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Remove ntree limit in python package. (#8345)

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- Remove `ntree_limit`. The parameter has been deprecated since 1.4.0.
- The SHAP package compatibility is broken.
This commit is contained in:
Jiaming Yuan 2023-03-31 19:01:55 +08:00 committed by GitHub
parent b647403baa
commit bac22734fb
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GPG Key ID: 4AEE18F83AFDEB23
17 changed files with 284 additions and 357 deletions
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16
python-package/xgboost/callback.py
View File

@ -23,13 +23,7 @@ from typing import (
import numpy
from . import collective
from .core import (
Booster,
DMatrix,
XGBoostError,
_get_booster_layer_trees,
_parse_eval_str,
)
from .core import Booster, DMatrix, XGBoostError, _parse_eval_str
__all__ = [
"TrainingCallback",
@ -177,22 +171,14 @@ class CallbackContainer:
assert isinstance(model, Booster), msg
if not self.is_cv:
num_parallel_tree, _ = _get_booster_layer_trees(model)
if model.attr("best_score") is not None:
model.best_score = float(cast(str, model.attr("best_score")))
model.best_iteration = int(cast(str, model.attr("best_iteration")))
# num_class is handled internally
model.set_attr(
best_ntree_limit=str((model.best_iteration + 1) * num_parallel_tree)
)
model.best_ntree_limit = int(cast(str, model.attr("best_ntree_limit")))
else:
# Due to compatibility with version older than 1.4, these attributes are
# added to Python object even if early stopping is not used.
model.best_iteration = model.num_boosted_rounds() - 1
model.set_attr(best_iteration=str(model.best_iteration))
model.best_ntree_limit = (model.best_iteration + 1) * num_parallel_tree
model.set_attr(best_ntree_limit=str(model.best_ntree_limit))
return model

67
python-package/xgboost/core.py
View File

@ -126,25 +126,6 @@ def _parse_eval_str(result: str) -> List[Tuple[str, float]]:
IterRange = TypeVar("IterRange", Optional[Tuple[int, int]], Tuple[int, int])
def _convert_ntree_limit(
booster: "Booster", ntree_limit: Optional[int], iteration_range: IterRange
) -> IterRange:
if ntree_limit is not None and ntree_limit != 0:
warnings.warn(
"ntree_limit is deprecated, use `iteration_range` or model "
"slicing instead.",
UserWarning,
)
if iteration_range is not None and iteration_range[1] != 0:
raise ValueError(
"Only one of `iteration_range` and `ntree_limit` can be non zero."
)
num_parallel_tree, _ = _get_booster_layer_trees(booster)
num_parallel_tree = max([num_parallel_tree, 1])
iteration_range = (0, ntree_limit // num_parallel_tree)
return iteration_range
def _expect(expectations: Sequence[Type], got: Type) -> str:
"""Translate input error into string.
@ -1508,41 +1489,6 @@ Objective = Callable[[np.ndarray, DMatrix], Tuple[np.ndarray, np.ndarray]]
Metric = Callable[[np.ndarray, DMatrix], Tuple[str, float]]
def _get_booster_layer_trees(model: "Booster") -> Tuple[int, int]:
"""Get number of trees added to booster per-iteration. This function will be removed
once `best_ntree_limit` is dropped in favor of `best_iteration`. Returns
`num_parallel_tree` and `num_groups`.
"""
config = json.loads(model.save_config())
booster = config["learner"]["gradient_booster"]["name"]
if booster == "gblinear":
num_parallel_tree = 0
elif booster == "dart":
num_parallel_tree = int(
config["learner"]["gradient_booster"]["gbtree"]["gbtree_model_param"][
"num_parallel_tree"
]
)
elif booster == "gbtree":
try:
num_parallel_tree = int(
config["learner"]["gradient_booster"]["gbtree_model_param"][
"num_parallel_tree"
]
)
except KeyError:
num_parallel_tree = int(
config["learner"]["gradient_booster"]["gbtree_train_param"][
"num_parallel_tree"
]
)
else:
raise ValueError(f"Unknown booster: {booster}")
num_groups = int(config["learner"]["learner_model_param"]["num_class"])
return num_parallel_tree, num_groups
def _configure_metrics(params: BoosterParam) -> BoosterParam:
if (
isinstance(params, dict)
@ -1576,11 +1522,11 @@ class Booster:
"""
Parameters
----------
params : dict
params :
Parameters for boosters.
cache : list
cache :
List of cache items.
model_file : string/os.PathLike/Booster/bytearray
model_file :
Path to the model file if it's string or PathLike.
"""
cache = cache if cache is not None else []
@ -2100,7 +2046,6 @@ class Booster:
self,
data: DMatrix,
output_margin: bool = False,
ntree_limit: int = 0,
pred_leaf: bool = False,
pred_contribs: bool = False,
approx_contribs: bool = False,
@ -2127,9 +2072,6 @@ class Booster:
output_margin :
Whether to output the raw untransformed margin value.
ntree_limit :
Deprecated, use `iteration_range` instead.
pred_leaf :
When this option is on, the output will be a matrix of (nsample,
ntrees) with each record indicating the predicted leaf index of
@ -2196,7 +2138,6 @@ class Booster:
raise TypeError("Expecting data to be a DMatrix object, got: ", type(data))
if validate_features:
self._validate_dmatrix_features(data)
iteration_range = _convert_ntree_limit(self, ntree_limit, iteration_range)
args = {
"type": 0,
"training": training,
@ -2522,8 +2463,6 @@ class Booster:
self.best_iteration = int(self.attr("best_iteration")) # type: ignore
if self.attr("best_score") is not None:
self.best_score = float(self.attr("best_score")) # type: ignore
if self.attr("best_ntree_limit") is not None:
self.best_ntree_limit = int(self.attr("best_ntree_limit")) # type: ignore
def num_boosted_rounds(self) -> int:
"""Get number of boosted rounds. For gblinear this is reset to 0 after

9
python-package/xgboost/dask.py
View File

@ -1653,14 +1653,11 @@ class DaskScikitLearnBase(XGBModel):
self,
X: _DataT,
output_margin: bool = False,
ntree_limit: Optional[int] = None,
validate_features: bool = True,
base_margin: Optional[_DaskCollection] = None,
iteration_range: Optional[Tuple[int, int]] = None,
) -> Any:
_assert_dask_support()
msg = "`ntree_limit` is not supported on dask, use `iteration_range` instead."
assert ntree_limit is None, msg
return self.client.sync(
self._predict_async,
X,
@ -1694,12 +1691,9 @@ class DaskScikitLearnBase(XGBModel):
def apply(
self,
X: _DataT,
ntree_limit: Optional[int] = None,
iteration_range: Optional[Tuple[int, int]] = None,
) -> Any:
_assert_dask_support()
msg = "`ntree_limit` is not supported on dask, use `iteration_range` instead."
assert ntree_limit is None, msg
return self.client.sync(self._apply_async, X, iteration_range=iteration_range)
def __await__(self) -> Awaitable[Any]:
@ -1993,14 +1987,11 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierMixIn, XGBClassifierBa
def predict_proba(
self,
X: _DaskCollection,
ntree_limit: Optional[int] = None,
validate_features: bool = True,
base_margin: Optional[_DaskCollection] = None,
iteration_range: Optional[Tuple[int, int]] = None,
) -> Any:
_assert_dask_support()
msg = "`ntree_limit` is not supported on dask, use `iteration_range` instead."
assert ntree_limit is None, msg
return self._client_sync(
self._predict_proba_async,
X=X,

35
python-package/xgboost/sklearn.py
View File

@ -36,7 +36,6 @@ from .core import (
Objective,
QuantileDMatrix,
XGBoostError,
_convert_ntree_limit,
_deprecate_positional_args,
_parse_eval_str,
)
@ -391,8 +390,7 @@ __model_doc = f"""
metric will be used for early stopping.
- If early stopping occurs, the model will have three additional fields:
:py:attr:`best_score`, :py:attr:`best_iteration` and
:py:attr:`best_ntree_limit`.
:py:attr:`best_score`, :py:attr:`best_iteration`.
.. note::
@ -1117,7 +1115,6 @@ class XGBModel(XGBModelBase):
self,
X: ArrayLike,
output_margin: bool = False,
ntree_limit: Optional[int] = None,
validate_features: bool = True,
base_margin: Optional[ArrayLike] = None,
iteration_range: Optional[Tuple[int, int]] = None,
@ -1135,8 +1132,6 @@ class XGBModel(XGBModelBase):
Data to predict with.
output_margin :
Whether to output the raw untransformed margin value.
ntree_limit :
Deprecated, use `iteration_range` instead.
validate_features :
When this is True, validate that the Booster's and data's feature_names are
identical. Otherwise, it is assumed that the feature_names are the same.
@ -1156,9 +1151,6 @@ class XGBModel(XGBModelBase):
"""
with config_context(verbosity=self.verbosity):
iteration_range = _convert_ntree_limit(
self.get_booster(), ntree_limit, iteration_range
)
iteration_range = self._get_iteration_range(iteration_range)
if self._can_use_inplace_predict():
try:
@ -1197,7 +1189,6 @@ class XGBModel(XGBModelBase):
def apply(
self,
X: ArrayLike,
ntree_limit: int = 0,
iteration_range: Optional[Tuple[int, int]] = None,
) -> np.ndarray:
"""Return the predicted leaf every tree for each sample. If the model is trained
@ -1211,9 +1202,6 @@ class XGBModel(XGBModelBase):
iteration_range :
See :py:meth:`predict`.
ntree_limit :
Deprecated, use ``iteration_range`` instead.
Returns
-------
X_leaves : array_like, shape=[n_samples, n_trees]
@ -1223,9 +1211,6 @@ class XGBModel(XGBModelBase):
"""
with config_context(verbosity=self.verbosity):
iteration_range = _convert_ntree_limit(
self.get_booster(), ntree_limit, iteration_range
)
iteration_range = self._get_iteration_range(iteration_range)
test_dmatrix = DMatrix(
X,
@ -1309,10 +1294,6 @@ class XGBModel(XGBModelBase):
"""
return int(self._early_stopping_attr("best_iteration"))
@property
def best_ntree_limit(self) -> int:
return int(self._early_stopping_attr("best_ntree_limit"))
@property
def feature_importances_(self) -> np.ndarray:
"""Feature importances property, return depends on `importance_type`
@ -1562,7 +1543,6 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
self,
X: ArrayLike,
output_margin: bool = False,
ntree_limit: Optional[int] = None,
validate_features: bool = True,
base_margin: Optional[ArrayLike] = None,
iteration_range: Optional[Tuple[int, int]] = None,
@ -1571,7 +1551,6 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
class_probs = super().predict(
X=X,
output_margin=output_margin,
ntree_limit=ntree_limit,
validate_features=validate_features,
base_margin=base_margin,
iteration_range=iteration_range,
@ -1599,7 +1578,6 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
def predict_proba(
self,
X: ArrayLike,
ntree_limit: Optional[int] = None,
validate_features: bool = True,
base_margin: Optional[ArrayLike] = None,
iteration_range: Optional[Tuple[int, int]] = None,
@ -1614,8 +1592,6 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
----------
X : array_like
Feature matrix. See :ref:`py-data` for a list of supported types.
ntree_limit : int
Deprecated, use `iteration_range` instead.
validate_features : bool
When this is True, validate that the Booster's and data's feature_names are
identical. Otherwise, it is assumed that the feature_names are the same.
@ -1642,7 +1618,6 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
if self.objective == "multi:softmax":
raw_predt = super().predict(
X=X,
ntree_limit=ntree_limit,
validate_features=validate_features,
base_margin=base_margin,
iteration_range=iteration_range,
@ -1652,7 +1627,6 @@ class XGBClassifier(XGBModel, XGBClassifierMixIn, XGBClassifierBase):
return class_prob
class_probs = super().predict(
X=X,
ntree_limit=ntree_limit,
validate_features=validate_features,
base_margin=base_margin,
iteration_range=iteration_range,
@ -2074,7 +2048,6 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
self,
X: ArrayLike,
output_margin: bool = False,
ntree_limit: Optional[int] = None,
validate_features: bool = True,
base_margin: Optional[ArrayLike] = None,
iteration_range: Optional[Tuple[int, int]] = None,
@ -2083,20 +2056,18 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
return super().predict(
X,
output_margin,
ntree_limit,
validate_features,
base_margin,
iteration_range,
iteration_range=iteration_range,
)
def apply(
self,
X: ArrayLike,
ntree_limit: int = 0,
iteration_range: Optional[Tuple[int, int]] = None,
) -> ArrayLike:
X, _ = _get_qid(X, None)
return super().apply(X, ntree_limit, iteration_range)
return super().apply(X, iteration_range)
def score(self, X: ArrayLike, y: ArrayLike) -> float:
"""Evaluate score for data using the last evaluation metric. If the model is

4
python-package/xgboost/spark/data.py
View File

@ -11,7 +11,6 @@ from xgboost import DataIter, DMatrix, QuantileDMatrix, XGBModel
from xgboost.compat import concat
from .._typing import ArrayLike
from ..core import _convert_ntree_limit
from .utils import get_logger # type: ignore
@ -343,8 +342,7 @@ def pred_contribs(
strict_shape: bool = False,
) -> np.ndarray:
"""Predict contributions with data with the full model."""
iteration_range = _convert_ntree_limit(model.get_booster(), None, None)
iteration_range = model._get_iteration_range(iteration_range)
iteration_range = model._get_iteration_range(None)
data_dmatrix = DMatrix(
data,
base_margin=base_margin,

1
tests/ci_build/conda_env/aarch64_test.yml
View File

@ -31,6 +31,5 @@ dependencies:
- pyspark
- cloudpickle
- pip:
- shap
- awscli
- auditwheel

1
tests/ci_build/conda_env/linux_cpu_test.yml
View File

@ -37,7 +37,6 @@ dependencies:
- pyarrow
- protobuf
- cloudpickle
- shap>=0.41
- modin
# TODO: Replace it with pyspark>=3.4 once 3.4 released.
# - https://ml-team-public-read.s3.us-west-2.amazonaws.com/pyspark-3.4.0.dev0.tar.gz

1
tests/ci_build/lint_python.py
View File

@ -146,6 +146,7 @@ def main(args: argparse.Namespace) -> None:
"tests/python/test_config.py",
"tests/python/test_data_iterator.py",
"tests/python/test_dt.py",
"tests/python/test_predict.py",
"tests/python/test_quantile_dmatrix.py",
"tests/python/test_tree_regularization.py",
"tests/python-gpu/test_gpu_data_iterator.py",

16
tests/python/test_basic_models.py
View File

@ -64,7 +64,7 @@ class TestModels:
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist)
# this is prediction
preds = bst.predict(dtest, ntree_limit=num_round)
preds = bst.predict(dtest, iteration_range=(0, num_round))
labels = dtest.get_label()
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
@ -83,7 +83,7 @@ class TestModels:
bst2 = xgb.Booster(params=param, model_file=model_path)
dtest2 = xgb.DMatrix(dtest_path)
preds2 = bst2.predict(dtest2, ntree_limit=num_round)
preds2 = bst2.predict(dtest2, iteration_range=(0, num_round))
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
@ -96,7 +96,7 @@ class TestModels:
# check whether custom evaluation metrics work
bst = xgb.train(param, dtrain, num_round, watchlist,
feval=my_logloss)
preds3 = bst.predict(dtest, ntree_limit=num_round)
preds3 = bst.predict(dtest, iteration_range=(0, num_round))
assert all(preds3 == preds)
# check whether sample_type and normalize_type work
@ -110,7 +110,7 @@ class TestModels:
param['sample_type'] = p[0]
param['normalize_type'] = p[1]
bst = xgb.train(param, dtrain, num_round, watchlist)
preds = bst.predict(dtest, ntree_limit=num_round)
preds = bst.predict(dtest, iteration_range=(0, num_round))
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1
@ -472,8 +472,8 @@ class TestModels:
X, y = load_iris(return_X_y=True)
cls = xgb.XGBClassifier(n_estimators=2)
cls.fit(X, y, early_stopping_rounds=1, eval_set=[(X, y)])
assert cls.get_booster().best_ntree_limit == 2
assert cls.best_ntree_limit == cls.get_booster().best_ntree_limit
assert cls.get_booster().best_iteration == cls.n_estimators - 1
assert cls.best_iteration == cls.get_booster().best_iteration
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, "cls.json")
@ -481,8 +481,8 @@ class TestModels:
cls = xgb.XGBClassifier(n_estimators=2)
cls.load_model(path)
assert cls.get_booster().best_ntree_limit == 2
assert cls.best_ntree_limit == cls.get_booster().best_ntree_limit
assert cls.get_booster().best_iteration == cls.n_estimators - 1
assert cls.best_iteration == cls.get_booster().best_iteration
def run_slice(
self,

1
tests/python/test_cli.py
View File

@ -102,7 +102,6 @@ eval[test] = {data_path}
booster.feature_names = None
booster.feature_types = None
booster.set_attr(best_iteration=None)
booster.set_attr(best_ntree_limit=None)
booster.save_model(model_out_py)
py_predt = booster.predict(data)

30
tests/python/test_predict.py
View File

@ -1,4 +1,4 @@
'''Tests for running inplace prediction.'''
"""Tests for running inplace prediction."""
from concurrent.futures import ThreadPoolExecutor
import numpy as np
@ -17,7 +17,7 @@ def run_threaded_predict(X, rows, predict_func):
per_thread = 20
with ThreadPoolExecutor(max_workers=10) as e:
for i in range(0, rows, int(rows / per_thread)):
if hasattr(X, 'iloc'):
if hasattr(X, "iloc"):
predictor = X.iloc[i : i + per_thread, :]
else:
predictor = X[i : i + per_thread, ...]
@ -61,13 +61,16 @@ def run_predict_leaf(predictor):
validate_leaf_output(leaf, num_parallel_tree)
ntree_limit = 2
n_iters = 2
sliced = booster.predict(
m, pred_leaf=True, ntree_limit=num_parallel_tree * ntree_limit, strict_shape=True
m,
pred_leaf=True,
iteration_range=(0, n_iters),
strict_shape=True,
)
first = sliced[0, ...]
assert np.prod(first.shape) == classes * num_parallel_tree * ntree_limit
assert np.prod(first.shape) == classes * num_parallel_tree * n_iters
# When there's only 1 tree, the output is a 1 dim vector
booster = xgb.train({"tree_method": "hist"}, num_boost_round=1, dtrain=m)
@ -77,11 +80,12 @@ def run_predict_leaf(predictor):
def test_predict_leaf():
run_predict_leaf('cpu_predictor')
run_predict_leaf("cpu_predictor")
def test_predict_shape():
from sklearn.datasets import fetch_california_housing
X, y = fetch_california_housing(return_X_y=True)
reg = xgb.XGBRegressor(n_estimators=1)
reg.fit(X, y)
@ -119,7 +123,8 @@ def test_predict_shape():
class TestInplacePredict:
'''Tests for running inplace prediction'''
"""Tests for running inplace prediction"""
@classmethod
def setup_class(cls):
cls.rows = 1000
@ -139,7 +144,7 @@ class TestInplacePredict:
cls.test = xgb.DMatrix(cls.X[:10, ...], missing=cls.missing)
cls.num_boost_round = 10
cls.booster = xgb.train({'tree_method': 'hist'}, dtrain, num_boost_round=10)
cls.booster = xgb.train({"tree_method": "hist"}, dtrain, num_boost_round=10)
def test_predict(self):
booster = self.booster
@ -162,28 +167,22 @@ class TestInplacePredict:
predt_from_array = booster.inplace_predict(
X[:10, ...], iteration_range=(0, 4), missing=self.missing
)
predt_from_dmatrix = booster.predict(test, ntree_limit=4)
predt_from_dmatrix = booster.predict(test, iteration_range=(0, 4))
np.testing.assert_allclose(predt_from_dmatrix, predt_from_array)
with pytest.raises(ValueError):
booster.predict(test, ntree_limit=booster.best_ntree_limit + 1)
with pytest.raises(ValueError):
booster.predict(test, iteration_range=(0, booster.best_iteration + 2))
default = booster.predict(test)
range_full = booster.predict(test, iteration_range=(0, self.num_boost_round))
ntree_full = booster.predict(test, ntree_limit=self.num_boost_round)
np.testing.assert_allclose(range_full, default)
np.testing.assert_allclose(ntree_full, default)
range_full = booster.predict(
test, iteration_range=(0, booster.best_iteration + 1)
)
ntree_full = booster.predict(test, ntree_limit=booster.best_ntree_limit)
np.testing.assert_allclose(range_full, default)
np.testing.assert_allclose(ntree_full, default)
def predict_dense(x):
inplace_predt = booster.inplace_predict(x)
@ -251,6 +250,7 @@ class TestInplacePredict:
@pytest.mark.skipif(**tm.no_pandas())
def test_pd_dtypes(self) -> None:
from pandas.api.types import is_bool_dtype
for orig, x in pd_dtypes():
dtypes = orig.dtypes if isinstance(orig, pd.DataFrame) else [orig.dtypes]
if isinstance(orig, pd.DataFrame) and is_bool_dtype(dtypes[0]):

2
tests/python/test_ranking.py
View File

@ -60,7 +60,7 @@ def test_ranking_with_weighted_data():
assert all(p <= q for p, q in zip(auc_rec, auc_rec[1:]))
for i in range(1, 11):
pred = bst.predict(dtrain, ntree_limit=i)
pred = bst.predict(dtrain, iteration_range=(0, i))
# is_sorted[i]: is i-th group correctly sorted by the ranking predictor?
is_sorted = []
for k in range(0, 20, 5):

39
tests/python/test_training_continuation.py
View File

@ -95,44 +95,39 @@ class TestTrainingContinuation:
res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
assert res1 == res2
gbdt_04 = xgb.train(xgb_params_02, dtrain_2class,
num_boost_round=3)
assert gbdt_04.best_ntree_limit == (gbdt_04.best_iteration +
1) * self.num_parallel_tree
gbdt_04 = xgb.train(xgb_params_02, dtrain_2class, num_boost_round=3)
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(y_2class,
res2 = mean_squared_error(
y_2class,
gbdt_04.predict(
dtrain_2class,
ntree_limit=gbdt_04.best_ntree_limit))
dtrain_2class, iteration_range=(0, gbdt_04.best_iteration + 1)
)
)
assert res1 == res2
gbdt_04 = xgb.train(xgb_params_02, dtrain_2class,
num_boost_round=7, xgb_model=gbdt_04)
assert gbdt_04.best_ntree_limit == (
gbdt_04.best_iteration + 1) * self.num_parallel_tree
gbdt_04 = xgb.train(
xgb_params_02, dtrain_2class, num_boost_round=7, xgb_model=gbdt_04
)
res1 = mean_squared_error(y_2class, gbdt_04.predict(dtrain_2class))
res2 = mean_squared_error(y_2class,
res2 = mean_squared_error(
y_2class,
gbdt_04.predict(
dtrain_2class,
ntree_limit=gbdt_04.best_ntree_limit))
dtrain_2class, iteration_range=(0, gbdt_04.best_iteration + 1)
)
)
assert res1 == res2
gbdt_05 = xgb.train(xgb_params_03, dtrain_5class,
num_boost_round=7)
assert gbdt_05.best_ntree_limit == (
gbdt_05.best_iteration + 1) * self.num_parallel_tree
gbdt_05 = xgb.train(xgb_params_03,
dtrain_5class,
num_boost_round=3,
xgb_model=gbdt_05)
assert gbdt_05.best_ntree_limit == (
gbdt_05.best_iteration + 1) * self.num_parallel_tree
res1 = gbdt_05.predict(dtrain_5class)
res2 = gbdt_05.predict(dtrain_5class,
ntree_limit=gbdt_05.best_ntree_limit)
res2 = gbdt_05.predict(
dtrain_5class, iteration_range=(0, gbdt_05.best_iteration + 1)
)
np.testing.assert_almost_equal(res1, res2)
@pytest.mark.skipif(**tm.no_sklearn())

6
tests/python/test_with_shap.py
View File

@ -13,9 +13,9 @@ except Exception:
pytestmark = pytest.mark.skipif(shap is None, reason="Requires shap package")
# Check integration is not broken from xgboost side
# Changes in binary format may cause problems
def test_with_shap():
# xgboost removed ntree_limit in 2.0, which breaks the SHAP package.
@pytest.mark.xfail
def test_with_shap() -> None:
from sklearn.datasets import fetch_california_housing
X, y = fetch_california_housing(return_X_y=True)

39
tests/python/test_with_sklearn.py
View File

@ -63,9 +63,15 @@ def test_multiclass_classification(objective):
assert xgb_model.get_booster().num_boosted_rounds() == 100
preds = xgb_model.predict(X[test_index])
# test other params in XGBClassifier().fit
preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3)
preds2 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=(0, 1)
)
preds3 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=None
)
preds4 = xgb_model.predict(
X[test_index], output_margin=False, iteration_range=(0, 1)
)
labels = y[test_index]
check_pred(preds, labels, output_margin=False)
@ -86,25 +92,21 @@ def test_multiclass_classification(objective):
assert proba.shape[1] == cls.n_classes_
def test_best_ntree_limit():
def test_best_iteration():
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
def train(booster, forest):
def train(booster: str, forest: Optional[int]) -> None:
rounds = 4
cls = xgb.XGBClassifier(
n_estimators=rounds, num_parallel_tree=forest, booster=booster
).fit(
X, y, eval_set=[(X, y)], early_stopping_rounds=3
)
assert cls.best_iteration == rounds - 1
if forest:
assert cls.best_ntree_limit == rounds * forest
else:
assert cls.best_ntree_limit == 0
# best_ntree_limit is used by default, assert that under gblinear it's
# best_iteration is used by default, assert that under gblinear it's
# automatically ignored due to being 0.
cls.predict(X)
@ -430,12 +432,15 @@ def test_regression():
preds = xgb_model.predict(X[test_index])
# test other params in XGBRegressor().fit
preds2 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=3)
preds3 = xgb_model.predict(X[test_index], output_margin=True,
ntree_limit=0)
preds4 = xgb_model.predict(X[test_index], output_margin=False,
ntree_limit=3)
preds2 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=(0, 3)
)
preds3 = xgb_model.predict(
X[test_index], output_margin=True, iteration_range=None
)
preds4 = xgb_model.predict(
X[test_index], output_margin=False, iteration_range=(0, 3)
)
labels = y[test_index]
assert mean_squared_error(preds, labels) < 25

169
tests/test_distributed/test_with_spark/test_spark_local.py
View File

@ -169,7 +169,7 @@ def reg_with_weight(
)
RegData = namedtuple("RegData", ("reg_df_train", "reg_df_test"))
RegData = namedtuple("RegData", ("reg_df_train", "reg_df_test", "reg_params"))
@pytest.fixture
@ -181,6 +181,13 @@ def reg_data(spark: SparkSession) -> Generator[RegData, None, None]:
predt0 = reg1.predict(X)
pred_contrib0: np.ndarray = pred_contribs(reg1, X, None, False)
reg_params = {
"max_depth": 5,
"n_estimators": 10,
"iteration_range": [0, 5],
"max_bin": 9,
}
# convert np array to pyspark dataframe
reg_df_train_data = [
(Vectors.dense(X[0, :]), int(y[0])),
@ -188,26 +195,34 @@ def reg_data(spark: SparkSession) -> Generator[RegData, None, None]:
]
reg_df_train = spark.createDataFrame(reg_df_train_data, ["features", "label"])
reg2 = xgb.XGBRegressor(max_depth=5, n_estimators=10)
reg2.fit(X, y)
predt2 = reg2.predict(X, iteration_range=[0, 5])
# array([0.22185266, 0.77814734], dtype=float32)
reg_df_test = spark.createDataFrame(
[
(
Vectors.dense(X[0, :]),
float(predt0[0]),
pred_contrib0[0, :].tolist(),
float(predt2[0]),
),
(
Vectors.sparse(3, {1: 1.0, 2: 5.5}),
float(predt0[1]),
pred_contrib0[1, :].tolist(),
float(predt2[1]),
),
],
[
"features",
"expected_prediction",
"expected_pred_contribs",
"expected_prediction_with_params",
],
)
yield RegData(reg_df_train, reg_df_test)
yield RegData(reg_df_train, reg_df_test, reg_params)
MultiClfData = namedtuple("MultiClfData", ("multi_clf_df_train", "multi_clf_df_test"))
@ -740,6 +755,76 @@ class TestPySparkLocal:
model = classifier.fit(clf_data.cls_df_train)
model.transform(clf_data.cls_df_test).collect()
def test_regressor_model_save_load(self, reg_data: RegData) -> None:
with tempfile.TemporaryDirectory() as tmpdir:
path = "file:" + tmpdir
regressor = SparkXGBRegressor(**reg_data.reg_params)
model = regressor.fit(reg_data.reg_df_train)
model.save(path)
loaded_model = SparkXGBRegressorModel.load(path)
assert model.uid == loaded_model.uid
for k, v in reg_data.reg_params.items():
assert loaded_model.getOrDefault(k) == v
pred_result = loaded_model.transform(reg_data.reg_df_test).collect()
for row in pred_result:
assert np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
with pytest.raises(AssertionError, match="Expected class name"):
SparkXGBClassifierModel.load(path)
assert_model_compatible(model, tmpdir)
def test_regressor_with_params(self, reg_data: RegData) -> None:
regressor = SparkXGBRegressor(**reg_data.reg_params)
all_params = dict(
**(regressor._gen_xgb_params_dict()),
**(regressor._gen_fit_params_dict()),
**(regressor._gen_predict_params_dict()),
)
check_sub_dict_match(
reg_data.reg_params, all_params, excluding_keys=_non_booster_params
)
model = regressor.fit(reg_data.reg_df_train)
all_params = dict(
**(model._gen_xgb_params_dict()),
**(model._gen_fit_params_dict()),
**(model._gen_predict_params_dict()),
)
check_sub_dict_match(
reg_data.reg_params, all_params, excluding_keys=_non_booster_params
)
pred_result = model.transform(reg_data.reg_df_test).collect()
for row in pred_result:
assert np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
def test_regressor_model_pipeline_save_load(self, reg_data: RegData) -> None:
with tempfile.TemporaryDirectory() as tmpdir:
path = "file:" + tmpdir
regressor = SparkXGBRegressor()
pipeline = Pipeline(stages=[regressor])
pipeline = pipeline.copy(
extra=get_params_map(reg_data.reg_params, regressor)
)
model = pipeline.fit(reg_data.reg_df_train)
model.save(path)
loaded_model = PipelineModel.load(path)
for k, v in reg_data.reg_params.items():
assert loaded_model.stages[0].getOrDefault(k) == v
pred_result = loaded_model.transform(reg_data.reg_df_test).collect()
for row in pred_result:
assert np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
assert_model_compatible(model.stages[0], tmpdir)
class XgboostLocalTest(SparkTestCase):
def setUp(self):
@ -918,12 +1003,6 @@ class XgboostLocalTest(SparkTestCase):
def get_local_tmp_dir(self):
return self.tempdir + str(uuid.uuid4())
def assert_model_compatible(self, model: XGBModel, model_path: str):
bst = xgb.Booster()
path = glob.glob(f"{model_path}/**/model/part-00000", recursive=True)[0]
bst.load_model(path)
self.assertEqual(model.get_booster().save_raw("json"), bst.save_raw("json"))
def test_convert_to_sklearn_model_reg(self) -> None:
regressor = SparkXGBRegressor(
n_estimators=200, missing=2.0, max_depth=3, sketch_eps=0.5
@ -1007,80 +1086,6 @@ class XgboostLocalTest(SparkTestCase):
== "float64"
)
def test_regressor_with_params(self):
regressor = SparkXGBRegressor(**self.reg_params)
all_params = dict(
**(regressor._gen_xgb_params_dict()),
**(regressor._gen_fit_params_dict()),
**(regressor._gen_predict_params_dict()),
)
check_sub_dict_match(
self.reg_params, all_params, excluding_keys=_non_booster_params
)
model = regressor.fit(self.reg_df_train)
all_params = dict(
**(model._gen_xgb_params_dict()),
**(model._gen_fit_params_dict()),
**(model._gen_predict_params_dict()),
)
check_sub_dict_match(
self.reg_params, all_params, excluding_keys=_non_booster_params
)
pred_result = model.transform(self.reg_df_test).collect()
for row in pred_result:
self.assertTrue(
np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
)
def test_regressor_model_save_load(self):
tmp_dir = self.get_local_tmp_dir()
path = "file:" + tmp_dir
regressor = SparkXGBRegressor(**self.reg_params)
model = regressor.fit(self.reg_df_train)
model.save(path)
loaded_model = SparkXGBRegressorModel.load(path)
self.assertEqual(model.uid, loaded_model.uid)
for k, v in self.reg_params.items():
self.assertEqual(loaded_model.getOrDefault(k), v)
pred_result = loaded_model.transform(self.reg_df_test).collect()
for row in pred_result:
self.assertTrue(
np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
)
with self.assertRaisesRegex(AssertionError, "Expected class name"):
SparkXGBClassifierModel.load(path)
self.assert_model_compatible(model, tmp_dir)
def test_regressor_model_pipeline_save_load(self):
tmp_dir = self.get_local_tmp_dir()
path = "file:" + tmp_dir
regressor = SparkXGBRegressor()
pipeline = Pipeline(stages=[regressor])
pipeline = pipeline.copy(extra=get_params_map(self.reg_params, regressor))
model = pipeline.fit(self.reg_df_train)
model.save(path)
loaded_model = PipelineModel.load(path)
for k, v in self.reg_params.items():
self.assertEqual(loaded_model.stages[0].getOrDefault(k), v)
pred_result = loaded_model.transform(self.reg_df_test).collect()
for row in pred_result:
self.assertTrue(
np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
)
self.assert_model_compatible(model.stages[0], tmp_dir)
def test_callbacks(self):
from xgboost.callback import LearningRateScheduler

167
tests/test_distributed/test_with_spark/test_spark_local_cluster.py
View File

@ -1,16 +1,24 @@
import json
import logging
import os
import random
import tempfile
import uuid
from collections import namedtuple
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.callback import LearningRateScheduler
pytestmark = pytest.mark.skipif(**tm.no_spark())
from typing import Generator
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession
from xgboost.spark import SparkXGBClassifier, SparkXGBRegressor
from xgboost.spark.utils import _get_max_num_concurrent_tasks
@ -18,43 +26,70 @@ from xgboost.spark.utils import _get_max_num_concurrent_tasks
from .utils import SparkLocalClusterTestCase
class XgboostLocalClusterTestCase(SparkLocalClusterTestCase):
def setUp(self):
random.seed(2020)
@pytest.fixture
def spark() -> Generator[SparkSession, None, None]:
config = {
"spark.master": "local-cluster[2, 2, 1024]",
"spark.python.worker.reuse": "false",
"spark.driver.host": "127.0.0.1",
"spark.task.maxFailures": "1",
"spark.sql.execution.pyspark.udf.simplifiedTraceback.enabled": "false",
"spark.sql.pyspark.jvmStacktrace.enabled": "true",
"spark.cores.max": "4",
"spark.task.cpus": "1",
"spark.executor.cores": "2",
}
self.n_workers = _get_max_num_concurrent_tasks(self.session.sparkContext)
# The following code use xgboost python library to train xgb model and predict.
#
# >>> import numpy as np
# >>> import xgboost
# >>> X = np.array([[1.0, 2.0, 3.0], [0.0, 1.0, 5.5]])
# >>> y = np.array([0, 1])
# >>> reg1 = xgboost.XGBRegressor()
# >>> reg1.fit(X, y)
# >>> reg1.predict(X)
# array([8.8363886e-04, 9.9911636e-01], dtype=float32)
# >>> def custom_lr(boosting_round, num_boost_round):
# ... return 1.0 / (boosting_round + 1)
# ...
# >>> reg1.fit(X, y, callbacks=[xgboost.callback.reset_learning_rate(custom_lr)])
# >>> reg1.predict(X)
builder = SparkSession.builder.appName("XGBoost PySpark Python API Tests")
for k, v in config.items():
builder.config(k, v)
logging.getLogger("pyspark").setLevel(logging.INFO)
sess = builder.getOrCreate()
yield sess
sess.stop()
sess.sparkContext.stop()
RegData = namedtuple("RegData", ("reg_df_train", "reg_df_test", "reg_params"))
@pytest.fixture
def reg_data(spark: SparkSession) -> Generator[RegData, None, None]:
reg_params = {"max_depth": 5, "n_estimators": 10, "iteration_range": (0, 5)}
X = np.array([[1.0, 2.0, 3.0], [0.0, 1.0, 5.5]])
y = np.array([0, 1])
def custom_lr(boosting_round):
return 1.0 / (boosting_round + 1)
reg1 = xgb.XGBRegressor(callbacks=[LearningRateScheduler(custom_lr)])
reg1.fit(X, y)
predt1 = reg1.predict(X)
# array([0.02406833, 0.97593164], dtype=float32)
# >>> reg2 = xgboost.XGBRegressor(max_depth=5, n_estimators=10)
# >>> reg2.fit(X, y)
# >>> reg2.predict(X, ntree_limit=5)
reg2 = xgb.XGBRegressor(max_depth=5, n_estimators=10)
reg2.fit(X, y)
predt2 = reg2.predict(X, iteration_range=(0, 5))
# array([0.22185263, 0.77814734], dtype=float32)
self.reg_params = {"max_depth": 5, "n_estimators": 10, "ntree_limit": 5}
self.reg_df_train = self.session.createDataFrame(
reg_df_train = spark.createDataFrame(
[
(Vectors.dense(1.0, 2.0, 3.0), 0),
(Vectors.sparse(3, {1: 1.0, 2: 5.5}), 1),
],
["features", "label"],
)
self.reg_df_test = self.session.createDataFrame(
reg_df_test = spark.createDataFrame(
[
(Vectors.dense(1.0, 2.0, 3.0), 0.0, 0.2219, 0.02406),
(Vectors.sparse(3, {1: 1.0, 2: 5.5}), 1.0, 0.7781, 0.9759),
(Vectors.dense(1.0, 2.0, 3.0), 0.0, float(predt2[0]), float(predt1[0])),
(
Vectors.sparse(3, {1: 1.0, 2: 5.5}),
1.0,
float(predt2[1]),
float(predt1[1]),
),
],
[
"features",
@ -63,6 +98,47 @@ class XgboostLocalClusterTestCase(SparkLocalClusterTestCase):
"expected_prediction_with_callbacks",
],
)
yield RegData(reg_df_train, reg_df_test, reg_params)
class TestPySparkLocalCluster:
def test_regressor_basic_with_params(self, reg_data: RegData) -> None:
regressor = SparkXGBRegressor(**reg_data.reg_params)
model = regressor.fit(reg_data.reg_df_train)
pred_result = model.transform(reg_data.reg_df_test).collect()
for row in pred_result:
assert np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
def test_callbacks(self, reg_data: RegData) -> None:
with tempfile.TemporaryDirectory() as tmpdir:
path = os.path.join(tmpdir, str(uuid.uuid4()))
def custom_lr(boosting_round):
return 1.0 / (boosting_round + 1)
cb = [LearningRateScheduler(custom_lr)]
regressor = SparkXGBRegressor(callbacks=cb)
# Test the save/load of the estimator instead of the model, since
# the callbacks param only exists in the estimator but not in the model
regressor.save(path)
regressor = SparkXGBRegressor.load(path)
model = regressor.fit(reg_data.reg_df_train)
pred_result = model.transform(reg_data.reg_df_test).collect()
for row in pred_result:
assert np.isclose(
row.prediction, row.expected_prediction_with_callbacks, atol=1e-3
)
class XgboostLocalClusterTestCase(SparkLocalClusterTestCase):
def setUp(self):
random.seed(2020)
self.n_workers = _get_max_num_concurrent_tasks(self.session.sparkContext)
# Distributed section
# Binary classification
@ -218,42 +294,6 @@ class XgboostLocalClusterTestCase(SparkLocalClusterTestCase):
self.reg_best_score_eval = 5.239e-05
self.reg_best_score_weight_and_eval = 4.850e-05
def test_regressor_basic_with_params(self):
regressor = SparkXGBRegressor(**self.reg_params)
model = regressor.fit(self.reg_df_train)
pred_result = model.transform(self.reg_df_test).collect()
for row in pred_result:
self.assertTrue(
np.isclose(
row.prediction, row.expected_prediction_with_params, atol=1e-3
)
)
def test_callbacks(self):
from xgboost.callback import LearningRateScheduler
path = os.path.join(self.tempdir, str(uuid.uuid4()))
def custom_learning_rate(boosting_round):
return 1.0 / (boosting_round + 1)
cb = [LearningRateScheduler(custom_learning_rate)]
regressor = SparkXGBRegressor(callbacks=cb)
# Test the save/load of the estimator instead of the model, since
# the callbacks param only exists in the estimator but not in the model
regressor.save(path)
regressor = SparkXGBRegressor.load(path)
model = regressor.fit(self.reg_df_train)
pred_result = model.transform(self.reg_df_test).collect()
for row in pred_result:
self.assertTrue(
np.isclose(
row.prediction, row.expected_prediction_with_callbacks, atol=1e-3
)
)
def test_classifier_distributed_basic(self):
classifier = SparkXGBClassifier(num_workers=self.n_workers, n_estimators=100)
model = classifier.fit(self.cls_df_train_distributed)
@ -409,7 +449,6 @@ class XgboostLocalClusterTestCase(SparkLocalClusterTestCase):
pred_result = model.transform(
self.cls_df_test_distributed_lower_estimators
).collect()
print(pred_result)
for row in pred_result:
self.assertTrue(np.isclose(row.expected_label, row.prediction, atol=1e-3))
self.assertTrue(