Writing and Running SAS Programs :: SAS(R) LASR(TM) Analytic Server 2.4: Reference Guide

Log On to SAS Studio

Start a web browser and direct it to a URL that is similar to the following example:

http://hostname.example.com/SASStudio

In addition to the host name being different, the protocol might be HTTPS and you might need to specify a port number.

Tip

After you log on, click go interactive

in the toolbar to enable interactive mode. This mode is needed for working interactively with the IMSTAT procedure.

Start a SAS LASR Analytic Server

You start a distributed server with the LASR procedure. See the following example:

options set=GRIDHOST="grid001.example.com";
options set=GRIDINSTALLLOC="/opt/TKGrid_REP"; 1

proc lasr create port=10011;
  performance nodes=all;
run;

1	Using a TKGrid_REP installation location enables reading data in parallel with SAS/ACCESS engines from distributed databases and Hadoop clusters.

Load a Table to Memory

Use the SAS LASR Analytic Server Engine

A common way to load tables into memory is to transfer them to the server using the SAS LASR Analytic Server engine. The following example transfers a table that is named Webscore to the server.

libname lasrlib sasiola host="grid001.example.com" port=10011;

data lasrlib.webscore;
    set somelib.webscore;
run;

Use the Hadoop Engine

Your Hadoop cluster might already have data in HDFS that you want to analyze, such as DBMS tables that were imported with Sqoop or log files that were imported with Flume. In this case, you can use the SAS/ACCESS Interface to Hadoop to access that data.

options set=HADOOP_JAR_FILES_PATH="/opt/hadoopjars";

libname hdplib hadoop server="grid001.example.com"  1
        config="/home/sasdemo/config.xml"
        hdfs_metadir="/user/sasdemo/meta" 
        hdfs_datadir="/user/sasdemo/data";

proc hdmd name=hdplib.webdata file_format=delimited  2
     encoding=utf8 sep="|" data_file="web-data.txt";
    column id int;
    column links varchar(256);
    column var1 double;
    column var2 double;
    column var3 double;
    column var4 double;
    column var5 double;
    column var6 double;
    column var7 double;
    column var8 double;
run;

proc lasr add data=hdplib.webdata port=10010;
    performance host="grid001.example.com"; 
run;

1	The LIBNAME statement with the Hadoop engine uses the HDFS_METADIR= option. This option enables working with XML-based table definitions called SASHDMD descriptors.
2	The HDMD procedure is used to create the SASHDMD descriptors from existing data in HDFS, which in this case is a delimited file.

For information about the Hadoop engine and the HDMD procedure, see SAS/ACCESS for Relational Databases: Reference.

Sample the Data

The following statements show one way to sample data. A calculated column is created with a number that is uniformly distributed between 0 and 1. The sampling is done based on the value of the new column.

%let seed = 12345;

proc imstat;
    table lasrlib.webdata;  1
    tableinfo;
    columninfo;
    frequency goalVar; 
run;

    compute sampkey "sampkey = ranuni(&seed.)";  2
run;

    table lasrlib.webdata;  3
    deleterows;
    where sampkey ge 0.31 and goalVar = 0;
run;

1	The TABLE statement specifies the Webdata table that was loaded to memory as the active table. The following three statements, TABLEINFO, COLUMNINFO, and FREQUENCY provide information about the table and a variable that is named Goalvar.
2	The COMPUTE statement creates a column that is named Sampkey. The column is permanent and is added to the table.
3	The TABLE statement is used again to reopen the table. This enables SAS to access the newly created column, Sampkey. The DELETEROWS statement is subject to the WHERE clause and marks 70% of the table for deletion where the goal was not met. Because the PURGE option is not used, the rows are not actually deleted. Instead, the rows are just disregarded in subsequent analyses that use the table.

Create a Forest of Decision Trees, Assess, and Score

The following code sample demonstrates using the RANDOMWOODS statement with training and validation data.

   table lasrlib.webdata;
    where sampkey ge 0.3;                        /* training set */
    randomwoods goalVar /
            input   = ( browser var1-var8 )
            nominal = ( browser )
            nbins=100 maxlevel=10 maxbranches=2  /* tree specs */
            greedy gain leafsize=50         
            
            ntree=100 seed=1314 m=5              /* forest spec */
            treeinfo bootstrap=0.3                                         
            temptable
    ;
run;
    table lasrlib.&_templast_;
    promote RF;                                 /* promote the model into */
                                                /* a permanent table */
run;

    table lasrlib.webdata;
    where sampkey lt 0.3;                       /* validation set */
    randomwoods /
         lasrtree  = lasrlib.RF
         nominal   = ( browser )
         temptable
         assess
         vars      = ( userid goalVar )
    ;
run;

    table lasrlib.&_templast_;                  /* assess */
    where strip(_RF_Level_) eq '1';
    assess _RF_P_/ y = goalVar event = '1' 
           nbins = 10 step = 0.001;
run;

    table lasrlib.webscore;                     /* score */
    compute goalVar "goalVar = 2";
    randomwoods /
             lasrtree  = lasrlib.RF
             nominal   = ( browser )
             temptable
             assess
             vars      = ( userid )
         ;
run;
    table lasrlib.&_templast_;
    promote scoreresult;
quit;