"""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 os import inspect import pkgutil import warnings import sys import functools import scipy as sp import scipy.io from functools import wraps from operator import itemgetter try: # Python 2 from urllib2 import urlopen from urllib2 import HTTPError except ImportError: # Python 3+ from urllib.request import urlopen from urllib.error import HTTPError import tempfile import shutil import os.path as op import atexit import unittest # WindowsError only exist on Windows try: WindowsError except NameError: WindowsError = None from numpy.testing import assert_allclose from numpy.testing import assert_almost_equal from numpy.testing import assert_approx_equal from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal from numpy.testing import assert_array_less import numpy as np import sklearn from sklearn.base import (BaseEstimator, ClassifierMixin, ClusterMixin, RegressorMixin, TransformerMixin) from sklearn.utils import deprecated, IS_PYPY, _IS_32BIT from sklearn.utils._joblib import joblib from sklearn.utils._unittest_backport import TestCase from sklearn.utils.fixes import signature additional_names_in_all = [] try: from nose.tools import raises as _nose_raises deprecation_message = ( 'sklearn.utils.testing.raises has been deprecated in version 0.20 ' 'and will be removed in 0.22. Please use ' 'sklearn.utils.testing.assert_raises instead.') raises = deprecated(deprecation_message)(_nose_raises) additional_names_in_all.append('raises') except ImportError: pass try: from nose.tools import with_setup as _with_setup deprecation_message = ( 'sklearn.utils.testing.with_setup has been deprecated in version 0.20 ' 'and will be removed in 0.22.' 'If your code relies on with_setup, please use' ' nose.tools.with_setup instead.') with_setup = deprecated(deprecation_message)(_with_setup) additional_names_in_all.append('with_setup') except ImportError: pass __all__ = ["assert_equal", "assert_not_equal", "assert_raises", "assert_raises_regexp", "assert_true", "assert_false", "assert_almost_equal", "assert_array_equal", "assert_array_almost_equal", "assert_array_less", "assert_less", "assert_less_equal", "assert_greater", "assert_greater_equal", "assert_approx_equal", "assert_allclose", "SkipTest"] __all__.extend(additional_names_in_all) _dummy = TestCase('__init__') assert_equal = _dummy.assertEqual assert_not_equal = _dummy.assertNotEqual assert_true = _dummy.assertTrue assert_false = _dummy.assertFalse assert_raises = _dummy.assertRaises SkipTest = unittest.case.SkipTest assert_dict_equal = _dummy.assertDictEqual assert_in = _dummy.assertIn assert_not_in = _dummy.assertNotIn assert_less = _dummy.assertLess assert_greater = _dummy.assertGreater assert_less_equal = _dummy.assertLessEqual assert_greater_equal = _dummy.assertGreaterEqual 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 assert_warns(warning_class, func, *args, **kw): """Test that a certain warning occurs. Parameters ---------- warning_class : the warning class The class to test for, e.g. UserWarning. func : callable Callable object to trigger warnings. *args : the positional arguments to `func`. **kw : the keyword arguments to `func` Returns ------- result : the return value of `func` """ clean_warning_registry() with warnings.catch_warnings(record=True) as w: # Cause all warnings to always be triggered. warnings.simplefilter("always") # Trigger a warning. result = func(*args, **kw) if hasattr(np, 'VisibleDeprecationWarning'): # Filter out numpy-specific warnings in numpy >= 1.9 w = [e for e in w if e.category is not np.VisibleDeprecationWarning] # Verify some things if not len(w) > 0: raise AssertionError("No warning raised when calling %s" % func.__name__) found = any(warning.category is warning_class for warning in w) if not found: raise AssertionError("%s did not give warning: %s( is %s)" % (func.__name__, warning_class, w)) return result def assert_warns_message(warning_class, message, func, *args, **kw): # very important to avoid uncontrolled state propagation """Test that a certain warning occurs and with a certain message. Parameters ---------- warning_class : the warning class The class to test for, e.g. UserWarning. message : str | callable The message or a substring of the message to test for. If callable, it takes a string as the argument and will trigger an AssertionError if the callable returns `False`. func : callable Callable object to trigger warnings. *args : the positional arguments to `func`. **kw : the keyword arguments to `func`. Returns ------- result : the return value of `func` """ clean_warning_registry() with warnings.catch_warnings(record=True) as w: # Cause all warnings to always be triggered. warnings.simplefilter("always") if hasattr(np, 'VisibleDeprecationWarning'): # Let's not catch the numpy internal DeprecationWarnings warnings.simplefilter('ignore', np.VisibleDeprecationWarning) # Trigger a warning. result = func(*args, **kw) # Verify some things if not len(w) > 0: raise AssertionError("No warning raised when calling %s" % func.__name__) found = [issubclass(warning.category, warning_class) for warning in w] if not any(found): raise AssertionError("No warning raised for %s with class " "%s" % (func.__name__, warning_class)) message_found = False # Checks the message of all warnings belong to warning_class for index in [i for i, x in enumerate(found) if x]: # substring will match, the entire message with typo won't msg = w[index].message # For Python 3 compatibility msg = str(msg.args[0] if hasattr(msg, 'args') else msg) if callable(message): # add support for certain tests check_in_message = message else: check_in_message = lambda msg: message in msg if check_in_message(msg): message_found = True break if not message_found: raise AssertionError("Did not receive the message you expected " "('%s') for <%s>, got: '%s'" % (message, func.__name__, msg)) return result def assert_warns_div0(func, *args, **kw): """Assume that numpy's warning for divide by zero is raised Handles the case of platforms that do not support warning on divide by zero Parameters ---------- func *args **kw """ with np.errstate(divide='warn', invalid='warn'): try: assert_warns(RuntimeWarning, np.divide, 1, np.zeros(1)) except AssertionError: # This platform does not report numpy divide by zeros return func(*args, **kw) return assert_warns_message(RuntimeWarning, 'invalid value encountered', func, *args, **kw) # To remove when we support numpy 1.7 def assert_no_warnings(func, *args, **kw): """ Parameters ---------- func *args **kw """ # very important to avoid uncontrolled state propagation clean_warning_registry() with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') result = func(*args, **kw) if hasattr(np, 'VisibleDeprecationWarning'): # Filter out numpy-specific warnings in numpy >= 1.9 w = [e for e in w if e.category is not np.VisibleDeprecationWarning] if len(w) > 0: raise AssertionError("Got warnings when calling %s: [%s]" % (func.__name__, ', '.join(str(warning) for warning in w))) return result 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 or None callable where you want to ignore the warnings. category : warning class, defaults to Warning. The category to filter. If Warning, all categories will be muted. Examples -------- >>> 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(object): """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 to 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): clean_warning_registry() 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 clean_warning_registry() 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[:] = [] clean_warning_registry() 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_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 or sparse matrix First array to compare. y : array-like or sparse matrix Second array to compare. rtol : float, optional relative tolerance; see numpy.allclose atol : float, optional 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 : string, 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.") @deprecated('deprecated in version 0.20 to be removed in version 0.22') def fake_mldata(columns_dict, dataname, matfile, ordering=None): """Create a fake mldata data set. .. deprecated:: 0.20 Will be removed in version 0.22 Parameters ---------- columns_dict : dict, keys=str, values=ndarray Contains data as columns_dict[column_name] = array of data. dataname : string Name of data set. matfile : string or file object The file name string or the file-like object of the output file. ordering : list, default None List of column_names, determines the ordering in the data set. Notes ----- This function transposes all arrays, while fetch_mldata only transposes 'data', keep that into account in the tests. """ datasets = dict(columns_dict) # transpose all variables for name in datasets: datasets[name] = datasets[name].T if ordering is None: ordering = sorted(list(datasets.keys())) # NOTE: setting up this array is tricky, because of the way Matlab # re-packages 1D arrays datasets['mldata_descr_ordering'] = sp.empty((1, len(ordering)), dtype='object') for i, name in enumerate(ordering): datasets['mldata_descr_ordering'][0, i] = name scipy.io.savemat(matfile, datasets, oned_as='column') @deprecated('deprecated in version 0.20 to be removed in version 0.22') class mock_mldata_urlopen(object): """Object that mocks the urlopen function to fake requests to mldata. When requesting a dataset with a name that is in mock_datasets, this object creates a fake dataset in a StringIO object and returns it. Otherwise, it raises an HTTPError. .. deprecated:: 0.20 Will be removed in version 0.22 Parameters ---------- mock_datasets : dict A dictionary of {dataset_name: data_dict}, or {dataset_name: (data_dict, ordering). `data_dict` itself is a dictionary of {column_name: data_array}, and `ordering` is a list of column_names to determine the ordering in the data set (see :func:`fake_mldata` for details). """ def __init__(self, mock_datasets): self.mock_datasets = mock_datasets def __call__(self, urlname): """ Parameters ---------- urlname : string The url """ dataset_name = urlname.split('/')[-1] if dataset_name in self.mock_datasets: resource_name = '_' + dataset_name from io import BytesIO matfile = BytesIO() dataset = self.mock_datasets[dataset_name] ordering = None if isinstance(dataset, tuple): dataset, ordering = dataset fake_mldata(dataset, resource_name, matfile, ordering) matfile.seek(0) return matfile else: raise HTTPError(urlname, 404, dataset_name + " is not available", [], None) def install_mldata_mock(mock_datasets): """ Parameters ---------- mock_datasets : dict A dictionary of {dataset_name: data_dict}, or {dataset_name: (data_dict, ordering). `data_dict` itself is a dictionary of {column_name: data_array}, and `ordering` is a list of column_names to determine the ordering in the data set (see :func:`fake_mldata` for details). """ # Lazy import to avoid mutually recursive imports from sklearn import datasets datasets.mldata.urlopen = mock_mldata_urlopen(mock_datasets) def uninstall_mldata_mock(): # Lazy import to avoid mutually recursive imports from sklearn import datasets datasets.mldata.urlopen = urlopen # Meta estimators need another estimator to be instantiated. META_ESTIMATORS = ["OneVsOneClassifier", "MultiOutputEstimator", "MultiOutputRegressor", "MultiOutputClassifier", "OutputCodeClassifier", "OneVsRestClassifier", "RFE", "RFECV", "BaseEnsemble", "ClassifierChain", "RegressorChain"] # estimators that there is no way to default-construct sensibly OTHER = ["Pipeline", "FeatureUnion", "GridSearchCV", "RandomizedSearchCV", "SelectFromModel", "ColumnTransformer"] # some strange ones DONT_TEST = ['SparseCoder', 'DictVectorizer', 'LabelBinarizer', 'LabelEncoder', 'MultiLabelBinarizer', 'TfidfTransformer', 'TfidfVectorizer', 'IsotonicRegression', 'OneHotEncoder', 'RandomTreesEmbedding', 'OrdinalEncoder', 'FeatureHasher', 'DummyClassifier', 'DummyRegressor', 'TruncatedSVD', 'PolynomialFeatures', 'GaussianRandomProjectionHash', 'HashingVectorizer', 'CheckingClassifier', 'PatchExtractor', 'CountVectorizer', # GradientBoosting base estimators, maybe should # exclude them in another way 'ZeroEstimator', 'ScaledLogOddsEstimator', 'QuantileEstimator', 'MeanEstimator', 'LogOddsEstimator', 'PriorProbabilityEstimator', '_SigmoidCalibration', 'VotingClassifier'] def all_estimators(include_meta_estimators=False, include_other=False, type_filter=None, include_dont_test=False): """Get a list of all estimators from sklearn. This function crawls the module and gets all classes that inherit from BaseEstimator. Classes that are defined in test-modules are not included. By default meta_estimators such as GridSearchCV are also not included. Parameters ---------- include_meta_estimators : boolean, default=False Whether to include meta-estimators that can be constructed using an estimator as their first argument. These are currently BaseEnsemble, OneVsOneClassifier, OutputCodeClassifier, OneVsRestClassifier, RFE, RFECV. include_other : boolean, default=False Wether to include meta-estimators that are somehow special and can not be default-constructed sensibly. These are currently Pipeline, FeatureUnion and GridSearchCV type_filter : string, list of string, or None, default=None Which kind of estimators should be returned. If None, no filter is applied and all estimators are returned. Possible values are 'classifier', 'regressor', 'cluster' and 'transformer' to get estimators only of these specific types, or a list of these to get the estimators that fit at least one of the types. include_dont_test : boolean, default=False Whether to include "special" label estimator or test processors. Returns ------- estimators : list of tuples List of (name, class), where ``name`` is the class name as string and ``class`` is the actuall type of the class. """ def is_abstract(c): if not(hasattr(c, '__abstractmethods__')): return False if not len(c.__abstractmethods__): return False return True all_classes = [] # get parent folder path = sklearn.__path__ for importer, modname, ispkg in pkgutil.walk_packages( path=path, prefix='sklearn.', onerror=lambda x: None): if ".tests." in modname: continue if IS_PYPY and ('_svmlight_format' in modname or 'feature_extraction._hashing' in modname): continue module = __import__(modname, fromlist="dummy") classes = inspect.getmembers(module, inspect.isclass) all_classes.extend(classes) all_classes = set(all_classes) estimators = [c for c in all_classes if (issubclass(c[1], BaseEstimator) and c[0] != 'BaseEstimator')] # get rid of abstract base classes estimators = [c for c in estimators if not is_abstract(c[1])] if not include_dont_test: estimators = [c for c in estimators if not c[0] in DONT_TEST] if not include_other: estimators = [c for c in estimators if not c[0] in OTHER] # possibly get rid of meta estimators if not include_meta_estimators: estimators = [c for c in estimators if not c[0] in META_ESTIMATORS] if type_filter is not None: if not isinstance(type_filter, list): type_filter = [type_filter] else: type_filter = list(type_filter) # copy filtered_estimators = [] filters = {'classifier': ClassifierMixin, 'regressor': RegressorMixin, 'transformer': TransformerMixin, 'cluster': ClusterMixin} for name, mixin in filters.items(): if name in type_filter: type_filter.remove(name) filtered_estimators.extend([est for est in estimators if issubclass(est[1], mixin)]) estimators = filtered_estimators if type_filter: raise ValueError("Parameter type_filter must be 'classifier', " "'regressor', 'transformer', 'cluster' or " "None, got" " %s." % repr(type_filter)) # drop duplicates, sort for reproducibility # itemgetter is used to ensure the sort does not extend to the 2nd item of # the tuple return sorted(set(estimators), key=itemgetter(0)) 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, optional, default=0 Pseudo random number generator state. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. """ if "random_state" in estimator.get_params(): estimator.set_params(random_state=random_state) def if_matplotlib(func): """Test decorator that skips test if matplotlib not installed. Parameters ---------- func """ @wraps(func) def run_test(*args, **kwargs): try: import matplotlib matplotlib.use('Agg', warn=False) # this fails if no $DISPLAY specified import matplotlib.pyplot as plt plt.figure() except ImportError: raise SkipTest('Matplotlib not available.') else: return func(*args, **kwargs) return run_test try: import pytest skip_if_32bit = pytest.mark.skipif(_IS_32BIT, reason='skipped on 32bit platforms') skip_travis = pytest.mark.skipif(os.environ.get('TRAVIS') == 'true', reason='skip on travis') fails_if_pypy = pytest.mark.xfail(IS_PYPY, raises=NotImplementedError, reason='not compatible with PyPy') skip_if_no_parallel = pytest.mark.skipif(not joblib.parallel.mp, reason="joblib is in serial mode") # 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 clean_warning_registry(): """Clean Python warning registry for easier testing of warning messages. We may not need to do this any more when getting rid of Python 2, not entirely sure. See https://bugs.python.org/issue4180 and https://bugs.python.org/issue21724 for more details. """ reg = "__warningregistry__" for mod_name, mod in list(sys.modules.items()): if 'six.moves' in mod_name: continue if hasattr(mod, reg): getattr(mod, reg).clear() 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 WindowsError: if warn: warnings.warn("Could not delete temporary folder %s" % folder_path) class TempMemmap(object): """ Parameters ---------- data mmap_mode """ 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 return_folder """ 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, class_name=None): """Get function full name Parameters ---------- func : callable The function object. class_name : string, optional (default: None) If ``func`` is a class method and the class name is known specify class_name for the error message. Returns ------- name : str The function name. """ parts = [] module = inspect.getmodule(func) if module: parts.append(module.__name__) if class_name is not None: parts.append(class_name) elif hasattr(func, 'im_class'): parts.append(func.im_class.__name__) parts.append(func.__name__) return '.'.join(parts) def check_docstring_parameters(func, doc=None, ignore=None, class_name=None): """Helper to check docstring Parameters ---------- func : callable The function object to test. doc : str, optional (default: None) Docstring if it is passed manually to the test. ignore : None | list Parameters to ignore. class_name : string, optional (default: None) If ``func`` is a class method and the class name is known specify class_name for the error message. 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, class_name=class_name) 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 args = list(filter(lambda x: x not in ignore, _get_args(func))) # drop self if len(args) > 0 and args[0] == 'self': args.remove('self') if doc is None: with warnings.catch_warnings(record=True) as w: try: doc = docscrape.FunctionDoc(func) except Exception as exp: incorrect += [func_name + ' parsing error: ' + str(exp)] return incorrect if len(w): raise RuntimeError('Error for %s:\n%s' % (func_name, w[0])) param_names = [] for name, type_definition, param_doc in doc['Parameters']: 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())] if '*' not in name: param_names.append(name.split(':')[0].strip('` ')) param_names = list(filter(lambda x: x not in ignore, param_names)) if len(param_names) != len(args): bad = str(sorted(list(set(param_names) ^ set(args)))) incorrect += [func_name + ' arg mismatch: ' + bad] else: for n1, n2 in zip(param_names, args): if n1 != n2: incorrect += [func_name + ' ' + n1 + ' != ' + n2] return incorrect