Upload blackbox yahpo-iaml_super

yahpo-iaml_super/best_params_resnet.json

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+{"d": 512, "d_hidden_factor": 3.9989508868016372, "hidden_dropout": 0.04468688309244354, "lr": 0.00011999115120321, "mixup": true, "n_layers": 6, "opt_tfms_auc": false, "opt_tfms_f1": false, "opt_tfms_glmnet.alpha": false, "opt_tfms_glmnet.s": true, "opt_tfms_ias": true, "opt_tfms_logloss": false, "opt_tfms_mec": false, "opt_tfms_mmce": true, "opt_tfms_rammodel": false, "opt_tfms_rampredict": false, "opt_tfms_ramtrain": false, "opt_tfms_ranger.min.node.size": true, "opt_tfms_ranger.mtry.ratio": false, "opt_tfms_ranger.num.random.splits": false, "opt_tfms_ranger.num.trees": true, "opt_tfms_ranger.sample.fraction": true, "opt_tfms_rpart.cp": false, "opt_tfms_rpart.maxdepth": false, "opt_tfms_rpart.minbucket": true, "opt_tfms_rpart.minsplit": false, "opt_tfms_timepredict": true, "opt_tfms_timetrain": false, "opt_tfms_trainsize": false, "opt_tfms_xgboost.alpha": false, "opt_tfms_xgboost.colsample_bylevel": false, "opt_tfms_xgboost.colsample_bytree": false, "opt_tfms_xgboost.eta": true, "opt_tfms_xgboost.gamma": false, "opt_tfms_xgboost.lambda": true, "opt_tfms_xgboost.max_depth": true, "opt_tfms_xgboost.min_child_weight": true, "opt_tfms_xgboost.nrounds": true, "opt_tfms_xgboost.rate_drop": true, "opt_tfms_xgboost.skip_drop": false, "opt_tfms_xgboost.subsample": false, "tfms_glmnet.s": "tlog", "tfms_ias": "tnexp", "tfms_mmce": "tlog", "tfms_ranger.min.node.size": "tnexp", "tfms_ranger.num.trees": "tlog", "tfms_ranger.sample.fraction": "tnexp", "tfms_rpart.minbucket": "tlog", "tfms_timepredict": "tnexp", "tfms_xgboost.eta": "tlog", "tfms_xgboost.lambda": "tlog", "tfms_xgboost.max_depth": "tlog", "tfms_xgboost.min_child_weight": "tnexp", "tfms_xgboost.nrounds": "tlog", "tfms_xgboost.rate_drop": "tlog", "use_residual_dropout": false}

yahpo-iaml_super/config_space.json

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+{
+  "hyperparameters": [
+    {
+      "name": "learner",
+      "type": "categorical",
+      "choices": [
+        "ranger",
+        "glmnet",
+        "xgboost",
+        "rpart"
+      ],
+      "default": "ranger",
+      "probabilities": null
+    },
+    {
+      "name": "task_id",
+      "type": "categorical",
+      "choices": [
+        "40981",
+        "41146",
+        "1489",
+        "1067"
+      ],
+      "default": "40981",
+      "probabilities": null
+    },
+    {
+      "name": "trainsize",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.03,
+      "upper": 1.0,
+      "default": 0.525
+    },
+    {
+      "name": "glmnet.alpha",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.0,
+      "upper": 1.0,
+      "default": 0.5
+    },
+    {
+      "name": "glmnet.s",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.00010000000000000009,
+      "upper": 999.9999999999998,
+      "default": 0.316227766
+    },
+    {
+      "name": "ranger.min.node.size",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 100,
+      "default": 50
+    },
+    {
+      "name": "ranger.mtry.ratio",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.0,
+      "upper": 1.0,
+      "default": 0.5
+    },
+    {
+      "name": "ranger.num.trees",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 2000,
+      "default": 1000
+    },
+    {
+      "name": "ranger.replace",
+      "type": "categorical",
+      "choices": [
+        "TRUE",
+        "FALSE"
+      ],
+      "default": "TRUE",
+      "probabilities": null
+    },
+    {
+      "name": "ranger.respect.unordered.factors",
+      "type": "categorical",
+      "choices": [
+        "ignore",
+        "order",
+        "partition"
+      ],
+      "default": "ignore",
+      "probabilities": null
+    },
+    {
+      "name": "ranger.sample.fraction",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.1,
+      "upper": 1.0,
+      "default": 0.55
+    },
+    {
+      "name": "ranger.splitrule",
+      "type": "categorical",
+      "choices": [
+        "gini",
+        "extratrees"
+      ],
+      "default": "gini",
+      "probabilities": null
+    },
+    {
+      "name": "rpart.cp",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.00010000000000000009,
+      "upper": 1.0,
+      "default": 0.01
+    },
+    {
+      "name": "rpart.maxdepth",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 30,
+      "default": 16
+    },
+    {
+      "name": "rpart.minbucket",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 100,
+      "default": 50
+    },
+    {
+      "name": "rpart.minsplit",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 100,
+      "default": 50
+    },
+    {
+      "name": "xgboost.alpha",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.00010000000000000009,
+      "upper": 999.9999999999998,
+      "default": 0.316227766
+    },
+    {
+      "name": "xgboost.booster",
+      "type": "categorical",
+      "choices": [
+        "gblinear",
+        "gbtree",
+        "dart"
+      ],
+      "default": "gblinear",
+      "probabilities": null
+    },
+    {
+      "name": "xgboost.lambda",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.00010000000000000009,
+      "upper": 999.9999999999998,
+      "default": 0.316227766
+    },
+    {
+      "name": "xgboost.nrounds",
+      "type": "uniform_int",
+      "log": true,
+      "lower": 3,
+      "upper": 2000,
+      "default": 77
+    },
+    {
+      "name": "xgboost.subsample",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.1,
+      "upper": 1.0,
+      "default": 0.55
+    },
+    {
+      "name": "ranger.num.random.splits",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 100,
+      "default": 50
+    },
+    {
+      "name": "xgboost.colsample_bylevel",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.01,
+      "upper": 1.0,
+      "default": 0.505
+    },
+    {
+      "name": "xgboost.colsample_bytree",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.01,
+      "upper": 1.0,
+      "default": 0.505
+    },
+    {
+      "name": "xgboost.eta",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.00010000000000000009,
+      "upper": 1.0,
+      "default": 0.01
+    },
+    {
+      "name": "xgboost.gamma",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.00010000000000000009,
+      "upper": 6.999999999999999,
+      "default": 0.0264575131
+    },
+    {
+      "name": "xgboost.max_depth",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 15,
+      "default": 8
+    },
+    {
+      "name": "xgboost.min_child_weight",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 2.718281828459045,
+      "upper": 149.99999999999997,
+      "default": 20.1926292064
+    },
+    {
+      "name": "xgboost.rate_drop",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.0,
+      "upper": 1.0,
+      "default": 0.5
+    },
+    {
+      "name": "xgboost.skip_drop",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.0,
+      "upper": 1.0,
+      "default": 0.5
+    }
+  ],
+  "conditions": [
+    {
+      "child": "glmnet.alpha",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "glmnet"
+    },
+    {
+      "child": "glmnet.s",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "glmnet"
+    },
+    {
+      "child": "ranger.min.node.size",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.mtry.ratio",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.num.trees",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.replace",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.respect.unordered.factors",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.sample.fraction",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "ranger.splitrule",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "ranger"
+    },
+    {
+      "child": "rpart.cp",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "rpart"
+    },
+    {
+      "child": "rpart.maxdepth",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "rpart"
+    },
+    {
+      "child": "rpart.minbucket",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "rpart"
+    },
+    {
+      "child": "rpart.minsplit",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "rpart"
+    },
+    {
+      "child": "xgboost.alpha",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "xgboost"
+    },
+    {
+      "child": "xgboost.booster",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "xgboost"
+    },
+    {
+      "child": "xgboost.lambda",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "xgboost"
+    },
+    {
+      "child": "xgboost.nrounds",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "xgboost"
+    },
+    {
+      "child": "xgboost.subsample",
+      "parent": "learner",
+      "type": "EQ",
+      "value": "xgboost"
+    },
+    {
+      "child": "ranger.num.random.splits",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "ranger.num.random.splits",
+          "parent": "ranger.splitrule",
+          "type": "EQ",
+          "value": "extratrees"
+        },
+        {
+          "child": "ranger.num.random.splits",
+          "parent": "learner",
+          "type": "EQ",
+          "value": "ranger"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.colsample_bylevel",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.colsample_bylevel",
+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
+            "dart",
+            "gbtree"
+          ]
+        },
+        {
+          "child": "xgboost.colsample_bylevel",
+          "parent": "learner",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.colsample_bytree",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.colsample_bytree",
+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
+            "dart",
+            "gbtree"
+          ]
+        },
+        {
+          "child": "xgboost.colsample_bytree",
+          "parent": "learner",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.eta",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.eta",
+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
+            "dart",
+            "gbtree"
+          ]
+        },
+        {
+          "child": "xgboost.eta",
+          "parent": "learner",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.gamma",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.gamma",
+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
+            "dart",
+            "gbtree"
+          ]
+        },
+        {
+          "child": "xgboost.gamma",
+          "parent": "learner",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    },
+    {
+      "child": "xgboost.max_depth",
+      "type": "AND",
+      "conditions": [
+        {
+          "child": "xgboost.max_depth",
+          "parent": "xgboost.booster",
+          "type": "IN",
+          "values": [
+            "dart",
+            "gbtree"
+          ]
+        },
+        {
+          "child": "xgboost.max_depth",
+          "parent": "learner",
+          "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",
+          "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",
+          "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",
+          "type": "EQ",
+          "value": "xgboost"
+        }
+      ]
+    }
+  ],
+  "forbiddens": [],
+  "python_module_version": "0.4.19",
+  "json_format_version": 0.2
+}

yahpo-iaml_super/encoding.json

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+{"learner": {"#na#": 0, "glmnet": 1, "ranger": 2, "rpart": 3, "xgboost": 4}, "ranger.replace": {"#na#": 0, "FALSE": 1, "TRUE": 2}, "ranger.respect.unordered.factors": {"#na#": 0, "ignore": 1, "order": 2, "partition": 3}, "ranger.splitrule": {"#na#": 0, "extratrees": 1, "gini": 2}, "task_id": {"#na#": 0, "1067": 1, "1489": 2, "40981": 3, "41146": 4}, "xgboost.booster": {"#na#": 0, "dart": 1, "gblinear": 2, "gbtree": 3}}

yahpo-iaml_super/metadata.json

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+{"metric_elapsed_time": "time", "metric_default": "val_accuracy", "resource_attr": "st_worker_iter"}

yahpo-iaml_super/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9318f8f985dcf068838b55bebe404f1d19b19717d2cc32cf0f6465cebc2eba46
+size 75816168

yahpo-iaml_super/param_set.R

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+search_space = ps(
+  learner = p_fct(levels = c("ranger", "glmnet", "xgboost", "rpart")),
+  ranger.num.trees = p_int(lower = 1L, upper = 2000L, depends = learner == "ranger"),
+  ranger.replace = p_lgl(depends = learner == "ranger"),
+  ranger.sample.fraction = p_dbl(lower = 0.1, upper = 1, depends = learner == "ranger"),
+  ranger.mtry.ratio = p_dbl(lower = 0, upper = 1, depends = learner == "ranger"),
+  ranger.respect.unordered.factors = p_fct(levels = c("ignore", "order", "partition"), depends = learner == "ranger"),
+  ranger.min.node.size = p_int(lower = 1L, upper = 100L, depends = learner == "ranger"),
+  ranger.splitrule = p_fct(levels = c("gini", "extratrees"), depends = learner == "ranger"),
+  ranger.num.random.splits = p_int(lower = 1L, upper = 100L, depends = ranger.splitrule == "extratrees" && learner == "ranger"),
+
+  glmnet.alpha = p_dbl(lower = 0, upper = 1, depends = learner == "glmnet"),
+  glmnet.s = p_dbl(lower = log(1e-4), upper = log(1000), tags = "log", trafo = function(x) exp(x), depends = learner == "glmnet"),
+
+  xgboost.booster = p_fct(levels = c("gblinear", "gbtree", "dart"), depends = learner == "xgboost"),
+  xgboost.nrounds = p_dbl(lower = 1, upper = log(2000), tags = c("int", "log"), trafo = function(x) as.integer(round(exp(x))), depends = learner == "xgboost"),
+  xgboost.eta = p_dbl(lower = log(1e-4), upper = 0, tags = "log", trafo = function(x) exp(x), depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.gamma = p_dbl(lower = log(1e-4), upper = log(7), tags = "log", trafo = function(x) exp(x), depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.lambda = p_dbl(lower = log(1e-4), upper = log(1000), tags = "log", trafo = function(x) exp(x), depends = learner == "xgboost"),
+  xgboost.alpha = p_dbl(lower = log(1e-4), upper = log(1000), tags = "log", trafo = function(x) exp(x), depends = learner == "xgboost"),
+  xgboost.subsample = p_dbl(lower = 0.1, upper = 1, depends = learner == "xgboost"),
+  xgboost.max_depth = p_int(lower = 1L, upper = 15L, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.min_child_weight = p_dbl(lower = 1, upper = log(150), tags = "log", trafo = function(x) exp(x), depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.colsample_bytree = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.colsample_bylevel = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.rate_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart" && learner == "xgboost"),
+  xgboost.skip_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart" && learner == "xgboost"),
+
+  rpart.cp = p_dbl(lower = log(1e-4), upper = 0, tags = "log", trafo = function(x) exp(x), depends = learner == "rpart"),
+  rpart.maxdepth = p_int(lower = 1L, upper = 30L, depends = learner == "rpart"),
+  rpart.minbucket = p_int(lower = 1L, upper = 100L, depends = learner == "rpart"),
+  rpart.minsplit = p_int(lower = 1L, upper = 100L, depends = learner == "rpart"),
+
+  trainsize = p_dbl(lower = 0.03, upper = 1, tags = "budget"),
+  task_id = p_fct(levels = c("40981", "41146", "1489", "1067"), tags = "task_id")
+)
+
+domain = ps(
+  learner = p_fct(levels = c("ranger", "glmnet", "xgboost", "rpart")),
+  ranger.num.trees = p_int(lower = 1L, upper = 2000L, depends = learner == "ranger"),
+  ranger.replace = p_lgl(depends = learner == "ranger"),
+  ranger.sample.fraction = p_dbl(lower = 0.1, upper = 1, depends = learner == "ranger"),
+  ranger.mtry.ratio = p_dbl(lower = 0, upper = 1, depends = learner == "ranger"),
+  ranger.respect.unordered.factors = p_fct(levels = c("ignore", "order", "partition"), depends = learner == "ranger"),
+  ranger.min.node.size = p_int(lower = 1L, upper = 100L, depends = learner == "ranger"),
+  ranger.splitrule = p_fct(levels = c("gini", "extratrees"), depends = learner == "ranger"),
+  ranger.num.random.splits = p_int(lower = 1L, upper = 100L, depends = ranger.splitrule == "extratrees" && learner == "ranger"),
+
+  glmnet.alpha = p_dbl(lower = 0, upper = 1, depends = learner == "glmnet"),
+  glmnet.s = p_dbl(lower = 1e-4, upper = 1000, depends = learner == "glmnet"),
+
+  xgboost.booster = p_fct(levels = c("gblinear", "gbtree", "dart"), depends = learner == "xgboost"),
+  xgboost.nrounds = p_int(lower = 3, upper = 2000, depends = learner == "xgboost"),
+  xgboost.eta = p_dbl(lower = 1e-4, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.gamma = p_dbl(lower = 1e-4, upper = 7, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.lambda = p_dbl(lower = 1e-4, upper = 1000, depends = learner == "xgboost"),
+  xgboost.alpha = p_dbl(lower = 1e-4, upper = 1000, depends = learner == "xgboost"),
+  xgboost.subsample = p_dbl(lower = 0.1, upper = 1, depends = learner == "xgboost"),
+  xgboost.max_depth = p_int(lower = 1L, upper = 15L, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.min_child_weight = p_dbl(lower = exp(1), upper = 150, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.colsample_bytree = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.colsample_bylevel = p_dbl(lower = 0.01, upper = 1, depends = xgboost.booster %in% c("dart", "gbtree") && learner == "xgboost"),
+  xgboost.rate_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart" && learner == "xgboost"),
+  xgboost.skip_drop = p_dbl(lower = 0, upper = 1, depends = xgboost.booster == "dart" && learner == "xgboost"),
+
+  rpart.cp = p_dbl(lower = 1e-4, upper = 1, depends = learner == "rpart"),
+  rpart.maxdepth = p_int(lower = 1L, upper = 30L, depends = learner == "rpart"),
+  rpart.minbucket = p_int(lower = 1L, upper = 100L, depends = learner == "rpart"),
+  rpart.minsplit = p_int(lower = 1L, upper = 100L, depends = learner == "rpart"),
+
+  trainsize = p_dbl(lower = 0.03, upper = 1, tags = "budget"),
+  task_id = p_fct(levels = c("40981", "41146", "1489", "1067"), tags = "task_id")
+)
+
+codomain = ps(
+  mmce = p_dbl(lower = 0, upper = 1, tags = "minimize"),
+  f1 = p_dbl(lower = 0, upper = 1, tags = "maximize"),
+  auc = p_dbl(lower = 0, upper = 1, tags = "maximize"),
+  logloss = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  ramtrain = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  rammodel = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  rampredict = 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"),
+  mec = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  ias = p_dbl(lower = 0, upper = Inf, tags = "minimize"),
+  nf = p_dbl(lower = 0, upper = Inf, tags = "minimize")
+)