414 lines
16 KiB
Python
414 lines
16 KiB
Python
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# Pytest customization
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import json
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import os
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import warnings
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import tempfile
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from contextlib import contextmanager
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import numpy as np
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import numpy.testing as npt
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import pytest
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import hypothesis
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from scipy._lib._fpumode import get_fpu_mode
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from scipy._lib._testutils import FPUModeChangeWarning
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from scipy._lib._array_api import SCIPY_ARRAY_API, SCIPY_DEVICE
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from scipy._lib import _pep440
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try:
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from scipy_doctest.conftest import dt_config
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HAVE_SCPDT = True
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except ModuleNotFoundError:
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HAVE_SCPDT = False
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def pytest_configure(config):
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config.addinivalue_line("markers",
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"slow: Tests that are very slow.")
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config.addinivalue_line("markers",
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"xslow: mark test as extremely slow (not run unless explicitly requested)")
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config.addinivalue_line("markers",
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"xfail_on_32bit: mark test as failing on 32-bit platforms")
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try:
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import pytest_timeout # noqa:F401
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except Exception:
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config.addinivalue_line(
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"markers", 'timeout: mark a test for a non-default timeout')
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try:
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# This is a more reliable test of whether pytest_fail_slow is installed
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# When I uninstalled it, `import pytest_fail_slow` didn't fail!
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from pytest_fail_slow import parse_duration # type: ignore[import-not-found] # noqa:F401,E501
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except Exception:
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config.addinivalue_line(
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"markers", 'fail_slow: mark a test for a non-default timeout failure')
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config.addinivalue_line("markers",
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"skip_xp_backends(*backends, reasons=None, np_only=False, cpu_only=False): "
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"mark the desired skip configuration for the `skip_xp_backends` fixture.")
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def pytest_runtest_setup(item):
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mark = item.get_closest_marker("xslow")
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if mark is not None:
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try:
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v = int(os.environ.get('SCIPY_XSLOW', '0'))
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except ValueError:
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v = False
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if not v:
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pytest.skip("very slow test; "
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"set environment variable SCIPY_XSLOW=1 to run it")
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mark = item.get_closest_marker("xfail_on_32bit")
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if mark is not None and np.intp(0).itemsize < 8:
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pytest.xfail(f'Fails on our 32-bit test platform(s): {mark.args[0]}')
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# Older versions of threadpoolctl have an issue that may lead to this
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# warning being emitted, see gh-14441
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with npt.suppress_warnings() as sup:
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sup.filter(pytest.PytestUnraisableExceptionWarning)
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try:
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from threadpoolctl import threadpool_limits
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HAS_THREADPOOLCTL = True
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except Exception: # observed in gh-14441: (ImportError, AttributeError)
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# Optional dependency only. All exceptions are caught, for robustness
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HAS_THREADPOOLCTL = False
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if HAS_THREADPOOLCTL:
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# Set the number of openmp threads based on the number of workers
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# xdist is using to prevent oversubscription. Simplified version of what
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# sklearn does (it can rely on threadpoolctl and its builtin OpenMP helper
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# functions)
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try:
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xdist_worker_count = int(os.environ['PYTEST_XDIST_WORKER_COUNT'])
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except KeyError:
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# raises when pytest-xdist is not installed
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return
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if not os.getenv('OMP_NUM_THREADS'):
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max_openmp_threads = os.cpu_count() // 2 # use nr of physical cores
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threads_per_worker = max(max_openmp_threads // xdist_worker_count, 1)
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try:
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threadpool_limits(threads_per_worker, user_api='blas')
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except Exception:
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# May raise AttributeError for older versions of OpenBLAS.
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# Catch any error for robustness.
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return
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@pytest.fixture(scope="function", autouse=True)
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def check_fpu_mode(request):
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"""
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Check FPU mode was not changed during the test.
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"""
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old_mode = get_fpu_mode()
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yield
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new_mode = get_fpu_mode()
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if old_mode != new_mode:
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warnings.warn(f"FPU mode changed from {old_mode:#x} to {new_mode:#x} during "
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"the test",
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category=FPUModeChangeWarning, stacklevel=0)
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# Array API backend handling
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xp_available_backends = {'numpy': np}
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if SCIPY_ARRAY_API and isinstance(SCIPY_ARRAY_API, str):
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# fill the dict of backends with available libraries
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try:
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import array_api_strict
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xp_available_backends.update({'array_api_strict': array_api_strict})
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if _pep440.parse(array_api_strict.__version__) < _pep440.Version('2.0'):
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raise ImportError("array-api-strict must be >= version 2.0")
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array_api_strict.set_array_api_strict_flags(
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api_version='2023.12'
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)
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except ImportError:
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pass
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try:
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import torch # type: ignore[import-not-found]
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xp_available_backends.update({'pytorch': torch})
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# can use `mps` or `cpu`
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torch.set_default_device(SCIPY_DEVICE)
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except ImportError:
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pass
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try:
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import cupy # type: ignore[import-not-found]
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xp_available_backends.update({'cupy': cupy})
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except ImportError:
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pass
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try:
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import jax.numpy # type: ignore[import-not-found]
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xp_available_backends.update({'jax.numpy': jax.numpy})
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jax.config.update("jax_enable_x64", True)
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jax.config.update("jax_default_device", jax.devices(SCIPY_DEVICE)[0])
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except ImportError:
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pass
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# by default, use all available backends
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if SCIPY_ARRAY_API.lower() not in ("1", "true"):
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SCIPY_ARRAY_API_ = json.loads(SCIPY_ARRAY_API)
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if 'all' in SCIPY_ARRAY_API_:
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pass # same as True
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else:
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# only select a subset of backend by filtering out the dict
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try:
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xp_available_backends = {
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backend: xp_available_backends[backend]
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for backend in SCIPY_ARRAY_API_
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}
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except KeyError:
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msg = f"'--array-api-backend' must be in {xp_available_backends.keys()}"
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raise ValueError(msg)
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if 'cupy' in xp_available_backends:
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SCIPY_DEVICE = 'cuda'
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array_api_compatible = pytest.mark.parametrize("xp", xp_available_backends.values())
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skip_xp_invalid_arg = pytest.mark.skipif(SCIPY_ARRAY_API,
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reason = ('Test involves masked arrays, object arrays, or other types '
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'that are not valid input when `SCIPY_ARRAY_API` is used.'))
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@pytest.fixture
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def skip_xp_backends(xp, request):
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"""
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Skip based on the ``skip_xp_backends`` marker.
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Parameters
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----------
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*backends : tuple
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Backends to skip, e.g. ``("array_api_strict", "torch")``.
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These are overriden when ``np_only`` is ``True``, and are not
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necessary to provide for non-CPU backends when ``cpu_only`` is ``True``.
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reasons : list, optional
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A list of reasons for each skip. When ``np_only`` is ``True``,
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this should be a singleton list. Otherwise, this should be a list
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of reasons, one for each corresponding backend in ``backends``.
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If unprovided, default reasons are used. Note that it is not possible
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to specify a custom reason with ``cpu_only``. Default: ``None``.
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np_only : bool, optional
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When ``True``, the test is skipped for all backends other
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than the default NumPy backend. There is no need to provide
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any ``backends`` in this case. To specify a reason, pass a
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singleton list to ``reasons``. Default: ``False``.
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cpu_only : bool, optional
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When ``True``, the test is skipped on non-CPU devices.
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There is no need to provide any ``backends`` in this case,
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but any ``backends`` will also be skipped on the CPU.
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Default: ``False``.
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"""
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if "skip_xp_backends" not in request.keywords:
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return
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backends = request.keywords["skip_xp_backends"].args
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kwargs = request.keywords["skip_xp_backends"].kwargs
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np_only = kwargs.get("np_only", False)
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cpu_only = kwargs.get("cpu_only", False)
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if np_only:
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reasons = kwargs.get("reasons", ["do not run with non-NumPy backends."])
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reason = reasons[0]
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if xp.__name__ != 'numpy':
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pytest.skip(reason=reason)
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return
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if cpu_only:
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reason = "do not run with `SCIPY_ARRAY_API` set and not on CPU"
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if SCIPY_ARRAY_API and SCIPY_DEVICE != 'cpu':
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if xp.__name__ == 'cupy':
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pytest.skip(reason=reason)
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elif xp.__name__ == 'torch':
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if 'cpu' not in xp.empty(0).device.type:
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pytest.skip(reason=reason)
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elif xp.__name__ == 'jax.numpy':
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for d in xp.empty(0).devices():
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if 'cpu' not in d.device_kind:
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pytest.skip(reason=reason)
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if backends is not None:
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reasons = kwargs.get("reasons", False)
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for i, backend in enumerate(backends):
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if xp.__name__ == backend:
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if not reasons:
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reason = f"do not run with array API backend: {backend}"
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else:
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reason = reasons[i]
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pytest.skip(reason=reason)
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# Following the approach of NumPy's conftest.py...
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# Use a known and persistent tmpdir for hypothesis' caches, which
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# can be automatically cleared by the OS or user.
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hypothesis.configuration.set_hypothesis_home_dir(
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os.path.join(tempfile.gettempdir(), ".hypothesis")
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)
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# We register two custom profiles for SciPy - for details see
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# https://hypothesis.readthedocs.io/en/latest/settings.html
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# The first is designed for our own CI runs; the latter also
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# forces determinism and is designed for use via scipy.test()
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hypothesis.settings.register_profile(
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name="nondeterministic", deadline=None, print_blob=True,
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)
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hypothesis.settings.register_profile(
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name="deterministic",
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deadline=None, print_blob=True, database=None, derandomize=True,
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suppress_health_check=list(hypothesis.HealthCheck),
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)
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# Profile is currently set by environment variable `SCIPY_HYPOTHESIS_PROFILE`
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# In the future, it would be good to work the choice into dev.py.
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SCIPY_HYPOTHESIS_PROFILE = os.environ.get("SCIPY_HYPOTHESIS_PROFILE",
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"deterministic")
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hypothesis.settings.load_profile(SCIPY_HYPOTHESIS_PROFILE)
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############################################################################
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# doctesting stuff
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if HAVE_SCPDT:
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# FIXME: populate the dict once
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@contextmanager
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def warnings_errors_and_rng(test=None):
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"""Temporarily turn (almost) all warnings to errors.
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Filter out known warnings which we allow.
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"""
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known_warnings = dict()
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# these functions are known to emit "divide by zero" RuntimeWarnings
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divide_by_zero = [
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'scipy.linalg.norm', 'scipy.ndimage.center_of_mass',
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]
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for name in divide_by_zero:
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known_warnings[name] = dict(category=RuntimeWarning,
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message='divide by zero')
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# Deprecated stuff in scipy.signal and elsewhere
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deprecated = [
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'scipy.signal.cwt', 'scipy.signal.morlet', 'scipy.signal.morlet2',
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'scipy.signal.ricker',
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'scipy.integrate.simpson',
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'scipy.interpolate.interp2d',
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]
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for name in deprecated:
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known_warnings[name] = dict(category=DeprecationWarning)
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from scipy import integrate
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# the funcions are known to emit IntergrationWarnings
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integration_w = ['scipy.special.ellip_normal',
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'scipy.special.ellip_harm_2',
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]
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for name in integration_w:
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known_warnings[name] = dict(category=integrate.IntegrationWarning,
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message='The occurrence of roundoff')
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# scipy.stats deliberately emits UserWarnings sometimes
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user_w = ['scipy.stats.anderson_ksamp', 'scipy.stats.kurtosistest',
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'scipy.stats.normaltest', 'scipy.sparse.linalg.norm']
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for name in user_w:
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known_warnings[name] = dict(category=UserWarning)
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# additional one-off warnings to filter
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dct = {
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'scipy.sparse.linalg.norm':
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dict(category=UserWarning, message="Exited at iteration"),
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# tutorials
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'linalg.rst':
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dict(message='the matrix subclass is not',
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category=PendingDeprecationWarning),
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'stats.rst':
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dict(message='The maximum number of subdivisions',
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category=integrate.IntegrationWarning),
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}
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known_warnings.update(dct)
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# these legitimately emit warnings in examples
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legit = set('scipy.signal.normalize')
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# Now, the meat of the matter: filter warnings,
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# also control the random seed for each doctest.
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# XXX: this matches the refguide-check behavior, but is a tad strange:
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# makes sure that the seed the old-fashioned np.random* methods is
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# *NOT* reproducible but the new-style `default_rng()` *IS* repoducible.
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# Should these two be either both repro or both not repro?
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from scipy._lib._util import _fixed_default_rng
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import numpy as np
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with _fixed_default_rng():
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np.random.seed(None)
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with warnings.catch_warnings():
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if test and test.name in known_warnings:
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warnings.filterwarnings('ignore',
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**known_warnings[test.name])
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yield
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elif test and test.name in legit:
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||
|
|
yield
|
||
|
|
else:
|
||
|
|
warnings.simplefilter('error', Warning)
|
||
|
|
yield
|
||
|
|
|
||
|
|
|
||
|
|
dt_config.user_context_mgr = warnings_errors_and_rng
|
||
|
|
dt_config.skiplist = set([
|
||
|
|
'scipy.linalg.LinAlgError', # comes from numpy
|
||
|
|
'scipy.fftpack.fftshift', # fftpack stuff is also from numpy
|
||
|
|
'scipy.fftpack.ifftshift',
|
||
|
|
'scipy.fftpack.fftfreq',
|
||
|
|
'scipy.special.sinc', # sinc is from numpy
|
||
|
|
'scipy.optimize.show_options', # does not have much to doctest
|
||
|
|
'scipy.signal.normalize', # manipulates warnings (XXX temp skip)
|
||
|
|
'scipy.sparse.linalg.norm', # XXX temp skip
|
||
|
|
])
|
||
|
|
|
||
|
|
# these are affected by NumPy 2.0 scalar repr: rely on string comparison
|
||
|
|
if np.__version__ < "2":
|
||
|
|
dt_config.skiplist.update(set([
|
||
|
|
'scipy.io.hb_read',
|
||
|
|
'scipy.io.hb_write',
|
||
|
|
'scipy.sparse.csgraph.connected_components',
|
||
|
|
'scipy.sparse.csgraph.depth_first_order',
|
||
|
|
'scipy.sparse.csgraph.shortest_path',
|
||
|
|
'scipy.sparse.csgraph.floyd_warshall',
|
||
|
|
'scipy.sparse.csgraph.dijkstra',
|
||
|
|
'scipy.sparse.csgraph.bellman_ford',
|
||
|
|
'scipy.sparse.csgraph.johnson',
|
||
|
|
'scipy.sparse.csgraph.yen',
|
||
|
|
'scipy.sparse.csgraph.breadth_first_order',
|
||
|
|
'scipy.sparse.csgraph.reverse_cuthill_mckee',
|
||
|
|
'scipy.sparse.csgraph.structural_rank',
|
||
|
|
'scipy.sparse.csgraph.construct_dist_matrix',
|
||
|
|
'scipy.sparse.csgraph.reconstruct_path',
|
||
|
|
'scipy.ndimage.value_indices',
|
||
|
|
'scipy.stats.mstats.describe',
|
||
|
|
]))
|
||
|
|
|
||
|
|
# help pytest collection a bit: these names are either private
|
||
|
|
# (distributions), or just do not need doctesting.
|
||
|
|
dt_config.pytest_extra_ignore = [
|
||
|
|
"scipy.stats.distributions",
|
||
|
|
"scipy.optimize.cython_optimize",
|
||
|
|
"scipy.test",
|
||
|
|
"scipy.show_config",
|
||
|
|
]
|
||
|
|
|
||
|
|
dt_config.pytest_extra_xfail = {
|
||
|
|
# name: reason
|
||
|
|
"io.rst": "",
|
||
|
|
"ND_regular_grid.rst": "ReST parser limitation",
|
||
|
|
"extrapolation_examples.rst": "ReST parser limitation",
|
||
|
|
"sampling_pinv.rst": "__cinit__ unexpected argument",
|
||
|
|
"sampling_srou.rst": "nan in scalar_power",
|
||
|
|
"probability_distributions.rst": "integration warning",
|
||
|
|
}
|
||
|
|
|
||
|
|
# tutorials
|
||
|
|
dt_config.pseudocode = set(['integrate.nquad(func,'])
|
||
|
|
dt_config.local_resources = {'io.rst': ["octave_a.mat"]}
|
||
|
|
############################################################################
|