Using the Apache SystemML API in a Spark shell with IBM Analytics Engine

SystemML in a Spark shell using the IBM Analytics Engine (IAE)? Yes!

A very simple way of using SystemML for all of your machine learning and big data needs. This tutorial will get you set up and running SystemML in a Spark shell using IAE like a star.

Not familiar with Apache SystemML?

At a high-level, SystemML is what is used for the machine learning and mathematical part of your data science project. You can log into Spark shell, load SystemML in the shell, load your data and write your linear algebra, statistical equations, matrices, etc. in code much shorter than it would be in the typical Spark shell syntax. It helps not only with mathematical exploration and machine learning algorithms, but it also allows you to be on Spark where you can do all of the above with really big data that you couldn't use on your local computer.

Not familiar with how to set up an Apache Spark cluster?

By using the IBM Analytics Engine you can spin up a Spark cluster in just a few minutes using the web user interface.

With both of these tools, I'll walk you through how to set up your computer for all of SystemML's assumptions, how to set up IAE and your Spark cluster, SSH in to connect to your Spark cluster on your computer and start a Spark shell and import SystemML,then load some data and do a few examples in scala. Whew that's a lot, but we I promise I go through it all!

Now let's get going on our learning. First step: Let's set up IAE.

Go to https://developer.ibm.com/clouddataservices/docs/ibm-analytics-engine/

(This assumes you have an account, so if you do not, go ahead and set one up and come back to this step.)

1) Select "IBM Analytics Engine service on Bluemix" beneath the video (which is super handy if you want to watch it!)
2) Now choose which selections you want (you can leave everything at its default for the purpose of this tutorial) and   push "Create" at the bottom of the page. This may take a few minutes.

3) Once your cluster has been created, make sure you are in the "Manage" section. If you are not, navigate to it! In this section you'll notice there is a lot of information. The areas you want to focus on are username, password and SSH (under "Connection Details".

4) Start by copying your SSH line. You will also need the password given to you after you SSH to your cluster.

5) Go to your terminal and paste the SSH line in it and press enter. 

6)You will be prompted for a password. Use your the password given to you on the "Manage" console.

Logged in? You're a rockstar! Now we can start SystemML!

First download SystemML (we are still at your terminal)

wget https://sparktc.ibmcloud.com/repo/latest/SystemML.jar  

Now type the following code to access the Spark shell with SystemML.

spark-shell --executor-memory 4G --driver-memory 4G --jars SystemML.jar  

Now, using the Spark Shell (Scala), import the MLContext to start SystemML.

import org.apache.sysml.api.mlcontext._  
import org.apache.sysml.api.mlcontext.ScriptFactory._  
val ml = new MLContext(sc)  

Congratulations!! NOW YOU ARE IN APACHE SYSTEMML!! Look at you.

In the future you will just need to do the last two steps to get this going and you can also repeat these last steps on a local Spark Shell.

Let's now figure out how to load a script and run it as well as load data and run some examples so that you can get familiar with Spark Shell and SystemML.

Here's a quick example: Script from a URL.

Here s1 is created by reading Univar-Stats.dml from a URL address.

val uniUrl = "https://github.com/raw/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml"  
val s1 = ScriptFactory.dmlFromUrl(uniUrl)  

Our next step is to parallelize the information, read in two matrices as RDDs, getting the sum of the first, the sum of the second and a message.

val data1 = sc.parallelize(Array("1.0,2.0", "3.0,4.0"))  
val data2 = sc.parallelize(Array("5.0,6.0", "7.0,8.0"))  
val s = """  
 | s1 = sum(m1);
 | s2 = sum(m2);
 | if (s1 > s2) {
 |  message = "s1 is greater"
 | } else if (s2 > s1) {
 |  message = "s2 is greater"
 | } else {
 |  message = "s1 and s2 are equal"
 | }
 | """

val script = dml(s).in("m1",data1).in("m2", data2).out("s1","s2", "message")  

You should get:

script: org.apache.sysml.api.mlcontext.Script =  
Inputs:  
[1] (RDD) m1: ParallelCollectionRDD[0] at parallelize at <console>:33
[2] (RDD) m2: ParallelCollectionRDD[1] at parallelize at <console>:33

Outputs:  
[1] s1
[2] s2
[3] message

Now print your script info.

println(script.info) 

You should see:

Script Type: DML

Inputs:  
[1] (RDD) m1: ParallelCollectionRDD[0] at parallelize at <console>:33
[2] (RDD) m2: ParallelCollectionRDD[1] at parallelize at <console>:33

Outputs:  
[1] s1
[2] s2
[3] message

Input Parameters:  
None

Input Variables:  
[1] m1
[2] m2

Output Variables:  
[1] s1
[2] s2
[3] message

Symbol Table:  
[1] (Matrix) m1: Matrix: null, [-1 x -1, nnz=-1, blocks (1 x 1)], csv, not-dirty
[2] (Matrix) m2: Matrix: null, [-1 x -1, nnz=-1, blocks (1 x 1)], csv, not-dirty

Script String:

s1 = sum(m1);  
s2 = sum(m2);  
if (s1 > s2) {  
message = "s1 is greater"
} else if (s2 > s1) {
message = "s2 is greater"
} else {
message = "s1 and s2 are equal"
}

Script Execution String:  
m1 = read('');  
m2 = read('');

s1 = sum(m1);  
s2 = sum(m2);  
if (s1 > s2) {  
 message = "s1 is greater"
} else if (s2 > s1) {
message = "s2 is greater"
} else {
 message = "s1 and s2 are equal"
}
write(s1, '');  
write(s2, '');  
write(message, '');  

Now execute your script and get your results!

val results = ml.execute(script) 

You should get:

results: org.apache.sysml.api.mlcontext.MLResults =  
[1] (Double) s1: 10.0
[2] (Double) s2: 26.0
[3] (String) message: s2 is greater

Just as an example, you can set your value as x and get your results in Double form.

val x = results.getDouble("s1") 

You should get:

x: Double = 10.0

Set value as y:

val y = results.getDouble("s2") 

You should get:

y: Double = 26.0

Simple example of adding both:

x + y

You should get:

res1: Double = 36.0  

Here is another version of the example. Because the API is very Scala friendly, you can pull out your results as a Scala tuple.

val (firstSum, secondSum, sumMessage) = results.getTuple[Double, Double, String]("s1", "s2", "message")  

You should get:

firstSum: Double = 10.0  
secondSum: Double = 26.0  
sumMessage: String = s2 is greater  

Here is another really handy part. As another example you can load in your data, type the short code and get a whole table of standard statistical measures for each feature!

To do this, let's first get our data into Spark.

We first want to make sure our data is clean and ready to go. Let's load in some data and run a SystemML script.

val habermanUrl = "http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data"
val habermanList = scala.io.Source.fromURL(habermanUrl).mkString.split("\n")
val habermanRDD = sc.parallelize(habermanList)
val habermanMetadata = new MatrixMetadata(306, 4)
val typesRDD = sc.parallelize(Array("1.0,1.0,1.0,2.0"))
val typesMetadata = new MatrixMetadata(1, 4)
val scriptUrl = "https://github.com/raw/apache/systemml/master/scripts/algorithms/Univar-Stats.dml"
val script = dmlFromUrl(scriptUrl).in("A", habermanRDD, habermanMetadata).in("K", typesRDD, typesMetadata).in("$CONSOLE_OUTPUT", true).out("baseStats")

val results = ml.execute(script)

You should see:

Feature [1]: Scale  
 (01) Minimum             | 30.0
 (02) Maximum             | 83.0
 (03) Range               | 53.0
 (04) Mean                | 52.45751633986928
 (05) Variance            | 116.71458266366658
 (06) Std deviation       | 10.803452349303281
 (07) Std err of mean     | 0.6175922641866753
 (08) Coeff of variation  | 0.20594669940735139
 (09) Skewness            | 0.1450718616532357
 (10) Kurtosis            | -0.6150152487211726
 (11) Std err of skewness | 0.13934809593495995
 (12) Std err of kurtosis | 0.277810485320835
 (13) Median              | 52.0
 (14) Interquartile mean  | 52.16013071895425

---

Feature [2]: Scale  
 (01) Minimum             | 58.0
 (02) Maximum             | 69.0
 (03) Range               | 11.0
 (04) Mean                | 62.85294117647059
 (05) Variance            | 10.558630665380907
 (06) Std deviation       | 3.2494046632238507
 (07) Std err of mean     | 0.18575610076612029
 (08) Coeff of variation  | 0.051698529971741194
 (09) Skewness            | 0.07798443581479181
 (10) Kurtosis            | -1.1324380182967442
 (11) Std err of skewness | 0.13934809593495995
 (12) Std err of kurtosis | 0.277810485320835
 (13) Median              | 63.0
 (14) Interquartile mean  | 62.80392156862745

---

Feature [3]: Scale  
 (01) Minimum             | 0.0
 (02) Maximum             | 52.0
 (03) Range               | 52.0
 (04) Mean                | 4.026143790849673
 (05) Variance            | 51.691117539912135
 (06) Std deviation       | 7.189653506248555
 (07) Std err of mean     | 0.41100513466216837
 (08) Coeff of variation  | 1.7857418611299172
 (09) Skewness            | 2.954633471088322
 (10) Kurtosis            | 11.425776549251449
 (11) Std err of skewness | 0.13934809593495995
 (12) Std err of kurtosis | 0.277810485320835
 (13) Median              | 1.0
 (14) Interquartile mean  | 1.2483660130718954

---

Feature [4]: Categorical (Nominal)  
 (15) Num of categories   | 2
 (16) Mode                | 1
 (17) Num of modes        | 1
results: org.apache.sysml.api.mlcontext.MLResults =  
[1] (Matrix) baseStats: Matrix: scratch_space/_p5250_9.31.116.229/parfor/2_resultmerge1, [17 x 4, nnz=44, blocks (1000 x 1000)], binaryblock, dirty

You can also ask for the base stats as a SystemML Matrix.

val baseStats = results.getMatrix("baseStats") 

You should get:

baseStats: org.apache.sysml.api.mlcontext.Matrix = org.apache.sysml.api.mlcontext.Matrix@237cd4e5

With that Matrix you can convert it into a bunch of different things!

baseStats.      
asDataFrame          asDoubleMatrix       asInstanceOf         asJavaRDDStringCSV   asJavaRDDStringIJV   asMLMatrix           asMatrixObject       asRDDStringCSV  
asRDDStringIJV       isInstanceOf         toString             

Such as you can get the base stats as an RDD. Note: IJV leaves out non values and CSV includes them. Here's an example of both:

baseStats.toRDDString  

You should get these options:

toRDDStringCSV   toRDDStringIJV   

Try as CSV:

baseStats.toRDDStringCSV.collect 

You should get:

res4: Array[String] = Array(30.0,58.0,0.0,0.0, 83.0,69.0,52.0,0.0, 53.0,11.0,52....1.0)

Try as IJV:

baseStats.toRDDStringIJV.collect 

You should get:

res5: Array[String] = Array(1 1 30.0, 1 2 58.0, 1 3 0.0, 1 4 0.0, 2 1 83.0, 2 2 69.0, 2 3 52.0, 2 4 0.0, ... 1...  

I think that's a great start to using SystemML with Spark Shell on the IBM Analytics Engine! Once you're done you can quit to exit.

:quit

You have successfully set up your computer for running SystemML and Spark on the IBM Analytics Engine (IAE), loaded the Spark shell, ran scripts, loaded data and run some examples!! Congrats!! Now go save the world with these awesome new skills.

Copyright 2017 IBM Corp. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

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