1195 lines
40 KiB
Python
1195 lines
40 KiB
Python
|
"""Testing utilities."""
|
||
|
|
|
||
|
|
# Copyright (c) 2011, 2012
|
||
|
|
# Authors: Pietro Berkes,
|
||
|
|
# Andreas Muller
|
||
|
|
# Mathieu Blondel
|
||
|
|
# Olivier Grisel
|
||
|
|
# Arnaud Joly
|
||
|
|
# Denis Engemann
|
||
|
|
# Giorgio Patrini
|
||
|
|
# Thierry Guillemot
|
||
|
|
# License: BSD 3 clause
|
||
|
|
import atexit
|
||
|
|
import contextlib
|
||
|
|
import functools
|
||
|
|
import importlib
|
||
|
|
import inspect
|
||
|
|
import os
|
||
|
|
import os.path as op
|
||
|
|
import re
|
||
|
|
import shutil
|
||
|
|
import sys
|
||
|
|
import tempfile
|
||
|
|
import unittest
|
||
|
|
import warnings
|
||
|
|
from collections.abc import Iterable
|
||
|
|
from dataclasses import dataclass
|
||
|
|
from functools import wraps
|
||
|
|
from inspect import signature
|
||
|
|
from subprocess import STDOUT, CalledProcessError, TimeoutExpired, check_output
|
||
|
|
from unittest import TestCase
|
||
|
|
|
||
|
|
import joblib
|
||
|
|
import numpy as np
|
||
|
|
import scipy as sp
|
||
|
|
from numpy.testing import assert_allclose as np_assert_allclose
|
||
|
|
from numpy.testing import (
|
||
|
|
assert_almost_equal,
|
||
|
|
assert_approx_equal,
|
||
|
|
assert_array_almost_equal,
|
||
|
|
assert_array_equal,
|
||
|
|
assert_array_less,
|
||
|
|
assert_no_warnings,
|
||
|
|
)
|
||
|
|
|
||
|
|
import sklearn
|
||
|
|
from sklearn.utils._array_api import _check_array_api_dispatch
|
||
|
|
from sklearn.utils.fixes import (
|
||
|
|
_IS_32BIT,
|
||
|
|
_IS_PYPY,
|
||
|
|
VisibleDeprecationWarning,
|
||
|
|
_in_unstable_openblas_configuration,
|
||
|
|
parse_version,
|
||
|
|
sp_version,
|
||
|
|
)
|
||
|
|
from sklearn.utils.multiclass import check_classification_targets
|
||
|
|
from sklearn.utils.validation import (
|
||
|
|
check_array,
|
||
|
|
check_is_fitted,
|
||
|
|
check_X_y,
|
||
|
|
)
|
||
|
|
|
||
|
|
__all__ = [
|
||
|
|
"assert_raises",
|
||
|
|
"assert_raises_regexp",
|
||
|
|
"assert_array_equal",
|
||
|
|
"assert_almost_equal",
|
||
|
|
"assert_array_almost_equal",
|
||
|
|
"assert_array_less",
|
||
|
|
"assert_approx_equal",
|
||
|
|
"assert_allclose",
|
||
|
|
"assert_run_python_script_without_output",
|
||
|
|
"assert_no_warnings",
|
||
|
|
"SkipTest",
|
||
|
|
]
|
||
|
|
|
||
|
|
_dummy = TestCase("__init__")
|
||
|
|
assert_raises = _dummy.assertRaises
|
||
|
|
SkipTest = unittest.case.SkipTest
|
||
|
|
assert_dict_equal = _dummy.assertDictEqual
|
||
|
|
|
||
|
|
assert_raises_regex = _dummy.assertRaisesRegex
|
||
|
|
# assert_raises_regexp is deprecated in Python 3.4 in favor of
|
||
|
|
# assert_raises_regex but lets keep the backward compat in scikit-learn with
|
||
|
|
# the old name for now
|
||
|
|
assert_raises_regexp = assert_raises_regex
|
||
|
|
|
||
|
|
|
||
|
|
def ignore_warnings(obj=None, category=Warning):
|
||
|
|
"""Context manager and decorator to ignore warnings.
|
||
|
|
|
||
|
|
Note: Using this (in both variants) will clear all warnings
|
||
|
|
from all python modules loaded. In case you need to test
|
||
|
|
cross-module-warning-logging, this is not your tool of choice.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
obj : callable, default=None
|
||
|
|
callable where you want to ignore the warnings.
|
||
|
|
category : warning class, default=Warning
|
||
|
|
The category to filter. If Warning, all categories will be muted.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import warnings
|
||
|
|
>>> from sklearn.utils._testing import ignore_warnings
|
||
|
|
>>> with ignore_warnings():
|
||
|
|
... warnings.warn('buhuhuhu')
|
||
|
|
|
||
|
|
>>> def nasty_warn():
|
||
|
|
... warnings.warn('buhuhuhu')
|
||
|
|
... print(42)
|
||
|
|
|
||
|
|
>>> ignore_warnings(nasty_warn)()
|
||
|
|
42
|
||
|
|
"""
|
||
|
|
if isinstance(obj, type) and issubclass(obj, Warning):
|
||
|
|
# Avoid common pitfall of passing category as the first positional
|
||
|
|
# argument which result in the test not being run
|
||
|
|
warning_name = obj.__name__
|
||
|
|
raise ValueError(
|
||
|
|
"'obj' should be a callable where you want to ignore warnings. "
|
||
|
|
"You passed a warning class instead: 'obj={warning_name}'. "
|
||
|
|
"If you want to pass a warning class to ignore_warnings, "
|
||
|
|
"you should use 'category={warning_name}'".format(warning_name=warning_name)
|
||
|
|
)
|
||
|
|
elif callable(obj):
|
||
|
|
return _IgnoreWarnings(category=category)(obj)
|
||
|
|
else:
|
||
|
|
return _IgnoreWarnings(category=category)
|
||
|
|
|
||
|
|
|
||
|
|
class _IgnoreWarnings:
|
||
|
|
"""Improved and simplified Python warnings context manager and decorator.
|
||
|
|
|
||
|
|
This class allows the user to ignore the warnings raised by a function.
|
||
|
|
Copied from Python 2.7.5 and modified as required.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
category : tuple of warning class, default=Warning
|
||
|
|
The category to filter. By default, all the categories will be muted.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, category):
|
||
|
|
self._record = True
|
||
|
|
self._module = sys.modules["warnings"]
|
||
|
|
self._entered = False
|
||
|
|
self.log = []
|
||
|
|
self.category = category
|
||
|
|
|
||
|
|
def __call__(self, fn):
|
||
|
|
"""Decorator to catch and hide warnings without visual nesting."""
|
||
|
|
|
||
|
|
@wraps(fn)
|
||
|
|
def wrapper(*args, **kwargs):
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("ignore", self.category)
|
||
|
|
return fn(*args, **kwargs)
|
||
|
|
|
||
|
|
return wrapper
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
args = []
|
||
|
|
if self._record:
|
||
|
|
args.append("record=True")
|
||
|
|
if self._module is not sys.modules["warnings"]:
|
||
|
|
args.append("module=%r" % self._module)
|
||
|
|
name = type(self).__name__
|
||
|
|
return "%s(%s)" % (name, ", ".join(args))
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
if self._entered:
|
||
|
|
raise RuntimeError("Cannot enter %r twice" % self)
|
||
|
|
self._entered = True
|
||
|
|
self._filters = self._module.filters
|
||
|
|
self._module.filters = self._filters[:]
|
||
|
|
self._showwarning = self._module.showwarning
|
||
|
|
warnings.simplefilter("ignore", self.category)
|
||
|
|
|
||
|
|
def __exit__(self, *exc_info):
|
||
|
|
if not self._entered:
|
||
|
|
raise RuntimeError("Cannot exit %r without entering first" % self)
|
||
|
|
self._module.filters = self._filters
|
||
|
|
self._module.showwarning = self._showwarning
|
||
|
|
self.log[:] = []
|
||
|
|
|
||
|
|
|
||
|
|
def assert_raise_message(exceptions, message, function, *args, **kwargs):
|
||
|
|
"""Helper function to test the message raised in an exception.
|
||
|
|
|
||
|
|
Given an exception, a callable to raise the exception, and
|
||
|
|
a message string, tests that the correct exception is raised and
|
||
|
|
that the message is a substring of the error thrown. Used to test
|
||
|
|
that the specific message thrown during an exception is correct.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
exceptions : exception or tuple of exception
|
||
|
|
An Exception object.
|
||
|
|
|
||
|
|
message : str
|
||
|
|
The error message or a substring of the error message.
|
||
|
|
|
||
|
|
function : callable
|
||
|
|
Callable object to raise error.
|
||
|
|
|
||
|
|
*args : the positional arguments to `function`.
|
||
|
|
|
||
|
|
**kwargs : the keyword arguments to `function`.
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
function(*args, **kwargs)
|
||
|
|
except exceptions as e:
|
||
|
|
error_message = str(e)
|
||
|
|
if message not in error_message:
|
||
|
|
raise AssertionError(
|
||
|
|
"Error message does not include the expected"
|
||
|
|
" string: %r. Observed error message: %r" % (message, error_message)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# concatenate exception names
|
||
|
|
if isinstance(exceptions, tuple):
|
||
|
|
names = " or ".join(e.__name__ for e in exceptions)
|
||
|
|
else:
|
||
|
|
names = exceptions.__name__
|
||
|
|
|
||
|
|
raise AssertionError("%s not raised by %s" % (names, function.__name__))
|
||
|
|
|
||
|
|
|
||
|
|
def assert_allclose(
|
||
|
|
actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True
|
||
|
|
):
|
||
|
|
"""dtype-aware variant of numpy.testing.assert_allclose
|
||
|
|
|
||
|
|
This variant introspects the least precise floating point dtype
|
||
|
|
in the input argument and automatically sets the relative tolerance
|
||
|
|
parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64
|
||
|
|
in scikit-learn).
|
||
|
|
|
||
|
|
`atol` is always left to 0. by default. It should be adjusted manually
|
||
|
|
to an assertion-specific value in case there are null values expected
|
||
|
|
in `desired`.
|
||
|
|
|
||
|
|
The aggregate tolerance is `atol + rtol * abs(desired)`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
actual : array_like
|
||
|
|
Array obtained.
|
||
|
|
desired : array_like
|
||
|
|
Array desired.
|
||
|
|
rtol : float, optional, default=None
|
||
|
|
Relative tolerance.
|
||
|
|
If None, it is set based on the provided arrays' dtypes.
|
||
|
|
atol : float, optional, default=0.
|
||
|
|
Absolute tolerance.
|
||
|
|
equal_nan : bool, optional, default=True
|
||
|
|
If True, NaNs will compare equal.
|
||
|
|
err_msg : str, optional, default=''
|
||
|
|
The error message to be printed in case of failure.
|
||
|
|
verbose : bool, optional, default=True
|
||
|
|
If True, the conflicting values are appended to the error message.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
AssertionError
|
||
|
|
If actual and desired are not equal up to specified precision.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
numpy.testing.assert_allclose
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> from sklearn.utils._testing import assert_allclose
|
||
|
|
>>> x = [1e-5, 1e-3, 1e-1]
|
||
|
|
>>> y = np.arccos(np.cos(x))
|
||
|
|
>>> assert_allclose(x, y, rtol=1e-5, atol=0)
|
||
|
|
>>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32)
|
||
|
|
>>> assert_allclose(a, 1e-5)
|
||
|
|
"""
|
||
|
|
dtypes = []
|
||
|
|
|
||
|
|
actual, desired = np.asanyarray(actual), np.asanyarray(desired)
|
||
|
|
dtypes = [actual.dtype, desired.dtype]
|
||
|
|
|
||
|
|
if rtol is None:
|
||
|
|
rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes]
|
||
|
|
rtol = max(rtols)
|
||
|
|
|
||
|
|
np_assert_allclose(
|
||
|
|
actual,
|
||
|
|
desired,
|
||
|
|
rtol=rtol,
|
||
|
|
atol=atol,
|
||
|
|
equal_nan=equal_nan,
|
||
|
|
err_msg=err_msg,
|
||
|
|
verbose=verbose,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def assert_allclose_dense_sparse(x, y, rtol=1e-07, atol=1e-9, err_msg=""):
|
||
|
|
"""Assert allclose for sparse and dense data.
|
||
|
|
|
||
|
|
Both x and y need to be either sparse or dense, they
|
||
|
|
can't be mixed.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
x : {array-like, sparse matrix}
|
||
|
|
First array to compare.
|
||
|
|
|
||
|
|
y : {array-like, sparse matrix}
|
||
|
|
Second array to compare.
|
||
|
|
|
||
|
|
rtol : float, default=1e-07
|
||
|
|
relative tolerance; see numpy.allclose.
|
||
|
|
|
||
|
|
atol : float, default=1e-9
|
||
|
|
absolute tolerance; see numpy.allclose. Note that the default here is
|
||
|
|
more tolerant than the default for numpy.testing.assert_allclose, where
|
||
|
|
atol=0.
|
||
|
|
|
||
|
|
err_msg : str, default=''
|
||
|
|
Error message to raise.
|
||
|
|
"""
|
||
|
|
if sp.sparse.issparse(x) and sp.sparse.issparse(y):
|
||
|
|
x = x.tocsr()
|
||
|
|
y = y.tocsr()
|
||
|
|
x.sum_duplicates()
|
||
|
|
y.sum_duplicates()
|
||
|
|
assert_array_equal(x.indices, y.indices, err_msg=err_msg)
|
||
|
|
assert_array_equal(x.indptr, y.indptr, err_msg=err_msg)
|
||
|
|
assert_allclose(x.data, y.data, rtol=rtol, atol=atol, err_msg=err_msg)
|
||
|
|
elif not sp.sparse.issparse(x) and not sp.sparse.issparse(y):
|
||
|
|
# both dense
|
||
|
|
assert_allclose(x, y, rtol=rtol, atol=atol, err_msg=err_msg)
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Can only compare two sparse matrices, not a sparse matrix and an array."
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def set_random_state(estimator, random_state=0):
|
||
|
|
"""Set random state of an estimator if it has the `random_state` param.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
estimator : object
|
||
|
|
The estimator.
|
||
|
|
random_state : int, RandomState instance or None, default=0
|
||
|
|
Pseudo random number generator state.
|
||
|
|
Pass an int for reproducible results across multiple function calls.
|
||
|
|
See :term:`Glossary <random_state>`.
|
||
|
|
"""
|
||
|
|
if "random_state" in estimator.get_params():
|
||
|
|
estimator.set_params(random_state=random_state)
|
||
|
|
|
||
|
|
|
||
|
|
try:
|
||
|
|
_check_array_api_dispatch(True)
|
||
|
|
ARRAY_API_COMPAT_FUNCTIONAL = True
|
||
|
|
except ImportError:
|
||
|
|
ARRAY_API_COMPAT_FUNCTIONAL = False
|
||
|
|
|
||
|
|
try:
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
skip_if_32bit = pytest.mark.skipif(_IS_32BIT, reason="skipped on 32bit platforms")
|
||
|
|
fails_if_pypy = pytest.mark.xfail(_IS_PYPY, reason="not compatible with PyPy")
|
||
|
|
fails_if_unstable_openblas = pytest.mark.xfail(
|
||
|
|
_in_unstable_openblas_configuration(),
|
||
|
|
reason="OpenBLAS is unstable for this configuration",
|
||
|
|
)
|
||
|
|
skip_if_no_parallel = pytest.mark.skipif(
|
||
|
|
not joblib.parallel.mp, reason="joblib is in serial mode"
|
||
|
|
)
|
||
|
|
skip_if_array_api_compat_not_configured = pytest.mark.skipif(
|
||
|
|
not ARRAY_API_COMPAT_FUNCTIONAL,
|
||
|
|
reason="requires array_api_compat installed and a new enough version of NumPy",
|
||
|
|
)
|
||
|
|
|
||
|
|
# Decorator for tests involving both BLAS calls and multiprocessing.
|
||
|
|
#
|
||
|
|
# Under POSIX (e.g. Linux or OSX), using multiprocessing in conjunction
|
||
|
|
# with some implementation of BLAS (or other libraries that manage an
|
||
|
|
# internal posix thread pool) can cause a crash or a freeze of the Python
|
||
|
|
# process.
|
||
|
|
#
|
||
|
|
# In practice all known packaged distributions (from Linux distros or
|
||
|
|
# Anaconda) of BLAS under Linux seems to be safe. So we this problem seems
|
||
|
|
# to only impact OSX users.
|
||
|
|
#
|
||
|
|
# This wrapper makes it possible to skip tests that can possibly cause
|
||
|
|
# this crash under OS X with.
|
||
|
|
#
|
||
|
|
# Under Python 3.4+ it is possible to use the `forkserver` start method
|
||
|
|
# for multiprocessing to avoid this issue. However it can cause pickling
|
||
|
|
# errors on interactively defined functions. It therefore not enabled by
|
||
|
|
# default.
|
||
|
|
|
||
|
|
if_safe_multiprocessing_with_blas = pytest.mark.skipif(
|
||
|
|
sys.platform == "darwin", reason="Possible multi-process bug with some BLAS"
|
||
|
|
)
|
||
|
|
except ImportError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
|
||
|
|
def check_skip_network():
|
||
|
|
if int(os.environ.get("SKLEARN_SKIP_NETWORK_TESTS", 0)):
|
||
|
|
raise SkipTest("Text tutorial requires large dataset download")
|
||
|
|
|
||
|
|
|
||
|
|
def _delete_folder(folder_path, warn=False):
|
||
|
|
"""Utility function to cleanup a temporary folder if still existing.
|
||
|
|
|
||
|
|
Copy from joblib.pool (for independence).
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
if os.path.exists(folder_path):
|
||
|
|
# This can fail under windows,
|
||
|
|
# but will succeed when called by atexit
|
||
|
|
shutil.rmtree(folder_path)
|
||
|
|
except OSError:
|
||
|
|
if warn:
|
||
|
|
warnings.warn("Could not delete temporary folder %s" % folder_path)
|
||
|
|
|
||
|
|
|
||
|
|
class TempMemmap:
|
||
|
|
"""
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
data
|
||
|
|
mmap_mode : str, default='r'
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, data, mmap_mode="r"):
|
||
|
|
self.mmap_mode = mmap_mode
|
||
|
|
self.data = data
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
data_read_only, self.temp_folder = create_memmap_backed_data(
|
||
|
|
self.data, mmap_mode=self.mmap_mode, return_folder=True
|
||
|
|
)
|
||
|
|
return data_read_only
|
||
|
|
|
||
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
||
|
|
_delete_folder(self.temp_folder)
|
||
|
|
|
||
|
|
|
||
|
|
def create_memmap_backed_data(data, mmap_mode="r", return_folder=False):
|
||
|
|
"""
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
data
|
||
|
|
mmap_mode : str, default='r'
|
||
|
|
return_folder : bool, default=False
|
||
|
|
"""
|
||
|
|
temp_folder = tempfile.mkdtemp(prefix="sklearn_testing_")
|
||
|
|
atexit.register(functools.partial(_delete_folder, temp_folder, warn=True))
|
||
|
|
filename = op.join(temp_folder, "data.pkl")
|
||
|
|
joblib.dump(data, filename)
|
||
|
|
memmap_backed_data = joblib.load(filename, mmap_mode=mmap_mode)
|
||
|
|
result = (
|
||
|
|
memmap_backed_data if not return_folder else (memmap_backed_data, temp_folder)
|
||
|
|
)
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
# Utils to test docstrings
|
||
|
|
|
||
|
|
|
||
|
|
def _get_args(function, varargs=False):
|
||
|
|
"""Helper to get function arguments."""
|
||
|
|
|
||
|
|
try:
|
||
|
|
params = signature(function).parameters
|
||
|
|
except ValueError:
|
||
|
|
# Error on builtin C function
|
||
|
|
return []
|
||
|
|
args = [
|
||
|
|
key
|
||
|
|
for key, param in params.items()
|
||
|
|
if param.kind not in (param.VAR_POSITIONAL, param.VAR_KEYWORD)
|
||
|
|
]
|
||
|
|
if varargs:
|
||
|
|
varargs = [
|
||
|
|
param.name
|
||
|
|
for param in params.values()
|
||
|
|
if param.kind == param.VAR_POSITIONAL
|
||
|
|
]
|
||
|
|
if len(varargs) == 0:
|
||
|
|
varargs = None
|
||
|
|
return args, varargs
|
||
|
|
else:
|
||
|
|
return args
|
||
|
|
|
||
|
|
|
||
|
|
def _get_func_name(func):
|
||
|
|
"""Get function full name.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
func : callable
|
||
|
|
The function object.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
name : str
|
||
|
|
The function name.
|
||
|
|
"""
|
||
|
|
parts = []
|
||
|
|
module = inspect.getmodule(func)
|
||
|
|
if module:
|
||
|
|
parts.append(module.__name__)
|
||
|
|
|
||
|
|
qualname = func.__qualname__
|
||
|
|
if qualname != func.__name__:
|
||
|
|
parts.append(qualname[: qualname.find(".")])
|
||
|
|
|
||
|
|
parts.append(func.__name__)
|
||
|
|
return ".".join(parts)
|
||
|
|
|
||
|
|
|
||
|
|
def check_docstring_parameters(func, doc=None, ignore=None):
|
||
|
|
"""Helper to check docstring.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
func : callable
|
||
|
|
The function object to test.
|
||
|
|
doc : str, default=None
|
||
|
|
Docstring if it is passed manually to the test.
|
||
|
|
ignore : list, default=None
|
||
|
|
Parameters to ignore.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
incorrect : list
|
||
|
|
A list of string describing the incorrect results.
|
||
|
|
"""
|
||
|
|
from numpydoc import docscrape
|
||
|
|
|
||
|
|
incorrect = []
|
||
|
|
ignore = [] if ignore is None else ignore
|
||
|
|
|
||
|
|
func_name = _get_func_name(func)
|
||
|
|
if not func_name.startswith("sklearn.") or func_name.startswith(
|
||
|
|
"sklearn.externals"
|
||
|
|
):
|
||
|
|
return incorrect
|
||
|
|
# Don't check docstring for property-functions
|
||
|
|
if inspect.isdatadescriptor(func):
|
||
|
|
return incorrect
|
||
|
|
# Don't check docstring for setup / teardown pytest functions
|
||
|
|
if func_name.split(".")[-1] in ("setup_module", "teardown_module"):
|
||
|
|
return incorrect
|
||
|
|
# Dont check estimator_checks module
|
||
|
|
if func_name.split(".")[2] == "estimator_checks":
|
||
|
|
return incorrect
|
||
|
|
# Get the arguments from the function signature
|
||
|
|
param_signature = list(filter(lambda x: x not in ignore, _get_args(func)))
|
||
|
|
# drop self
|
||
|
|
if len(param_signature) > 0 and param_signature[0] == "self":
|
||
|
|
param_signature.remove("self")
|
||
|
|
|
||
|
|
# Analyze function's docstring
|
||
|
|
if doc is None:
|
||
|
|
records = []
|
||
|
|
with warnings.catch_warnings(record=True):
|
||
|
|
warnings.simplefilter("error", UserWarning)
|
||
|
|
try:
|
||
|
|
doc = docscrape.FunctionDoc(func)
|
||
|
|
except UserWarning as exp:
|
||
|
|
if "potentially wrong underline length" in str(exp):
|
||
|
|
# Catch warning raised as of numpydoc 1.2 when
|
||
|
|
# the underline length for a section of a docstring
|
||
|
|
# is not consistent.
|
||
|
|
message = str(exp).split("\n")[:3]
|
||
|
|
incorrect += [f"In function: {func_name}"] + message
|
||
|
|
return incorrect
|
||
|
|
records.append(str(exp))
|
||
|
|
except Exception as exp:
|
||
|
|
incorrect += [func_name + " parsing error: " + str(exp)]
|
||
|
|
return incorrect
|
||
|
|
if len(records):
|
||
|
|
raise RuntimeError("Error for %s:\n%s" % (func_name, records[0]))
|
||
|
|
|
||
|
|
param_docs = []
|
||
|
|
for name, type_definition, param_doc in doc["Parameters"]:
|
||
|
|
# Type hints are empty only if parameter name ended with :
|
||
|
|
if not type_definition.strip():
|
||
|
|
if ":" in name and name[: name.index(":")][-1:].strip():
|
||
|
|
incorrect += [
|
||
|
|
func_name
|
||
|
|
+ " There was no space between the param name and colon (%r)" % name
|
||
|
|
]
|
||
|
|
elif name.rstrip().endswith(":"):
|
||
|
|
incorrect += [
|
||
|
|
func_name
|
||
|
|
+ " Parameter %r has an empty type spec. Remove the colon"
|
||
|
|
% (name.lstrip())
|
||
|
|
]
|
||
|
|
|
||
|
|
# Create a list of parameters to compare with the parameters gotten
|
||
|
|
# from the func signature
|
||
|
|
if "*" not in name:
|
||
|
|
param_docs.append(name.split(":")[0].strip("` "))
|
||
|
|
|
||
|
|
# If one of the docstring's parameters had an error then return that
|
||
|
|
# incorrect message
|
||
|
|
if len(incorrect) > 0:
|
||
|
|
return incorrect
|
||
|
|
|
||
|
|
# Remove the parameters that should be ignored from list
|
||
|
|
param_docs = list(filter(lambda x: x not in ignore, param_docs))
|
||
|
|
|
||
|
|
# The following is derived from pytest, Copyright (c) 2004-2017 Holger
|
||
|
|
# Krekel and others, Licensed under MIT License. See
|
||
|
|
# https://github.com/pytest-dev/pytest
|
||
|
|
|
||
|
|
message = []
|
||
|
|
for i in range(min(len(param_docs), len(param_signature))):
|
||
|
|
if param_signature[i] != param_docs[i]:
|
||
|
|
message += [
|
||
|
|
"There's a parameter name mismatch in function"
|
||
|
|
" docstring w.r.t. function signature, at index %s"
|
||
|
|
" diff: %r != %r" % (i, param_signature[i], param_docs[i])
|
||
|
|
]
|
||
|
|
break
|
||
|
|
if len(param_signature) > len(param_docs):
|
||
|
|
message += [
|
||
|
|
"Parameters in function docstring have less items w.r.t."
|
||
|
|
" function signature, first missing item: %s"
|
||
|
|
% param_signature[len(param_docs)]
|
||
|
|
]
|
||
|
|
|
||
|
|
elif len(param_signature) < len(param_docs):
|
||
|
|
message += [
|
||
|
|
"Parameters in function docstring have more items w.r.t."
|
||
|
|
" function signature, first extra item: %s"
|
||
|
|
% param_docs[len(param_signature)]
|
||
|
|
]
|
||
|
|
|
||
|
|
# If there wasn't any difference in the parameters themselves between
|
||
|
|
# docstring and signature including having the same length then return
|
||
|
|
# empty list
|
||
|
|
if len(message) == 0:
|
||
|
|
return []
|
||
|
|
|
||
|
|
import difflib
|
||
|
|
import pprint
|
||
|
|
|
||
|
|
param_docs_formatted = pprint.pformat(param_docs).splitlines()
|
||
|
|
param_signature_formatted = pprint.pformat(param_signature).splitlines()
|
||
|
|
|
||
|
|
message += ["Full diff:"]
|
||
|
|
|
||
|
|
message.extend(
|
||
|
|
line.strip()
|
||
|
|
for line in difflib.ndiff(param_signature_formatted, param_docs_formatted)
|
||
|
|
)
|
||
|
|
|
||
|
|
incorrect.extend(message)
|
||
|
|
|
||
|
|
# Prepend function name
|
||
|
|
incorrect = ["In function: " + func_name] + incorrect
|
||
|
|
|
||
|
|
return incorrect
|
||
|
|
|
||
|
|
|
||
|
|
def assert_run_python_script_without_output(source_code, pattern=".+", timeout=60):
|
||
|
|
"""Utility to check assertions in an independent Python subprocess.
|
||
|
|
|
||
|
|
The script provided in the source code should return 0 and the stdtout +
|
||
|
|
stderr should not match the pattern `pattern`.
|
||
|
|
|
||
|
|
This is a port from cloudpickle https://github.com/cloudpipe/cloudpickle
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
source_code : str
|
||
|
|
The Python source code to execute.
|
||
|
|
pattern : str
|
||
|
|
Pattern that the stdout + stderr should not match. By default, unless
|
||
|
|
stdout + stderr are both empty, an error will be raised.
|
||
|
|
timeout : int, default=60
|
||
|
|
Time in seconds before timeout.
|
||
|
|
"""
|
||
|
|
fd, source_file = tempfile.mkstemp(suffix="_src_test_sklearn.py")
|
||
|
|
os.close(fd)
|
||
|
|
try:
|
||
|
|
with open(source_file, "wb") as f:
|
||
|
|
f.write(source_code.encode("utf-8"))
|
||
|
|
cmd = [sys.executable, source_file]
|
||
|
|
cwd = op.normpath(op.join(op.dirname(sklearn.__file__), ".."))
|
||
|
|
env = os.environ.copy()
|
||
|
|
try:
|
||
|
|
env["PYTHONPATH"] = os.pathsep.join([cwd, env["PYTHONPATH"]])
|
||
|
|
except KeyError:
|
||
|
|
env["PYTHONPATH"] = cwd
|
||
|
|
kwargs = {"cwd": cwd, "stderr": STDOUT, "env": env}
|
||
|
|
# If coverage is running, pass the config file to the subprocess
|
||
|
|
coverage_rc = os.environ.get("COVERAGE_PROCESS_START")
|
||
|
|
if coverage_rc:
|
||
|
|
kwargs["env"]["COVERAGE_PROCESS_START"] = coverage_rc
|
||
|
|
|
||
|
|
kwargs["timeout"] = timeout
|
||
|
|
try:
|
||
|
|
try:
|
||
|
|
out = check_output(cmd, **kwargs)
|
||
|
|
except CalledProcessError as e:
|
||
|
|
raise RuntimeError(
|
||
|
|
"script errored with output:\n%s" % e.output.decode("utf-8")
|
||
|
|
)
|
||
|
|
|
||
|
|
out = out.decode("utf-8")
|
||
|
|
if re.search(pattern, out):
|
||
|
|
if pattern == ".+":
|
||
|
|
expectation = "Expected no output"
|
||
|
|
else:
|
||
|
|
expectation = f"The output was not supposed to match {pattern!r}"
|
||
|
|
|
||
|
|
message = f"{expectation}, got the following output instead: {out!r}"
|
||
|
|
raise AssertionError(message)
|
||
|
|
except TimeoutExpired as e:
|
||
|
|
raise RuntimeError(
|
||
|
|
"script timeout, output so far:\n%s" % e.output.decode("utf-8")
|
||
|
|
)
|
||
|
|
finally:
|
||
|
|
os.unlink(source_file)
|
||
|
|
|
||
|
|
|
||
|
|
def _convert_container(
|
||
|
|
container,
|
||
|
|
constructor_name,
|
||
|
|
columns_name=None,
|
||
|
|
dtype=None,
|
||
|
|
minversion=None,
|
||
|
|
categorical_feature_names=None,
|
||
|
|
):
|
||
|
|
"""Convert a given container to a specific array-like with a dtype.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
container : array-like
|
||
|
|
The container to convert.
|
||
|
|
constructor_name : {"list", "tuple", "array", "sparse", "dataframe", \
|
||
|
|
"series", "index", "slice", "sparse_csr", "sparse_csc", \
|
||
|
|
"sparse_csr_array", "sparse_csc_array", "pyarrow", "polars", \
|
||
|
|
"polars_series"}
|
||
|
|
The type of the returned container.
|
||
|
|
columns_name : index or array-like, default=None
|
||
|
|
For pandas container supporting `columns_names`, it will affect
|
||
|
|
specific names.
|
||
|
|
dtype : dtype, default=None
|
||
|
|
Force the dtype of the container. Does not apply to `"slice"`
|
||
|
|
container.
|
||
|
|
minversion : str, default=None
|
||
|
|
Minimum version for package to install.
|
||
|
|
categorical_feature_names : list of str, default=None
|
||
|
|
List of column names to cast to categorical dtype.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
converted_container
|
||
|
|
"""
|
||
|
|
if constructor_name == "list":
|
||
|
|
if dtype is None:
|
||
|
|
return list(container)
|
||
|
|
else:
|
||
|
|
return np.asarray(container, dtype=dtype).tolist()
|
||
|
|
elif constructor_name == "tuple":
|
||
|
|
if dtype is None:
|
||
|
|
return tuple(container)
|
||
|
|
else:
|
||
|
|
return tuple(np.asarray(container, dtype=dtype).tolist())
|
||
|
|
elif constructor_name == "array":
|
||
|
|
return np.asarray(container, dtype=dtype)
|
||
|
|
elif constructor_name in ("pandas", "dataframe"):
|
||
|
|
pd = pytest.importorskip("pandas", minversion=minversion)
|
||
|
|
result = pd.DataFrame(container, columns=columns_name, dtype=dtype, copy=False)
|
||
|
|
if categorical_feature_names is not None:
|
||
|
|
for col_name in categorical_feature_names:
|
||
|
|
result[col_name] = result[col_name].astype("category")
|
||
|
|
return result
|
||
|
|
elif constructor_name == "pyarrow":
|
||
|
|
pa = pytest.importorskip("pyarrow", minversion=minversion)
|
||
|
|
array = np.asarray(container)
|
||
|
|
if columns_name is None:
|
||
|
|
columns_name = [f"col{i}" for i in range(array.shape[1])]
|
||
|
|
data = {name: array[:, i] for i, name in enumerate(columns_name)}
|
||
|
|
result = pa.Table.from_pydict(data)
|
||
|
|
if categorical_feature_names is not None:
|
||
|
|
for col_idx, col_name in enumerate(result.column_names):
|
||
|
|
if col_name in categorical_feature_names:
|
||
|
|
result = result.set_column(
|
||
|
|
col_idx, col_name, result.column(col_name).dictionary_encode()
|
||
|
|
)
|
||
|
|
return result
|
||
|
|
elif constructor_name == "polars":
|
||
|
|
pl = pytest.importorskip("polars", minversion=minversion)
|
||
|
|
result = pl.DataFrame(container, schema=columns_name, orient="row")
|
||
|
|
if categorical_feature_names is not None:
|
||
|
|
for col_name in categorical_feature_names:
|
||
|
|
result = result.with_columns(pl.col(col_name).cast(pl.Categorical))
|
||
|
|
return result
|
||
|
|
elif constructor_name == "series":
|
||
|
|
pd = pytest.importorskip("pandas", minversion=minversion)
|
||
|
|
return pd.Series(container, dtype=dtype)
|
||
|
|
elif constructor_name == "polars_series":
|
||
|
|
pl = pytest.importorskip("polars", minversion=minversion)
|
||
|
|
return pl.Series(values=container)
|
||
|
|
elif constructor_name == "index":
|
||
|
|
pd = pytest.importorskip("pandas", minversion=minversion)
|
||
|
|
return pd.Index(container, dtype=dtype)
|
||
|
|
elif constructor_name == "slice":
|
||
|
|
return slice(container[0], container[1])
|
||
|
|
elif "sparse" in constructor_name:
|
||
|
|
if not sp.sparse.issparse(container):
|
||
|
|
# For scipy >= 1.13, sparse array constructed from 1d array may be
|
||
|
|
# 1d or raise an exception. To avoid this, we make sure that the
|
||
|
|
# input container is 2d. For more details, see
|
||
|
|
# https://github.com/scipy/scipy/pull/18530#issuecomment-1878005149
|
||
|
|
container = np.atleast_2d(container)
|
||
|
|
|
||
|
|
if "array" in constructor_name and sp_version < parse_version("1.8"):
|
||
|
|
raise ValueError(
|
||
|
|
f"{constructor_name} is only available with scipy>=1.8.0, got "
|
||
|
|
f"{sp_version}"
|
||
|
|
)
|
||
|
|
if constructor_name in ("sparse", "sparse_csr"):
|
||
|
|
# sparse and sparse_csr are equivalent for legacy reasons
|
||
|
|
return sp.sparse.csr_matrix(container, dtype=dtype)
|
||
|
|
elif constructor_name == "sparse_csr_array":
|
||
|
|
return sp.sparse.csr_array(container, dtype=dtype)
|
||
|
|
elif constructor_name == "sparse_csc":
|
||
|
|
return sp.sparse.csc_matrix(container, dtype=dtype)
|
||
|
|
elif constructor_name == "sparse_csc_array":
|
||
|
|
return sp.sparse.csc_array(container, dtype=dtype)
|
||
|
|
|
||
|
|
|
||
|
|
def raises(expected_exc_type, match=None, may_pass=False, err_msg=None):
|
||
|
|
"""Context manager to ensure exceptions are raised within a code block.
|
||
|
|
|
||
|
|
This is similar to and inspired from pytest.raises, but supports a few
|
||
|
|
other cases.
|
||
|
|
|
||
|
|
This is only intended to be used in estimator_checks.py where we don't
|
||
|
|
want to use pytest. In the rest of the code base, just use pytest.raises
|
||
|
|
instead.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
excepted_exc_type : Exception or list of Exception
|
||
|
|
The exception that should be raised by the block. If a list, the block
|
||
|
|
should raise one of the exceptions.
|
||
|
|
match : str or list of str, default=None
|
||
|
|
A regex that the exception message should match. If a list, one of
|
||
|
|
the entries must match. If None, match isn't enforced.
|
||
|
|
may_pass : bool, default=False
|
||
|
|
If True, the block is allowed to not raise an exception. Useful in
|
||
|
|
cases where some estimators may support a feature but others must
|
||
|
|
fail with an appropriate error message. By default, the context
|
||
|
|
manager will raise an exception if the block does not raise an
|
||
|
|
exception.
|
||
|
|
err_msg : str, default=None
|
||
|
|
If the context manager fails (e.g. the block fails to raise the
|
||
|
|
proper exception, or fails to match), then an AssertionError is
|
||
|
|
raised with this message. By default, an AssertionError is raised
|
||
|
|
with a default error message (depends on the kind of failure). Use
|
||
|
|
this to indicate how users should fix their estimators to pass the
|
||
|
|
checks.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
raised_and_matched : bool
|
||
|
|
True if an exception was raised and a match was found, False otherwise.
|
||
|
|
"""
|
||
|
|
return _Raises(expected_exc_type, match, may_pass, err_msg)
|
||
|
|
|
||
|
|
|
||
|
|
class _Raises(contextlib.AbstractContextManager):
|
||
|
|
# see raises() for parameters
|
||
|
|
def __init__(self, expected_exc_type, match, may_pass, err_msg):
|
||
|
|
self.expected_exc_types = (
|
||
|
|
expected_exc_type
|
||
|
|
if isinstance(expected_exc_type, Iterable)
|
||
|
|
else [expected_exc_type]
|
||
|
|
)
|
||
|
|
self.matches = [match] if isinstance(match, str) else match
|
||
|
|
self.may_pass = may_pass
|
||
|
|
self.err_msg = err_msg
|
||
|
|
self.raised_and_matched = False
|
||
|
|
|
||
|
|
def __exit__(self, exc_type, exc_value, _):
|
||
|
|
# see
|
||
|
|
# https://docs.python.org/2.5/whatsnew/pep-343.html#SECTION000910000000000000000
|
||
|
|
|
||
|
|
if exc_type is None: # No exception was raised in the block
|
||
|
|
if self.may_pass:
|
||
|
|
return True # CM is happy
|
||
|
|
else:
|
||
|
|
err_msg = self.err_msg or f"Did not raise: {self.expected_exc_types}"
|
||
|
|
raise AssertionError(err_msg)
|
||
|
|
|
||
|
|
if not any(
|
||
|
|
issubclass(exc_type, expected_type)
|
||
|
|
for expected_type in self.expected_exc_types
|
||
|
|
):
|
||
|
|
if self.err_msg is not None:
|
||
|
|
raise AssertionError(self.err_msg) from exc_value
|
||
|
|
else:
|
||
|
|
return False # will re-raise the original exception
|
||
|
|
|
||
|
|
if self.matches is not None:
|
||
|
|
err_msg = self.err_msg or (
|
||
|
|
"The error message should contain one of the following "
|
||
|
|
"patterns:\n{}\nGot {}".format("\n".join(self.matches), str(exc_value))
|
||
|
|
)
|
||
|
|
if not any(re.search(match, str(exc_value)) for match in self.matches):
|
||
|
|
raise AssertionError(err_msg) from exc_value
|
||
|
|
self.raised_and_matched = True
|
||
|
|
|
||
|
|
return True
|
||
|
|
|
||
|
|
|
||
|
|
class MinimalClassifier:
|
||
|
|
"""Minimal classifier implementation without inheriting from BaseEstimator.
|
||
|
|
|
||
|
|
This estimator should be tested with:
|
||
|
|
|
||
|
|
* `check_estimator` in `test_estimator_checks.py`;
|
||
|
|
* within a `Pipeline` in `test_pipeline.py`;
|
||
|
|
* within a `SearchCV` in `test_search.py`.
|
||
|
|
"""
|
||
|
|
|
||
|
|
_estimator_type = "classifier"
|
||
|
|
|
||
|
|
def __init__(self, param=None):
|
||
|
|
self.param = param
|
||
|
|
|
||
|
|
def get_params(self, deep=True):
|
||
|
|
return {"param": self.param}
|
||
|
|
|
||
|
|
def set_params(self, **params):
|
||
|
|
for key, value in params.items():
|
||
|
|
setattr(self, key, value)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def fit(self, X, y):
|
||
|
|
X, y = check_X_y(X, y)
|
||
|
|
check_classification_targets(y)
|
||
|
|
self.classes_, counts = np.unique(y, return_counts=True)
|
||
|
|
self._most_frequent_class_idx = counts.argmax()
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict_proba(self, X):
|
||
|
|
check_is_fitted(self)
|
||
|
|
X = check_array(X)
|
||
|
|
proba_shape = (X.shape[0], self.classes_.size)
|
||
|
|
y_proba = np.zeros(shape=proba_shape, dtype=np.float64)
|
||
|
|
y_proba[:, self._most_frequent_class_idx] = 1.0
|
||
|
|
return y_proba
|
||
|
|
|
||
|
|
def predict(self, X):
|
||
|
|
y_proba = self.predict_proba(X)
|
||
|
|
y_pred = y_proba.argmax(axis=1)
|
||
|
|
return self.classes_[y_pred]
|
||
|
|
|
||
|
|
def score(self, X, y):
|
||
|
|
from sklearn.metrics import accuracy_score
|
||
|
|
|
||
|
|
return accuracy_score(y, self.predict(X))
|
||
|
|
|
||
|
|
|
||
|
|
class MinimalRegressor:
|
||
|
|
"""Minimal regressor implementation without inheriting from BaseEstimator.
|
||
|
|
|
||
|
|
This estimator should be tested with:
|
||
|
|
|
||
|
|
* `check_estimator` in `test_estimator_checks.py`;
|
||
|
|
* within a `Pipeline` in `test_pipeline.py`;
|
||
|
|
* within a `SearchCV` in `test_search.py`.
|
||
|
|
"""
|
||
|
|
|
||
|
|
_estimator_type = "regressor"
|
||
|
|
|
||
|
|
def __init__(self, param=None):
|
||
|
|
self.param = param
|
||
|
|
|
||
|
|
def get_params(self, deep=True):
|
||
|
|
return {"param": self.param}
|
||
|
|
|
||
|
|
def set_params(self, **params):
|
||
|
|
for key, value in params.items():
|
||
|
|
setattr(self, key, value)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def fit(self, X, y):
|
||
|
|
X, y = check_X_y(X, y)
|
||
|
|
self.is_fitted_ = True
|
||
|
|
self._mean = np.mean(y)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict(self, X):
|
||
|
|
check_is_fitted(self)
|
||
|
|
X = check_array(X)
|
||
|
|
return np.ones(shape=(X.shape[0],)) * self._mean
|
||
|
|
|
||
|
|
def score(self, X, y):
|
||
|
|
from sklearn.metrics import r2_score
|
||
|
|
|
||
|
|
return r2_score(y, self.predict(X))
|
||
|
|
|
||
|
|
|
||
|
|
class MinimalTransformer:
|
||
|
|
"""Minimal transformer implementation without inheriting from
|
||
|
|
BaseEstimator.
|
||
|
|
|
||
|
|
This estimator should be tested with:
|
||
|
|
|
||
|
|
* `check_estimator` in `test_estimator_checks.py`;
|
||
|
|
* within a `Pipeline` in `test_pipeline.py`;
|
||
|
|
* within a `SearchCV` in `test_search.py`.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, param=None):
|
||
|
|
self.param = param
|
||
|
|
|
||
|
|
def get_params(self, deep=True):
|
||
|
|
return {"param": self.param}
|
||
|
|
|
||
|
|
def set_params(self, **params):
|
||
|
|
for key, value in params.items():
|
||
|
|
setattr(self, key, value)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def fit(self, X, y=None):
|
||
|
|
check_array(X)
|
||
|
|
self.is_fitted_ = True
|
||
|
|
return self
|
||
|
|
|
||
|
|
def transform(self, X, y=None):
|
||
|
|
check_is_fitted(self)
|
||
|
|
X = check_array(X)
|
||
|
|
return X
|
||
|
|
|
||
|
|
def fit_transform(self, X, y=None):
|
||
|
|
return self.fit(X, y).transform(X, y)
|
||
|
|
|
||
|
|
|
||
|
|
def _array_api_for_tests(array_namespace, device):
|
||
|
|
try:
|
||
|
|
array_mod = importlib.import_module(array_namespace)
|
||
|
|
except ModuleNotFoundError:
|
||
|
|
raise SkipTest(
|
||
|
|
f"{array_namespace} is not installed: not checking array_api input"
|
||
|
|
)
|
||
|
|
try:
|
||
|
|
import array_api_compat # noqa
|
||
|
|
except ImportError:
|
||
|
|
raise SkipTest(
|
||
|
|
"array_api_compat is not installed: not checking array_api input"
|
||
|
|
)
|
||
|
|
|
||
|
|
# First create an array using the chosen array module and then get the
|
||
|
|
# corresponding (compatibility wrapped) array namespace based on it.
|
||
|
|
# This is because `cupy` is not the same as the compatibility wrapped
|
||
|
|
# namespace of a CuPy array.
|
||
|
|
xp = array_api_compat.get_namespace(array_mod.asarray(1))
|
||
|
|
if (
|
||
|
|
array_namespace == "torch"
|
||
|
|
and device == "cuda"
|
||
|
|
and not xp.backends.cuda.is_built()
|
||
|
|
):
|
||
|
|
raise SkipTest("PyTorch test requires cuda, which is not available")
|
||
|
|
elif array_namespace == "torch" and device == "mps":
|
||
|
|
if os.getenv("PYTORCH_ENABLE_MPS_FALLBACK") != "1":
|
||
|
|
# For now we need PYTORCH_ENABLE_MPS_FALLBACK=1 for all estimators to work
|
||
|
|
# when using the MPS device.
|
||
|
|
raise SkipTest(
|
||
|
|
"Skipping MPS device test because PYTORCH_ENABLE_MPS_FALLBACK is not "
|
||
|
|
"set."
|
||
|
|
)
|
||
|
|
if not xp.backends.mps.is_built():
|
||
|
|
raise SkipTest(
|
||
|
|
"MPS is not available because the current PyTorch install was not "
|
||
|
|
"built with MPS enabled."
|
||
|
|
)
|
||
|
|
elif array_namespace in {"cupy", "cupy.array_api"}: # pragma: nocover
|
||
|
|
import cupy
|
||
|
|
|
||
|
|
if cupy.cuda.runtime.getDeviceCount() == 0:
|
||
|
|
raise SkipTest("CuPy test requires cuda, which is not available")
|
||
|
|
return xp
|
||
|
|
|
||
|
|
|
||
|
|
def _get_warnings_filters_info_list():
|
||
|
|
@dataclass
|
||
|
|
class WarningInfo:
|
||
|
|
action: "warnings._ActionKind"
|
||
|
|
message: str = ""
|
||
|
|
category: type[Warning] = Warning
|
||
|
|
|
||
|
|
def to_filterwarning_str(self):
|
||
|
|
if self.category.__module__ == "builtins":
|
||
|
|
category = self.category.__name__
|
||
|
|
else:
|
||
|
|
category = f"{self.category.__module__}.{self.category.__name__}"
|
||
|
|
|
||
|
|
return f"{self.action}:{self.message}:{category}"
|
||
|
|
|
||
|
|
return [
|
||
|
|
WarningInfo("error", category=DeprecationWarning),
|
||
|
|
WarningInfo("error", category=FutureWarning),
|
||
|
|
WarningInfo("error", category=VisibleDeprecationWarning),
|
||
|
|
# TODO: remove when pyamg > 5.0.1
|
||
|
|
# Avoid a deprecation warning due pkg_resources usage in pyamg.
|
||
|
|
WarningInfo(
|
||
|
|
"ignore",
|
||
|
|
message="pkg_resources is deprecated as an API",
|
||
|
|
category=DeprecationWarning,
|
||
|
|
),
|
||
|
|
WarningInfo(
|
||
|
|
"ignore",
|
||
|
|
message="Deprecated call to `pkg_resources",
|
||
|
|
category=DeprecationWarning,
|
||
|
|
),
|
||
|
|
# pytest-cov issue https://github.com/pytest-dev/pytest-cov/issues/557 not
|
||
|
|
# fixed although it has been closed. https://github.com/pytest-dev/pytest-cov/pull/623
|
||
|
|
# would probably fix it.
|
||
|
|
WarningInfo(
|
||
|
|
"ignore",
|
||
|
|
message=(
|
||
|
|
"The --rsyncdir command line argument and rsyncdirs config variable are"
|
||
|
|
" deprecated"
|
||
|
|
),
|
||
|
|
category=DeprecationWarning,
|
||
|
|
),
|
||
|
|
# XXX: Easiest way to ignore pandas Pyarrow DeprecationWarning in the
|
||
|
|
# short-term. See https://github.com/pandas-dev/pandas/issues/54466 for
|
||
|
|
# more details.
|
||
|
|
WarningInfo(
|
||
|
|
"ignore",
|
||
|
|
message=r"\s*Pyarrow will become a required dependency",
|
||
|
|
category=DeprecationWarning,
|
||
|
|
),
|
||
|
|
# warnings has been fixed from dateutil main but not released yet, see
|
||
|
|
# https://github.com/dateutil/dateutil/issues/1314
|
||
|
|
WarningInfo(
|
||
|
|
"ignore",
|
||
|
|
message="datetime.datetime.utcfromtimestamp",
|
||
|
|
category=DeprecationWarning,
|
||
|
|
),
|
||
|
|
# Python 3.12 warnings from joblib fixed in master but not released yet,
|
||
|
|
# see https://github.com/joblib/joblib/pull/1518
|
||
|
|
WarningInfo(
|
||
|
|
"ignore", message="ast.Num is deprecated", category=DeprecationWarning
|
||
|
|
),
|
||
|
|
WarningInfo(
|
||
|
|
"ignore", message="Attribute n is deprecated", category=DeprecationWarning
|
||
|
|
),
|
||
|
|
# Python 3.12 warnings from sphinx-gallery fixed in master but not
|
||
|
|
# released yet, see
|
||
|
|
# https://github.com/sphinx-gallery/sphinx-gallery/pull/1242
|
||
|
|
WarningInfo(
|
||
|
|
"ignore", message="ast.Str is deprecated", category=DeprecationWarning
|
||
|
|
),
|
||
|
|
WarningInfo(
|
||
|
|
"ignore", message="Attribute s is deprecated", category=DeprecationWarning
|
||
|
|
),
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def get_pytest_filterwarning_lines():
|
||
|
|
warning_filters_info_list = _get_warnings_filters_info_list()
|
||
|
|
return [
|
||
|
|
warning_info.to_filterwarning_str()
|
||
|
|
for warning_info in warning_filters_info_list
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
def turn_warnings_into_errors():
|
||
|
|
warnings_filters_info_list = _get_warnings_filters_info_list()
|
||
|
|
for warning_info in warnings_filters_info_list:
|
||
|
|
warnings.filterwarnings(
|
||
|
|
warning_info.action,
|
||
|
|
message=warning_info.message,
|
||
|
|
category=warning_info.category,
|
||
|
|
)
|