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Use pytest conventions consistently (#6337)

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* Do not derive from unittest.TestCase (not needed for pytest)

* assertRaises -> pytest.raises

* Simplify test_empty_dmatrix with test parametrization

* setUpClass -> setup_class, tearDownClass -> teardown_class

* Don't import unittest; import pytest

* Use plain assert

* Use parametrized tests in more places

* Fix test_gpu_with_sklearn.py

* Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls

* Fix test_eta_decay_gpu_hist

* Add parametrized tests for monotone constraints

* Fix test names

* Remove test parametrization

* Revise test_slice to be not flaky
This commit is contained in:
Philip Hyunsu Cho 2020-11-19 17:00:15 -08:00 committed by GitHub
parent c763b50dd0
commit 9c9070aea2
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GPG Key ID: 4AEE18F83AFDEB23
34 changed files with 200 additions and 223 deletions
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3
tests/python-gpu/load_pickle.py
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@ -1,6 +1,5 @@
'''Loading a pickled model generated by test_pickling.py, only used by
`test_gpu_with_dask.py`'''
import unittest
import os
import numpy as np
import xgboost as xgb
@ -14,7 +13,7 @@ sys.path.append("tests/python")
import testing as tm
class TestLoadPickle(unittest.TestCase):
class TestLoadPickle:
def test_load_pkl(self):
'''Test whether prediction is correct.'''
assert os.environ['CUDA_VISIBLE_DEVICES'] == '-1'

3
tests/python-gpu/test_device_quantile_dmatrix.py
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@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import unittest
import pytest
import sys
@ -9,7 +8,7 @@ sys.path.append("tests/python")
import testing as tm
class TestDeviceQuantileDMatrix(unittest.TestCase):
class TestDeviceQuantileDMatrix:
def test_dmatrix_numpy_init(self):
data = np.random.randn(5, 5)
with pytest.raises(TypeError,

4
tests/python-gpu/test_gpu_basic_models.py
View File

@ -1,15 +1,15 @@
import sys
import os
import unittest
import numpy as np
import xgboost as xgb
import pytest
sys.path.append("tests/python")
# Don't import the test class, otherwise they will run twice.
import test_callback as test_cb # noqa
rng = np.random.RandomState(1994)
class TestGPUBasicModels(unittest.TestCase):
class TestGPUBasicModels:
cputest = test_cb.TestCallbacks()
def run_cls(self, X, y, deterministic):

3
tests/python-gpu/test_gpu_interaction_constraints.py
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@ -1,5 +1,4 @@
import numpy as np
import unittest
import sys
sys.path.append("tests/python")
# Don't import the test class, otherwise they will run twice.
@ -7,7 +6,7 @@ import test_interaction_constraints as test_ic # noqa
rng = np.random.RandomState(1994)
class TestGPUInteractionConstraints(unittest.TestCase):
class TestGPUInteractionConstraints:
cputest = test_ic.TestInteractionConstraints()
def test_interaction_constraints(self):

3
tests/python-gpu/test_gpu_pickling.py
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@ -1,6 +1,5 @@
'''Test model IO with pickle.'''
import pickle
import unittest
import numpy as np
import subprocess
import os
@ -35,7 +34,7 @@ def load_pickle(path):
return bst
class TestPickling(unittest.TestCase):
class TestPickling:
args_template = [
"pytest",
"--verbose",

4
tests/python-gpu/test_gpu_prediction.py
View File

@ -1,6 +1,4 @@
import sys
import json
import unittest
import pytest
import numpy as np
@ -26,7 +24,7 @@ predict_parameter_strategy = strategies.fixed_dictionaries({
})
class TestGPUPredict(unittest.TestCase):
class TestGPUPredict:
def test_predict(self):
iterations = 10
np.random.seed(1)

7
tests/python-gpu/test_gpu_ranking.py
View File

@ -1,16 +1,15 @@
import numpy as np
import xgboost
import os
import unittest
import itertools
import shutil
import urllib.request
import zipfile
class TestRanking(unittest.TestCase):
class TestRanking:
@classmethod
def setUpClass(cls):
def setup_class(cls):
"""
Download and setup the test fixtures
"""
@ -75,7 +74,7 @@ class TestRanking(unittest.TestCase):
'predictor': 'cpu_predictor'}
@classmethod
def tearDownClass(cls):
def teardown_class(cls):
"""
Cleanup test artifacts from download and unpacking
:return:

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

@ -1,4 +1,3 @@
import unittest
import numpy as np
import xgboost as xgb
import json
@ -6,7 +5,7 @@ import json
rng = np.random.RandomState(1994)
class TestGPUTrainingContinuation(unittest.TestCase):
class TestGPUTrainingContinuation:
def run_training_continuation(self, use_json):
kRows = 64
kCols = 32

8
tests/python-gpu/test_gpu_with_sklearn.py
View File

@ -2,7 +2,6 @@ import xgboost as xgb
import pytest
import sys
import numpy as np
import unittest
sys.path.append("tests/python")
import testing as tm # noqa
@ -33,8 +32,5 @@ def test_gpu_binary_classification():
assert err < 0.1
class TestGPUBoostFromPrediction(unittest.TestCase):
cpu_test = twskl.TestBoostFromPrediction()
def test_boost_from_prediction_gpu_hist(self):
self.cpu_test.run_boost_from_prediction('gpu_hist')
def test_boost_from_prediction_gpu_hist():
cpu_test = twskl.run_boost_from_prediction('gpu_hist')

44
tests/python-gpu/test_monotonic_constraints.py
View File

@ -1,7 +1,6 @@
import sys
import numpy as np
import unittest
import pytest
import xgboost as xgb
@ -38,26 +37,27 @@ def assert_constraint(constraint, tree_method):
assert non_increasing(pred)
class TestMonotonicConstraints(unittest.TestCase):
@pytest.mark.skipif(**tm.no_sklearn())
def test_gpu_hist_basic(self):
assert_constraint(1, 'gpu_hist')
assert_constraint(-1, 'gpu_hist')
@pytest.mark.skipif(**tm.no_sklearn())
def test_gpu_hist_basic():
assert_constraint(1, 'gpu_hist')
assert_constraint(-1, 'gpu_hist')
def test_gpu_hist_depthwise(self):
params = {
'tree_method': 'gpu_hist',
'grow_policy': 'depthwise',
'monotone_constraints': '(1, -1)'
}
model = xgb.train(params, tmc.training_dset)
tmc.is_correctly_constrained(model)
def test_gpu_hist_lossguide(self):
params = {
'tree_method': 'gpu_hist',
'grow_policy': 'lossguide',
'monotone_constraints': '(1, -1)'
}
model = xgb.train(params, tmc.training_dset)
tmc.is_correctly_constrained(model)
def test_gpu_hist_depthwise():
params = {
'tree_method': 'gpu_hist',
'grow_policy': 'depthwise',
'monotone_constraints': '(1, -1)'
}
model = xgb.train(params, tmc.training_dset)
tmc.is_correctly_constrained(model)
def test_gpu_hist_lossguide():
params = {
'tree_method': 'gpu_hist',
'grow_policy': 'lossguide',
'monotone_constraints': '(1, -1)'
}
model = xgb.train(params, tmc.training_dset)
tmc.is_correctly_constrained(model)

34
tests/python/test_basic.py
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@ -2,7 +2,7 @@
import numpy as np
import os
import xgboost as xgb
import unittest
import pytest
import json
from pathlib import Path
import tempfile
@ -12,7 +12,7 @@ dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestBasic(unittest.TestCase):
class TestBasic:
def test_compat(self):
from xgboost.compat import lazy_isinstance
a = np.array([1, 2, 3])
@ -119,30 +119,28 @@ class TestBasic(unittest.TestCase):
# number of feature importances should == number of features
dump1 = bst.get_dump()
self.assertEqual(len(dump1), 1, "Expected only 1 tree to be dumped.")
self.assertEqual(len(dump1[0].splitlines()), 3,
"Expected 1 root and 2 leaves - 3 lines in dump.")
assert len(dump1) == 1, 'Expected only 1 tree to be dumped.'
len(dump1[0].splitlines()) == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
dump2 = bst.get_dump(with_stats=True)
self.assertEqual(dump2[0].count('\n'), 3,
"Expected 1 root and 2 leaves - 3 lines in dump.")
self.assertGreater(dump2[0].find('\n'), dump1[0].find('\n'),
"Expected more info when with_stats=True is given.")
assert dump2[0].count('\n') == 3, 'Expected 1 root and 2 leaves - 3 lines in dump.'
assert (dump2[0].find('\n') > dump1[0].find('\n'),
'Expected more info when with_stats=True is given.')
dump3 = bst.get_dump(dump_format="json")
dump3j = json.loads(dump3[0])
self.assertEqual(dump3j["nodeid"], 0, "Expected the root node on top.")
assert dump3j['nodeid'] == 0, 'Expected the root node on top.'
dump4 = bst.get_dump(dump_format="json", with_stats=True)
dump4j = json.loads(dump4[0])
self.assertIn("gain", dump4j, "Expected 'gain' to be dumped in JSON.")
assert 'gain' in dump4j, "Expected 'gain' to be dumped in JSON."
def test_load_file_invalid(self):
self.assertRaises(xgb.core.XGBoostError, xgb.Booster,
model_file='incorrect_path')
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file='incorrect_path')
self.assertRaises(xgb.core.XGBoostError, xgb.Booster,
model_file=u'不正なパス')
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file=u'不正なパス')
def test_dmatrix_numpy_init_omp(self):
@ -226,7 +224,7 @@ class TestBasic(unittest.TestCase):
assert output == solution
class TestBasicPathLike(unittest.TestCase):
class TestBasicPathLike:
"""Unit tests using pathlib.Path for file interaction."""
def test_DMatrix_init_from_path(self):
@ -253,8 +251,8 @@ class TestBasicPathLike(unittest.TestCase):
def test_Booster_init_invalid_path(self):
"""An invalid model_file path should raise XGBoostError."""
self.assertRaises(xgb.core.XGBoostError, xgb.Booster,
model_file=Path("invalidpath"))
with pytest.raises(xgb.core.XGBoostError):
xgb.Booster(model_file=Path("invalidpath"))
def test_Booster_save_and_load(self):

1
tests/python/test_basic_models.py
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@ -1,6 +1,5 @@
import numpy as np
import xgboost as xgb
import unittest
import os
import json
import testing as tm

64
tests/python/test_callback.py
View File

@ -1,5 +1,4 @@
import xgboost as xgb
import unittest
import pytest
import os
import testing as tm
@ -9,9 +8,9 @@ import tempfile
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
class TestCallbacks(unittest.TestCase):
class TestCallbacks:
@classmethod
def setUpClass(cls):
def setup_class(cls):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
cls.X = X
@ -148,9 +147,9 @@ class TestCallbacks(unittest.TestCase):
early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
save_best=True)
cls = xgb.XGBClassifier(booster='gblinear', n_estimators=10)
self.assertRaises(ValueError, lambda: cls.fit(X, y, eval_set=[(X, y)],
eval_metric=tm.eval_error_metric,
callbacks=[early_stop]))
with pytest.raises(ValueError):
cls.fit(X, y, eval_set=[(X, y)], eval_metric=tm.eval_error_metric,
callbacks=[early_stop])
# No error
early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
@ -172,17 +171,20 @@ class TestCallbacks(unittest.TestCase):
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 4
warning_check = pytest.warns(UserWarning) if deprecated_callback else tm.noop_context()
# learning_rates as a list
# init eta with 0 to check whether learning_rates work
param = {'max_depth': 2, 'eta': 0, 'verbosity': 0,
'objective': 'binary:logistic', 'eval_metric': 'error',
'tree_method': tree_method}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[scheduler([
0.8, 0.7, 0.6, 0.5
])],
evals_result=evals_result)
with warning_check:
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[scheduler([
0.8, 0.7, 0.6, 0.5
])],
evals_result=evals_result)
eval_errors_0 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
# validation error should decrease, if eta > 0
@ -193,10 +195,11 @@ class TestCallbacks(unittest.TestCase):
'objective': 'binary:logistic', 'eval_metric': 'error',
'tree_method': tree_method}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[scheduler(
[0.8, 0.7, 0.6, 0.5])],
evals_result=evals_result)
with warning_check:
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[scheduler(
[0.8, 0.7, 0.6, 0.5])],
evals_result=evals_result)
eval_errors_1 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
# validation error should decrease, if learning_rate > 0
@ -208,11 +211,12 @@ class TestCallbacks(unittest.TestCase):
'eval_metric': 'error', 'tree_method': tree_method
}
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[scheduler(
[0, 0, 0, 0]
)],
evals_result=evals_result)
with warning_check:
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[scheduler(
[0, 0, 0, 0]
)],
evals_result=evals_result)
eval_errors_2 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
# validation error should not decrease, if eta/learning_rate = 0
@ -223,11 +227,12 @@ class TestCallbacks(unittest.TestCase):
return num_boost_round / (ithround + 1)
evals_result = {}
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[
scheduler(eta_decay)
],
evals_result=evals_result)
with warning_check:
bst = xgb.train(param, dtrain, num_round, watchlist,
callbacks=[
scheduler(eta_decay)
],
evals_result=evals_result)
eval_errors_3 = list(map(float, evals_result['eval']['error']))
assert isinstance(bst, xgb.core.Booster)
@ -238,18 +243,15 @@ class TestCallbacks(unittest.TestCase):
assert eval_errors_3[i] != eval_errors_2[i]
def test_eta_decay_hist(self):
with pytest.warns(UserWarning):
self.run_eta_decay('hist', True)
self.run_eta_decay('hist', True)
self.run_eta_decay('hist', False)
def test_eta_decay_approx(self):
with pytest.warns(UserWarning):
self.run_eta_decay('approx', True)
self.run_eta_decay('approx', True)
self.run_eta_decay('approx', False)
def test_eta_decay_exact(self):
with pytest.warns(UserWarning):
self.run_eta_decay('exact', True)
self.run_eta_decay('exact', True)
self.run_eta_decay('exact', False)
def test_check_point(self):

3
tests/python/test_cli.py
View File

@ -1,6 +1,5 @@
import os
import tempfile
import unittest
import platform
import xgboost
import subprocess
@ -9,7 +8,7 @@ import json
import testing as tm
class TestCLI(unittest.TestCase):
class TestCLI:
template = '''
booster = gbtree
objective = reg:squarederror

85
tests/python/test_dmatrix.py
View File

@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import unittest
import scipy.sparse
import pytest
from scipy.sparse import rand, csr_matrix
@ -12,7 +11,7 @@ dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestDMatrix(unittest.TestCase):
class TestDMatrix:
def test_warn_missing(self):
from xgboost import data
with pytest.warns(UserWarning):
@ -48,15 +47,19 @@ class TestDMatrix(unittest.TestCase):
assert dm.num_col() == 2
# 0d array
self.assertRaises(ValueError, xgb.DMatrix, np.array(1))
with pytest.raises(ValueError):
xgb.DMatrix(np.array(1))
# 1d array
self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3]))
with pytest.raises(ValueError):
xgb.DMatrix(np.array([1, 2, 3]))
# 3d array
data = np.random.randn(5, 5, 5)
self.assertRaises(ValueError, xgb.DMatrix, data)
with pytest.raises(ValueError):
xgb.DMatrix(data)
# object dtype
data = np.array([['a', 'b'], ['c', 'd']])
self.assertRaises(ValueError, xgb.DMatrix, data)
with pytest.raises(ValueError):
xgb.DMatrix(data)
def test_csr(self):
indptr = np.array([0, 2, 3, 6])
@ -106,56 +109,50 @@ class TestDMatrix(unittest.TestCase):
def test_slice(self):
X = rng.randn(100, 100)
y = rng.randint(low=0, high=3, size=100)
y = rng.randint(low=0, high=3, size=100).astype(np.float32)
d = xgb.DMatrix(X, y)
np.testing.assert_equal(d.get_label(), y.astype(np.float32))
np.testing.assert_equal(d.get_label(), y)
fw = rng.uniform(size=100).astype(np.float32)
d.set_info(feature_weights=fw)
eval_res_0 = {}
booster = xgb.train(
{'num_class': 3, 'objective': 'multi:softprob',
'eval_metric': 'merror'},
d,
num_boost_round=2, evals=[(d, 'd')], evals_result=eval_res_0)
predt = booster.predict(d)
predt = predt.reshape(100 * 3, 1)
d.set_base_margin(predt)
# base margin is per-class in multi-class classifier
base_margin = rng.randn(100, 3).astype(np.float32)
d.set_base_margin(base_margin.flatten())
ridxs = [1, 2, 3, 4, 5, 6]
sliced = d.slice(ridxs)
sliced_margin = sliced.get_float_info('base_margin')
assert sliced_margin.shape[0] == len(ridxs) * 3
# Slicing works with label and other meta info fields
np.testing.assert_equal(sliced.get_label(), y[1:7])
np.testing.assert_equal(sliced.get_float_info('feature_weights'), fw)
np.testing.assert_equal(sliced.get_base_margin(), base_margin[1:7, :].flatten())
np.testing.assert_equal(sliced.get_base_margin(), sliced.get_float_info('base_margin'))
eval_res_1 = {}
xgb.train(
# Slicing a DMatrix results into a DMatrix that's equivalent to a DMatrix that's
# constructed from the corresponding NumPy slice
d2 = xgb.DMatrix(X[1:7, :], y[1:7])
d2.set_base_margin(base_margin[1:7, :].flatten())
eval_res = {}
_ = xgb.train(
{'num_class': 3, 'objective': 'multi:softprob',
'eval_metric': 'merror'},
sliced,
num_boost_round=2, evals=[(sliced, 'd')], evals_result=eval_res_1)
eval_res_0 = eval_res_0['d']['merror']
eval_res_1 = eval_res_1['d']['merror']
for i in range(len(eval_res_0)):
assert abs(eval_res_0[i] - eval_res_1[i]) < 0.02
'eval_metric': 'mlogloss'},
d,
num_boost_round=2, evals=[(d2, 'd2'), (sliced, 'sliced')], evals_result=eval_res)
np.testing.assert_equal(eval_res['d2']['mlogloss'], eval_res['sliced']['mlogloss'])
def test_feature_names_slice(self):
data = np.random.randn(5, 5)
# different length
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=list('abcdef'))
with pytest.raises(ValueError):
xgb.DMatrix(data, feature_names=list('abcdef'))
# contains duplicates
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=['a', 'b', 'c', 'd', 'd'])
with pytest.raises(ValueError):
xgb.DMatrix(data, feature_names=['a', 'b', 'c', 'd', 'd'])
# contains symbol
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=['a', 'b', 'c', 'd', 'e<1'])
with pytest.raises(ValueError):
xgb.DMatrix(data, feature_names=['a', 'b', 'c', 'd', 'e<1'])
dm = xgb.DMatrix(data)
dm.feature_names = list('abcde')
@ -170,14 +167,12 @@ class TestDMatrix(unittest.TestCase):
dm.feature_types = list('qiqiq')
assert dm.feature_types == list('qiqiq')
def incorrect_type_set():
with pytest.raises(ValueError):
dm.feature_types = list('abcde')
self.assertRaises(ValueError, incorrect_type_set)
# reset
dm.feature_names = None
self.assertEqual(dm.feature_names, ['f0', 'f1', 'f2', 'f3', 'f4'])
assert dm.feature_names == ['f0', 'f1', 'f2', 'f3', 'f4']
assert dm.feature_types is None
def test_feature_names(self):
@ -209,7 +204,8 @@ class TestDMatrix(unittest.TestCase):
# different feature name must raises error
dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
self.assertRaises(ValueError, bst.predict, dm)
with pytest.raises(ValueError):
bst.predict(dm)
def test_get_info(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
@ -234,9 +230,8 @@ class TestDMatrix(unittest.TestCase):
fw -= 1
def assign_weight():
with pytest.raises(ValueError):
m.set_info(feature_weights=fw)
self.assertRaises(ValueError, assign_weight)
def test_sparse_dmatrix_csr(self):
nrow = 100

6
tests/python/test_dt.py
View File

@ -1,5 +1,4 @@
# -*- coding: utf-8 -*-
import unittest
import pytest
import numpy as np
@ -17,7 +16,7 @@ pytestmark = pytest.mark.skipif(
reason=tm.no_dt()['reason'] + ' or ' + tm.no_pandas()['reason'])
class TestDataTable(unittest.TestCase):
class TestDataTable:
def test_dt(self):
df = pd.DataFrame([[1, 2., True], [2, 3., False]],
@ -43,7 +42,8 @@ class TestDataTable(unittest.TestCase):
df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
columns=['a', 'b', 'c'])
dtable = dt.Frame(df)
self.assertRaises(ValueError, xgb.DMatrix, dtable)
with pytest.raises(ValueError):
xgb.DMatrix(dtable)
df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
dtable = dt.Frame(df)

3
tests/python/test_early_stopping.py
View File

@ -1,13 +1,12 @@
import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1994)
class TestEarlyStopping(unittest.TestCase):
class TestEarlyStopping:
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping_nonparallel(self):

3
tests/python/test_eval_metrics.py
View File

@ -1,13 +1,12 @@
import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1337)
class TestEvalMetrics(unittest.TestCase):
class TestEvalMetrics:
xgb_params_01 = {
'verbosity': 0,
'nthread': 1,

3
tests/python/test_interaction_constraints.py
View File

@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
import numpy as np
import xgboost
import unittest
import testing as tm
import pytest
@ -9,7 +8,7 @@ dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestInteractionConstraints(unittest.TestCase):
class TestInteractionConstraints:
def run_interaction_constraints(self, tree_method):
x1 = np.random.normal(loc=1.0, scale=1.0, size=1000)
x2 = np.random.normal(loc=1.0, scale=1.0, size=1000)

3
tests/python/test_monotone_constraints.py
View File

@ -1,6 +1,5 @@
import numpy as np
import xgboost as xgb
import unittest
import testing as tm
import pytest
@ -61,7 +60,7 @@ y = (
training_dset = xgb.DMatrix(x, label=y)
class TestMonotoneConstraints(unittest.TestCase):
class TestMonotoneConstraints:
def test_monotone_constraints_for_exact_tree_method(self):

3
tests/python/test_openmp.py
View File

@ -1,10 +1,9 @@
# -*- coding: utf-8 -*-
import xgboost as xgb
import unittest
import numpy as np
class TestOMP(unittest.TestCase):
class TestOMP:
def test_omp(self):
dpath = 'demo/data/'
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')

3
tests/python/test_parse_tree.py
View File

@ -1,5 +1,4 @@
import xgboost as xgb
import unittest
import numpy as np
import pytest
import testing as tm
@ -12,7 +11,7 @@ dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestTreesToDataFrame(unittest.TestCase):
class TestTreesToDataFrame:
def build_model(self, max_depth, num_round):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')

3
tests/python/test_pickling.py
View File

@ -2,7 +2,6 @@ import pickle
import numpy as np
import xgboost as xgb
import os
import unittest
kRows = 100
@ -15,7 +14,7 @@ def generate_data():
return X, y
class TestPickling(unittest.TestCase):
class TestPickling:
def run_model_pickling(self, xgb_params):
X, y = generate_data()
dtrain = xgb.DMatrix(X, y)

3
tests/python/test_plotting.py
View File

@ -3,7 +3,6 @@ import numpy as np
import xgboost as xgb
import testing as tm
import unittest
import pytest
try:
@ -20,7 +19,7 @@ pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(),
dpath = 'demo/data/agaricus.txt.train'
class TestPlotting(unittest.TestCase):
class TestPlotting:
def test_plotting(self):
m = xgb.DMatrix(dpath)
booster = xgb.train({'max_depth': 2, 'eta': 1,

3
tests/python/test_predict.py
View File

@ -1,5 +1,4 @@
'''Tests for running inplace prediction.'''
import unittest
from concurrent.futures import ThreadPoolExecutor
import numpy as np
from scipy import sparse
@ -66,7 +65,7 @@ def test_predict_leaf():
run_predict_leaf('cpu_predictor')
class TestInplacePredict(unittest.TestCase):
class TestInplacePredict:
'''Tests for running inplace prediction'''
def test_predict(self):
rows = 1000

12
tests/python/test_ranking.py
View File

@ -2,7 +2,6 @@ import numpy as np
from scipy.sparse import csr_matrix
import xgboost
import os
import unittest
import itertools
import shutil
import urllib.request
@ -73,10 +72,10 @@ def test_ranking_with_weighted_data():
assert all(p <= q for p, q in zip(is_sorted, is_sorted[1:]))
class TestRanking(unittest.TestCase):
class TestRanking:
@classmethod
def setUpClass(cls):
def setup_class(cls):
"""
Download and setup the test fixtures
"""
@ -119,7 +118,7 @@ class TestRanking(unittest.TestCase):
}
@classmethod
def tearDownClass(cls):
def teardown_class(cls):
"""
Cleanup test artifacts from download and unpacking
:return:
@ -144,8 +143,9 @@ class TestRanking(unittest.TestCase):
cv = xgboost.cv(self.params, self.dtrain, num_boost_round=2500,
early_stopping_rounds=10, nfold=10, as_pandas=False)
assert isinstance(cv, dict)
self.assertSetEqual(set(cv.keys()), {'test-ndcg-mean', 'train-ndcg-mean', 'test-ndcg-std', 'train-ndcg-std'},
"CV results dict key mismatch")
assert (set(cv.keys()) == {'test-ndcg-mean', 'train-ndcg-mean', 'test-ndcg-std',
'train-ndcg-std'},
'CV results dict key mismatch.')
def test_cv_no_shuffle(self):
"""

3
tests/python/test_shap.py
View File

@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import unittest
import itertools
import re
import scipy
@ -11,7 +10,7 @@ dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestSHAP(unittest.TestCase):
class TestSHAP:
def test_feature_importances(self):
data = np.random.randn(100, 5)

3
tests/python/test_training_continuation.py
View File

@ -1,13 +1,12 @@
import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1337)
class TestTrainingContinuation(unittest.TestCase):
class TestTrainingContinuation:
num_parallel_tree = 3
def generate_parameters(self, use_json):

3
tests/python/test_tree_regularization.py
View File

@ -1,5 +1,4 @@
import numpy as np
import unittest
import xgboost as xgb
from numpy.testing import assert_approx_equal
@ -7,7 +6,7 @@ from numpy.testing import assert_approx_equal
train_data = xgb.DMatrix(np.array([[1]]), label=np.array([1]))
class TestTreeRegularization(unittest.TestCase):
class TestTreeRegularization:
def test_alpha(self):
params = {
'tree_method': 'exact', 'verbosity': 0,

3
tests/python/test_updaters.py
View File

@ -1,5 +1,4 @@
import testing as tm
import unittest
import pytest
import xgboost as xgb
import numpy as np
@ -36,7 +35,7 @@ def train_result(param, dmat, num_rounds):
return result
class TestTreeMethod(unittest.TestCase):
class TestTreeMethod:
@given(exact_parameter_strategy, strategies.integers(1, 20),
tm.dataset_strategy)
@settings(deadline=None)

14
tests/python/test_with_modin.py
View File

@ -2,7 +2,6 @@
import numpy as np
import xgboost as xgb
import testing as tm
import unittest
import pytest
try:
@ -18,7 +17,7 @@ dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestModin(unittest.TestCase):
class TestModin:
def test_modin(self):
@ -43,7 +42,8 @@ class TestModin(unittest.TestCase):
# incorrect dtypes
df = md.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
columns=['a', 'b', 'c'])
self.assertRaises(ValueError, xgb.DMatrix, df)
with pytest.raises(ValueError):
xgb.DMatrix(df)
# numeric columns
df = md.DataFrame([[1, 2., True], [2, 3., False]])
@ -113,13 +113,13 @@ class TestModin(unittest.TestCase):
def test_modin_label(self):
# label must be a single column
df = md.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
self.assertRaises(ValueError, xgb.data._transform_pandas_df, df,
False, None, None, 'label', 'float')
with pytest.raises(ValueError):
xgb.data._transform_pandas_df(df, False, None, None, 'label', 'float')
# label must be supported dtype
df = md.DataFrame({'A': np.array(['a', 'b', 'c'], dtype=object)})
self.assertRaises(ValueError, xgb.data._transform_pandas_df, df,
False, None, None, 'label', 'float')
with pytest.raises(ValueError):
xgb.data._transform_pandas_df(df, False, None, None, 'label', 'float')
df = md.DataFrame({'A': np.array([1, 2, 3], dtype=int)})
result, _, _ = xgb.data._transform_pandas_df(df, False, None, None,

14
tests/python/test_with_pandas.py
View File

@ -2,7 +2,6 @@
import numpy as np
import xgboost as xgb
import testing as tm
import unittest
import pytest
try:
@ -18,7 +17,7 @@ dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestPandas(unittest.TestCase):
class TestPandas:
def test_pandas(self):
@ -43,7 +42,8 @@ class TestPandas(unittest.TestCase):
# incorrect dtypes
df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
columns=['a', 'b', 'c'])
self.assertRaises(ValueError, xgb.DMatrix, df)
with pytest.raises(ValueError):
xgb.DMatrix(df)
# numeric columns
df = pd.DataFrame([[1, 2., True], [2, 3., False]])
@ -139,13 +139,13 @@ class TestPandas(unittest.TestCase):
def test_pandas_label(self):
# label must be a single column
df = pd.DataFrame({'A': ['X', 'Y', 'Z'], 'B': [1, 2, 3]})
self.assertRaises(ValueError, xgb.data._transform_pandas_df, df,
False, None, None, 'label', 'float')
with pytest.raises(ValueError):
xgb.data._transform_pandas_df(df, False, None, None, 'label', 'float')
# label must be supported dtype
df = pd.DataFrame({'A': np.array(['a', 'b', 'c'], dtype=object)})
self.assertRaises(ValueError, xgb.data._transform_pandas_df, df,
False, None, None, 'label', 'float')
with pytest.raises(ValueError):
xgb.data._transform_pandas_df(df, False, None, None, 'label', 'float')
df = pd.DataFrame({'A': np.array([1, 2, 3], dtype=int)})
result, _, _ = xgb.data._transform_pandas_df(df, False, None, None,

61
tests/python/test_with_sklearn.py
View File

@ -7,7 +7,6 @@ import tempfile
import os
import shutil
import pytest
import unittest
import json
rng = np.random.RandomState(1994)
@ -1012,37 +1011,39 @@ def test_feature_weights():
assert poly_decreasing[0] < -0.08
class TestBoostFromPrediction(unittest.TestCase):
def run_boost_from_prediction(self, tree_method):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
model_0 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4,
tree_method=tree_method)
model_0.fit(X=X, y=y)
margin = model_0.predict(X, output_margin=True)
def run_boost_from_prediction(tree_method):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
model_0 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4,
tree_method=tree_method)
model_0.fit(X=X, y=y)
margin = model_0.predict(X, output_margin=True)
model_1 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4,
tree_method=tree_method)
model_1.fit(X=X, y=y, base_margin=margin)
predictions_1 = model_1.predict(X, base_margin=margin)
model_1 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=4,
tree_method=tree_method)
model_1.fit(X=X, y=y, base_margin=margin)
predictions_1 = model_1.predict(X, base_margin=margin)
cls_2 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8,
tree_method=tree_method)
cls_2.fit(X=X, y=y)
predictions_2 = cls_2.predict(X)
assert np.all(predictions_1 == predictions_2)
cls_2 = xgb.XGBClassifier(
learning_rate=0.3, random_state=0, n_estimators=8,
tree_method=tree_method)
cls_2.fit(X=X, y=y)
predictions_2 = cls_2.predict(X)
assert np.all(predictions_1 == predictions_2)
@pytest.mark.skipif(**tm.no_sklearn())
def test_boost_from_prediction_hist(self):
self.run_boost_from_prediction('hist')
@pytest.mark.skipif(**tm.no_sklearn())
def test_boost_from_prediction_approx(self):
self.run_boost_from_prediction('approx')
@pytest.mark.skipif(**tm.no_sklearn())
def test_boost_from_prediction_hist():
run_boost_from_prediction('hist')
@pytest.mark.skipif(**tm.no_sklearn())
def test_boost_from_prediction_exact(self):
self.run_boost_from_prediction('exact')
@pytest.mark.skipif(**tm.no_sklearn())
def test_boost_from_prediction_approx():
run_boost_from_prediction('approx')
@pytest.mark.skipif(**tm.no_sklearn())
def test_boost_from_prediction_exact():
run_boost_from_prediction('exact')

8
tests/python/testing.py
View File

@ -301,6 +301,14 @@ def captured_output():
sys.stdout, sys.stderr = old_out, old_err
try:
# Python 3.7+
from contextlib import nullcontext as noop_context
except ImportError:
# Python 3.6
from contextlib import suppress as noop_context
CURDIR = os.path.normpath(os.path.abspath(os.path.dirname(__file__)))
PROJECT_ROOT = os.path.normpath(
os.path.join(CURDIR, os.path.pardir, os.path.pardir))