robertwb commented on code in PR #26795: URL: https://github.com/apache/beam/pull/26795#discussion_r1236279360
########## sdks/python/apache_beam/ml/transforms/tft_transforms_test.py: ########## @@ -0,0 +1,393 @@ +# +# 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. +# + +from typing import List +from typing import NamedTuple + +import unittest +import numpy as np +from parameterized import parameterized + +import apache_beam as beam +from apache_beam.testing.util import assert_that +from apache_beam.testing.util import equal_to + +# pylint: disable=wrong-import-order, wrong-import-position +try: + from apache_beam.ml.transforms import base + from apache_beam.ml.transforms import tft_transforms + from apache_beam.ml.transforms import handlers +except ImportError: + tft_transforms = None + +skip_if_tft_not_available = unittest.skipIf( + tft_transforms is None, 'tensorflow_transform is not installed.') + + +class MyTypesUnbatched(NamedTuple): + x: List[int] + + +class MyTypesBatched(NamedTuple): + x: List[int] + + +z_score_expected = {'x_mean': 3.5, 'x_var': 2.9166666666666665} + + +def assert_z_score_artifacts(element): + element = element.as_dict() + assert 'x_mean' in element + assert 'x_var' in element + assert element['x_mean'] == z_score_expected['x_mean'] + assert element['x_var'] == z_score_expected['x_var'] + + +def assert_scale_to_0_1_artifacts(element): + element = element.as_dict() + assert 'x_min' in element + assert 'x_max' in element + assert element['x_min'] == 1 + assert element['x_max'] == 6 + + +def assert_bucketize_artifacts(element): + element = element.as_dict() + assert 'x_quantiles' in element + assert np.array_equal( + element['x_quantiles'], np.array([3, 5], dtype=np.float32)) + + +@skip_if_tft_not_available +class ScaleZScoreTest(unittest.TestCase): + def test_z_score_unbatched(self): + unbatched_data = [{ + 'x': 1 + }, { + 'x': 2 + }, { + 'x': 3 + }, { + 'x': 4 + }, { + 'x': 5 + }, { + 'x': 6 + }] + + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + unbatched_result = ( + p + | "unbatchedCreate" >> beam.Create(unbatched_data) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesUnbatched) + | "unbatchedMLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.Scale_To_ZScore(columns=['x']))) + _ = (unbatched_result | beam.Map(assert_z_score_artifacts)) + + def test_z_score_batched(self): + batched_data = [{'x': [1, 2, 3]}, {'x': [4, 5, 6]}] + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + batched_result = ( + p + | "batchedCreate" >> beam.Create(batched_data) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesBatched) + | "batchedMLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.Scale_To_ZScore(columns=['x']))) + _ = (batched_result | beam.Map(assert_z_score_artifacts)) + + +@skip_if_tft_not_available +class ScaleTo01Test(unittest.TestCase): + def test_scale_to_0_1_batched(self): + batched_data = [{'x': [1, 2, 3]}, {'x': [4, 5, 6]}] + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + batched_result = ( + p + | "batchedCreate" >> beam.Create(batched_data) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesBatched) + | "batchedMLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.Scale_To_0_1(columns=['x']))) + _ = (batched_result | beam.Map(assert_scale_to_0_1_artifacts)) + + expected_output = [ + np.array([0, 0.2, 0.4], dtype=np.float32), + np.array([0.6, 0.8, 1], dtype=np.float32) + ] + actual_output = (batched_result | beam.Map(lambda x: x.x)) + assert_that( + actual_output, equal_to(expected_output, equals_fn=np.array_equal)) + + def test_scale_to_0_1_unbatched(self): + unbatched_data = [{ + 'x': 1 + }, { + 'x': 2 + }, { + 'x': 3 + }, { + 'x': 4 + }, { + 'x': 5 + }, { + 'x': 6 + }] + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + unbatched_result = ( + p + | "unbatchedCreate" >> beam.Create(unbatched_data) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesUnbatched) + | "unbatchedMLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.Scale_To_0_1(columns=['x']))) + + _ = (unbatched_result | beam.Map(assert_scale_to_0_1_artifacts)) + expected_output = ( + np.array([0], dtype=np.float32), + np.array([0.2], dtype=np.float32), + np.array([0.4], dtype=np.float32), + np.array([0.6], dtype=np.float32), + np.array([0.8], dtype=np.float32), + np.array([1], dtype=np.float32)) + actual_output = (unbatched_result | beam.Map(lambda x: x.x)) + assert_that( + actual_output, equal_to(expected_output, equals_fn=np.array_equal)) + + +@skip_if_tft_not_available +class BucketizeTest(unittest.TestCase): + def test_bucketize_unbatched(self): + unbatched = [{'x': 1}, {'x': 2}, {'x': 3}, {'x': 4}, {'x': 5}, {'x': 6}] + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + unbatched_result = ( + p + | "unbatchedCreate" >> beam.Create(unbatched) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesUnbatched) + | "unbatchedMLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.Bucketize(columns=['x'], num_buckets=3))) + _ = (unbatched_result | beam.Map(assert_bucketize_artifacts)) + + transformed_data = (unbatched_result | beam.Map(lambda x: x.x)) + expected_data = [ + np.array([0]), + np.array([0]), + np.array([1]), + np.array([1]), + np.array([2]), + np.array([2]) + ] + assert_that( + transformed_data, equal_to(expected_data, equals_fn=np.array_equal)) + + def test_bucketize_batched(self): + batched = [{'x': [1, 2, 3]}, {'x': [4, 5, 6]}] + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + batched_result = ( + p + | "batchedCreate" >> beam.Create(batched) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesBatched) + | "batchedMLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.Bucketize(columns=['x'], num_buckets=3))) + _ = (batched_result | beam.Map(assert_bucketize_artifacts)) + + transformed_data = ( + batched_result + | "TransformedColumnX" >> beam.Map(lambda ele: ele.x)) + expected_data = [ + np.array([0, 0, 1], dtype=np.int64), + np.array([1, 2, 2], dtype=np.int64) + ] + assert_that( + transformed_data, equal_to(expected_data, equals_fn=np.array_equal)) + + @parameterized.expand([ + (range(1, 10), [4, 7]), + (range(9, 0, -1), [4, 7]), + (range(19, 0, -1), [10]), + (range(1, 100), [25, 50, 75]), + # similar to the above but with odd number of elements + (range(1, 100, 2), [25, 51, 75]), + (range(99, 0, -1), range(10, 100, 10)) + ]) + def test_bucketize_boundaries(self, test_input, expected_boundaries): + # boundaries are outputted as artifacts for the Bucketize transform. + data = [{'x': [i]} for i in test_input] + num_buckets = len(expected_boundaries) + 1 + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + result = ( + p + | "Create" >> beam.Create(data) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesUnbatched) + | "MLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.Bucketize(columns=['x'], num_buckets=num_buckets))) + actual_boundaries = ( + result + | beam.Map(lambda x: x.as_dict()) + | beam.Map(lambda x: x['x_quantiles'])) + + def assert_boundaries(actual_boundaries): + assert np.array_equal(actual_boundaries, expected_boundaries) + + _ = (actual_boundaries | beam.Map(assert_boundaries)) + + +@skip_if_tft_not_available +class ApplyBucketsTest(unittest.TestCase): + @parameterized.expand([ + (range(1, 100), [25, 50, 75]), + (range(1, 100, 2), [25, 51, 75]), + ]) + def test_apply_buckets(self, test_inputs, bucket_boundaries): + with beam.Pipeline() as p: + data = [{'x': [i]} for i in test_inputs] + process_handler = handlers.TFTProcessHandlerSchema() + result = ( + p + | "Create" >> beam.Create(data) + | beam.Map(lambda x: MyTypesBatched(**x)).with_output_types( + MyTypesUnbatched) + | "MLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.ApplyBuckets( + columns=['x'], bucket_boundaries=bucket_boundaries))) + expected_output = [] + bucket = 0 + for x in sorted(test_inputs): + # Increment the bucket number when crossing the boundary + if (bucket < len(bucket_boundaries) and x >= bucket_boundaries[bucket]): + bucket += 1 + expected_output.append(np.array([bucket])) + + actual_output = (result | beam.Map(lambda x: x.x)) + assert_that( + actual_output, equal_to(expected_output, equals_fn=np.array_equal)) + + +class ComputeAndVocabUnbatchedInputType(NamedTuple): + x: str + + +class ComputeAndVocabBatchedInputType(NamedTuple): + x: List[str] + + +@skip_if_tft_not_available +class ComputeAndApplyVocabTest(unittest.TestCase): + def test_compute_and_apply_vocabulary_unbatched_inputs(self): + batch_size = 100 + num_instances = batch_size + 1 + input_data = [{ + 'x': '%.10i' % i, # Front-padded to facilitate lexicographic sorting. + } for i in range(num_instances)] + + expected_data = [{ + 'x': (len(input_data) - 1) - i, # Due to reverse lexicographic sorting. + } for i in range(len(input_data))] + + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + actual_data = ( + p + | "Create" >> beam.Create(input_data) + | beam.Map(lambda x: ComputeAndVocabUnbatchedInputType(**x) + ).with_output_types(ComputeAndVocabUnbatchedInputType) + | "MLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( + tft_transforms.ComputeAndApplyVocabulary(columns=['x']))) + actual_data |= beam.Map(lambda x: x.as_dict()) + + assert_that(actual_data, equal_to(expected_data)) + + def test_compute_and_apply_vocabulary_batched(self): + batch_size = 100 + num_instances = batch_size + 1 + input_data = [ + { + 'x': ['%.10i' % i, '%.10i' % (i + 1), '%.10i' % (i + 2)], + # Front-padded to facilitate lexicographic sorting. + } for i in range(0, num_instances, 3) + ] + + # since we have 3 elements in a single batch, multiply with 3 for + # each iteration i on the expected output. + excepted_data = [ + np.array([(len(input_data) * 3 - 1) - i, + (len(input_data) * 3 - 1) - i - 1, + (len(input_data) * 3 - 1) - i - 2], + dtype=np.int64) # Front-padded to facilitate lexicographic + # sorting. + for i in range(0, len(input_data) * 3, 3) + ] + + with beam.Pipeline() as p: + process_handler = handlers.TFTProcessHandlerSchema() + result = ( + p + | "Create" >> beam.Create(input_data) + | beam.Map(lambda x: ComputeAndVocabBatchedInputType(**x) + ).with_output_types(ComputeAndVocabBatchedInputType) + | "MLTransform" >> + base.MLTransform(process_handler=process_handler).with_transform( Review Comment: Yes, I think we should make this internal rather than exposed to the user. -- This is an automated message from the Apache Git Service. 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