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Refactor tests for training continuation. (#9997)

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Jiaming Yuan 2024-01-24 16:07:19 +08:00 committed by GitHub
parent 5062a3ab46
commit d12cc1090a
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4 changed files with 105 additions and 86 deletions
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58
python-package/xgboost/testing/continuation.py Normal file
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@ -0,0 +1,58 @@
"""Tests for training continuation."""
import json
from typing import Any, Dict, TypeVar
import numpy as np
import pytest
import xgboost as xgb
# pylint: disable=too-many-locals
def run_training_continuation_model_output(device: str, tree_method: str) -> None:
"""Run training continuation test."""
datasets = pytest.importorskip("sklearn.datasets")
n_samples = 64
n_features = 32
X, y = datasets.make_regression(n_samples, n_features, random_state=1)
dtrain = xgb.DMatrix(X, y)
params = {
"tree_method": tree_method,
"max_depth": "2",
"gamma": "0.1",
"alpha": "0.01",
"device": device,
}
bst_0 = xgb.train(params, dtrain, num_boost_round=64)
dump_0 = bst_0.get_dump(dump_format="json")
bst_1 = xgb.train(params, dtrain, num_boost_round=32)
bst_1 = xgb.train(params, dtrain, num_boost_round=32, xgb_model=bst_1)
dump_1 = bst_1.get_dump(dump_format="json")
T = TypeVar("T", Dict[str, Any], float, str, int, list)
def recursive_compare(obj_0: T, obj_1: T) -> None:
if isinstance(obj_0, float):
assert np.isclose(obj_0, obj_1, atol=1e-6)
elif isinstance(obj_0, str):
assert obj_0 == obj_1
elif isinstance(obj_0, int):
assert obj_0 == obj_1
elif isinstance(obj_0, dict):
for i in range(len(obj_0.items())):
assert list(obj_0.keys())[i] == list(obj_1.keys())[i]
if list(obj_0.keys())[i] != "missing":
recursive_compare(list(obj_0.values()), list(obj_1.values()))
else:
for i, lhs in enumerate(obj_0):
rhs = obj_1[i]
recursive_compare(lhs, rhs)
assert len(dump_0) == len(dump_1)
for i, lhs in enumerate(dump_0):
obj_0 = json.loads(lhs)
obj_1 = json.loads(dump_1[i])
recursive_compare(obj_0, obj_1)

2
tests/ci_build/lint_python.py
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@ -28,6 +28,7 @@ class LintersPaths:
"tests/python/test_predict.py",
"tests/python/test_quantile_dmatrix.py",
"tests/python/test_tree_regularization.py",
"tests/python/test_training_continuation.py",
"tests/python/test_shap.py",
"tests/python/test_model_io.py",
"tests/python/test_with_pandas.py",
@ -91,6 +92,7 @@ class LintersPaths:
"tests/python/test_multi_target.py",
"tests/python-gpu/test_gpu_data_iterator.py",
"tests/python-gpu/load_pickle.py",
"tests/python-gpu/test_gpu_training_continuation.py",
"tests/python/test_model_io.py",
"tests/test_distributed/test_with_spark/test_data.py",
"tests/test_distributed/test_gpu_with_spark/test_data.py",

52
tests/python-gpu/test_gpu_training_continuation.py
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@ -1,54 +1,12 @@
import json
import numpy as np
import pytest
import xgboost as xgb
from xgboost.testing.continuation import run_training_continuation_model_output
rng = np.random.RandomState(1994)
class TestGPUTrainingContinuation:
def test_training_continuation(self):
kRows = 64
kCols = 32
X = np.random.randn(kRows, kCols)
y = np.random.randn(kRows)
dtrain = xgb.DMatrix(X, y)
params = {
"tree_method": "gpu_hist",
"max_depth": "2",
"gamma": "0.1",
"alpha": "0.01",
}
bst_0 = xgb.train(params, dtrain, num_boost_round=64)
dump_0 = bst_0.get_dump(dump_format="json")
bst_1 = xgb.train(params, dtrain, num_boost_round=32)
bst_1 = xgb.train(params, dtrain, num_boost_round=32, xgb_model=bst_1)
dump_1 = bst_1.get_dump(dump_format="json")
def recursive_compare(obj_0, obj_1):
if isinstance(obj_0, float):
assert np.isclose(obj_0, obj_1, atol=1e-6)
elif isinstance(obj_0, str):
assert obj_0 == obj_1
elif isinstance(obj_0, int):
assert obj_0 == obj_1
elif isinstance(obj_0, dict):
keys_0 = list(obj_0.keys())
keys_1 = list(obj_1.keys())
values_0 = list(obj_0.values())
values_1 = list(obj_1.values())
for i in range(len(obj_0.items())):
assert keys_0[i] == keys_1[i]
if list(obj_0.keys())[i] != "missing":
recursive_compare(values_0[i], values_1[i])
else:
for i in range(len(obj_0)):
recursive_compare(obj_0[i], obj_1[i])
assert len(dump_0) == len(dump_1)
for i in range(len(dump_0)):
obj_0 = json.loads(dump_0[i])
obj_1 = json.loads(dump_1[i])
recursive_compare(obj_0, obj_1)
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
def test_model_output(self, tree_method: str) -> None:
run_training_continuation_model_output("cuda", tree_method)

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)