@@ -92,24 +92,24 @@ test_that("ResultAssignerSurrogate passes internal tuned values", {
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validate = 0.2 ,
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early_stopping = TRUE ,
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x = to_tune(0.2 , 0.3 ),
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- iter = to_tune(upper = 1000 , internal = TRUE , aggr = function (x ) 99 ))
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+ iter = to_tune(upper = 1000L , internal = TRUE , aggr = function (x ) 99L ))
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instance = ti(
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task = tsk(" pima" ),
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learner = learner ,
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- resampling = rsmp(" cv" , folds = 3 ),
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+ resampling = rsmp(" cv" , folds = 3L ),
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measures = msr(" classif.ce" ),
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- terminator = trm(" evals" , n_evals = 20 ),
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+ terminator = trm(" evals" , n_evals = 20L ),
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store_benchmark_result = TRUE
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)
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surrogate = SurrogateLearner $ new(REGR_KM_DETERM )
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acq_function = AcqFunctionEI $ new()
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acq_optimizer = AcqOptimizer $ new(opt(" random_search" , batch_size = 2L ), terminator = trm(" evals" , n_evals = 2L ))
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tuner = tnr(" mbo" , result_assigner = result_assigner )
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- expect_data_table(tuner $ optimize(instance ), nrows = 1 )
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- expect_list(instance $ archive $ data $ internal_tuned_values , len = 20 , types = " list" )
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- expect_equal(instance $ archive $ data $ internal_tuned_values [[1 ]], list ( iter = 99 ) )
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+ expect_data_table(tuner $ optimize(instance ), nrows = 1L )
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+ expect_list(instance $ archive $ data $ internal_tuned_values , len = 20L , types = " list" )
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+ expect_equal(instance $ archive $ data $ internal_tuned_values [[1L ]] $ iter , 99L )
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expect_false(instance $ result_learner_param_vals $ early_stopping )
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- expect_equal(instance $ result_learner_param_vals $ iter , 99 )
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+ expect_equal(instance $ result_learner_param_vals $ iter , 99L )
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})
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