Nothing
test_that("ArchiveTuning access methods work", {
instance = TuningInstanceSingleCrit$new(task = tsk("iris"), learner = lrn("classif.rpart"),
resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = TEST_MAKE_PS1(n_dim = 1),
terminator = trm("evals", n_evals = 4))
tuner = tnr("random_search", batch_size = 2)
tuner$optimize(instance)
# learner
walk(instance$archive$data$uhash, function(uhash) {
expect_learner(instance$archive$learner(uhash = uhash))
})
walk(seq_row(instance$archive$data), function(i) {
expect_learner(instance$archive$learner(i))
})
# learner param values
walk(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$learner_param_vals(uhash = uhash))
expect_named(instance$archive$learner_param_vals(uhash = uhash), c("xval" ,"cp"))
})
walk(seq_row(instance$archive$data), function(i) {
expect_list(instance$archive$learner_param_vals(i))
expect_named(instance$archive$learner_param_vals(i), c("xval" ,"cp"))
})
# learners
walk(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$learners(uhash = uhash))
expect_learner(instance$archive$learners(uhash = uhash)[[1]])
})
walk(seq_row(instance$archive$data), function(i) {
expect_list(instance$archive$learners(i))
expect_learner(instance$archive$learners(i)[[1]])
})
# predictions
walk(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$predictions(uhash = uhash))
expect_prediction(instance$archive$predictions(uhash = uhash)[[1]])
})
walk(seq_row(instance$archive$data), function(i) {
expect_list(instance$archive$predictions(i))
expect_prediction(instance$archive$predictions(i)[[1]])
})
# resample result
walk(instance$archive$data$uhash, function(uhash) {
expect_resample_result(instance$archive$resample_result(uhash = uhash))
})
walk(seq_row(instance$archive$data), function(i) {
expect_resample_result(instance$archive$resample_result(i))
})
# learner
walk(instance$archive$data$uhash, function(uhash) {
expect_learner(instance$archive$learner(uhash = uhash))
})
walk(seq_row(instance$archive$data), function(i) {
expect_learner(instance$archive$learner(i))
})
# learner param values
walk(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$learner_param_vals(uhash = uhash))
expect_named(instance$archive$learner_param_vals(uhash = uhash), c("xval" ,"cp"))
})
walk(seq_row(instance$archive$data), function(i) {
expect_list(instance$archive$learner_param_vals(i))
expect_named(instance$archive$learner_param_vals(i), c("xval" ,"cp"))
})
# learners
walk(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$learners(uhash = uhash))
expect_learner(instance$archive$learners(uhash = uhash)[[1]])
})
walk(seq_row(instance$archive$data), function(i) {
expect_list(instance$archive$learners(i))
expect_learner(instance$archive$learners(i)[[1]])
})
# predictions
walk(instance$archive$data$uhash, function(uhash) {
expect_list(instance$archive$predictions(uhash = uhash))
expect_prediction(instance$archive$predictions(uhash = uhash)[[1]])
})
walk(seq_row(instance$archive$data), function(i) {
expect_list(instance$archive$predictions(i))
expect_prediction(instance$archive$predictions(i)[[1]])
})
# resample result
walk(instance$archive$data$uhash, function(uhash) {
expect_resample_result(instance$archive$resample_result(uhash = uhash))
})
walk(seq_row(instance$archive$data), function(i) {
expect_resample_result(instance$archive$resample_result(i))
})
})
test_that("ArchiveTuning as.data.table function works", {
instance = TuningInstanceSingleCrit$new(task = tsk("pima"), learner = lrn("classif.rpart"),
resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = TEST_MAKE_PS1(n_dim = 1),
terminator = trm("evals", n_evals = 4))
tuner = tnr("random_search", batch_size = 2)
tuner$optimize(instance)
# default
tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 4, ncols = 9)
expect_named(tab, c("cp", "classif.ce", "x_domain_cp", "runtime_learners", "timestamp", "batch_nr", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# extra measure
tab = as.data.table(instance$archive, measures = msr("classif.acc"))
expect_data_table(tab, nrows = 4, ncols = 10)
expect_named(tab, c("cp", "classif.ce", "classif.acc", "x_domain_cp", "runtime_learners", "timestamp", "batch_nr", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# extra measures
tab = as.data.table(instance$archive, measures = msrs(c("classif.acc", "classif.mcc")))
expect_data_table(tab, nrows = 4, ncols = 11)
expect_named(tab, c("cp", "classif.ce", "classif.acc", "classif.mcc", "x_domain_cp", "runtime_learners", "timestamp", "batch_nr", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# exclude column
tab = as.data.table(instance$archive, exclude_columns = "timestamp")
expect_data_table(tab, nrows = 4, ncols = 9)
expect_named(tab, c("cp", "classif.ce", "x_domain_cp", "runtime_learners", "batch_nr", "uhash", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# exclude columns
tab = as.data.table(instance$archive, exclude_columns = c("timestamp", "uhash"))
expect_data_table(tab, nrows = 4, ncols = 8)
expect_named(tab, c("cp", "classif.ce", "x_domain_cp", "runtime_learners", "batch_nr", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# no exclude
tab = as.data.table(instance$archive, exclude_columns = NULL)
expect_data_table(tab, nrows = 4, ncols = 10)
expect_named(tab, c("cp", "classif.ce", "x_domain_cp", "runtime_learners", "timestamp", "batch_nr", "uhash", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# no unnest
tab = as.data.table(instance$archive, unnest = NULL)
expect_data_table(tab, nrows = 4, ncols = 9)
expect_named(tab, c("cp", "classif.ce" ,"runtime_learners", "timestamp", "batch_nr", "x_domain", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# without benchmark result
instance = TuningInstanceSingleCrit$new(task = tsk("pima"), learner = lrn("classif.rpart"),
resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = TEST_MAKE_PS1(n_dim = 1),
terminator = trm("evals", n_evals = 4), store_benchmark_result = FALSE)
tuner = tnr("random_search", batch_size = 2)
tuner$optimize(instance)
tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 4, ncols = 8)
expect_named(tab, c("cp", "classif.ce", "x_domain_cp", "runtime_learners", "timestamp", "batch_nr", "errors", "warnings"), ignore.order =TRUE)
# empty archive
instance = TuningInstanceSingleCrit$new(task = tsk("pima"), learner = lrn("classif.rpart"),
resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = TEST_MAKE_PS1(n_dim = 1),
terminator = trm("evals", n_evals = 4))
expect_data_table(as.data.table(instance$archive), nrows = 0, ncols = 0)
# new ids in x_domain
search_space = ps(
x1 = p_int(1, 12),
x2 = p_dbl(0.01, 0.1),
.extra_trafo = function(x, param_set) {
x$minsplit = x$x1
x$cp = x$x2
x$x1 = NULL
x$x2 = NULL
x
}
)
instance = tune(tuner = tnr("random_search", batch_size = 2), task = tsk("pima"), learner = lrn("classif.rpart"),
resampling = rsmp("holdout"), measures = msr("classif.ce"), search_space = search_space,
term_evals = 4)
tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 4, ncols = 11)
expect_named(tab, c("x1", "x2", "classif.ce", "x_domain_minsplit", "x_domain_cp", "runtime_learners", "timestamp", "batch_nr", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# new ids in x_domain switch
search_space = ps(
x1 = p_int(1, 12),
x2 = p_dbl(0.01, 0.1),
.extra_trafo = function(x, param_set) {
if (x$x1 > 3) x$minsplit = x$x1
x$cp = x$x2
x$x1 = NULL
x$x2 = NULL
x
}
)
instance = tune(tuner = tnr("random_search", batch_size = 2), task = tsk("pima"), learner = lrn("classif.rpart"),
resampling = rsmp("holdout"), measures = msr("classif.ce"), search_space = search_space,
term_evals = 100)
tab = as.data.table(instance$archive)
expect_data_table(tab, nrows = 100, ncols = 11)
expect_named(tab, c("x1", "x2", "classif.ce", "x_domain_minsplit", "x_domain_cp", "runtime_learners", "timestamp", "batch_nr", "resample_result", "errors", "warnings"), ignore.order =TRUE)
# row order
instance = TuningInstanceSingleCrit$new(task = tsk("pima"), learner = lrn("classif.rpart"),
resampling = rsmp("holdout"), measure = msr("classif.ce"), search_space = TEST_MAKE_PS1(n_dim = 1),
terminator = trm("evals", n_evals = 10))
tuner = tnr("random_search", batch_size = 1)
tuner$optimize(instance)
tab = as.data.table(instance$archive)
expect_equal(tab$batch_nr, 1:10)
})
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