Delete yahpo-iaml_xgboost

yahpo-iaml_xgboost/best_params_resnet.json

@@ -1 +0,0 @@
-{"d": 384, "d_hidden_factor": 2.7930060842224904, "hidden_dropout": 0.09343650341288146, "lr": 0.0007388150356328408, "mixup": false, "n_layers": 6, "opt_tfms_alpha": true, "opt_tfms_auc": true, "opt_tfms_colsample_bylevel": true, "opt_tfms_colsample_bytree": true, "opt_tfms_eta": false, "opt_tfms_f1": false, "opt_tfms_gamma": false, "opt_tfms_ias": false, "opt_tfms_lambda": true, "opt_tfms_logloss": false, "opt_tfms_max_depth": false, "opt_tfms_mec": false, "opt_tfms_min_child_weight": true, "opt_tfms_mmce": true, "opt_tfms_nrounds": true, "opt_tfms_rammodel": false, "opt_tfms_rampredict": true, "opt_tfms_ramtrain": true, "opt_tfms_rate_drop": false, "opt_tfms_skip_drop": true, "opt_tfms_subsample": true, "opt_tfms_timepredict": false, "opt_tfms_timetrain": false, "opt_tfms_trainsize": false, "tfms_alpha": "tlog", "tfms_auc": "tnexp", "tfms_colsample_bylevel": "tnexp", "tfms_colsample_bytree": "tnexp", "tfms_lambda": "tlog", "tfms_min_child_weight": "tnexp", "tfms_mmce": "tlog", "tfms_nrounds": "tlog", "tfms_rampredict": "tnexp", "tfms_ramtrain": "tnexp", "tfms_skip_drop": "tnexp", "tfms_subsample": "tlog", "use_residual_dropout": false}

yahpo-iaml_xgboost/config_space.json

@@ -1,202 +0,0 @@
-{
-  "hyperparameters": [
-    {
-      "name": "alpha",
-      "type": "uniform_float",
-      "log": true,
-      "lower": 0.00010000000000000009,
-      "upper": 999.9999999999998,
-      "default": 0.316227766
-    },
-    {
-      "name": "booster",
-      "type": "categorical",
-      "choices": [
-        "gblinear",
-        "gbtree",
-        "dart"
-      ],
-      "default": "gblinear",
-      "probabilities": null
-    },
-    {
-      "name": "lambda",
-      "type": "uniform_float",
-      "log": true,
-      "lower": 0.00010000000000000009,
-      "upper": 999.9999999999998,
-      "default": 0.316227766
-    },
-    {
-      "name": "nrounds",
-      "type": "uniform_int",
-      "log": true,
-      "lower": 3,
-      "upper": 2000,
-      "default": 77
-    },
-    {
-      "name": "subsample",
-      "type": "uniform_float",
-      "log": false,
-      "lower": 0.1,
-      "upper": 1.0,
-      "default": 0.55
-    },
-    {
-      "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": "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": 0.00010000000000000009,
-      "upper": 1.0,
-      "default": 0.01
-    },
-    {
-      "name": "gamma",
-      "type": "uniform_float",
-      "log": true,
-      "lower": 0.00010000000000000009,
-      "upper": 6.999999999999999,
-      "default": 0.0264575131
-    },
-    {
-      "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": 149.99999999999997,
-      "default": 20.1926292064
-    },
-    {
-      "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": "0.4.19",
-  "json_format_version": 0.2
-}

yahpo-iaml_xgboost/encoding.json

@@ -1 +0,0 @@
-{"booster": {"#na#": 0, "dart": 1, "gblinear": 2, "gbtree": 3}, "task_id": {"#na#": 0, "1067": 1, "1489": 2, "40981": 3, "41146": 4}}

yahpo-iaml_xgboost/metadata.json

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

yahpo-iaml_xgboost/model.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dd51307d61b7cd746f5cee82b174aeecf05f9459da07e36ca8bf267d8d2d4d94
-size 29827930

yahpo-iaml_xgboost/param_set.R

@@ -1,50 +0,0 @@
-search_space = ps(
-  booster = p_fct(levels = c("gblinear", "gbtree", "dart")),
-  nrounds = p_dbl(lower = 1, upper = log(2000), tags = c("int", "log"), trafo = function(x) as.integer(round(exp(x)))),
-  eta = p_dbl(lower = log(1e-4), upper = log(1), tags = "log", trafo = function(x) exp(x), depends = booster %in% c("dart", "gbtree")),
-  gamma = p_dbl(lower = log(1e-4), upper = log(7), tags = "log", trafo = function(x) exp(x), depends = booster %in% c("dart", "gbtree")),
-  lambda = p_dbl(lower = log(1e-4), upper = log(1000), tags = "log", trafo = function(x) exp(x)),
-  alpha = p_dbl(lower = log(1e-4), upper = log(1000), 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 = log(150), 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"),
-  task_id = p_fct(levels = c("40981", "41146", "1489", "1067"), tags = "task_id")
-)
-
-domain = ps(
-  booster = p_fct(levels = c("gblinear", "gbtree", "dart")),
-  nrounds = p_int(lower = 3, upper = 2000),
-  eta = p_dbl(lower = 1e-4, upper = 1, depends = booster %in% c("dart", "gbtree")),
-  gamma = p_dbl(lower = 1e-4, upper = 7, depends = booster %in% c("dart", "gbtree")),
-  lambda = p_dbl(lower = 1e-4, upper = 1000),
-  alpha = p_dbl(lower = 1e-4, upper = 1000),
-  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 = 150, 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"),
-  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")
-)