using parameterized to simplify testing logic

https://github.com/wolever/parameterized

Author: phi-dbq

python/requirements.txt

@@ -4,6 +4,7 @@ h5py>=2.7.0
 keras==2.0.4 # NOTE: this package has only been tested with keras 2.0.4 and may not work with other releases
 nose>=1.3.7  # for testing
 numpy>=1.11.2
+parameterized>=0.6.1 # for testing
 pillow>=4.1.1,<4.2
 pygments>=2.2.0
 tensorflow==1.3.0

python/tests/param/params_test.py

@@ -15,97 +15,36 @@
 from __future__ import absolute_import, division, print_function
 
 from collections import namedtuple
-from six import with_metaclass
+# Use this to create parameterized test cases
+from parameterized import parameterized
 
 from sparkdl.param.converters import SparkDLTypeConverters
 
 from ..tests import PythonUnitTestCase
 
-
-class TestGenMeta(type):
-    """
-    This meta-class add test cases to the main unit-test class.
-    To add test cases, implement the test logic in a function
-    >>> `def _my_test_impl(): ...`
-    then call the following function
-    >>>  `_register_test_case(fn_impl=_my_test_impl, name=..., doc=...)`
-    """
-    def __new__(mcs, name, bases, attrs):
-        _add_invalid_col2tnsr_mapping_tests()
-        attrs.update(_TEST_FUNCTIONS_REGISTRY)
-        return super(TestGenMeta, mcs).__new__(mcs, name, bases, attrs)
-
-
-# Stores test function name mapped to implementation body
-_TEST_FUNCTIONS_REGISTRY = {}
-
 TestCase = namedtuple('TestCase', ['data', 'description'])
 
-def _register_test_case(fn_impl, name, doc):
-    """ Add an individual test case """
-    fn_impl.__name__ = name
-    fn_impl.__doc__ = doc
-    _TEST_FUNCTIONS_REGISTRY[name] = fn_impl
-
-def _add_invalid_col2tnsr_mapping_tests():
-    """ Create a list of test cases and construct individual test functions for each case """
-    shared_test_cases = [
-        TestCase(data=['a1', 'b2'], description='required pair but get single element'),
-        TestCase(data=('c3', 'd4'), description='required pair but get single element'),
-        TestCase(data=[('a', 1), ('b', 2)], description='only accept dict, but get list'),
-        TestCase(data={1: 'a', 2.0: 'b'}, description='wrong mapping type'),
-        TestCase(data={'a': 1.0, 'b': 2}, description='wrong mapping type'),
-    ]
-
-    # Specify test cases for `asColumnToTensorNameMap`
-    # Add additional test cases specific to this one
-    col2tnsr_test_cases = shared_test_cases + [
-        TestCase(data={'colA': 'tnsrOpA', 'colB': 'tnsrOpB'},
-                 description='strict tensor name required'),
-    ]
-    _fn_name_template = 'test_invalid_col2tnsr_{idx}'
-    _fn_doc_template = 'Test invalid column => tensor name mapping: {description}'
-
-    for idx, test_case in enumerate(col2tnsr_test_cases):
-        # Add the actual test logic here
-        def test_fn_impl(self):
-            with self.assertRaises(TypeError, msg=test_case.description):
-                SparkDLTypeConverters.asColumnToTensorNameMap(test_case.data)
-
-        _name = _fn_name_template.format(idx=idx)
-        _doc = _fn_doc_template.format(description=test_case.description)
-        _register_test_case(fn_impl=test_fn_impl, name=_name, doc=_doc)
-
-
-    # Specify tests for `asTensorNameToColumnMap`
-    tnsr2col_test_cases = shared_test_cases + [
-        TestCase(data={'tnsrOpA': 'colA', 'tnsrOpB': 'colB'},
-                 description='strict tensor name required'),
-    ]
-    _fn_name_template = 'test_invalid_tnsr2col_{idx}'
-    _fn_doc_template = 'Test invalid tensor name => column mapping: {description}'
-
-    for idx, test_case in enumerate(tnsr2col_test_cases):
-        # Add the actual test logic here
-        def test_fn_impl(self):  # pylint: disable=function-redefined
-            with self.assertRaises(TypeError, msg=test_case.description):
-                SparkDLTypeConverters.asTensorNameToColumnMap(test_case.data)
-
-        _name = _fn_name_template.format(idx=idx)
-        _doc = _fn_doc_template.format(description=test_case.description)
-        _register_test_case(fn_impl=test_fn_impl, name=_name, doc=_doc)
-
-
-class ParamsConverterTest(with_metaclass(TestGenMeta, PythonUnitTestCase)):
+_shared_invalid_test_cases = [
+    TestCase(data=['a1', 'b2'], description='required pair but get single element'),
+    TestCase(data=('c3', 'd4'), description='required pair but get single element'),
+    TestCase(data=[('a', 1), ('b', 2)], description='only accept dict, but get list'),
+    TestCase(data={1: 'a', 2.0: 'b'}, description='wrong mapping type'),
+    TestCase(data={'a': 1.0, 'b': 2}, description='wrong mapping type'),
+]
+_col2tnsr_test_cases = _shared_invalid_test_cases + [
+    TestCase(data={'colA': 'tnsrOpA', 'colB': 'tnsrOpB'},
+             description='strict tensor name required'),
+]
+_tnsr2col_test_cases = _shared_invalid_test_cases + [
+    TestCase(data={'tnsrOpA': 'colA', 'tnsrOpB': 'colB'},
+             description='strict tensor name required'),
+]
+
+class ParamsConverterTest(PythonUnitTestCase):
     """
     Test MLlib Params introduced in Spark Deep Learning Pipeline
     Additional test cases are attached via the meta class `TestGenMeta`.
     """
-    # pylint: disable=protected-access
-
-    @classmethod
-    def setUpClass(cls):
-        print(repr(cls), cls)
 
     def test_tf_input_mapping_converter(self):
         """ Test valid input mapping conversion """
@@ -122,3 +61,13 @@ def test_tf_output_mapping_converter(self):
 
         res = SparkDLTypeConverters.asTensorNameToColumnMap(valid_tnsr_output)
         self.assertEqual(valid_output_mapping_result, res)
+
+    @parameterized.expand(_col2tnsr_test_cases)
+    def test_invalid_input_mapping(self, data, description):
+        with self.assertRaises(TypeError, msg=description):
+            SparkDLTypeConverters.asColumnToTensorNameMap(data)
+
+    @parameterized.expand(_tnsr2col_test_cases)
+    def test_invalid_output_mapping(self, data, description):
+        with self.assertRaises(TypeError, msg=description):
+            SparkDLTypeConverters.asTensorNameToColumnMap(data)