Python skip.SkipTest方法代码示例

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_format_docstrings():
    """
    Test if docstrings are well formatted.
    """
    # Disabled for now
    return True

    try:
        verify_format_docstrings()
    except SkipTest as e:
        import traceback
        traceback.print_exc(e)
        raise AssertionError(
            "Some file raised SkipTest on import, and inadvertently"
            " canceled the documentation testing."
        ) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def verify_format_docstrings():
    """
    Implementation of `test_format_docstrings`. The implementation is
    factored out so it can be placed inside a guard against SkipTest.
    """
    format_infractions = []

    for path in list_files(".py"):
        rel_path = os.path.relpath(path, cleverhans.__path__[0])
        if rel_path in whitelist_docstrings:
            continue
        try:
            format_infractions.extend(docstring_errors(path))
        except Exception as e:
            format_infractions.append(["%s failed to run so format cannot "
                                       "be checked. Error message:\n %s" %
                                       (rel_path, e)])

    if len(format_infractions) > 0:
        msg = "\n".join(':'.join(line) for line in format_infractions)
        raise AssertionError("Docstring format not respected:\n%s" % msg) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_course_credits_inst_200_ok(self):
        raise SkipTest()
        client = Client()
        client.login_user("ggbaker")

        url = reverse('planning.views.view_teaching_credits_inst')
        response = basic_page_tests(self, client, url)
        self.assertEqual(response.status_code, 200)

        url = reverse('planning.views.view_teaching_equivalent_inst', kwargs={'equivalent_id': 1})
        response = basic_page_tests(self, client, url)
        self.assertEqual(response.status_code, 200)

        url = reverse('planning.views.new_teaching_equivalent_inst')
        response = basic_page_tests(self, client, url)
        self.assertEqual(response.status_code, 200)

        url = reverse('planning.views.edit_teaching_equivalent_inst', kwargs={'equivalent_id': 1})
        response = basic_page_tests(self, client, url)
        self.assertEqual(response.status_code, 200) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_local_csm_grad_c():
    raise SkipTest("Opt disabled as it don't support unsorted indices")
    if not theano.config.cxx:
        raise SkipTest("G++ not available, so we need to skip this test.")
    data = tensor.vector()
    indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
                              tensor.ivector())
    mode = theano.compile.mode.get_default_mode()

    if theano.config.mode == 'FAST_COMPILE':
        mode = theano.compile.Mode(linker='c|py', optimizer='fast_compile')

    mode = mode.including("specialize", "local_csm_grad_c")
    for CS, cast in [(sparse.CSC, sp.csc_matrix), (sparse.CSR, sp.csr_matrix)]:
        cost = tensor.sum(sparse.DenseFromSparse()(CS(data, indices, indptr, shape)))
        f = theano.function(
            [data, indices, indptr, shape],
            tensor.grad(cost, data),
            mode=mode)
        assert not any(isinstance(node.op, sparse.CSMGrad) for node
                       in f.maker.fgraph.toposort())
        v = cast(random_lil((10, 40),
                            config.floatX, 3))
        f(v.data, v.indices, v.indptr, v.shape) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_local_mul_s_d():
    if not theano.config.cxx:
        raise SkipTest("G++ not available, so we need to skip this test.")
    mode = theano.compile.mode.get_default_mode()
    mode = mode.including("specialize", "local_mul_s_d")

    for sp_format in sparse.sparse_formats:
        inputs = [getattr(theano.sparse, sp_format + '_matrix')(),
                  tensor.matrix()]

        f = theano.function(inputs,
                            sparse.mul_s_d(*inputs),
                            mode=mode)

        assert not any(isinstance(node.op, sparse.MulSD) for node
                       in f.maker.fgraph.toposort()) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_local_mul_s_v():
    if not theano.config.cxx:
        raise SkipTest("G++ not available, so we need to skip this test.")
    mode = theano.compile.mode.get_default_mode()
    mode = mode.including("specialize", "local_mul_s_v")

    for sp_format in ['csr']:  # Not implemented for other format
        inputs = [getattr(theano.sparse, sp_format + '_matrix')(),
                  tensor.vector()]

        f = theano.function(inputs,
                            sparse.mul_s_v(*inputs),
                            mode=mode)

        assert not any(isinstance(node.op, sparse.MulSV) for node
                       in f.maker.fgraph.toposort()) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_local_structured_add_s_v():
    if not theano.config.cxx:
        raise SkipTest("G++ not available, so we need to skip this test.")
    mode = theano.compile.mode.get_default_mode()
    mode = mode.including("specialize", "local_structured_add_s_v")

    for sp_format in ['csr']:  # Not implemented for other format
        inputs = [getattr(theano.sparse, sp_format + '_matrix')(),
                  tensor.vector()]

        f = theano.function(inputs,
                            sparse.structured_add_s_v(*inputs),
                            mode=mode)

        assert not any(isinstance(node.op, sparse.StructuredAddSV) for node
                       in f.maker.fgraph.toposort()) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_local_sampling_dot_csr():
    if not theano.config.cxx:
        raise SkipTest("G++ not available, so we need to skip this test.")
    mode = theano.compile.mode.get_default_mode()
    mode = mode.including("specialize", "local_sampling_dot_csr")

    for sp_format in ['csr']:  # Not implemented for other format
        inputs = [tensor.matrix(),
                  tensor.matrix(),
                  getattr(theano.sparse, sp_format + '_matrix')()]

        f = theano.function(inputs,
                            sparse.sampling_dot(*inputs),
                            mode=mode)

        if theano.config.blas.ldflags:
            assert not any(isinstance(node.op, sparse.SamplingDot) for node
                       in f.maker.fgraph.toposort())
        else:
            # SamplingDotCSR's C implementation needs blas, so it should not
            # be inserted
            assert not any(isinstance(node.op, sparse.opt.SamplingDotCSR) for node
                       in f.maker.fgraph.toposort()) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def __generalized_ss_test(self, theanop, symbolicType, testOp, scipyType):

        scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]

        if (bool(scipy_ver < [0, 13])):
            raise SkipTest("comparison operators need newer release of scipy")

        x = symbolicType()
        y = symbolicType()

        op = theanop(x, y)

        f = theano.function([x, y], op)

        m1 = scipyType(random_lil((10, 40), config.floatX, 3))
        m2 = scipyType(random_lil((10, 40), config.floatX, 3))

        self.assertTrue(numpy.array_equal(f(m1, m2).data, testOp(m1, m2).data)) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def __generalized_sd_test(self, theanop, symbolicType, testOp, scipyType):

        scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]

        if (bool(scipy_ver < [0, 13])):
            raise SkipTest("comparison operators need newer release of scipy")

        x = symbolicType()
        y = theano.tensor.matrix()

        op = theanop(x, y)

        f = theano.function([x, y], op)

        m1 = scipyType(random_lil((10, 40), config.floatX, 3))
        m2 = self._rand_ranged(1000, -1000, [10, 40])

        self.assertTrue(numpy.array_equal(f(m1, m2).data, testOp(m1, m2).data)) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def __generalized_ds_test(self, theanop, symbolicType, testOp, scipyType):

        scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]

        if (bool(scipy_ver < [0, 13])):
            raise SkipTest("comparison operators need newer release of scipy")

        x = symbolicType()
        y = theano.tensor.matrix()

        op = theanop(y, x)

        f = theano.function([y, x], op)

        m1 = scipyType(random_lil((10, 40), config.floatX, 3))
        m2 = self._rand_ranged(1000, -1000, [10, 40])

        self.assertTrue(numpy.array_equal(f(m2, m1).data, testOp(m2, m1).data)) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_equality_case(self):
        """
        Test assuring normal behaviour when values
        in the matrices are equal
        """

        scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]

        if (bool(scipy_ver < [0, 13])):
            raise SkipTest("comparison operators need newer release of scipy")

        x = sparse.csc_matrix()
        y = theano.tensor.matrix()

        m1 = sp.csc_matrix((2, 2), dtype=theano.config.floatX)
        m2 = numpy.asarray([[0, 0], [0, 0]], dtype=theano.config.floatX)

        for func in self.testsDic:

            op = func(y, x)
            f = theano.function([y, x], op)

            self.assertTrue(numpy.array_equal(f(m2, m1),
                                              self.testsDic[func](m2, m1))) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_grad(self):
        c = T.matrix()
        p_y = T.exp(c) / T.exp(c).sum(axis=1).dimshuffle(0, 'x')

        # test that function contains softmax and softmaxgrad
        w = T.matrix()
        backup = config.warn.sum_div_dimshuffle_bug
        config.warn.sum_div_dimshuffle_bug = False
        try:
            g = theano.function([c, w], T.grad((p_y * w).sum(), c))
            hasattr(g.maker.fgraph.outputs[0].tag, 'trace')
        finally:
            config.warn.sum_div_dimshuffle_bug = backup
        g_ops = [n.op for n in g.maker.fgraph.toposort()]
        # print '--- g ='
        # printing.debugprint(g)
        # print '==='

        raise SkipTest('Optimization not enabled for the moment')
        assert len(g_ops) == 2
        assert softmax_op in g_ops
        assert softmax_grad in g_ops
        g(self.rng.rand(3, 4), self.rng.uniform(.5, 1, (3, 4))) 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_transpose_basic(self):
        # this should be a transposed softmax
        c = T.matrix()
        p_y = T.exp(c) / T.exp(c).sum(axis=0)

        # test that function contains softmax and no div.
        f = theano.function([c], p_y)
        # printing.debugprint(f)

        # test that function contains softmax and no div.
        backup = config.warn.sum_div_dimshuffle_bug
        config.warn.sum_div_dimshuffle_bug = False
        try:
            g = theano.function([c], T.grad(p_y.sum(), c))
            hasattr(g.maker.fgraph.outputs[0].tag, 'trace')
        finally:
            config.warn.sum_div_dimshuffle_bug = backup
        # printing.debugprint(g)
        raise SkipTest('Optimization not enabled for the moment') 

# 需要导入模块: from nose.plugins import skip [as 别名]
# 或者: from nose.plugins.skip import SkipTest [as 别名]
def test_1d_basic(self):
        # this should be a softmax, but of a one-row matrix
        c = T.vector()
        p_y = T.exp(c) / T.exp(c).sum()

        # test that function contains softmax and no div.
        f = theano.function([c], p_y)
        hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
        # printing.debugprint(f)

        # test that function contains softmax and no div.
        backup = config.warn.sum_div_dimshuffle_bug
        config.warn.sum_div_dimshuffle_bug = False
        try:
            g = theano.function([c], T.grad(p_y.sum(), c))
            hasattr(g.maker.fgraph.outputs[0].tag, 'trace')
        finally:
            config.warn.sum_div_dimshuffle_bug = backup
        # printing.debugprint(g)
        raise SkipTest('Optimization not enabled for the moment')

    # REPEAT 3 CASES in presence of log(softmax) with the advanced indexing
    # etc.