Upload blackbox yahpo-rbv2_super

yahpo/rbv2_super/best_params_resnet.json

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+{"d": 512, "d_hidden_factor": 3.141331955873597, "hidden_dropout": 0.15835721248635748, "lr": 0.004207643040634978, "mixup": false, "n_layers": 1, "opt_tfms_acc": true, "opt_tfms_aknn.M": false, "opt_tfms_aknn.ef": false, "opt_tfms_aknn.ef_construction": false, "opt_tfms_aknn.k": true, "opt_tfms_auc": true, "opt_tfms_bac": true, "opt_tfms_brier": true, "opt_tfms_f1": false, "opt_tfms_glmnet.alpha": false, "opt_tfms_glmnet.s": false, "opt_tfms_logloss": true, "opt_tfms_memory": true, "opt_tfms_ranger.min.node.size": false, "opt_tfms_ranger.mtry.power": false, "opt_tfms_ranger.num.random.splits": true, "opt_tfms_ranger.num.trees": false, "opt_tfms_ranger.sample.fraction": true, "opt_tfms_repl": true, "opt_tfms_rpart.cp": false, "opt_tfms_rpart.maxdepth": false, "opt_tfms_rpart.minbucket": true, "opt_tfms_rpart.minsplit": false, "opt_tfms_svm.cost": true, "opt_tfms_svm.degree": false, "opt_tfms_svm.gamma": false, "opt_tfms_svm.tolerance": false, "opt_tfms_timepredict": false, "opt_tfms_timetrain": false, "opt_tfms_trainsize": false, "opt_tfms_xgboost.alpha": false, "opt_tfms_xgboost.colsample_bylevel": false, "opt_tfms_xgboost.colsample_bytree": true, "opt_tfms_xgboost.eta": false, "opt_tfms_xgboost.gamma": true, "opt_tfms_xgboost.lambda": false, "opt_tfms_xgboost.max_depth": false, "opt_tfms_xgboost.min_child_weight": true, "opt_tfms_xgboost.nrounds": true, "opt_tfms_xgboost.rate_drop": false, "opt_tfms_xgboost.skip_drop": true, "opt_tfms_xgboost.subsample": true, "residual_dropout": 0.03256172522342702, "tfms_acc": "tlog", "tfms_aknn.k": "tnexp", "tfms_auc": "tlog", "tfms_bac": "tnexp", "tfms_brier": "tlog", "tfms_logloss": "tlog", "tfms_memory": "tnexp", "tfms_ranger.num.random.splits": "tlog", "tfms_ranger.sample.fraction": "tnexp", "tfms_repl": "tnexp", "tfms_rpart.minbucket": "tlog", "tfms_svm.cost": "tlog", "tfms_xgboost.colsample_bytree": "tnexp", "tfms_xgboost.gamma": "tlog", "tfms_xgboost.min_child_weight": "tlog", "tfms_xgboost.nrounds": "tlog", "tfms_xgboost.skip_drop": "tlog", "tfms_xgboost.subsample": "tlog", "use_residual_dropout": true}

yahpo/rbv2_super/config_space.json

@@ -0,0 +1,727 @@
+{
+  "hyperparameters": [
+    {
+      "name": "learner_id",
+      "type": "categorical",
+      "choices": [
+        "aknn",
+        "glmnet",
+        "ranger",
+        "rpart",
+        "svm",
+        "xgboost"
+      ],
+      "default": "aknn",
+      "probabilities": null
+    },
+    {
+      "name": "num.impute.selected.cpo",
+      "type": "categorical",
+      "choices": [
+        "impute.mean",
+        "impute.median",
+        "impute.hist"
+      ],
+      "default": "impute.mean",
+      "probabilities": null
+    },
+    {
+      "name": "repl",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 10,
+      "default": 6
+    },
+    {
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+      "default": "1040",
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+    {
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+      "lower": 0.03,
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+      "lower": 18,
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+    {
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+      "choices": [
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+        "cosine",
+        "ip"
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+      "lower": 7,
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+      "choices": [
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+    {
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+      "value": "aknn"
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+    {
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+      "type": "EQ",
+      "value": "aknn"
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+    {
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+      "parent": "learner_id",
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+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "ranger"
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+    {
+      "child": "ranger.mtry.power",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.num.trees",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "ranger"
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+    {
+      "child": "ranger.respect.unordered.factors",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.sample.fraction",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.splitrule",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "rpart.cp",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "rpart"
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+    {
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+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "rpart"
+    },
+    {
+      "child": "rpart.minbucket",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "rpart"
+    },
+    {
+      "child": "rpart.minsplit",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "rpart"
+    },
+    {
+      "child": "svm.cost",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "svm"
+    },
+    {
+      "child": "svm.kernel",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "svm"
+    },
+    {
+      "child": "svm.tolerance",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "svm"
+    },
+    {
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+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "xgboost"
+    },
+    {
+      "child": "xgboost.booster",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "xgboost"
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+    {
+      "child": "xgboost.lambda",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "xgboost"
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+    {
+      "child": "xgboost.nrounds",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "xgboost"
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+    {
+      "child": "xgboost.subsample",
+      "parent": "learner_id",
+      "type": "EQ",
+      "value": "xgboost"
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+    {
+      "child": "ranger.num.random.splits",
+      "type": "AND",
+      "conditions": [
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+          "parent": "ranger.splitrule",
+          "type": "EQ",
+          "value": "extratrees"
+        },
+        {
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+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "ranger"
+        }
+      ]
+    },
+    {
+      "child": "svm.degree",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "svm.degree",
+          "parent": "svm.kernel",
+          "type": "EQ",
+          "value": "polynomial"
+        },
+        {
+          "child": "svm.degree",
+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "svm"
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+    {
+      "child": "svm.gamma",
+      "type": "AND",
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+          "value": "radial"
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+        {
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+    {
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+      "type": "AND",
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+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
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+      ]
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+    {
+      "child": "xgboost.colsample_bytree",
+      "type": "AND",
+      "conditions": [
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+          "parent": "xgboost.booster",
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+          ]
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+        {
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+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
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+      ]
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+    {
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+      "type": "AND",
+      "conditions": [
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+          "parent": "xgboost.booster",
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+          "values": [
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+          ]
+        },
+        {
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+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.gamma",
+      "type": "AND",
+      "conditions": [
+        {
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+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
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+          ]
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+        {
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+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.max_depth",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.max_depth",
+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
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+            "gbtree"
+          ]
+        },
+        {
+          "child": "xgboost.max_depth",
+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.min_child_weight",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.min_child_weight",
+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
+            "dart",
+            "gbtree"
+          ]
+        },
+        {
+          "child": "xgboost.min_child_weight",
+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.rate_drop",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.rate_drop",
+          "parent": "xgboost.booster",
+          "type": "EQ",
+          "value": "dart"
+        },
+        {
+          "child": "xgboost.rate_drop",
+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.skip_drop",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.skip_drop",
+          "parent": "xgboost.booster",
+          "type": "EQ",
+          "value": "dart"
+        },
+        {
+          "child": "xgboost.skip_drop",
+          "parent": "learner_id",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    }
+  ],
+  "forbiddens": [],
+  "python_module_version": "0.4.18",
+  "json_format_version": 0.2
+}

yahpo/rbv2_super/encoding.json

@@ -0,0 +1 @@
+{"aknn.distance": {"#na#": 0, "cosine": 1, "ip": 2, "l2": 3}, "learner_id": {"#na#": 0, "aknn": 1, "glmnet": 2, "ranger": 3, "rpart": 4, "svm": 5, "xgboost": 6}, "num.impute.selected.cpo": {"#na#": 0, "impute.hist": 1, "impute.mean": 2, "impute.median": 3}, "ranger.respect.unordered.factors": {"#na#": 0, "ignore": 1, "order": 2, "partition": 3}, "ranger.splitrule": {"#na#": 0, "extratrees": 1, "gini": 2}, "svm.kernel": {"#na#": 0, "linear": 1, "polynomial": 2, "radial": 3}, "task_id": {"#na#": 0, "1040": 1, "1049": 2, "1050": 3, "1053": 4, "1056": 5, "1063": 6, "1067": 7, "1068": 8, "11": 9, "1111": 10, "12": 11, "1220": 12, "14": 13, "1457": 14, "1461": 15, "1462": 16, "1464": 17, "1468": 18, "1475": 19, "1476": 20, "1478": 21, "1479": 22, "1480": 23, "1485": 24, "1486": 25, "1487": 26, "1489": 27, "1493": 28, "1494": 29, "1497": 30, "15": 31, "1501": 32, "151": 33, "1510": 34, "1515": 35, "1590": 36, "16": 37, "18": 38, "181": 39, "182": 40, "188": 41, "22": 42, "23": 43, "23381": 44, "24": 45, "28": 46, "29": 47, "3": 48, "300": 49, "307": 50, "31": 51, "312": 52, "32": 53, "334": 54, "37": 55, "375": 56, "377": 57, "38": 58, "40496": 59, "40498": 60, "40499": 61, "40536": 62, "40668": 63, "40670": 64, "40685": 65, "40701": 66, "40900": 67, "40966": 68, "40975": 69, "40978": 70, "40979": 71, "40981": 72, "40982": 73, "40983": 74, "40984": 75, "40994": 76, "41027": 77, "41138": 78, "41142": 79, "41143": 80, "41146": 81, "41156": 82, "41157": 83, "41162": 84, "41163": 85, "41164": 86, "41169": 87, "41212": 88, "4134": 89, "4154": 90, "42": 91, "44": 92, "4534": 93, "4538": 94, "458": 95, "46": 96, "469": 97, "470": 98, "50": 99, "54": 100, "6": 101, "60": 102, "6332": 103}, "xgboost.booster": {"#na#": 0, "dart": 1, "gblinear": 2, "gbtree": 3}}

yahpo/rbv2_super/metadata.json

@@ -0,0 +1 @@
+{"metric_elapsed_time": "time", "metric_default": "val_accuracy", "resource_attr": "st_worker_iter"}

yahpo/rbv2_super/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28b25f92be0623bce7809cda0a68670046bc48a994c6081e5fe3b54257581ccd
+size 10227923

yahpo/rbv2_super/param_set.R

@@ -0,0 +1,150 @@
+search_space = ps(
+    # svm
+    svm.kernel = p_fct(levels = c("linear", "polynomial", "radial")),
+    svm.cost = p_dbl(lower = -10, upper = 10, tags = "log", trafo = function(x) exp(x)),
+    svm.gamma = p_dbl(lower = -10, upper = 10, tags = "log", trafo = function(x) exp(x), depends = svm.kernel == "radial"),
+    svm.tolerance = p_dbl(lower = -10, upper = log(2), tags = "log", trafo = function(x) exp(x)),
+    svm.degree = p_int(lower = 2L, upper = 5L, depends = svm.kernel == "polynomial"),
+    # glmnet
+    glmnet.alpha = p_dbl(lower = 0, upper = 1),
+    glmnet.s = p_dbl(lower = -7, upper = 7, tags = "log", trafo = function(x) exp(x)),
+    # rpart
+    rpart.cp = p_dbl(lower = -7, upper = 0, tags = "log", trafo = function(x) exp(x)),
+    rpart.maxdepth = p_int(lower = 1L, upper = 30L),
+    rpart.minbucket = p_int(lower = 1L, upper = 100L),
+    rpart.minsplit = p_int(lower = 1L, upper = 100L),
+    # ranger
+    ranger.num.trees = p_int(lower = 1L, upper = 2000L),
+    ranger.sample.fraction = p_dbl(lower = 0.1, upper = 1),
+    ranger.mtry.power = p_int(lower = 0, upper = 1),
+    ranger.respect.unordered.factors = p_fct(levels = c("ignore", "order", "partition")),
+    ranger.min.node.size = p_int(lower = 1L, upper = 100L),
+    ranger.splitrule = p_fct(levels = c("gini", "extratrees")),
+    ranger.num.random.splits = p_int(lower = 1L, upper = 100L, depends = ranger.splitrule == "extratrees"),
+    # aknn
+    aknn.k = p_int(lower = 1L, upper = 50L),
+    aknn.distance = p_fct(levels = c("l2", "cosine", "ip")),
+    aknn.M = p_int(lower = 18L, upper = 50L),
+    aknn.ef = p_dbl(lower = 2, upper = 6, tags = c("int", "log"), trafo = function(x) as.integer(round(exp(x)))),
+    aknn.ef_construction = p_dbl(lower = 2, upper = 7, tags = c("int", "log"), trafo = function(x) as.integer(round(exp(x)))),
+    # xgboost
+    xgboost.booster = p_fct(levels = c("gblinear", "gbtree", "dart")),
+    xgboost.nrounds = p_dbl(lower = 2, upper = 8, tags = c("int", "log"), trafo = function(x) as.integer(round(exp(x)))),
+    xgboost.eta = p_dbl(lower = -7, upper = 0, tags = "log", trafo = function(x) exp(x), depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.gamma = p_dbl(lower = -10, upper = 2, tags = "log", trafo = function(x) exp(x), depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.lambda = p_dbl(lower = -7, upper = 7, tags = "log", trafo = function(x) exp(x)),
+    xgboost.alpha = p_dbl(lower = -7, upper = 7, tags = "log", trafo = function(x) exp(x)),
+    xgboost.subsample = p_dbl(lower = 0.1, upper = 1),
+    xgboost.max_depth = p_int(lower = 1L, upper = 15L, depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.min_child_weight = p_dbl(lower = 1, upper = 5, tags = "log", trafo = function(x) exp(x), depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.colsample_bytree = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.colsample_bylevel = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.rate_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart"),
+    xgboost.skip_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart"),
+    trainsize = p_dbl(lower = 0.03, upper = 1, tags = "budget"),
+    repl = p_int(lower = 1L, upper = 10L, tags = "budget"),
+    num.impute.selected.cpo = p_fct(levels = c("impute.mean", "impute.median", "impute.hist")),
+    learner_id = p_fct(levels = c("aknn", "glmnet", "ranger", "rpart", "svm", "xgboost")),
+    task_id = p_fct(levels = c("41138", "40981", "4134", "1220", "4154", "41163", "4538",
+    "40978", "375", "1111", "40496", "40966", "4534", "40900", "40536",
+    "41156", "1590", "1457", "458", "469", "41157", "11", "1461",
+    "1462", "1464", "15", "40975", "41142", "40701", "40994", "23",
+    "1468", "40668", "29", "31", "6332", "37", "40670", "23381",
+    "151", "188", "41164", "1475", "1476", "1478", "41169", "1479",
+    "41212", "1480", "300", "41143", "1053", "41027", "1067", "1063",
+    "41162", "3", "6", "1485", "1056", "12", "14", "16", "18", "40979",
+    "22", "1515", "334", "24", "1486", "1493", "28", "1487", "1068",
+    "1050", "1049", "32", "1489", "470", "1494", "182", "312", "40984",
+    "1501", "40685", "38", "42", "44", "46", "40982", "1040", "41146",
+    "377", "40499", "50", "54", "307", "1497", "60", "1510", "40983",
+    "40498", "181"),
+      tags = "task_id"
+    )
+)
+# Add dependencies
+map(search_space$params$learner_id$levels, function(x) {
+    nms = names(search_space$params)[startsWith(names(search_space$params), x)]
+    map(nms, function(nm) search_space$add_dep(nm, "learner_id", CondEqual$new(x)))
+})
+
+domain = ps(
+    # svm
+    svm.kernel = p_fct(levels = c("linear", "polynomial", "radial")),
+    svm.cost = p_dbl(lower = exp(-10), upper = exp(10)),
+    svm.gamma = p_dbl(lower = exp(-10), upper = exp(10), depends = svm.kernel == "radial"),
+    svm.tolerance = p_dbl(lower = exp(-10), upper = 2),
+    svm.degree = p_int(lower = 2L, upper = 5L, depends = svm.kernel == "polynomial"),
+    # glmnet
+    glmnet.alpha = p_dbl(lower = 0, upper = 1),
+    glmnet.s = p_dbl(lower = exp(-7), upper = exp(7)),
+    # rpart
+    rpart.cp = p_dbl(lower = exp(-7), upper = exp(0)),
+    rpart.maxdepth = p_int(lower = 1L, upper = 30L),
+    rpart.minbucket = p_int(lower = 1L, upper = 100L),
+    rpart.minsplit = p_int(lower = 1L, upper = 100L),
+    # ranger
+    ranger.num.trees = p_int(lower = 1L, upper = 2000L),
+    ranger.sample.fraction = p_dbl(lower = 0.1, upper = 1),
+    ranger.mtry.power = p_dbl(lower = 0, upper = 1),
+    ranger.respect.unordered.factors = p_fct(levels = c("ignore", "order", "partition")),
+    ranger.min.node.size = p_int(lower = 1L, upper = 100L),
+    ranger.splitrule = p_fct(levels = c("gini", "extratrees")),
+    ranger.num.random.splits = p_int(lower = 1, upper = 100L, depends = ranger.splitrule == "extratrees"),
+    # aknn
+    aknn.k = p_int(lower = 1L, upper = 50L),
+    aknn.distance = p_fct(levels = c("l2", "cosine", "ip")),
+    aknn.M = p_int(lower = 18L, upper = 50L),
+    aknn.ef = p_int(lower = 7L, upper = 403L),
+    aknn.ef_construction = p_int(lower = 7L, upper = 403L),
+    # xgboost
+    xgboost.booster = p_fct(levels = c("gblinear", "gbtree", "dart")),
+    xgboost.nrounds = p_int(lower = 7L, upper = 2981L),
+    xgboost.eta = p_dbl(lower = exp(-7), upper = exp(0),depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.gamma = p_dbl(lower = exp(-10), upper = exp(2), depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.lambda = p_dbl(lower = exp(-7), upper = exp(7)),
+    xgboost.alpha = p_dbl(lower = exp(-7), upper = exp(7)),
+    xgboost.subsample = p_dbl(lower = 0.1, upper = 1),
+    xgboost.max_depth = p_int(lower = 1L, upper = 15L, depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.min_child_weight = p_dbl(lower = exp(1), upper = exp(5), depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.colsample_bytree = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.colsample_bylevel = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree")),
+    xgboost.rate_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart"),
+    xgboost.skip_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart"),
+    # learner_id
+    trainsize = p_dbl(lower = 0.03, upper = 1, tags = "budget"),
+    repl = p_int(lower = 1L, upper = 10L, tags = "budget"),
+    num.impute.selected.cpo = p_fct(levels = c("impute.mean", "impute.median", "impute.hist")),
+    learner_id = p_fct(levels = c("aknn", "glmnet", "ranger", "rpart", "svm", "xgboost")),
+    task_id = p_fct(levels = c("41138", "40981", "4134", "1220", "4154", "41163", "4538",
+    "40978", "375", "1111", "40496", "40966", "4534", "40900", "40536",
+    "41156", "1590", "1457", "458", "469", "41157", "11", "1461",
+    "1462", "1464", "15", "40975", "41142", "40701", "40994", "23",
+    "1468", "40668", "29", "31", "6332", "37", "40670", "23381",
+    "151", "188", "41164", "1475", "1476", "1478", "41169", "1479",
+    "41212", "1480", "300", "41143", "1053", "41027", "1067", "1063",
+    "41162", "3", "6", "1485", "1056", "12", "14", "16", "18", "40979",
+    "22", "1515", "334", "24", "1486", "1493", "28", "1487", "1068",
+    "1050", "1049", "32", "1489", "470", "1494", "182", "312", "40984",
+    "1501", "40685", "38", "42", "44", "46", "40982", "1040", "41146",
+    "377", "40499", "50", "54", "307", "1497", "60", "1510", "40983",
+    "40498", "181"),
+      tags = "task_id"
+    )
+)
+# Add dependencies
+map(domain$params$learner_id$levels, function(x) {
+    nms = names(domain$params)[startsWith(names(domain$params), x)]
+    map(nms, function(nm) domain$add_dep(nm, "learner_id", CondEqual$new(x)))
+})
+
+codomain = ps(
+  acc = p_dbl(lower = 0, upper = 1, tags = "maximize"),
+  bac = p_dbl(lower = 0, upper = 1, tags = "maximize"),
+  f1 = p_dbl(lower = 0, upper = 1, tags = "maximize"),
+  auc = p_dbl(lower = 0, upper = 1, tags = "maximize"),
+  brier = p_dbl(lower = 0, upper = 1, tags = "minimize"),
+  logloss = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  timetrain = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  timepredict = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  memory = p_dbl(lower = 0, upper = Inf, tags = "minimize")
+)