The European Union’s General Data Protection Regulation (GDPR) taking effect on 25 May 2018 pertains not only to organizations located within the EU; it applies to all companies processing and holding the personal data of data subjects residing in the European Union, regardless of the company’s location.

If the GDPR acronym does not mean much to you, think of the one that does – HIPAA, FERPA, COPPA, CIPSEA, or any other that is relevant to your jurisdiction – this blog post is equally applicable to all of them.

The GDPR prohibits personal data processing revealing such individual characteristics as race or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, as well as the processing of genetic data, biometric data for the purpose of uniquely identifying a natural person, data concerning health, and data concerning a natural person’s sex life or sexual orientation. It also has special rules for data relating to criminal convictions or offenses and the processing of children’s personal data.

Whenever SAS users produce reports on demographic data, there is always a risk of inadvertently revealing personal data protected by law, especially when reports are generated automatically or interactively via dynamic data queries. Even for aggregate reports there is a high potential for such exposure.

Suppose you produce an aggregate cross-tabulation report on a small demographic group, representing a count distribution by students’ grade and race. It is highly probable that you can get the count of 1 for some cells in the report, which will unequivocally identify persons and thus disclose their education record (grade) by race. Even if the count is not equal to 1, but is equal to some other small number, there is still a risk of possible deducing or disaggregating of Personally Identifiable Information (PII) from surrounding data (other cells, row and column totals) or related reports on that small demographic group.

The following are the four selected SAS tools that allow you to take care of protecting personal data in SAS reports by suppressing counts in small demographic group reports.

1. Automatic data suppression in SAS reports

This blog post explains the fundamental concepts of data suppression algorithms. It takes you behind the scenes of the iterative process of complementary data suppression and walks you through SAS code implementing a primary and secondary complementary suppression algorithm. The suppression code uses BASE SAS – DATA STEPs, SAS macros, PROC FORMAT, PROC MEANS, and PROC REPORT.

2. Implementing Privacy Protection-Compliant SAS® Aggregate Reports

This SAS Global Forum 2018 paper solidifies and expands on the above blog post. It walks you through the intricate logic of an enhanced complementary suppression process, and demonstrates SAS coding techniques to implement and automatically generate aggregate tabular reports compliant with privacy protection law. The result is a set of SAS macros ready for use in any reporting organization responsible for compliance with privacy protection.

4. Is it sensitive? Mask it with data suppression

This blog post provides an example of using the above Data Suppression type aggregated measures derived data items in SAS Visual Analytics.

We want to hear from you.  Is this blog post useful? How do you comply with GDPR (or other Privacy Law of your jurisdiction) in your organization? What SAS privacy protection features would you like to see in future SAS releases?

SAS tools for GDPR privacy compliant reporting was published on SAS Users.

Analyzing ticket sales and customer data for large sports and entertainment events is a complex endeavor. But SAS Visual Analytics makes it easy, with location analytics, customer segmentation, predictive artificial intelligence (AI) capabilities – and more. This blog post covers a brief overview of these features by using a fictitious event company [...]

A future of flying cars and Minority Report-styled predictive dashboards may still be some time away, but the possibilities of robotics and Artificial Intelligence (AI)-powered automation are a reality today. From connected cars to smart homes and offices, we see daily how big data and the Internet of Things (IoT) [...]

IoT use cases on display at new innovation centre was published on SAS Voices by Randy Goh

A very common coding technique SAS programmers use is identifying the largest value for a given column using DATA Step BY statement with the DESCENDING option. In this example I wanted to find the largest value of the number of runs (nRuns) by each team in the SASHELP.BASEBALL dataset. Using a SAS workspace server, one would write:

Figure 1. Single Threaded DATA Step in SAS Workspace Server

Figure 2 shows the results of the code we ran in Figure 1:

Figure 2. Result from SAS Code Displayed in Figure 1

To run this DATA Step distributed we will leveraging the SAS® Cloud Analytic Services in SAS® Viya. Notice in Figure 3, there is no need for the PROC SORT step which is required when running DATA Step single threaded in a SAS workspace server. This is because SAS® Cloud Analytic Services in SAS® Viya . Instead we will use

Figure 3. Distributed DATA Step in SAS® Cloud Analytic Services in SAS® Viya

Figure 4 shows the results when running distributed DATA Step in SAS® Cloud Analytic Services in SAS® Viya.

Figure 4. Results of Distributed DATA Step in SAS® Cloud Analytic Services in SAS® Viya

Conclusion

Until the BY statement running in the SAS® Cloud Analytic Services in SAS® Viya supports DESCENDING use this technique to ensure your DATA Step runs distributed.

SAS Visual Forecasting 8.2 effectively models and forecasts time series in large scale. It is built on SAS Viya and powered by SAS Cloud Analytic Services (CAS).

In this blog post, I will build a Visual Forecasting (VF) Pipeline, which is a process flow diagram whose nodes represent tasks in the VF Process.  The objective is to show how to perform the full analytics life cycle with large volumes of data: from accessing data and assigning variable roles accurately, to building forecasting models, to select a champion model and overriding the system generated forecast. In this blog post I will use 1,337 time series related to a chemical company and will illustrate the main steps you would use for your own applications and datasets.

In future posts, I will work in the Programming application, a collection of SAS procedures and CAS actions for direct coding or access through tasks in SAS Studio, and will develop and assess VF models via Python code.

In a VF pipeline, teams can easily save forecast components to the Toolbox to later share in this collaborative environment.

Forecasting Node in Visual Analytics

This section briefly describes what is available in SAS Visual Analytics, the rest of the blog discusses SAS Visual Forecasting 8.2.

In SAS Visual Analytics the Forecast object will select the model that best fits the data out of these models: ARIMA, Damped trend exponential smoothing, Linear exponential smoothing, Seasonal exponential smoothing, Simple exponential smoothing, Winters method (additive), or Winters method (multiplicative). Currently there are no diagnostic statistics (MAPE, RMSE) for the model selected.

You can do “what-if analysis” using the Scenario Analysis and Goal Seeking functionalities. Scenario Analysis enables you to forecast hypothetical scenarios by specifying the future values for one or more underlying factors that contribute to the forecast. For example, if you forecast the profit of a company, and material cost is an underlying factor, then you might use scenario analysis to determine how the forecasted profit would change if the material cost increased by 10%. Goal Seeking enables you to specify a target value for your forecast measure, and then determine the values of underlying factors that would be required to achieve the target value. For example, if you forecast the profit of a company, and material cost is an underlying factor, then you might use Goal Seeking to determine what value for material cost would be required to achieve a 10% increase in profit.

Another neat feature in SAS Visual Analytics is that one can apply different filters to the final forecast. Filters are underlying factors or different levels of the hierarchy, and the resulting plot incorporates those filters.

Data Requirements for Forecasting

There are specific data requirements when working in a forecasting project, a time series dataset that contains at least two variables: 1) the variable that you want to forecast which is known as the target of your analysis, for example, Revenue and 2) a time ID variable that contains the time stamps of the target variable. The intervals of this variable are regularly spaced. Your time series table can contain other time-varying variables. When your time series table contains more than one individual series, you might have classification variables, as shown in the photo below. Distribution Center and Product Key are classification variables. Optionally, you can designate numerical variables (ex: Discount) as independent variables in your models.

You also have the option of adding a table of attributes to the time series table. Attributes are categorical variables that define qualities of the time series. Attribute variables are similar to BY variables, but are not used to identify the series that you want to forecast. In this post, the data I am using includes Distribution Center, Supplier Name, Product Type, Venue and Product Category. Notice that the attributes are time invariant, and that the attribute table is much smaller than the time series table.

The two data sets (SkinProduct and SkinProductAttributes) used in this blog contain 1,337 time series related to a chemical company. This picture shows a few rows of the two data sets used in this post, note that DATE intervals are regularly spaced weeks. The SkinProduct dataset is referred as the Time Series table in the SkinProductAttributes dataset as the Attribute Data.

Developing Models in SAS Visual Forecasting 8.2

Step One: Create a Forecasting Project and Assign Variables

From the SAS Home menu select the action Build Models that will take you to SAS Model Studio, where you select New Project, and enter 1) the Name of your project, 2) the Type of project, make sure you enter “Forecasting” and 3) the data source.

In the Data tab,  assign the variables roles by using the icon in the upper right corner.

The roles assigned in this post are typical of role assignments in forecasting projects. Notice these variables are in the Time Series table. Also, notice that the classification variables are ordered from highest to lowest hierarchy:

Time Variable: Date
Dependent Variable: Revenue
Classification Variables: Distribution Center and Product Key

In the Time Series table, you might have additional variables you’d like to assign the role “independent” that should be considered for model generation. Independent variables are the explanatory, input, predictor, or causal variables that can be used to model and forecast the dependent variable. In this post, the variable “Discount” is assigned the role “independent”. To do this assignment: right click on the variable, and select Edit Variables.

To bring in the second dataset with the Attribute variables, follow the steps in this photo:

Step two: Automated Modeling with Pipelines

The objective in this step is to select a champion model.  Working in the Pipelines tab, one explores the time series plots, uses the code editor to modify the default model, adds a 2nd model to the pipeline, compares the models and selects a champion model.

After step one is completed, select the Pipelines Tab

The first node in the VF pipelines is the Data node. After right-clicking and running this node, one can see the time series by selecting Explore Time Series. Notice that one can filter by the attribute variables, and that the table shows the exact historical data values.

Auto-forecasting is the next node in the default pipeline. Remember that we are modeling 1,332 time series. For each time series, the Auto-forecasting node automatically diagnoses the statistical characteristics of the time series, generates a list of appropriate time series models, automatically selects the model, and generates forecasts. This node evaluates and selects for each time series the ARIMAX and exponential smoothing models.

One can customize and modify the forecasting models (except the Hierarchical Forecasting model) by editing the model’s code. For example, to add the class of models for intermittent demand to the auto- forecasting node, one could open the code editor for that node and replace these lines
rc = diagspec.setESM();
rc = diagspec.setARIMAX();
with:
rc = diagspec.setIDM();

To open the code editor see photo below. After changes, save code and close the editor.

At this point, you can run the Auto Forecasting node, and after looking at its results, save it to the toolbox, so the editing changes are saved and later reused or shared with team members.

By expanding the Nodes pane and the Forecasting Modeling pane on the left, you can select from several models and add a 2nd modeling node to the pipeline

The next photo shows a pipeline with the Naïve Forecast as the second model. It was added to the pipeline by dropping its node into the parent node (data node). This is the resulting pipeline:

After running the Model Comparison node, compare the WMAE (Weighted Mean Absolute Error) and WMAPE (Weighted Mean Absolute Percent Error) and select a champion model.

You can build several pipelines using different model strategies. In order to select a champion model from all the models developed in the pipelines one uses the Pipeline Comparison tab.

Before you work on any overrides for your forecasting project, you need to make sure that you are working with the best pipeline and modeling node for your data. SAS Visual Forecasting selects the best fit model in each pipeline. After each pipeline is run, the champion pipeline is selected based on the statistics of fit that you chose for the selection criteria. If necessary, you can change the selected champion pipeline.

Step Three: Overrides

The Overrides tab is used to manually adjust the forecasts in the future. For example, if you want to account for some promotions that your company and its competitors are running and that are not captured by the models.

The Overrides module allows users to select subsets of time series at the aggregate level by selecting attribute values in the attribute table that you defined in the data tab. The filters based on the attributes are highly customizable and do not restrict you to use the hierarchy that was used for the modeling. The section of a filter using attribute is often referred to as faceted search. Whenever you create a new filter based on a selection of values of the attributes (also known as facets), the aggregate for all series that match the facets will be displayed on your main panel.

There is a wealth of information in the Overrides overview: 1) a list of the BY variables, as well as attribute variables, available to use as filters.

2) a Plot of the Time Series Aggregation and Overrides displaying historical and forecast data, and

3) a Forecast and Overrides table which can be used to create, edit and submit, override values for a time series based on external factors that are not included in the forecast models

Conclusion

Using SAS Visual Forecasting 8.2 you can effectively model and forecast time series in large scale. The Visual Forecasting Pipeline greatly facilitates the automatic forecasting of large volumes of data, and provides a structured and robust method with efficient and flexible processes.

References

An introduction to SAS Visual Forecasting 8.2 was published on SAS Users.

The release of SAS Viya 3.3 has brought some nice data quality features. In addition to the visual applications like Data Studio or Data Explorer that are part of the Data Preparation offering, one can leverage data quality capabilities from a programming perspective.

For the time being, SAS Viya provides two ways to programmatically perform data quality processing on CAS data:

• The Data Step Data Quality functions.
• The profile CAS action.

To use Data Quality programming capabilities in CAS, a Data Quality license is required (or a Data Preparation license which includes Data Quality).

Data Step Data Quality functions

The list of the Data Quality functions currently supported in CAS are listed here and below:

They cover casing, parsing, field extraction, gender analysis, identification analysis, match codes and standardize capabilities.

As for now, they are only available in the CAS Data Step. You can’t use them in DS2 or in FedSQL.

To run in CAS certain conditions must be met. These include:

• Both the input and output data must be CAS tables.
• All language elements must be supported in the CAS Data Step.
• Others.

Let’s look at an example:

cas mysession sessopts=(caslib="casuser") ;   libname casuser cas caslib="casuser" ;   data casuser.baseball2 ; length gender $1 mcName parsedValue tokenNames lastName firstName varchar(100) ; set casuser.baseball ; gender=dqGender(name,'NAME','ENUSA') ; mcName=dqMatch(name,'NAME',95,'ENUSA') ; parsedValue=dqParse(name,'NAME','ENUSA') ; tokenNames=dqParseInfoGet('NAME','ENUSA') ; if _n_=1 then put tokenNames= ; lastName=dqParseTokenGet(parsedValue,'Family Name','NAME','ENUSA') ; firstName=dqParseTokenGet(parsedValue,'Given Name','NAME','ENUSA') ; run ; Here, my input and output tables are CAS tables, and I’m using CAS-enabled statements and functions. So, this will run in CAS, in multiple threads, in massively parallel mode across all my CAS workers on in-memory data. You can confirm this by looking for the following message in the log: NOTE: Running DATA step in Cloud Analytic Services. NOTE: The DATA step will run in multiple threads. I’m doing simple data quality processing here: • Determine the gender of an individual based on his(her) name, with the dqGender function. • Create a match code for the name for a later deduplication, with the dqMatch function. • Parse the name using the dqParse function. • Identify the name of the tokens produced by the parsing function, with the dqParseInfoGet function. • Write the token names in the log, the tokens for this definition are: Prefix,Given Name,Middle Name,Family Name,Suffix,Title/Additional Info • Extract the “Family Name” token from the parsed value, using dqParseTokenGet. • Extract the “Given Name” token from the parsed value, again using dqParseTokenGet. I get the following table as a result: Performing this kind of data quality processing on huge tables in memory and in parallel is simply awesome! The dataDiscovery.profile CAS action This CAS action enables you to profile a CAS table: • It offers 2 algorithms, one is faster but uses more memory. • It offers multiple options to control your profiling job: • Columns to be profiled. • Number of distinct values to be profiled (high-cardinality columns). • Number of distinct values/outliers to report. • It provides identity analysis using RegEx expressions. • It outputs the results to another CAS table. The resulting table is a transposed table of all the metrics for all the columns. This table requires some post-processing to be analyzed properly. Example: proc cas; dataDiscovery.profile / algorithm="PRIMARY" table={caslib="casuser" name="product_dim"} columns={"ProductBrand","ProductLine","Product","ProductDescription","ProductQuality"} cutoff=20 frequencies=10 outliers=5 casOut={caslib="casuser" name="product_dim_profiled" replace=true} ; quit ; In this example, you can see: • How to specify the profiling algorithm (quite simple: PRIMARY=best performance, SECONDARY=less memory). • How to specify the input table and the columns you want to profile. • How to reduce the number of distinct values to process using the cutoff option (it prevents excessive memory use for high-cardinality columns, but might show incomplete results). • How to reduce the number of distinct values reported using the frequencies option. • How to specify where to store the results (casout). So, the result is not a report but a table. The RowId column needs to be matched with A few comments/cautions on this results table: • DoubleValue, DecSextValue, or IntegerValue fields can appear on the output table if numeric fields have been profiled. • DecSextValue can contain the mean (metric #1008), median (#1009), standard deviation (#1022) and standard error (#1023) if a numeric column was profiled. • It can also contain frequency distributions, maximum, minimum, and mode if the source column is of DecSext data type which is not possible yet. • DecSext is a 192-bit fixed-decimal data type that is not supported yet in CAS, and consequently is converted into a double most of the time. Also, SAS Studio cannot render correctly new CAS data types. As of today, those metrics might not be very reliable. • Also, some percentage calculations might be rounded due to the use of integers in the Count field. • The legend for metric 1001 is not documented. Here it is: 1: CHAR 2: VARCHAR 3: DATE 4: DATETIME 5: DECQUAD 6: DECSEXT 7: DOUBLE 8: INT32 9: INT64 10: TIME A last word on the profile CAS action. It can help you to perform some identity analysis using patterns defined as RegEx expressions (this does not use the QKB). Here is an example: proc cas; dataDiscovery.profile / table={caslib="casuser" name="customers"} identities={ {pattern="PAT=-]? ?999[- ]9999",type="USPHONE"}, {pattern= "PAT=^99999[- ]9999$",type="ZIP4"}, {pattern= "PAT=^99999$",type="ZIP"}, {pattern= "[^ @]+@[^ @]+\.[A-Z]{2,4}",type="EMAIL"}, {pattern= "^(?i:A[LKZR]|C[AOT]|DE|FL|GA|HI|I[ADLN]|K[SY]|LA|M[ADEINOST]|N[CDEHJMVY]|O[HKR]|PA|RI|S[CD]|T[NX]|UT|V[AT]|W[AIVY])$",type="STATE"} } casOut={caslib="casuser" name="customers_profiled" replace="true"} ; quit ;

I hope this post has been helpful.

As a follow on from my previous blog post, where we looked at the different use cases for using Kerberos in SAS Viya 3.3, in this post will delve into more details on the requirements for use case 4, where we use Kerberos authentication through-out both the SAS 9.4 and SAS Viya 3.3 environments. We won’t cover the configuration of this setup as that is a topic too broad for a single blog post.

As a reminder the use case we are considering is shown here:

Here the SAS 9.4 Workspace Server is launched with Kerberos credentials, the Service Principal for the SAS 9.4 Object Spawner will need to be trusted for delegation. This means that a Kerberos credential for the end-user is available to the SAS 9.4 Workspace Server. The SAS 9.4 Workspace Server can use this end-user Kerberos credential to request a Service Ticket for the connection to SAS Cloud Analytic Services. While SAS Cloud Analytic Services is provided with a Kerberos keytab and principal it can use to validate this Service Ticket. Validating the Service Ticket authenticates the SAS 9.4 end-user to SAS Cloud Analytic Services. The principal for SAS Cloud Analytic Services must also be trusted for delegation. We need the SAS Cloud Analytic Services session to have access to the Kerberos credentials of the SAS 9.4 end-user.

These Kerberos credentials made available to the SAS Cloud Analytic Services are used for two purposes. First, they are used to make a Kerberized connection to the SAS Viya Logon Manager, this is to obtain the SAS Viya internal OAuth token. As a result, the SAS Viya Logon Manager must be configured to accept Kerberos connections. Secondly, the Kerberos credentials of the SAS 9.4 end-user are used to connect to the Secure Hadoop environment.

In this case, since all the various principals are trusted for delegation, our SAS 9.4 end-user can perform multiple authentication hops using Kerberos with each component. This means that through the use of Kerberos authentication the SAS 9.4 end-user is authenticated into SAS Cloud Analytic Services and out to the Secure Hadoop environment.

Reasons for doing it…

To start with, why would we look to use this use case? From all the use cases we considered in the previous blog post this provides the strongest authentication between SAS 9.4 Maintenance 5 and SAS Viya 3.3. At no point do we have a username/password combination passing between the SAS 9.4 environment and the SAS Viya 3.3. In fact, the only credential set (username/password) sent over the network in the whole environment is the credential set used by the Identities microservice to fetch user and group information for SAS Viya 3.3. Something we could also eliminate if the LDAP provider supported anonymous binds for fetching user details.

Also, this use case provides true single sign-on from SAS 9.4 Maintenance 5 to SAS Viya 3.3 and all the way out to the Secured Hadoop environment. Each operating system run-time process will be launched as the end-user and no cached or stored username/password combination is required.

High-Level Requirements

At a high-level, we need to have both sides configured for Kerberos delegated authentication. This means both the SAS 9.4 Maintenance 5 and the SAS Viya 3.3 environments must be configured for Kerberos authentication.

The following SAS components and tiers need to be configured:

• SAS 9.4 Middle-Tier
• SAS 9.4 Compute Tier
• SAS Viya 3.3 SAS Logon Manager
• SAS Viya 3.3 SAS Cloud Analytic Services

Detailed Requirements

First let’s talk about Service Principal Names. We need to have a Service Principal Name (SPN) registered for each of the components/tiers in our list above. Specifically, we need a SPN registered for:

• HTTP/<HOSTNAME> for the SAS 9.4 Middle-Tier
• SAS/<HOSTNAME> for the SAS 9.4 Metadata Tier
• SAS/<HOSTNAME> for the SAS 9.4 Compute Tier
• HTTP/<HOSTNAME> for the SAS Viya 3.3 SAS Logon Manager
• sascas/<HOSTNAME> for the SAS Viya 3.3 SAS Cloud Analytic Services

Where the <HOSTNAME> part should be the fully qualified hostname of the machines where the component is running. This means that some of these might be combined, for example if the SAS 9.4 Metadata Tier and Compute Tier are running on the same host we will only have one SPN for both. Conversely, we might require more SPNs, if for example, we are running a SAS 9.4 Metadata Cluster.

The SPN needs to be registered against something. Since our aim is to support single sign-on from the end-user’s desktop we’ll probably be registering the SPNs in Active Directory. In Active Directory we can register against either a user or computer object. For both the SAS 9.4 Metadata and Compute Tier the registration can be performed automatically if the processes run as the local system account on a Microsoft Windows host and will be against the computer object. Otherwise, and for the other tiers and components, the SPN must be registered manually. We recommend, that while you can register multiple SPNs against a single object, that you register each SPN against a separate object.

Since the entire aim of this configuration is to delegate the Kerberos authentication from one tier/component onto the next we need to ensure the objects, namely users or computer objects, are trusted for delegation. The SAS 9.4 Middle-Tier will only support un-constrained delegation, whereas the other tiers and components support Microsoft’s constrained delegation. If you choose to go down the path of constrained delegation you need to specify each and every Kerberos service the object is trusted to delegate authentication to.

Finally, we need to provide a Kerberos keytab for the majority of the tiers/components. The Kerberos keytab will contain the long-term keys for the object the SPN is registered against. The only exceptions being the SAS 9.4 Metadata and Compute Tiers if these are running on Windows hosts.

Conclusion

You can now enable Kerberos delegation across the SAS Platform, using a single strong authentication mechanism across that single platform. As always with configuring Kerberos authentication the prerequisites, in terms of Service Principal Names, service accounts, delegation settings, and keytabs are important for success.

SAS Viya 3.3 Kerberos Delegation from SAS 9.4M5 was published on SAS Users.

BREAKING NEWS. Today, shortly after midnight on the U.S. East Coast, Cary, NC-based SAS Institute successfully completed its first space exploration mission.

This interplanetary expedition was conducted on a SAS-designed manned spacecraft powered by our state-of-the-art atomic Collider Acceleration System (CAS) engine. A crew of three SAS volunteers took part in that undertaking. These brave souls were:

All three specialize in terrestrial communications (i.e. social media) and received special training on extra-terrestrial travel and communications. Their mission was to study the Venus space area up close to find out what causes the gravitational field anomaly that has recently been observed there.

How it all started

Those of us who attended last year’s SAS Global Forum in Orlando must remember the inspiring speech by Canadian astronaut Chris Hadfield. The main idea I took away from his speech was that success is not a good teacher, as it teaches us nothing; failure, on the hand, is a very good teacher, at least for those of us who are willing to learn the lesson.  But, as we all know, there is a time to fail/learn, and there is a time to succeed.

I can’t speak for all of you, but, man, were we inspired by that speech! We at SAS knew right then that we, too, wanted to explore the “final frontier.” As a data analytics software company, all our studies start with data explorations. Our public relations group worked tirelessly with the major stakeholder Government agencies and private companies (NASA, SpaceX, ROSCOSMOS, etc.) to get ahold of the data. When our analysts finally did get access to the data, they were overwhelmed by its size. That was really BIG data (literally of cosmic proportions) – data about every little pocket of spacetime in our Solar system, collected over multiple years of astronomical observations and Space exploration programs.

Predictive Modeling

Using our flagship SAS Viya Analytics software, we mined these vast data archives, employing various predictive modeling, computational, and heuristic techniques such as automatic machine deep space learning, 3D artificial intelligence simulation, and, most importantly, natural, coffee-stimulated human intelligence.

What caught our attention was the space area around Venus. Planet Venus is notorious for being an outlier. First of all, it spins slower than any other planet in the Solar system, even slower than it revolves around the Sun. In fact it spins about 243 times slower than Earth. That means that a day there lasts approximately 243 Earth-days, making it longer than a Venusian year, which is only about 225 Earth-days long.

Second of all, it spins backwards, in the opposite direction from most other planets, including Earth, so that on Venus the sun rises in the west.

Third, it has the highest mean surface temperature of all the Solar System planets – reaching up to 726 °K (452 °C or 870 °F), which is 1.6 times hotter than Mercury, the closest planet to the Sun. This is because of Venus’ thick atmosphere composed mostly of greenhouse gases (carbon dioxide and sulfur dioxide), which trap a good portion of the Sun’s heat.

However, the most unusual thing that we discovered was an aberration in Venus’ gravitational field, suggesting a significant mass (possibly large enough to be a planet) hidden behind it.

The following bubble plot uses a logarithmic scale for x-axis (distance from the Sun) and visualizes our finding:

Now we can see it clearly. Not only does it show an unknown small planet behind Venus, it also explains why it is not visible from Earth: its period of revolution around the Sun is exactly the same as that of Venus, which is why it has always been obstructed by Venus, and not visible from Earth.

Due to its very close proximity to Venus, there is a good chance that even a slight tangential nudge experienced by planet “2-?” might break gravitational equilibrium, causing it to start orbiting Venus as a moon rather than the Sun.  We will be observing this situation carefully.

Another interesting finding is that this unknown planet is a much more hospitable place than Venus, as it has an oxygen-dominated atmosphere and a relaxing surface temperature only slightly higher than that of Earth, as it is shown in the following chart:

At this point we had had enough modeling and needed some hard proof.

Space exploration mission

We leveraged our best human intelligence resources around the world to progress rapidly through all the required phases of spacecraft design and construction, crew selection and training, and finally launching and completing the space mission.

All in all, it took just under a year (11 months to be precise) to bring this project to completion. The data collection and exploration phase took around one month, which is in line with the capacity of the SAS Viya analytical environment. The design and build phase took about five months, and it was conducted in parallel with crew selection and training; finally, the fly phase also took 5 months including launch, travel to the Venus space area with a flyby of Venus and the planet X, and a return to Earth. As you can see from the picture at the very top, the unknown planet X hidden behind Venus does indeed exist. However, further analysis and study will be necessary to determine the nature of the observed surface irregularities.

Your participation and input are requested

If you would like you to engage in the fascinating field of space exploration, you are welcome to use the following SAS-generated summary data table:

If you are a detail-oriented type, it will be obvious to you that the circumference of the new planet oddly equals 200π2 (km), which defies all the canons of geometry. You are welcome to prove or disprove the possibility of such an unusual occurrence.

So far, we have referred to this new planet as “2-?” and “planet X”, but it’s about time to give it a name, a real name as all other 8 planets of our Solar System have.

Our first inclination was to name it after our newest SAS analytical software environment, Viya. But do we really want to dilute the brand by applying it to two different though prominent objects?!

That is why we decided to reach out to you, our readers, to solicit ideas for the name of the new planet.  Please provide a brief justification for your name suggestion. We also welcome any insights, hypotheses and data stories you might come up with based on the collected data. We greatly appreciate your input.

Disclosure

SAS discovers a new planet in the Solar System was published on SAS Users.

Gradient boosting is one of the most widely used machine learning models in practice, with more and more people like to use it in Kaggle competitions. Are you interested in seeing how to use gradient boosting model for classification in SAS Visual Data Mining and Machine Learning? Here I play with the classification of Fisher’s Iris flower dataset using gradient boosting, and this may serve as a start point to those interested in trying the classification models in SAS Visual Data Mining and Machine Learning product.

Fisher’s Iris data is a well-known dataset in data mining. Per Wikipedia, Fisher developed a linear discriminant model to distinguish the species from each other by the features provided in the dataset. You may already see people run different classification models on this dataset, such as neural network. What I am interested in, is to see how well SAS gradient boosting model will do the species classification.

#1  Explore the dataset

We can easily load Fisher’s Iris dataset from SASHelp.Iris into SAS Viya. The dataset consists of 50 samples each species of Iris Setosa Virginica and Versicolor, totally 150 records with five attributes: Petal Length, Petal Width, Sepal Length, Sepal width and Iris Species. The dataset itself is already well-formed, with neither missing values, nor outliers. Take a quick look of the dataset in SAS Visual Analytics as below.

From the chart, we see that the iris species of ‘Setosa’ can be easily distinguished from the ‘Versicolor’ and ‘Virginica’ species by the length and width of their petals and sepals. However, this is not the case for the latter two species, some of them are staggered closely, which makes it a little hard to distinguish each other by these features.

#2  Prepare Data

There is not much effort needed to prepare the data for the prediction. But one thing I’d like to mention here is about the standardization of measure variables. By viewing the measure details in SAS Visual Analytics, we see that neither Petal Length distribution nor Petal Width distribution is normal. You may wonder if we need to normalize the data before applying it to the model for analysis, but this leads to one great thing I like the Gradient Boosting model. Users do not need to explicitly standardize quantitative data. Tree-base models should be robust to such problem in an input feature, since the algorithm is based on node splits. (Here is an article discussing a similar problem.)

So, here my data preparation is just doing the data partitioning before starting the classification on iris species. I need to make sure each partition will follow the same distribution on different species in the iris dataset. This can be achieved easily in SAS Visual Analytics by adding a partition data item - by setting the Sampling method to ‘Stratified sampling’ and add the ‘Iris Species’ as the column to be stratified by. I define two partitions so I have training partition, validation partition. I set 60% for training, and 40% for validation partition, with random seed 1234. Thus, a categorical data item ‘Partition’ is added, with value of 0 for validation, 1 for training partition. (For easier understanding in the charts, I’ve created a custom category called ‘Partitions’ based on the ‘Partition’ data item values.)

The charts below show that the 150 rows in Fisher’s Iris dataset are distributed equally into three species, and the created partitions are sampled with the same percentage among the three species.

#3  Train the gradient boosting model

Training various models in SAS Visual Data Mining and Machine Learning allows us to appreciate the advantages of visualization, and it’s very straight-forward for users. In ‘Objects’ tab, drag and drop the ‘Gradient Boosting’ to the canvas. Assign the ‘Iris Species’ as response variable, and ‘Petal Length, Petal Width, Sepal Length, Sepal width’ as predictors. Then set the ‘Partition’ data item for Partition ID. After that, the system will train the model and show the model assessment. I’ve taken a screenshot for ‘Virginica’ event as below.

The response variable of Iris Species has three event levels – ‘Setosa’, ‘Versicolor’ and ‘Virginica’, and we can choose desired event level to have a look of the model output. In addition, we may switch the assessment plot of Lift to ROC plot, or to Misclassification plot (Note: the misclassification plot is based on event level, thus it will show the ‘Setosa’ and ‘NOT Setosa’ species if we choose the ‘Setosa’ event.). Below is a screenshot with ROC plot and the model assessment statistics.

In practice, training models usually cost a lot of effort in tuning model parameters. SAS Visual Data Mining and Machine Learning has provided the ‘Autotune’ feature that can help this, users may decide some settings like maximum iterations, seconds, and evaluations and the product will choose the optimal values for the hyperparameters of the model. Considering that this dataset only has 150 samples, I won’t bother to do the hyperparameters tuning.

#4  Make prediction by the model

Now I can start to make predictions from the gradient boosting model for the data in testing partition. There are several ways to go here. In Visual Data Mining and Machine Learning, on the right-button mouse menu, either click the ‘Export model…’ or click the ‘Derive predicted…’ menu. The first one will export the model codes, so you can run it in SAS Studio with your data to be predicted. The latter one is very straight-forward in SAS Visual Data Mining and Machine Learning. It will pop up the ‘New Prediction Items’ page, where you may choose to get the predicted value and its probability values for all the levels of Iris Species. These data items will be added to the iris CAS table for further evaluation. Since the iris dataset has three species in the sample, I need to set ‘All levels’ so the prediction will give out the classification in three species and their probabilities.

#5  Review the prediction result

In the model assessment tab, we already see the model assessment statistics for model evaluation. We may also switch to ‘Variable Importance’ tab, or ‘Lift’ tab, ‘ROC’ tab, and ‘Misclassification’ tab to see more about the model. Here I’d like to visually compare the predicted species value with the iris species value provided in the dataset.

To show how many failures of the classification visually, I perform following actions:

• In SAS Visual Analytics, create a list table to show all 150 rows of the iris dataset. Since there is no primary key in the dataset, the SAS Visual Analytics list table will do aggregation for measure variables by default, so be sure to set the ‘Detail data’ option in the Options tab.
• Create a calculated item (named ‘equals’) to compare if the values of ‘Iris Species’ and ‘Predicted: Iris Species’ columns are equal: {IF ( 'Iris Species'n = 'Predicted: Iris Species'n ) RETURN 1 ELSE 0. }
• Define a display rule with the calculated item to highlight the misclassified rows. I’ve sorted the table by above ‘equals’ value so those rows without equal value of ‘Iris Species’ and ‘Predicted : Iris Species’ columns are shown on top.

We see four rows are misclassified by the model, 3 of them are from training partition and 1 from validation partition. So far, the result looks not bad, right?

We may continue to tune the parameters of gradient boosting model easily in SAS Visual Data Mining and Machine Learning, to improve the model. For example, if I set smaller leaf size value to 2 instead of the default value of 5, the model accuracy will be improved (too good to be true?). See below screenshot for a comparison.

Of course, people may like to try tuning other parameters, or to generate more features to refine the model. Anyway, it is easy-to-use and straight-forwarded to do classification using gradient boosting model in SAS Visual Data Mining and Machine Learning. In addition, there are many other models in SAS Visual Data Mining and Machine Learning people may like to run for classification. Do you like to play with the other models for practicing?