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Traktor/myenv/Lib/site-packages/sklearn/utils/_testing.py

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losowanie zdjec
2024-05-26 05:12:46 +02:00
"""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,
)
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