SystemML Quick Start Guide


This tutorial provides a quick introduction to using SystemML by running existing SystemML algorithms in standalone mode. More information about running SystemML in distributed execution mode (Hadoop, Spark) will be added soon. For more in-depth information, please refer to the Algorithms Reference and the DML Language Reference.

What is SystemML

SystemML enables large-scale machine learning (ML) via a high-level declarative language with R-like syntax called DML. This language allows data scientists to express their ML algorithms with full flexibility but without the need to fine-tune distributed runtime execution plans and system configurations. These ML programs are dynamically compiled and optimized based on data and cluster characteristics using rule- and cost-based optimization techniques. The compiler automatically generates hybrid runtime execution plans ranging from in-memory, single node execution to distributed computation for Hadoop M/R or Spark Batch execution. SystemML features a suite of algorithms for Descriptive Statistics, Classification, Clustering, Regression, Matrix Factorization, and Survival Analysis. Detailed descriptions of these algorithms can be found in the Algorithms Reference.


Download SystemML

Binary releases of SystemML are available for download at https://github.com/SparkTC/systemml/releases.

The SystemML project is available on GitHub at https://github.com/SparkTC/systemml. SystemML can be downloaded from GitHub and built with Maven. Instructions to build and test SystemML can be found in the GitHub README file.


Standalone vs Distributed Execution Mode

SystemML’s standalone mode is designed to allow data scientists to rapidly prototype algorithms on a single machine. The standalone release packages all required libraries into a single distribution file. In standalone mode, all operations occur on a single node in a non-Hadoop environment. Standalone mode is not appropriate for large datasets.

For large-scale production environments, SystemML algorithm execution can be distributed across a multi-node cluster using Apache Hadoop or Apache Spark. We will make use of standalone mode throughout this tutorial.

The examples described in docs/SystemML_Algorithms_Reference.pdf are written primarily for the hadoop distributed environment. To run those examples in standalone mode, modify the commands by replacing “hadoop jar SystemML.jar -f ...” with “./runStandaloneSystemML.sh ...” on Mac/UNIX or with “./runStandaloneSystemML.bat ...” on Windows.


Contents of the SystemML Standalone Package

To follow along with this guide, first build a standalone package of SystemML using Apache Maven and unpack it to your working directory, i.e. ~/systemml-tutorial.

$ git clone https://github.com/SparkTC/systemml.git
$ mvn clean package
$ tar -xzf `find . -name 'system-ml*standalone.tar.gz'` -C ~/systemml-tutorial

The extracted package should have these contents:

$ ls -lF ~/systemml-tutorial
total 56
drwxr-xr-x  23   algorithms/
drwxr-xr-x   5   docs/
drwxr-xr-x  35   lib/
-rw-r--r--   1   log4j.properties
-rw-r--r--   1   readme.txt
-rwxr-xr-x   1   runStandaloneSystemML.bat*
-rwxr-xr-x   1   runStandaloneSystemML.sh*
-rw-r--r--   1   SystemML-config.xml

Refer to docs/SystemML_Algorithms_Reference.pdf for more information about each algorithm included in the algorithms folder.

For the rest of the tutorial we switch our working directory to ~/systemml-tutorial.

$ cd  ~/systemml-tutorial


Choosing Test Data

In this tutorial we will use the Haberman’s Survival Data Set which can be downloaded in CSV format from the Center for Machine Learning and Intelligent Systems

$ wget -P data/ http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data

The Haberman Data Set has 306 instances and 4 attributes (including the class attribute):

  1. Age of patient at time of operation (numerical)
  2. Patient’s year of operation (year - 1900, numerical)
  3. Number of positive axillary nodes detected (numerical)
  4. Survival status (class attribute)
    • 1 = the patient survived 5 years or longer
    • 2 = the patient died within 5 year

We will need to create a metadata file (MTD) which stores metadata information about the content of the data file. The name of the MTD file associated with the data file <filename> must be <filename>.mtd. 

$ echo '{"rows": 306, "cols": 4, "format": "csv"}' > data/haberman.data.mtd


Example 1 - Univariate Statistics

Let’s start with a simple example, computing certain univariate statistics for each feature column using the algorithm Univar-Stats.dml which requires 3 arguments:

We need to create a file types.csv that describes the type of each column in the data along with it’s metadata file types.csv.mtd.

$ echo '1,1,1,2' > data/types.csv
$ echo '{"rows": 1, "cols": 4, "format": "csv"}' > data/types.csv.mtd

To run the Univar-Stats.dml algorithm, issue the following command:

$ ./runStandaloneSystemML.sh algorithms/Univar-Stats.dml -nvargs X=data/haberman.data TYPES=data/types.csv STATS=data/univarOut.mtx

The resulting matrix has one row per each univariate statistic and one column per input feature. The output file univarOut.mtx describes that matrix. The elements of the first column denote the number of the statistic, the elements of the second column refer to the number of the feature column in the input data, and the elements of the third column show the value of the univariate statistic.

1 1 30.0
1 2 58.0
2 1 83.0
2 2 69.0
2 3 52.0
3 1 53.0
3 2 11.0
3 3 52.0
4 1 52.45751633986928
4 2 62.85294117647059
4 3 4.026143790849673
5 1 116.71458266366658
5 2 10.558630665380907
5 3 51.691117539912135
6 1 10.803452349303281
6 2 3.2494046632238507
6 3 7.189653506248555
7 1 0.6175922641866753
7 2 0.18575610076612029
7 3 0.41100513466216837
8 1 0.20594669940735139
8 2 0.051698529971741194
8 3 1.7857418611299172
9 1 0.1450718616532357
9 2 0.07798443581479181
9 3 2.954633471088322
10 1 -0.6150152487211726
10 2 -1.1324380182967442
10 3 11.425776549251449
11 1 0.13934809593495995
11 2 0.13934809593495995
11 3 0.13934809593495995
12 1 0.277810485320835
12 2 0.277810485320835
12 3 0.277810485320835
13 1 52.0
13 2 63.0
13 3 1.0
14 1 52.16013071895425
14 2 62.80392156862745
14 3 1.2483660130718954
15 4 2.0
16 4 1.0
17 4 1.0

The following table lists the number and name of each univariate statistic. The row numbers below correspond to the elements of the first column in the output matrix above. The signs “+” show applicability to scale or/and to categorical features.

Row Name of Statistic Scale Categ.
1 Minimum +  
2 Maximum +  
3 Range +  
4 Mean +  
5 Variance +  
6 Standard deviation +  
7 Standard error of mean +  
8 Coefficient of variation +  
9 Skewness +  
10 Kurtosis +  
11 Standard error of skewness +  
12 Standard error of kurtosis +  
13 Median +  
14 Inter quartile mean +  
15 Number of categories   +
16 Mode   +
17 Number of modes   +



Example 2 - Binary-class Support Vector Machines

Let’s take the same haberman.data to explore the binary-class support vector machines algorithm l2-svm.dml. This example also illustrates how to use of the sampling algorithm sample.dml and the data split algorithm spliXY.dml.

Sampling the Test Data

First we need to use the sample.dml algorithm to separate the input into one training data set and one data set for model prediction.

Parameters:

We will create the file perc.csv and perc.csv.mtd to define the sampling vector with a sampling rate of 50% to generate 2 data sets: 

$ printf "0.5\n0.5" > data/perc.csv
$ echo '{"rows": 2, "cols": 1, "format": "csv"}' > data/perc.csv.mtd

Let’s run the sampling algorithm to create the two data samples:

$ ./runStandaloneSystemML.sh algorithms/utils/sample.dml -nvargs X=data/haberman.data sv=data/perc.csv O=data/haberman.part ofmt="csv"

Splitting Labels from Features

Next we use the splitXY.dml algorithm to separate the feature columns from the label column(s).

Parameters:

We specify y=4 as the 4th column contains the labels to be predicted and run the splitXY.dml algorithm on our training and test data sets.

$ ./runStandaloneSystemML.sh algorithms/utils/splitXY.dml -nvargs X=data/haberman.part/1 y=4 OX=data/haberman.train.data.csv OY=data/haberman.train.labels.csv ofmt="csv"

$ ./runStandaloneSystemML.sh algorithms/utils/splitXY.dml -nvargs X=data/haberman.part/2 y=4 OX=data/haberman.test.data.csv  OY=data/haberman.test.labels.csv  ofmt="csv"

Training and Testing the Model

Now we need to train our model using the l2-svm.dml algorithm.

Parameters:

The l2-svm.dml algorithm is used on our training data sample to train the model.

$ ./runStandaloneSystemML.sh algorithms/l2-svm.dml -nvargs X=data/haberman.train.data.csv Y=data/haberman.train.labels.csv model=data/l2-svm-model.csv fmt="csv" Log=data/l2-svm-log.csv

The l2-svm-predict.dml algorithm is used on our test data sample to predict the labels based on the trained model.

$ ./runStandaloneSystemML.sh algorithms/l2-svm-predict.dml -nvargs X=data/haberman.test.data.csv Y=data/haberman.test.labels.csv model=data/l2-svm-model.csv fmt="csv" confusion=data/l2-svm-confusion.csv

The console output should show the accuracy of the trained model in percent, i.e.:

15/09/01 01:32:51 INFO api.DMLScript: BEGIN DML run 09/01/2015 01:32:51
15/09/01 01:32:51 INFO conf.DMLConfig: Updating localtmpdir with value /tmp/systemml
15/09/01 01:32:51 INFO conf.DMLConfig: Updating scratch with value scratch_space
15/09/01 01:32:51 INFO conf.DMLConfig: Updating optlevel with value 2
15/09/01 01:32:51 INFO conf.DMLConfig: Updating numreducers with value 10
15/09/01 01:32:51 INFO conf.DMLConfig: Updating jvmreuse with value false
15/09/01 01:32:51 INFO conf.DMLConfig: Updating defaultblocksize with value 1000
15/09/01 01:32:51 INFO conf.DMLConfig: Updating dml.yarn.appmaster with value false
15/09/01 01:32:51 INFO conf.DMLConfig: Updating dml.yarn.appmaster.mem with value 2048
15/09/01 01:32:51 INFO conf.DMLConfig: Updating dml.yarn.mapreduce.mem with value 2048
15/09/01 01:32:51 INFO conf.DMLConfig: Updating dml.yarn.app.queue with value default
15/09/01 01:32:51 INFO conf.DMLConfig: Updating cp.parallel.matrixmult with value true
15/09/01 01:32:51 INFO conf.DMLConfig: Updating cp.parallel.textio with value true
Accuracy (%): 74.14965986394557
15/09/01 01:32:52 INFO api.DMLScript: SystemML Statistics:
Total execution time:		0.130 sec.
Number of executed MR Jobs:	0.

The generated file l2-svm-confusion.csv should contain the following confusion matrix of this form:

|t1 t2|
|t3 t4|

If the confusion matrix looks like this ..

107.0,38.0
0.0,2.0

… then the accuracy of the model is (t1+t4)/(t1+t2+t3+t4) = (107+2)/107+38+0+2) = 0.741496599


Refer to the Algorithms Reference for more details.


Troubleshooting

If you encounter a "java.lang.OutOfMemoryError" you can edit the invocation script (runStandaloneSystemML.sh or runStandaloneSystemML.bat) to increase the memory available to the JVM, i.e: 

java -Xmx16g -Xms4g -Xmn1g -cp ${CLASSPATH} com.ibm.bi.dml.api.DMLScript \
     -f ${SCRIPT_FILE} -exec singlenode -config=SystemML-config.xml \
     $@


Next Steps

Check out the Algorithms Reference and run some of the other pre-packaged algorithms.

Follow the DML and PyDML Programming Guide to become familiar with DML and PyDML.