), and whether we would use parallel processing (if we have more than one cores available, then we would use parallel processing). We return the rank and the value of the fitness function as our output for each model.
# This function will evaluate and rank the fitness of each model based on # targeted objective function # the default is a linear model assessed with AIC # the output is the value and rank of the fitness function for each model # the inputs are: # generation_t0 is a matrix of parent chromosomes to be evaluated # the columns correspond to predictors/genes and rows correspond to parents/chromosomes # Y is a vector of response variable # family describes the distribution of errors and the link function used # if family is Gaussian, we will fit the data using lm # otherwise, we will implement the corresponding glm # objective_function is the selected criteria to assess the fitness of a model # nCores is an integer indicating the number of parallel processes # to run when evaluating fitness # rank_objective_function a function that ranks parents by their fitness # as determined by optimize criteria evaluate_fitness <- function(generation_t0, Y, X, family, nCores, minimize, objective_function, rank_objective_function) { #number parent chromosomes P <- dim(generation_t0)[1] ###### #evaluate and rank each chromosome with selected objective function ###### # serial ---------------- if (nCores == 1) { # lm ---------------- if (family == "gaussian") { obj_fun_output <- sapply(1:P, function(i) { mod <- stats::lm(Y ~ X[, generation_t0[i, ] == 1]) return(objective_function(mod)) }) # glm ---------------- } else if(family != "gaussian") { obj_fun_output <- sapply(1:P, function(i) { mod <- stats::glm(Y ~ X[, generation_t0[i, ] == 1], family = family) return(objective_function(mod)) }) } # parallel ---------------- } else if (nCores > 1) { # lm ---------------- if (family == "gaussian") { obj_fun_output <- unlist(parallel::mclapply(1:P, function(i) { mod <- stats::lm(Y ~ X[, generation_t0[i, ] == 1]) return(objective_function(mod)) }, mc.preschedule = TRUE, mc.cores = nCores)) # glm ---------------- } else if(family != "gaussian") { obj_fun_output <- unlist(parallel::mclapply(1:P, function(i) { mod <- stats::glm(Y ~ X[, generation_t0[i, ] == 1], family = family) return(objective_function(mod)) }, mc.preschedule = TRUE, mc.cores = nCores)) } } # rank ---------------- parent_rank <- rank_objective_function(obj_fun_output, minimize) # return rankings ---------------- return(parent_rank) }
In the third step of our algorithm, we create a new generation using selection, crossover and mutation through the corresponding functions and the function "create_next_generation". The population size of a future generation remains the same as the initial generation, so we would still have P individuals or models in the next generation.
We consider several methods for selection, crossover and mutation.
For selection, we select the first parent by the selection probability and the other parent randomly. The probability of selection is calculated as $\phi_{i}=\frac{2 r_{i}}{P(P+1)}$. The algorithm can also implement the user-defined selection method through using match.fun().
A pair of parents has a probability of 0.8 to crossover, and we define three possible methods:
First method: uses three-point crossover, where we choose three non-overlapping points within the length of the chromosome or the number of the design variables in each model. We swap the elements between these points and render two children.
Second method: defines a crossover probability based on the ranks of fitness of each parent, which gives a parent with a higher rank higher probability of passing its genes to the offsprings. Specifically, this crossover probability is a weighted average of the two parents, where the weight for a parent is defined as the proportion of his rank in the summation of the two parents' ranks.
Third method: passes the same variables between parents directly to the offsprings and only considers crossover between the non-concordant variables of parents. Deciding between these non-concordant variables based on a selection probability, which gives a parent with higher rank a higher probability of retaining a variable.
For mutation, we will either use the user-defined mutation probability or the default, which is $\frac{1}{P\sqrt{C}}$.
############## # auxiliary function III ############## # This function will select optimal parents # based on the selection probability # the input is: # parent_rank is a vector indicating the rank of parents chromosomes, # ranking order is inverse: parent chromosomes with lowest fitness rank will # have rank == 1 select_parents <- function(parent_rank) { # get number of chromosomes P <- length(parent_rank) # probability of selection phi <- (2 * parent_rank) / (P * (P + 1)) # select first parent by parent_rank, second random parent1 <- sample(1:P, 1, prob=phi, replace = T) parent2 <- sample((1:P)[-parent1], 1, replace = T) return(c(parent1, parent2)) } # This function defines three crossover methods # the inputs are: # generation_t0 is a matrix of parent chromosomes to be evaluated # the columns correspond to predictors/genes and rows correspond to # parents/chromosomes # parentInd is a vector containing the rank indexes of two parents selected for crossover # crossover_method is a character string indicating which method of crossover to be used # the default is method 1, which is a three-point crossover # pCrossover is a number between 0 and 1 indicating the probability of # crossover for each mate pair, the default is 0.8 # parent_rank is an integer vector of fitness ranks for parent chromosomes crossover_parents <- function(generation_t0, parentInd, crossover_method, pCrossover, parent_rank) { # get parent info parent1 <- generation_t0[parentInd[1], ] parent2 <- generation_t0[parentInd[2], ] C <- length(parent1) parent1r <- parent_rank[parentInd[1]] parent2r <- parent_rank[parentInd[2]] if (stats::rbinom(1, 1, pCrossover) == 1 ) { if (crossover_method == "method1") { #METHOD 1 ---------------- #multipoint crossover: three crossover points cross <- sort(sample(seq(2,(C - 2), 2), 3, replace = F)) child1 <- c(parent1[1:cross[1]], parent2[(cross[1] + 1):cross[2]], parent1[(cross[2] + 1):cross[3]], parent2[(cross[3] + 1):C]) child2 <- c(parent2[1:cross[1]], parent1[(cross[1] + 1):cross[2]], parent2[(cross[2] + 1):cross[3]], parent2[(cross[3] + 1):C]) } else if (crossover_method == "method2") { #METHOD 2 ---------------- #method upweights parent with higher parent_rank high childProb <- parent1 * parent1r[1] / (parent1r + parent2r) + parent2 * parent2r / (parent1r + parent2r) child1 <- stats::rbinom(C, 1, prob = childProb) child2 <- stats::rbinom(C, 1, prob = childProb) } else if (crossover_method == "method3") { #METHOD 3 ---------------- #randomly samples non-concordant variables between parents # slightly upweights parent selected by prob. proportional to parent_rank child1 <- parent1 child2 <- parent2 child1[parent1 != parent2] <- stats::rbinom(sum(parent1 - parent2 != 0), 1, prob = parent1r / (parent1r + parent2r)) child2[parent1 != parent2] <- stats::rbinom(sum(parent1 - parent2 != 0), 1, prob = parent2r / (parent1r + parent2r)) } return(rbind(as.integer(child1), as.integer(child2))) } else { child1 <- parent1 child2 <- parent2 return(rbind(child1, child2)) } } # This function defines the ways of mutation # the mutation will be implemented based on # the user-defined probability or the default # if the user input an invalid probability (not between 0 and 1) # we generate an error message # which is 1/P*(C)^0.5 # mutation_rate is an optional numeric value between 0 # and 1 indicating mutation rate # child is a vector containing the chromosome of child produced by crossover # P is an integer indicating the number of parent chromosomes # C is an integer indicating the number of predictor variables or genes mutate_child <- function(mutation_rate, child, P, C) { if (is.null(mutation_rate)) { return(as.integer(abs(round(child, 0) - stats::rbinom(C, 1, prob = 1 / (P * sqrt(C)))))) } else { if (mutation_rate > 1 | mutation_rate < 0) { stop("Error: mutation_rate must be between 0 and 1") } return(as.integer(abs(round(child, 0) - stats::rbinom(C, 1, prob = mutation_rate)))) } }
We then create each of the P individuals in the next generation through the function "create_next_generation". We ensure that each child/new model should have at least one 1 in their genes/design variables, and the output of the function "create_next_generation" is the next generation "generation_t1".
# This function will create children through different methods of # selection, crossover and mutation # a future generation will be of the same size of the previous generation # the output is the child generation # the inputs are: # obj_fun_output is a numeric vector containg the objective function output # for each chromosome for ranking # select_parents, crossover_parents, and mutate_child # are sub functions defined above # pCrossover is the probability of crossover between the parents # the default is 0.8 # mutation_rate is the probability of mutation between parents # the default is 1/P*(C)^0.5 create_next_generation <- function(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate) { # set variables P <- dim(generation_t0)[1] C <- dim(generation_t0)[2] parent_rank <- obj_fun_output[, 2] #Create matrix for next generation generation_t1 <- matrix(NA, dim(generation_t0)[1], dim(generation_t0)[2]) ######### #Selection, Crossover, and Mutation ######### i <- 1 #initialize while loop while(i <= dim(generation_t1)[1]) { # Selection ---------------- parentInd <- select_parents(parent_rank) # Crossover ---------------- children <- crossover_parents(generation_t0, parentInd, crossover_method, pCrossover, parent_rank) # Mutation ---------------- child1 <- mutate_child(mutation_rate, children[1, ], P, C) child2 <- mutate_child(mutation_rate, children[2, ], P, C) # Check if all zeros ----------------- if (!all(child1 == 0) & !all(child2 == 0)) { generation_t1[c(i, i + 1), ] <- rbind(child1, child2) # update counter i <- i + 2 } } # return new new generation return(generation_t1) }
We at last implementing the genetic algorithm to a dataset to select the most relevant predictors (X) for the response variable (Y) through using our primary function -- "select". We first check whether each input's type is reasonable, then we implement the genetic algorithm in the following steps: 1. we generate the initial population using function "generate_founders". 2. we evaluate the fitness of each model in the initial population using function "evaluate_fitness" and select the optimal parent pairs. 3. we create a child generation through different selection, crossover, and mutation methods. 4. we repeat steps 2 and 3 until either the maximum allowed number of iterations is reached or the result converges to a specific optimum. Note that we define the criteria for convergence as the top 25% in the population having imperceptible difference in their fitness values. Also, users can input their desired maximum number of iterations or use the default of 100.
############## #primary function for package ############## # This function will implement the steps above and continue to # generate future generations until criteria is met # it will also check the reasonableness of the type of each input # the output is the best model selected, its fitness value, # the number of iterations the algorithm takes, whether the # algorithm converges, and the time each iteration takes # the inputs are: # Y is a vector of response variable # X is a matrix or dataframe of predictor variables # family is a character string describing the error distribution # and link function to be used in the model, the default is gaussian # objective_function is a function for computing objective # the default is AIC, user can specify customized function # crossover_parents_function is a function for crossover between mate pairs # user can specify customized function # crossover_method is a character string describing crossover method # the default is a three-point crossover # pCrossover is a numeric value for he probability of crossover for each mate pair # start_chrom a numeric value for the size of the popuation of chromosomes # mutation_rate a numeric value for rate of mutation # converge a logical value indicating whether algorithm should attempt to # converge or run for specified number of iterations # tol a numeric value indicating convergence tolerance # the default is 1e-4 # iter an integer specifying maximum number of generations algorithm will produce # the default is 100 select <- function(Y, X, family = "gaussian", objective_function = stats::AIC, crossover_parents_function = crossover_parents, crossover_method = c("method1", "method2", "method3"), pCrossover = 0.8, start_chrom = NULL, mutation_rate = NULL, converge = TRUE, tol = 1e-4, iter = 100, minimize = TRUE, nCores = 1L) { ######## #error checking ######## # X if (!is.matrix(X) & !is.data.frame(X)) stop("X must be matrix or dataframe") # Y if (!is.vector(Y) & !is.matrix(Y)) stop("Y must be vector or 1 column matrix") if (is.matrix(Y)) { if(ncol(Y) > 1) stop("Y must be vector or 1 column matrix") } # family if (family == "gaussian" & all(Y %% 1 == 0)) {cat("Warning: outcome distribution is are 1, 0 integer, family == 'gaussian' may not be suitable")} if (family == "gamma" & sum(Y > 0) > 0) {cat("Warning: outcome values < 0, family == 'gamma' may produce errors")} # objective_function if (!is.function(objective_function)) stop("Error: objective_function must be a function") # crossover_parents if (!is.function(crossover_parents_function)) stop("Error: crossover_parents must be a function") # crossover_method if (!is.character(crossover_method)) stop("Error: crossover_method should be a character string") if (length(crossover_method) > 1) crossover_method <- crossover_method[1] # pCrossover if (!is.numeric(pCrossover) | pCrossover < .Machine$double.eps | pCrossover > 1) stop("Error: pCrossover must be number between 0 and 1") if (pCrossover < 0.5) cat("Warning: pCrossover < 0.5 may not reach optimum") # mutation_rate if (!is.null(mutation_rate)) { if(!is.numeric(mutation_rate)) stop("Error: mutation rate must be numeric") if(mutation_rate < 0 | mutation_rate > 1) stop("Error: mutation rate must be bewteen 0 and 1") } # converge if (!is.logical(converge)) stop("Error: converge must be logical (TRUE/FALSE). Default is TRUE") # tol if (!is.numeric(tol)) stop("Error: tol must be numeric. Default is 1e-4") # iter if (!is.numeric(iter)) stop("Error: iter must be numeric") if (length(iter) > 1) stop("Error: iter be of length one") # minimize if (!is.logical(minimize)) stop("Error: minimize must be logical (TRUE/FALSE). Default is TRUE") # nCores if (!is.integer(nCores)) stop("Error: nCores must be integer of length 1") if (nCores > parallel::detectCores()) stop("Error: nCores cannot be larger than detectCores()") if (nCores < 1) stop("Error: nCores must be >= 1") ########## # Perform genetic algorithm ######### t1 <- Sys.time() # timing # Step 1: Generate founders ---------------- generation_t0 <- generate_founders(X, start_chrom) P <- nrow(generation_t0) #num chromosomes cat("1. Generate founders: ", P, "chromosomes") # Step 2. Evaluate founder fitness Fitness of inital pop ---------------- cat("\n2. Evaluate founders") obj_fun_output_t0 <- evaluate_fitness(generation_t0, Y, X, family, nCores, minimize, objective_function, rank_objective_function) #create array to store fitness data for each iteration convergeData <- array(dim = c(P, 2, 1)) #P x 2 x iter #save founder fitness evaluation data convergeData[, , 1] <- obj_fun_output_t0[ order(obj_fun_output_t0[, 2], decreasing = T), c(1, 3)] # Step 3. loop through successive generations ---------------- cat("\n3. Begin breeding \n Generations: ") t1 <- c(t1, Sys.time()) for (i in 1:iter) { # 1. create next generation ---------------- if (i == 1) { generation_t1 <- generation_t0 obj_fun_output_t1 <- obj_fun_output_t0 } generation_t1 <- create_next_generation(generation_t1, obj_fun_output_t1, select_parents, crossover_method, crossover_parents_function, pCrossover, mutate_child, mutation_rate) # 2. evaluate children fitness ---------------- obj_fun_output_t1 <- evaluate_fitness(generation_t1, Y, X, family, nCores, minimize, objective_function, rank_objective_function) # store fitness data convergeData <- abind::abind(convergeData, obj_fun_output_t1[order(obj_fun_output_t1[, 2], decreasing = T), c(1, 3)]) # cat generation and save timing cat(i, "-", sep = "") # 3. check convergence ---------------- if (i > 10 & isTRUE(converge)) { if(isTRUE(all.equal(mean(convergeData[1:(P * 0.25), 2, i]), convergeData[1, 2, i], check.names = F, tolerance = tol)) & isTRUE(all.equal(mean(convergeData[1:(P * 0.25), 2, (i - 1)]), convergeData[1, 2, i], check.names = F, tolerance = tol))) { #if((abs(convergeData[1, 2, i] - convergeData[1, 2, (i - 1)]) / # abs( convergeData[1, 2, (i - 1)])) < tol) { cat("\n#### Converged! ####") break } } } # Step 4. process output ---------------- t1 <- c(t1, Sys.time()) # get models with the best fitness best_scores <- convergeData[, , i] if (sum(best_scores[, 2] == best_scores[1, 2]) > 1) { num_best_scores <- sum(best_scores[, 2] == best_scores[1, 2]) } else {num_best_scores <- 1} bestModel <- generation_t1[convergeData[, 1, i], ] # other output information value <- convergeData[1, 2, dim(convergeData)[3]] if(dim(convergeData)[3] < iter) { converged <- "Yes" } else { converged <- "No" } # create output list output <- list("Best_model" = colnames(X)[round(colMeans(bestModel[1:dim(best_scores)[1], ]), 0) == 1], optimize = list("obj_func" = paste(substitute(objective_function))[3], value = as.numeric(round(value, 4)), minimize = minimize, method = crossover_method), iter = dim(convergeData)[3], converged = converged, convergeData = convergeData, timing = t1) # set class class(output) <- c("GA", class(output)) return(output) }
In addition to the functions above, we also define a function named "plot.GA", which will plot the values of fitness for each model in a generation, the mean fitness of each generation, as well as the best fitness obtained in each generation.
# This function will plot the output of the function select # the inputs are: # x is a GA object and it must be a class of GA # col1 is a character string for color of chromosomes scatter plotpoints # the default is blue # col2 is a character string for color of chromosomes mean and max lines # the default is red plot.GA <- function(x, ..., col1 = "blue", col2 = "red") { # error checking if (!is.character(col1)) stop("Error: col1 must be a character string of length 1") if (length(col1) > 1) stop("Error: col1 must be a character string of length 1") if (!is.character(col2)) stop("Error: col1 must be a character string of length 1") if (length(col2) > 1) stop("Error: col1 must be a character string of length 1") # get plot data from GA object convergeData <- x$convergeData obj_fun <- as.character(x$optimize[1]) minimize <- x$optimize[3] method <- as.character(x$optimize[4]) iter <- x$iter # scatter plot of chromosome data across generations ---------------- # check for scales packages if (requireNamespace("scales", quietly = TRUE)) { # with scales graphics::plot(jitter(rep(1, nrow(convergeData))), convergeData[, 2, 1], type = "p", pch = 19, col = scales::alpha(col1, 0.1), ylim = c(min(convergeData[ , 2, ], na.rm = T), max(convergeData[ , 2, ], na.rm = T)), xlim = c(1, iter), xlab = "Generations", ylab = obj_fun, main = paste("GA performance: \n ", obj_fun, method)) for (i in 2:iter) { graphics::points(jitter(rep(i, nrow(convergeData))), convergeData[, 2, i], type = "p", pch = 19, col = scales::alpha(col1, 0.25)) } } else { # without scales graphics::plot(jitter(rep(1, nrow(convergeData))), convergeData[, 2, 1], type = "p", pch = 19, col = col1, ylim = c(min(convergeData[ , 2, ], na.rm = T), max(convergeData[ , 2, ], na.rm = T)), xlim = c(1, iter), xlab = "Generations", ylab = obj_fun, main = paste("GA performance: \n ", obj_fun, method)) for (i in 2:iter) { graphics::points(jitter(rep(i, nrow(convergeData))), convergeData[, 2, i], type = "p", pch = 19, col = "blue") } } # line plots of mean and max fitness per generation graphics::lines(1:iter, sapply(1:iter, function(x) convergeData[1, 2, x]), type = "l", col = col2, lwd = 2) graphics::lines(1:iter, sapply(1:iter, function(x) mean(convergeData[, 2, x])), col = col2, lty = 2, lwd = 2) # legend if (minimize == TRUE) { location <- "topright" } else { location <- "bottomright" } graphics::legend(location, c("Chr fitness", "Best fitness", "Mean Fitness"), col = c(col1, col2, col2), pch = c(19, NA, NA), lwd = c(NA, 2, 2), lty = c(NA, 1, 2), bty = "n") }
We then perform formal testing for each of the function in our algorithm.
library(testthat) library(MASS)
We use "mtcars" to test the function "generate_founders", where we test two cases: the user defines the number of chromsomes/models in a population or not. For each case, we test whether the output from "generate_founders" is a $P*C$ matrix, and whether each row of the output contains only numerical values of 0 and 1.
y <- mtcars$mpg x <- as.matrix(mtcars[,c(-1)]) C <- dim(x)[2] P <- 2 * C t0 <- generate_founders(x,12) # test the dimension, class, and type of the output test_that('generate_founders works', {test <- generate_founders(x, NULL) expect_equal(dim(test)[2], C) expect_equal(dim(test)[1], P) expect_true(is.matrix(test)) expect_true(is.numeric(test)) expect_false(any(rowSums(test == 0) == C))}) test_that('generate_founders works with user defined chrom_start', {test <- generate_founders(x, 25) expect_equal(dim(test)[2], C) expect_equal(dim(test)[1], 25) expect_true(is.matrix(test)) expect_true(is.numeric(test)) expect_false(any(rowSums(test == 0) == C))})
We test the function "rank_objective_function" with simulated data, and consider when the user desires a maximization or a minimization of the targeted fitness function. In each case, we consider the dimension, class, and type of the output.
objfunout <- runif(100, 0, 1) rnk1 <- rank_objective_function(objfunout, T) # Test the dimension, class, and type of the output of rank_objective_function # with minimize set to TRUE. test_that('test rank_objective_function works',{ expect_equal(dim(rnk1), c(100,3)) expect_true(is.numeric(rnk1)) expect_true(is.matrix(rnk1)) }) rnk2 <- rank_objective_function(objfunout, F) # Test the dimension, class, and type of the output of rank_objective_function # with minimize set to FALSE. test_that('test rank_objective_function works',{ expect_equal(dim(rnk2), c(100,3)) expect_true(is.numeric(rnk2)) expect_true(is.matrix(rnk2)) })
We test the function "evaluate_fitness" with the dataset "mtcars". We first test whether the parallel processing works and check the type of the function output. While the default of the objective function is AIC, we check the functionality of this function by inputting some other possible objective function, such as the log-likehood function.
# test data ---------------- Y <- as.matrix(mtcars$mpg) X <- as.matrix(mtcars[2:ncol(mtcars)]) # get input data C <- dim(X)[2] # number genes P <- 2 * C # number of chromosomes # generate chromosomes to test geneSample <- sample(c(0, 1), replace = TRUE, size = ceiling(1.2 * C * P)) indices <- seq_along(geneSample) firstGen <- split(geneSample, ceiling(indices / C)) generation_t0 <- matrix(unlist(unique(firstGen)[1:P]), ncol = C, byrow = TRUE) generation_t0 <- generation_t0[apply(generation_t0, 1, function(x) !all(x == 0)), ] # serial evaluation test_that('serial fitness evaluation works', {test <- evaluate_fitness(generation_t0, Y, X, family = "gaussian", nCores = 1, minimize = TRUE, objective_function = stats::AIC, rank_objective_function) expect_is(test, "matrix") expect_type(test, "double") }) # parallel evaluation test_that('parallel fitness evaluation works', {test <- evaluate_fitness(generation_t0, Y, X, family = "gaussian", nCores = 2, minimize = TRUE, objective_function = stats::AIC, rank_objective_function) expect_is(test, "matrix") expect_type(test, "double") }) # test maximize evaluation and other objective functions test_that('Other objective_functions work', {test <- evaluate_fitness(generation_t0, Y, X, family = "gaussian", nCores = 1, minimize = FALSE, objective_function = stats::logLik, rank_objective_function) expect_is(test, "matrix") expect_type(test, "double") })
We then test the functions implemented in the selection, crossover, and mutation step using the dataset "mtcars". For the function "select_parents", we check the type, length, and values containing in the output (at least one of the design variable should be 1). For the function "crossover_parents", we implementing the formal testing for each of the three methods, where we consider the class, dimension, and the values involved in the output (we should only have 0's and 1's in the output, and the output should have at least one 1). We test the function "mutate_child" using random binomial data, and we consider three cases: the mutation happens under the default mutation rate, a valid mutation rate (a probability between 0 and 1), and an invalid rate. We test the class, dimension, and the values involved in the output in each case.
#test data Y <- as.matrix(mtcars$mpg) X <- as.matrix(mtcars[2:ncol(mtcars)]) dim(X) # get input data C <- dim(X)[2] # number genes P <- 2 * C # number of chromosomes # generate chromosomes to test geneSample <- sample(c(0, 1), replace = TRUE, size = ceiling(1.2 * C * P)) indices <- seq_along(geneSample) firstGen <- split(geneSample, ceiling(indices / C)) generation_t0 <- matrix(unlist(unique(firstGen)[1:P]), ncol = C, byrow = TRUE) generation_t0 <- generation_t0[apply(generation_t0, 1, function(x) !all(x == 0)), ] parentInd <- sample(1:P, 2, replace = F) parent_rank <- 1:P # select_parents ---------------- test_that("select_parents works ", {test <- select_parents(parent_rank) expect_is(test, "integer") expect_type(test, "integer") expect_true(all(!is.na(test))) # not all 0's expect_equal(length(test), 2) # 2 parents expect_true(all(test > 0 & test <= P)) # C chromosomes }) # crossover_parents ---------------- test_that("crossover_parents works and crossover method 1 works", {test <- crossover_parents(generation_t0, parentInd, crossover_method = "method1", pCrossover = 1, parent_rank) expect_is(test, "matrix") expect_type(test, "integer") expect_true(any(test == 1 | test == 0)) # O's and 1's expect_true(!all(test == 0)) # not all 0's expect_equal(dim(test)[1], 2) # 2 children expect_equal(dim(test)[2], C) # C chromosomes }) test_that("crossover_parents works and crossover method 2 works", {test <- crossover_parents(generation_t0, parentInd, crossover_method = "method2", pCrossover = 1, parent_rank) expect_is(test, "matrix") expect_type(test, "integer") expect_true(any(test == 1 | test == 0)) # O's and 1's expect_true(!all(test == 0)) # not all 0's expect_equal(dim(test)[1], 2) # 2 children expect_equal(dim(test)[2], C) # C chromosomes }) test_that("crossover_parents works and crossover method 3 works", {test <- crossover_parents(generation_t0, parentInd, crossover_method = "method3", pCrossover = 1, parent_rank) expect_is(test, "matrix") expect_type(test, "integer") expect_true(any(test == 1 | test == 0)) # O's and 1's expect_true(!all(test == 0)) # not all 0's expect_equal(dim(test)[1], 2) # 2 children expect_equal(dim(test)[2], C) # C chromosomes }) # mutate_child ---------------- mutation_rate <- NULL child <- stats::rbinom(C, 1, runif(1, min = 0.35, max = 0.65)) test_that("mutate_child works when mutation_rate is null", {test <- mutate_child(mutation_rate, child, P, C) expect_type(test, "integer") expect_true(any(test == 1 | test == 0)) # O's and 1's expect_true(!all(test == 0)) # not all 0's expect_equal(length(test), C) # C chromosomes }) test_that("mutate_child works when user specifies a mutation rate", {test <- mutate_child(mutation_rate = 0.01, child, P, C) expect_type(test, "integer") expect_true(any(test == 1 | test == 0)) # O's and 1's expect_true(!all(test == 0)) # not all 0's expect_equal(length(test), C) # C chromosomes }) test_that("mutate_child works when user specifies a mutation rate", {test <- mutate_child(mutation_rate = 0.01, child, P, C) expect_type(test, "integer") expect_true(any(test == 1 | test == 0)) # O's and 1's expect_true(!all(test == 0)) # not all 0's expect_equal(length(test), C) # C chromosomes }) test_that("mutate_child breaks when user specifies an incorrect mutation rate", {expect_error(mutate_child(mutation_rate = 1.2, child, P, C)) expect_error(mutate_child(mutation_rate = -0.2, child, P, C)) })
We test the function "create_next_generation" with the dataset "mtcars". We first test its functionality when the first corssover method is applied. For valid inputs of the probabilities for crossover and mutation, we check the dimension, class and type of the function, and for invalid inputs of these probabilities, we checked if the function will throw an error message. We also test if "create_next_generation" functions correctly when the second and third crossover methods are implemented.
Y <- as.matrix(mtcars$mpg) X <- as.matrix(mtcars[2:ncol(mtcars)]) dim(X) # get input data C <- dim(X)[2] # number genes P <- 2* C # number of chromosomes # generate chromosomes to test geneSample <- sample(c(0, 1), replace = TRUE, size = ceiling(1.2 * C * P)) indices <- seq_along(geneSample) firstGen <- split(geneSample, ceiling(indices / C)) generation_t0 <- matrix(unlist(unique(firstGen)[1:P]), ncol = C, byrow = TRUE) generation_t0 <- generation_t0[apply(generation_t0, 1, function(x) !all(x == 0)), ] dim(generation_t0) obj_fun_output <- evaluate_fitness(generation_t0, Y, X, family = "gaussian", nCores = 1, minimize = TRUE, objective_function = stats::AIC, rank_objective_function) crossover_method<-"method1" # basic evaluation test_that('create next generation works', { #Assign a probability in crossovber pCrossover<-1 #Assign mutation rate mutation_rate<-0.1 test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate) expect_equal(dim(test), c(20,10)) expect_is(test, "matrix") expect_type(test, "integer") }) # another one with different probability test_that('other probability works', { #Assign a probability in crossovber pCrossover<-0 #Assign mutation rate mutation_rate<-1 test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate) expect_equal(dim(test), c(20,10)) expect_is(test, "matrix") expect_type(test, "integer") }) # test whether invalid input will throw error test_that('serial fitness evaluation works', { #Assign a probability in crossovber pCrossover<-1.1 #Assign mutation rate mutation_rate<-1 expect_error(test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate)) }) test_that('serial fitness evaluation works', { #Assign a probability in crossovber pCrossover<--0.1 #Assign mutation rate mutation_rate<-1 expect_error(test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate)) }) test_that('serial fitness evaluation works', { #Assign a probability in crossovber pCrossover<-1 #Assign mutation rate mutation_rate<--1 expect_error(test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate)) }) test_that('serial fitness evaluation works', { #Assign a probability in crossovber pCrossover<-1 #Assign mutation rate mutation_rate<-1.3 expect_error(test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate)) }) #Make sure other method works test_that('methods 2 works', { #Assign a probability in crossovber pCrossover<-1 #Assign Different methods crossover_method<-"method2" #Assign mutation rate mutation_rate<-1 test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate) expect_equal(dim(test), c(20,10)) expect_is(test, "matrix") expect_type(test, "integer") }) test_that('methods 3 works', { #Assign a probability in crossovber pCrossover<-1 #Assign mutation rate mutation_rate<-1 #Assign Method crossover_method<-"method3" test <- create_next_generation(generation_t0, obj_fun_output, select_parents, crossover_method, crossover_parents, pCrossover, mutate_child, mutation_rate) expect_equal(dim(test), c(20,10)) expect_is(test, "matrix") expect_type(test, "integer") })
At last, we test our primary function "select" with the "mtcars" data, where we check the closeness between the optimal fitness value obtained from our genetic algorithm with the value of this fitness function in linear regression. We test each of the three crossover methods under an either the default function of AIC or an alternative function such as BIC, and the set the tolerance of the difference to be 1 in this case.
fit <- lm(mpg~., data = mtcars) step <- stepAIC(fit, direction="both") step ## test for the closeness between AIC # test for method1 test_that('Variable selection using genetic algorithms',{ test1 <- select(Y, X, family = "gaussian", crossover_method = 'method1') expect_equal(test1$optimize$value, AIC(lm(mpg~wt+qsec+am, data = mtcars)), tolerance = 1e0) }) # test for method2 test_that('Variable selection using genetic algorithms',{ test2 <- select(Y, X, family = "gaussian", crossover_method = 'method2') expect_equal(test2$optimize$value, AIC(lm(mpg~ wt+qsec+am, data = mtcars)), tolerance = 1e0) }) # test for method3 test_that('Variable selection using genetic algorithms',{ test3 <- select(Y, X, family = "gaussian", crossover_method = 'method3') expect_equal(test3$optimize$value, AIC(lm(mpg~wt+qsec+am, data = mtcars)), tolerance = 1e0) }) ## test for the closeness between BIC # test for method1 test_that('Variable selection using genetic algorithms',{ test1 <- select(Y, X, family = "gaussian", crossover_method = 'method1') expect_equal(test1$optimize$value, BIC(lm(mpg~wt+qsec+am, data = mtcars)), tolerance = 1e0) }) # test for method2 test_that('Variable selection using genetic algorithms',{ test1 <- select(Y, X, family = "gaussian", crossover_method = 'method2') expect_equal(test1$optimize$value, BIC(lm(mpg~wt+qsec+am, data = mtcars)), tolerance = 1e0) }) # test for method3 test_that('Variable selection using genetic algorithms',{ test1 <- select(Y, X, family = "gaussian", crossover_method = 'method3') expect_equal(test1$optimize$value, BIC(lm(mpg~wt+qsec+am, data = mtcars)), tolerance = 1e0) })
From the output, we can see that each function in the algorithm passes the test successfully.
In addition to testing our algorithm on the "mtcars" dataset, we also implement our algorithm on a larger dataset--"Ionosphere", which has 30+ possible predictors in this case. The variables "V1" and "V2" are two nominal and the variable "Class" is factor, we ignore these three variables, and fit all other variables on "V34". Again, we compare the optimal fitness value obtained from our genetic algorithm with that in a linear regression model (note that this linear model is selected to be the optimal model obtained from the stepwise selection by AIC). We also plot the results from the genetic algorithm for each of the three methods using the function "GA.plot".
library('mlbench') data("Ionosphere") Ionosphere$V1<-NULL Ionosphere$V2<-NULL Ionosphere$Class<-NULL y<-Ionosphere[,32] x<- as.matrix(Ionosphere[,-32]) library(testthat) library(MASS) fit <- lm(V34 ~ ., data = Ionosphere) step <- stepAIC(fit, direction="both") step # the optimal model selected by stepwise AIC is # V34 ~ V4 + V5 + V7 + V8 + V9 + V11 + V12 + V13 + # V14 + V15 + V16 + V18 + V19 + V20 + V23 + V25 + V27 + V29 + # V30 + V31 + V32 + V33 AIC(lm( V34 ~ V4 + V5 + V7 + V8 + V9 + V11 + V12 + V13 + V14 + V15 + V16 + V18 + V19 + V20 + V23 + V25 + V27 + V29 + V30 + V31 + V32 + V33, data = Ionosphere)) test_that('Variable selection using genetic algorithms',{ test1 <-GA::select(y, x, crossover_method = 'method1', nCores = 1L) expect_equal(test1$optimize$value, AIC(lm( V34 ~ V4 + V5 + V7 + V8 + V9 + V11 + V12 + V13 + V14 + V15 + V16 + V18 + V19 + V20 + V23 + V25 + V27 + V29 + V30 + V31 + V32 + V33, data = Ionosphere)), tolerance = 2e0) }) test1 <-GA::select(y, x, crossover_method = 'method1', nCores = 1L) test1$optimize$value plot(test1, col1 = "blue", col2 = "red") test_that('Variable selection using genetic algorithms',{ test2 <-GA::select(y, x, crossover_method = 'method2', nCores = 1L) expect_equal(test2$optimize$value, AIC(lm( V34 ~ V4 + V5 + V7 + V8 + V9 + V11 + V12 + V13 + V14 + V15 + V16 + V18 + V19 + V20 + V23 + V25 + V27 + V29 + V30 + V31 + V32 + V33, data = Ionosphere)), tolerance = 2e0) }) test2 <-GA::select(y, x, crossover_method = 'method2', nCores = 1L) test2$optimize$value plot(test2, col1 = "blue", col2 = "red") test_that('Variable selection using genetic algorithms',{ test1 <-GA::select(y, x, crossover_method = 'method3', nCores = 1L) expect_equal(test1$optimize$value, AIC(lm( V34 ~ V4 + V5 + V7 + V8 + V9 + V11 + V12 + V13 + V14 + V15 + V16 + V18 + V19 + V20 + V23 + V25 + V27 + V29 + V30 + V31 + V32 + V33, data = Ionosphere)), tolerance = 2e0) }) test3 <-GA::select(y, x, crossover_method = 'method3', nCores = 1L) test3$optimize$value plot(test3, col1 = "blue", col2 = "red")
We can see that all three crossover methods in the algorithm pass the formal testing. From the plots, we can see that the three-point crossover method converges more slowly than the other methods, and it still has relatively large variation in the objective function (AIC in this case) among different chomosomes/models. The second crossover method seems to perform the best in terms of the speed of convergence, and it also has smaller variance in the fitness function for the latter generations. The third crossover method seems to perform better than the three-point crossover method, while it has slower convergence and more variation compared to the second method.
In this project, all members in the group help test the practicability of each function and propose constructive suggestions in designing the algorithm, especially on the set-up of the crossover and mutation methods, as well as the criteria of convergence. Then each group member is also responsible for the following major parts:
Cameron Adams: wrote and continuously improve the functionality of functions including "select", developed the third crossover method, wrote the help information "select.Rd" and the formal testing for functions such as "select_parents".
Yuwen Chen: wrote the formal testing for functions including "generate_founders" and "select", constructed the GA package and ensure that it is operational, debugged some impracticabilities in the algorithm.
Weijie Xu: constructed the basic structure of the algorithm and wrote functions including "generate_founders", developed the second crossover method , wrote the formal testing for functions such as "create_next_generation".
Yilin Zhou: checked the functionality of each step and the improve the consistensy of the presentation, constructed and wrote this documentation, double check to ensure the project attains all the requirements.
Geof H. Givens, Jennifer A. Hoeting (2013) Combinatorial Optimization (italicize). Chapter 3 of Computational Statistics (italicize).
Henderson and Velleman (1981), Building multiple regression models interatively. Biometrics, 37, 391-411.
Sigillito, V. G., Wing, S. P., Hutton, L. V., Baker, K. B. (1989). Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest, 10, 262-266.
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