Posts in category: Amazon AWS

It’s 5 PM on a Friday. You’ve spent all afternoon coding out a complex, sophisticated feature engineering strategy. It just started working on your Amazon SageMaker Studio t3.medium notebook, and all you want to do is plug this onto a massive instance, scale it out over the rest of your dataset, and go home. You could upgrade your notebook instance, but the job would stop as soon as you close your laptop. Why not schedule the job from your notebook directly?

Amazon SageMaker provides a fully-managed solution for building, training, and deploying machine learning (ML) models. In this post, we demonstrate using Amazon SageMaker Processing Jobs to execute Jupyter notebooks with the open-source project Papermill. The combination of Amazon SageMaker with Amazon CloudWatch, AWS Lambda, and the entire AWS stack have always provided the modular backbone you need to scale up jobs, like feature engineering, both on the fly and on a schedule. We’re happy to provide a do-it-yourself toolkit to simplify this process, using AWS CloudFormation to set up permissions, Lambda to launch the job, and Amazon Elastic Container Registry (Amazon ECR) to create a customized execution environment. It includes a library and CLI to initiate notebook execution from any AWS client and a Jupyter plugin for a seamless user experience.

As of this writing, you can write code in a Jupyter notebook and run it on an Amazon SageMaker ephemeral instance with the click of a button, either immediately or on a schedule. With the tools provided here, you can do this from anywhere: at a shell prompt, in JupyterLab on Amazon SageMaker, in another JupyterLab environment you have, or automated in a program you’ve written. We’ve written sample code that simplifies setup by using AWS CloudFormation to handle the heavy lifting and provides convenience tools to run and monitor executions.

For more information about executing notebooks, see the GitHub repo. All the source code is available in aws-samples on GitHub. Read on to learn all about how to use scheduled notebook execution.

When to use this solution

This toolkit is especially useful for running nightly reports. For example, you may want to analyze all the training jobs your data science team ran that day, run a cost/benefit analysis, and generate a report about the business value your models are going to bring after you deploy them into production. That would be a perfect fit for a scheduled notebook—all the graphs, tables, and charts are generated by your code, the same as if you stepped through the notebook yourself, except now they are handled automatically, in addition to persisting in Amazon Simple Storage Service (Amazon S3). You can start your day with the latest notebook, executed overnight, to move your analysis forward.

Or, imagine that you want to scale up a feature engineering step. You’ve already perfected the for-loop to knock out all your Pandas transformations, and all you need is time and compute to run this on the full 20 GB of data. No problem—just drop your notebook into the toolkit, run a job, close your laptop, and you’re done. Your code continues to run on the scheduled instance, regardless of whether or not you’re actively using Jupyter at the moment.

Perhaps you’re on a data science team that still trains models on local laptops or Amazon SageMaker notebooks, and haven’t yet adopted the Amazon SageMaker ephemeral instances for training jobs. With this toolkit, you can easily use the advanced compute options only for the time you’re training a model. You can spin up a p3.xlarge only for the hour your model trains but use your Studio environment all day on the affordable t3.medium. You can easily connect these resources to the Experiments SDK with a few lines of code. Although it’s still fully supported to run Amazon SageMaker notebooks and Amazon SageMaker Studio on p3 instances, developing a habit of using the largest instances only for short periods is a net cost-savings exercise.

You may have an S3 bucket full of objects and need to run a full notebook on each object. These could be dates of phone call records in your call center or Tweet-streams from particular users in your social network. You can easily write a for-loop over those objects by using this toolkit, which schedules a job for each file, runs it on its dedicated instance, and stores the completed notebook in Amazon S3. These could even be model artifacts loaded in from your preferred training environment—package up your inference code in a notebook and use the toolkit to easily deploy them!

Finally, customers tell us that reporting on the performance of their models is a key asset for their stakeholders. With this toolkit, you can implement a human-in-the-loop solution that analyzes feature importance, produces ROC curves, and estimates how your model will perform on the tricky edge cases that are crucial to your final product. You can build a model profiler that all the data scientists on your team can easily access. You can trigger this model profiler to run after every training job is complete, closing the loop on the value of your analysis to your stakeholders.

Three Ways to Execute Notebooks on a Schedule in SageMaker

To execute a notebook in Amazon SageMaker, you use a Lambda function that sets up and runs an Amazon SageMaker Processing job. The function can be invoked directly by the user or added as a target of an Amazon EventBridge rule to run on a schedule or in response to an event. The notebook to run is stored as an Amazon S3 object so it’s available to run even if you’re not online when the execution happens. The following diagram illustrates this architecture.

We outline three different ways to install and use this capability that let you work with notebooks and schedules just the way you want.

Using the AWS APIs or CLI directly

You can use the AWS APIs directly to execute and schedule notebooks. To make the process easier, we have provided a CloudFormation template to set up the Lambda function you need and some AWS Identity and Access Management (IAM) roles and policies that you use when running notebooks. We also provided scripts for building and customizing the Docker container images that Amazon SageMaker Processing Jobs uses when running the notebooks.

After you instantiate the CloudFormation template and create a container image, you can run a notebook like this with the following code:

$ aws lambda invoke --function-name RunNotebook 
             --payload '{"input_path": "s3://mybucket/mynotebook.ipynb", 
                         "parameters": {"p": 0.75}}' result.json

To create a schedule, enter the following code, replacing the Region and account number in the arn, as well as the input_path to your S3 bucket.

$ aws events put-rule --name "RunNotebook-test" --schedule "cron(15 1 * * ? *)"
$ aws lambda add-permission --statement-id EB-RunNotebook-test 
                            --action lambda:InvokeFunction 
                            --function-name RunNotebook 
                            --principal events.amazonaws.com 
                            --source-arn arn:aws:events:us-east-1:123456789:rule/RunNotebook-test
$ aws events put-targets --rule RunNotebook-test 
                         --targets '[{"Id": "Default", 
                                      "Arn": "arn:aws:lambda:us-east-1:123456789:function:RunNotebook", 
                                      "Input": "{ "input_path": "s3://mybucket/mynotebook.ipynb", 
                                                  "parameters": {"p": 0.75}}"}]‘

With this approach, you manage moving the notebook to Amazon S3, monitoring Amazon SageMaker Processing Jobs, and retrieving the output notebook from Amazon S3.

This is a great solution when you’re a knowledgeable AWS user who wants to craft a solution without taking on extra dependencies. You can even modify the Lambda function we’ve written or the Papermill execution container to meet your exact needs.

For more information about scheduling notebooks with the AWS APIs, see the full setup instructions on the GitHub repo.

Making things easier with a convenience package

To make it easier to schedule notebooks (especially if you aren’t an AWS expert), we’ve created a convenience package that wraps the AWS tools in a CLI and Python library that give you a more natural interface to running and scheduling notebooks. This package lets you build customized execution environments without Docker, via AWS CodeBuild instead, and manages the Amazon S3 interactions and job monitoring for you.

After you run the setup, execute the notebook with the following code:

$ run-notebook run mynotebook.ipynb -p p=0.5 -p n=200

Schedule a notebook with the following code:

$ run-notebook schedule --at "cron(15 1 * * ? *)" --name nightly weather.ipynb -p "name=Boston, MA"

The convenience package also contains tools to monitor jobs and view schedules. See the following code:

$ run-notebook list-runs
Date                 Rule                 Notebook              Parameters           Status     Job
2020-06-15 15:31:40                       fraud-analysis.ipynb  name=Tom             Completed  papermill-fraud-analysis-2020-06-15-22-31-39
2020-06-15 01:00:08  DailyForecastSeattle DailyForecast.ipynb   place=Seattle, WA    Completed  papermill-DailyForecast-2020-06-15-08-00-08
2020-06-15 01:00:03  DailyForecastNewYork DailyForecast.ipynb   place=New York, NY   Completed  papermill-DailyForecast-2020-06-15-08-00-02
2020-06-12 22:34:06                       powers.ipynb          p=0.5                Completed  papermill-powers-2020-06-13-05-34-05
                                                                n=20
$ 

For more information about the convenience package, see the GitHub repo.

Executing notebooks directly from JupyterLab with a GUI

For those who prefer an interactive experience, the convenience package includes a JupyterLab extension that you can enable for JupyterLab running locally, in Amazon SageMaker Studio, or on an Amazon SageMaker notebook instance.

After you set up the Jupyter extension for Amazon SageMaker Studio users, you see the new notebook execution sidebar (the rocket ship icon). The sidebar lets you execute or schedule the notebook you’re viewing . You can use the notebook-runner container that was created by the default setup or any other container you built. Enter the ARN for the execution role these jobs utilize and your instance preference, and you’re ready to go!

After you choose Run Now, the Lambda function picks up your notebook and runs it on an Amazon SageMaker Processing job. You can view the status of that job by choosing Runs. See the following screenshot.

When the job is complete, the finished notebook is stored in Amazon S3. Remember, this means your previous runs will persist, so you can easily revert back to them.

Finally, import the output notebook by choosing View Output and Import Notebook. If you don’t import the notebook, it’s never copied to your local directory. This is great when you want to see what happened, but don’t want to clutter things up with lots of extra notebooks.

For instructions on setting up the JupyterLab extension and using the GUI to run and monitor your notebooks, see the GitHub repo.

Summary

This post discussed how you can combine the modular capabilities of Amazon SageMaker and the AWS cloud to give data scientists and ML engineers the seamless experience of running notebooks on ephemeral instances. We are releasing an open-source toolkit to simplify this process, including a CLI, convenience package, and Jupyter widget. We discussed a variety of use cases for this, from running nightly reports to scaling up feature engineering to profiling models on the latest datasets. We shared examples from the various ways of running the toolkit. Feel free to walk through the Quick Start on GitHub and step through even more examples on the GitHub repo.

Author Bios

Emily Webber is a machine learning specialist SA at AWS, who alternates between data scientist, machine learning architect, and research scientist based on the day of the week. She lives in Chicago, and you can find her on YouTube, LinkedIn, GitHub, or Twitch. When not helping customers and attempting to invent the next generation of machine learning experiences, she enjoys running along beautiful Lake Shore Drive, escaping into her Kindle, and exploring the road less traveled.

Tom Faulhaber is a Principal Engineer on the Amazon SageMaker team. Lately, he has been focusing on unlocking all the potential uses of the richness of Jupyter notebooks and how they can add to the data scientist’s toolbox in non-traditional ways. In his spare time, Tom is usually found biking and hiking to discover all the wild spaces around Seattle with his kids.

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We are excited to announce the launch of AWS DeepComposer Chartbusters, a monthly challenge where developers can use AWS DeepComposer to create original compositions and compete to top the charts and win prizes. AWS DeepComposer gives developers a creative way to get started with machine learning (ML) and generative AI techniques. With AWS DeepComposer, developers, regardless of their background in ML, can get started with generative AI techniques to learn how to train and optimize their models to create original music. The first AWS DeepComposer Chartbusters challenge, Bach to the Future, requires developers to use a new generative AI algorithm provided in the AWS DeepComposer console to create compositions in the style of Bach.

Every month through October, 2020, AWS will release a new Chartbusters challenge that has a different monthly theme to introduce you to a variety of generative AI techniques. You don’t need any musical knowledge to participate in the challenge. Before participating in a challenge, you can use learning capsules available in the AWS DeepComposer console to learn the generative AI concepts required for each month’s challenge. Learning capsules provide easy-to-consume, bite-size content to help you learn the concepts of generative AI algorithms.

How to participate in the challenge

The challenge is open worldwide for developers to participate. To get started, you will need to use one of the generative AI algorithms available in the AWS DeepComposer console to create compositions. Once you are ready to submit your composition for the challenge, select Submit a composition in the console to submit your creations to SoundCloud. AWS DeepComposer will add your submission to the Chartbuster challenge playlist on SoundCloud.

You can invite your family and friends to listen and like your composition by using the social sharing buttons available on SoundCloud. At the end of each challenge period, AWS will shortlist the top 20 compositions using a sum of customer likes and count of plays on SoundCloud. Our human AWS experts and DeepComposer AI judge will evaluate the shortlist based on musical quality and creativity to select the top 10 ranked compositions. The DeepComposer AI judge is trained on original Bach compositions and scores how similar your composition is to Bach’s style.

At the end of each challenge period, we will announce the top 10 compositions in an AWS ML blog post and feature them in an exclusive AWS top 10 playlist on SoundCloud and in the AWS DeepComposer console. The winner for each month’s challenge will receive an AWS DeepComposer Chartbusters gold record mailed to their physical address. Additionally, we will interview the winner to share their experience and feature them in an AWS ML blog post. You can continue to participate in multiple challenges so that if you don’t make it to the top 10 in one challenge, you can participate in the next challenge for another chance to top the charts.

Bach to the Future challenge

The first AWS DeepComposer Chartbuster challenge titled Bach to the Future launches today and is open until July 25^th. To participate in the challenge, you will need to use the autoregressive CNN (AR-CNN) algorithm available in the AWS DeepComposer console to create compositions in the style of Bach. AWS will announce the top 10 compositions for the Bach to the Future challenge and the theme for the next challenge on July 31st, 2020, in an AWS ML blog post.

The AR-CNN algorithm enhances the original input melody by adding or removing musical notes from the input melody. If the algorithm detects off-key or extraneous notes, it may choose to remove them. If it identifies additional specific notes that are highly probable in a Bach composition, it may decide to add them. Listen to the following example of a composition that is generated by applying the AR-CNN algorithm. You might recognize this tune from Jonathan Coulton, as it is available as a sample input melody in the AWS DeepComposer console.

Input: me-and-my-jar-with-extra-missing-notes.midi

Enhanced composition: me-and-my-jar-enhanced.midi

You can use the Introduction to autoregressive convolutional neural network learning capsule available in the AWS DeepComposer console to learn the concepts. To access the learning capsule in the console, navigate to learning capsules using the left navigation menu. Choose Introduction to autoregressive convolutional neural network to begin learning.

Creating and submitting a composition

To get started, log in to the AWS DeepComposer console and navigate to the music studio using the left navigation menu. You can use either the sample melodies provided in the console, record a custom melody using the keyboard, or import your own input track. You can adjust the tempo and pitch for your melody in the music studio.

Choose Autoregressive generative AI technique, and then choose Autoregressive CNN Bach model. You have four parameters that you can choose to adjust: Maximum notes to add, Maximum notes to remove, Sampling iterations, and Creative risk. For this example, let’s choose the defaults and select Enhance input melody.

The AR-CNN algorithm allows you to collaborate iteratively with the machine learning algorithm by experimenting with the parameters; you can use the output from one iteration of the AR-CNN algorithm as input to the next iteration.

To submit your composition for the challenge, choose Chartbusters using the left navigation menu, and select Submit a composition. Choose your composition from the drop-down menu, provide a track name for your composition, and select Submit. AWS DeepComposer will submit your composition to the Bach to the Future playlist on SoundCloud. You can select the Vote on SoundCloud button in the console to review and listen to other submissions for the challenge.

Congratulations! You have submitted your first entry for the AWS DeepComposer Chartbusters challenge. Invite your friends and family to listen to and like your composition.

To celebrate the launch of the first Chartbuster challenge, we are offering the AWS DeepComposer keyboard at a special price of $79 for a limited period from 6/23/2020 to 7/15/2020 on amazon.com. The pricing includes the keyboard and a 3-month free trial of AWS DeepComposer services.

Learn more about AWS DeepComposer Chartbusters at https://aws.amazon.com/deepcomposer/chartbusters.

About the Author

Jyothi Nookula is a Principal Product Manager for AWS AI devices. She loves to build products that delight her customers. In her spare time, she loves to paint and host charity fund raisers for her art exhibitions.

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Fraudulent users and malicious accounts can result in billions of dollars in lost revenue annually for businesses. Although many businesses use rule-based filters to prevent malicious activity in their systems, these filters are often brittle and may not capture the full range of malicious behavior.

However, some solutions, such as graph techniques, are especially suited for detecting fraudsters and malicious users. Fraudsters can evolve their behavior to fool rule-based systems or simple feature-based models, but it’s difficult to fake the graph structure and relationships between users and other entities captured in transaction or interaction logs. Graph neural networks (GNNs) combine information from the graph structure with attributes of users or transactions to learn meaningful representations that can distinguish malicious users and events from legitimate ones.

This post shows how to use Amazon SageMaker and Deep Graph Library (DGL) to train GNN models and detect malicious users or fraudulent transactions. Businesses looking for a fully-managed AWS AI service for fraud detection can also use Amazon Fraud Detector, which makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud.

In this blog post, we focus on the data preprocessing and model training with Amazon SageMaker.  To train the GNN model, you must first construct a heterogeneous graph using information from transaction tables or access logs. A heterogeneous graph is one that contains different types of nodes and edges. In the case where nodes represent users or transactions, the nodes can have several kinds of distinct relationships with other users and possibly other entities, such as device identifiers, institutions, applications, IP addresses and so on.

Some examples of use cases that fit under this include:

• A financial network where users transact with other users and specific financial institutions or applications
• A gaming network where users interact with other users but also with distinct games or devices
• A social network where users can have different types of links to other users

The following diagram illustrates a heterogeneous financial transaction network.

GNNs can incorporate user features like demographic information or transaction features like activity frequency. In other words, you can enrich the heterogeneous graph representation with features for nodes and edges as metadata. After the node and relations in the heterogeneous graph are established, with their associated features, you can train a GNN model to learn to classify different nodes as malicious or legitimate, using both the node or edge features as well as the graph structure. The model training is set up in a semi-supervised manner—you have a subset of nodes in the graph already labeled as fraudulent or legitimate. You use this labeled subset as a training signal to learn the parameters of the GNN. The trained GNN model can then predict the labels for the remaining unlabeled nodes in the graph.

Architecture

To get started, you can use the full solution architecture that uses Amazon SageMaker to run the processing jobs and training jobs. You can trigger the Amazon SageMaker jobs automatically with AWS Lambda functions that respond to Amazon Simple Storage Service (Amazon S3) put events, or manually by running cells in an example Amazon SageMaker notebook. The following diagram is a visual depiction of the architecture.

The full implementation is available on the GitHub repo with an AWS CloudFormation template that launches the architecture in your AWS account.

Data preprocessing for fraud detection with GNNs

In this section, we show how to preprocess an example dataset and identify the relations that will make up the heterogeneous graph.

Dataset

For this use case, we use the IEEE-CIS fraud dataset to benchmark the modeling approach. This is an anonymized dataset that contains 500 thousand transactions between users. The dataset has two main tables:

• Transactions table – Contains information about transactions or interactions between users
• Identity table – Contains information about access logs, device, and network information for users performing transactions

You use a subset of these transactions with their labels as a supervision signal for the model training. For the transactions in the test dataset, their labels are masked during training. The task is to predict which masked transactions are fraudulent and which are not.

The following code example gets the data and uploads it to an S3 bucket that Amazon SageMaker uses to access the dataset during preprocessing and training (run this in a Jupyter notebook cell):

# Replace with an S3 location or local path to point to your own dataset
raw_data_location = 's3://sagemaker-solutions-us-west-2/Fraud-detection-in-financial-networks/data'

bucket = 'SAGEMAKER_S3_BUCKET'
prefix = 'dgl'
input_data = 's3://{}/{}/raw-data'.format(bucket, prefix)

!aws s3 cp --recursive $raw_data_location $input_data

# Set S3 locations to store processed data for training and post-training results and artifacts respectively
train_data = 's3://{}/{}/processed-data'.format(bucket, prefix)
train_output = 's3://{}/{}/output'.format(bucket, prefix)

Despite the efforts of fraudsters to mask their behavior, fraudulent or malicious activities often have telltale signs like high out-degree or activity aggregation in the graph structure. The following sections show how to perform feature extraction and graph construction to allow the GNN models to take advantage of these patterns to predict fraud.

Feature extraction

Feature extraction consists of performing numerical encoding on categorical features and some transformation of numerical columns. For example, the transaction amounts are logarithmically transformed to indicate the relative magnitude of the amounts, and categorical attributes can be converted to numerical form by performing one hot encoding. For each transaction, the feature vector contains attributes from the transaction tables with information about the time delta between previous transactions, name and addresses matches, and match counts.

Constructing the graph

To construct the full interaction graph, split the relational information in the data into edge lists for each relation type. Each edge list is a bipartite graph between transaction nodes and other entity types. These entity types each constitute an identifying attribute about the transaction. For example, you can have an entity type for the kind of card (debit or credit) used in the transaction, the IP address of the device the transaction was completed with, and the device ID or operating system of the device used. The entity types used for graph construction consist of all the attributes in the identity table and a subset of attributes in the transactions table, like credit card information or email domain. The heterogeneous graph is constructed with the set of per relation type edge lists and the feature matrix for the nodes.

Using Amazon SageMaker Processing

You can execute the data preprocessing and feature extraction step using Amazon SageMaker Processing. Amazon SageMaker Processing is a feature of Amazon SageMaker that lets you run preprocessing and postprocessing workloads on fully managed infrastructure. For more information, see Process Data and Evaluate Models.

First define a container for the Amazon SageMaker Processing job to use. This container should contain all the dependencies that the data preprocessing script requires. Because the data preprocessing here only depends on the pandas library, you can have a minimal Dockerfile to define the container. See the following code:

FROM python:3.7-slim-buster

RUN pip3 install pandas==0.24.2
ENV PYTHONUNBUFFERED=TRUE

ENTRYPOINT ["python3"]

You can build the container and push the built container to an Amazon Elastic Container Registry (Amazon ECR) repository by entering the following of code:

import boto3

region = boto3.session.Session().region_name
account_id = boto3.client('sts').get_caller_identity().get('Account')
ecr_repository = 'sagemaker-preprocessing-container'
ecr_repository_uri = '{}.dkr.ecr.{}.amazonaws.com/{}:latest'.format(account_id, region, ecr_repository)

!bash data-preprocessing/container/build_and_push.sh $ecr_repository docker

When the data preprocessing container is ready, you can create an Amazon SageMaker ScriptProcessor that sets up a Processing job environment using the preprocessing container. You can then use the ScriptProcessor to run a Python script, which has the data preprocessing implementation, in the environment defined by the container. The Processing job terminates when the Python script execution is complete and the preprocessed data has been saved back to Amazon S3. This process is completely managed by Amazon SageMaker. When running the ScriptProcessor, you have the option of passing in arguments to the data preprocessing script. Specify what columns in the transaction table should be considered as identity columns and what columns are categorical features. All other columns are assumed to be numerical features. See the following code:

from sagemaker.processing import ScriptProcessor, ProcessingInput, ProcessingOutput

script_processor = ScriptProcessor(command=['python3'],
                                   image_uri=ecr_repository_uri,
                                   role=role,
                                   instance_count=1,
                                   instance_type='ml.r5.24xlarge')

script_processor.run(code='data-preprocessing/graph_data_preprocessor.py',
                     inputs=[ProcessingInput(source=input_data,
                                             destination='/opt/ml/processing/input')],
                     outputs=[ProcessingOutput(destination=train_data,
                                               source='/opt/ml/processing/output')],
                     arguments=['--id-cols', 'card1,card2,card3,card4,card5,card6,ProductCD,addr1,addr2,P_emaildomain,R_emaildomain',
                                '--cat-cols',' M1,M2,M3,M4,M5,M6,M7,M8,M9'])

The following code example shows the outputs of the Amazon SageMaker Processing job stored in Amazon S3:

from os import path
from sagemaker.s3 import S3Downloader
processed_files = S3Downloader.list(train_data)
print("===== Processed Files =====")
print('n'.join(processed_files))Output:

===== Processed Files =====
s3://graph-fraud-detection/dgl/processed-data/features.csv
s3://graph-fraud-detection/dgl/processed-data/relation_DeviceInfo_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_DeviceType_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_P_emaildomain_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_ProductCD_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_R_emaildomain_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_TransactionID_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_addr1_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_addr2_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_card1_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_card2_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_card3_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_card4_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_card5_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_card6_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_01_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_02_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_03_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_04_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_05_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_06_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_07_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_08_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_09_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_10_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_11_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_12_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_13_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_14_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_15_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_16_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_17_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_18_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_19_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_20_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_21_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_22_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_23_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_24_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_25_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_26_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_27_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_28_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_29_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_30_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_31_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_32_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_33_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_34_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_35_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_36_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_37_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/relation_id_38_edgelist.csv
s3://graph-fraud-detection/dgl/processed-data/tags.csv
s3://graph-fraud-detection/dgl/processed-data/test.csv

All the relation edgelist files represent the different kinds of edges used to construct the heterogenous graph during training. Features.csv contains the final transformed features of the transaction nodes, and tags.csv contains the labels of the nodes used as the training supervision signal. Test.csv contains the TransactionID data to use as a test dataset to evaluate the performance of the model. The labels for these nodes are masked during training.

GNN model training

Now you can use Deep Graph Library (DGL) to create the graph and define a GNN model, and use Amazon SageMaker to launch the infrastructure to train the GNN. Specifically,  a relational graph convolutional neural network model can be used to learn embeddings for the nodes in the heterogeneous graph, and a fully connected layer for the final node classification.

Hyperparameters

To train the GNN, you need to define a few hyperparameters that are fixed before the training process, such as the kind of graph you’re constructing, the class of GNN models you’re using, the network architecture, and the optimizer and optimization parameters. See the following code:

edges = ",".join(map(lambda x: x.split("/")[-1], [file for file in processed_files if "relation" in file]))
params = {'nodes' : 'features.csv',
          'edges': 'relation*.csv',
          'labels': 'tags.csv',
          'model': 'rgcn',
          'num-gpus': 1,
          'batch-size': 10000,
          'embedding-size': 64,
          'n-neighbors': 1000,
          'n-layers': 2,
          'n-epochs': 10,
          'optimizer': 'adam',
          'lr': 1e-2
        }

The preceding code shows a few of the hyperparameters. For more information about all the hyperparameters and their default values, see estimator_fns.py in the GitHub repo.

Model training with Amazon SageMaker

With the hyperparameters defined, you can now kick off the training job. The training job uses DGL, with MXNet as the backend deep learning framework, to define and train the GNN. Amazon SageMaker makes it easy to train GNN models with the framework estimators, which have the deep learning framework environments already set up. For more information about training GNNs with DGL on Amazon SageMaker, see Train a Deep Graph Network.

You can now create an Amazon SageMaker MXNet estimator and pass in the model training script, hyperparameters, and the number and type of training instances you want. You can then call fit on the estimator and pass in the training data location in Amazon S3. See the following code:

from sagemaker.mxnet import MXNet

estimator = MXNet(entry_point='train_dgl_mxnet_entry_point.py',
                  source_dir='dgl-fraud-detection',
                  role=role, 
                  train_instance_count=1, 
                  train_instance_type='ml.p2.xlarge',
                  framework_version="1.6.0",
                  py_version='py3',
                  hyperparameters=params,
                  output_path=train_output,
                  code_location=train_output,
                  sagemaker_session=sess)

estimator.fit({'train': train_data})

Results

After training the GNN, the model learns to distinguish legitimate transactions from fraudulent ones. The training job produces a pred.csv file, which contains the model’s predictions for the transactions in test.csv. The ROC curve depicts the relationship between the true positive rate and the false positive rate at various thresholds, and the Area Under the Curve (AUC) can be used as an evaluation metric. The following graph shows that the GNN model we trained outperforms both fully connected feed forward networks and gradient boosted trees that use the features but don’t fully take advantage of the graph structure.

Conclusion

In this post, we showed how to construct a heterogeneous graph from user transactions and activity and use that graph and other collected features to train a GNN model to predict which transactions are fraudulent. This post also showed how to use DGL and Amazon SageMaker to define and train a GNN that achieves high performance on this task. For more information about the full implementation of the project and other GNN models for the task, see the GitHub repo.

Additionally, we showed how to perform data processing to extract useful features and relations from raw transaction data logs using Amazon SageMaker Processing. You can get started with the project by deploying the provided CloudFormation template and passing in your own dataset to detect malicious users and fraudulent transactions in your data.

About the Author

Soji Adeshina is a Machine Learning Developer who works on developing deep learning based solutions for AWS customers. Currently, he’s working on graph learning with applications in financial services and advertising but he also has a background in computer vision and recommender systems. In his spare time, he likes to cook and read philosophical texts.

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Customer service conversations typically revolve around one or more topics and contain related questions. Answering these questions seamlessly is essential for a good conversational experience. For example, as part of a car rental reservation, you have queries such as, “What’s the charge for an additional driver?” or, “Do you have car seats for kids?” Starting today, you can use a search intent in your Amazon Lex bots to integrate with Amazon Kendra, so your bots can surface answers from Kendra.

Amazon Kendra was recently made generally available to all AWS customers, with exciting new features. Amazon Kendra provides you with a highly accurate and easy-to-use enterprise search service powered by machine learning. It offers a more intuitive way to search—using natural language—and returns more accurate answers, so your end-users can discover information stored within your organization’s vast amount of content.

We previously described how you can add an AWS Lambda function in an Amazon Lex bot to integrate with Kendra. For more information, see Smarter FAQ bots with Amazon Kendra. With this approach, you need to write and maintain integration code, and any updates to the configuration require updating this code. Now you can can add a search intent to get the answers you’re looking for from FAQ documents. You simply define the search index parameters in the intent as part of the bot definition. The bot queries the Amazon Kendra search index during the interaction if the user input doesn’t match any of the other configured intents. The search query responses are populated with request attributes that the bot can use in its response. You don’t have to write any Lambda code. The integration is simple, and you can seamlessly deliver natural language answers from Amazon Kendra in your Amazon Lex conversations.

This post shows how you can integrate an Amazon Lex bot with Amazon Kendra using a search intent, so you can quickly build a bot to provide answers in a conversation. The following diagram illustrates this solution.

Building a Lex bot

We will use the same conversation from the previous post to model a bot conversation:

User: Where’s the IT Help Desk?
Agent: 2nd floor; room 201 (two rights after you get out of the elevators).
User: Can I come in at noon?
Agent: Sure. What’s the nature of your problem?
User: I have a cracked laptop screen.
Agent: For verification purposes, can I get the last four digits of your employee ID?
User: 1234.
Agent: Got it, we’ll see you then to take a look at your screen.
User: How long does it take to fix a screen?
Agent: Laptop screen repairs typically take 4 hours.
User: What do I need to bring with me for a screen repair?
Agent: For a laptop screen repair, please bring your employee badge, laptop, and VPN fob.
User: How do I access my documents while my laptop is being repaired?
Agent: You can use Amazon Workdocs to access your documents.
User: Can I use WorkDocs on my tablet?
Agent: Yes. Users can access Amazon WorkDocs using free applications for iPad, Kindle Fire, and Android tablets that you can download from iTunes, Amazon Appstore for Android, and Google Play. Users can also access Amazon WorkDocs using the web browser on any mobile device.
User: When does the help desk close?
Agent: Our hours are from 11 AM to 2 PM.
User: Is there a coffee shop nearby?
Agent: There is one on the third floor and it’s open from 8 AM to 4 PM daily.

The first step is to build an Amazon Lex bot with intents to book an appointment and check appointment status. The MakeAppointment intent elicits the information required to make an appointment, such as the date, time, employee ID, and the nature of the issue. The CheckAppointmentStatus intent provides the status of the appointment. When a user asks a question that the Lex bot can’t answer with these intents, it uses the built-in KendraSearchIntent intent to connect to Amazon Kendra to search for an appropriate answer.

Deploying the sample bot

To create the sample bot, complete the following steps. This creates an Amazon Lex bot called help_desk_bot and a Lambda fulfillment function called help_desk_bot_handler.

1. Download the Amazon Lex definition and Lambda code.
2. In the AWS Lambda console, choose Create function.
3. Enter the function name help_desk_bot_handler.
4. Choose the latest Python runtime (for example, Python 3.8).
5. For Permissions, choose Create a new role with basic Lambda permissions.
6. Choose Create function.
7. Once your new Lambda function is available, in the Function code section, choose Actions, choose Upload a .zip file, choose Upload, and select the help_desk_bot_lambda_handler.zip file that you downloaded.
8. Choose Save.
9. On the Amazon Lex console, choose Actions, and then Import.
10. Choose the file help_desk_bot.zip that you downloaded, and choose Import.
11. On the Amazon Lex console, choose the bot help_desk_bot.
12. For each of the intents, choose AWS Lambda function in the Fulfillment section, and select the help_desk_bot_handler function in the dropdown list. If you are prompted “You are about to give Amazon Lex permission to invoke your Lambda Function”, choose OK.
13. When all the intents are updated, choose Build.

At this point, you should have a working bot that is not yet connected to Amazon Kendra.

Creating an Amazon Kendra index

You’re now ready to create an Amazon Kendra index for your documents and FAQ. Complete the following steps:

1. On the Amazon Kendra console, choose Launch Amazon Kendra.
2. If you have existing Amazon Kendra indexes, choose Create index.
3. For Index name, enter a name, such as it-helpdesk.
4. For Description, enter an optional description, such as IT Help Desk FAQs.
5. For IAM role, choose Create a new role to create a role to allow Amazon Kendra to access Amazon CloudWatch Logs.
6. For Role name, enter a name, such as cloudwatch-logs. Kendra will prefix the name with AmazonKendra and the AWS region.
7. Choose Next.
   
8. For Provisioning editions, choose Developer edition.
   
9. Choose Create.

Adding your FAQ content

While Amazon Kendra creates your new index, upload your content to an Amazon Simple Storage Service (Amazon S3) bucket.

1. On the Amazon S3 console, create a new bucket, such as kendra-it-helpdesk-docs-<your-account#>.
2. Keep the default settings and choose Create bucket.
3. Download the following sample files and upload them to your new S3 bucket:
   • help-desk-faq.csv
   • workdocs-faq.csv

When the index creation is complete, you can add your FAQ content.

4. On the Amazon Kendra console, choose your index, then choose FAQs, and Add FAQ.
5. For FAQ name, enter a name, such as it-helpdesk-faq.
6. For Description, enter an optional description, such as FAQ for the IT Help Desk.
7. For S3, browse Amazon S3 to find your bucket, and choose help-desk-faq.csv.
8. For IAM role, choose Create a new role to allow Amazon Kendra to access your S3 bucket.
9. For Role name, enter a name, such as s3-access. Kendra will prefix your role name with AmazonKendra-.
10. Choose Add.
    
11. Stay on the page while Amazon Kendra creates your FAQ.
12. When the FAQ is complete, choose Add FAQ to add another FAQ.
13. For FAQ name, enter a name, such as workdocs-faq.
14. For Description, enter a description, such as FAQ for Amazon WorkDocs mobile and web access.
15. For S3, browse Amazon S3 to find your bucket, and choose workdocs-faq.csv.
16. For IAM role, choose the same role you created in step 9.
17. Choose Add.
    

After you create your FAQs, you can try some Kendra searches by choosing Search console. For example:

• When is the help desk open?
• When does the help desk close?
• Where is the help desk?
• Can I access WorkDocs from my phone?

Adding a search intent

Now that you have a working Amazon Kendra index, you need to add a search intent.

1. On the Amazon Lex console, choose help_desk_bot.
2. Under Intents, choose the + icon next to add an intent.
3. Choose Search existing intents.
4. Under Built-in intents, choose KendraSearchIntent.
5. Enter a name for your intent, such as help_desk_kendra_search.
6. Choose Add.
7. Under Amazon Kendra query, choose the index you created (it-helpdesk).
8. For IAM role, choose Add Amazon Kendra permissions.
9. For Fulfillment, leave the default value Return parameters to client selected.
   
   
10. For Response, choose Message, enter the following message value and choose + to add it:

    ((x-amz-lex:kendra-search-response-question_answer-answer-1))

11. Choose Save intent.
12. Choose Build.

The message value you used in step 10 is a request attribute, which is set automatically by the Amazon Kendra search intent. This response is only selected if Kendra surfaces an answer.  For more information on request attributes, see the AMAZON.KendraSearchIntent documentation.

Your bot can now execute Amazon Kendra queries. You can test this on the Amazon Lex console. For example, you can try the sample conversation from the beginning of this post.

Deploying on a Slack channel

You can put this solution in a real chat environment, such as Slack, so that users can easily get information. To create a Slack channel association with your bot, complete the following steps:

1. On the Amazon Lex console, choose Settings.
2. Choose Publish.
3. For Create an alias, enter an alias name, such as test.
4. Choose Publish.
5. When your alias is published, choose the Channels
6. Under Channels, choose Slack.
7. Enter a Channel Name, such as slack_help_desk_bot.
8. For Channel Description, add an optional description.
9. From the KMS Key drop-down menu, leave aws/lex selected.
10. For Alias, choose test.
11. Provide the Client Id, Client Secret, and Verification Token for your Slack application.
    
12. Choose Activate to generate the OAuth URL and Postback URL.

Use the OAuth URL and Postback URL on the Slack application portal to complete the integration. For more information about setting up a Slack application and integrating with Amazon Lex, see Integrating an Amazon Lex Bot with Slack.

Conclusion

This post demonstrates how to integrate Amazon Lex and Amazon Kendra using a search intent. Amazon Kendra can extract specific answers from unstructured data. No pre-training is required; you simply point Amazon Kendra at your content, and it provides specific answers to natural language queries. For more information about incorporating these techniques into your bots, please see the AMAZON.KendraSearchIntent documentation.

About the authors

Brian Yost is a Senior Consultant with the AWS Professional Services Conversational AI team. In his spare time, he enjoys mountain biking, home brewing, and tinkering with technology.

As a Product Manager on the Amazon Lex team, Harshal Pimpalkhute spends his time trying to get machines to engage (nicely) with humans.

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