def test_tensor_vector_mtable_input_output(self):
input_schema_str = "col0 TENSOR, col1 DENSE_VECTOR, col2 MTABLE"
t = Tensor.fromNdarray(np.array([[1, 2], [3, 4]], dtype=np.int32))
v = DenseVector.ones(10)
m = MTable.fromDataframe(
pd.DataFrame([[1.0, "A", 0, 0, 0], [2.0, "B", 1, 1, 0],
[3.0, "C", 2, 2, 1], [4.0, "D", 3, 3, 1]]),
'f0 double, f1 string, f2 int, f3 int, label int')
values = [t, v, m]
# input
j_csv_util_cls = get_java_class(
"com.alibaba.alink.common.utils.TableUtil")
j_input_col_types: List[JavaObject] = j_csv_util_cls.getColTypes(
input_schema_str)
j_row = LocalPredictor._create_input_j_row(values, j_input_col_types)
j_row_type_adapter = get_java_class(
"com.alibaba.alink.python.utils.RowTypeAdapter")
j_row_type_adapter.checkRowType(j_row, j_input_col_types)
# output
arr = LocalPredictor._j_row_to_arr(j_row)
self.assertEqual(3, len(arr))
self.assertTrue(isinstance(arr[0], (Tensor, )))
self.assertTrue(isinstance(arr[0], (IntTensor, )))
self.assertTrue(isinstance(arr[1], (DenseVector, )))
self.assertTrue(isinstance(arr[1], (Vector, )))
self.assertTrue(isinstance(arr[2], (MTable, )))
def test_create_input_j_row(self):
j_csv_util_cls = get_java_class(
"com.alibaba.alink.common.utils.TableUtil")
input_schema_str = "col0 boolean, col1 byte, col2 short, col3 int, col4 long, col5 float, col6 double, col7 varchar"
values = [True, 1, 1, 1, 1, 2., 4., "123"]
j_input_col_types: List[JavaObject] = j_csv_util_cls.getColTypes(
input_schema_str)
j_row = LocalPredictor._create_input_j_row(values, j_input_col_types)
j_row_type_adapter = get_java_class(
"com.alibaba.alink.python.utils.RowTypeAdapter")
j_row_type_adapter.checkRowType(j_row, j_input_col_types)
def test_existence_of_java_class_method(self):
AlinkGlobalConfiguration.setPrintProcessInfo(True)
j_check_wrapper_util_cls = get_java_class(
"com.alibaba.alink.python.utils.CheckWrapperUtil")
py_classes = get_all_subclasses(
JavaObjectWrapperWithAutoTypeConversion)
inconsistency = []
for py_class in py_classes:
j_cls_name = py_class._j_cls_name
py_func_set = set(
filter(
lambda d: not d.startswith("_") and d != "get_j_obj" and d
!= "j_cls_name", dir(py_class)))
if hasattr(py_class, '_unsupported_j_methods'):
py_func_set = py_func_set.union(
set(py_class._unsupported_j_methods))
j_func_set = set(
j_check_wrapper_util_cls.getJMethodNames(j_cls_name))
if py_func_set != j_func_set:
inconsistency.append((j_cls_name, py_func_set - j_func_set,
j_func_set - py_func_set))
self.assertTrue(
len(inconsistency) == 0, "Inconsistency: {}".format(inconsistency))
def test_tensor_methods_coverage(self):
py_class = Tensor
j_cls_name = py_class._j_cls_name
py_func_set = set(filter(
lambda d: not d.startswith("_") and d != "get_j_obj" and d != "j_cls_name",
dir(py_class)))
py_func_set -= {'toNdarray', 'fromNdarray'}
if hasattr(py_class, '_unsupported_j_methods'):
py_func_set = py_func_set.union(set(py_class._unsupported_j_methods))
j_check_wrapper_util_cls = get_java_class("com.alibaba.alink.python.utils.CheckWrapperUtil")
j_func_set = set(j_check_wrapper_util_cls.getJMethodNames(j_cls_name))
self.assertSetEqual(py_func_set, j_func_set)