"""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 re import platform import struct 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 # WindowsError only exist on Windows try: WindowsError except NameError: WindowsError = None import sklearn from sklearn.base import BaseEstimator from sklearn.externals import joblib # Conveniently import all assertions in one place. from nose.tools import assert_equal from nose.tools import assert_not_equal from nose.tools import assert_true from nose.tools import assert_false from nose.tools import assert_raises from nose.tools import raises try: from nose.tools import assert_dict_equal except ImportError: # Not in old versions of nose, but is only for formatting anyway assert_dict_equal = assert_equal from nose import SkipTest from nose import with_setup from numpy.testing import assert_almost_equal from numpy.testing import assert_array_equal from numpy.testing import assert_array_almost_equal from numpy.testing import assert_array_less from numpy.testing import assert_approx_equal import numpy as np from sklearn.base import (ClassifierMixin, RegressorMixin, TransformerMixin, ClusterMixin) from sklearn.cluster import DBSCAN __all__ = ["assert_equal", "assert_not_equal", "assert_raises", "assert_raises_regexp", "raises", "with_setup", "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"] try: from nose.tools import assert_in, assert_not_in except ImportError: # Nose < 1.0.0 def assert_in(x, container): assert_true(x in container, msg="%r in %r" % (x, container)) def assert_not_in(x, container): assert_false(x in container, msg="%r in %r" % (x, container)) try: from nose.tools import assert_raises_regex except ImportError: # for Python 2 def assert_raises_regex(expected_exception, expected_regexp, callable_obj=None, *args, **kwargs): """Helper function to check for message patterns in exceptions.""" not_raised = False try: callable_obj(*args, **kwargs) not_raised = True except expected_exception as e: error_message = str(e) if not re.compile(expected_regexp).search(error_message): raise AssertionError("Error message should match pattern " "%r. %r does not." % (expected_regexp, error_message)) if not_raised: raise AssertionError("%s not raised by %s" % (expected_exception.__name__, callable_obj.__name__)) # 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_less(a, b, msg=None): message = "%r is not lower than %r" % (a, b) if msg is not None: message += ": " + msg assert a < b, message def _assert_greater(a, b, msg=None): message = "%r is not greater than %r" % (a, b) if msg is not None: message += ": " + msg assert a > b, message def assert_less_equal(a, b, msg=None): message = "%r is not lower than or equal to %r" % (a, b) if msg is not None: message += ": " + msg assert a <= b, message def assert_greater_equal(a, b, msg=None): message = "%r is not greater than or equal to %r" % (a, b) if msg is not None: message += ": " + msg assert a >= b, message 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 Calable object to trigger warnings. *args : the positional arguments to `func`. **kw : the keyword arguments to `func` Returns ------- result : the return value of `func` """ # very important to avoid uncontrolled state propagation 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 entire message or a substring to test for. If callable, it takes a string as argument and will trigger an assertion error if it returns `False`. func : callable Calable 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 # To remove when we support numpy 1.7 def assert_no_warnings(func, *args, **kw): # XXX: once we may depend on python >= 2.6, this can be replaced by the # warnings module context manager. # 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 ---------- 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 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 to ignore the warnings raise by a function. Copied from Python 2.7.5 and modified as required. Parameters ---------- category : tuple of warning class, defaut 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): # very important to avoid uncontrolled state propagation 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): clean_warning_registry() # be safe and not propagate state + chaos warnings.simplefilter("ignore", self.category) 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 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() # be safe and not propagate state + chaos try: from nose.tools import assert_less except ImportError: assert_less = _assert_less try: from nose.tools import assert_greater except ImportError: assert_greater = _assert_greater def _assert_allclose(actual, desired, rtol=1e-7, atol=0, err_msg='', verbose=True): actual, desired = np.asanyarray(actual), np.asanyarray(desired) if np.allclose(actual, desired, rtol=rtol, atol=atol): return msg = ('Array not equal to tolerance rtol=%g, atol=%g: ' 'actual %s, desired %s') % (rtol, atol, actual, desired) raise AssertionError(msg) if hasattr(np.testing, 'assert_allclose'): assert_allclose = np.testing.assert_allclose else: assert_allclose = _assert_allclose def assert_raise_message(exceptions, message, function, *args, **kwargs): """Helper function to test error messages in exceptions. Parameters ---------- exceptions : exception or tuple of exception Name of the estimator function : callable Calable object to raise error *args : the positional arguments to `function`. **kw : 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 fake_mldata(columns_dict, dataname, matfile, ordering=None): """Create a fake mldata data set. 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') class mock_mldata_urlopen(object): def __init__(self, mock_datasets): """Object that mocks the urlopen function to fake requests to mldata. `mock_datasets` is 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 `fake_mldata` for details). 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. """ self.mock_datasets = mock_datasets def __call__(self, urlname): 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): # 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"] # estimators that there is no way to default-construct sensibly OTHER = ["Pipeline", "FeatureUnion", "GridSearchCV", "RandomizedSearchCV", "SelectFromModel"] # some trange ones DONT_TEST = ['SparseCoder', 'EllipticEnvelope', 'DictVectorizer', 'LabelBinarizer', 'LabelEncoder', 'MultiLabelBinarizer', 'TfidfTransformer', 'TfidfVectorizer', 'IsotonicRegression', 'OneHotEncoder', 'RandomTreesEmbedding', '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 include_dont_test : boolean, default=False Whether to include "special" label estimator or test processors. 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. 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 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. Classes for whom random_state is deprecated are ignored. Currently DBSCAN is one such class. """ if isinstance(estimator, DBSCAN): return 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.""" @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 def skip_if_32bit(func): """Test decorator that skips tests on 32bit platforms.""" @wraps(func) def run_test(*args, **kwargs): bits = 8 * struct.calcsize("P") if bits == 32: raise SkipTest('Test skipped on 32bit platforms.') else: return func(*args, **kwargs) return run_test def if_not_mac_os(versions=('10.7', '10.8', '10.9'), message='Multi-process bug in Mac OS X >= 10.7 ' '(see issue #636)'): """Test decorator that skips test if OS is Mac OS X and its major version is one of ``versions``. """ warnings.warn("if_not_mac_os is deprecated in 0.17 and will be removed" " in 0.19: use the safer and more generic" " if_safe_multiprocessing_with_blas instead", DeprecationWarning) mac_version, _, _ = platform.mac_ver() skip = '.'.join(mac_version.split('.')[:2]) in versions def decorator(func): if skip: @wraps(func) def func(*args, **kwargs): raise SkipTest(message) return func return decorator def if_safe_multiprocessing_with_blas(func): """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. """ @wraps(func) def run_test(*args, **kwargs): if sys.platform == 'darwin': raise SkipTest( "Possible multi-process bug with some BLAS") return func(*args, **kwargs) return run_test def clean_warning_registry(): """Safe way to reset warnings.""" warnings.resetwarnings() 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 check_skip_travis(): """Skip test if being run on Travis.""" if os.environ.get('TRAVIS') == "true": raise SkipTest("This test needs to be skipped on Travis") 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): def __init__(self, data, mmap_mode='r'): self.temp_folder = tempfile.mkdtemp(prefix='sklearn_testing_') self.mmap_mode = mmap_mode self.data = data def __enter__(self): fpath = op.join(self.temp_folder, 'data.pkl') joblib.dump(self.data, fpath) data_read_only = joblib.load(fpath, mmap_mode=self.mmap_mode) atexit.register(lambda: _delete_folder(self.temp_folder, warn=True)) return data_read_only def __exit__(self, exc_type, exc_val, exc_tb): _delete_folder(self.temp_folder) with_network = with_setup(check_skip_network) with_travis = with_setup(check_skip_travis) class _named_check(object): """Wraps a check to show a useful description Parameters ---------- check : function Must have ``__name__`` and ``__call__`` arg_text : str A summary of arguments to the check """ # Setting the description on the function itself can give incorrect results # in failing tests def __init__(self, check, arg_text): self.check = check self.description = ("{0[1]}.{0[3]}:{1.__name__}({2})".format( inspect.stack()[1], check, arg_text)) def __call__(self, *args, **kwargs): return self.check(*args, **kwargs)