196 lines
6.2 KiB
Meson
196 lines
6.2 KiB
Meson
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fs = import('fs')
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cython_args = []
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# Platform detection
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is_windows = host_machine.system() == 'windows'
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is_mingw = is_windows and cc.get_id() == 'gcc'
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# Adapted from Scipy. mingw is untested and not officially supported. If you
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# ever bump into issues when trying to compile for mingw, please open an issue
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# in the scikit-learn issue tracker
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if is_mingw
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# For mingw-w64, link statically against the UCRT.
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gcc_link_args = ['-lucrt', '-static']
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add_project_link_arguments(gcc_link_args, language: ['c', 'cpp'])
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# Force gcc to float64 long doubles for compatibility with MSVC
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# builds, for C only.
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add_project_arguments('-mlong-double-64', language: 'c')
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endif
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# Adapted from scipy, each project seems to have its own tweaks for this. One
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# day using dependency('numpy') will be a thing, see
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# https://github.com/mesonbuild/meson/issues/9598.
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# NumPy include directory - needed in all submodules
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# Relative paths are needed when for example a virtualenv is
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# placed inside the source tree; Meson rejects absolute paths to places inside
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# the source tree. The try-except is needed because when things are split
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# across drives on Windows, there is no relative path and an exception gets
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# raised. There may be other such cases, so add a catch-all and switch to
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# an absolute path.
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# For cross-compilation it is often not possible to run the Python interpreter
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# in order to retrieve numpy's include directory. It can be specified in the
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# cross file instead:
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# [properties]
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# numpy-include-dir = /abspath/to/host-pythons/site-packages/numpy/core/include
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#
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# This uses the path as is, and avoids running the interpreter.
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incdir_numpy = meson.get_external_property('numpy-include-dir', 'not-given')
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if incdir_numpy == 'not-given'
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incdir_numpy = run_command(py,
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[
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'-c',
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'''
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import os
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import numpy as np
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try:
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incdir = os.path.relpath(np.get_include())
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except Exception:
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incdir = np.get_include()
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print(incdir)
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'''
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],
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check: true
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).stdout().strip()
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endif
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inc_np = include_directories(incdir_numpy)
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np_dep = declare_dependency(include_directories: inc_np)
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openmp_dep = dependency('OpenMP', language: 'c', required: false)
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if not openmp_dep.found()
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warn_about_missing_openmp = true
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# On Apple Clang avoid a misleading warning if compiler variables are set.
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# See https://github.com/scikit-learn/scikit-learn/issues/28710 for more
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# details. This may be removed if the OpenMP detection on Apple Clang improves,
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# see https://github.com/mesonbuild/meson/issues/7435#issuecomment-2047585466.
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if host_machine.system() == 'darwin' and cc.get_id() == 'clang'
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compiler_env_vars_with_openmp = run_command(py,
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[
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'-c',
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'''
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import os
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compiler_env_vars_to_check = ["CPPFLAGS", "CFLAGS", "CXXFLAGS"]
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compiler_env_vars_with_openmp = [
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var for var in compiler_env_vars_to_check if "-fopenmp" in os.getenv(var, "")]
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print(compiler_env_vars_with_openmp)
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'''], check: true).stdout().strip()
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warn_about_missing_openmp = compiler_env_vars_with_openmp == '[]'
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endif
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if warn_about_missing_openmp
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warning(
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'''
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***********
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* WARNING *
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***********
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It seems that scikit-learn cannot be built with OpenMP.
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- Make sure you have followed the installation instructions:
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https://scikit-learn.org/dev/developers/advanced_installation.html
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- If your compiler supports OpenMP but you still see this
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message, please submit a bug report at:
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https://github.com/scikit-learn/scikit-learn/issues
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- The build will continue with OpenMP-based parallelism
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disabled. Note however that some estimators will run in
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sequential mode instead of leveraging thread-based
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parallelism.
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***
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''')
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else
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warning(
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'''It looks like compiler environment variables were set to enable OpenMP support.
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Check the output of "import sklearn; sklearn.show_versions()" after the build
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to make sure that scikit-learn was actually built with OpenMP support.
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''')
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endif
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endif
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# For now, we keep supporting SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES variable
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# (see how it is done in sklearn/_build_utils/__init__.py when building with
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# setuptools). Accessing environment variables in meson.build is discouraged,
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# so once we drop setuptools this functionality should be behind a meson option
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# or buildtype
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boundscheck = run_command(py,
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[
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'-c',
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'''
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import os
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if os.environ.get("SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES", "0") != "0":
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print(True)
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else:
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print(False)
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'''
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],
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check: true
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).stdout().strip()
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scikit_learn_cython_args = [
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'-X language_level=3', '-X boundscheck=' + boundscheck, '-X wraparound=False',
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'-X initializedcheck=False', '-X nonecheck=False', '-X cdivision=True',
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'-X profile=False',
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# Needed for cython imports across subpackages, e.g. cluster pyx that
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# cimports metrics pxd
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'--include-dir', meson.global_build_root(),
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]
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cython_args += scikit_learn_cython_args
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# Write file in Meson build dir to be able to figure out from Python code
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# whether scikit-learn was built with Meson. Adapted from pandas
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# _version_meson.py.
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custom_target('write_built_with_meson_file',
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output: '_built_with_meson.py',
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command: [
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py, '-c', 'with open("sklearn/_built_with_meson.py", "w") as f: f.write("")'
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],
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install: true,
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install_dir: py.get_install_dir() / 'sklearn'
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)
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extensions = ['_isotonic']
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py.extension_module(
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'_isotonic',
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'_isotonic.pyx',
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cython_args: cython_args,
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install: true,
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subdir: 'sklearn',
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)
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# Need for Cython cimports across subpackages to work, i.e. avoid errors like
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# relative cimport from non-package directory is not allowed
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sklearn_root_cython_tree = [
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fs.copyfile('__init__.py')
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]
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sklearn_dir = py.get_install_dir() / 'sklearn'
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# Subpackages are mostly in alphabetical order except to handle Cython
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# dependencies across subpackages
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subdir('__check_build')
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subdir('_loss')
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# utils needs to be early since plenty of other modules cimports utils .pxd
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subdir('utils')
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# metrics needs to be to be before cluster since cluster cimports metrics .pxd
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subdir('metrics')
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subdir('cluster')
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subdir('datasets')
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subdir('decomposition')
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subdir('ensemble')
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subdir('feature_extraction')
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subdir('linear_model')
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subdir('manifold')
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subdir('neighbors')
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subdir('preprocessing')
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subdir('svm')
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subdir('tree')
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