def test_empty_alias(self):
hps = hpv.Hyperparameters(
hpv.ContinuousHyperparameter(name="a",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False))
with self.assertRaises(exc.AlgorithmError):
hps.declare_alias("b", "c")
with self.assertRaises(exc.UserError):
hps.validate({"abc": "0.5"})
result = hps.validate({"a": "0.667"})
self.assertEqual(result["a"], 0.667)
def test_simple_alias(self):
hps = hpv.Hyperparameters(
hpv.ContinuousHyperparameter(name="gamma",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=True))
hps.declare_alias("gamma", "min_split_loss")
with self.assertRaises(exc.UserError):
hps.validate({})
with self.assertRaises(exc.UserError):
hps.validate({"min_split_long": "0.5"})
result = hps.validate({"min_split_loss": "0.667"})
self.assertEqual(result["gamma"], 0.667)
def test_continuous_tunable_range(self):
hyperparameter = hpv.ContinuousHyperparameter(
name="x",
range=hpv.Interval(min_open=0),
tunable=True,
tunable_recommended_range=hpv.Interval(
min_open=0, max_open=100, scale=hpv.Interval.LINEAR_SCALE),
required=True)
self.assertEqual(
hyperparameter.format_tunable_range(), {
'ContinuousParameterRanges': [{
'MaxValue': '100',
'MinValue': '0',
'Name': 'x',
'ScalingType': 'Linear'
}]
})
def test_simple_continuous(self):
hps = hpv.Hyperparameters(
hpv.ContinuousHyperparameter(name="gamma",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=True))
with self.assertRaises(exc.UserError):
hps.validate({})
with self.assertRaises(exc.UserError):
hps.validate({"gamma": "ha"})
with self.assertRaises(exc.UserError):
hps.validate({"gamma": "-1"})
result = hps.validate({"gamma": "0.667"})
self.assertEqual(result["gamma"], 0.667)
def test_multiple_alias(self):
hps = hpv.Hyperparameters(
hpv.ContinuousHyperparameter(name="a",
default=0.5,
range=hpv.Interval(min_closed=0,
max_closed=1),
required=True))
hps.declare_alias("a", "alpha")
hps.declare_alias("a", "beta")
with self.assertRaises(exc.UserError):
hps.validate({})
with self.assertRaises(exc.UserError):
hps.validate({"abc": "0.5"})
result = hps.validate({"alpha": "0.667"})
self.assertEqual(result["a"], 0.667)
result = hps.validate({"beta": "0.667"})
self.assertEqual(result["a"], 0.667)
def initialize(metrics):
@hpv.range_validator(["auto", "exact", "approx", "hist", "gpu_hist"])
def tree_method_range_validator(CATEGORIES, value):
return value in CATEGORIES
@hpv.dependencies_validator(["booster", "process_type"])
def updater_validator(value, dependencies):
valid_tree_plugins = [
'grow_colmaker', 'distcol', 'grow_histmaker',
'grow_local_histmaker', 'grow_skmaker', 'sync', 'refresh', 'prune',
'grow_quantile_histmaker'
]
valid_tree_build_plugins = [
'grow_colmaker', 'distcol', 'grow_histmaker',
'grow_local_histmaker', 'grow_colmaker', 'grow_quantile_histmaker'
]
valid_linear_plugins = ['shotgun', 'coord_descent']
valid_process_update_plugins = ['refresh', 'prune']
if dependencies.get('booster') == 'gblinear':
# validate only one linear updater is selected
if not (len(value) == 1 and value[0] in valid_linear_plugins):
raise exc.UserError(
"Linear updater should be one of these options: {}.".
format(', '.join("'{0}'".format(
valid_updater
for valid_updater in valid_linear_plugins))))
elif dependencies.get('process_type') == 'update':
if not all(x in valid_process_update_plugins for x in value):
raise exc.UserError(
"process_type 'update' can only be used with updater 'refresh' and 'prune'"
)
else:
if not all(x in valid_tree_plugins for x in value):
raise exc.UserError(
"Tree updater should be selected from these options: 'grow_colmaker', 'distcol', 'grow_histmaker', "
"'grow_local_histmaker', 'grow_skmaker', 'grow_quantile_histmaker', 'sync', 'refresh', 'prune', "
"'shortgun', 'coord_descent'.")
# validate only one tree updater is selected
counter = 0
for tmp in value:
if tmp in valid_tree_build_plugins:
counter += 1
if counter > 1:
raise exc.UserError(
"Only one tree grow plugin can be selected. Choose one from the"
"following: 'grow_colmaker', 'distcol', 'grow_histmaker', "
"'grow_local_histmaker', 'grow_skmaker'")
@hpv.range_validator(["auto", "cpu_predictor", "gpu_predictor"])
def predictor_validator(CATEGORIES, value):
return value in CATEGORIES
@hpv.dependencies_validator(["num_class"])
def objective_validator(value, dependencies):
num_class = dependencies.get("num_class")
if value in ("multi:softmax", "multi:softprob") and num_class is None:
raise exc.UserError(
"Require input for parameter 'num_class' for multi-classification"
)
if value is None and num_class is not None:
raise exc.UserError(
"Do not need to setup parameter 'num_class' for learning task other than "
"multi-classification.")
@hpv.range_validator(XGB_MAXIMIZE_METRICS + XGB_MINIMIZE_METRICS)
def eval_metric_range_validator(SUPPORTED_METRIC, metric):
if "<function" in metric:
raise exc.UserError(
"User defined evaluation metric {} is not supported yet.".
format(metric))
if "@" in metric:
metric_name = metric.split('@')[0].strip()
metric_threshold = metric.split('@')[1].strip()
if metric_name not in ["error", "ndcg", "map"]:
raise exc.UserError(
"Metric '{}' is not supported. Parameter 'eval_metric' with customized threshold should "
"be one of these options: 'error', 'ndcg', 'map'.".format(
metric))
try:
float(metric_threshold)
except ValueError:
raise exc.UserError(
"Threshold value 't' in '{}@t' expects float input.".
format(metric_name))
return True
return metric in SUPPORTED_METRIC
@hpv.dependencies_validator(["objective"])
def eval_metric_dep_validator(value, dependencies):
objective = dependencies["objective"]
if "auc" in value:
if not any(
objective.startswith(metric_type)
for metric_type in ['binary:', 'rank:']):
raise exc.UserError(
"Metric 'auc' can only be applied for classification and ranking problems."
)
if "aft-nloglik" in value:
if objective not in ["survival:aft"]:
raise exc.UserError(
"Metric 'aft-nloglik' can only be applied for 'survival:aft' objective."
)
@hpv.dependencies_validator(["tree_method"])
def monotone_constraints_validator(value, dependencies):
tree_method = dependencies.get("tree_method")
if value is not None and tree_method not in ("exact", "hist"):
raise exc.UserError(
"monotone_constraints can be used only when the tree_method parameter is set to "
"either 'exact' or 'hist'.")
@hpv.dependencies_validator(["tree_method"])
def interaction_constraints_validator(value, dependencies):
tree_method = dependencies.get("tree_method")
if value is not None and tree_method not in ("exact", "hist",
"approx"):
raise exc.UserError(
"interaction_constraints can be used only when the tree_method parameter is set to "
"either 'exact', 'hist' or 'approx'.")
hyperparameters = hpv.Hyperparameters(
hpv.IntegerHyperparameter(name="num_round",
required=True,
range=hpv.Interval(min_closed=1),
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=1,
max_closed=4000,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.IntegerHyperparameter(name="csv_weights",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False),
hpv.IntegerHyperparameter(name="early_stopping_rounds",
range=hpv.Interval(min_closed=1),
required=False),
hpv.CategoricalHyperparameter(name="booster",
range=["gbtree", "gblinear", "dart"],
required=False),
hpv.IntegerHyperparameter(name="verbosity",
range=hpv.Interval(min_closed=0,
max_closed=3),
required=False),
hpv.IntegerHyperparameter(name="nthread",
range=hpv.Interval(min_closed=1),
required=False),
hpv.ContinuousHyperparameter(name="eta",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0.1,
max_closed=0.5,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="gamma",
range=hpv.Interval(min_closed=0),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0,
max_closed=5,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.IntegerHyperparameter(name="max_depth",
range=hpv.Interval(min_closed=0),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0,
max_closed=10,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="min_child_weight",
range=hpv.Interval(min_closed=0),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0,
max_closed=120,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="max_delta_step",
range=hpv.Interval(min_closed=0),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0,
max_closed=10,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="subsample",
range=hpv.Interval(min_open=0,
max_closed=1),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0.5,
max_closed=1,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="colsample_bytree",
range=hpv.Interval(min_open=0,
max_closed=1),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0.5,
max_closed=1,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="colsample_bylevel",
range=hpv.Interval(min_open=0,
max_closed=1),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0.1,
max_closed=1,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="colsample_bynode",
range=hpv.Interval(min_open=0,
max_closed=1),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0.1,
max_closed=1,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="lambda",
range=hpv.Interval(min_closed=0),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0,
max_closed=1000,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.ContinuousHyperparameter(name="alpha",
range=hpv.Interval(min_closed=0),
required=False,
tunable=True,
tunable_recommended_range=hpv.Interval(
min_closed=0,
max_closed=1000,
scale=hpv.Interval.LINEAR_SCALE)),
hpv.CategoricalHyperparameter(name="tree_method",
range=tree_method_range_validator,
required=False),
hpv.ContinuousHyperparameter(name="sketch_eps",
range=hpv.Interval(min_open=0,
max_open=1),
required=False),
hpv.ContinuousHyperparameter(name="scale_pos_weight",
range=hpv.Interval(min_open=0),
required=False),
hpv.CommaSeparatedListHyperparameter(
name="updater",
range=[
'grow_colmaker', 'distcol', 'grow_histmaker',
'grow_local_histmaker', 'grow_skmaker', 'sync', 'refresh',
'prune', 'grow_colmaker', 'distcol', 'grow_histmaker',
'grow_local_histmaker', 'grow_colmaker', 'shotgun',
'coord_descent', 'refresh', 'prune'
],
dependencies=updater_validator,
required=False),
hpv.CategoricalHyperparameter(name="dsplit",
range=["row", "col"],
required=False),
hpv.IntegerHyperparameter(name="refresh_leaf",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False),
hpv.CategoricalHyperparameter(name="process_type",
range=["default", "update"],
required=False),
hpv.CategoricalHyperparameter(name="grow_policy",
range=["depthwise", "lossguide"],
required=False),
hpv.IntegerHyperparameter(name="max_leaves",
range=hpv.Interval(min_closed=0),
required=False),
hpv.IntegerHyperparameter(name="max_bin",
range=hpv.Interval(min_closed=0),
required=False),
hpv.CategoricalHyperparameter(name="predictor",
range=predictor_validator,
required=False),
hpv.TupleHyperparameter(name="monotone_constraints",
range=[-1, 0, 1],
required=False,
dependencies=monotone_constraints_validator),
hpv.NestedListHyperparameter(
name="interaction_constraints",
range=hpv.Interval(min_closed=1),
required=False,
dependencies=interaction_constraints_validator),
hpv.CategoricalHyperparameter(name="sample_type",
range=["uniform", "weighted"],
required=False),
hpv.CategoricalHyperparameter(name="normalize_type",
range=["tree", "forest"],
required=False),
hpv.ContinuousHyperparameter(name="rate_drop",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False),
hpv.IntegerHyperparameter(name="one_drop",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False),
hpv.ContinuousHyperparameter(name="skip_drop",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False),
hpv.ContinuousHyperparameter(name="lambda_bias",
range=hpv.Interval(min_closed=0,
max_closed=1),
required=False),
hpv.ContinuousHyperparameter(name="tweedie_variance_power",
range=hpv.Interval(min_open=1,
max_open=2),
required=False),
hpv.CategoricalHyperparameter(
name="objective",
range=[
"aft_loss_distribution", "binary:logistic", "binary:logitraw",
"binary:hinge", "count:poisson", "multi:softmax",
"multi:softprob", "rank:pairwise", "rank:ndcg", "rank:map",
"reg:linear", "reg:squarederror", "reg:logistic", "reg:gamma",
"reg:pseudohubererror", "reg:squaredlogerror", "reg:tweedie",
"survival:aft", "survival:cox"
],
dependencies=objective_validator,
required=False),
hpv.IntegerHyperparameter(name="num_class",
range=hpv.Interval(min_closed=2),
required=False),
hpv.ContinuousHyperparameter(name="base_score",
range=hpv.Interval(min_closed=0),
required=False),
hpv.CategoricalHyperparameter(name="_tuning_objective_metric",
range=metrics.names,
required=False),
hpv.CommaSeparatedListHyperparameter(
name="eval_metric",
range=eval_metric_range_validator,
dependencies=eval_metric_dep_validator,
required=False),
hpv.IntegerHyperparameter(name="seed",
range=hpv.Interval(min_open=-2**31,
max_open=2**31 - 1),
required=False),
hpv.IntegerHyperparameter(name="num_parallel_tree",
range=hpv.Interval(min_closed=1),
required=False),
hpv.CategoricalHyperparameter(name="save_model_on_termination",
range=["true", "false"],
required=False),
hpv.CategoricalHyperparameter(name="aft_loss_distribution",
range=["normal", "logistic", "extreme"],
required=False),
hpv.ContinuousHyperparameter(name="aft_loss_distribution_scale",
range=hpv.Interval(min_closed=0),
required=False),
hpv.CategoricalHyperparameter(name="single_precision_histogram",
range=["true", "false"],
required=False),
hpv.CategoricalHyperparameter(name="deterministic_histogram",
range=["true", "false"],
required=False),
)
hyperparameters.declare_alias("eta", "learning_rate")
hyperparameters.declare_alias("gamma", "min_split_loss")
hyperparameters.declare_alias("lambda", "reg_lambda")
hyperparameters.declare_alias("alpha", "reg_alpha")
return hyperparameters