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Refactor tests for training continuation. (#9997)

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This commit is contained in:
Jiaming Yuan
2024-01-24 16:07:19 +08:00
committed by GitHub
parent 5062a3ab46
commit d12cc1090a
4 changed files with 105 additions and 86 deletions
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79
tests/python/test_training_continuation.py
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@@ -6,6 +6,7 @@ import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.continuation import run_training_continuation_model_output
rng = np.random.RandomState(1337)
@@ -15,54 +16,51 @@ class TestTrainingContinuation:
def generate_parameters(self):
xgb_params_01_binary = {
'nthread': 1,
"nthread": 1,
}
xgb_params_02_binary = {
'nthread': 1,
'num_parallel_tree': self.num_parallel_tree
"nthread": 1,
"num_parallel_tree": self.num_parallel_tree,
}
xgb_params_03_binary = {
'nthread': 1,
'num_class': 5,
'num_parallel_tree': self.num_parallel_tree
"nthread": 1,
"num_class": 5,
"num_parallel_tree": self.num_parallel_tree,
}
return [
xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary
]
return [xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary]
def run_training_continuation(self, xgb_params_01, xgb_params_02,
xgb_params_03):
def run_training_continuation(self, xgb_params_01, xgb_params_02, xgb_params_03):
from sklearn.datasets import load_digits
from sklearn.metrics import mean_squared_error
digits_2class = load_digits(n_class=2)
digits_5class = load_digits(n_class=5)
X_2class = digits_2class['data']
y_2class = digits_2class['target']
X_2class = digits_2class["data"]
y_2class = digits_2class["target"]
X_5class = digits_5class['data']
y_5class = digits_5class['target']
X_5class = digits_5class["data"]
y_5class = digits_5class["target"]
dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
gbdt_01 = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=10)
gbdt_01 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=10)
ntrees_01 = len(gbdt_01.get_dump())
assert ntrees_01 == 10
gbdt_02 = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=0)
gbdt_02.save_model('xgb_tc.json')
gbdt_02 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=0)
gbdt_02.save_model("xgb_tc.json")
gbdt_02a = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=10, xgb_model=gbdt_02)
gbdt_02b = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=10, xgb_model="xgb_tc.json")
gbdt_02a = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model=gbdt_02
)
gbdt_02b = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model="xgb_tc.json"
)
ntrees_02a = len(gbdt_02a.get_dump())
ntrees_02b = len(gbdt_02b.get_dump())
assert ntrees_02a == 10
@@ -76,20 +74,21 @@ class TestTrainingContinuation:
res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
assert res1 == res2
gbdt_03 = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=3)
gbdt_03.save_model('xgb_tc.json')
gbdt_03 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=3)
gbdt_03.save_model("xgb_tc.json")
gbdt_03a = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=7, xgb_model=gbdt_03)
gbdt_03b = xgb.train(xgb_params_01, dtrain_2class,
num_boost_round=7, xgb_model="xgb_tc.json")
gbdt_03a = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model=gbdt_03
)
gbdt_03b = xgb.train(
xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model="xgb_tc.json"
)
ntrees_03a = len(gbdt_03a.get_dump())
ntrees_03b = len(gbdt_03b.get_dump())
assert ntrees_03a == 10
assert ntrees_03b == 10
os.remove('xgb_tc.json')
os.remove("xgb_tc.json")
res1 = mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
@@ -113,16 +112,14 @@ class TestTrainingContinuation:
y_2class,
gbdt_04.predict(
dtrain_2class, iteration_range=(0, gbdt_04.num_boosted_rounds())
)
),
)
assert res1 == res2
gbdt_05 = xgb.train(xgb_params_03, dtrain_5class,
num_boost_round=7)
gbdt_05 = xgb.train(xgb_params_03,
dtrain_5class,
num_boost_round=3,
xgb_model=gbdt_05)
gbdt_05 = xgb.train(xgb_params_03, dtrain_5class, num_boost_round=7)
gbdt_05 = xgb.train(
xgb_params_03, dtrain_5class, num_boost_round=3, xgb_model=gbdt_05
)
res1 = gbdt_05.predict(dtrain_5class)
res2 = gbdt_05.predict(
@@ -163,3 +160,7 @@ class TestTrainingContinuation:
clf.set_params(eval_metric="error")
clf.fit(X, y, eval_set=[(X, y)], xgb_model=loaded)
assert tm.non_increasing(clf.evals_result()["validation_0"]["error"])
@pytest.mark.parametrize("tree_method", ["hist", "approx", "exact"])
def test_model_output(self, tree_method: str) -> None:
run_training_continuation_model_output("cpu", tree_method)