Commit | Line | Data |
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504afaad BA |
1 | library(agghoo) |
2 | ||
3 | standardCV <- function(data, target, task = NULL, gmodel = NULL, params = NULL, | |
4 | loss = NULL, CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE) | |
5 | ) { | |
6 | if (!is.null(task)) | |
7 | task = match.arg(task, c("classification", "regression")) | |
8 | if (is.character(gmodel)) | |
9 | gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree")) | |
10 | if (is.numeric(params) || is.character(params)) | |
11 | params <- as.list(params) | |
12 | if (is.null(task)) { | |
13 | if (is.numeric(target)) | |
14 | task = "regression" | |
15 | else | |
16 | task = "classification" | |
17 | } | |
18 | ||
19 | if (is.null(loss)) { | |
20 | loss <- function(y1, y2) { | |
21 | if (task == "classification") { | |
22 | if (is.null(dim(y1))) | |
23 | mean(y1 != y2) | |
24 | else { | |
25 | if (!is.null(dim(y2))) | |
26 | mean(rowSums(abs(y1 - y2))) | |
27 | else { | |
28 | y2 <- as.character(y2) | |
29 | names <- colnames(y1) | |
30 | positions <- list() | |
31 | for (idx in seq_along(names)) | |
32 | positions[[ names[idx] ]] <- idx | |
33 | mean(vapply( | |
34 | seq_along(y2), | |
35 | function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])), | |
36 | 0)) | |
37 | } | |
38 | } | |
39 | } | |
1fdc3c34 BA |
40 | else |
41 | mean(abs(y1 - y2)) | |
504afaad BA |
42 | } |
43 | } | |
44 | ||
45 | n <- nrow(data) | |
46 | shuffle_inds <- NULL | |
47 | if (CV$type == "vfold" && CV$shuffle) | |
48 | shuffle_inds <- sample(n, n) | |
49 | get_testIndices <- function(v, shuffle_inds) { | |
50 | if (CV$type == "vfold") { | |
51 | first_index = round((v-1) * n / CV$V) + 1 | |
52 | last_index = round(v * n / CV$V) | |
53 | test_indices = first_index:last_index | |
54 | if (!is.null(shuffle_inds)) | |
55 | test_indices <- shuffle_inds[test_indices] | |
56 | } | |
57 | else | |
58 | test_indices = sample(n, round(n * CV$test_size)) | |
59 | test_indices | |
60 | } | |
61 | list_testinds <- list() | |
62 | for (v in seq_len(CV$V)) | |
63 | list_testinds[[v]] <- get_testIndices(v, shuffle_inds) | |
64 | ||
65 | gmodel <- agghoo::Model$new(data, target, task, gmodel, params) | |
66 | best_error <- Inf | |
67 | best_model <- NULL | |
68 | for (p in seq_len(gmodel$nmodels)) { | |
69 | error <- 0 | |
70 | for (v in seq_len(CV$V)) { | |
71 | testIdx <- list_testinds[[v]] | |
72 | dataHO <- data[-testIdx,] | |
73 | testX <- data[testIdx,] | |
74 | targetHO <- target[-testIdx] | |
75 | testY <- target[testIdx] | |
76 | if (!is.matrix(dataHO) && !is.data.frame(dataHO)) | |
77 | dataHO <- as.matrix(dataHO) | |
78 | if (!is.matrix(testX) && !is.data.frame(testX)) | |
79 | testX <- as.matrix(testX) | |
80 | model_pred <- gmodel$get(dataHO, targetHO, p) | |
81 | prediction <- model_pred(testX) | |
82 | error <- error + loss(prediction, testY) | |
83 | } | |
84 | if (error <= best_error) { | |
85 | newModel <- list(model=model_pred, param=gmodel$getParam(p)) | |
86 | if (error == best_error) | |
87 | best_model[[length(best_model)+1]] <- newModel | |
88 | else { | |
89 | best_model <- list(newModel) | |
90 | best_error <- error | |
91 | } | |
92 | } | |
93 | } | |
94 | best_model[[ sample(length(best_model), 1) ]] | |
95 | } | |
96 | ||
e86bf24d | 97 | compareToCV <- function(df, t_idx, task=NULL, rseed=-1, verbose=TRUE, ...) { |
504afaad BA |
98 | if (rseed >= 0) |
99 | set.seed(rseed) | |
100 | if (is.null(task)) | |
101 | task <- ifelse(is.numeric(df[,t_idx]), "regression", "classification") | |
102 | n <- nrow(df) | |
103 | test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) ) | |
7b5193cd BA |
104 | data <- as.matrix(df[-test_indices,-t_idx]) |
105 | target <- df[-test_indices,t_idx] | |
106 | test <- as.matrix(df[test_indices,-t_idx]) | |
107 | a <- agghoo(data, target, task, ...) | |
504afaad BA |
108 | a$fit() |
109 | if (verbose) { | |
110 | print("Parameters:") | |
111 | print(unlist(a$getParams())) | |
112 | } | |
7b5193cd | 113 | pa <- a$predict(test) |
504afaad BA |
114 | err_a <- ifelse(task == "classification", |
115 | mean(pa != df[test_indices,t_idx]), | |
116 | mean(abs(pa - df[test_indices,t_idx]))) | |
117 | if (verbose) | |
118 | print(paste("error agghoo:", err_a)) | |
119 | # Compare with standard cross-validation: | |
7b5193cd | 120 | s <- standardCV(data, target, task, ...) |
504afaad BA |
121 | if (verbose) |
122 | print(paste( "Parameter:", s$param )) | |
7b5193cd | 123 | ps <- s$model(test) |
504afaad BA |
124 | err_s <- ifelse(task == "classification", |
125 | mean(ps != df[test_indices,t_idx]), | |
126 | mean(abs(ps - df[test_indices,t_idx]))) | |
127 | if (verbose) | |
128 | print(paste("error CV:", err_s)) | |
e86bf24d | 129 | invisible(c(err_a, err_s)) |
504afaad BA |
130 | } |
131 | ||
132 | library(parallel) | |
e86bf24d | 133 | compareMulti <- function(df, t_idx, task = NULL, N = 100, nc = NA, ...) { |
504afaad BA |
134 | if (is.na(nc)) |
135 | nc <- detectCores() | |
7b5193cd BA |
136 | compareOne <- function(n) { |
137 | print(n) | |
138 | compareToCV(df, t_idx, task, n, verbose=FALSE, ...) | |
139 | } | |
140 | errors <- if (nc >= 2) { | |
141 | mclapply(1:N, compareOne, mc.cores = nc) | |
142 | } else { | |
143 | lapply(1:N, compareOne) | |
144 | } | |
504afaad BA |
145 | print("error agghoo vs. cross-validation:") |
146 | Reduce('+', errors) / N | |
147 | } |