Upload blackbox yahpo-rbv2_xgboost

yahpo/rbv2_xgboost/best_params_resnet.json

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+{"d": 128, "d_hidden_factor": 3.2778673260890114, "hidden_dropout": 0.04797084974229543, "lr": 0.00044845753754503033, "mixup": false, "n_layers": 5, "opt_tfms_acc": true, "opt_tfms_alpha": true, "opt_tfms_auc": true, "opt_tfms_bac": true, "opt_tfms_brier": true, "opt_tfms_colsample_bylevel": false, "opt_tfms_colsample_bytree": false, "opt_tfms_eta": true, "opt_tfms_f1": true, "opt_tfms_gamma": true, "opt_tfms_lambda": true, "opt_tfms_logloss": true, "opt_tfms_max_depth": false, "opt_tfms_memory": false, "opt_tfms_min_child_weight": true, "opt_tfms_nrounds": false, "opt_tfms_rate_drop": true, "opt_tfms_repl": true, "opt_tfms_skip_drop": false, "opt_tfms_subsample": true, "opt_tfms_timepredict": true, "opt_tfms_timetrain": false, "opt_tfms_trainsize": false, "residual_dropout": 0.14237779501259495, "tfms_acc": "tlog", "tfms_alpha": "tlog", "tfms_auc": "tlog", "tfms_bac": "tnexp", "tfms_brier": "tlog", "tfms_eta": "tnexp", "tfms_f1": "tnexp", "tfms_gamma": "tlog", "tfms_lambda": "tlog", "tfms_logloss": "tnexp", "tfms_min_child_weight": "tlog", "tfms_rate_drop": "tlog", "tfms_repl": "tlog", "tfms_subsample": "tnexp", "tfms_timepredict": "tnexp", "use_residual_dropout": true}

yahpo/rbv2_xgboost/config_space.json

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+{
+  "hyperparameters": [
+    {
+      "name": "alpha",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.0009118819655545162,
+      "upper": 1096.6331584284585,
+      "default": 1.0
+    },
+    {
+      "name": "booster",
+      "type": "categorical",
+      "choices": [
+        "gblinear",
+        "gbtree",
+        "dart"
+      ],
+      "default": "gblinear",
+      "probabilities": null
+    },
+    {
+      "name": "lambda",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.0009118819655545162,
+      "upper": 1096.6331584284585,
+      "default": 1.0
+    },
+    {
+      "name": "nrounds",
+      "type": "uniform_int",
+      "log": true,
+      "lower": 7,
+      "upper": 2981,
+      "default": 144
+    },
+    {
+      "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
+    },
+    {
+      "name": "subsample",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.1,
+      "upper": 1.0,
+      "default": 0.55
+    },
+    {
+      "name": "task_id",
+      "type": "categorical",
+      "choices": [
+        "16","40923","41143","470","1487","40499","40966","41164","1497","40975","1461","41278","11","54","300","40984","31","1067","1590","40983","41163","41165","182","1220","41159","41169","42","188","1457","1480","6332","181","1479","40670","40536","41138","41166","6","14","29","458","1056","1462","1494","40701","12","1493","44","307","334","40982","41142","38","1050","469","23381","41157","15","4541","23","4134","40927","40981","41156","3","1049","40900","1063","23512","40979","1040","1068","41161","22","1489","41027","24","4135","23517","1053","1468","312","377","1515","18","1476","1510","41162","28","375","1464","40685","40996","41146","41216","40668","41212","32","60","4538","40496","41150","37","46","554","1475","1485","1501","1111","4534","41168","151","4154","40978","40994","50","1478","1486","40498"
+      ],
+      "default": "1040",
+      "probabilities": null
+    },
+    {
+      "name": "trainsize",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.03,
+      "upper": 1.0,
+      "default": 0.525
+    },
+    {
+      "name": "colsample_bylevel",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.01,
+      "upper": 1.0,
+      "default": 0.505
+    },
+    {
+      "name": "colsample_bytree",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.01,
+      "upper": 1.0,
+      "default": 0.505
+    },
+    {
+      "name": "eta",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 0.0009118819655545162,
+      "upper": 1.0,
+      "default": 0.0301973834
+    },
+    {
+      "name": "gamma",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 4.5399929762484854e-05,
+      "upper": 7.38905609893065,
+      "default": 0.0183156389
+    },
+    {
+      "name": "max_depth",
+      "type": "uniform_int",
+      "log": false,
+      "lower": 1,
+      "upper": 15,
+      "default": 8
+    },
+    {
+      "name": "min_child_weight",
+      "type": "uniform_float",
+      "log": true,
+      "lower": 2.718281828459045,
+      "upper": 148.4131591025766,
+      "default": 20.0855369232
+    },
+    {
+      "name": "rate_drop",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.0,
+      "upper": 1.0,
+      "default": 0.5
+    },
+    {
+      "name": "skip_drop",
+      "type": "uniform_float",
+      "log": false,
+      "lower": 0.0,
+      "upper": 1.0,
+      "default": 0.5
+    }
+  ],
+  "conditions": [
+    {
+      "child": "colsample_bylevel",
+      "parent": "booster",
+      "type": "IN",
+      "values": [
+        "dart",
+        "gbtree"
+      ]
+    },
+    {
+      "child": "colsample_bytree",
+      "parent": "booster",
+      "type": "IN",
+      "values": [
+        "dart",
+        "gbtree"
+      ]
+    },
+    {
+      "child": "eta",
+      "parent": "booster",
+      "type": "IN",
+      "values": [
+        "dart",
+        "gbtree"
+      ]
+    },
+    {
+      "child": "gamma",
+      "parent": "booster",
+      "type": "IN",
+      "values": [
+        "dart",
+        "gbtree"
+      ]
+    },
+    {
+      "child": "max_depth",
+      "parent": "booster",
+      "type": "IN",
+      "values": [
+        "dart",
+        "gbtree"
+      ]
+    },
+    {
+      "child": "min_child_weight",
+      "parent": "booster",
+      "type": "IN",
+      "values": [
+        "dart",
+        "gbtree"
+      ]
+    },
+    {
+      "child": "rate_drop",
+      "parent": "booster",
+      "type": "EQ",
+      "value": "dart"
+    },
+    {
+      "child": "skip_drop",
+      "parent": "booster",
+      "type": "EQ",
+      "value": "dart"
+    }
+  ],
+  "forbiddens": [],
+  "python_module_version": "0.4.18",
+  "json_format_version": 0.2
+}

yahpo/rbv2_xgboost/encoding.json

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+{"booster": {"#na#": 0, "dart": 1, "gblinear": 2, "gbtree": 3}, "num.impute.selected.cpo": {"#na#": 0, "impute.hist": 1, "impute.mean": 2, "impute.median": 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, "23512": 45, "23517": 46, "24": 47, "28": 48, "29": 49, "3": 50, "300": 51, "307": 52, "31": 53, "312": 54, "32": 55, "334": 56, "37": 57, "375": 58, "377": 59, "38": 60, "40496": 61, "40498": 62, "40499": 63, "40536": 64, "40668": 65, "40670": 66, "40685": 67, "40701": 68, "40900": 69, "40923": 70, "40927": 71, "40966": 72, "40975": 73, "40978": 74, "40979": 75, "40981": 76, "40982": 77, "40983": 78, "40984": 79, "40994": 80, "40996": 81, "41027": 82, "41138": 83, "41142": 84, "41143": 85, "41146": 86, "41150": 87, "41156": 88, "41157": 89, "41159": 90, "41161": 91, "41162": 92, "41163": 93, "41164": 94, "41165": 95, "41166": 96, "41168": 97, "41169": 98, "41212": 99, "41216": 100, "41278": 101, "4134": 102, "4135": 103, "4154": 104, "42": 105, "44": 106, "4534": 107, "4538": 108, "4541": 109, "458": 110, "46": 111, "469": 112, "470": 113, "50": 114, "54": 115, "554": 116, "6": 117, "60": 118, "6332": 119}}

yahpo/rbv2_xgboost/metadata.json

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

yahpo/rbv2_xgboost/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:85125b6d97f553d10cd50f9e632e1a3f1ad44bcb00300dd2880d70dfd043d9f3
+size 3335828

yahpo/rbv2_xgboost/param_set.R

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+search_space = ps(
+  booster = p_fct(levels = c("gblinear", "gbtree", "dart")),
+  nrounds = p_dbl(lower = 2, upper = 8, tags = c("int", "log"), trafo = function(x) as.integer(round(exp(x)))),
+  eta = p_dbl(lower = -7, upper = 0, tags = "log", trafo = function(x) exp(x), depends = booster %in% c("dart", "gbtree")),
+  gamma = p_dbl(lower = -10, upper = 2, tags = "log", trafo = function(x) exp(x), depends = booster %in% c("dart", "gbtree")),
+  lambda = p_dbl(lower = -7, upper = 7, tags = "log", trafo = function(x) exp(x)),
+  alpha = p_dbl(lower = -7, upper = 7, tags = "log", trafo = function(x) exp(x)),
+  subsample = p_dbl(lower = 0.1, upper = 1),
+  max_depth = p_int(lower = 1L, upper = 15L, depends = booster %in% c("dart", "gbtree")),
+  min_child_weight = p_dbl(lower = 1, upper = 5, tags = "log", trafo = function(x) exp(x), depends = booster %in% c("dart", "gbtree")),
+  colsample_bytree = p_dbl(lower = 0.01, upper = 1, depends = booster %in% c("dart", "gbtree")),
+  colsample_bylevel = p_dbl(lower = 0.01, upper = 1, depends = booster %in% c("dart", "gbtree")),
+  rate_drop = p_dbl(lower = 0, upper = 1, depends = booster == "dart"),
+  skip_drop = p_dbl(lower = 0, upper = 1, depends = 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")),
+  task_id = p_fct(levels = c("16", "40923", "41143", "470", "1487", "40499", "40966", "41164",
+"1497", "40975", "1461", "41278", "11", "54", "300", "40984",
+"31", "1067", "1590", "40983", "41163", "41165", "182", "1220",
+"41159", "41169", "42", "188", "1457", "1480", "6332", "181",
+"1479", "40670", "40536", "41138", "41166", "6", "14", "29",
+"458", "1056", "1462", "1494", "40701", "12", "1493", "44", "307",
+"334", "40982", "41142", "38", "1050", "469", "23381", "41157",
+"15", "4541", "23", "4134", "40927", "40981", "41156", "3", "1049",
+"40900", "1063", "23512", "40979", "1040", "1068", "41161", "22",
+"1489", "41027", "24", "4135", "23517", "1053", "1468", "312",
+"377", "1515", "18", "1476", "1510", "41162", "28", "375", "1464",
+"40685", "40996", "41146", "41216", "40668", "41212", "32", "60",
+"4538", "40496", "41150", "37", "46", "554", "1475", "1485",
+"1501", "1111", "4534", "41168", "151", "4154", "40978", "40994",
+"50", "1478", "1486", "40498"),
+    tags = "task_id"
+  )
+)
+
+domain = ps(
+  booster = p_fct(levels = c("gblinear", "gbtree", "dart")),
+  nrounds = p_int(lower = 7L, upper = 2981L),
+  eta = p_dbl(lower = exp(-7), upper = exp(0),depends = booster %in% c("dart", "gbtree")),
+  gamma = p_dbl(lower = exp(-10), upper = exp(2), depends = booster %in% c("dart", "gbtree")),
+  lambda = p_dbl(lower = exp(-7), upper = exp(7)),
+  alpha = p_dbl(lower = exp(-7), upper = exp(7)),
+  subsample = p_dbl(lower = 0.1, upper = 1),
+  max_depth = p_int(lower = 1L, upper = 15L, depends = booster %in% c("dart", "gbtree")),
+  min_child_weight = p_dbl(lower = exp(1), upper = exp(5), depends = booster %in% c("dart", "gbtree")),
+  colsample_bytree = p_dbl(lower = 0.01, upper = 1, depends = booster %in% c("dart", "gbtree")),
+  colsample_bylevel = p_dbl(lower = 0.01, upper = 1, depends = booster %in% c("dart", "gbtree")),
+  rate_drop = p_dbl(lower = 0, upper = 1, depends = booster == "dart"),
+  skip_drop = p_dbl(lower = 0, upper = 1, depends = 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")),
+  task_id = p_fct(levels = c("16", "40923", "41143", "470", "1487", "40499", "40966", "41164",
+"1497", "40975", "1461", "41278", "11", "54", "300", "40984",
+"31", "1067", "1590", "40983", "41163", "41165", "182", "1220",
+"41159", "41169", "42", "188", "1457", "1480", "6332", "181",
+"1479", "40670", "40536", "41138", "41166", "6", "14", "29",
+"458", "1056", "1462", "1494", "40701", "12", "1493", "44", "307",
+"334", "40982", "41142", "38", "1050", "469", "23381", "41157",
+"15", "4541", "23", "4134", "40927", "40981", "41156", "3", "1049",
+"40900", "1063", "23512", "40979", "1040", "1068", "41161", "22",
+"1489", "41027", "24", "4135", "23517", "1053", "1468", "312",
+"377", "1515", "18", "1476", "1510", "41162", "28", "375", "1464",
+"40685", "40996", "41146", "41216", "40668", "41212", "32", "60",
+"4538", "40496", "41150", "37", "46", "554", "1475", "1485",
+"1501", "1111", "4534", "41168", "151", "4154", "40978", "40994",
+"50", "1478", "1486", "40498"),
+    tags = "task_id"
+  )
+)
+
+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")
+)