repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
xgboost | xgboost-master/tests/ci_build/change_version.py | """
1. Modify ``CMakeLists.txt`` in source tree and ``python-package/xgboost/VERSION`` if
needed, run CMake .
If this is a RC release, the Python version has the form <major>.<minor>.<patch>rc1
2. Modify ``DESCRIPTION`` and ``configure.ac`` in R-package. Run ``autoreconf``.
3. Run ``mvn`` in ``jvm-packages``
If... | 5,198 | 31.49375 | 88 | py |
xgboost | xgboost-master/tests/python/test_data_iterator.py | from typing import Callable, Dict, List
import numpy as np
import pytest
from hypothesis import given, settings, strategies
from scipy.sparse import csr_matrix
import xgboost as xgb
from xgboost import testing as tm
from xgboost.data import SingleBatchInternalIter as SingleBatch
from xgboost.testing import IteratorFo... | 5,556 | 29.201087 | 89 | py |
xgboost | xgboost-master/tests/python/test_dmatrix.py | import os
import tempfile
import numpy as np
import pytest
import scipy.sparse
from hypothesis import given, settings, strategies
from scipy.sparse import csr_matrix, rand
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.data import np_dtypes
rng = np.random.RandomState(1)
dpath = 'demo/... | 16,765 | 34.748401 | 95 | py |
xgboost | xgboost-master/tests/python/test_survival.py | import json
import os
from typing import List, Optional, Tuple, cast
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
dpath = tm.data_dir(__file__)
@pytest.fixture(scope="module")
def toy_data() -> Tuple[xgb.DMatrix, np.ndarray, np.ndarray]:
X = np.array([1, 2, 3, 4, 5])... | 5,895 | 33.887574 | 87 | py |
xgboost | xgboost-master/tests/python/test_interaction_constraints.py | import numpy as np
import pytest
import xgboost
from xgboost import testing as tm
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestInteractionConstraints:
def run_interaction_constraints(
self, tree_method, feature_names=None, interaction_constraints='[[0, 1]]'
):
x1 = np.ran... | 4,864 | 39.882353 | 91 | py |
xgboost | xgboost-master/tests/python/test_cli.py | import json
import os
import platform
import subprocess
import tempfile
import numpy
import xgboost
from xgboost import testing as tm
class TestCLI:
template = '''
booster = gbtree
objective = reg:squarederror
eta = 1.0
gamma = 1.0
seed = {seed}
min_child_weight = 0
max_depth = 3
task = {task}
model_in = {model... | 7,100 | 35.603093 | 79 | py |
xgboost | xgboost-master/tests/python/test_plotting.py | import json
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
try:
import matplotlib
matplotlib.use('Agg')
from graphviz import Source
from matplotlib.axes import Axes
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotli... | 3,420 | 34.268041 | 77 | py |
xgboost | xgboost-master/tests/python/test_callback.py | import json
import os
import tempfile
from contextlib import nullcontext
from typing import Union
import pytest
import xgboost as xgb
from xgboost import testing as tm
# We use the dataset for tests.
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
class TestCallbacks:
@classmethod
def setup_class(cls):
... | 19,424 | 39.72327 | 89 | py |
xgboost | xgboost-master/tests/python/test_tracker.py | import re
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import RabitTracker, collective
from xgboost import testing as tm
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
def test_rabit_tracker():
tracker = RabitTrack... | 3,138 | 31.030612 | 87 | py |
xgboost | xgboost-master/tests/python/test_ranking.py | import itertools
import json
import os
import shutil
from typing import Optional
import numpy as np
import pytest
from hypothesis import given, note, settings
from scipy.sparse import csr_matrix
import xgboost
from xgboost import testing as tm
from xgboost.testing.data import RelDataCV, simulate_clicks, sort_ltr_samp... | 10,581 | 35.743056 | 87 | py |
xgboost | xgboost-master/tests/python/test_model_compatibility.py | import copy
import json
import os
import urllib.request
import zipfile
import generate_models as gm
import pytest
import xgboost
from xgboost import testing as tm
def run_model_param_check(config):
assert config['learner']['learner_model_param']['num_feature'] == str(4)
assert config['learner']['learner_tra... | 5,321 | 36.744681 | 89 | py |
xgboost | xgboost-master/tests/python/test_with_sklearn.py | import json
import os
import pickle
import random
import tempfile
import warnings
from typing import Callable, Optional
import numpy as np
import pytest
from sklearn.utils.estimator_checks import parametrize_with_checks
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.ranking import run_ra... | 51,130 | 32.462696 | 95 | py |
xgboost | xgboost-master/tests/python/test_basic.py | import json
import os
import tempfile
from pathlib import Path
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestBasic:
def test_compat(self):
from xgboost.compat import lazy_isinstance
a = n... | 12,151 | 35.93617 | 92 | py |
xgboost | xgboost-master/tests/python/test_with_modin.py | import numpy as np
import pytest
from test_dmatrix import set_base_margin_info
import xgboost as xgb
from xgboost import testing as tm
try:
import modin.pandas as md
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_modin())
class TestModin:
@pytest.mark.xfail
def test_modin(self):
... | 5,475 | 36.506849 | 82 | py |
xgboost | xgboost-master/tests/python/test_dt.py | import numpy as np
import pytest
import xgboost as xgb
dt = pytest.importorskip("datatable")
pd = pytest.importorskip("pandas")
class TestDataTable:
def test_dt(self) -> None:
df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]], columns=["a", "b", "c"])
dtable = dt.Frame(df)
labels = dt.... | 1,376 | 31.785714 | 88 | py |
xgboost | xgboost-master/tests/python/test_pickling.py | import json
import os
import pickle
import numpy as np
import xgboost as xgb
kRows = 100
kCols = 10
def generate_data():
X = np.random.randn(kRows, kCols)
y = np.random.randn(kRows)
return X, y
class TestPickling:
def run_model_pickling(self, xgb_params) -> str:
X, y = generate_data()
... | 1,611 | 24.1875 | 82 | py |
xgboost | xgboost-master/tests/python/test_config.py | import multiprocessing
from concurrent.futures import ThreadPoolExecutor
import pytest
import xgboost as xgb
@pytest.mark.parametrize("verbosity_level", [0, 1, 2, 3])
def test_global_config_verbosity(verbosity_level):
def get_current_verbosity():
return xgb.get_config()["verbosity"]
old_verbosity =... | 2,195 | 32.272727 | 75 | py |
xgboost | xgboost-master/tests/python/generate_models.py | import os
import numpy as np
import xgboost
kRounds = 2
kRows = 1000
kCols = 4
kForests = 2
kMaxDepth = 2
kClasses = 3
X = np.random.randn(kRows, kCols)
w = np.random.uniform(size=kRows)
version = xgboost.__version__
np.random.seed(1994)
target_dir = 'models'
def booster_bin(model):
return os.path.join(targ... | 4,998 | 31.673203 | 91 | py |
xgboost | xgboost-master/tests/python/test_with_pandas.py | from typing import Type
import numpy as np
import pytest
from test_dmatrix import set_base_margin_info
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.data import pd_arrow_dtypes, pd_dtypes
try:
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.n... | 14,507 | 39.188366 | 88 | py |
xgboost | xgboost-master/tests/python/test_quantile_dmatrix.py | from typing import Any, Dict, List
import numpy as np
import pytest
from hypothesis import given, settings, strategies
from scipy import sparse
import xgboost as xgb
from xgboost.testing import (
IteratorForTest,
make_batches,
make_batches_sparse,
make_categorical,
make_ltr,
make_sparse_regres... | 11,842 | 34.142433 | 86 | py |
xgboost | xgboost-master/tests/python/test_openmp.py | import os
import subprocess
import tempfile
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
pytestmark = tm.timeout(10)
class TestOMP:
def test_omp(self):
dtrain, dtest = tm.load_agaricus(__file__)
param = {'booster': 'gbtree',
'objecti... | 3,285 | 29.146789 | 85 | py |
xgboost | xgboost-master/tests/python/test_eval_metrics.py | import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.metrics import check_precision_score, check_quantile_error
rng = np.random.RandomState(1337)
class TestEvalMetrics:
xgb_params_01 = {
'verbosity': 0,
'nthread': 1,
'eval_metric':... | 12,366 | 36.935583 | 90 | py |
xgboost | xgboost-master/tests/python/test_training_continuation.py | import os
import tempfile
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
rng = np.random.RandomState(1337)
class TestTrainingContinuation:
num_parallel_tree = 3
def generate_parameters(self):
xgb_params_01_binary = {
'nthread': 1,
}
... | 5,883 | 34.878049 | 79 | py |
xgboost | xgboost-master/tests/python/test_linear.py | from hypothesis import given, note, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
pytestmark = tm.timeout(20)
parameter_strategy = strategies.fixed_dictionaries({
'booster': strategies.just('gblinear'),
'eta': strategies.floats(0.01, 0.25),
'tolerance': strategies.floats(1... | 3,673 | 35.376238 | 97 | py |
xgboost | xgboost-master/tests/python/test_shap.py | import itertools
import re
import numpy as np
import scipy
import scipy.special
import xgboost as xgb
from xgboost import testing as tm
class TestSHAP:
def test_feature_importances(self) -> None:
rng = np.random.RandomState(1994)
data = rng.randn(100, 5)
target = np.array([0, 1] * 50)
... | 10,334 | 38.903475 | 102 | py |
xgboost | xgboost-master/tests/python/test_predict.py | """Tests for running inplace prediction."""
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import pandas as pd
import pytest
from scipy import sparse
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.data import np_dtypes, pd_dtypes
from xgboost.testing.shared import v... | 8,720 | 31.909434 | 87 | py |
xgboost | xgboost-master/tests/python/test_tree_regularization.py | import numpy as np
from numpy.testing import assert_approx_equal
import xgboost as xgb
train_data = xgb.DMatrix(np.array([[1]]), label=np.array([1]))
class TestTreeRegularization:
def test_alpha(self):
params = {
"tree_method": "exact",
"verbosity": 0,
"objective": "r... | 2,356 | 27.059524 | 78 | py |
xgboost | xgboost-master/tests/python/test_collective.py | import multiprocessing
import socket
import sys
import time
import numpy as np
import pytest
import xgboost as xgb
from xgboost import RabitTracker, build_info, federated
if sys.platform.startswith("win"):
pytest.skip("Skipping collective tests on Windows", allow_module_level=True)
def run_rabit_worker(rabit_e... | 4,053 | 36.192661 | 104 | py |
xgboost | xgboost-master/tests/python/test_basic_models.py | import json
import locale
import os
import tempfile
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
dpath = tm.data_dir(__file__)
rng = np.random.RandomState(1994)
def json_model(model_path: str, parameters: dict) -> dict:
datasets = pytest.importorskip("sklearn.datase... | 26,002 | 37.183554 | 88 | py |
xgboost | xgboost-master/tests/python/with_omp_limit.py | import sys
from sklearn.datasets import make_classification
from sklearn.metrics import roc_auc_score
import xgboost as xgb
def run_omp(output_path: str):
X, y = make_classification(
n_samples=200, n_features=32, n_classes=3, n_informative=8
)
Xy = xgb.DMatrix(X, y, nthread=16)
booster = xgb... | 683 | 23.428571 | 72 | py |
xgboost | xgboost-master/tests/python/test_demos.py | import os
import subprocess
import sys
import tempfile
import pytest
import xgboost
from xgboost import testing as tm
pytestmark = tm.timeout(30)
DEMO_DIR = tm.demo_dir(__file__)
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, 'guide-python')
CLI_DEMO_DIR = os.path.join(DEMO_DIR, 'CLI')
def test_basic_walkthrough():
... | 6,699 | 28.777778 | 78 | py |
xgboost | xgboost-master/tests/python/test_monotone_constraints.py | import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
dpath = 'demo/data/'
def is_increasing(y):
return np.count_nonzero(np.diff(y) < 0.0) == 0
def is_decreasing(y):
return np.count_nonzero(np.diff(y) > 0.0) == 0
def is_correctly_constrained(learner, feature_names=None... | 5,360 | 34.269737 | 92 | py |
xgboost | xgboost-master/tests/python/test_with_arrow.py | import os
import unittest
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
try:
import pandas as pd
import pyarrow as pa
import pyarrow.csv as pc
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_arrow()["condition"] or tm.no_pandas()["condit... | 3,020 | 32.197802 | 84 | py |
xgboost | xgboost-master/tests/python/test_early_stopping.py | import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.updater import get_basescore
rng = np.random.RandomState(1994)
class TestEarlyStopping:
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping_nonparallel(self):
from sklearn.dataset... | 4,658 | 37.825 | 90 | py |
xgboost | xgboost-master/tests/python/test_updaters.py | import json
from string import ascii_lowercase
from typing import Any, Dict, List
import numpy as np
import pytest
from hypothesis import given, note, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.params import (
cat_parameter_strategy,
exact_parameter_strat... | 19,708 | 34.384201 | 105 | py |
xgboost | xgboost-master/tests/python/test_with_shap.py | import numpy as np
import pytest
import xgboost as xgb
try:
import shap
except Exception:
shap = None
pass
pytestmark = pytest.mark.skipif(shap is None, reason="Requires shap package")
# xgboost removed ntree_limit in 2.0, which breaks the SHAP package.
@pytest.mark.xfail
def test_with_shap() -> None:... | 832 | 24.242424 | 77 | py |
xgboost | xgboost-master/tests/python/test_parse_tree.py | import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_pandas())
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestTreesToDataFrame:
def build_model(self, max_depth, num_round):
dtrain, _ = tm.load_agaricus(__file... | 2,770 | 36.958904 | 81 | py |
xgboost | xgboost-master/tests/python-gpu/test_large_input.py | import cupy as cp
import numpy as np
import pytest
import xgboost as xgb
# Test for integer overflow or out of memory exceptions
def test_large_input():
available_bytes, _ = cp.cuda.runtime.memGetInfo()
# 15 GB
required_bytes = 1.5e10
if available_bytes < required_bytes:
pytest.skip("Not enou... | 670 | 25.84 | 77 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_linear.py | import pytest
from hypothesis import assume, given, note, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
pytestmark = tm.timeout(10)
parameter_strategy = strategies.fixed_dictionaries({
'booster': strategies.just('gblinear'),
'eta': strategies.floats(0.01, 0.25),
'tolerance'... | 2,973 | 36.175 | 93 | py |
xgboost | xgboost-master/tests/python-gpu/test_device_quantile_dmatrix.py | import sys
import numpy as np
import pytest
from hypothesis import given, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.data import check_inf
sys.path.append("tests/python")
import test_quantile_dmatrix as tqd
class TestQuantileDMatrix:
cputest = tqd.TestQuan... | 7,477 | 30.821277 | 85 | py |
xgboost | xgboost-master/tests/python-gpu/conftest.py | import pytest
from xgboost import testing as tm
def has_rmm():
return tm.no_rmm()["condition"]
@pytest.fixture(scope="session", autouse=True)
def setup_rmm_pool(request, pytestconfig):
tm.setup_rmm_pool(request, pytestconfig)
def pytest_addoption(parser: pytest.Parser) -> None:
parser.addoption(
... | 992 | 28.205882 | 81 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_data_iterator.py | import sys
import pytest
from hypothesis import given, settings, strategies
from xgboost.testing import no_cupy
sys.path.append("tests/python")
from test_data_iterator import run_data_iterator
from test_data_iterator import test_single_batch as cpu_single_batch
def test_gpu_single_batch() -> None:
cpu_single_b... | 1,008 | 23.609756 | 83 | py |
xgboost | xgboost-master/tests/python-gpu/load_pickle.py | """Loading a pickled model generated by test_pickling.py, only used by
`test_gpu_with_dask.py`"""
import json
import os
import numpy as np
import pytest
from test_gpu_pickling import build_dataset, load_pickle, model_path
import xgboost as xgb
from xgboost import testing as tm
class TestLoadPickle:
def test_loa... | 2,464 | 35.25 | 88 | py |
xgboost | xgboost-master/tests/python-gpu/test_monotonic_constraints.py | import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_monotone_constraints as tmc
rng = np.random.RandomState(1994)
def non_decreasing(L):
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
def non_increasing(L):
... | 1,609 | 23.769231 | 78 | py |
xgboost | xgboost-master/tests/python-gpu/test_from_cudf.py | import json
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
from test_dmatrix import set_base_margin_info
def dmatrix_from_cudf(input_type, DMatrixT, missing=np.NAN):
'''Test constructing DMatrix from cudf'''
import cudf
... | 12,553 | 33.584022 | 96 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_updaters.py | import sys
from typing import Any, Dict
import numpy as np
import pytest
from hypothesis import assume, given, note, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.params import cat_parameter_strategy, hist_parameter_strategy
from xgboost.testing.updater import (
... | 9,527 | 33.647273 | 88 | py |
xgboost | xgboost-master/tests/python-gpu/test_from_cupy.py | import json
import sys
import numpy as np
import pytest
import xgboost as xgb
sys.path.append("tests/python")
from test_dmatrix import set_base_margin_info
from xgboost import testing as tm
cupy = pytest.importorskip("cupy")
def test_array_interface() -> None:
arr = cupy.array([[1, 2, 3, 4], [1, 2, 3, 4]])
... | 7,908 | 31.681818 | 95 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_with_sklearn.py | import json
import os
import sys
import tempfile
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.ranking import run_ranking_qid_df
sys.path.append("tests/python")
import test_with_sklearn as twskl # noqa
pytestmark = pytest.mark.skipif(**tm.no_sklearn()... | 4,774 | 28.115854 | 80 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_basic_models.py | import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_basic_models as test_bm
# Don't import the test class, otherwise they will run twice.
import test_callback as test_cb # noqa
rng = np.random.RandomState(1994)
... | 2,714 | 30.941176 | 82 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_training_continuation.py | import json
import numpy as np
import xgboost as xgb
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)
... | 1,870 | 34.980769 | 78 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_eval_metrics.py | import json
import sys
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.metrics import check_precision_score, check_quantile_error
sys.path.append("tests/python")
import test_eval_metrics as test_em # noqa
class TestGPUEvalMetrics:
cpu_test = test_em.TestEvalMetrics()
@... | 2,281 | 29.837838 | 85 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_demos.py | import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
sys.path.append("tests/python")
import test_demos as td # noqa
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(td.PYTHON_DEMO_DIR, 'quantile_data_iterator.py')
cmd = ['python', script... | 669 | 21.333333 | 74 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_plotting.py | import sys
import pytest
from xgboost import testing as tm
sys.path.append("tests/python")
import test_plotting as tp
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(), tm.no_graphviz()))
class TestPlotting:
cputest = tp.TestPlotting()
@pytest.mark.skipif(**tm.no_pandas())
def test_... | 388 | 19.473684 | 87 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_pickling.py | """Test model IO with pickle."""
import os
import pickle
import subprocess
import numpy as np
import pytest
import xgboost as xgb
from xgboost import XGBClassifier
from xgboost import testing as tm
model_path = "./model.pkl"
pytestmark = tm.timeout(30)
def build_dataset():
N = 10
x = np.linspace(0, N * N,... | 5,400 | 27.882353 | 84 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_ranking.py | import os
from typing import Dict
import numpy as np
import pytest
import xgboost
from xgboost import testing as tm
pytestmark = tm.timeout(30)
def comp_training_with_rank_objective(
dtrain: xgboost.DMatrix,
dtest: xgboost.DMatrix,
rank_objective: str,
metric_name: str,
tolerance: float = 1e-02... | 3,649 | 27.294574 | 87 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_prediction.py | import sys
from copy import copy
import numpy as np
import pytest
from hypothesis import assume, given, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.compat import PANDAS_INSTALLED
if PANDAS_INSTALLED:
from hypothesis.extra.pandas import column, data_frames, range_inde... | 20,606 | 34.963351 | 88 | py |
xgboost | xgboost-master/tests/python-gpu/test_gpu_interaction_constraints.py | import sys
import numpy as np
import pandas as pd
import xgboost as xgb
sys.path.append("tests/python")
# Don't import the test class, otherwise they will run twice.
import test_interaction_constraints as test_ic # noqa
rng = np.random.RandomState(1994)
class TestGPUInteractionConstraints:
cputest = test_ic.... | 1,551 | 30.04 | 82 | py |
xgboost | xgboost-master/tests/test_distributed/test_federated/test_federated.py | #!/usr/bin/python
import multiprocessing
import sys
import time
import xgboost as xgb
import xgboost.federated
SERVER_KEY = 'server-key.pem'
SERVER_CERT = 'server-cert.pem'
CLIENT_KEY = 'client-key.pem'
CLIENT_CERT = 'client-cert.pem'
def run_server(port: int, world_size: int, with_ssl: bool) -> None:
if with_s... | 3,078 | 34.390805 | 94 | py |
xgboost | xgboost-master/tests/test_distributed/test_with_dask/test_with_dask.py | """Copyright 2019-2022 XGBoost contributors"""
import asyncio
import json
import os
import pickle
import socket
import tempfile
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from itertools import starmap
from math import ceil
from operator import attrgetter, getitem
from pathlib import... | 78,178 | 34.089318 | 96 | py |
xgboost | xgboost-master/tests/test_distributed/test_with_dask/test_demos.py | import os
import subprocess
import pytest
from xgboost import testing as tm
@pytest.mark.skipif(**tm.no_dask())
def test_dask_cpu_training_demo():
script = os.path.join(tm.demo_dir(__file__), "dask", "cpu_training.py")
cmd = ["python", script]
subprocess.check_call(cmd)
@pytest.mark.skipif(**tm.no_das... | 1,004 | 26.162162 | 83 | py |
xgboost | xgboost-master/tests/test_distributed/test_gpu_with_spark/test_data.py | import pytest
from xgboost import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_spark())
from ..test_with_spark.test_data import run_dmatrix_ctor
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.parametrize(
"is_feature_cols,is_qdm",
[(True, True), (True, False), (False, True), (False, False)],
)
d... | 446 | 25.294118 | 67 | py |
xgboost | xgboost-master/tests/test_distributed/test_gpu_with_spark/test_gpu_spark.py | import json
import logging
import subprocess
import pytest
import sklearn
from xgboost import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_spark())
from pyspark.ml.linalg import Vectors
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.sql import SparkSession
from xgboost.spark i... | 8,564 | 34.83682 | 88 | py |
xgboost | xgboost-master/tests/test_distributed/test_with_spark/test_data.py | from typing import List
import numpy as np
import pandas as pd
import pytest
from xgboost import testing as tm
pytestmark = [pytest.mark.skipif(**tm.no_spark())]
from xgboost import DMatrix, QuantileDMatrix
from xgboost.spark.data import (
_read_csr_matrix_from_unwrapped_spark_vec,
alias,
create_dmatrix... | 5,132 | 31.903846 | 88 | py |
xgboost | xgboost-master/tests/test_distributed/test_with_spark/utils.py | import contextlib
import logging
import shutil
import sys
import tempfile
import unittest
from io import StringIO
import pytest
from xgboost import testing as tm
pytestmark = [pytest.mark.skipif(**tm.no_spark())]
from pyspark.sql import SparkSession
from xgboost.spark.utils import _get_default_params_from_func
c... | 4,058 | 27.1875 | 92 | py |
xgboost | xgboost-master/tests/test_distributed/test_with_spark/test_spark_local.py | import glob
import logging
import random
import tempfile
import uuid
from collections import namedtuple
from typing import Generator, Sequence, Type
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.spark.data import pred_contribs
pytestmark = [tm.timeout(60), pyte... | 55,161 | 37.227304 | 98 | py |
xgboost | xgboost-master/tests/test_distributed/test_with_spark/test_spark_local_cluster.py | import json
import logging
import os
import random
import tempfile
import uuid
from collections import namedtuple
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.callback import LearningRateScheduler
pytestmark = pytest.mark.skipif(**tm.no_spark())
from typing i... | 18,886 | 37.623722 | 106 | py |
xgboost | xgboost-master/tests/test_distributed/test_gpu_with_dask/conftest.py | from typing import Generator, Sequence
import pytest
from xgboost import testing as tm
@pytest.fixture(scope="session", autouse=True)
def setup_rmm_pool(request, pytestconfig: pytest.Config) -> None:
tm.setup_rmm_pool(request, pytestconfig)
@pytest.fixture(scope="class")
def local_cuda_client(request, pytestc... | 1,302 | 29.302326 | 86 | py |
xgboost | xgboost-master/tests/test_distributed/test_gpu_with_dask/test_gpu_demos.py | import os
import subprocess
import pytest
from xgboost import testing as tm
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.mgpu
def test_dask_training():
script = os.path.join(tm.demo_dir(__file__), "dask", "gpu_training.py")
cmd... | 644 | 23.807692 | 83 | py |
xgboost | xgboost-master/tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py | """Copyright 2019-2022 XGBoost contributors"""
import asyncio
import json
from collections import OrderedDict
from inspect import signature
from typing import Any, Dict, Type, TypeVar
import numpy as np
import pytest
from hypothesis import given, note, settings, strategies
from hypothesis._settings import duration
im... | 20,069 | 33.484536 | 86 | py |
xgboost | xgboost-master/tests/benchmark/benchmark_linear.py | #pylint: skip-file
import argparse
import xgboost as xgb
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import time
import ast
rng = np.random.RandomState(1994)
def run_benchmark(args):
try:
dtest = xgb.DMatrix('dtest.dm')
... | 2,912 | 40.614286 | 143 | py |
xgboost | xgboost-master/tests/benchmark/benchmark_tree.py | """Run benchmark on the tree booster."""
import argparse
import ast
import time
import numpy as np
import xgboost as xgb
RNG = np.random.RandomState(1994)
def run_benchmark(args):
"""Runs the benchmark."""
try:
dtest = xgb.DMatrix('dtest.dm')
dtrain = xgb.DMatrix('dtrain.dm')
if no... | 3,021 | 33.735632 | 100 | py |
xgboost | xgboost-master/demo/nvflare/horizontal/custom/controller.py | """
Example of training controller with NVFlare
===========================================
"""
import multiprocessing
from nvflare.apis.client import Client
from nvflare.apis.fl_context import FLContext
from nvflare.apis.impl.controller import Controller, Task
from nvflare.apis.shareable import Shareable
from nvflare... | 2,587 | 36.507246 | 89 | py |
xgboost | xgboost-master/demo/nvflare/horizontal/custom/trainer.py | import os
from nvflare.apis.executor import Executor
from nvflare.apis.fl_constant import FLContextKey, ReturnCode
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable, make_reply
from nvflare.apis.signal import Signal
import xgboost as xgb
from xgboost import callback
class Su... | 3,971 | 42.648352 | 84 | py |
xgboost | xgboost-master/demo/nvflare/vertical/custom/controller.py | """
Example of training controller with NVFlare
===========================================
"""
import multiprocessing
from nvflare.apis.client import Client
from nvflare.apis.fl_context import FLContext
from nvflare.apis.impl.controller import Controller, Task
from nvflare.apis.shareable import Shareable
from nvflare... | 2,587 | 36.507246 | 89 | py |
xgboost | xgboost-master/demo/nvflare/vertical/custom/trainer.py | import os
from nvflare.apis.executor import Executor
from nvflare.apis.fl_constant import FLContextKey, ReturnCode
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable, make_reply
from nvflare.apis.signal import Signal
import xgboost as xgb
from xgboost import callback
class Su... | 4,015 | 39.979592 | 95 | py |
xgboost | xgboost-master/demo/aft_survival/aft_survival_demo_with_optuna.py | """
Demo for survival analysis (regression) with Optuna.
====================================================
Demo for survival analysis (regression) using Accelerated Failure Time (AFT) model,
using Optuna to tune hyperparameters
"""
import numpy as np
import optuna
import pandas as pd
from sklearn.model_selection i... | 3,655 | 42.52381 | 112 | py |
xgboost | xgboost-master/demo/aft_survival/aft_survival_viz_demo.py | """
Visual demo for survival analysis (regression) with Accelerated Failure Time (AFT) model.
=========================================================================================
This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble
model starts out as a flat line and evolves in... | 4,167 | 28.985612 | 89 | py |
xgboost | xgboost-master/demo/aft_survival/aft_survival_demo.py | """
Demo for survival analysis (regression).
========================================
Demo for survival analysis (regression). using Accelerated Failure Time (AFT) model.
"""
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import ShuffleSplit
import xgboost as xgb
# The Veterans' Admi... | 2,291 | 35.380952 | 87 | py |
xgboost | xgboost-master/demo/multiclass_classification/train.py | #!/usr/bin/python
from __future__ import division
import numpy as np
import xgboost as xgb
# label need to be 0 to num_class -1
data = np.loadtxt('./dermatology.data', delimiter=',',
converters={33: lambda x:int(x == '?'), 34: lambda x:int(x) - 1})
sz = data.shape
train = data[:int(sz[0] * 0.7), :]
test = ... | 1,553 | 28.884615 | 73 | py |
xgboost | xgboost-master/demo/gpu_acceleration/cover_type.py | import time
from sklearn.datasets import fetch_covtype
from sklearn.model_selection import train_test_split
import xgboost as xgb
# Fetch dataset using sklearn
cov = fetch_covtype()
X = cov.data
y = cov.target
# Create 0.75/0.25 train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.... | 1,333 | 30.761905 | 90 | py |
xgboost | xgboost-master/demo/dask/cpu_survival.py | """
Example of training survival model with Dask on CPU
===================================================
"""
import os
import dask.dataframe as dd
from dask.distributed import Client, LocalCluster
import xgboost as xgb
from xgboost.dask import DaskDMatrix
def main(client):
# Load an example survival data f... | 2,404 | 32.402778 | 91 | py |
xgboost | xgboost-master/demo/dask/sklearn_cpu_training.py | """
Use scikit-learn regressor interface with CPU histogram tree method
===================================================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
import xgboost
def main(client):
# generate some random data for demonstration
n = 100... | 1,186 | 27.95122 | 74 | py |
xgboost | xgboost-master/demo/dask/sklearn_gpu_training.py | """
Use scikit-learn regressor interface with GPU histogram tree method
===================================================================
"""
from dask import array as da
from dask.distributed import Client
# It's recommended to use dask_cuda for GPU assignment
from dask_cuda import LocalCUDACluster
import xgboost... | 1,375 | 28.276596 | 73 | py |
xgboost | xgboost-master/demo/dask/gpu_training.py | """
Example of training with Dask on GPU
====================================
"""
import dask_cudf
from dask import array as da
from dask import dataframe as dd
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
import xgboost as xgb
from xgboost import dask as dxgb
from xgboost.dask import Das... | 2,919 | 33.352941 | 88 | py |
xgboost | xgboost-master/demo/dask/cpu_training.py | """
Example of training with Dask on CPU
====================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
import xgboost as xgb
from xgboost.dask import DaskDMatrix
def main(client):
# generate some random data for demonstration
m = 100000
n = 100
... | 1,408 | 27.755102 | 70 | py |
xgboost | xgboost-master/demo/dask/dask_callbacks.py | """
Example of using callbacks with Dask
====================================
"""
import numpy as np
from dask.distributed import Client, LocalCluster
from dask_ml.datasets import make_regression
from dask_ml.model_selection import train_test_split
import xgboost as xgb
from xgboost.dask import DaskDMatrix
def proba... | 2,965 | 31.955556 | 87 | py |
xgboost | xgboost-master/demo/guide-python/predict_first_ntree.py | """
Demo for prediction using number of trees
=========================================
"""
import os
import numpy as np
from sklearn.datasets import load_svmlight_file
import xgboost as xgb
CURRENT_DIR = os.path.dirname(__file__)
train = os.path.join(CURRENT_DIR, "../data/agaricus.txt.train")
test = os.path.join(CU... | 1,942 | 30.852459 | 87 | py |
xgboost | xgboost-master/demo/guide-python/external_memory.py | """
Experimental support for external memory
========================================
This is similar to the one in `quantile_data_iterator.py`, but for external memory
instead of Quantile DMatrix. The feature is not ready for production use yet.
.. versionadded:: 1.5.0
See :doc:`the tutorial </tutorials/exter... | 3,179 | 30.8 | 89 | py |
xgboost | xgboost-master/demo/guide-python/quantile_data_iterator.py | """
Demo for using data iterator with Quantile DMatrix
==================================================
.. versionadded:: 1.2.0
The demo that defines a customized iterator for passing batches of data into
:py:class:`xgboost.QuantileDMatrix` and use this ``QuantileDMatrix`` for
training. The feature is used pri... | 3,624 | 28.713115 | 86 | py |
xgboost | xgboost-master/demo/guide-python/callbacks.py | '''
Demo for using and defining callback functions
==============================================
.. versionadded:: 1.3.0
'''
import argparse
import os
import tempfile
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_... | 4,631 | 33.311111 | 92 | py |
xgboost | xgboost-master/demo/guide-python/generalized_linear_model.py | """
Demo for GLM
============
"""
import os
import xgboost as xgb
##
# this script demonstrate how to fit generalized linear model in xgboost
# basically, we are using linear model, instead of tree for our boosters
##
CURRENT_DIR = os.path.dirname(__file__)
dtrain = xgb.DMatrix(
os.path.join(CURRENT_DIR, "../da... | 1,425 | 26.423077 | 80 | py |
xgboost | xgboost-master/demo/guide-python/learning_to_rank.py | """
Getting started with learning to rank
=====================================
.. versionadded:: 2.0.0
This is a demonstration of using XGBoost for learning to rank tasks using the
MSLR_10k_letor dataset. For more infomation about the dataset, please visit its
`description page <https://www.microsoft.com/en-us/res... | 6,345 | 28.516279 | 87 | py |
xgboost | xgboost-master/demo/guide-python/continuation.py | """
Demo for training continuation
==============================
"""
import os
import pickle
import tempfile
from sklearn.datasets import load_breast_cancer
import xgboost
def training_continuation(tmpdir: str, use_pickle: bool) -> None:
"""Basic training continuation."""
# Train 128 iterations in 1 sessi... | 3,807 | 33.306306 | 88 | py |
xgboost | xgboost-master/demo/guide-python/custom_rmsle.py | """
Demo for defining a custom regression objective and metric
==========================================================
Demo for defining customized metric and objective. Notice that for simplicity reason
weight is not used in following example. In this script, we implement the Squared Log
Error (SLE) objective and... | 6,450 | 31.094527 | 86 | py |
xgboost | xgboost-master/demo/guide-python/cat_in_the_dat.py | """
Train XGBoost with cat_in_the_dat dataset
=========================================
A simple demo for categorical data support using dataset from Kaggle categorical data
tutorial.
The excellent tutorial is at:
https://www.kaggle.com/shahules/an-overview-of-encoding-techniques
And the data can be found at:
https:... | 3,744 | 28.722222 | 86 | py |
xgboost | xgboost-master/demo/guide-python/sklearn_examples.py | '''
Collection of examples for using sklearn interface
==================================================
For an introduction to XGBoost's scikit-learn estimator interface, see
:doc:`/python/sklearn_estimator`.
Created on 1 Apr 2015
@author: Jamie Hall
'''
import pickle
import numpy as np
from sklearn.datasets impo... | 2,644 | 32.910256 | 79 | py |
xgboost | xgboost-master/demo/guide-python/update_process.py | """
Demo for using `process_type` with `prune` and `refresh`
========================================================
Modifying existing trees is not a well established use for XGBoost, so feel free to
experiment.
"""
import numpy as np
from sklearn.datasets import fetch_california_housing
import xgboost as xgb
d... | 3,247 | 32.833333 | 89 | py |
xgboost | xgboost-master/demo/guide-python/feature_weights.py | """
Demo for using feature weight to change column sampling
=======================================================
.. versionadded:: 1.3.0
"""
import argparse
import numpy as np
from matplotlib import pyplot as plt
import xgboost
def main(args: argparse.Namespace) -> None:
rng = np.random.RandomState(199... | 1,383 | 22.066667 | 85 | py |
xgboost | xgboost-master/demo/guide-python/gamma_regression.py | """
Demo for gamma regression
=========================
"""
import numpy as np
import xgboost as xgb
# this script demonstrates how to fit gamma regression model (with log link function)
# in xgboost, before running the demo you need to generate the autoclaims dataset
# by running gen_autoclaims.R located in xgboo... | 1,098 | 35.633333 | 99 | py |
xgboost | xgboost-master/demo/guide-python/categorical.py | """
Getting started with categorical data
=====================================
Experimental support for categorical data.
In before, users need to run an encoder themselves before passing the data into XGBoost,
which creates a sparse matrix and potentially increase memory usage. This demo
showcases the experimental... | 2,833 | 31.204545 | 88 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.