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[CI] Refactor tests to reduce CI time. (#8312)

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This commit is contained in:
Rory Mitchell
2022-10-12 11:32:06 +02:00
committed by GitHub
parent 39afdac3be
commit ce0382dcb0
19 changed files with 95 additions and 122 deletions
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13
tests/python-gpu/test_gpu_data_iterator.py
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@@ -5,10 +5,9 @@ import pytest
import sys
sys.path.append("tests/python")
from test_data_iterator import SingleBatch, make_batches
from test_data_iterator import test_single_batch as cpu_single_batch
from test_data_iterator import run_data_iterator
from testing import IteratorForTest, no_cupy
from testing import no_cupy
def test_gpu_single_batch() -> None:
@@ -21,16 +20,14 @@ def test_gpu_single_batch() -> None:
strategies.integers(1, 7),
strategies.integers(0, 8),
strategies.booleans(),
strategies.booleans(),
)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_gpu_data_iterator(
n_samples_per_batch: int, n_features: int, n_batches: int, subsample: bool
n_samples_per_batch: int, n_features: int, n_batches: int, subsample: bool, use_cupy: bool
) -> None:
run_data_iterator(
n_samples_per_batch, n_features, n_batches, "gpu_hist", subsample, True
)
run_data_iterator(
n_samples_per_batch, n_features, n_batches, "gpu_hist", subsample, False
n_samples_per_batch, n_features, n_batches, "gpu_hist", subsample, use_cupy
)

6
tests/python-gpu/test_gpu_linear.py
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@@ -6,6 +6,8 @@ sys.path.append("tests/python")
import testing as tm
pytestmark = pytest.mark.timeout(10)
parameter_strategy = strategies.fixed_dictionaries({
'booster': strategies.just('gblinear'),
'eta': strategies.floats(0.01, 0.25),
@@ -30,7 +32,7 @@ def train_result(param, dmat, num_rounds):
class TestGPULinear:
@given(parameter_strategy, strategies.integers(10, 50),
tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_gpu_coordinate(self, param, num_rounds, dataset):
assume(len(dataset.y) > 0)
param['updater'] = 'gpu_coord_descent'
@@ -49,7 +51,7 @@ class TestGPULinear:
strategies.floats(1e-5, 0.8),
strategies.floats(1e-5, 0.8)
)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_gpu_coordinate_regularised(self, param, num_rounds, dataset, alpha, lambd):
assume(len(dataset.y) > 0)
param['updater'] = 'gpu_coord_descent'

3
tests/python-gpu/test_gpu_pickling.py
View File

@@ -15,6 +15,8 @@ import testing as tm
model_path = './model.pkl'
pytestmark = pytest.mark.timeout(30)
def build_dataset():
N = 10
x = np.linspace(0, N*N, N*N)
@@ -65,6 +67,7 @@ class TestPickling:
assert status == 0
os.remove(model_path)
# TODO: This test is too slow
@pytest.mark.skipif(**tm.no_sklearn())
def test_pickling(self):
x, y = build_dataset()

11
tests/python-gpu/test_gpu_prediction.py
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@@ -32,6 +32,7 @@ predict_parameter_strategy = strategies.fixed_dictionaries({
'num_parallel_tree': strategies.sampled_from([1, 4]),
})
pytestmark = pytest.mark.timeout(20)
class TestGPUPredict:
def test_predict(self):
@@ -264,7 +265,7 @@ class TestGPUPredict:
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_shap(self, num_rounds, dataset, param):
if dataset.name.endswith("-l1"): # not supported by the exact tree method
return
@@ -280,7 +281,7 @@ class TestGPUPredict:
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None, max_examples=20, print_blob=True)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_shap_interactions(self, num_rounds, dataset, param):
if dataset.name.endswith("-l1"): # not supported by the exact tree method
return
@@ -333,14 +334,14 @@ class TestGPUPredict:
np.testing.assert_equal(cpu_leaf, gpu_leaf)
@given(predict_parameter_strategy, tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_predict_leaf_gbtree(self, param, dataset):
param['booster'] = 'gbtree'
param['tree_method'] = 'gpu_hist'
self.run_predict_leaf_booster(param, 10, dataset)
@given(predict_parameter_strategy, tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_predict_leaf_dart(self, param, dataset):
param['booster'] = 'dart'
param['tree_method'] = 'gpu_hist'
@@ -351,7 +352,7 @@ class TestGPUPredict:
@given(df=data_frames([column('x0', elements=strategies.integers(min_value=0, max_value=3)),
column('x1', elements=strategies.integers(min_value=0, max_value=5))],
index=range_indexes(min_size=20, max_size=50)))
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_predict_categorical_split(self, df):
from sklearn.metrics import mean_squared_error

4
tests/python-gpu/test_gpu_ranking.py
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@@ -6,10 +6,12 @@ import shutil
import urllib.request
import zipfile
import sys
import pytest
sys.path.append("tests/python")
import testing as tm # noqa
pytestmark = pytest.mark.timeout(10)
class TestRanking:
@classmethod
@@ -96,7 +98,7 @@ class TestRanking:
# specify validations set to watch performance
watchlist = [(cls.dtest, 'eval'), (cls.dtrain, 'train')]
num_trees = 2500
num_trees = 100
check_metric_improvement_rounds = 10
evals_result = {}

2
tests/python-gpu/test_gpu_spark/test_gpu_spark.py
View File

@@ -7,7 +7,7 @@ import sklearn
sys.path.append("tests/python")
import testing as tm
if tm.no_dask()["condition"]:
if tm.no_spark()["condition"]:
pytest.skip(msg=tm.no_spark()["reason"], allow_module_level=True)
if sys.platform.startswith("win"):
pytest.skip("Skipping PySpark tests on Windows", allow_module_level=True)

19
tests/python-gpu/test_gpu_updaters.py
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@@ -1,7 +1,6 @@
from typing import Dict, Any
import numpy as np
import sys
import gc
import pytest
import xgboost as xgb
from hypothesis import given, strategies, assume, settings, note
@@ -10,6 +9,7 @@ sys.path.append("tests/python")
import testing as tm
import test_updaters as test_up
pytestmark = pytest.mark.timeout(30)
parameter_strategy = strategies.fixed_dictionaries({
'max_depth': strategies.integers(0, 11),
@@ -46,7 +46,7 @@ class TestGPUUpdaters:
cputest = test_up.TestTreeMethod()
@given(parameter_strategy, strategies.integers(1, 20), tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=50, print_blob=True)
def test_gpu_hist(self, param, num_rounds, dataset):
param["tree_method"] = "gpu_hist"
param = dataset.set_params(param)
@@ -73,7 +73,7 @@ class TestGPUUpdaters:
@given(strategies.integers(10, 400), strategies.integers(3, 8),
strategies.integers(1, 2), strategies.integers(4, 7))
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
@pytest.mark.skipif(**tm.no_pandas())
def test_categorical_ohe(self, rows, cols, rounds, cats):
self.cputest.run_categorical_ohe(rows, cols, rounds, cats, "gpu_hist")
@@ -85,7 +85,7 @@ class TestGPUUpdaters:
test_up.cat_parameter_strategy,
strategies.integers(4, 32),
)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
@pytest.mark.skipif(**tm.no_pandas())
def test_categorical(
self,
@@ -106,7 +106,7 @@ class TestGPUUpdaters:
test_up.hist_parameter_strategy,
test_up.cat_parameter_strategy,
)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_categorical_ames_housing(
self,
hist_parameters: Dict[str, Any],
@@ -125,7 +125,7 @@ class TestGPUUpdaters:
strategies.integers(3, 8),
strategies.integers(4, 7)
)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
@pytest.mark.skipif(**tm.no_pandas())
def test_categorical_missing(self, rows, cols, cats):
self.cputest.run_categorical_missing(rows, cols, cats, "gpu_hist")
@@ -149,7 +149,7 @@ class TestGPUUpdaters:
@pytest.mark.skipif(**tm.no_cupy())
@given(parameter_strategy, strategies.integers(1, 20),
tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_gpu_hist_device_dmatrix(self, param, num_rounds, dataset):
# We cannot handle empty dataset yet
assume(len(dataset.y) > 0)
@@ -159,9 +159,9 @@ class TestGPUUpdaters:
note(result)
assert tm.non_increasing(result['train'][dataset.metric], tolerance=1e-3)
@given(parameter_strategy, strategies.integers(1, 20),
@given(parameter_strategy, strategies.integers(1, 3),
tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_external_memory(self, param, num_rounds, dataset):
if dataset.name.endswith("-l1"):
return
@@ -172,7 +172,6 @@ class TestGPUUpdaters:
m = dataset.get_external_dmat()
external_result = train_result(param, m, num_rounds)
del m
gc.collect()
assert tm.non_increasing(external_result['train'][dataset.metric])
def test_empty_dmatrix_prediction(self):

2
tests/python-gpu/test_gpu_with_sklearn.py
View File

@@ -61,7 +61,7 @@ def test_boost_from_prediction_gpu_hist():
def test_num_parallel_tree():
twskl.run_calif_housing_rf_regression("gpu_hist")
twskl.run_housing_rf_regression("gpu_hist")
@pytest.mark.skipif(**tm.no_pandas())