sdks/python/apache_beam/examples/inference/milk_quality_prediction_windowing.py - beam - Git at Google

 #
 # 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.
 #

 """A streaming pipeline that uses RunInference API and windowing that classifies
 the quality of milk as good, bad or medium based on pH, temperature,
 taste, odor, fat, turbidity and color. Each minute new measurements come in
 and a sliding window aggregates the number of good, bad and medium
 samples.

 This example uses the milk quality prediction dataset from kaggle.
 https://www.kaggle.com/datasets/cpluzshrijayan/milkquality


 In order to set this example up, you will need two things.
 1. Download the data in csv format from kaggle and host it.
 2. Split the dataset in a training set and test set (preprocess_data function).
 3. Train the classifier.
 """

 import argparse
 import logging
 import time
 from typing import NamedTuple

 import pandas
 from sklearn.model_selection import train_test_split

 import apache_beam as beam
 import xgboost
 from apache_beam import window
 from apache_beam.ml.inference import RunInference
 from apache_beam.ml.inference.base import PredictionResult
 from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas
 from apache_beam.options.pipeline_options import PipelineOptions
 from apache_beam.options.pipeline_options import SetupOptions
 from apache_beam.runners.runner import PipelineResult
 from apache_beam.testing.test_stream import TestStream


 def parse_known_args(argv):
   """Parses args for the workflow."""
   parser = argparse.ArgumentParser()
   parser.add_argument(
       '--dataset',
       dest='dataset',
       required=True,
       help='Path to the csv containing Kaggle Milk Quality dataset.')
   parser.add_argument(
       '--pipeline_input_data',
       dest='pipeline_input_data',
       required=True,
       help='Path to store the csv containing input data for the pipeline.'
       'This will be generated as part of preprocessing the data.')
   parser.add_argument(
       '--training_set',
       dest='training_set',
       required=True,
       help='Path to store the csv containing the training set.'
       'This will be generated as part of preprocessing the data.')
   parser.add_argument(
       '--labels',
       dest='labels',
       required=True,
       help='Path to store the csv containing the labels used in training.'
       'This will be generated as part of preprocessing the data.')
   parser.add_argument(
       '--model_state',
       dest='model_state',
       required=True,
       help='Path to the state of the XGBoost model loaded for Inference.')
   return parser.parse_known_args(argv)


 def preprocess_data(
     dataset_path: str,
     training_set_path: str,
     labels_path: str,
     test_set_path: str):
   """
     Helper function to split the dataset into a training set
     and its labels and a test set. The training set and
     its labels are used to train a lightweight model.
     The test set is used to create a test streaming pipeline.
     Args:
         dataset_path: path to csv file containing the Kaggle
          milk quality dataset
         training_set_path: path to output the training samples
         labels_path:  path to output the labels for the training set
         test_set_path: path to output the test samples
     """
   df = pandas.read_csv(dataset_path)
   df['Grade'].replace(['low', 'medium', 'high'], [0, 1, 2], inplace=True)
   x = df.drop(columns=['Grade'])
   y = df['Grade']
   x_train, x_test, y_train, _ = \
       train_test_split(x, y, test_size=0.60, random_state=99)
   x_train.to_csv(training_set_path, index=False)
   y_train.to_csv(labels_path, index=False)
   x_test.to_csv(test_set_path, index=False)


 def train_model(
     samples_path: str, labels_path: str, model_state_output_path: str):
   """Function to train the XGBoost model.
     Args:
       samples_path: path to csv file containing the training data
       labels_path: path to csv file containing the labels for the training data
       model_state_output_path: Path to store the trained model
   """
   samples = pandas.read_csv(samples_path)
   labels = pandas.read_csv(labels_path)
   xgb = xgboost.XGBClassifier(max_depth=3)
   xgb.fit(samples, labels)
   xgb.save_model(model_state_output_path)
   return xgb


 class MilkQualityAggregation(NamedTuple):
   bad_quality_measurements: int
   medium_quality_measurements: int
   high_quality_measurements: int


 class AggregateMilkQualityResults(beam.CombineFn):
   """Simple aggregation to keep track of the number
    of samples with good, bad and medium quality milk."""
   def create_accumulator(self):
     return MilkQualityAggregation(0, 0, 0)

   def add_input(
       self, accumulator: MilkQualityAggregation, element: PredictionResult):
     quality = element.inference[0]
     if quality == 0:
       return MilkQualityAggregation(
           accumulator.bad_quality_measurements + 1,
           accumulator.medium_quality_measurements,
           accumulator.high_quality_measurements)
     elif quality == 1:
       return MilkQualityAggregation(
           accumulator.bad_quality_measurements,
           accumulator.medium_quality_measurements + 1,
           accumulator.high_quality_measurements)
     else:
       return MilkQualityAggregation(
           accumulator.bad_quality_measurements,
           accumulator.medium_quality_measurements,
           accumulator.high_quality_measurements + 1)

   def merge_accumulators(self, accumulators: MilkQualityAggregation):
     return MilkQualityAggregation(
         sum(
             aggregation.bad_quality_measurements
             for aggregation in accumulators),
         sum(
             aggregation.medium_quality_measurements
             for aggregation in accumulators),
         sum(
             aggregation.high_quality_measurements
             for aggregation in accumulators),
     )

   def extract_output(self, accumulator: MilkQualityAggregation):
     return accumulator


 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

   milk_quality_data = pandas.read_csv(known_args.pipeline_input_data)

   start = time.mktime(time.strptime('2023/06/29 10:00:00', '%Y/%m/%d %H:%M:%S'))

   # Create a test stream
   test_stream = TestStream()

   # Watermark is set to 10:00:00
   test_stream.advance_watermark_to(start)

   # Split the dataframe in individual samples
   samples = [
       milk_quality_data.iloc[i:i + 1] for i in range(len(milk_quality_data))
   ]

   for watermark_offset, sample in enumerate(samples, 1):
     test_stream.advance_watermark_to(start + watermark_offset)
     test_stream.add_elements([sample])

   test_stream.advance_watermark_to_infinity()

   model_handler = XGBoostModelHandlerPandas(
       model_class=xgboost.XGBClassifier, model_state=known_args.model_state)

   with beam.Pipeline() as p:
     _ = (
         p | test_stream
         | 'window' >> beam.WindowInto(window.SlidingWindows(30, 5))
         | "RunInference" >> RunInference(model_handler)
         | 'Count number of elements in window' >> beam.CombineGlobally(
             AggregateMilkQualityResults()).without_defaults()
         | 'Print' >> beam.Map(print))


 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)

   known_args, _ = parse_known_args(None)

   preprocess_data(
       known_args.dataset,
       training_set_path=known_args.training_set,
       labels_path=known_args.labels,
       test_set_path=known_args.pipeline_input_data)
   train_model(
       samples_path=known_args.training_set,
       labels_path=known_args.labels,
       model_state_output_path=known_args.model_state)
   run()
	#
	# 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.
	#

	"""A streaming pipeline that uses RunInference API and windowing that classifies
	the quality of milk as good, bad or medium based on pH, temperature,
	taste, odor, fat, turbidity and color. Each minute new measurements come in
	and a sliding window aggregates the number of good, bad and medium
	samples.

	This example uses the milk quality prediction dataset from kaggle.
	https://www.kaggle.com/datasets/cpluzshrijayan/milkquality


	In order to set this example up, you will need two things.
	1. Download the data in csv format from kaggle and host it.
	2. Split the dataset in a training set and test set (preprocess_data function).
	3. Train the classifier.
	"""

	import argparse
	import logging
	import time
	from typing import NamedTuple

	import pandas
	from sklearn.model_selection import train_test_split

	import apache_beam as beam
	import xgboost
	from apache_beam import window
	from apache_beam.ml.inference import RunInference
	from apache_beam.ml.inference.base import PredictionResult
	from apache_beam.ml.inference.xgboost_inference import XGBoostModelHandlerPandas
	from apache_beam.options.pipeline_options import PipelineOptions
	from apache_beam.options.pipeline_options import SetupOptions
	from apache_beam.runners.runner import PipelineResult
	from apache_beam.testing.test_stream import TestStream


	def parse_known_args(argv):
	"""Parses args for the workflow."""
	parser = argparse.ArgumentParser()
	parser.add_argument(
	'--dataset',
	dest='dataset',
	required=True,
	help='Path to the csv containing Kaggle Milk Quality dataset.')
	parser.add_argument(
	'--pipeline_input_data',
	dest='pipeline_input_data',
	required=True,
	help='Path to store the csv containing input data for the pipeline.'
	'This will be generated as part of preprocessing the data.')
	parser.add_argument(
	'--training_set',
	dest='training_set',
	required=True,
	help='Path to store the csv containing the training set.'
	'This will be generated as part of preprocessing the data.')
	parser.add_argument(
	'--labels',
	dest='labels',
	required=True,
	help='Path to store the csv containing the labels used in training.'
	'This will be generated as part of preprocessing the data.')
	parser.add_argument(
	'--model_state',
	dest='model_state',
	required=True,
	help='Path to the state of the XGBoost model loaded for Inference.')
	return parser.parse_known_args(argv)


	def preprocess_data(
	dataset_path: str,
	training_set_path: str,
	labels_path: str,
	test_set_path: str):
	"""
	Helper function to split the dataset into a training set
	and its labels and a test set. The training set and
	its labels are used to train a lightweight model.
	The test set is used to create a test streaming pipeline.
	Args:
	dataset_path: path to csv file containing the Kaggle
	milk quality dataset
	training_set_path: path to output the training samples
	labels_path: path to output the labels for the training set
	test_set_path: path to output the test samples
	"""
	df = pandas.read_csv(dataset_path)
	df['Grade'].replace(['low', 'medium', 'high'], [0, 1, 2], inplace=True)
	x = df.drop(columns=['Grade'])
	y = df['Grade']
	x_train, x_test, y_train, _ = \
	train_test_split(x, y, test_size=0.60, random_state=99)
	x_train.to_csv(training_set_path, index=False)
	y_train.to_csv(labels_path, index=False)
	x_test.to_csv(test_set_path, index=False)


	def train_model(
	samples_path: str, labels_path: str, model_state_output_path: str):
	"""Function to train the XGBoost model.
	Args:
	samples_path: path to csv file containing the training data
	labels_path: path to csv file containing the labels for the training data
	model_state_output_path: Path to store the trained model
	"""
	samples = pandas.read_csv(samples_path)
	labels = pandas.read_csv(labels_path)
	xgb = xgboost.XGBClassifier(max_depth=3)
	xgb.fit(samples, labels)
	xgb.save_model(model_state_output_path)
	return xgb


	class MilkQualityAggregation(NamedTuple):
	bad_quality_measurements: int
	medium_quality_measurements: int
	high_quality_measurements: int


	class AggregateMilkQualityResults(beam.CombineFn):
	"""Simple aggregation to keep track of the number
	of samples with good, bad and medium quality milk."""
	def create_accumulator(self):
	return MilkQualityAggregation(0, 0, 0)

	def add_input(
	self, accumulator: MilkQualityAggregation, element: PredictionResult):
	quality = element.inference[0]
	if quality == 0:
	return MilkQualityAggregation(
	accumulator.bad_quality_measurements + 1,
	accumulator.medium_quality_measurements,
	accumulator.high_quality_measurements)
	elif quality == 1:
	return MilkQualityAggregation(
	accumulator.bad_quality_measurements,
	accumulator.medium_quality_measurements + 1,
	accumulator.high_quality_measurements)
	else:
	return MilkQualityAggregation(
	accumulator.bad_quality_measurements,
	accumulator.medium_quality_measurements,
	accumulator.high_quality_measurements + 1)

	def merge_accumulators(self, accumulators: MilkQualityAggregation):
	return MilkQualityAggregation(
	sum(
	aggregation.bad_quality_measurements
	for aggregation in accumulators),
	sum(
	aggregation.medium_quality_measurements
	for aggregation in accumulators),
	sum(
	aggregation.high_quality_measurements
	for aggregation in accumulators),
	)

	def extract_output(self, accumulator: MilkQualityAggregation):
	return accumulator


	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

	milk_quality_data = pandas.read_csv(known_args.pipeline_input_data)

	start = time.mktime(time.strptime('2023/06/29 10:00:00', '%Y/%m/%d %H:%M:%S'))

	# Create a test stream
	test_stream = TestStream()

	# Watermark is set to 10:00:00
	test_stream.advance_watermark_to(start)

	# Split the dataframe in individual samples
	samples = [
	milk_quality_data.iloc[i:i + 1] for i in range(len(milk_quality_data))
	]

	for watermark_offset, sample in enumerate(samples, 1):
	test_stream.advance_watermark_to(start + watermark_offset)
	test_stream.add_elements([sample])

	test_stream.advance_watermark_to_infinity()

	model_handler = XGBoostModelHandlerPandas(
	model_class=xgboost.XGBClassifier, model_state=known_args.model_state)

	with beam.Pipeline() as p:
	_ = (
	p \| test_stream
	\| 'window' >> beam.WindowInto(window.SlidingWindows(30, 5))
	\| "RunInference" >> RunInference(model_handler)
	\| 'Count number of elements in window' >> beam.CombineGlobally(
	AggregateMilkQualityResults()).without_defaults()
	\| 'Print' >> beam.Map(print))


	if __name__ == '__main__':
	logging.getLogger().setLevel(logging.INFO)

	known_args, _ = parse_known_args(None)

	preprocess_data(
	known_args.dataset,
	training_set_path=known_args.training_set,
	labels_path=known_args.labels,
	test_set_path=known_args.pipeline_input_data)
	train_model(
	samples_path=known_args.training_set,
	labels_path=known_args.labels,
	model_state_output_path=known_args.model_state)
	run()