Changed comments to docstrings where necessary in theano/sparse/tests

theano/sparse/tests/test_basic.py

@@ -98,31 +98,30 @@ def random_lil(shape, dtype, nnz):
 
 def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None,
                          explicit_zero=False, unsorted_indices=False):
-    # Return a tuple containing everything needed to
-    # perform a test.
-    #
-    # If `out_dtype` is `None`, theano.config.floatX is
-    # used.
-    #
-    # :param format: Sparse format.
-    # :param shape: Shape of data.
-    # :param n: Number of variable.
-    # :param out_dtype: dtype of output.
-    # :param p: Sparsity proportion.
-    # :param gap: Tuple for the range of the random sample. When
-    #             length is 1, it is assumed to be the exclusive
-    #             max, when `gap` = (`a`, `b`) it provide a sample
-    #             from [a, b[. If `None` is used, it provide [0, 1]
-    #             for float dtypes and [0, 50[ for integer dtypes.
-    # :param explicit_zero: When True, we add explicit zero in the
-    #                       returned sparse matrix
-    # :param unsorted_indices: when True, we make sure there is
-    #                          unsorted indices in the returned
-    #                          sparse matrix.
-    # :return: (variable, data) where both `variable`
-    #          and `data` are list.
-    #
-    # :note: explicit_zero and unsorted_indices was added in Theano 0.6rc4
+    """
+    Return a tuple containing everything needed to perform a test.
+
+    If `out_dtype` is `None`, theano.config.floatX is used.
+
+    :param format: Sparse format.
+    :param shape: Shape of data.
+    :param n: Number of variable.
+    :param out_dtype: dtype of output.
+    :param p: Sparsity proportion.
+    :param gap: Tuple for the range of the random sample. When
+                length is 1, it is assumed to be the exclusive
+                max, when `gap` = (`a`, `b`) it provide a sample
+                from [a, b[. If `None` is used, it provide [0, 1]
+                for float dtypes and [0, 50[ for integer dtypes.
+    :param explicit_zero: When True, we add explicit zero in the
+                          returned sparse matrix
+    :param unsorted_indices: when True, we make sure there is
+                             unsorted indices in the returned
+                             sparse matrix.
+    :return: (variable, data) where both `variable` and `data` are list.
+
+    :note: explicit_zero and unsorted_indices was added in Theano 0.6rc4
+    """
 
     if out_dtype is None:
         out_dtype = theano.config.floatX
@@ -182,25 +181,27 @@ def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None,
 
 
 def verify_grad_sparse(op, pt, structured=False, *args, **kwargs):
-    # Wrapper for theano.test.unittest_tools.py:verify_grad wich
-    # converts sparse variables back and forth.
-    #
-    # Parameters
-    # ----------
-    # op
-    #     Op to check.
-    # pt
-    #     List of inputs to realize the tests.
-    # structured
-    #     True to tests with a structured grad, False otherwise.
-    # args
-    #     Other `verify_grad` parameters if any.
-    # kwargs
-    #     Other `verify_grad` keywords if any.
-    #
-    # Returns
-    # -------
-    # None
+    """
+    Wrapper for theano.test.unittest_tools.py:verify_grad wich
+    converts sparse variables back and forth.
+
+    Parameters
+    ----------
+    op
+        Op to check.
+    pt
+        List of inputs to realize the tests.
+    structured
+        True to tests with a structured grad, False otherwise.
+    args
+        Other `verify_grad` parameters if any.
+    kwargs
+        Other `verify_grad` keywords if any.
+
+    Returns
+    -------
+    None
+    """
 
     def conv_none(x):
         return x
@@ -1427,7 +1428,9 @@ class DotTests(utt.InferShapeTester):
 
 
 class UsmmTests(unittest.TestCase):
-    # Test the Usmm and UsmmCscDense class and related optimization
+    """
+    Test the Usmm and UsmmCscDense class and related optimization
+    """
     def setUp(self):
         x_size = (10, 100)
         y_size = (100, 200)
@@ -2470,8 +2473,9 @@ def _format_info(nb):
 
 
 class _HVStackTester(utt.InferShapeTester):
-    # Test for both HStack and VStack.
-
+    """
+    Test for both HStack and VStack.
+    """
     nb = 3  # Number of sparse matrix to stack
     x, mat = _format_info(nb)
 
@@ -2517,11 +2521,13 @@ class _HVStackTester(utt.InferShapeTester):
 
 
 def _hv_switch(op, expected_function):
-    # Return the right test class for HStack or VStack.
-    #
-    # :Parameters:
-    # - `op`: HStack or VStack class.
-    # - `expected_function`: function from scipy for comparaison.
+    """
+    Return the right test class for HStack or VStack.
+
+    :Parameters:
+    - `op`: HStack or VStack class.
+    - `expected_function`: function from scipy for comparaison.
+    """
 
     class XStackTester(_HVStackTester):
         op_class = op
@@ -2585,28 +2591,30 @@ class AddSSDataTester(utt.InferShapeTester):
 
 def elemwise_checker(op, expected_f, gap=None, test_dtypes=None,
                      grad_test=True, name=None, gap_grad=None):
-    # Return the appropriate test class for the elemwise on sparse.
-    #
-    # :param op: Op to test.
-    # :expected_f: Function use to compare. This function must act
-    #              on dense matrix. If the op is structured
-    #              see the `structure_function` decorator to make
-    #              this function structured.
-    # :param gap: Tuple for the range of the random sample. When
-    #             length is 1, it is assumed to be the exclusive
-    #             max, when `gap` = (`a`, `b`) it provide a sample
-    #             from [a, b[. If `None` is used, it provide [0, 1]
-    #             for float dtypes and [0, 50[ for integer dtypes.
-    # :param test_dtypes: Particular dtypes for testing the op.
-    #                     If `None`, this is set to the most common
-    #                     dtypes.
-    # :param grad_test: True for testing the grad. False will
-    #                   skip this test.
-    # :param gap_grad: If None, we reuse gap. Otherwise it is the same as gap
-    #                  but for testing the gradiant of the op.
-    #
-    # :return: The class that perform the tests, not an instance
-    #          of the class.
+    """
+    Return the appropriate test class for the elemwise on sparse.
+
+    :param op: Op to test.
+    :expected_f: Function use to compare. This function must act
+                 on dense matrix. If the op is structured
+                 see the `structure_function` decorator to make
+                 this function structured.
+    :param gap: Tuple for the range of the random sample. When
+                length is 1, it is assumed to be the exclusive
+                max, when `gap` = (`a`, `b`) it provide a sample
+                from [a, b[. If `None` is used, it provide [0, 1]
+                for float dtypes and [0, 50[ for integer dtypes.
+    :param test_dtypes: Particular dtypes for testing the op.
+                        If `None`, this is set to the most common
+                        dtypes.
+    :param grad_test: True for testing the grad. False will
+                      skip this test.
+    :param gap_grad: If None, we reuse gap. Otherwise it is the same as gap
+                     but for testing the gradiant of the op.
+
+    :return: The class that perform the tests, not an instance
+             of the class.
+    """
 
     if test_dtypes is None:
         test_dtypes = sparse.all_dtypes
@@ -2782,19 +2790,21 @@ def test_hstack_vstack():
 
 
 def structure_function(f, index=0):
-    # Decorator to structure a function wich
-    # apply on dense matrix.
-    #
-    # Here, the inputs of the function must be
-    # dense matrix. The sparse pattern is
-    # determined by finding the zeros.
-    #
-    # :param index: The index of the parameter
-    #               from wich the function must
-    #               be structured.
-    #
-    # :return: The structured function for its
-    #          `index` parameter.
+    """
+    Decorator to structure a function wich
+    apply on dense matrix.
+
+    Here, the inputs of the function must be
+    dense matrix. The sparse pattern is
+    determined by finding the zeros.
+
+    :param index: The index of the parameter
+                  from wich the function must
+                  be structured.
+
+    :return: The structured function for its
+             `index` parameter.
+    """
 
     def structured_function(*args):
         pattern = args[index]