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17ea2f13 BA |
1 | #' standardCV_core |
2 | #' | |
3 | #' Cross-validation method, added here as an example. | |
4 | #' Parameters are described in ?agghoo and ?AgghooCV | |
afa67660 | 5 | standardCV_core <- function(data, target, task, gmodel, params, loss, CV) { |
43a6578d BA |
6 | n <- nrow(data) |
7 | shuffle_inds <- NULL | |
8 | if (CV$type == "vfold" && CV$shuffle) | |
9 | shuffle_inds <- sample(n, n) | |
43a6578d BA |
10 | list_testinds <- list() |
11 | for (v in seq_len(CV$V)) | |
afa67660 | 12 | list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds) |
43a6578d BA |
13 | gmodel <- agghoo::Model$new(data, target, task, gmodel, params) |
14 | best_error <- Inf | |
15 | best_model <- NULL | |
16 | for (p in seq_len(gmodel$nmodels)) { | |
afa67660 | 17 | error <- Reduce('+', lapply(seq_len(CV$V), function(v) { |
43a6578d | 18 | testIdx <- list_testinds[[v]] |
afa67660 BA |
19 | d <- splitTrainTest(data, target, testIdx) |
20 | model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p) | |
21 | prediction <- model_pred(d$dataTest) | |
22 | loss(prediction, d$targetTest) | |
23 | }) ) | |
43a6578d | 24 | if (error <= best_error) { |
afa67660 BA |
25 | newModel <- list(model=gmodel$get(data, target, p), |
26 | param=gmodel$getParam(p)) | |
43a6578d BA |
27 | if (error == best_error) |
28 | best_model[[length(best_model)+1]] <- newModel | |
29 | else { | |
30 | best_model <- list(newModel) | |
31 | best_error <- error | |
32 | } | |
33 | } | |
34 | } | |
35 | best_model[[ sample(length(best_model), 1) ]] | |
36 | } | |
37 | ||
17ea2f13 BA |
38 | #' standardCV_run |
39 | #' | |
40 | #' Run and eval the standard cross-validation procedure. | |
41 | #' Parameters are rather explicit except "floss", which corresponds to the | |
42 | #' "final" loss function, applied to compute the error on testing dataset. | |
43 | #' | |
44 | #' @export | |
43a6578d | 45 | standardCV_run <- function( |
17ea2f13 | 46 | dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ... |
43a6578d | 47 | ) { |
afa67660 BA |
48 | args <- list(...) |
49 | task <- checkTask(args$task, targetTrain) | |
50 | modPar <- checkModPar(args$gmodel, args$params) | |
51 | loss <- checkLoss(args$loss, task) | |
17ea2f13 | 52 | CV <- checkCV(args$CV) |
afa67660 BA |
53 | s <- standardCV_core( |
54 | dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV) | |
43a6578d BA |
55 | if (verbose) |
56 | print(paste( "Parameter:", s$param )) | |
afa67660 BA |
57 | p <- s$model(dataTest) |
58 | err <- floss(p, targetTest) | |
43a6578d | 59 | if (verbose) |
afa67660 BA |
60 | print(paste("error CV:", err)) |
61 | invisible(err) | |
43a6578d BA |
62 | } |
63 | ||
17ea2f13 BA |
64 | #' agghoo_run |
65 | #' | |
66 | #' Run and eval the agghoo procedure. | |
67 | #' Parameters are rather explicit except "floss", which corresponds to the | |
68 | #' "final" loss function, applied to compute the error on testing dataset. | |
69 | #' | |
70 | #' @export | |
43a6578d | 71 | agghoo_run <- function( |
17ea2f13 | 72 | dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ... |
43a6578d | 73 | ) { |
17ea2f13 BA |
74 | args <- list(...) |
75 | CV <- checkCV(args$CV) | |
76 | # Must remove CV arg, or agghoo will complain "error: unused arg" | |
77 | args$CV <- NULL | |
78 | a <- do.call(agghoo, c(list(data=dataTrain, target=targetTrain), args)) | |
43a6578d BA |
79 | a$fit(CV) |
80 | if (verbose) { | |
81 | print("Parameters:") | |
82 | print(unlist(a$getParams())) | |
83 | } | |
84 | pa <- a$predict(dataTest) | |
85 | err <- floss(pa, targetTest) | |
86 | if (verbose) | |
87 | print(paste("error agghoo:", err)) | |
afa67660 | 88 | invisible(err) |
43a6578d BA |
89 | } |
90 | ||
17ea2f13 BA |
91 | #' compareTo |
92 | #' | |
93 | #' Compare a list of learning methods (or run only one), on data/target. | |
94 | #' | |
95 | #' @param data Data matrix or data.frame | |
96 | #' @param target Target vector (generally) | |
97 | #' @param method_s Either a single function, or a list | |
98 | #' (examples: agghoo_run, standardCV_run) | |
99 | #' @param rseed Seed of the random generator (-1 means "random seed") | |
100 | #' @param floss Loss function to compute the error on testing dataset. | |
101 | #' @param verbose TRUE to request methods to be verbose. | |
102 | #' @param ... arguments passed to method_s function(s) | |
103 | #' | |
104 | #' @export | |
43a6578d | 105 | compareTo <- function( |
afa67660 | 106 | data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ... |
43a6578d BA |
107 | ) { |
108 | if (rseed >= 0) | |
109 | set.seed(rseed) | |
110 | n <- nrow(data) | |
111 | test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) ) | |
afa67660 | 112 | d <- splitTrainTest(data, target, test_indices) |
43a6578d BA |
113 | |
114 | # Set error function to be used on model outputs (not in core method) | |
afa67660 | 115 | task <- checkTask(list(...)$task, target) |
43a6578d BA |
116 | if (is.null(floss)) { |
117 | floss <- function(y1, y2) { | |
118 | ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2))) | |
119 | } | |
120 | } | |
121 | ||
122 | # Run (and compare) all methods: | |
123 | runOne <- function(o) { | |
17ea2f13 | 124 | o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, floss, verbose, ...) |
43a6578d | 125 | } |
afa67660 | 126 | errors <- c() |
43a6578d BA |
127 | if (is.list(method_s)) |
128 | errors <- sapply(method_s, runOne) | |
129 | else if (is.function(method_s)) | |
130 | errors <- runOne(method_s) | |
43a6578d BA |
131 | invisible(errors) |
132 | } | |
133 | ||
17ea2f13 BA |
134 | #' compareMulti |
135 | #' | |
136 | #' Run compareTo N times in parallel. | |
137 | #' | |
138 | #' @inheritParams compareTo | |
139 | #' @param N Number of calls to method(s) | |
140 | #' @param nc Number of cores. Set to parallel::detectCores() if undefined. | |
141 | #' Set it to any value <=1 to say "no parallelism". | |
142 | #' @param verbose TRUE to print task numbers and "Errors:" in the end. | |
143 | #' | |
144 | #' @export | |
43a6578d | 145 | compareMulti <- function( |
17ea2f13 | 146 | data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ... |
43a6578d | 147 | ) { |
afa67660 | 148 | require(parallel) |
43a6578d BA |
149 | if (is.na(nc)) |
150 | nc <- parallel::detectCores() | |
afa67660 BA |
151 | |
152 | # "One" comparison for each method in method_s (list) | |
43a6578d | 153 | compareOne <- function(n) { |
17ea2f13 BA |
154 | if (verbose) |
155 | print(n) | |
afa67660 | 156 | compareTo(data, target, method_s, n, floss, verbose=FALSE, ...) |
43a6578d | 157 | } |
afa67660 | 158 | |
43a6578d | 159 | errors <- if (nc >= 2) { |
43a6578d BA |
160 | parallel::mclapply(1:N, compareOne, mc.cores = nc) |
161 | } else { | |
162 | lapply(1:N, compareOne) | |
163 | } | |
17ea2f13 BA |
164 | if (verbose) |
165 | print("Errors:") | |
43a6578d BA |
166 | Reduce('+', errors) / N |
167 | } | |
17ea2f13 BA |
168 | |
169 | #' compareRange | |
170 | #' | |
171 | #' Run compareMulti on several values of the parameter V. | |
172 | #' | |
173 | #' @inheritParams compareMulti | |
174 | #' @param V_range Values of V to be tested. | |
175 | #' | |
176 | #' @export | |
177 | compareRange <- function( | |
178 | data, target, method_s, N=100, nc=NA, floss=NULL, V_range=c(10,15,20,), ... | |
179 | ) { | |
180 | args <- list(...) | |
181 | # Avoid warnings if V is left unspecified: | |
182 | CV <- suppressWarnings( checkCV(args$CV) ) | |
183 | errors <- lapply(V_range, function(V) { | |
184 | args$CV$V <- V | |
185 | do.call(compareMulti, c(list(data=data, target=target, method_s=method_s, | |
186 | N=N, nc=nc, floss=floss, verbose=F), args)) | |
187 | }) | |
188 | print(paste(V_range, errors)) | |
189 | } |