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Move num_parallel_tree to model parameter. (#7751)

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The size of forest should be a property of model itself instead of a training
hyper-parameter.
This commit is contained in:
Jiaming Yuan
2022-03-29 02:32:42 +08:00
committed by GitHub
parent 8b3ecfca25
commit 3c9b04460a
11 changed files with 158 additions and 101 deletions
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107
tests/python/test_basic_models.py
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@@ -8,8 +8,6 @@ import locale
import tempfile
dpath = os.path.join(tm.PROJECT_ROOT, 'demo/data/')
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
rng = np.random.RandomState(1994)
@@ -38,6 +36,8 @@ class TestModels:
param = {'verbosity': 0, 'objective': 'binary:logistic',
'booster': 'gblinear', 'alpha': 0.0001, 'lambda': 1,
'nthread': 1}
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 4
bst = xgb.train(param, dtrain, num_round, watchlist)
@@ -124,7 +124,7 @@ class TestModels:
predt_1 = bst.predict(margined)
assert np.any(np.abs(predt_1 - predt_0) > 1e-6)
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
bst = xgb.train({'tree_method': 'hist'}, dtrain, 2)
predt_2 = bst.predict(dtrain)
assert np.all(np.abs(predt_2 - predt_1) < 1e-6)
@@ -150,6 +150,8 @@ class TestModels:
'objective': 'reg:logistic',
"tree_method": tree_method
}
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 10
@@ -195,6 +197,8 @@ class TestModels:
self.run_custom_objective()
def test_multi_eval_metric(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
param = {'max_depth': 2, 'eta': 0.2, 'verbosity': 1,
'objective': 'binary:logistic'}
@@ -216,6 +220,7 @@ class TestModels:
param['scale_pos_weight'] = ratio
return (dtrain, dtest, param)
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'auc'}, seed=0, fpreproc=fpreproc)
@@ -223,6 +228,7 @@ class TestModels:
param = {'max_depth': 2, 'eta': 1, 'verbosity': 0,
'objective': 'binary:logistic'}
num_round = 2
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
xgb.cv(param, dtrain, num_round, nfold=5,
metrics={'error'}, seed=0, show_stdv=False)
@@ -331,6 +337,7 @@ class TestModels:
os.remove(model_path)
try:
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
xgb.train({'objective': 'foo'}, dtrain, num_boost_round=1)
except ValueError as e:
e_str = str(e)
@@ -422,68 +429,58 @@ class TestModels:
assert cls.get_booster().best_ntree_limit == 2
assert cls.best_ntree_limit == cls.get_booster().best_ntree_limit
@pytest.mark.skipif(**tm.no_sklearn())
@pytest.mark.parametrize('booster', ['gbtree', 'dart'])
def test_slice(self, booster):
from sklearn.datasets import make_classification
num_classes = 3
X, y = make_classification(n_samples=1000, n_informative=5,
n_classes=num_classes)
dtrain = xgb.DMatrix(data=X, label=y)
num_parallel_tree = 4
num_boost_round = 16
total_trees = num_parallel_tree * num_classes * num_boost_round
booster = xgb.train({
'num_parallel_tree': 4, 'subsample': 0.5, 'num_class': 3, 'booster': booster,
'objective': 'multi:softprob'},
num_boost_round=num_boost_round, dtrain=dtrain)
booster.feature_types = ["q"] * X.shape[1]
assert len(booster.get_dump()) == total_trees
def run_slice(
self,
booster: xgb.Booster,
dtrain: xgb.DMatrix,
num_parallel_tree: int,
num_classes: int,
num_boost_round: int
):
beg = 3
end = 7
sliced: xgb.Booster = booster[beg: end]
sliced: xgb.Booster = booster[beg:end]
assert sliced.feature_types == booster.feature_types
sliced_trees = (end - beg) * num_parallel_tree * num_classes
assert sliced_trees == len(sliced.get_dump())
sliced_trees = sliced_trees // 2
sliced: xgb.Booster = booster[beg: end: 2]
sliced = booster[beg:end:2]
assert sliced_trees == len(sliced.get_dump())
sliced: xgb.Booster = booster[beg: ...]
sliced = booster[beg: ...]
sliced_trees = (num_boost_round - beg) * num_parallel_tree * num_classes
assert sliced_trees == len(sliced.get_dump())
sliced: xgb.Booster = booster[beg:]
sliced = booster[beg:]
sliced_trees = (num_boost_round - beg) * num_parallel_tree * num_classes
assert sliced_trees == len(sliced.get_dump())
sliced: xgb.Booster = booster[:end]
sliced = booster[:end]
sliced_trees = end * num_parallel_tree * num_classes
assert sliced_trees == len(sliced.get_dump())
sliced: xgb.Booster = booster[...:end]
sliced = booster[...: end]
sliced_trees = end * num_parallel_tree * num_classes
assert sliced_trees == len(sliced.get_dump())
with pytest.raises(ValueError, match=r'>= 0'):
booster[-1: 0]
with pytest.raises(ValueError, match=r">= 0"):
booster[-1:0]
# we do not accept empty slice.
with pytest.raises(ValueError):
booster[1:1]
# stop can not be smaller than begin
with pytest.raises(ValueError, match=r'Invalid.*'):
with pytest.raises(ValueError, match=r"Invalid.*"):
booster[3:0]
with pytest.raises(ValueError, match=r'Invalid.*'):
with pytest.raises(ValueError, match=r"Invalid.*"):
booster[3:-1]
# negative step is not supported.
with pytest.raises(ValueError, match=r'.*>= 1.*'):
with pytest.raises(ValueError, match=r".*>= 1.*"):
booster[0:2:-1]
# step can not be 0.
with pytest.raises(ValueError, match=r'.*>= 1.*'):
with pytest.raises(ValueError, match=r".*>= 1.*"):
booster[0:2:0]
trees = [_ for _ in booster]
@@ -492,12 +489,12 @@ class TestModels:
with pytest.raises(TypeError):
booster["wrong type"]
with pytest.raises(IndexError):
booster[:num_boost_round+1]
booster[: num_boost_round + 1]
with pytest.raises(ValueError):
booster[1, 2] # too many dims
booster[1, 2] # too many dims
# setitem is not implemented as model is immutable during slicing.
with pytest.raises(TypeError):
booster[...:end] = booster
booster[...: end] = booster
sliced_0 = booster[1:3]
np.testing.assert_allclose(
@@ -525,6 +522,44 @@ class TestModels:
single = booster[1:7].predict(dtrain, output_margin=True)
np.testing.assert_allclose(merged, single, atol=1e-6)
@pytest.mark.skipif(**tm.no_sklearn())
@pytest.mark.parametrize("booster", ["gbtree", "dart"])
def test_slice(self, booster):
from sklearn.datasets import make_classification
num_classes = 3
X, y = make_classification(
n_samples=1000, n_informative=5, n_classes=num_classes
)
dtrain = xgb.DMatrix(data=X, label=y)
num_parallel_tree = 4
num_boost_round = 16
total_trees = num_parallel_tree * num_classes * num_boost_round
booster = xgb.train(
{
"num_parallel_tree": num_parallel_tree,
"subsample": 0.5,
"num_class": num_classes,
"booster": booster,
"objective": "multi:softprob",
},
num_boost_round=num_boost_round,
dtrain=dtrain,
)
booster.feature_types = ["q"] * X.shape[1]
assert len(booster.get_dump()) == total_trees
self.run_slice(booster, dtrain, num_parallel_tree, num_classes, num_boost_round)
bytesarray = booster.save_raw(raw_format="ubj")
booster = xgb.Booster(model_file=bytesarray)
self.run_slice(booster, dtrain, num_parallel_tree, num_classes, num_boost_round)
bytesarray = booster.save_raw(raw_format="deprecated")
booster = xgb.Booster(model_file=bytesarray)
self.run_slice(booster, dtrain, num_parallel_tree, num_classes, num_boost_round)
@pytest.mark.skipif(**tm.no_pandas())
def test_feature_info(self):
import pandas as pd

4
tests/python/test_with_dask.py
View File

@@ -530,7 +530,7 @@ def test_dask_regressor(model: str, client: "Client") -> None:
forest = int(
json.loads(regressor.get_booster().save_config())["learner"][
"gradient_booster"
]["gbtree_train_param"]["num_parallel_tree"]
]["gbtree_model_param"]["num_parallel_tree"]
)
if model == "boosting":
@@ -584,7 +584,7 @@ def run_dask_classifier(
assert n_threads != 0 and n_threads != os.cpu_count()
forest = int(
config["learner"]["gradient_booster"]["gbtree_train_param"]["num_parallel_tree"]
config["learner"]["gradient_booster"]["gbtree_model_param"]["num_parallel_tree"]
)
if model == "boosting":
assert len(history["validation_0"][metric]) == 2

18
tests/python/test_with_sklearn.py
View File

@@ -329,21 +329,27 @@ def test_select_feature():
def test_num_parallel_tree():
from sklearn.datasets import fetch_california_housing
reg = xgb.XGBRegressor(n_estimators=4, num_parallel_tree=4,
tree_method='hist')
reg = xgb.XGBRegressor(n_estimators=4, num_parallel_tree=4, tree_method="hist")
X, y = fetch_california_housing(return_X_y=True)
bst = reg.fit(X=X, y=y)
dump = bst.get_booster().get_dump(dump_format='json')
dump = bst.get_booster().get_dump(dump_format="json")
assert len(dump) == 16
reg = xgb.XGBRFRegressor(n_estimators=4)
bst = reg.fit(X=X, y=y)
dump = bst.get_booster().get_dump(dump_format='json')
dump = bst.get_booster().get_dump(dump_format="json")
assert len(dump) == 4
config = json.loads(bst.get_booster().save_config())
assert int(config['learner']['gradient_booster']['gbtree_train_param'][
'num_parallel_tree']) == 4
assert (
int(
config["learner"]["gradient_booster"]["gbtree_model_param"][
"num_parallel_tree"
]
)
== 4
)
def test_calif_housing_regression():