tests/testthat/test.tpr.dag.R
In marconotaro/hemdag: Hierarchical Ensemble Methods for Directed Acyclic Graphs
library(HEMDAG);
source("make.test.data.R");
context("test tpr-dag methods");
test_that("tpr.dag works",{
S <- make.scores();
g <- make.graph();
root <- root.node(g);
S.noroot <- S[,-which(colnames(S)==root)];
tmp <- tempfile();
## all 18 tpr-dag variants
S.tprTF <- tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="htd");
S.tprT <- tpr.dag(S, g, root, positive="children", bottomup="threshold", topdown="htd", t=0.5);
S.tprW <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold.free", topdown="htd", w=0.5);
S.tprWT <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold", topdown="htd", t=0.5, w=0.5);
S.descensTF <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold.free", topdown="htd");
S.descensT <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="htd", t=0.5);
S.descensW <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold.free", topdown="htd", w=0.5);
S.descensWT <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold", topdown="htd", t=0.5, w=05);
S.descensTAU <- tpr.dag(S, g, root, positive="descendants", bottomup="tau", topdown="htd", t=0.5);
S.isotprTF <- tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="gpav");
S.isotprT <- tpr.dag(S, g, root, positive="children", bottomup="threshold", topdown="gpav", t=0.5);
S.isotprW <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold.free", topdown="gpav", w=0.5);
S.isotprWT <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold", topdown="gpav", t=0.5, w=0.5);
S.isodescensTF <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold.free", topdown="gpav");
S.isodescensT <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="gpav", t=0.5);
S.isodescensW <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold.free", topdown="gpav", w=0.5);
S.isodescensWT <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold", topdown="gpav", t=0.5, w=0.5);
S.isodescensTAU <- tpr.dag(S, g, root, positive="descendants", bottomup="tau", topdown="gpav", t=0.5);
write.table(S.tprTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprTF, S.check);
write.table(S.tprT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprT, S.check);
write.table(S.tprW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprW, S.check);
write.table(S.tprWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprWT, S.check);
write.table(S.descensTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensTF, S.check);
write.table(S.descensT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensT, S.check);
write.table(S.descensW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensW, S.check);
write.table(S.descensWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensWT, S.check);
write.table(S.descensTAU, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensTAU, S.check);
write.table(S.isotprTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprTF, S.check);
write.table(S.isotprT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprT, S.check);
write.table(S.isotprW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprW, S.check);
write.table(S.isotprWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprWT, S.check);
write.table(S.isodescensTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensTF, S.check);
write.table(S.isodescensT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensT, S.check);
write.table(S.isodescensW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensW, S.check);
write.table(S.isodescensWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensWT, S.check);
write.table(S.isodescensTAU, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensTAU, S.check);
## no root testing
S.hier.noroot <- tpr.dag(S.noroot, g, root, positive="children", bottomup="threshold.free", topdown="htd");
## class mismatch
S.error <- S[,-which(colnames(S) %in% c("D","H"))];
expect_error(tpr.dag(S.error, g, root, positive="children", bottomup="threshold.free", topdown="htd"),
"mismatch between the number of nodes of the graph g and the number of classes of the score matrix S");
## stop/warnings messages
expect_error(tpr.dag(S, g, root, positive="child", bottomup="threshold.free", topdown="htd"),
"positive or bottomup or topdown value misspelled");
expect_error(tpr.dag(S, g, root, positive="children", bottomup="tau", topdown="htd"),
"tau is a descens variant. Please set positive to descendants");
expect_warning(tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="htd", t=1),
"when t or w is equal to 1, tpr-dag is reduced to htd-dag");
expect_warning(tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="gpav", parallel=TRUE, ncores=1),
"increase number of cores to exploit the gpav parallel version");
expect_warning(tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="gpav", parallel=FALSE, ncores=3),
"set parallel to TRUE to exploit the gpav parallel version");
expect_warning(tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="htd", parallel=TRUE, ncores=3),
"does not exist a parallel version of htd");
})
test_that("tpr.dag.cv works",{
S <- make.scores()
g <- make.graph();
ann <- make.ann();
root <- root.node(g);
S.noroot <- S[,-which(colnames(S)%in%root)];
## degenerate case: train and test made of one row/example
S.degen <- S[1:2,];
ann.degen <- ann[1:2,];
threshold <- seq(0.1, 0.9, 0.2);
weight <- seq(0.1, 0.9, 0.2);
expect_output(tpr.dag.cv(S, g, ann=NULL, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL), "tpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=3, seed=23, metric="auprc", n.round=NULL),
"training fold:\\t1\\ttop auprc avg found:\\t0.236183\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t1\\ttop auprc avg found:\\t0.3748124\\tbest threshold:\\t0.5\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.2832962\\tbest threshold:\\t0.1\\t\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold.free", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=0, weight=weight, kk=3, seed=23, metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.8113208\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.7933067\\tbest weight:\\t0.1\\t\\ntraining fold:\\t3\\ttop fmax avg found:\\t0.776699\\tbest weight:\\t0.1\\t\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.8113208\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.8219138\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntraining fold:\\t3\\ttop fmax avg found:\\t0.8224138\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=NULL, norm=TRUE, norm.type="qnorm", positive="children", bottomup="threshold.free", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"qnorm normalization: done\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S.degen, g, ann=ann.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="auprc", n.round=NULL), "tpr-dag correction done");
expect_output(tpr.dag.cv(S.degen, g, ann=ann.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="fmax", n.round=3), "tpr-dag correction done");
expect_output(tpr.dag.cv(S.noroot, g, ann=NULL, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL), "tpr-dag correction done");
## n.round autoset to NULL
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=3, seed=23, metric="auprc", n.round=3),
"training fold:\\t1\\ttop auprc avg found:\\t0.236183\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t1\\ttop auprc avg found:\\t0.3748124\\tbest threshold:\\t0.5\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.2832962\\tbest threshold:\\t0.1\\t\\ntpr-dag correction done");
## error messages
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=TRUE, norm.type="maxnorm", positive="descendantss", bottomup="threshold.free", topdown="htd", W=NULL,
parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"positive or bottomup or topdown value misspelled");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=TRUE, norm.type="maxnorm", positive="children", bottomup="tau", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tau is a descens variant. Please set positive to descendants");
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=FALSE, norm.type="maxnorm", positive="children", bottomup="threshold.free", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"do you wanna or not normalize the matrix S\\? norm and norm.type inconsistent");
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=TRUE, norm.type=NULL, positive="children", bottomup="threshold.free", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"choose a normalization methods among those available");
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=3, seed=23, metric="auprc", n.round=NULL),
"the annotation matrix must be provided to maximize the hyper-parameter\\(s\\) of the chosen tpr-dag ensemble variant");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=1, seed=23, metric="auprc", n.round=NULL),
"smallest number of folds to define test and training set is 2. Set kk larger or equal to 2");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric=NULL, n.round=NULL),
"the bottom-up approach weighted.threshold is parametric. Select the metric on which maximize according to those available");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="auc", n.round=NULL),
"value of parameter metric misspelled");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=3, seed=NULL, metric="auprc", n.round=NULL),
"set seed to create folds");
expect_error(tpr.dag.cv(S.degen, g, ann=ann.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=3, seed=23, metric="fmax", n.round=3),
"number of folds selected too high: some folds have no examples. Please reduce the number of folds")
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="fmax", n.round=NULL), "set n.round properly");
})
test_that("tpr.dag.holdout works",{
S <- make.scores()
g <- make.graph();
ann <- make.ann();
root <- root.node(g);
testIndex <- 4;
S.noroot <- S[,-which(colnames(S)%in%root)];
## degenerate case: train and test made of one row/example
S.degen <- S[1:2,];
ann.degen <- ann[1:2,];
testIndex.degen <- 1;
threshold <- seq(0.1, 0.9, 0.2);
weight <- seq(0.1, 0.9, 0.2);
expect_output(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S.degen, g, ann=NULL, testIndex=testIndex.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="descendants",
bottomup="weighted.threshold.free", topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=weight, kk=2, seed=23,
metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.7272727\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.8244898\\tbest weight:\\t0.1\\t\\nacross\\t2 training folds\\tbest fmax avg found:\\t0.8244898\\tbest weight:\\t0.1\\t\\ntpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="auprc", n.round=NULL),
"training fold:\\t2\\ttop auprc avg found:\\t0.4801396\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.6534264\\tbest threshold:\\t0.9\\t\\nacross\\t2 training folds\\tbest auprc avg found:\\t0.6534264\\tbest threshold:\\t0.9\\t\\ntpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S.noroot, g, ann=NULL, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=2, seed=23, metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.8\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.8244898\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\nacross\\t2 training folds\\tbest fmax avg found:\\t0.8244898\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=TRUE, norm.type="maxnorm", positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"maxnorm normalization done\\ntpr-dag holdout correction done");
## n.round autoset to NULL
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="auprc", n.round=3),
"training fold:\\t2\\ttop auprc avg found:\\t0.3068528\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.4801396\\tbest threshold:\\t0.5\\t\\nacross\\t2 training folds\\tbest auprc avg found:\\t0.4801396\\tbest threshold:\\t0.5\\t\\ntpr-dag holdout correction done");
## error messages
expect_error(tpr.dag.holdout(S.degen, g, ann=ann.degen, testIndex=testIndex.degen, norm=FALSE, norm.type=NULL, positive="children",
bottomup="threshold", topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23,
metric="auprc", n.round=NULL),
"training matrix too small \\(only one example/row\\) for hyper-parameters tuning. Please use threshold.free strategy instead");
expect_error(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=TRUE, norm.type="maxnorm", positive="descendantss", bottomup="threshold.free",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"positive or bottomup or topdown value misspelled");
expect_error(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=TRUE, norm.type="maxnorm", positive="children", bottomup="tau", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tau is a descens variant. Please set positive to descendants");
expect_error(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=FALSE, norm.type="maxnorm", positive="children", bottomup="threshold.free",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"do you wanna or not normalize the matrix S\\? norm and norm.type inconsistent");
expect_error(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=TRUE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"choose a normalization methods among those available");
expect_error(tpr.dag.holdout(S, g, ann=NULL,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=3, seed=23, metric="auprc", n.round=NULL),
"the annotation matrix must be provided to maximize the hyper-parameter\\(s\\) of the chosen tpr-dag ensemble variant");
expect_error(tpr.dag.holdout(S, g, ann=ann,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=1, seed=23, metric="auprc", n.round=NULL),
"smallest number of folds to define test and training set is 2. Set kk larger or equal to 2");
expect_error(tpr.dag.holdout(S, g, ann=ann,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric=NULL, n.round=NULL),
"the bottom-up approach weighted.threshold is parametric. Select the metric on which maximize according to those available");
expect_error(tpr.dag.holdout(S, g, ann=ann,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="auc", n.round=NULL),
"value of parameter metric misspelled");
expect_error(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=NULL, metric="auprc", n.round=NULL),
"set seed to create folds");
expect_error(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="fmax", n.round=NULL),
"set n.round properly");
})
marconotaro/hemdag documentation built on Nov. 21, 2024, 10:05 p.m.
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library(HEMDAG);
source("make.test.data.R");
context("test tpr-dag methods");
test_that("tpr.dag works",{
S <- make.scores();
g <- make.graph();
root <- root.node(g);
S.noroot <- S[,-which(colnames(S)==root)];
tmp <- tempfile();
## all 18 tpr-dag variants
S.tprTF <- tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="htd");
S.tprT <- tpr.dag(S, g, root, positive="children", bottomup="threshold", topdown="htd", t=0.5);
S.tprW <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold.free", topdown="htd", w=0.5);
S.tprWT <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold", topdown="htd", t=0.5, w=0.5);
S.descensTF <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold.free", topdown="htd");
S.descensT <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="htd", t=0.5);
S.descensW <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold.free", topdown="htd", w=0.5);
S.descensWT <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold", topdown="htd", t=0.5, w=05);
S.descensTAU <- tpr.dag(S, g, root, positive="descendants", bottomup="tau", topdown="htd", t=0.5);
S.isotprTF <- tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="gpav");
S.isotprT <- tpr.dag(S, g, root, positive="children", bottomup="threshold", topdown="gpav", t=0.5);
S.isotprW <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold.free", topdown="gpav", w=0.5);
S.isotprWT <- tpr.dag(S, g, root, positive="children", bottomup="weighted.threshold", topdown="gpav", t=0.5, w=0.5);
S.isodescensTF <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold.free", topdown="gpav");
S.isodescensT <- tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="gpav", t=0.5);
S.isodescensW <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold.free", topdown="gpav", w=0.5);
S.isodescensWT <- tpr.dag(S, g, root, positive="descendants", bottomup="weighted.threshold", topdown="gpav", t=0.5, w=0.5);
S.isodescensTAU <- tpr.dag(S, g, root, positive="descendants", bottomup="tau", topdown="gpav", t=0.5);
write.table(S.tprTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprTF, S.check);
write.table(S.tprT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprT, S.check);
write.table(S.tprW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprW, S.check);
write.table(S.tprWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.tprWT, S.check);
write.table(S.descensTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensTF, S.check);
write.table(S.descensT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensT, S.check);
write.table(S.descensW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensW, S.check);
write.table(S.descensWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensWT, S.check);
write.table(S.descensTAU, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.descensTAU, S.check);
write.table(S.isotprTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprTF, S.check);
write.table(S.isotprT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprT, S.check);
write.table(S.isotprW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprW, S.check);
write.table(S.isotprWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isotprWT, S.check);
write.table(S.isodescensTF, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensTF, S.check);
write.table(S.isodescensT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensT, S.check);
write.table(S.isodescensW, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensW, S.check);
write.table(S.isodescensWT, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensWT, S.check);
write.table(S.isodescensTAU, row.names=TRUE, col.names=TRUE, quote=FALSE, file=tmp);
S.check <- as.matrix(read.table(tmp));
expect_equal(S.isodescensTAU, S.check);
## no root testing
S.hier.noroot <- tpr.dag(S.noroot, g, root, positive="children", bottomup="threshold.free", topdown="htd");
## class mismatch
S.error <- S[,-which(colnames(S) %in% c("D","H"))];
expect_error(tpr.dag(S.error, g, root, positive="children", bottomup="threshold.free", topdown="htd"),
"mismatch between the number of nodes of the graph g and the number of classes of the score matrix S");
## stop/warnings messages
expect_error(tpr.dag(S, g, root, positive="child", bottomup="threshold.free", topdown="htd"),
"positive or bottomup or topdown value misspelled");
expect_error(tpr.dag(S, g, root, positive="children", bottomup="tau", topdown="htd"),
"tau is a descens variant. Please set positive to descendants");
expect_warning(tpr.dag(S, g, root, positive="descendants", bottomup="threshold", topdown="htd", t=1),
"when t or w is equal to 1, tpr-dag is reduced to htd-dag");
expect_warning(tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="gpav", parallel=TRUE, ncores=1),
"increase number of cores to exploit the gpav parallel version");
expect_warning(tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="gpav", parallel=FALSE, ncores=3),
"set parallel to TRUE to exploit the gpav parallel version");
expect_warning(tpr.dag(S, g, root, positive="children", bottomup="threshold.free", topdown="htd", parallel=TRUE, ncores=3),
"does not exist a parallel version of htd");
})
test_that("tpr.dag.cv works",{
S <- make.scores()
g <- make.graph();
ann <- make.ann();
root <- root.node(g);
S.noroot <- S[,-which(colnames(S)%in%root)];
## degenerate case: train and test made of one row/example
S.degen <- S[1:2,];
ann.degen <- ann[1:2,];
threshold <- seq(0.1, 0.9, 0.2);
weight <- seq(0.1, 0.9, 0.2);
expect_output(tpr.dag.cv(S, g, ann=NULL, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL), "tpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=3, seed=23, metric="auprc", n.round=NULL),
"training fold:\\t1\\ttop auprc avg found:\\t0.236183\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t1\\ttop auprc avg found:\\t0.3748124\\tbest threshold:\\t0.5\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.2832962\\tbest threshold:\\t0.1\\t\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold.free", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=0, weight=weight, kk=3, seed=23, metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.8113208\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.7933067\\tbest weight:\\t0.1\\t\\ntraining fold:\\t3\\ttop fmax avg found:\\t0.776699\\tbest weight:\\t0.1\\t\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.8113208\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.8219138\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntraining fold:\\t3\\ttop fmax avg found:\\t0.8224138\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S, g, ann=NULL, norm=TRUE, norm.type="qnorm", positive="children", bottomup="threshold.free", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"qnorm normalization: done\\ntpr-dag correction done");
expect_output(tpr.dag.cv(S.degen, g, ann=ann.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="auprc", n.round=NULL), "tpr-dag correction done");
expect_output(tpr.dag.cv(S.degen, g, ann=ann.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="fmax", n.round=3), "tpr-dag correction done");
expect_output(tpr.dag.cv(S.noroot, g, ann=NULL, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL), "tpr-dag correction done");
## n.round autoset to NULL
expect_output(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=3, seed=23, metric="auprc", n.round=3),
"training fold:\\t1\\ttop auprc avg found:\\t0.236183\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t1\\ttop auprc avg found:\\t0.3748124\\tbest threshold:\\t0.5\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.2832962\\tbest threshold:\\t0.1\\t\\ntpr-dag correction done");
## error messages
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=TRUE, norm.type="maxnorm", positive="descendantss", bottomup="threshold.free", topdown="htd", W=NULL,
parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"positive or bottomup or topdown value misspelled");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=TRUE, norm.type="maxnorm", positive="children", bottomup="tau", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tau is a descens variant. Please set positive to descendants");
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=FALSE, norm.type="maxnorm", positive="children", bottomup="threshold.free", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"do you wanna or not normalize the matrix S\\? norm and norm.type inconsistent");
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=TRUE, norm.type=NULL, positive="children", bottomup="threshold.free", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"choose a normalization methods among those available");
expect_error(tpr.dag.cv(S, g, ann=NULL, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=0, weight=0, kk=3, seed=23, metric="auprc", n.round=NULL),
"the annotation matrix must be provided to maximize the hyper-parameter\\(s\\) of the chosen tpr-dag ensemble variant");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=1, seed=23, metric="auprc", n.round=NULL),
"smallest number of folds to define test and training set is 2. Set kk larger or equal to 2");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric=NULL, n.round=NULL),
"the bottom-up approach weighted.threshold is parametric. Select the metric on which maximize according to those available");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="auc", n.round=NULL),
"value of parameter metric misspelled");
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=0, kk=3, seed=NULL, metric="auprc", n.round=NULL),
"set seed to create folds");
expect_error(tpr.dag.cv(S.degen, g, ann=ann.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav", W=NULL,
parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=3, seed=23, metric="fmax", n.round=3),
"number of folds selected too high: some folds have no examples. Please reduce the number of folds")
expect_error(tpr.dag.cv(S, g, ann=ann, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold", topdown="gpav", W=NULL, parallel=FALSE,
ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="fmax", n.round=NULL), "set n.round properly");
})
test_that("tpr.dag.holdout works",{
S <- make.scores()
g <- make.graph();
ann <- make.ann();
root <- root.node(g);
testIndex <- 4;
S.noroot <- S[,-which(colnames(S)%in%root)];
## degenerate case: train and test made of one row/example
S.degen <- S[1:2,];
ann.degen <- ann[1:2,];
testIndex.degen <- 1;
threshold <- seq(0.1, 0.9, 0.2);
weight <- seq(0.1, 0.9, 0.2);
expect_output(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S.degen, g, ann=NULL, testIndex=testIndex.degen, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="descendants",
bottomup="weighted.threshold.free", topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=weight, kk=2, seed=23,
metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.7272727\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.8244898\\tbest weight:\\t0.1\\t\\nacross\\t2 training folds\\tbest fmax avg found:\\t0.8244898\\tbest weight:\\t0.1\\t\\ntpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="auprc", n.round=NULL),
"training fold:\\t2\\ttop auprc avg found:\\t0.4801396\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.6534264\\tbest threshold:\\t0.9\\t\\nacross\\t2 training folds\\tbest auprc avg found:\\t0.6534264\\tbest threshold:\\t0.9\\t\\ntpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S.noroot, g, ann=NULL, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=2, seed=23, metric="fmax", n.round=3),
"training fold:\\t1\\ttop fmax avg found:\\t0.8\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntraining fold:\\t2\\ttop fmax avg found:\\t0.8244898\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\nacross\\t2 training folds\\tbest fmax avg found:\\t0.8244898\\tbest threshold:\\t0.1\\tbest weight:\\t0.1\\t\\ntpr-dag holdout correction done");
expect_output(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=TRUE, norm.type="maxnorm", positive="children", bottomup="threshold.free",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"maxnorm normalization done\\ntpr-dag holdout correction done");
## n.round autoset to NULL
expect_output(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="gpav",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23, metric="auprc", n.round=3),
"training fold:\\t2\\ttop auprc avg found:\\t0.3068528\\tbest threshold:\\t0.1\\t\\ntraining fold:\\t2\\ttop auprc avg found:\\t0.4801396\\tbest threshold:\\t0.5\\t\\nacross\\t2 training folds\\tbest auprc avg found:\\t0.4801396\\tbest threshold:\\t0.5\\t\\ntpr-dag holdout correction done");
## error messages
expect_error(tpr.dag.holdout(S.degen, g, ann=ann.degen, testIndex=testIndex.degen, norm=FALSE, norm.type=NULL, positive="children",
bottomup="threshold", topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=2, seed=23,
metric="auprc", n.round=NULL),
"training matrix too small \\(only one example/row\\) for hyper-parameters tuning. Please use threshold.free strategy instead");
expect_error(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=TRUE, norm.type="maxnorm", positive="descendantss", bottomup="threshold.free",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"positive or bottomup or topdown value misspelled");
expect_error(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=TRUE, norm.type="maxnorm", positive="children", bottomup="tau", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"tau is a descens variant. Please set positive to descendants");
expect_error(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=FALSE, norm.type="maxnorm", positive="children", bottomup="threshold.free",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"do you wanna or not normalize the matrix S\\? norm and norm.type inconsistent");
expect_error(tpr.dag.holdout(S, g, ann=NULL, testIndex=testIndex, norm=TRUE, norm.type=NULL, positive="children", bottomup="threshold.free",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=NULL, seed=NULL, metric=NULL, n.round=NULL),
"choose a normalization methods among those available");
expect_error(tpr.dag.holdout(S, g, ann=NULL,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=0, weight=0, kk=3, seed=23, metric="auprc", n.round=NULL),
"the annotation matrix must be provided to maximize the hyper-parameter\\(s\\) of the chosen tpr-dag ensemble variant");
expect_error(tpr.dag.holdout(S, g, ann=ann,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=0, kk=1, seed=23, metric="auprc", n.round=NULL),
"smallest number of folds to define test and training set is 2. Set kk larger or equal to 2");
expect_error(tpr.dag.holdout(S, g, ann=ann,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric=NULL, n.round=NULL),
"the bottom-up approach weighted.threshold is parametric. Select the metric on which maximize according to those available");
expect_error(tpr.dag.holdout(S, g, ann=ann,testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="htd", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="auc", n.round=NULL),
"value of parameter metric misspelled");
expect_error(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="threshold", topdown="htd",
W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=NULL, metric="auprc", n.round=NULL),
"set seed to create folds");
expect_error(tpr.dag.holdout(S, g, ann=ann, testIndex=testIndex, norm=FALSE, norm.type=NULL, positive="children", bottomup="weighted.threshold",
topdown="gpav", W=NULL, parallel=FALSE, ncores=1, threshold=threshold, weight=weight, kk=3, seed=23, metric="fmax", n.round=NULL),
"set n.round properly");
})
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