FIX update sklearn.__all__ to include all end-user submodules

Conflicts:
	sklearn/__init__.py
	sklearn/tests/test_common.py

Author: jnothman

sklearn/__init__.py

@@ -36,6 +36,7 @@
 else:
     from . import __check_build
     from .base import clone
+    __check_build  # avoid flakes unused variable error
 
     def test(*args, **kwargs):
         import warnings
@@ -60,14 +61,17 @@ def test(*args, **kwargs):
     # "test" as a test function
     test.__test__ = False
 
-    __all__ = ['cross_validation', 'cluster', 'covariance',
-               'datasets', 'decomposition', 'feature_extraction',
-               'feature_selection', 'semi_supervised',
-               'gaussian_process', 'grid_search', 'hmm', 'lda', 'linear_model',
-               'metrics', 'mixture', 'naive_bayes', 'neighbors', 'pipeline',
-               'preprocessing', 'qda', 'svm', 'clone',
-               'cross_decomposition',
-               'isotonic', 'pls']
+    __all__ = ['cluster', 'covariance', 'cross_decomposition',
+               'cross_validation', 'datasets', 'decomposition', 'dummy',
+               'ensemble', 'externals', 'feature_extraction',
+               'feature_selection', 'gaussian_process', 'grid_search', 'hmm',
+               'isotonic', 'kernel_approximation', 'lda', 'learning_curve',
+               'linear_model', 'manifold', 'metrics', 'mixture', 'multiclass',
+               'naive_bayes', 'neighbors', 'neural_network', 'pipeline', 'pls',
+               'preprocessing', 'qda', 'random_projection', 'semi_supervised',
+               'svm', 'tree',
+               # Non-modules:
+               'clone']
 
 
 def setup_module(module):

sklearn/tests/test_common.py

@@ -31,8 +31,10 @@
 from sklearn.utils.testing import meta_estimators
 from sklearn.utils.testing import set_random_state
 from sklearn.utils.testing import assert_greater
+from sklearn.utils.testing import assert_in
 from sklearn.utils.testing import SkipTest
 from sklearn.utils.testing import check_skip_travis
+from sklearn.utils.testing import ignore_warnings
 
 import sklearn
 from sklearn.base import (clone, ClassifierMixin, RegressorMixin,
@@ -1143,12 +1145,14 @@ def check_cluster_overwrite_params(name, Clustering, X, y):
                      % (name, k, v, new_params[k]))
 
 
+@ignore_warnings
 def test_import_all_consistency():
     # Smoke test to check that any name in a __all__ list is actually defined
     # in the namespace of the module or package.
     pkgs = pkgutil.walk_packages(path=sklearn.__path__, prefix='sklearn.',
                                  onerror=lambda _: None)
-    for importer, modname, ispkg in pkgs:
+    submods = [modname for _, modname, _ in pkgs]
+    for modname in submods + ['sklearn']:
         if ".tests." in modname:
             continue
         package = __import__(modname, fromlist="dummy")
@@ -1159,6 +1163,15 @@ def test_import_all_consistency():
                         modname, name))
 
 
+def test_root_import_all_completeness():
+    EXCEPTIONS = ('utils', 'tests', 'base', 'setup')
+    for _, modname, _ in pkgutil.walk_packages(path=sklearn.__path__,
+                                               onerror=lambda _: None):
+        if '.' in modname or modname.startswith('_') or modname in EXCEPTIONS:
+            continue
+        assert_in(modname, sklearn.__all__)
+
+
 def test_sparsify_estimators():
     """Test if predict with sparsified estimators works.