View source: R/addExperiments.R

addExperiments | R Documentation |

Add experiments for running algorithms on problems to the registry, so they can be executed later.

addExperiments( reg, prob.designs, algo.designs, repls = 1L, skip.defined = FALSE )

`reg` |
[ |

`prob.designs` |
[ |

`algo.designs` |
[ |

`repls` |
[ |

`skip.defined` |
[ |

Invisibly returns vector of ids of added experiments.

Other add:
`addAlgorithm()`

,
`addProblem()`

### EXAMPLE 1 ### reg = makeExperimentRegistry(id = "example1", file.dir = tempfile()) # Define a problem: # Subsampling from the iris dataset. data(iris) subsample = function(static, ratio) { n = nrow(static) train = sample(n, floor(n * ratio)) test = setdiff(seq(n), train) list(test = test, train = train) } addProblem(reg, id = "iris", static = iris, dynamic = subsample, seed = 123) # Define algorithm "tree": # Decision tree on the iris dataset, modeling Species. tree.wrapper = function(static, dynamic, ...) { library(rpart) mod = rpart(Species ~ ., data = static[dynamic$train, ], ...) pred = predict(mod, newdata = static[dynamic$test, ], type = "class") table(static$Species[dynamic$test], pred) } addAlgorithm(reg, id = "tree", fun = tree.wrapper) # Define algorithm "forest": # Random forest on the iris dataset, modeling Species. forest.wrapper = function(static, dynamic, ...) { library(randomForest) mod = randomForest(Species ~ ., data = static, subset = dynamic$train, ...) pred = predict(mod, newdata = static[dynamic$test, ]) table(static$Species[dynamic$test], pred) } addAlgorithm(reg, id = "forest", fun = forest.wrapper) # Define problem parameters: pars = list(ratio = c(0.67, 0.9)) iris.design = makeDesign("iris", exhaustive = pars) # Define decision tree parameters: pars = list(minsplit = c(10, 20), cp = c(0.01, 0.1)) tree.design = makeDesign("tree", exhaustive = pars) # Define random forest parameters: pars = list(ntree = c(100, 500)) forest.design = makeDesign("forest", exhaustive = pars) # Add experiments to the registry: # Use previously defined experimental designs. addExperiments(reg, prob.designs = iris.design, algo.designs = list(tree.design, forest.design), repls = 2) # usually you would set repls to 100 or more. # Optional: Short summary over problems and algorithms. summarizeExperiments(reg) # Optional: Test one decision tree job and one expensive (ntree = 1000) # random forest job. Use findExperiments to get the right job ids. do.tests = FALSE if (do.tests) { id1 = findExperiments(reg, algo.pattern = "tree")[1] id2 = findExperiments(reg, algo.pattern = "forest", algo.pars = (ntree == 1000))[1] testJob(reg, id1) testJob(reg, id2) } # Submit the jobs to the batch system submitJobs(reg) # Calculate the misclassification rate for all (already done) jobs. reduce = function(job, res) { n = sum(res) list(mcr = (n-sum(diag(res)))/n) } res = reduceResultsExperiments(reg, fun = reduce) print(res) # Aggregate results using 'ddply' from package 'plyr': # Calculate the mean over all replications of identical experiments # (same problem, same algorithm and same parameters) library(plyr) vars = setdiff(names(res), c("repl", "mcr")) aggr = ddply(res, vars, summarise, mean.mcr = mean(mcr)) print(aggr) ## Not run: ### EXAMPLE 2 ### # define two simple test functions testfun1 = function(x) sum(x^2) testfun2 = function(x) -exp(-sum(abs(x))) # Define ExperimentRegistry: reg = makeExperimentRegistry("example02", seed = 123, file.dir = tempfile()) # Add the testfunctions to the registry: addProblem(reg, "testfun1", static = testfun1) addProblem(reg, "testfun2", static = testfun2) # Use SimulatedAnnealing on the test functions: addAlgorithm(reg, "sann", fun = function(static, dynamic) { upp = rep(10, 2) low = -upp start = sample(c(-10, 10), 2) res = optim(start, fn = static, lower = low, upper = upp, method = "SANN") res = res[c("par", "value", "counts", "convergence")] res$start = start return(res) }) # add experiments and submit addExperiments(reg, repls = 10) submitJobs(reg) # Gather informations from the experiments, in this case function value # and whether the algorithm convergenced: reduceResultsExperiments(reg, fun = function(job, res) res[c("value", "convergence")]) ## End(Not run)

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