AnandInguva commented on code in PR #24965: URL: https://github.com/apache/beam/pull/24965#discussion_r1067299492
########## sdks/python/apache_beam/ml/inference/xgboost_inference_it_test.py: ########## @@ -0,0 +1,314 @@ +# +# 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 logging + +import pytest +import unittest +import uuid + +from apache_beam.examples.inference import xgboost_iris_classification +from apache_beam.io.filesystems import FileSystems +from apache_beam.testing.test_pipeline import TestPipeline + + +def process_outputs(filepath): + with FileSystems().open(filepath) as f: + lines = f.readlines() + lines = [l.decode('utf-8').strip('\n') for l in lines] + return lines + + [email protected]_xgboost [email protected]_postcommit +class XGBoostInference(unittest.TestCase): + def test_iris_classification_numpy_single_batch(self): + test_pipeline = TestPipeline(is_integration_test=True) + input_type = 'numpy' + output_file_dir = '/tmp' Review Comment: Can we have output file directory to gcs since it gets cleaned up periodically? https://github.com/apache/beam/blob/c0e689331c2a6573ecf267b9bef133a85ea8a36c/sdks/python/apache_beam/ml/inference/pytorch_inference_it_test.py#L74 ########## sdks/python/apache_beam/ml/inference/xgboost_inference_test.py: ########## @@ -0,0 +1,443 @@ +# +# 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 os +import shutil +import sys +import tempfile +import unittest +import zipfile +from typing import Any +from typing import Tuple + +import datatable +import numpy +import pandas +import pytest +import scipy + +import apache_beam as beam +from apache_beam.ml.inference import RunInference +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerDatatable +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerNumpy +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerSciPy +from apache_beam.testing.test_pipeline import TestPipeline +from apache_beam.testing.util import assert_that +from apache_beam.testing.util import equal_to + +try: + import xgboost +except ImportError: + raise unittest.SkipTest('XGBoost dependencies are not installed') + + +def _compare_prediction_result(a: PredictionResult, b: PredictionResult): + if isinstance(a.example, scipy.sparse.csr_matrix) and isinstance( Review Comment: For unittests to run, let us add these to the tox.ini file with the dependencies needed for xgboost tests similar to torch https://github.com/apache/beam/blob/3a98ecbdbfad59ae8cb96d7eaef544d25d9c437f/sdks/python/tox.ini#L317 ########## sdks/python/apache_beam/examples/inference/xgboost_iris_classification.py: ########## @@ -0,0 +1,169 @@ +# +# 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 logging +from typing import Callable +from typing import Iterable +from typing import List +from typing import Tuple +from typing import Union + +import datatable +import numpy +import pandas +import scipy +import xgboost +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split + +import apache_beam as beam +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerDatatable +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerNumpy +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerSciPy +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.runners.runner import PipelineResult + + +def _train_model(model_state_output_path: str = '/tmp/model.json', seed=999): + """Function to train an XGBoost Classifier using the sklearn Iris dataset""" + dataset = load_iris() + x_train, _, y_train, _ = train_test_split( + dataset['data'], dataset['target'], test_size=.2, random_state=seed) + booster = xgboost.XGBClassifier( + n_estimators=2, max_depth=2, learning_rate=1, objective='binary:logistic') + booster.fit(x_train, y_train) + booster.save_model(model_state_output_path) + return booster + + +class PostProcessor(beam.DoFn): + """Process the PredictionResult to get the predicted label. + Returns a comma separated string with true label and predicted label. + """ + def process(self, element: Tuple[int, PredictionResult]) -> Iterable[str]: + label, prediction_result = element + prediction = prediction_result.inference + yield '{},{}'.format(label, prediction) + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--input-type', + dest='input_type', + required=True, + choices=['numpy', 'pandas', 'scipy', 'datatable'], + help= + 'Datatype of the input data.' + ) + parser.add_argument( + '--output', + dest='output', + required=True, + help='Path to save output predictions.') + parser.add_argument( + '--model-state', Review Comment: ```suggestion '--model-state', '--model_state' ``` I would replace `-` with `_` ########## sdks/python/container/py310/base_image_requirements.txt: ########## @@ -154,4 +155,5 @@ urllib3==1.26.13 websocket-client==1.4.2 Werkzeug==2.2.2 wrapt==1.14.1 +xgboost==1.7.1 Review Comment: I would like to push back on adding these dependencies to container because they would increase the size of the container image since these are not dependencies of Apache Beam ########## sdks/python/apache_beam/examples/inference/xgboost_iris_classification.py: ########## @@ -0,0 +1,169 @@ +# +# 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 logging +from typing import Callable +from typing import Iterable +from typing import List +from typing import Tuple +from typing import Union + +import datatable +import numpy +import pandas +import scipy +import xgboost +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split + +import apache_beam as beam +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerDatatable +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerNumpy +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerSciPy +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.runners.runner import PipelineResult + + +def _train_model(model_state_output_path: str = '/tmp/model.json', seed=999): + """Function to train an XGBoost Classifier using the sklearn Iris dataset""" + dataset = load_iris() + x_train, _, y_train, _ = train_test_split( + dataset['data'], dataset['target'], test_size=.2, random_state=seed) + booster = xgboost.XGBClassifier( + n_estimators=2, max_depth=2, learning_rate=1, objective='binary:logistic') + booster.fit(x_train, y_train) + booster.save_model(model_state_output_path) + return booster + + +class PostProcessor(beam.DoFn): + """Process the PredictionResult to get the predicted label. + Returns a comma separated string with true label and predicted label. + """ + def process(self, element: Tuple[int, PredictionResult]) -> Iterable[str]: + label, prediction_result = element + prediction = prediction_result.inference + yield '{},{}'.format(label, prediction) + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--input-type', + dest='input_type', + required=True, + choices=['numpy', 'pandas', 'scipy', 'datatable'], + help= + 'Datatype of the input data.' + ) + parser.add_argument( + '--output', + dest='output', + required=True, + help='Path to save output predictions.') + parser.add_argument( + '--model-state', Review Comment: In the other arguments as well ########## sdks/python/apache_beam/examples/inference/xgboost_iris_classification.py: ########## @@ -0,0 +1,169 @@ +# +# 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 logging +from typing import Callable +from typing import Iterable +from typing import List +from typing import Tuple +from typing import Union + +import datatable +import numpy +import pandas +import scipy +import xgboost +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split + +import apache_beam as beam +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerDatatable +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerNumpy +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas +from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerSciPy +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from apache_beam.runners.runner import PipelineResult + + +def _train_model(model_state_output_path: str = '/tmp/model.json', seed=999): + """Function to train an XGBoost Classifier using the sklearn Iris dataset""" + dataset = load_iris() + x_train, _, y_train, _ = train_test_split( + dataset['data'], dataset['target'], test_size=.2, random_state=seed) + booster = xgboost.XGBClassifier( + n_estimators=2, max_depth=2, learning_rate=1, objective='binary:logistic') + booster.fit(x_train, y_train) + booster.save_model(model_state_output_path) + return booster + + +class PostProcessor(beam.DoFn): + """Process the PredictionResult to get the predicted label. + Returns a comma separated string with true label and predicted label. + """ + def process(self, element: Tuple[int, PredictionResult]) -> Iterable[str]: + label, prediction_result = element + prediction = prediction_result.inference + yield '{},{}'.format(label, prediction) + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--input-type', + dest='input_type', + required=True, + choices=['numpy', 'pandas', 'scipy', 'datatable'], + help= + 'Datatype of the input data.' + ) + parser.add_argument( + '--output', + dest='output', + required=True, + help='Path to save output predictions.') + parser.add_argument( + '--model-state', + dest='model_state', + required=True, + help='Path to the state of the XGBoost model loaded for Inference.' + ) + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument('--split', action='store_true', dest='split') + group.add_argument('--no-split', action='store_false', dest='split') + return parser.parse_known_args(argv) + + +def load_sklearn_iris_test_data( + data_type: Callable, + split: bool = True, + seed: int = 999) -> List[Union[numpy.array, pandas.DataFrame]]: + """ + Loads test data from the sklearn Iris dataset in a given format, + either in a single or multiple batches. + Args: + data_type: Datatype of the iris test dataset. + split: Split the dataset in different batches or return single batch. + seed: Random state for splitting the train and test set. + """ + dataset = load_iris() + _, x_test, _, _ = train_test_split( + dataset['data'], dataset['target'], test_size=.2, random_state=seed) + + if split: + return [(index, data_type(sample.reshape(1, -1))) for index, + sample in enumerate(x_test)] + return [(0, data_type(x_test))] + + +def run( + argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult: + """ + Args: + argv: Command line arguments defined for this example. + save_main_session: Used for internal testing. + test_pipeline: Used for internal testing. + """ + known_args, pipeline_args = parse_known_args(argv) + pipeline_options = PipelineOptions(pipeline_args) + pipeline_options.view_as(SetupOptions).save_main_session = save_main_session + + data_types = { + 'numpy': (numpy.array, XGBoostModelHandlerNumpy), + 'pandas': (pandas.DataFrame, XGBoostModelHandlerPandas), + 'scipy': (scipy.sparse.csr_matrix, XGBoostModelHandlerSciPy), + 'datatable': (datatable.Frame, XGBoostModelHandlerDatatable), + } + + input_data_type, model_handler = data_types[known_args.input_type] + + xgboost_model_handler = KeyedModelHandler( + model_handler( + model_class=xgboost.XGBClassifier, + model_state=known_args.model_state)) + + input_data = load_sklearn_iris_test_data( + data_type=input_data_type, split=known_args.split) + + pipeline = test_pipeline + if not test_pipeline: + pipeline = beam.Pipeline(options=pipeline_options) + + predictions = ( + pipeline + | "ReadInputData" >> beam.Create(input_data) + | "RunInference" >> RunInference(xgboost_model_handler) + | "PostProcessOutputs" >> beam.ParDo(PostProcessor())) + + _ = predictions | "WriteOutput" >> beam.io.WriteToText( + known_args.output, shard_name_template='', append_trailing_newlines=True) + + result = pipeline.run() + result.wait_until_finish() + return result + + +if __name__ == '__main__': + logging.getLogger().setLevel(logging.INFO) + _train_model() Review Comment: How long does it take to train the model? I hope this would take less time since it will run on user's machine ########## sdks/python/apache_beam/ml/inference/xgboost_inference.py: ########## @@ -0,0 +1,212 @@ +# +# 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 sys +from abc import ABC +from typing import Any +from typing import Callable +from typing import Dict +from typing import Iterable +from typing import Optional +from typing import Sequence +from typing import Union + +import datatable +import numpy +import pandas +import scipy +import xgboost + +from apache_beam.ml.inference.base import ExampleT +from apache_beam.ml.inference.base import ModelHandler +from apache_beam.ml.inference.base import ModelT +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import PredictionT + + +class XGBoostModelHandler(ModelHandler[ExampleT, PredictionT, ModelT], ABC): Review Comment: Users could(may be) use it if they want to use their own run_inference method. We can also document in docstring in similar way https://github.com/apache/beam/blob/c0e689331c2a6573ecf267b9bef133a85ea8a36c/sdks/python/apache_beam/pvalue.py#L351 ########## sdks/python/container/py310/base_image_requirements.txt: ########## @@ -154,4 +155,5 @@ urllib3==1.26.13 websocket-client==1.4.2 Werkzeug==2.2.2 wrapt==1.14.1 +xgboost==1.7.1 Review Comment: I wouldn't put `xgboost` here since it is not a dependency of beam. We can have a requirements file and gradle task. We can add this gradle task to the PostCommit suites similarly on torch tests run. For direct runner, https://github.com/apache/beam/blob/c0e689331c2a6573ecf267b9bef133a85ea8a36c/sdks/python/test-suites/direct/common.gradle#L284 For Dataflow runner, https://github.com/apache/beam/blob/c0e689331c2a6573ecf267b9bef133a85ea8a36c/sdks/python/test-suites/dataflow/common.gradle#L403 If IT tests are light weight and easy to run, we can add them to DirectRunner. ########## sdks/python/apache_beam/ml/inference/xgboost_inference_it_test.py: ########## @@ -0,0 +1,314 @@ +# +# 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 logging + +import pytest +import unittest +import uuid + +from apache_beam.examples.inference import xgboost_iris_classification +from apache_beam.io.filesystems import FileSystems +from apache_beam.testing.test_pipeline import TestPipeline + + +def process_outputs(filepath): + with FileSystems().open(filepath) as f: + lines = f.readlines() + lines = [l.decode('utf-8').strip('\n') for l in lines] + return lines + + [email protected]_xgboost [email protected]_postcommit +class XGBoostInference(unittest.TestCase): + def test_iris_classification_numpy_single_batch(self): + test_pipeline = TestPipeline(is_integration_test=True) + input_type = 'numpy' + output_file_dir = '/tmp' Review Comment: Can we have this change for all the tests? -- This is an automated message from the Apache Git Service. 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