python_code stringlengths 0 679k | repo_name stringlengths 9 41 | file_path stringlengths 6 149 |
|---|---|---|
import numpy as np
import xgboost as xgb
import pytest
try:
import shap
except ImportError:
shap = None
pass
pytestmark = pytest.mark.skipif(shap is None, reason="Requires shap package")
# Check integration is not broken from xgboost side
# Changes in binary format may cause problems
def test_with_shap(... | spark-xgboost-nv-release_1.4.0 | tests/python/test_with_shap.py |
import xgboost
import numpy as np
import os
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(target... | spark-xgboost-nv-release_1.4.0 | tests/python/generate_models.py |
import xgboost as xgb
import pytest
import os
import testing as tm
import tempfile
# We use the dataset for tests.
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
class TestCallbacks:
@classmethod
def setup_class(cls):
from sklearn.datasets import load_breast_cancer
X, y = load_breast_canc... | spark-xgboost-nv-release_1.4.0 | tests/python/test_callback.py |
import numpy as np
import xgboost as xgb
import os
import json
import testing as tm
import pytest
import locale
import tempfile
dpath = os.path.join(tm.PROJECT_ROOT, 'demo/data/')
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
rng = np.random.RandomState(1994)
de... | spark-xgboost-nv-release_1.4.0 | tests/python/test_basic_models.py |
import testing as tm
import pytest
import numpy as np
import xgboost as xgb
import json
import os
dpath = os.path.join(tm.PROJECT_ROOT, 'demo', 'data')
def test_aft_survival_toy_data():
# See demo/aft_survival/aft_survival_viz_demo.py
X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
INF = np.inf
y_lowe... | spark-xgboost-nv-release_1.4.0 | tests/python/test_survival.py |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import testing as tm
import xgboost as xgb
try:
import datatable as dt
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_dt()['condition'] or tm.no_pandas()['condition'],
reason=tm.no_dt()['reason'] + ' or ... | spark-xgboost-nv-release_1.4.0 | tests/python/test_dt.py |
# -*- coding: utf-8 -*-
import os
import tempfile
import numpy as np
import xgboost as xgb
import scipy.sparse
import pytest
from scipy.sparse import rand, csr_matrix
import testing as tm
rng = np.random.RandomState(1)
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestDMatrix:
def test_warn_miss... | spark-xgboost-nv-release_1.4.0 | tests/python/test_dmatrix.py |
'''Tests for running inplace prediction.'''
from concurrent.futures import ThreadPoolExecutor
import numpy as np
from scipy import sparse
import pytest
import pandas as pd
import testing as tm
import xgboost as xgb
def run_threaded_predict(X, rows, predict_func):
results = []
per_thread = 20
with ThreadP... | spark-xgboost-nv-release_1.4.0 | tests/python/test_predict.py |
import numpy as np
from scipy.sparse import csr_matrix
import testing as tm
import xgboost
import os
import itertools
import shutil
import urllib.request
import zipfile
def test_ranking_with_unweighted_data():
Xrow = np.array([1, 2, 6, 8, 11, 14, 16, 17])
Xcol = np.array([0, 0, 1, 1, 2, 2, 3, 3])
X = ... | spark-xgboost-nv-release_1.4.0 | tests/python/test_ranking.py |
from xgboost import RabitTracker
import xgboost as xgb
import pytest
import testing as tm
import numpy as np
import sys
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
def test_rabit_tracker():
tracker = RabitTracker(hostIP='127.0.0.1', nslave=1)
... | spark-xgboost-nv-release_1.4.0 | tests/python/test_tracker.py |
import pickle
import numpy as np
import xgboost as xgb
import os
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):
X, y = generate_data()
dtrain = xgb.DM... | spark-xgboost-nv-release_1.4.0 | tests/python/test_pickling.py |
# -*- coding: utf-8 -*-
import xgboost as xgb
import pytest
import testing as tm
@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 = get_current_verbosity()
w... | spark-xgboost-nv-release_1.4.0 | tests/python/test_config.py |
import os
import subprocess
import pytest
import testing as tm
import sys
ROOT_DIR = tm.PROJECT_ROOT
DEMO_DIR = os.path.join(ROOT_DIR, 'demo')
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, 'guide-python')
CLI_DEMO_DIR = os.path.join(DEMO_DIR, 'CLI')
def test_basic_walkthrough():
script = os.path.join(PYTHON_DEMO_DIR... | spark-xgboost-nv-release_1.4.0 | tests/python/test_demos.py |
import testing as tm
from hypothesis import strategies, given, settings, note
import xgboost as xgb
parameter_strategy = strategies.fixed_dictionaries({
'booster': strategies.just('gblinear'),
'eta': strategies.floats(0.01, 0.25),
'tolerance': strategies.floats(1e-5, 1e-2),
'nthread': strategies.intege... | spark-xgboost-nv-release_1.4.0 | tests/python/test_linear.py |
from pathlib import Path
import pickle
import testing as tm
import pytest
import xgboost as xgb
import sys
import numpy as np
import scipy
import json
from typing import List, Tuple, Dict, Optional, Type, Any
import asyncio
from functools import partial
from concurrent.futures import ThreadPoolExecutor
import tempfile
... | spark-xgboost-nv-release_1.4.0 | tests/python/test_with_dask.py |
import testing as tm
import pytest
import xgboost as xgb
import numpy as np
from hypothesis import given, strategies, settings, note
exact_parameter_strategy = strategies.fixed_dictionaries({
'nthread': strategies.integers(1, 4),
'max_depth': strategies.integers(1, 11),
'min_child_weight': strategies.float... | spark-xgboost-nv-release_1.4.0 | tests/python/test_updaters.py |
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import testing as tm
import pytest
try:
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_pandas())
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestPandas:
def test_pandas(self):
... | spark-xgboost-nv-release_1.4.0 | tests/python/test_with_pandas.py |
# coding: utf-8
import os
import urllib
import zipfile
import sys
from contextlib import contextmanager
from io import StringIO
from xgboost.compat import SKLEARN_INSTALLED, PANDAS_INSTALLED
from xgboost.compat import DASK_INSTALLED
import pytest
import tempfile
import xgboost as xgb
import numpy as np
hypothesis = py... | spark-xgboost-nv-release_1.4.0 | tests/python/testing.py |
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import testing as tm
import pytest
try:
import modin.pandas as md
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_modin())
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestModin:
def test_modin(self):
... | spark-xgboost-nv-release_1.4.0 | tests/python/test_with_modin.py |
# -*- coding: utf-8 -*-
import xgboost as xgb
import numpy as np
class TestOMP:
def test_omp(self):
dpath = 'demo/data/'
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'booster': 'gbtree',
'objective': ... | spark-xgboost-nv-release_1.4.0 | tests/python/test_openmp.py |
import xgboost as xgb
import numpy as np
import pytest
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 = xgb.DMatrix(dpath + 'agaricus.txt.trai... | spark-xgboost-nv-release_1.4.0 | tests/python/test_parse_tree.py |
import os
import tempfile
import platform
import xgboost
import subprocess
import numpy
import json
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_in}
model_out ... | spark-xgboost-nv-release_1.4.0 | tests/python/test_cli.py |
import collections
import importlib.util
import numpy as np
import xgboost as xgb
import testing as tm
import tempfile
import os
import shutil
import pytest
import json
rng = np.random.RandomState(1994)
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
class TemporaryDirectory(object):
"""Context manager for t... | spark-xgboost-nv-release_1.4.0 | tests/python/test_with_sklearn.py |
import xgboost as xgb
import testing as tm
import numpy as np
import pytest
rng = np.random.RandomState(1337)
class TestEvalMetrics:
xgb_params_01 = {
'verbosity': 0,
'nthread': 1,
'eval_metric': 'error'
}
xgb_params_02 = {
'verbosity': 0,
'nthread': 1,
'e... | spark-xgboost-nv-release_1.4.0 | tests/python/test_eval_metrics.py |
import numpy as np
import xgboost as xgb
import testing as tm
import pytest
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):
n = 100
variable_x = np... | spark-xgboost-nv-release_1.4.0 | tests/python/test_monotone_constraints.py |
#!/usr/bin/python
import xgboost as xgb
import numpy as np
xgb.rabit.init()
X = [
[15.00,28.90,29.00,3143.70,0.00,0.10,69.90,90.00,13726.07,0.00,2299.70,0.00,0.05,
4327.03,0.00,24.00,0.18,3.00,0.41,3.77,0.00,0.00,4.00,0.00,150.92,0.00,2.00,0.00,
0.01,138.00,1.00,0.02,69.90,0.00,0.83,5.00,0.01,0.12,47.30,0.00,... | spark-xgboost-nv-release_1.4.0 | tests/distributed/test_issue3402.py |
#!/usr/bin/python
import xgboost as xgb
# Always call this before using distributed module
xgb.rabit.init()
# Load file, file will be automatically sharded in distributed mode.
dtrain = xgb.DMatrix('../../demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
# Specify parameters via... | spark-xgboost-nv-release_1.4.0 | tests/distributed/test_basic.py |
"""Distributed GPU tests."""
import sys
import xgboost as xgb
import os
import numpy as np
def run_test(name, params_fun):
"""Runs a distributed GPU test."""
# Always call this before using distributed module
xgb.rabit.init()
rank = xgb.rabit.get_rank()
world = xgb.rabit.get_world_size()
# Lo... | spark-xgboost-nv-release_1.4.0 | tests/distributed/distributed_gpu.py |
import numpy as np
import sys
import gc
import pytest
import xgboost as xgb
from hypothesis import given, strategies, assume, settings, note
sys.path.append("tests/python")
import testing as tm
parameter_strategy = strategies.fixed_dictionaries({
'max_depth': strategies.integers(0, 11),
'max_leaves': strategi... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_updaters.py |
import sys
import pytest
import numpy as np
import xgboost as xgb
from xgboost.compat import PANDAS_INSTALLED
from hypothesis import given, strategies, assume, settings, note
if PANDAS_INSTALLED:
from hypothesis.extra.pandas import column, data_frames, range_indexes
else:
def noop(*args, **kwargs):
p... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_prediction.py |
'''Loading a pickled model generated by test_pickling.py, only used by
`test_gpu_with_dask.py`'''
import os
import numpy as np
import xgboost as xgb
import json
import pytest
import sys
from test_gpu_pickling import build_dataset, model_path, load_pickle
sys.path.append("tests/python")
import testing as tm
class Te... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/load_pickle.py |
import sys
import pytest
import logging
sys.path.append("tests/python")
import testing as tm # noqa
def has_rmm():
try:
import rmm
return True
except ImportError:
return False
@pytest.fixture(scope='session', autouse=True)
def setup_rmm_pool(request, pytestcon... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/conftest.py |
import numpy as np
import xgboost as xgb
import sys
import pytest
sys.path.append("tests/python")
import testing as tm
def dmatrix_from_cupy(input_type, DMatrixT, missing=np.NAN):
'''Test constructing DMatrix from cupy'''
import cupy as cp
kRows = 80
kCols = 3
np_X = np.random.randn(kRows, kCol... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_from_cupy.py |
import numpy as np
import sys
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.TestInteractionConstraints()
def test_int... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_interaction_constraints.py |
'''Test model IO with pickle.'''
import pickle
import numpy as np
import subprocess
import os
import sys
import json
import pytest
import xgboost as xgb
from xgboost import XGBClassifier
sys.path.append("tests/python")
import testing as tm
model_path = './model.pkl'
def build_dataset():
N = 10
x = np.linspa... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_pickling.py |
import xgboost as xgb
import pytest
import sys
import numpy as np
sys.path.append("tests/python")
import testing as tm # noqa
import test_with_sklearn as twskl # noqa
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
rng = np.random.RandomState(1994)
def test_gpu_binary_classification():
from s... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_with_sklearn.py |
import sys
import xgboost
import pytest
sys.path.append("tests/python")
import test_eval_metrics as test_em # noqa
class TestGPUEvalMetrics:
cpu_test = test_em.TestEvalMetrics()
@pytest.mark.parametrize("n_samples", [4, 100, 1000])
def test_roc_auc_binary(self, n_samples):
self.cpu_test.run_roc... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_eval_metrics.py |
import sys
import os
from typing import Type, TypeVar, Any, Dict, List
import pytest
import numpy as np
import asyncio
import xgboost
import subprocess
from collections import OrderedDict
from inspect import signature
from hypothesis import given, strategies, settings, note
from hypothesis._settings import duration
fro... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_with_dask.py |
import numpy as np
import xgboost
import os
import itertools
import shutil
import urllib.request
import zipfile
import sys
sys.path.append("tests/python")
import testing as tm # noqa
class TestRanking:
@classmethod
def setup_class(cls):
"""
Download and setup the test fixtures
... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_ranking.py |
# -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import pytest
import sys
sys.path.append("tests/python")
import testing as tm
class TestDeviceQuantileDMatrix:
def test_dmatrix_numpy_init(self):
data = np.random.randn(5, 5)
with pytest.raises(TypeError, match='is not supported'):
... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_device_quantile_dmatrix.py |
import os
import subprocess
import sys
import pytest
sys.path.append("tests/python")
import testing as tm
import test_demos as td # noqa
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(td.PYTHON_DEMO_DIR, 'data_iterator.py')
cmd = ['python', script]
subprocess.c... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_demos.py |
import numpy as np
import xgboost as xgb
import sys
import pytest
sys.path.append("tests/python")
import testing as tm
def dmatrix_from_cudf(input_type, DMatrixT, missing=np.NAN):
'''Test constructing DMatrix from cudf'''
import cudf
import pandas as pd
kRows = 80
kCols = 3
na = np.random.r... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_from_cudf.py |
import numpy as np
import xgboost as xgb
import cupy as cp
import time
import pytest
# Test for integer overflow or out of memory exceptions
def test_large_input():
available_bytes, _ = cp.cuda.runtime.memGetInfo()
# 15 GB
required_bytes = 1.5e+10
if available_bytes < required_bytes:
pytest.sk... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_large_input.py |
import sys
import os
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:
cputest = test_cb.TestCallbacks()
def r... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_basic_models.py |
import sys
import numpy as np
import pytest
import xgboost as xgb
sys.path.append("tests/python")
import testing as tm
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):
return all((... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_monotonic_constraints.py |
import sys
from hypothesis import strategies, given, settings, assume
import pytest
import numpy
import xgboost as xgb
sys.path.append("tests/python")
import testing as tm
parameter_strategy = strategies.fixed_dictionaries({
'booster': strategies.just('gblinear'),
'eta': strategies.floats(0.01, 0.25),
'to... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_linear.py |
import numpy as np
import xgboost as xgb
import json
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)
... | spark-xgboost-nv-release_1.4.0 | tests/python-gpu/test_gpu_training_continuation.py |
"""Setup xgboost package."""
import os
import shutil
import subprocess
import logging
import distutils
import sys
from platform import system
from setuptools import setup, find_packages, Extension
from setuptools.command import build_ext, sdist, install_lib, install
# You can't use `pip install .` as pip copies setup.... | spark-xgboost-nv-release_1.4.0 | python-package/setup.py |
# coding: utf-8
# pylint: disable= invalid-name
"""Distributed XGBoost Rabit related API."""
import ctypes
import pickle
import numpy as np
from .core import _LIB, c_str, STRING_TYPES, _check_call
def _init_rabit():
"""internal library initializer."""
if _LIB is not None:
_LIB.RabitGetRank.restype = ... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/rabit.py |
# coding: utf-8
# pylint: disable=invalid-name, too-many-statements, no-self-use
# pylint: disable=too-many-arguments
"""Training Library containing training routines."""
from abc import ABC
import collections
import os
import pickle
from typing import Callable, List, Optional, Union, Dict, Tuple
import numpy
from . i... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/callback.py |
# pylint: disable=missing-function-docstring
"""Global configuration for XGBoost"""
import ctypes
import json
from contextlib import contextmanager
from functools import wraps
from .core import _LIB, _check_call, c_str, py_str
def config_doc(*, header=None, extra_note=None, parameters=None, returns=None,
... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/config.py |
# coding: utf-8
# pylint: disable= invalid-name, unused-import
"""For compatibility and optional dependencies."""
import sys
import types
import importlib.util
import logging
import numpy as np
assert (sys.version_info[0] == 3), 'Python 2 is no longer supported.'
# pylint: disable=invalid-name, redefined-builtin
STR... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/compat.py |
# pylint: disable=too-many-locals, too-many-arguments, invalid-name,
# pylint: disable=too-many-branches
# coding: utf-8
"""Plotting Library."""
from io import BytesIO
import numpy as np
from .core import Booster
from .sklearn import XGBModel
def plot_importance(booster, ax=None, height=0.2,
xlim=... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/plotting.py |
# coding: utf-8
"""XGBoost: eXtreme Gradient Boosting library.
Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
"""
import os
from .core import DMatrix, DeviceQuantileDMatrix, Booster
from .training import train, cv
from . import rabit # noqa
from . import tracker # noqa
from .tracker impo... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/__init__.py |
# coding: utf-8
# pylint: disable=too-many-arguments, too-many-branches, invalid-name
# pylint: disable=too-many-lines, too-many-locals, no-self-use
"""Core XGBoost Library."""
import collections
# pylint: disable=no-name-in-module,import-error
from collections.abc import Mapping
from typing import List, Optional, Any,... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/core.py |
# pylint: disable=too-many-arguments, too-many-locals, no-name-in-module
# pylint: disable=missing-class-docstring, invalid-name
# pylint: disable=too-many-lines, fixme
# pylint: disable=too-few-public-methods
# pylint: disable=import-error
"""Dask extensions for distributed training. See
https://xgboost.readthedocs.io... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/dask.py |
"""
This script is a variant of dmlc-core/dmlc_tracker/tracker.py,
which is a specialized version for xgboost tasks.
"""
# pylint: disable=invalid-name, missing-docstring, too-many-arguments, too-many-locals
# pylint: disable=too-many-branches, too-many-statements, too-many-instance-attributes
import socket
import str... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/tracker.py |
# coding: utf-8
"""Find the path to xgboost dynamic library files."""
import os
import platform
from typing import List
import sys
class XGBoostLibraryNotFound(Exception):
"""Error thrown by when xgboost is not found"""
def find_lib_path() -> List[str]:
"""Find the path to xgboost dynamic library files.
... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/libpath.py |
# coding: utf-8
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, E0012, R0912, C0302
"""Scikit-Learn Wrapper interface for XGBoost."""
import copy
import warnings
import json
from typing import Union, Optional, List, Dict, Callable, Tuple, Any, TypeVar
import numpy as np
from .core import Boo... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/sklearn.py |
# coding: utf-8
# pylint: disable=too-many-locals, too-many-arguments, invalid-name
# pylint: disable=too-many-branches, too-many-statements
"""Training Library containing training routines."""
import warnings
import copy
import numpy as np
from .core import Booster, XGBoostError, _get_booster_layer_trees
from .compat ... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/training.py |
# pylint: disable=too-many-arguments, too-many-branches
# pylint: disable=too-many-return-statements, import-error
'''Data dispatching for DMatrix.'''
import ctypes
import json
import warnings
import os
from typing import Any
import numpy as np
from .core import c_array, _LIB, _check_call, c_str, _array_interface
fro... | spark-xgboost-nv-release_1.4.0 | python-package/xgboost/data.py |
#!/usr/bin/env python
import errno
import argparse
import glob
import os
import platform
import shutil
import subprocess
import sys
from contextlib import contextmanager
from cudautils import cudaver
# Monkey-patch the API inconsistency between Python2.X and 3.X.
if sys.platform.startswith("linux"):
sys.platform =... | spark-xgboost-nv-release_1.4.0 | jvm-packages/create_jni.py |
#!/usr/bin/env python
import os
import re
import subprocess
import sys
# version -> classifier
# '' means default classifier
cuda_vers = {
'11.2': ['cuda11', '']
}
def check_classifier(classifier):
'''
Check the mapping from cuda version to jar classifier.
Used by maven build.
'''
cu_ver = detec... | spark-xgboost-nv-release_1.4.0 | jvm-packages/cudautils.py |
#
# Copyright (c) 2019 by Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/setup.py |
spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/ml/__init__.py | |
spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/ml/dmlc/__init__.py | |
spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/ml/dmlc/xgboost4j/__init__.py | |
spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/ml/dmlc/xgboost4j/scala/__init__.py | |
#
# Copyright (c) 2019 by Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/ml/dmlc/xgboost4j/scala/spark/__init__.py |
#
# Copyright (c) 2019 by Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/sparkxgb/rapids.py |
#
# Copyright (c) 2019 by Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/sparkxgb/util.py |
#
# Copyright (c) 2019 by Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/sparkxgb/__init__.py |
#
# Copyright (c) 2019 by Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/sparkxgb/common.py |
#
# Copyright (c) 2019 by Contributors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-spark/src/main/resources/sparkxgb/xgboost.py |
from sklearn.datasets import load_iris
import numpy as np
import pandas
X, y = load_iris(return_X_y=True)
y = y.astype(np.int)
df = pandas.DataFrame(data=X, columns=['sepal length', 'sepal width', 'petal length', 'petal width'])
class_id_to_name = {0:'Iris-setosa', 1:'Iris-versicolor', 2:'Iris-virginica'}
df['class'] ... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-tester/get_iris.py |
import sys
pom_template = """
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<g... | spark-xgboost-nv-release_1.4.0 | jvm-packages/xgboost4j-tester/generate_pom.py |
#!/usr/bin/python
"""
demo python script of rabit: Lazy preparation function
"""
import os
import sys
import numpy as np
# import rabit, the tracker script will setup the lib path correctly
# for normal run without tracker script, add following line
# sys.path.append(os.path.dirname(__file__) + '/../wrapper')
import ra... | spark-xgboost-nv-release_1.4.0 | rabit/guide/lazy_allreduce.py |
#!/usr/bin/python
"""
demo python script of rabit
"""
from __future__ import print_function
from builtins import range
import os
import sys
import numpy as np
# import rabit, the tracker script will setup the lib path correctly
# for normal run without tracker script, add following line
# sys.path.append(os.path.dirnam... | spark-xgboost-nv-release_1.4.0 | rabit/guide/basic.py |
#!/usr/bin/python
"""
demo python script of rabit
"""
from __future__ import print_function
import os
import sys
# add path to wrapper
# for normal run without tracker script, add following line
# sys.path.append(os.path.dirname(__file__) + '/../wrapper')
import rabit
rabit.init()
n = 3
rank = rabit.get_rank()
s = Non... | spark-xgboost-nv-release_1.4.0 | rabit/guide/broadcast.py |
# -*- coding: utf-8 -*-
"""Helper utilty function for customization."""
import sys
import os
import docutils
import subprocess
if os.environ.get('READTHEDOCS', None) == 'True':
subprocess.call('cd ..; rm -rf recommonmark;' +
'git clone https://github.com/tqchen/recommonmark', shell=True)
sys.p... | spark-xgboost-nv-release_1.4.0 | rabit/doc/sphinx_util.py |
# -*- coding: utf-8 -*-
#
# documentation build configuration file, created by
# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All confi... | spark-xgboost-nv-release_1.4.0 | rabit/doc/conf.py |
"""Query list of all contributors and reviewers in a release"""
from sh.contrib import git
import sys
import re
import requests
import json
if len(sys.argv) != 5:
print(f'Usage: {sys.argv[0]} [starting commit/tag] [ending commit/tag] [GitHub username] ' +
'[GitHub password]')
sys.exit(1)
from_comm... | spark-xgboost-nv-release_1.4.0 | dev/query_contributors.py |
"""Simple script for downloading and checking pypi release wheels.
tqdm, sh are required to run this script.
"""
from urllib.request import urlretrieve
import argparse
from typing import List
from sh.contrib import git
from distutils import version
import subprocess
import tqdm
import os
# The package building is man... | spark-xgboost-nv-release_1.4.0 | dev/release-pypi.py |
# -*- coding: utf-8 -*-
"""Helper utility function for customization."""
import sys
import os
import subprocess
READTHEDOCS_BUILD = (os.environ.get('READTHEDOCS', None) is not None)
if not os.path.exists('web-data'):
subprocess.call('rm -rf web-data;' +
'git clone https://github.com/dmlc/web-data'... | spark-xgboost-nv-release_1.4.0 | doc/sphinx_util.py |
# -*- coding: utf-8 -*-
#
# documentation build configuration file, created by
# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All confi... | spark-xgboost-nv-release_1.4.0 | doc/conf.py |
'''This is a simple script that converts a pickled XGBoost
Scikit-Learn interface object from 0.90 to a native model. Pickle
format is not stable as it's a direct serialization of Python object.
We advice not to use it when stability is needed.
'''
import pickle
import json
import os
import argparse
import numpy as n... | spark-xgboost-nv-release_1.4.0 | doc/python/convert_090to100.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | setup.py |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: megamolbart.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import message as _message
from ... | MegaMolBART-dev | generated/megamolbart_pb2.py |
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
import megamolbart_pb2 as megamolbart__pb2
class GenerativeSamplerStub(object):
"""import "google/protobuf/empty.proto";
python -m pip install grpcio
... | MegaMolBART-dev | generated/megamolbart_pb2_grpc.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/package_info.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/__init__.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/tokenizer/__init__.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/tokenizer/tokenizer.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/utils/__init__.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/models/__init__.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/models/megamolbart/megamolbart_model.py |
# Copyright (c) 2022, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | MegaMolBART-dev | nemo_chem/models/megamolbart/__init__.py |
import logging
import torch
from typing import List
from omegaconf import OmegaConf
from pytorch_lightning.trainer.trainer import Trainer
from nemo.collections.nlp.parts.nlp_overrides import (NLPDDPPlugin,
NLPSaveRestoreConnector)
from nemo.utils.app_state import... | MegaMolBART-dev | nemo_chem/models/megamolbart/infer.py |
import grpc
import torch
import logging
from concurrent import futures
from hydra import compose, initialize
from nemo_chem.models.megamolbart import NeMoMegaMolBARTWrapper
import megamolbart_pb2_grpc
from megamolbart_pb2 import OutputSpec
logger = logging.getLogger(__name__)
class InferenceService(megamolbart_pb2_g... | MegaMolBART-dev | nemo_chem/models/megamolbart/grpc/service.py |
MegaMolBART-dev | nemo_chem/models/megamolbart/grpc/__init__.py |
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