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"""Configure global settings and get information about the working environment."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
# Machine learning module for Python
# ==================================
#
# sklearn is a Python module integrating classical machine
# learning algorithms in the tightly-knit world of scientific Python
# packages (numpy, scipy, matplotlib).
#
# It aims to provide simple and efficient solutions to learning problems
# that are accessible to everybody and reusable in various contexts:
# machine-learning as a versatile tool for science and engineering.
#
# See https://scikit-learn.org for complete documentation.
import importlib as _importlib
import logging
import os
import random
from ._config import config_context, get_config, set_config
logger = logging.getLogger(__name__)
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
# X.Y.0 # For first release after an increment in Y
# X.Y.Z # For bugfix releases
#
# Admissible pre-release markers:
# X.Y.ZaN # Alpha release
# X.Y.ZbN # Beta release
# X.Y.ZrcN # Release Candidate
# X.Y.Z # Final release
#
# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
#
__version__ = "1.6.1"
# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
# simultaneously. This can happen for instance when calling BLAS inside a
# prange. Setting the following environment variable allows multiple OpenMP
# libraries to be loaded. It should not degrade performances since we manually
# take care of potential over-subcription performance issues, in sections of
# the code where nested OpenMP loops can happen, by dynamically reconfiguring
# the inner OpenMP runtime to temporarily disable it while under the scope of
# the outer OpenMP parallel section.
os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "True")
# Workaround issue discovered in intel-openmp 2019.5:
# https://github.com/ContinuumIO/anaconda-issues/issues/11294
os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
# `_distributor_init` allows distributors to run custom init code.
# For instance, for the Windows wheel, this is used to pre-load the
# vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
# sub-folder.
# It is necessary to do this prior to importing show_versions as the
# later is linked to the OpenMP runtime to make it possible to introspect
# it and importing it first would fail if the OpenMP dll cannot be found.
from . import ( # noqa: F401 E402
__check_build,
_distributor_init,
)
from .base import clone # noqa: E402
from .utils._show_versions import show_versions # noqa: E402
_submodules = [
"calibration",
"cluster",
"covariance",
"cross_decomposition",
"datasets",
"decomposition",
"dummy",
"ensemble",
"exceptions",
"experimental",
"externals",
"feature_extraction",
"feature_selection",
"frozen",
"gaussian_process",
"inspection",
"isotonic",
"kernel_approximation",
"kernel_ridge",
"linear_model",
"manifold",
"metrics",
"mixture",
"model_selection",
"multiclass",
"multioutput",
"naive_bayes",
"neighbors",
"neural_network",
"pipeline",
"preprocessing",
"random_projection",
"semi_supervised",
"svm",
"tree",
"discriminant_analysis",
"impute",
"compose",
]
__all__ = _submodules + [
# Non-modules:
"clone",
"get_config",
"set_config",
"config_context",
"show_versions",
]
def __dir__():
return __all__
def __getattr__(name):
if name in _submodules:
return _importlib.import_module(f"sklearn.{name}")
else:
try:
return globals()[name]
except KeyError:
raise AttributeError(f"Module 'sklearn' has no attribute '{name}'")
_BUILT_WITH_MESON = False
try:
import sklearn._built_with_meson # noqa: F401
_BUILT_WITH_MESON = True
except ModuleNotFoundError:
pass
def setup_module(module):
"""Fixture for the tests to assure globally controllable seeding of RNGs"""
import numpy as np
# Check if a random seed exists in the environment, if not create one.
_random_seed = os.environ.get("SKLEARN_SEED", None)
if _random_seed is None:
_random_seed = np.random.uniform() * np.iinfo(np.int32).max
_random_seed = int(_random_seed)
print("I: Seeding RNGs with %r" % _random_seed)
np.random.seed(_random_seed)
random.seed(_random_seed)
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