Upload blackbox yahpo-iaml_ranger

yahpo/iaml_ranger/best_params_resnet.json

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+{"d": 512, "d_hidden_factor": 2.2494375076022086, "hidden_dropout": 0.03397549242844672, "lr": 0.0005620479756574539, "mixup": false, "n_layers": 4, "opt_tfms_auc": false, "opt_tfms_f1": false, "opt_tfms_ias": true, "opt_tfms_logloss": false, "opt_tfms_mec": true, "opt_tfms_min.node.size": false, "opt_tfms_mmce": true, "opt_tfms_mtry.ratio": false, "opt_tfms_num.random.splits": false, "opt_tfms_num.trees": true, "opt_tfms_rammodel": true, "opt_tfms_rampredict": true, "opt_tfms_ramtrain": false, "opt_tfms_sample.fraction": true, "opt_tfms_timepredict": true, "opt_tfms_timetrain": false, "opt_tfms_trainsize": false, "tfms_ias": "tlog", "tfms_mec": "tlog", "tfms_mmce": "tnexp", "tfms_num.trees": "tlog", "tfms_rammodel": "tlog", "tfms_rampredict": "tlog", "tfms_sample.fraction": "tlog", "tfms_timepredict": "tlog", "use_residual_dropout": false}

yahpo/iaml_ranger/config_space.json

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+{
+  "hyperparameters": [
+    {
+      "name": "min.node.size",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 100,
+      "default": 50
+    },
+    {
+      "name": "mtry.ratio",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.0,
+      "upper": 1.0,
+      "default": 0.5
+    },
+    {
+      "name": "num.trees",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 2000,
+      "default": 1000
+    },
+    {
+      "name": "replace",
+      "type": "categorical",
+      "choices": [
+        "TRUE",
+        "FALSE"
+      ],
+      "default": "TRUE",
+      "probabilities": null
+    },
+    {
+      "name": "respect.unordered.factors",
+      "type": "categorical",
+      "choices": [
+        "ignore",
+        "order",
+        "partition"
+      ],
+      "default": "ignore",
+      "probabilities": null
+    },
+    {
+      "name": "sample.fraction",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.1,
+      "upper": 1.0,
+      "default": 0.55
+    },
+    {
+      "name": "splitrule",
+      "type": "categorical",
+      "choices": [
+        "gini",
+        "extratrees"
+      ],
+      "default": "gini",
+      "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": "num.random.splits",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 100,
+      "default": 50
+    }
+  ],
+  "conditions": [
+    {
+      "child": "num.random.splits",
+      "parent": "splitrule",
+      "type": "EQ",
+      "value": "extratrees"
+    }
+  ],
+  "forbiddens": [],
+  "python_module_version": "0.4.19",
+  "json_format_version": 0.2
+}

yahpo/iaml_ranger/encoding.json

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+{"replace": {"#na#": 0, "FALSE": 1, "TRUE": 2}, "respect.unordered.factors": {"#na#": 0, "ignore": 1, "order": 2, "partition": 3}, "splitrule": {"#na#": 0, "extratrees": 1, "gini": 2}, "task_id": {"#na#": 0, "1067": 1, "1489": 2, "40981": 3, "41146": 4}}

yahpo/iaml_ranger/metadata.json

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

yahpo/iaml_ranger/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ed2779d20f0938535512008779f28ef350dc479219beaab660d658e2e730e6c
+size 28482414

yahpo/iaml_ranger/param_set.R

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+search_space = ps(
+  num.trees = p_int(lower = 1L, upper = 2000L),
+  replace = p_lgl(),
+  sample.fraction = p_dbl(lower = 0.1, upper = 1),
+  mtry.ratio = p_dbl(lower = 0, upper = 1),
+  respect.unordered.factors = p_fct(levels = c("ignore", "order", "partition")),
+  min.node.size = p_int(lower = 1L, upper = 100L),
+  splitrule = p_fct(levels = c("gini", "extratrees")),
+  num.random.splits = p_int(lower = 1L, upper = 100L, depends = splitrule == "extratrees"),
+  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(
+  num.trees = p_int(lower = 1L, upper = 2000L),
+  replace = p_lgl(),
+  sample.fraction = p_dbl(lower = 0.1, upper = 1),
+  mtry.ratio = p_dbl(lower = 0, upper = 1),
+  respect.unordered.factors = p_fct(levels = c("ignore", "order", "partition")),
+  min.node.size = p_int(lower = 1L, upper = 100L),
+  splitrule = p_fct(levels = c("gini", "extratrees")),
+  num.random.splits = p_int(lower = 1L, upper = 100L, depends = splitrule == "extratrees"),
+  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")
+)