vibhatha commented on code in PR #12590: URL: https://github.com/apache/arrow/pull/12590#discussion_r862268470
########## python/pyarrow/tests/test_udf.py: ########## @@ -0,0 +1,498 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + + +import pytest + +import pyarrow as pa +from pyarrow import compute as pc + +# UDFs are all tested with a dataset scan +pytestmark = pytest.mark.dataset + + +try: + import pyarrow.dataset as ds +except ImportError: + ds = None + + +def mock_udf_context(batch_length=10): + from pyarrow._compute import _get_scalar_udf_context + return _get_scalar_udf_context(pa.default_memory_pool(), batch_length) + + [email protected](scope="session") +def unary_func_fixture(): + def unary_function(ctx, scalar1): + return pc.call_function("add", [scalar1, 1]) + func_name = "y=x+k" + unary_doc = {"summary": "add function", + "description": "test add function"} + pc.register_scalar_function(unary_function, + func_name, + unary_doc, + {"array": pa.int64()}, + pa.int64()) + return unary_function, func_name + + [email protected](scope="session") +def binary_func_fixture(): + def binary_function(ctx, m, x): + return pc.call_function("multiply", [m, x]) + func_name = "y=mx" + binary_doc = {"summary": "y=mx", + "description": "find y from y = mx"} + pc.register_scalar_function(binary_function, + func_name, + binary_doc, + {"m": pa.int64(), + "x": pa.int64(), + }, + pa.int64()) + return binary_function, func_name + + [email protected](scope="session") +def ternary_func_fixture(): + def ternary_function(ctx, m, x, c): + mx = pc.call_function("multiply", [m, x]) + return pc.call_function("add", [mx, c]) + ternary_doc = {"summary": "y=mx+c", + "description": "find y from y = mx + c"} + func_name = "y=mx+c" + pc.register_scalar_function(ternary_function, + func_name, + ternary_doc, + { + "array1": pa.int64(), + "array2": pa.int64(), + "array3": pa.int64(), + }, + pa.int64()) + return ternary_function, func_name + + [email protected](scope="session") +def varargs_func_fixture(): + def varargs_function(ctx, *values): + base_val = values[:2] + res = pc.call_function("add", base_val) + for other_val in values[2:]: + res = pc.call_function("add", [res, other_val]) + return res + func_name = "z=ax+by+c" + varargs_doc = {"summary": "z=ax+by+c", + "description": "find z from z = ax + by + c" + } + pc.register_scalar_function(varargs_function, + func_name, + varargs_doc, + { + "array1": pa.int64(), + "array2": pa.int64(), + "array3": pa.int64(), + "array4": pa.int64(), + "array5": pa.int64(), + }, + pa.int64()) + return varargs_function, func_name + + [email protected](scope="session") +def random_with_udf_ctx_func_fixture(): + def random_with_udf_ctx(context, one, two): + return pc.add(one, two, memory_pool=context.memory_pool) + + in_types = {"one": pa.int64(), + "two": pa.int64(), + } + func_doc = { + "summary": "test udf context", + "description": "udf context test" + } + func_name = "test_udf_context" + pc.register_scalar_function(random_with_udf_ctx, + func_name, func_doc, + in_types, + pa.int64()) + return random_with_udf_ctx, func_name + + [email protected](scope="session") +def output_check_func_fixture(): + # The objective of this fixture is to evaluate, + # how the UDF interface respond to unexpected + # output types. The types chosen at the test + # end are either of different Arrow data type + # or non-Arrow type. + def output_check(ctx, array): + ar = pc.call_function("add", [array, 1]) + ar = ar.cast(pa.int32()) + return ar + func_name = "test_output_value" + in_types = {"array": pa.int64()} + out_type = pa.int64() + doc = { + "summary": "add function scalar", + "description": "add function" + } + pc.register_scalar_function(output_check, func_name, doc, + in_types, out_type) + return output_check, func_name + + [email protected](scope="session") +def nullary_check_func_fixture(): + # this needs to return array values + def nullary_check(context): + return pa.array([42] * context.batch_length, type=pa.int64(), + memory_pool=context.memory_pool) + + func_doc = { + "summary": "random function", + "description": "generates a random value" + } + func_name = "test_random_func" + pc.register_scalar_function(nullary_check, + func_name, + func_doc, + {}, + pa.int64()) + + return nullary_check, func_name + + [email protected](scope="session") +def output_python_type_func_fixture(): + # This fixture helps to check the response + # when the function return value is not an Arrow + # defined data type. Instead here the returned value + # is of type int in Python. + def const_return(ctx, scalar): + return 42 + + func_name = "test_output_type" + in_types = {"array": pa.int64()} + out_type = pa.int64() + doc = { + "summary": "add function scalar", + "description": "add function" + } + pc.register_scalar_function(const_return, func_name, doc, + in_types, out_type) + return const_return, func_name + + [email protected](scope="session") +def varargs_check_func_fixture(): + def varargs_check(ctx, *values): + base_val = values[:2] + res = pc.call_function("add", base_val) + for other_val in values[2:]: + res = pc.call_function("add", [res, other_val]) + return res + func_name = "test_varargs_function" + in_types = {"array1": pa.int64(), + "array2": pa.int64(), + } + doc = {"summary": "n add function", + "description": "add N number of arrays" + } + pc.register_scalar_function(varargs_check, func_name, doc, + in_types, pa.int64()) + + return varargs_check, func_name + + [email protected](scope="session") +def raise_func_fixture(): + def raise_func(ctx): + raise ValueError("Test function with raise") + func_name = "test_raise" + doc = { + "summary": "test function with raise", + "description": "function with a raise" + } + pc.register_scalar_function(raise_func, func_name, doc, + {}, pa.int64()) + return raise_func, func_name + + +def check_scalar_function(func_fixture, + inputs, + run_in_dataset=True, + batch_length=None): + function, name = func_fixture + if batch_length is None: + for input in inputs: + try: + batch_length = len(inputs) + except TypeError: + pass + expected_output = function(mock_udf_context(batch_length), *inputs) + func = pc.get_function(name) + assert func.name == name + + result = pc.call_function(name, inputs) + assert result == expected_output + if run_in_dataset: + field_names = [f'field{index}' for index, in_arr in inputs] + table = pa.Table.from_arrays(inputs, field_names) + dataset = ds.dataset(table) + func_args = [ds.field(field_name) for field_name in field_names] + result_table = dataset.to_table( + columns={'result': ds.field('')._call(name, func_args)}) + assert result_table.column(0).chunks[0] == expected_output + + +def test_scalar_udf_array_unary(unary_func_fixture): + check_scalar_function(unary_func_fixture, + [ + pa.array([10, 20], pa.int64()) + ] + ) + + +def test_scalar_udf_array_binary(binary_func_fixture): + check_scalar_function(binary_func_fixture, + [ + pa.array([10, 20], pa.int64()), + pa.array([2, 4], pa.int64()) + ] + ) + + +def test_scalar_udf_array_ternary(ternary_func_fixture): + check_scalar_function(ternary_func_fixture, + [ + pa.array([10, 20], pa.int64()), + pa.array([2, 4], pa.int64()), + pa.array([5, 10], pa.int64()) + ] + ) + + +def test_scalar_udf_array_varargs(varargs_func_fixture): + check_scalar_function(varargs_func_fixture, + [ + pa.array([2, 3], pa.int64()), + pa.array([10, 20], pa.int64()), + pa.array([3, 7], pa.int64()), + pa.array([20, 30], pa.int64()), + pa.array([5, 10], pa.int64()) + ] + ) + + +def test_registration_errors(): + # validate function name + doc = { + "summary": "test udf input", + "description": "parameters are validated" + } + in_types = {"scalar": pa.int64()} + out_type = pa.int64() + + def test_reg_function(context): + return pa.array([10]) + + with pytest.raises(TypeError): + pc.register_scalar_function(test_reg_function, + None, doc, in_types, + out_type) + + # validate function + with pytest.raises(TypeError, match="func must be a callable"): + pc.register_scalar_function(None, "test_none_function", doc, in_types, + out_type) + + # validate output type + expected_expr = "DataType expected, got <class 'NoneType'>" + with pytest.raises(TypeError, match=expected_expr): + pc.register_scalar_function(test_reg_function, + "test_output_function", doc, in_types, + None) + + # validate input type + expected_expr = "in_types must be a dictionary of DataType" + with pytest.raises(TypeError, match=expected_expr): + pc.register_scalar_function(test_reg_function, + "test_input_function", doc, None, + out_type) + + # register an already registered function + # first registration + pc.register_scalar_function(test_reg_function, + "test_reg_function", doc, {}, + out_type) + # second registration + expected_expr = "Already have a function registered with name:" \ + + " test_reg_function" + with pytest.raises(pa.lib.ArrowKeyError, match=expected_expr): + pc.register_scalar_function(test_reg_function, + "test_reg_function", doc, {}, + out_type) + + +def test_varargs_function_validation(varargs_check_func_fixture): + _, func_name = varargs_check_func_fixture + func = pc.get_function(func_name) + + assert func.name == func_name + + error_msg = "VarArgs function 'test_varargs_function'" \ + + " needs at least 2 arguments" + + with pytest.raises(pa.lib.ArrowInvalid, match=error_msg): + pc.call_function(func_name, [pa.array([1, 10]), + ]) + + +def test_function_doc_validation(): + # validate arity + in_types = {"scalar": pa.int64()} + out_type = pa.int64() + + # doc with no summary + func_doc = { + "description": "desc" + } + + def add_const(ctx, scalar): + return pc.call_function("add", [scalar, 1]) + + expected_expr = "Function doc must contain a summary" + + with pytest.raises(ValueError, match=expected_expr): + pc.register_scalar_function(add_const, "test_no_summary", + func_doc, in_types, + out_type) + + # doc with no decription + func_doc = { + "summary": "test summary" + } + + expected_expr = "Function doc must contain a description" + + with pytest.raises(ValueError, match=expected_expr): + pc.register_scalar_function(add_const, "test_no_desc", + func_doc, in_types, + out_type) + + # doc with empty dictionary + func_doc = {} + expected_expr = "Function doc must contain a summary" + with pytest.raises(ValueError, match=expected_expr): + pc.register_scalar_function(add_const, + "test_empty_dictionary", + func_doc, in_types, + out_type) + + +def test_nullary_functions(nullary_check_func_fixture): + check_scalar_function(nullary_check_func_fixture, [], False, 1) Review Comment: Let's add a description and update the test case accordingly in future. -- This is an automated message from the Apache Git Service. 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