xgboost.testing.params Namespace Reference

Variables
	strategies = pytest.importorskip("hypothesis.strategies")
	exact_parameter_strategy
	hist_parameter_strategy
	hist_cache_strategy
	hist_multi_parameter_strategy
	cat_parameter_strategy
	lambdarank_parameter_strategy

Detailed Description

Strategies for updater tests.

Variable Documentation

cat_parameter_strategy

xgboost.testing.params.cat_parameter_strategy

Initial value:

=  strategies.fixed_dictionaries(
    {
        "max_cat_to_onehot": strategies.integers(1, 128),
        "max_cat_threshold": strategies.integers(1, 128),
    }
)

exact_parameter_strategy

xgboost.testing.params.exact_parameter_strategy

Initial value:

=  strategies.fixed_dictionaries(
    {
        "nthread": strategies.integers(1, 4),
        "max_depth": strategies.integers(1, 11),
        "min_child_weight": strategies.floats(0.5, 2.0),
        "alpha": strategies.floats(1e-5, 2.0),
        "lambda": strategies.floats(1e-5, 2.0),
        "eta": strategies.floats(0.01, 0.5),
        "gamma": strategies.floats(1e-5, 2.0),
        "seed": strategies.integers(0, 10),
        # We cannot enable subsampling as the training loss can increase
        # 'subsample': strategies.floats(0.5, 1.0),
        "colsample_bytree": strategies.floats(0.5, 1.0),
        "colsample_bylevel": strategies.floats(0.5, 1.0),
    }
)

hist_cache_strategy

xgboost.testing.params.hist_cache_strategy

Initial value:

=  strategies.fixed_dictionaries(
    {"max_cached_hist_node": strategies.sampled_from([1, 4, 1024, 2**31])}
)

hist_multi_parameter_strategy

xgboost.testing.params.hist_multi_parameter_strategy

Initial value:

=  strategies.fixed_dictionaries(
    {
        "max_depth": strategies.integers(1, 11),
        "max_leaves": strategies.integers(0, 1024),
        "max_bin": strategies.integers(2, 512),
        "multi_strategy": strategies.sampled_from(
            ["multi_output_tree", "one_output_per_tree"]
        ),
        "grow_policy": strategies.sampled_from(["lossguide", "depthwise"]),
        "min_child_weight": strategies.floats(0.5, 2.0),
        # We cannot enable subsampling as the training loss can increase
        # 'subsample': strategies.floats(0.5, 1.0),
        "colsample_bytree": strategies.floats(0.5, 1.0),
        "colsample_bylevel": strategies.floats(0.5, 1.0),
    }
).filter(
    lambda x: (cast(int, x["max_depth"]) > 0 or cast(int, x["max_leaves"]) > 0)
    and (cast(int, x["max_depth"]) > 0 or x["grow_policy"] == "lossguide")
)

hist_parameter_strategy

xgboost.testing.params.hist_parameter_strategy

Initial value:

=  strategies.fixed_dictionaries(
    {
        "max_depth": strategies.integers(1, 11),
        "max_leaves": strategies.integers(0, 1024),
        "max_bin": strategies.integers(2, 512),
        "grow_policy": strategies.sampled_from(["lossguide", "depthwise"]),
        "min_child_weight": strategies.floats(0.5, 2.0),
        # We cannot enable subsampling as the training loss can increase
        # 'subsample': strategies.floats(0.5, 1.0),
        "colsample_bytree": strategies.floats(0.5, 1.0),
        "colsample_bylevel": strategies.floats(0.5, 1.0),
    }
).filter(
    lambda x: (cast(int, x["max_depth"]) > 0 or cast(int, x["max_leaves"]) > 0)
    and (cast(int, x["max_depth"]) > 0 or x["grow_policy"] == "lossguide")
)

lambdarank_parameter_strategy

xgboost.testing.params.lambdarank_parameter_strategy

Initial value:

=  strategies.fixed_dictionaries(
    {
        "lambdarank_unbiased": strategies.sampled_from([True, False]),
        "lambdarank_pair_method": strategies.sampled_from(["topk", "mean"]),
        "lambdarank_num_pair_per_sample": strategies.integers(1, 8),
        "lambdarank_bias_norm": strategies.floats(0.5, 2.0),
        "objective": strategies.sampled_from(
            ["rank:ndcg", "rank:map", "rank:pairwise"]
        ),
    }
).filter(
    lambda x: not (x["lambdarank_unbiased"] and x["lambdarank_pair_method"] == "mean")
)