"""Testing utilities.""" # Authors: The scikit-learn developers # SPDX-License-Identifier: 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 textwrap import unittest import warnings from collections import defaultdict, namedtuple from collections.abc import Iterable from dataclasses import dataclass from difflib import context_diff from functools import wraps from inspect import signature from itertools import chain, groupby from subprocess import STDOUT, CalledProcessError, TimeoutExpired, check_output 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_array_almost_equal, assert_array_equal, assert_array_less, ) import sklearn from sklearn.utils import ( ClassifierTags, RegressorTags, Tags, TargetTags, TransformerTags, ) from sklearn.utils._array_api import _check_array_api_dispatch from sklearn.utils.fixes import ( _IS_32BIT, 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_array_equal", "assert_almost_equal", "assert_array_almost_equal", "assert_array_less", "assert_allclose", "assert_run_python_script_without_output", "SkipTest", ] SkipTest = unittest.case.SkipTest 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_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 `. """ if "random_state" in estimator.get_params(): estimator.set_params(random_state=random_state) def _is_numpydoc(): try: import numpydoc # noqa except (ImportError, AssertionError): return False else: return True 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_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" ) skip_if_no_numpydoc = pytest.mark.skipif( not _is_numpydoc(), reason="numpydoc is required to test the docstrings", ) 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 _check_item_included(item_name, args): """Helper to check if item should be included in checking.""" if args.include is not True and item_name not in args.include: return False if args.exclude is not None and item_name in args.exclude: return False return True def _diff_key(line): """Key for grouping output from `context_diff`.""" if line.startswith(" "): return " " elif line.startswith("- "): return "- " elif line.startswith("+ "): return "+ " elif line.startswith("! "): return "! " return None def _get_diff_msg(docstrings_grouped): """Get message showing the difference between type/desc docstrings of all objects. `docstrings_grouped` keys should be the type/desc docstrings and values are a list of objects with that docstring. Objects with the same type/desc docstring are thus grouped together. """ msg_diff = "" ref_str = "" ref_group = [] for docstring, group in docstrings_grouped.items(): if not ref_str and not ref_group: ref_str += docstring ref_group.extend(group) diff = list( context_diff( ref_str.split(), docstring.split(), fromfile=str(ref_group), tofile=str(group), n=8, ) ) # Add header msg_diff += "".join((diff[:3])) # Group consecutive 'diff' words to shorten error message for start, group in groupby(diff[3:], key=_diff_key): if start is None: msg_diff += "\n" + "\n".join(group) else: msg_diff += "\n" + start + " ".join(word[2:] for word in group) # Add new lines at end of diff, to separate comparisons msg_diff += "\n\n" return msg_diff def _check_consistency_items( items_docs, type_or_desc, section, n_objects, descr_regex_pattern="" ): """Helper to check docstring consistency of all `items_docs`. If item is not present in all objects, checking is skipped and warning raised. If `regex` provided, match descriptions to all descriptions. """ skipped = [] for item_name, docstrings_grouped in items_docs.items(): # If item not found in all objects, skip if sum([len(objs) for objs in docstrings_grouped.values()]) < n_objects: skipped.append(item_name) # If regex provided, match to all descriptions elif type_or_desc == "description" and descr_regex_pattern: not_matched = [] for docstring, group in docstrings_grouped.items(): if not re.search(descr_regex_pattern, docstring): not_matched.extend(group) if not_matched: msg = textwrap.fill( f"The description of {section[:-1]} '{item_name}' in {not_matched}" f" does not match 'descr_regex_pattern': {descr_regex_pattern} " ) raise AssertionError(msg) # Otherwise, if more than one key, docstrings not consistent between objects elif len(docstrings_grouped.keys()) > 1: msg_diff = _get_diff_msg(docstrings_grouped) obj_groups = " and ".join( str(group) for group in docstrings_grouped.values() ) msg = textwrap.fill( f"The {type_or_desc} of {section[:-1]} '{item_name}' is inconsistent " f"between {obj_groups}:" ) msg += msg_diff raise AssertionError(msg) if skipped: warnings.warn( f"Checking was skipped for {section}: {skipped} as they were " "not found in all objects." ) def assert_docstring_consistency( objects, include_params=False, exclude_params=None, include_attrs=False, exclude_attrs=None, include_returns=False, exclude_returns=None, descr_regex_pattern=None, ): r"""Check consistency between docstring parameters/attributes/returns of objects. Checks if parameters/attributes/returns have the same type specification and description (ignoring whitespace) across `objects`. Intended to be used for related classes/functions/data descriptors. Entries that do not appear across all `objects` are ignored. Parameters ---------- objects : list of {classes, functions, data descriptors} Objects to check. Objects may be classes, functions or data descriptors with docstrings that can be parsed by numpydoc. include_params : list of str or bool, default=False List of parameters to be included. If True, all parameters are included, if False, checking is skipped for parameters. Can only be set if `exclude_params` is None. exclude_params : list of str or None, default=None List of parameters to be excluded. If None, no parameters are excluded. Can only be set if `include_params` is True. include_attrs : list of str or bool, default=False List of attributes to be included. If True, all attributes are included, if False, checking is skipped for attributes. Can only be set if `exclude_attrs` is None. exclude_attrs : list of str or None, default=None List of attributes to be excluded. If None, no attributes are excluded. Can only be set if `include_attrs` is True. include_returns : list of str or bool, default=False List of returns to be included. If True, all returns are included, if False, checking is skipped for returns. Can only be set if `exclude_returns` is None. exclude_returns : list of str or None, default=None List of returns to be excluded. If None, no returns are excluded. Can only be set if `include_returns` is True. descr_regex_pattern : str, default=None Regular expression to match to all descriptions of included parameters/attributes/returns. If None, will revert to default behavior of comparing descriptions between objects. Examples -------- >>> from sklearn.metrics import (accuracy_score, classification_report, ... mean_absolute_error, mean_squared_error, median_absolute_error) >>> from sklearn.utils._testing import assert_docstring_consistency ... # doctest: +SKIP >>> assert_docstring_consistency([mean_absolute_error, mean_squared_error], ... include_params=['y_true', 'y_pred', 'sample_weight']) # doctest: +SKIP >>> assert_docstring_consistency([median_absolute_error, mean_squared_error], ... include_params=True) # doctest: +SKIP >>> assert_docstring_consistency([accuracy_score, classification_report], ... include_params=["y_true"], ... descr_regex_pattern=r"Ground truth \(correct\) (labels|target values)") ... # doctest: +SKIP """ from numpydoc.docscrape import NumpyDocString Args = namedtuple("args", ["include", "exclude", "arg_name"]) def _create_args(include, exclude, arg_name, section_name): if exclude and include is not True: raise TypeError( f"The 'exclude_{arg_name}' argument can be set only when the " f"'include_{arg_name}' argument is True." ) if include is False: return {} return {section_name: Args(include, exclude, arg_name)} section_args = { **_create_args(include_params, exclude_params, "params", "Parameters"), **_create_args(include_attrs, exclude_attrs, "attrs", "Attributes"), **_create_args(include_returns, exclude_returns, "returns", "Returns"), } objects_doc = dict() for obj in objects: if ( inspect.isdatadescriptor(obj) or inspect.isfunction(obj) or inspect.isclass(obj) ): objects_doc[obj.__name__] = NumpyDocString(inspect.getdoc(obj)) else: raise TypeError( "All 'objects' must be one of: function, class or descriptor, " f"got a: {type(obj)}." ) n_objects = len(objects) for section, args in section_args.items(): type_items = defaultdict(lambda: defaultdict(list)) desc_items = defaultdict(lambda: defaultdict(list)) for obj_name, obj_doc in objects_doc.items(): for item_name, type_def, desc in obj_doc[section]: if _check_item_included(item_name, args): # Normalize white space type_def = " ".join(type_def.strip().split()) desc = " ".join(chain.from_iterable(line.split() for line in desc)) # Use string type/desc as key, to group consistent objs together type_items[item_name][type_def].append(obj_name) desc_items[item_name][desc].append(obj_name) _check_consistency_items(type_items, "type specification", section, n_objects) _check_consistency_items( desc_items, "description", section, n_objects, descr_regex_pattern=descr_regex_pattern, ) 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`. """ 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)) def __sklearn_tags__(self): return Tags( estimator_type="classifier", classifier_tags=ClassifierTags(), regressor_tags=None, transformer_tags=None, target_tags=TargetTags(required=True), ) 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`. """ 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)) def __sklearn_tags__(self): return Tags( estimator_type="regressor", classifier_tags=None, regressor_tags=RegressorTags(), transformer_tags=None, target_tags=TargetTags(required=True), ) 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 __sklearn_tags__(self): return Tags( estimator_type="transformer", classifier_tags=None, regressor_tags=None, transformer_tags=TransformerTags(), target_tags=TargetTags(required=False), ) 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 == "cupy": # 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, )