ryanthompson591 commented on code in PR #17697: URL: https://github.com/apache/beam/pull/17697#discussion_r875902795
########## sdks/python/apache_beam/examples/inference/sklearn_inference_example.py: ########## @@ -0,0 +1,98 @@ +# +# 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 argparse +import numpy as np + +import apache_beam as beam + +from apache_beam.ml.inference.api import RunInference +from apache_beam.ml.inference.sklearn_inference import ModelFileType +from apache_beam.ml.inference.sklearn_inference import SklearnModelLoader +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions + + +class GetData(beam.DoFn): + def process(self, dataset_name): + if dataset_name == 'fetch_20newsgroups': + from sklearn.datasets import fetch_20newsgroups_vectorized + newsgroups_test = fetch_20newsgroups_vectorized( + subset='test', data_home='/tmp') + for x, y in zip(newsgroups_test['data'], newsgroups_test['target']): + # (n_feat,) is supported but not (1, n_feat) + yield y, np.squeeze(x.toarray()) + else: + raise NotImplementedError + + +class PostProcessor(beam.DoFn): + """Post process PredictionResult to output true_label and + prediction using numpy.""" + def process(self, element): + true_label, prediction_result = element + prediction = prediction_result.inference + yield true_label, prediction + + +def setup_pipeline(options: PipelineOptions, args=None): + """Sets up Sklearn RunInference pipeline""" + model_loader = SklearnModelLoader( + model_file_type=ModelFileType.PICKLE, model_uri=args.model_path) Review Comment: pickle is the default, so no need to specify. ########## sdks/python/apache_beam/examples/inference/sklearn_inference_example.py: ########## @@ -0,0 +1,98 @@ +# +# 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 argparse +import numpy as np + +import apache_beam as beam + +from apache_beam.ml.inference.api import RunInference +from apache_beam.ml.inference.sklearn_inference import ModelFileType +from apache_beam.ml.inference.sklearn_inference import SklearnModelLoader +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions + + +class GetData(beam.DoFn): + def process(self, dataset_name): + if dataset_name == 'fetch_20newsgroups': + from sklearn.datasets import fetch_20newsgroups_vectorized + newsgroups_test = fetch_20newsgroups_vectorized( + subset='test', data_home='/tmp') + for x, y in zip(newsgroups_test['data'], newsgroups_test['target']): + # (n_feat,) is supported but not (1, n_feat) + yield y, np.squeeze(x.toarray()) + else: + raise NotImplementedError + + +class PostProcessor(beam.DoFn): + """Post process PredictionResult to output true_label and + prediction using numpy.""" + def process(self, element): + true_label, prediction_result = element + prediction = prediction_result.inference + yield true_label, prediction + + +def setup_pipeline(options: PipelineOptions, args=None): + """Sets up Sklearn RunInference pipeline""" + model_loader = SklearnModelLoader( + model_file_type=ModelFileType.PICKLE, model_uri=args.model_path) + + with beam.Pipeline(options=options) as p: + data = ( + p | "Dataset by name" >> beam.Create([args.dataset]) + | "Get data" >> beam.ParDo(GetData())) + + predictions = ( + data | "RunInference" >> RunInference(model_loader) + | "PostProcessor" >> beam.ParDo(PostProcessor())) + + predictions | "Write output to GCS" >> beam.io.WriteToText( # pylint: disable=expression-not-assigned + args.output, + file_name_suffix='.txt', + shard_name_template='', + append_trailing_newlines=True) + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--dataset', + dest='dataset', + help='Sklearn built-in dataset name', + # datasets in sklearn.datasets module + choices=['olivetti_faces', 'fetch_20newsgroups']) Review Comment: I suppose this is fine, but IMO it would be better to use a hosted dataset. ########## sdks/python/apache_beam/ml/inference/sklearn_inference_it_test.py: ########## @@ -0,0 +1,54 @@ +# +# 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. +# + +"""End-to-End test for Sklearn Inference""" + +import logging +import pytest +import unittest +import uuid + +from apache_beam.io.filesystems import FileSystems +from apache_beam.examples.inference import sklearn_inference_example +from apache_beam.testing.test_pipeline import TestPipeline + + +class SklearnInference(unittest.TestCase): + @pytest.mark.it_postcommit + @pytest.mark.sickbay_direct + @pytest.mark.sickbay_spark + @pytest.mark.sickbay_flink + def test_predictions_output_file(self): + test_pipeline = TestPipeline(is_integration_test=True) + output_file_dir = 'gs://apache-beam-ml/temp_storage_end_to_end_testing/outputs' # pylint: disable=line-too-long + output_file = '/'.join([output_file_dir, str(uuid.uuid4()), 'result.txt']) + model_path = 'gs://apache-beam-ml/temp_storage_end_to_end_testing/models/fetch_20newsgroups/multinomialNB.pkl' # pylint: disable=line-too-long + extra_opts = { + 'dataset': 'fetch_20newsgroups', + 'output': output_file, + 'model_path': model_path, + } + + sklearn_inference_example.run( + test_pipeline.get_full_options_as_args(**extra_opts), + save_main_session=False) + self.assertEqual(FileSystems().exists(output_file), True) Review Comment: Is it possible to read anything from the file and validate it? ########## sdks/python/apache_beam/examples/inference/sklearn_inference_example.py: ########## @@ -0,0 +1,98 @@ +# +# 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 argparse +import numpy as np + +import apache_beam as beam + +from apache_beam.ml.inference.api import RunInference +from apache_beam.ml.inference.sklearn_inference import ModelFileType +from apache_beam.ml.inference.sklearn_inference import SklearnModelLoader +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions + + +class GetData(beam.DoFn): Review Comment: I think this would be a stronger example if it followed a more conventional approach like loading from a csv. Also in this case, even having labels is not important. Since I suppose in a true example if the labels were already known it wouldn't be valuable. Plus I don't think the labels are really a good key, later when we validate data. Because I don't think the data is guaranteed to come out ordered the same as it was input. A better key for later validating expectation might be just the index of the example. ########## sdks/python/apache_beam/examples/inference/sklearn_inference_example.py: ########## @@ -0,0 +1,98 @@ +# +# 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 argparse +import numpy as np + +import apache_beam as beam + +from apache_beam.ml.inference.api import RunInference +from apache_beam.ml.inference.sklearn_inference import ModelFileType +from apache_beam.ml.inference.sklearn_inference import SklearnModelLoader +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions + + +class GetData(beam.DoFn): + def process(self, dataset_name): + if dataset_name == 'fetch_20newsgroups': + from sklearn.datasets import fetch_20newsgroups_vectorized + newsgroups_test = fetch_20newsgroups_vectorized( + subset='test', data_home='/tmp') + for x, y in zip(newsgroups_test['data'], newsgroups_test['target']): + # (n_feat,) is supported but not (1, n_feat) + yield y, np.squeeze(x.toarray()) + else: + raise NotImplementedError + + +class PostProcessor(beam.DoFn): + """Post process PredictionResult to output true_label and Review Comment: Maybe rename true_label to expected_label or just label. ML people should know what that is. ########## sdks/python/apache_beam/ml/inference/sklearn_inference_it_test.py: ########## @@ -0,0 +1,54 @@ +# +# 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. +# + +"""End-to-End test for Sklearn Inference""" + +import logging +import pytest +import unittest +import uuid + +from apache_beam.io.filesystems import FileSystems +from apache_beam.examples.inference import sklearn_inference_example +from apache_beam.testing.test_pipeline import TestPipeline + + +class SklearnInference(unittest.TestCase): + @pytest.mark.it_postcommit + @pytest.mark.sickbay_direct Review Comment: Why do we want to sickbay spark/flink/direct. Shouldn't those all work? Or will they fail when data is hosted on gcs? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
