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Initial support for multi-target tree. (#8616)

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* Implement multi-target for hist.

- Add new hist tree builder.
- Move data fetchers for tests.
- Dispatch function calls in gbm base on the tree type.
This commit is contained in:
Jiaming Yuan
2023-03-22 23:49:56 +08:00
committed by GitHub
parent ea04d4c46c
commit 151882dd26
34 changed files with 856 additions and 389 deletions
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110
tests/python/test_updaters.py
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@@ -11,6 +11,7 @@ from xgboost import testing as tm
from xgboost.testing.params import (
cat_parameter_strategy,
exact_parameter_strategy,
hist_multi_parameter_strategy,
hist_parameter_strategy,
)
from xgboost.testing.updater import check_init_estimation, check_quantile_loss
@@ -18,11 +19,70 @@ from xgboost.testing.updater import check_init_estimation, check_quantile_loss
def train_result(param, dmat, num_rounds):
result = {}
xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
evals_result=result)
booster = xgb.train(
param,
dmat,
num_rounds,
[(dmat, "train")],
verbose_eval=False,
evals_result=result,
)
assert booster.num_features() == dmat.num_col()
assert booster.num_boosted_rounds() == num_rounds
assert booster.feature_names == dmat.feature_names
assert booster.feature_types == dmat.feature_types
return result
class TestTreeMethodMulti:
@given(
exact_parameter_strategy, strategies.integers(1, 20), tm.multi_dataset_strategy
)
@settings(deadline=None, print_blob=True)
def test_exact(self, param: dict, num_rounds: int, dataset: tm.TestDataset) -> None:
if dataset.name.endswith("-l1"):
return
param["tree_method"] = "exact"
param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), num_rounds)
assert tm.non_increasing(result["train"][dataset.metric])
@given(
exact_parameter_strategy,
hist_parameter_strategy,
strategies.integers(1, 20),
tm.multi_dataset_strategy,
)
@settings(deadline=None, print_blob=True)
def test_approx(self, param, hist_param, num_rounds, dataset):
param["tree_method"] = "approx"
param = dataset.set_params(param)
param.update(hist_param)
result = train_result(param, dataset.get_dmat(), num_rounds)
note(result)
assert tm.non_increasing(result["train"][dataset.metric])
@given(
exact_parameter_strategy,
hist_multi_parameter_strategy,
strategies.integers(1, 20),
tm.multi_dataset_strategy,
)
@settings(deadline=None, print_blob=True)
def test_hist(
self, param: dict, hist_param: dict, num_rounds: int, dataset: tm.TestDataset
) -> None:
if dataset.name.endswith("-l1"):
return
param["tree_method"] = "hist"
param = dataset.set_params(param)
param.update(hist_param)
result = train_result(param, dataset.get_dmat(), num_rounds)
note(result)
assert tm.non_increasing(result["train"][dataset.metric])
class TestTreeMethod:
USE_ONEHOT = np.iinfo(np.int32).max
USE_PART = 1
@@ -77,10 +137,14 @@ class TestTreeMethod:
# Second prune should not change the tree
assert after_prune == second_prune
@given(exact_parameter_strategy, hist_parameter_strategy, strategies.integers(1, 20),
tm.dataset_strategy)
@given(
exact_parameter_strategy,
hist_parameter_strategy,
strategies.integers(1, 20),
tm.dataset_strategy
)
@settings(deadline=None, print_blob=True)
def test_hist(self, param, hist_param, num_rounds, dataset):
def test_hist(self, param: dict, hist_param: dict, num_rounds: int, dataset: tm.TestDataset) -> None:
param['tree_method'] = 'hist'
param = dataset.set_params(param)
param.update(hist_param)
@@ -88,23 +152,6 @@ class TestTreeMethod:
note(result)
assert tm.non_increasing(result['train'][dataset.metric])
@given(tm.sparse_datasets_strategy)
@settings(deadline=None, print_blob=True)
def test_sparse(self, dataset):
param = {"tree_method": "hist", "max_bin": 64}
hist_result = train_result(param, dataset.get_dmat(), 16)
note(hist_result)
assert tm.non_increasing(hist_result['train'][dataset.metric])
param = {"tree_method": "approx", "max_bin": 64}
approx_result = train_result(param, dataset.get_dmat(), 16)
note(approx_result)
assert tm.non_increasing(approx_result['train'][dataset.metric])
np.testing.assert_allclose(
hist_result["train"]["rmse"], approx_result["train"]["rmse"]
)
def test_hist_categorical(self):
# hist must be same as exact on all-categorial data
dpath = 'demo/data/'
@@ -143,6 +190,23 @@ class TestTreeMethod:
w = [0, 0, 1, 0]
model.fit(X, y, sample_weight=w)
@given(tm.sparse_datasets_strategy)
@settings(deadline=None, print_blob=True)
def test_sparse(self, dataset):
param = {"tree_method": "hist", "max_bin": 64}
hist_result = train_result(param, dataset.get_dmat(), 16)
note(hist_result)
assert tm.non_increasing(hist_result['train'][dataset.metric])
param = {"tree_method": "approx", "max_bin": 64}
approx_result = train_result(param, dataset.get_dmat(), 16)
note(approx_result)
assert tm.non_increasing(approx_result['train'][dataset.metric])
np.testing.assert_allclose(
hist_result["train"]["rmse"], approx_result["train"]["rmse"]
)
def run_invalid_category(self, tree_method: str) -> None:
rng = np.random.default_rng()
# too large
@@ -365,7 +429,7 @@ class TestTreeMethod:
) -> None:
cat_parameters.update(hist_parameters)
dataset = tm.TestDataset(
"ames_housing", tm.get_ames_housing, "reg:squarederror", "rmse"
"ames_housing", tm.data.get_ames_housing, "reg:squarederror", "rmse"
)
cat_parameters["tree_method"] = tree_method
results = train_result(cat_parameters, dataset.get_dmat(), 16)