SAS Visual Analytics

4月 222019
 

Imagine a world where satisfying human-computer dialogues exist. With the resurgence of interest in natural language processing (NLP) and understanding (NLU) – that day may not be far off.

In order to provide more satisfying interactions with machines, researchers are designing smart systems that use artificial intelligence (AI) to develop better understanding of human requests and intent.

Last year, OpenAI used a machine learning technique called reinforcement learning to teach agents to design their own language. The AI agents were given a simple set of words and the ability to communicate with each other. They were then given a set of goals that were best achieved by cooperating (communicating) with other agents. The agents independently developed a simple ‘grounded’ language.

Grounded vs. inferred language


Human language is said to be grounded in experience. People grasp the meaning of many basic words by interaction – not by learning dictionary definitions by rote. They develop understanding in terms of sensory experience -- for example, words like red, heavy, above.

Abstract word meanings are built in relation to more concretely grounded terms. Grounding allows humans to acquire and understand words and sentences in context.

The opposite of a grounded language is an inferred language. Inferred languages derive meaning from the words themselves and not what they represent. In AI trained only on textual data, but not real-world representations, these methods lack true understanding of what the words mean.

What if the AI agent develops its own language we can’t understand?

It happens. Even if the researcher gives the agents simple English words the agent inevitably diverges to its own, unintelligible language. Recently researchers at Facebook, Google and OpenAI all experienced this phenomenon!

Agents are reward driven. If there is no reward for using English (or human language) then the agents will develop a more efficient shorthand for themselves.

That’s cool – why is that a problem?

When researchers at the Facebook Artificial Intelligence Research lab designed chatbots to negotiate with one another using machine learning, they had to tweak one of their models because otherwise the bot-to-bot conversation “led to divergence from human language as the agents developed their own language for negotiating.” They had to use what’s called a fixed supervised model instead.

The problem, there, is transparency. Machine learning techniques such as deep learning are black box technologies. A lot of data is fed into the AI, in this case a neural network, to train on and develop its own rules. The model is then fed new data which is used to spit out answers or information. The black box analogy is used because it is very hard, if not impossible in complex models, to know exactly how the AI derives the output (answers). If AI develops its own languages when talking to other AI, the transparency problem compounds. How can we fully trust an AI when we can’t follow how it is making its decisions and what it is telling other AI?

But it does demonstrate how machines are redefining people’s understanding of so many realms once believed to be exclusively human—like language. The Facebook researchers concluded that it offered a fascinating insight to human and machine language. The bots also proved to be very good negotiators, developing intelligent negotiating strategies.

These new insights, in turn, lead to smarter chatbots that have a greater understanding of the real world and the context of human dialog.

At SAS, we’re developing different ways to incorporate chatbots into business dashboards or analytics platforms. These capabilities have the potential to expand the audience for analytics results and attract new and less technical users.

“Chatbots are a key technology that could allow people to consume analytics without realizing that’s what they’re doing,” says Oliver Schabenberger, SAS Executive Vice President, Chief Operating Officer and Chief Technology Officer in a recent SAS Insights article. “Chatbots create a humanlike interaction that makes results accessible to all.” The evolution of NLP toward NLU has a lot of important implications for businesses and consumers alike.

Satisfying human-computer dialogues will soon exist, and will have applications in medicine, law, and the classroom-to name but a few. As the volume of unstructured information continues to grow exponentially, we will benefit from AI’s tireless ability to help us make sense of it all.

Further Resources:
Natural Language Processing: What it is and why it matters
White paper: Text Analytics for Executives: What Can Text Analytics Do for Your Organization?
SAS® Text Analytics for Business Applications: Concept Rules for Information Extraction Models, by Teresa Jade, Biljana Belamaric Wilsey, and Michael Wallis
Unstructured Data Analysis: Entity Resolution and Regular Expressions in SAS®, by Matthew Windham
SAS: What are chatbots?
Blog: Let’s chat about chatbots, by Wayne Thompson

Moving from natural language processing to natural language understanding was published on SAS Users.

4月 122019
 

At the end of my SAS Users blog post explaining how to install SAS Viya on the Azure Cloud for a SAS Hackathon in the Nordics, I promised to provide some technical background. I ended up with only one manual step by launching a shell script from a Linux machine and from there the whole process kicked off. In this post, I explain how we managed to automate this process as much as possible. Read on to discover the details of the script.

Pre-requisite

The script uses the Azure command-line interface (CLI) heavily. The CLI is Microsoft's cross-platform command-line experience for managing Azure resources. Make sure the CLI is installed, otherwise you cannot use the script.

The deployment process

The process contains three different steps:

  1. Test the availability of the SAS Viya installation repository.
  2. Launch a new Azure Virtual Machine. This action uses a previously created custom Azure image.
  3. Perform the actual installation.

Let’s examine the details of each step.

Test the availability of the SAS Viya installation repository

When deploying software in the cloud, Red Hat Enterprise Linux recommends using a mirror repository. Since the SAS Viya package allows for this installation method, we decided to use the mirror for the hackathon images. This is optional, but optimal, say if your deployment does not have access to the Internet or if you must always deploy the same version of software (such as for regulatory reasons or for testing/production purposes).

In our Azure Subscription we created an Azure Resource group with the name ‘Nordics Hackathon.’ Within that resource group, there is an Azure VM running a web server hosting the downloaded SAS Viya repository.

Azure VM running HTTPD Server and hosting a SAS Viya Mirror Repository

Of course, we cannot start the SAS Viya installation before being sure this VM – hosting all rpms to install SAS Viya – is running.
To validate that the VM is running, we issue the start command from the CLI:

az vm start -g [Azure Resource Group] -n [AZ VM name]

Something like:

az vm start -g my_resourcegroup -n my_viyarepo34

If the server is already running, nothing happens. If not, the command starts the VM. We can also check the Azure console:

Azure Console with 'Running' VMs

Launching the VM

The second part of the script launches a new Azure VM. We use the custom Azure image we created earlier. The SAS Viya image creation is explained in the first blog post.

The Azure image used for the Nordics hackathon was the template for all other SAS Viya installations. On this Azure image we completed several valuable tasks:

  • We performed a SAS Viya pre-deployment assessment using the SAS Viya Administration Resource Kit (Viya ARK) utility tool. The Viya ARK - Pre-installation Playbook is a great tool that checks all prerequisites and performs many pre-deployment tasks before deploying SAS Viya software.
  • Installed R-Server and R-Studio
  • Installed Ansible
  • Created a SAS Viya Playbook using the SAS Orchestration CLI.
  • Customized Ansible playbooks created by SAS colleagues used to kickoff OpenLdap & JupyterHub installation.

Every time we launch our script, an exact copy of a new Azure Virtual machine launches, fully customized according to our needs for the Hackathon.
Below is the Azure CLI command used in the script which creates a new Azure VM.

az vm create --resource-group [Azure Resource Group]--name $NAME --image viya_Base \
--admin-username azureuser --admin-password [your_pw] --subnet [subnet_id] \
--nsg [optional existing network security group] --public-ip-address-allocation static \
--size [any Azure size] --tags name=$NAME

After the creation of the VM, we install SAS Viya in the third step of the process.

Installation

After running the script three times (using a different value for $NAME), we end up with the following high-level infrastructure:

SAS Viya on Azure Cloud deployemnt

After the launch of the Azure VM, the viya-install.sh script starts the install script using the original image in the /opt/sas/install/ location.
In the last step of the deployment process, the script installs OpenLdap, SAS Viya and JupyterHub. The following command runs the script:

az vm run-command invoke -g [Azure Resource Group] -n $NAME --command-id RunShellScript --scripts "sudo /opt/sas/install/viya-install.sh &"

The steps in the script should be familiar to those with experience installing SAS Viya and/or Ansible playbooks. Below is the script in its entirety.

#!/bin/bash
touch /start
####################################################################
echo "Starting with the installation of OPENLDAP. Check the openldap.log in the playbook directory for more information" > /var/log/myScriptLog.txt
####################################################################
# install openldap
cd /opt/sas/install/OpenLDAP
ansible-playbook openldapsetup.yml
if [ $? -ne 0 ]; then { echo "Failed the openldap setup, aborting." ; exit 1; } fi
cp ./sitedefault.yml /opt/sas/install/sas_viya_playbook/roles/consul/files/sitedefault.yml
if [ $? -ne 0 ]; then { echo "Failed to copy file, aborting." ; exit 1; } fi
####################################################################
echo "Starting Viya installation" >> /var/log/myScriptLog.txt
####################################################################
# install viya
cd /opt/sas/install/sas_viya_playbook
ansible-playbook site.yml
if [ $? -ne 0 ]; then { echo "Failed to install sas viya, aborting." ; exit 1; } fi
####################################################################
echo "Starting jupyterhub installation" >> /var/log/myScriptLog.txt
####################################################################
# install jupyterhub
cd /opt/sas/install/jupy-azure
ansible-playbook deploy_jupyter.yml
if [ $? -ne 0 ]; then { echo "Failed to install jupyterhub, aborting." ; exit 1; } fi
####################################################################
touch /finish 
####################################################################

Up next

In a future blog, I hope to show you how get up and running with SAS Viya Azure Quick Start. For now, the details I provided in this and the previous blog post is enough to get you started deploying your own SAS Viya environments in the cloud.

Script for a SAS Viya installation on Azure in just one click was published on SAS Users.

4月 112019
 

What's the impact of using data governance and analytics for the business side of education? It's an interesting question, and during a video interview, Dale Pietrzak, Ed.D., Director of Institutional Effectiveness and Accreditation (IEA) at the University of Idaho shared details on the results they're realizing from using SAS for data [...]

The impact of data governance and analytics: An interview with the U. of Idaho was published on SAS Voices by Georgia Mariani

4月 082019
 
The catch phrase “everything happens somewhere” is increasingly common these days.  That “somewhere” translates into a location on the Earth; a latitude and longitude.  When one of these “somewhere’s” is combined with many other “somewhere’s”, you quickly have a robust spatial data set that becomes actionable with the right analytic tools.

Opportunities for Spatial Analytics are increasing

In today’s modern world, GPS-enabled devices are ubiquitous, and their use continues to increase daily.  Cell phones, cars, fitness trackers, and cameras are all able to locate and track our position.  As a result, the location analytics market is expected to grow to over USD 16 Billion by 2021, up 17.6% from 2016 [1].

Waldo Tobler, an American-Swiss geographer and cartographer, developed his First Law of Geography based on this concept of everything happening somewhere.  He stated, “Everything is related to everything else, but near things are more related than distant things”[2].  As analytic professionals, we are accustomed to working with these correlations using scatterplots, heatmaps, or clustering models.  But what happens when we add a geographic map into the analysis?

Maps offer the ability to unlock a new level of insight into our data that traditional graphs do not offer: personal connection.  As humans, we naturally relate to our surroundings on a spatial level.   It helps build our perspective and frame of reference through which we view and navigate the world.  We feel a sense of loss when a physical landmark from our childhood – a building, tree, park, or route we used to walk to school – is destroyed or changed from the memories we have of it.  In this sense, we are connected, spatially and emotionally, to our surroundings.

We inherently understand how data relates to the world around us, at some level, just by viewing it on a map.  Whether it is a body of water or a mountain affecting a driving route or maybe a trendy area of a city causing housing prices to increase faster than the local average, a map connects us with these facts intuitively.  We come to these basic conclusions based solely on our experiences in the world and knowledge of the physical landmarks in the map.

One of the best examples of this is the 1854 Cholera outbreak in London.  Dr. John Snow was one of the first to use a map for understanding the origin of an epidemiological outbreak.  He created a map of the affected London neighborhood by plotting the location of all known Cholera deaths.  In addition to the deaths, he also plotted the location of 13 community wells that served as the public water supply.  Using this data, he was able to see a clustering of deaths around a single pump.  Armed with this information, Dr. Snow was able to convince local officials to remove the handle from the Broad Street pump.  Once removed, new cases of Cholera quickly began to diminish.  This helped prove his theory the outbreak’s origin was not air-borne as commonly believed during that time, but rather of a water-borne origin. [3]

1854 London Cholera deaths: Tabular data vs. Coordinate map [3]

Let’s look at how Dr. Snow’s map helped mitigate the outbreak and prove his theory.  The image above compares the data of the recorded deaths and community wells in tabular form to a Coordinate map.  It is obvious from the coordinate map that there is a clustering of points.  Town officials and those familiar with the neighborhood could easily get a sense of where the outbreak was concentrated.  The map told a better story by connecting their personal experience of the area to the locations of the deaths and ultimately to the wells.  Something a data table or traditional graph could not do.

Maps of London Cholera deaths with modern analytic overlays [3]

Today, with the computing power and modern analytic methods available to us, we can take the analysis even further.  The examples above show the same coordinate map with added Voronoi polygon and cluster analysis overlays.  The concentration around the Broad Street pump becomes even clearer, showing why Geographic Maps are an important tool to have in your analytic toolbox.

SAS Global Forum 2019 is being held April 28-May 1, 2019 in Dallas, Texas.  If you are planning to go to this year’s event, be sure to attend one of our presentations on the latest mapping features included in SAS Visual Analytics and BASE SAS.  While you’re there, don’t forget to stop by the SAS Mapping booth located in the QUAD to say ‘Hi!’ and let us help with your spatial data needs.  See you in Dallas!

Introduction to Esri Integration in SAS Visual Analytics

  • Monday, April 29, 4:30-5:30p, Room: Level 1, D162

There’s a Map for That! What’s New and Coming Soon in SAS Mapping Technologies

  • Tuesday April 30, 4:00-4:30p, Room: Level 1, D162

Creating Great Maps in ODS Graphics Using the SGMAP Procedure

  • Wednesday May 01, 11:30a-12:30p, Room: Level 1, D162

[1] https://www.marketsandmarkets.com/Market-Reports/location-analytics-market-177193456.html

[2] https://en.wikipedia.org/wiki/Tobler%27s_first_law_of_geography

[3] https://www1.udel.edu/johnmack/frec682/cholera/

How the 1854 Cholera outbreak showed us the importance of spatial analysis was published on SAS Users.

4月 052019
 

Recently, you may have heard about the release of the new SAS Analytics Cloud. The platform allows fast access to data-science applications in the cloud! Running on the SAS Cloud and using the latest container technology, Analytics Cloud eliminates the need to install, update, or maintain software or related infrastructure.

SAS Machine Learning on SAS Analytics Cloud is designed for SAS and open source data scientists to gain on-demand programmatic access to SAS Viya. All the algorithms provided by SAS Visual Data Mining and Machine Learning (VDMML), SAS Visual Statistics and SAS Visual Analytics are available through the offering. Developers and data scientists access SAS through a programming interface using either the SAS or Python programming languages.

A free trial for Analytics Cloud is available, and registration is simple. The trial environment allows users to manage and collaborate with others, share data, and create runtime models to analyze their data. The system is pre-loaded with sample data for learning, and allows users to upload their own data. My colleague Joe Furbee explains how to register for the trial and takes you on a tour of the system in his article, Zero to SAS in 60 Seconds- SAS Machine Learning on SAS Analytics Cloud.

Luckily, I had the privilege of being the technical writer for the documentation for SAS Analytics Cloud, and through this met two of my now close friends at SAS.

Alyssa Andrews (pictured left) and Mariah Bragg (pictured right) are both Software Developers at SAS, but worked on the UI for SAS Analytics Cloud. Mariah works in the Research and Development (R&D) division of SAS while Alyssa works in the Information Technology (IT) division. As you can see this project ended up being an interesting mix of SAS teams!

As Mariah told me the history, I learned that SAS Analytics Cloud “was a collaborative project between IT and R&D. The IT team presented the container technology idea to Dr. Goodnight but went to R&D because they wanted this idea run like an R&D project.”

As we prepared for the release of SAS Analytics Cloud to the public, I asked Mariah and Alyssa about their experience working on the UI for SAS Analytics Cloud, and about all the work that they had completed to bring this powerful platform to life!


What is SAS Analytics Cloud for you? How do you believe it will help SAS users?

Alyssa: For me, it is SAS getting to do Software as a Service. So now you can click on our SAS Software and it can magically run without having to add the complexity of shipping a technical support agent to the customers site to install a bunch of complex software.

Mariah: I agree. This will be a great opportunity for SAS to unify and have all our SAS products on cloud.

Alyssa: Now, you can trial and then pay for SAS products on the fly without having to go through any complexities.

What did you do on the project as UI Developers?

Alyssa: I was lent out to the SAS Analytics Cloud team from another team and given a tour-of-duty because I had a background in Django (a high-level Python Web design tool) which is another type of API framework you can build a UI on top of. Then I met Mariah, who came from an Angular background, and we decided to build the project on Angular. So, I would say Mariah was the lead developer and I was learning from her. She did more of the connecting to the API backend and building the store part out, and I did more of the tweaks and the overlays.

What is something you are proud of creating for SAS Analytics Cloud?

Mariah: I’m really proud to be a part of something that uses Angular. I think I was one of the first people to start using Angular at SAS and I am so excited that we have something out there that is using this new technology. I am also really proud of how our team works together, and I’m really proud of how we architectured the application. We went through multiple redesigns, but they were very manageable, and we really built and designed such that we could pull out components and modify parts without much stress.

Alyssa: That we implemented good design practices. It is a lot more work on the front-end, but it helps so much not to have just snowflake code (a term used by developers to describe code that isn’t reusable or extremely unique to where it becomes a problem later on and adds weight to the program) floating. Each piece of code is there for a reason, it’s very modular.

What are your hopes for the future of SAS Analytics Cloud?

Alyssa: I hope that it continues to grow and that we add even more applications to this new container technology, so that SAS can move even more into the cloud arena. I hope it brings success. It is a really cool platform, so I can’t wait to hear about users and their success with it.

Mariah:
I agree with Alyssa. I also hope it is successful so that we keep moving into the Cloud with SAS.

Learning more

As a Developmental Editor with SAS Press, it was a new and engaging experience to get to work with such an innovative technology like SAS Analytics Cloud. I was happy I got to work with such an exciting team and I also look forward to what is next for SAS Analytics Cloud.

And as a SAS Press team member, I hope you check out the new way to trial SAS Machine Learning with SAS Analytics Cloud. And while you are learning SAS, check out some of our great books that can help you get started with SAS Studio, like Ron Cody’s Biostatistics by Example Using SAS® Studio and also explore Geoff Der and Brian Everitt’s Essential Statistics Using SAS® University Edition.

Already experienced but want to know more about how to integrate R and Python into SAS? Check out Kevin D. Smith’s blogs on R and Python with SAS Viya. Also take a moment to investigate our new books on using open source R and Python with SAS Viya: SAS Viya: The R Perspective by Yue Qi, Kevin D. Smith, and XingXing Meng and SAS Viya: The Phyton Perspective by Kevin D. Smith and XingXing Meng.

These great books can set you on the right path to learning SAS before you begin your jump into SAS Analytics Cloud, the new way to experience SAS.

SAS® Analytics Cloud—an interview with the women involved was published on SAS Users.

3月 272019
 

SAS Visual Analytics supports region maps for Country, US states, and provinces out-of-the-box.  These work well for small scale maps covering the world, a continent, or a single country.  However, other regions are often needed.  Beginning in version 8.3, VA supports custom polygons to display regions such as sales territories, counties, or zip codes.

Region (choropleth) maps use a fill color to show relationships between the regions based upon a response value from your data.  Using custom polygons in VA follows the same steps outlined in previous posts for predefined or custom coordinate geography items, with just a few additional steps.  Here’s the basic flow:

  • Identify your data
  • Import polygon shapefile into SAS dataset
  • Import the shape dataset into VA
  • Create a Custom polygon provider
  • Create the geography item
  • Create and customize the map

Before we begin

VA supports two sources for creating custom polygons: Esri shapefiles and Esri Feature Services.  The goal for this post is to show how to create custom polygons using an Esri shapefile.

Typically, when working with custom polygons, you will have two datasets: the first defines the custom regions (shape data) and the second contains the data you wish to map (business data).  The shape data is derived from an Esri shapefile or feature service.  The business data can be in a shapefile or any format supported by VA (.sas7bdat, .csv, .xls, etc). It contains the information you want to analyze distributed across the regions defined by the shape data.

It is recommended that you verify the imported shape data before using it in your final map.  This will confirm the data is valid and make debugging an issue easier should you encounter any errors.  To verify, use the same dataset for both the shape and business data.  The example below will use this approach.

Access to a GIS application such as Esri’s ArcGIS or QGIS is recommended.  There are two areas where they can help you prepare to use custom polygons in your VA map:

  • Creating a shapefile to define polygons specific to your business need or application
  • Viewing the attribute table of existing shapefiles to determine its unique identifier column

For this example, we will be creating a map of registered Neighborhood Associations in Boise, Idaho. To follow along, download the data from the City of Boise open data site: Boise Neighborhood Associations

1. Identify your data

Shape data

The shape data defining the custom regions needs to be in an Esri shapefile format. These files can be created in a GIS application or obtained from a wide variety of online sources such as: the US Census Bureau (http://www.census.gov); local and state municipalities; state agencies such as the Department of Transportation; and university GIS departments.  Most municipalities now have Open Data portals that provide a wealth of reliable data for public use.  These sources are maintained by dedicated staff and are updated regularly.

Business data

The business data can be specific to your company’s operation or customer base.  Or it can be broad and general using census or demographic information.  It answers the question of What you want to analyze on the map.  The business data must contain a column that aligns with your shape data.  For example: If you want to map the age distribution and spending habits of your target customers across zip codes, then your business data must have a column for zip codes that allows it to be joined to a zip code region in the shape data.

2. Import polygon data into a SAS dataset

VA 8.3 does not support the native shapefile format. To use a shapefile in VA, you must first import it into SAS.  Included with Viya3.4, the %shpimprt macro will convert a shapefile into a SAS dataset and load it into CAS.  You can find the documentation for it here: %shpimprt documentation.

Alternatively, the shapefile can be manually imported with these basic steps:

  • Import the shapefile into SAS
  • Add a sequence column to the dataset
  • Reduce the density of the dataset
  • Limit the dataset based on the density value

Additional details and sample code for each of these steps can be found in the text file linked here: Manual shapefile import steps.

3. Import the shape dataset into VA

Next, we must import the dataset into VA, if using the manual shapefile import process.  To do this, locate the data pane on the left of VA.  From the ‘Open Data Source’ window, select Import > Local File.  Navigate to the location of the SAS dataset created from Step 2 and click the Open button.

Adjust the target location as needed, based on your VA installation, and make note of the location selected.  This path will be required to configure the custom polygon provider. Review and adjust the other options as needed.  Click the blue ‘Import Item’ button at the top of the window to start the import process.  A message will appear indicating the import status. Upon successful import, click the 'OK' button to open the dataset.

Since we are using the same dataset for the shape and business data, we need to make a copy of the category variable that will be used for our map. Right click on ‘ASSOCIATIO’ and select ‘Duplicate’.  Next, let’s change the names of both variables to better distinguish them from one another:

  • Change ‘ASSOCIATIO’ to ‘Business data’
  • Change ‘ASSOCIATIO (1)’ to ‘Shape data’

4. Create the geography item

We are now ready to start creating the geography item.  With Custom polygons, an additional step is required beyond what was described in previous posts with predefined and custom coordinates geography items.  We must define a Custom Polygon provider so VA knows how to locate and display the Boise Neighborhood Associations.  This is needed only once and is part of the geography item setup you are familiar with.

Our goal is to map the regions of the Boise Neighborhood Associations, so we will use ‘Shape data’ for our geography item.  Locate it in the VA data panel and change its Classification type to ‘Geography’.  From the ‘Geography data type’ dropdown, select ‘Custom polygonal shapes’. Several new fields will be displayed.  In the ‘Custom polygon provider’ dropdown, click the ‘Define new polygon provider’ button.

A ‘New Polygon Provider’ window will appear.  All fields shown are required.  The Advanced section has additional options, but they are not needed for this example.

Configure the fields based on the following:

  • Name / Label – Enter ‘Boise Neighborhoods’ for both (these values do not have to be the same)
  • Type – The default CAS Table is the correct option for this example.
  • Server / Library – These values must match those used for the data upload in Step 3.
  • Table – Select the name of the table uploaded in Step 3 (Boise_Neighborhoods)
  • ID Column – The unique identifier column of the dataset. Used to join the shape and business data together. (Select OBJECTID)
  • Sequence Column – This column is created during the import process from Step 2. Needed by VA to display the custom regions. (Select SEQUENCE)

The custom polygon provider is now configured.  All that is needed to finish the geography item setup, is to identify the Region ID.  This is the crucial step that will join the shape data to the business data.  The Region ID column must match the ID Column chosen when the custom polygon provider was setup.  Since we are using the same dataset in this example, that value is the same: OBJECTID.

In cases where different datasets are used for the shape and business data, the name of Region ID and ID Column may be different.  The column labels are not important, but their content must match for the join to occur.

Notice that once you select the correct RegionID value, the preview window will display the custom regions from the imported shape data.  The Latitude and Longitude columns are not required in this example.  Click the ‘OK’ button, to finish the setup.

5. Create and customize the map

You are now ready to create your map.  Drag the Boise Neighborhoods geography item to the report canvas.  Let’s enhance the appearance of our map by making a few style changes:

  • Set a Color role to shade the Neighborhood Association regions (Roles > Color > Business data)
  • Position the legend on the left of the map (Options > Legend)
  • Adjust the transparency of the fill color to 45% (Options > Map Transparency)
  • Change the map service to Esri World Street Map (Options > Map service)

Final map with custom polygons.

Congratulations!  You have just created your first custom region map.  In this post we discussed how to use the Custom Polygon provider to define your own regions using an Esri shapefile.  Compared to the Predefined and Custom Coordinate options, custom polygons give you additional flexibility and control over how your spatial data is analyzed.

Creating custom region maps with SAS Visual Analytics was published on SAS Users.

2月 272019
 

In this post, we continue our discussion of geography variables, the foundation of Visual Analytics Geo maps. This time we will look at Custom Coordinates.  As with any statistical graph, understanding your data is key.  But when using Custom Coordinates for geographic maps, this understanding becomes even more important.

Use the Custom Coordinate geography variable when your data does not match one of VA’s predefined geography types (see previous post, Fundamentals of SAS Visual Analytics geo maps).  For Custom coordinates, your data set must include latitude and longitude values as separate variables.   These values should be sourced from trustworthy providers and validated for accuracy prior to loading into VA.

When using Custom Coordinates, the Coordinate Space must also be considered.  The coordinate space defines the grid used to plot your data.  The underlying map is also based on a grid.  In order for your data to display correctly on a map, these grids must match.  Visual Analytics uses the World Geodetic System (WGS84) as the default coordinate space (grid).  This will work for most scenarios, including the example below.

Once you have selected a dataset and confirmed it contains the required spatial information, you can now create a Custom Geography variable.  In this example, I am using the variable Business Address from the dataset Wake_Co_Pizza.  Let’s get started.

  1. Begin by opening VA and navigate to the Data panel on the left of the application.
  2. Select the dataset and locate the variable that you wish to map. Click the down arrow to the right of the variable and chose ‘Geography’ from the Classification dropdown menu.
  3. The ‘Edit Geography Item’ window appears. Select Custom coordinates in the ‘Geography data type’ dropdown.   Three new dropdown lists appear that are specific to the Custom coordinates data type: ‘Latitude (y)’, ‘Longitude (x)’ and ‘Coordinate Space’.

When using the Custom coordinates data type, we must tell VA where to find the spatial data in our dataset.  We do this using the Latitude (y) and Longitude (x) dropdown lists.  They contain all measures from your dataset.  In this example, the variable ‘Latitude World Geodetic System’ contains our latitude values and the variable  ‘Longitude World Geodetic System’ contains our longitude values.   The ‘Coordinate Space’ dropdown defaults to World Geodetic System (WGS84) and is the correct choice for this example.

  1. Click the OK button to complete the setup once the latitude and longitude variables have been selected from their respective dropdown lists. You should see a new ‘Geography’ section in the Data panel.  The name of the variable (or its edited value) will be displayed beside a globe icon to indicate it is a geography variable.  In this case we see the variable Business Address.

 

Congratulations!  You have now created a custom geography variable and are ready to display it on a map.  To do this, simply drag it from the Data panel and drop it on the report canvas.  The auto-map feature of VA will recognize it as a geography variable and display the data as a bubble map with an OpenStreetMap background.

In this post, we created a custom geography variable using the default Coordinate Space.  Using a custom geography variable gives you the flexibility of mapping data sets that contain valid latitude and longitude values.  Next time, we will take our exploration of the geography variable one step further and explore using custom polygons in your maps.

Using Custom Coordinates for map creation in SAS Visual Analytics was published on SAS Users.

2月 082019
 

Creating a map with SAS Visual Analytics begins with the geographic variable.  The geographic variable is a special type of data variable where each item has a latitude and longitude value.  For maximum flexibility, VA supports three types of geography variables:

  1. Predefined
  2. Custom coordinates
  3. Custom polygons

This is the first in a series of posts that will discuss each type of geography variable and their creation. The predefined geography variable is the easiest and quickest way to begin and will be the focus of this post.

SAS Visual Analytics comes with nine (9) predefined geographic lookup types.  This lookup method requires that your data contains a variable matching one of these nine data types:

  • Country or Region Names – Full proper name of a country or region (ISO 3166-1)
  • Country or Region ISO 2-Letter Codes – Alpha-2 country code (ISO 3166-1)
  • Country or Region ISO Numeric Codes – Numeric-3 country code (ISO 3166-1)
  • Country or Region SAS Map ID Values – SAS ID values from MPASGFK continent data sets
  • Subdivision (State, Province) Names – Full proper name for level 2 admin regions (ISO 3166-2)
  • Subdivision (State, Province) SAS Map ID Values – SAS ID values from MAPSGFK continent data sets (Level 1)
  • US State Names – Full proper name for US State
  • US State Abbreviations – Two letter US State abbreviation
  • US Zip Codes – A 5-digit US zip code (no regions)

Once you have identified a variable in your dataset matching one of these types, you are ready to begin.  For our example map, the dataset 'Crime' and variable 'State name' will be used.  Let’s get started.

Creating a predefined geography variable in SAS Visual Analytics

  1. Begin by opening VA and navigate to the Data panel on the left of the application.
  2. Select the desired dataset and locate a variable that matches one of the predefined lookup types discussed above. Click the down arrow to the right of the variable and select ‘Geography’ from the Classification dropdown menu.
  3. The ‘Edit Geography Item’ window will open. Depending upon the type of geography variable selected, some of the options on this dialog will vary.  The 'Name' textbox is common for all types and will contain the variable selected from your dataset.  Edit this label as needed to make it more user friendly for your intended audience.
  4. The ‘Geography data type’ drop down list is where you select the desired type of geography variable.  In this example, we are using the default predefined option.
  5. Locate the 'Name or code context' dropdown list.  Select the type of predefined variable that matches the data type of the variable chosen from your data.  Once selected, VA scans your data and does an internal lookup on each data item.  This process identifies latitude and longitude values for each item of your dataset.  Lookup results are shown on the right of the window as a percentage and a thumbnail size map.  The thumbnail map displays the the first 100 matches.
  6. If there are any unmatched data items, the first 5 will be displayed.  This may provide a better understanding of your data.  In this example, it is clear from variable name as to what type should be selected (US State Names).  However, in most cases that choice will not be this obvious.  The lesson here, know your data!

Unmatched data items indicators

Once you are satisfied with the matched results, click the OK button to continue.  You should see a new section in the Data panel labeled ‘Geography’.  The name of the variable will be displayed beside a globe icon. This icon represents the geography variable and provides confirmation it was created successfully.

Icon change for geography variable

Now that the geography variable has been created, we are ready to create a map.  To do this, simply drag it from the Data panel and drop it on the VA report canvas.  The auto-map feature of VA will recognize the geography variable and create a bubble map with an OpenStreetMap background.  Congratulations!  You have just created your first map in VA.

Bubble map created with predefined geography variable

The concept of a geography variable was introduced in this post as the foundation for creating all maps in VA.  Using the predefined geography variable is the quickest way to get started with Geo maps.  In situations when the predefined type is not possible, using one of VA's custom geography types becomes necessary.  These scenarios will be discussed in future blog posts.

Fundamentals of SAS Visual Analytics geo maps was published on SAS Users.

2月 022019
 

SAS Visual Analytics

I don't know about you, but when I read challenges like:

  • Detecting hidden heart failure before it harms an individual
  • Can SAS Viya AI help to digitalize pension management?
  • How to recommend your next adventure based on travel data
  • How to use advanced analytics in building a relevant next best action
  • Can SAS help you find your future home?
  • When does a customer have their travel mood on, and to which destination will he travel?
  • How can SAS Viya, Machine Learning and Face Recognition help find missing people?

…I can continue with the list of ideas provided by the teams participating in the SAS Nordics User Group’s Hackathon. But one thing is for sure, I become enthusiastic and I'm eager to discover the answers and how analytics can help in solving these questions.

When the Nordics team asked for support for providing SAS Viya infrastructure on Azure Cloud platform, I didn't hesitate to agree and started planning the environment.

Environment needs

Colleagues from the Nordics countries informed us their Hackathon currently included fourteen registered teams. Hence, they needed at least fourteen different environments with the latest and greatest SAS Viya Tools like SAS Visual Analytics, SAS VDMML and SAS Text Analytics. In addition, participants wanted to get the chance to use open source technologies with SAS and asked us to install R-Studio and Jupyter. This would allow data scientists develop models in a programming language of choice and provide access to SAS predictive modeling capabilities.

The challenge I faced was how to automate this installation process. We didn't want to repeat an exact installation fourteen times! Also, in case of a failure we needed a way to quickly reinstall a fresh virtual machine in our environment. We wanted to create the virtual machines on the Azure Cloud platform. The goal was to quickly get SAS Viya instances up and running on Azure, with little user interaction. We ended up with a single script expecting one parameter: the name of the instance. Next, I provide an overview of how we accomplished our task.

The setup

As we need to deploy fourteen identical copies of the same SAS Viya software, we decided to make use of the SAS Mirror Manager, which is a utility for synchronizing SAS software repositories. After downloading the mirror repository, we moved the complete file structure to a Web Server hosted on a separate Nordics Hackathon repository virtual machine, but within a similar private network where the SAS Viya instances will run. This guarantees low latency when downloading the software.

Once the repository server is up and running, we have what we needed to create a SAS Viya base image. Within that image, we first need to make sure to meet the requirements described in the SAS Viya Deployment Guide. To complete this task, we turned to the Viya Infrastructure Resource Kit (VIRK). The VIRK is a collection of tools, created by Erwan Granger, that assist in infrastructure and readiness-verification tasks. The script is located in a repository on SAS software’s GitHub page. By running the VIRK script before creation of the base image, we guarantee all virtual machines based on the image meet the necessary requirements.

Next, we create within the base image the SAS Viya Playbook as described in the SAS Viya Deployment Guide. That allows us to kick off a SAS Viya installation later. The Viya installation must occur later during the initial launch of a new VM based on that image. We cannot install SAS Viya beforehand because one of the requirements is a static IP address and a static hostname, which is different for each VM we launch. However, we can install R-Studio server on the base image. Another important file we make available on this base image is a script to initiate the Ansible installations of OpenLdap, SAS Viya and Jupyter.

Deployment

After the common components are in place we follow the instructions from Azure on how to create a custom image of an Azure VM. This capability is available on other public cloud providers as well. Now all the prerequisites to create working Viya environments for the Hackathon are complete. Finally, we create a launch script to install a full SAS Viya environment with single command and one parameter, the hostname, from the Azure CLI.

$ ./launchscript.sh viya01
$ ./launchscript.sh viya02
$ ./launchscript.sh viya03
...
$ ./launchscript.sh viya12
$ ./launchscript.sh viya13
$ ./launchscript.sh viya14

The script

The main parts of this launch script are:

  1. Testing if the Nordics Hackathon Repository VM is running because we must download software from our own locally created repository.
  2. Launch a new VM, based on the SAS Viya Image we created during preparation, assign a public static IP address, and choose a Standard_E32-16s_v3 Azure VM.
  3. Launch our own Viya-install script to perform the following three sub-steps:
    • Install openLDAP as the identity provider
    • Install SAS Viya just as you would do by following the SAS Viya Deployment Guide.
    • Install Jupyter with a customized Ansible script made by my colleague Alexander Koller.

The result of this is we have fourteen full SAS Viya installations ready in about one hour and 45 minutes. We recently posted a Linkedin video describing the entire process.

Final thoughts

I am planning to write a blog on SAS Communities to share more technical insight on how we created the script. I am honored I was asked to be part of the jury for the Hackathon. I am looking forward to the analytical insights that the different teams will discover and how they will make use of SAS Viya running on the Azure Cloud platform.

Additional resources

Series of Webinars supporting the Nordic Hackathon

Installing SAS Viya Azure virtual machines with a single click was published on SAS Users.

12月 222018
 

This post rounds out the year and my series of articles on SAS REST APIs. The first two articles in the series: Using SAS Viya REST APIs to access images from SAS Visual Analytics and Using SAS Cloud Analytics Service REST APIs to run CAS Actions, examined how to use SAS Viya REST and SAS CAS REST APIs to access SAS data from external resources. Access the links to for a quick detour to get some background. This article takes things a step further and outlines how to use a simple application to interact with SAS Viya using REST APIs.

What do chocolate and toffee have to do with optimization? Read on and find out.

The application

When deciding on an example to use in this article, I wanted to focus on the interaction between the application and SAS, not app complexity. I decided to use an application created by my colleague, Deva Kumar. His OptModel1 is an application built on the restAF framework and demonstrates how SAS REST APIs can be used to build applications that exploit various SAS Viya functionalities. This application optimizes the quantities of chocolate and toffee to purchase based on a budget entered by the user.

Think of the application as comparable to the guns and butter economic model. The idea in the model is the more you spend on the military (guns), the less you spend on domestic programs and the civilian goods (butter). As President Johnson stated in 1968, "That bitch of a war, killed the lady I really loved -- the Great Society." In this article, I'll stick to chocolate and toffee, a much less debatable (and tastier) subject matter.

The OptModel1 application uses the runOptmodel CAS action to solve the optimization problem. The application launches and authenticates the user, the app requests a budget. Based on the amount entered, a purchase recommendation returns for chocolate and toffee. The user may also request a report based on returned values. In the application, OptModel1 and SAS interact through REST API calls. Refer to the diagram below for application code workflow.

Create the application

To create the application yourself, access the source code and install instructions on SAS' github page. I recommend cloning, or in the least, accessing the repository. I refer to code snippets from multiple files throughout the article.

Application Workflow

Represented below is the OptModel1 work flow. Highlighted in yellow is each API call.

OptModel1 Work Flow

OptModel1 Work Flow

Outlined in the following sections is each step in the work flow, with corresponding numbers from the diagram.

Launch the application

Enter url http://localhost:5006/optmodel in a browser, to access the login screen.

OptModel1 app login page

1. Login

Enter proper credentials and click the 'Sign In' button. The OptModel1 application initiates authentication in the logon.html file with this code:

        <script>
            function logonButton() {
                let store = restaf.initStore();
                store.logon(LOGONPAYLOAD)
                    .then(msg => console.log(msg))
                    .catch(err => alert(err));
            }
        </script>

Application landing page

After successfully logging in, the application's main page appears.

Application landing page

Notice how the host and access token are part of the resulting url. For now, this is as far as I'll go on authentication. I will cover this topic in depth in a future article.

As I stated earlier, this is the simplest of applications. I want to keep the focus on what is going on under the covers and not on a flashy application.

2a. Application initialization

Once the app confirms authentication, the application initialization steps ensue. The app needs to be available to multiple users at once, so each session gets their own copy of the template Visual Analytics (VA) report. This avoids users stepping on each other’s changes. This is accomplished through a series of API calls as explained below. The code for these calls is in vaSetup.js and reportViewer.js.

2b. Copy data

The app copies data from the Public caslib to a temporary worklib – a worklib is a standard caslib like casuser. The casl code below is submitted to CAS server for execution. The code to make the API call to CAS is in vaSetup.js. The relevant snippet of javascript code is:

  // create casl statements
    let casl = `
        /* Drop the table in memory */
        action table.dropTable/
        caslib='${appEnv.work.caslib}' name='${appEnv.work.table}' quiet=TRUE;
 
        /* Delete the table from the source */
        action table.deletesource / 
        caslib='${appEnv.work.caslib}' source='${appEnv.work.table}.sashdat' quiet=TRUE;
 
        /* Run data step to copy the template table to worklib */
        action datastep.runCode /
            code='
            data ${appEnv.work.caslib}.${appEnv.work.table}; 
            set ${appEnv.template.caslib}.${appEnv.template.table};
            run;';
 
        /* Save the new work table */
        action table.save /
            caslib  = '${appEnv.work.caslib}'
            name    = '${appEnv.work.table}'
            replace = TRUE
            table= {
                caslib = '${appEnv.work.caslib}'
                name   = '${appEnv.work.table}'
            };
 
        /* Drop the table to force report to reload the new table */
        action table.dropTable/
            caslib='${appEnv.work.caslib}' name='${appEnv.work.table}' quiet=TRUE;
 
 
    `;
 
    // run casl statements on the server via REST API
    let payload = {
        action: 'sccasl.runCasl',
        data: {code: casl}
    }
    await store.runAction(session, payload);

2c. Does report exist?

This step checks to see if the personal copy of the VA report already exists.

2d. Delete temporary report

If the personal report exists it is deleted so that a new one can be created using the latest VA report template.

// If temporary report exists delete it - allows for potential new template report
    let reportsList = await getReport( store, reports, `${APPENV.work.report}`);
    if ( reportsList !== null ) {
        await store.apiCall(reportsList.itemsCmd(reportsList.itemsList(0), 'delete'));
      };

2e. Create new report

A new personal report is created. This new report is associated with the table that was created in step 2b.

// make the service call to create the temporary report
    let changeData = reportTransforms.links('createDataMappedReport');
    let newReport = await store.apiCall(changeData, p);

2f. Save report info

A new personal report is created. This new report is associated with the table that was created in step 2b.

// create src parameter for the iframe
    let options = "&appSwitcherDisabled=true&reportViewOnly=true&printEnabled=true&sharedEnabled=true&informationEnabled=true&commentEnabled=true&reportViewOnly=true";
    let href = `${appEnv.host}/SASReportViewer/?reportUri=${reportUri}${options}`;
 
    // save href in appEnv to use for displaying VA report in an iframe
    appEnv.href = href;

3. Enter budget

Enter budget in the space provided (I use $10,000 in this example) and click the Optimize button. This action instructs the application calculate the amount of chocolate and toffee to purchase based on the model.

Enter budget and optimize

4. & 5. Generate and execute CASL code

The code to load the CAS action set, run the CAS action, and store the results in a table, is in the genCode.js file:

  /* Assumption: All necessary input tables are in memory */
	pgm = "${pgm}";
	/*Load action set and run optimization*/
	loadactionset 'optimization';
		action optimization.runOptmodel / 
		code=pgm printlevel=0; 
		run; 
 
	/* save result of optimization for VA to use */
	action table.save /
		caslib  = '${appEnv.work.caslib}'
		name    = '${appEnv.work.table}'
		replace = TRUE
		table= {
			caslib = '${appEnv.work.caslib}'
			name   = '${appEnv.work.table}'
		};
 
	/* fetch results to return for the UI to display */
	action table.fetch r=result /
		table= {caslib = '${appEnv.work.caslib}' name = '${appEnv.work.table}'};
	run;
 
	/* drop the table to force report to reload the new table */
	action table.dropTable/
		caslib='${appEnv.work.caslib}' name='${appEnv.work.table}' quiet=TRUE;

Note: The drop table step at the end of the preceding code is important to force VA to reload the data for the report.

6. Get the results - table form

The results return to the application in table form. We now know to buy quantities of 370 chocolate and 111 toffee with our $10,000 budget. Please refer to the casTableViewer for code details of this step.

Data view in table format

6. Get the results - report form

Select the View Graph button. This action instructs OptModel1 to display the interactive report with the new data (the report we created in step 2f). Please refer to the onReport function in index.html for code details of this step.

Data view in report format

Now that we know how much chocolate and toffee to buy, we can make enough treats for all of the holiday parties just around the corner. More importantly, we see how to integrate SAS REST APIs into our application. This completes the series on using SAS REST APIs. The conversation is not over however. I will continue to search out and report on other topics related to SAS, open source languages, and agile technologies. Happy Holidays!

SAS REST APIs: a sample application was published on SAS Users.