Nothing

```
context("PipeOpEnsemble")
test_that("PipeOpEnsemble - basic properties", {
op = PipeOpEnsemble$new(4, id = "ensemble", param_vals = list())
expect_pipeop(op)
expect_pipeop_class(PipeOpEnsemble, list(3, id = "ensemble", param_vals = list()))
expect_pipeop_class(PipeOpEnsemble, list(1, id = "ensemble", param_vals = list()))
expect_pipeop_class(PipeOpEnsemble, list(0, id = "ensemble", param_vals = list()))
truth = rnorm(70)
prds = replicate(4, PredictionRegr$new(row_ids = seq_len(70), truth = truth, response = truth + rnorm(70, sd = 0.1)))
expect_list(train_pipeop(op, rep(list(NULL), 4)), len = 1)
expect_error(predict_pipeop(op, prds), "Abstract")
op = PipeOpEnsemble$new(0, id = "ensemble", param_vals = list())
expect_pipeop(op)
# collect_multiplicity = TRUE
op = PipeOpEnsemble$new(0, collect_multiplicity = TRUE, id = "ensemble", param_vals = list())
expect_pipeop(op)
expect_list(train_pipeop(op, list(as.Multiplicity(rep(list(NULL), 4)))), len = 1)
expect_error(predict_pipeop(op, list(as.Multiplicity(prds))), "Abstract")
expect_error(PipeOpEnsemble$new(1, collect_multiplicity = TRUE, id = "ensemble", param_vals = list()), regexp = "collect_multiplicity only works with innum == 0")
})
test_that("PipeOpWeightedRegrAvg - train and predict", {
# Create 4 predictions
truth = rnorm(70)
prds = replicate(4, PredictionRegr$new(row_ids = seq_len(70), truth = truth, response = truth + rnorm(70, sd = 0.1)), simplify = FALSE)
po = PipeOpRegrAvg$new(4)
expect_pipeop(po)
expect_list(train_pipeop(po, rep(list(NULL), 4)), len = 1)
out = predict_pipeop(po, prds)
# Returns the same if weights are 1, rest 0
po = PipeOpRegrAvg$new(4)
po$param_set$values$weights = c(0, 0, 1, 0)
expect_list(train_pipeop(po, rep(list(NULL), 4)), len = 1)
out = predict_pipeop(po, prds)
expect_equal(out, list(output = prds[[3]]))
po = PipeOpRegrAvg$new()
expect_pipeop(po)
expect_list(train_pipeop(po, rep(list(NULL), 4)), len = 1)
out = predict_pipeop(po, prds)
# Returns the same if weights are 1, rest 0
po = PipeOpRegrAvg$new()
po$param_set$values$weights = c(0, 0, 1, 0)
expect_list(train_pipeop(po, rep(list(NULL), 4)), len = 1)
out = predict_pipeop(po, prds)
expect_equal(out, list(output = prds[[3]]))
})
## test_that("PipeOpNlOptRegrAvg - response - train and predict", {
## truth = rnorm(70)
## prds = replicate(7, set_class(list(row_ids = seq_len(70), response = truth + rnorm(70, sd = 0.1)),
## c("PredictionRegr", "Prediction")), simplify = FALSE)
## po = PipeOpNlOptRegrAvg$new(7)
## expect_pipeop(po)
## expect_list(train_pipeop(po, prds), len = 1)
## out = predict_pipeop(po, prds)
## expect_class(out[[1]], "PredictionRegr")
## })
test_that("PipeOpWeightedClassifAvg - response - train and predict", {
nulls = rep(list(NULL), 4)
prds = replicate(4,
make_prediction_obj_classif(n = 100, noise = TRUE,
predict_types = "response", nclasses = 3),
simplify = FALSE
)
lapply(prds, function(x) x$data$tab$truth = prds[[1]]$data$tab$truth) # works because of R6 reference semantics
po = PipeOpClassifAvg$new(4)
expect_pipeop(po)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
po = PipeOpClassifAvg$new(4)
po$param_set$values$weights = c(0, 0, 0, 1)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
expect_equal(out[[1]]$data$tab, prds[[4]]$data$tab)
po = PipeOpClassifAvg$new()
expect_pipeop(po)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
po = PipeOpClassifAvg$new()
po$param_set$values$weights = c(0, 0, 0, 1)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
expect_equal(out[[1]]$data$tab, prds[[4]]$data$tab)
})
test_that("PipeOpWeightedClassifAvg - prob - train and predict", {
nulls = rep(list(NULL), 4)
prds = replicate(4,
make_prediction_obj_classif(n = 100, noise = TRUE,
predict_types = c("response", "prob"), nclasses = 3),
simplify = FALSE
)
lapply(prds, function(x) x$data$truth = prds[[1]]$data$truth) # works because of R6 reference semantics
po = PipeOpClassifAvg$new(4)
expect_pipeop(po)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
po = PipeOpClassifAvg$new(4)
po$param_set$values$weights = c(0, 0, 0, 1)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
expect_equivalent(as.data.table(out[[1]]), as.data.table(prds[[4]]))
po = PipeOpClassifAvg$new()
expect_pipeop(po)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
po = PipeOpClassifAvg$new()
po$param_set$values$weights = c(0, 0, 0, 1)
expect_list(train_pipeop(po, nulls), len = 1)
out = predict_pipeop(po, prds)
expect_class(out[[1]], "PredictionClassif")
expect_equivalent(as.data.table(out[[1]]), as.data.table(prds[[4]]))
})
## test_that("PipeOpNlOptClassifAvg - response - train and predict", {
## prds = replicate(5,
## make_prediction_obj_classif(n = 100, noise = TRUE,
## predict_types = c("response"), nclasses = 3)
## )
## po = PipeOpNlOptClassifAvg$new(5)
## expect_pipeop(po)
## expect_list(train_pipeop(po, prds), len = 1)
## out = predict_pipeop(po, prds)
## expect_class(out[[1]], "PredictionClassif")
## })
## test_that("PipeOpNlOptClassifAvg - prob - train and predict", {
## prds = replicate(3,
## make_prediction_obj_classif(n = 100, noise = TRUE,
## predict_types = c("response", "prob"), nclasses = 2)
## )
## po = PipeOpNlOptClassifAvg$new(3)
## expect_pipeop(po)
## expect_list(train_pipeop(po, prds), len = 1)
## out = predict_pipeop(po, prds)
## expect_numeric(po$state$weights)
## expect_class(out[[1]], "PredictionClassif")
## po = PipeOpNlOptClassifAvg$new(3)
## expect_pipeop(po)
## expect_list(train_pipeop(po, prds), len = 1)
## out = predict_pipeop(po, prds)
## expect_numeric(po$state$weights)
## expect_class(out[[1]], "PredictionClassif")
## })
```

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