layout: global title: Spark MLContext Programming Guide description: Spark MLContext Programming Guide
- This will become a table of contents (this text will be scraped). {:toc}
Overview
The Spark MLContext API offers a programmatic interface for interacting with SystemML from Spark using languages such as Scala, Java, and Python. As a result, it offers a convenient way to interact with SystemML from the Spark Shell and from Notebooks such as Jupyter and Zeppelin.
Spark Shell Example
Start Spark Shell with SystemML
To use SystemML with Spark Shell, the SystemML jar can be referenced using Spark Shell's --jars option.
{% highlight bash %} spark-shell --executor-memory 4G --driver-memory 4G --jars SystemML.jar {% endhighlight %}
Create MLContext
All primary classes that a user interacts with are located in the org.apache.sysml.api.mlcontext package. For convenience, we can additionally add a static import of ScriptFactory to shorten the syntax for creating Script objects. An MLContext object can be created by passing its constructor a reference to the SparkContext. If successful, you should see a “Welcome to Apache SystemML!” message.
scala> import org.apache.sysml.api.mlcontext.ScriptFactory._ import org.apache.sysml.api.mlcontext.ScriptFactory._
scala> val ml = new MLContext(sc)
Welcome to Apache SystemML!
ml: org.apache.sysml.api.mlcontext.MLContext = org.apache.sysml.api.mlcontext.MLContext@12139db0
{% endhighlight %}
Hello World
The ScriptFactory class allows DML and PYDML scripts to be created from Strings, Files, URLs, and InputStreams. Here, we'll use the dml method to create a DML “hello world” script based on a String. Notice that the script reports that it has no inputs or outputs.
We execute the script using MLContext's execute method, which displays “hello world” to the console. The execute method returns an MLResults object, which contains no results since the script has no outputs.
Outputs: None
scala> ml.execute(helloScript) hello world res0: org.apache.sysml.api.mlcontext.MLResults = None
{% endhighlight %}
DataFrame Example
For demonstration purposes, we'll use Spark to create a DataFrame called df of random doubles from 0 to 1 consisting of 10,000 rows and 100 columns.
scala> import org.apache.spark.sql.types.{StructType,StructField,DoubleType} import org.apache.spark.sql.types.{StructType, StructField, DoubleType}
scala> import scala.util.Random import scala.util.Random
scala> val numRows = 10000 numRows: Int = 10000
scala> val numCols = 100 numCols: Int = 100
scala> val data = sc.parallelize(0 to numRows-1).map { _ => Row.fromSeq(Seq.fill(numCols)(Random.nextDouble)) } data: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[1] at map at :42
scala> val schema = StructType((0 to numCols-1).map { i => StructField(“C” + i, DoubleType, true) } ) schema: org.apache.spark.sql.types.StructType = StructType(StructField(C0,DoubleType,true), StructField(C1,DoubleType,true), StructField(C2,DoubleType,true), StructField(C3,DoubleType,true), StructField(C4,DoubleType,true), StructField(C5,DoubleType,true), StructField(C6,DoubleType,true), StructField(C7,DoubleType,true), StructField(C8,DoubleType,true), StructField(C9,DoubleType,true), StructField(C10,DoubleType,true), StructField(C11,DoubleType,true), StructField(C12,DoubleType,true), StructField(C13,DoubleType,true), StructField(C14,DoubleType,true), StructField(C15,DoubleType,true), StructField(C16,DoubleType,true), StructField(C17,DoubleType,true), StructField(C18,DoubleType,true), StructField(C19,DoubleType,true), StructField(C20,DoubleType,true), StructField(C21,DoubleType,true), ... scala> val df = spark.createDataFrame(data, schema) df: org.apache.spark.sql.DataFrame = [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, C8: double, C9: double, C10: double, C11: double, C12: double, C13: double, C14: double, C15: double, C16: double, C17: double, C18: double, C19: double, C20: double, C21: double, C22: double, C23: double, C24: double, C25: double, C26: double, C27: double, C28: double, C29: double, C30: double, C31: double, C32: double, C33: double, C34: double, C35: double, C36: double, C37: double, C38: double, C39: double, C40: double, C41: double, C42: double, C43: double, C44: double, C45: double, C46: double, C47: double, C48: double, C49: double, C50: double, C51: double, C52: double, C53: double, C54: double, C55: double, C56: double, C57: double, C58: double, C5...
{% endhighlight %}
We'll create a DML script to find the minimum, maximum, and mean values in a matrix. This script has one input variable, matrix Xin, and three output variables, minOut, maxOut, and meanOut.
For performance, we'll specify metadata indicating that the matrix has 10,000 rows and 100 columns.
We'll create a DML script using the ScriptFactory dml method with the minMaxMean script String. The input variable is specified to be our DataFrame df with MatrixMetadata mm. The output variables are specified to be minOut, maxOut, and meanOut. Notice that inputs are supplied by the in method, and outputs are supplied by the out method.
We execute the script and obtain the results as a Tuple by calling getTuple on the results, specifying the types and names of the output variables.
{% endhighlight %}
scala> val mm = new MatrixMetadata(numRows, numCols) mm: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 10000, columns: 100, non-zeros: None, rows per block: None, columns per block: None
scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...
Outputs: [1] minOut [2] maxOut [3] meanOut
scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”) min: Double = 2.6257349849956313E-8 max: Double = 0.9999999686609718 mean: Double = 0.49996223966662934
{% endhighlight %}
Many different types of input and output variables are automatically allowed. These types include Boolean, Long, Double, String, Array[Array[Double]], RDD<String> and JavaRDD<String> in CSV (dense) and IJV (sparse) formats, DataFrame, BinaryBlockMatrix, Matrix, and Frame. RDDs and JavaRDDs are assumed to be CSV format unless MatrixMetadata is supplied indicating IJV format.
RDD Example
Let‘s take a look at an example of input matrices as RDDs in CSV format. We’ll create two 2x2 matrices and input these into a DML script. This script will sum each matrix and create a message based on which sum is greater. We will output the sums and the message.
For fun, we‘ll write the script String to a file and then use ScriptFactory’s dmlFromFile method to create the script object based on the file. We'll also specify the inputs using a Map, although we could have also chained together two in methods to specify the same inputs.
scala> val rdd2 = sc.parallelize(Array(“5.0,6.0”, “7.0,8.0”)) rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[43] at parallelize at :38
scala> val sums = """ | 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” | } | """ sums: 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” } "
scala> scala.tools.nsc.io.File(“sums.dml”).writeAll(sums)
scala> val sumScript = dmlFromFile(“sums.dml”).in(Map(“m1”-> rdd1, “m2”-> rdd2)).out(“s1”, “s2”, “message”) sumScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m1: ParallelCollectionRDD[42] at parallelize at :38 [2] (RDD) m2: ParallelCollectionRDD[43] at parallelize at :38
Outputs: [1] s1 [2] s2 [3] message
scala> val sumResults = ml.execute(sumScript) sumResults: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) s1: 10.0 [2] (Double) s2: 26.0 [3] (String) message: s2 is greater
scala> val s1 = sumResults.getDouble(“s1”) s1: Double = 10.0
scala> val s2 = sumResults.getDouble(“s2”) s2: Double = 26.0
scala> val message = sumResults.getString(“message”) message: String = s2 is greater
{% endhighlight %}
If you have metadata that you would like to supply along with the input matrices, this can be accomplished using a Scala Seq, List, or Array.
{% endhighlight %}
scala> val rdd2Metadata = new MatrixMetadata(2, 2) rdd2Metadata: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 2, columns: 2, non-zeros: None, rows per block: None, columns per block: None
scala> val sumScript = dmlFromFile(“sums.dml”).in(Seq((“m1”, rdd1, rdd1Metadata), (“m2”, rdd2, rdd2Metadata))).out(“s1”, “s2”, “message”) sumScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m1: ParallelCollectionRDD[42] at parallelize at :38 [2] (RDD) m2: ParallelCollectionRDD[43] at parallelize at :38
Outputs: [1] s1 [2] s2 [3] message
scala> val (firstSum, secondSum, sumMessage) = ml.execute(sumScript).getTuple[Double, Double, String](“s1”, “s2”, “message”) firstSum: Double = 10.0 secondSum: Double = 26.0 sumMessage: String = s2 is greater
{% endhighlight %}
The same inputs with metadata can be supplied by chaining in methods, as in the example below, which shows that out methods can also be chained.
{% endhighlight %}
Outputs: [1] s1 [2] s2 [3] message
scala> val (firstSum, secondSum, sumMessage) = ml.execute(sumScript).getTuple[Double, Double, String](“s1”, “s2”, “message”) firstSum: Double = 10.0 secondSum: Double = 26.0 sumMessage: String = s2 is greater
{% endhighlight %}
Matrix Output
Let‘s look at an example of reading a matrix out of SystemML. We’ll create a DML script in which we create a 2x2 matrix m. We'll set the variable n to be the sum of the cells in the matrix.
We create a script object using String s, and we set m and n as the outputs. We execute the script, and in the results we see we have Matrix m and Double n. The n output variable has a value of 110.0.
We get Matrix m and Double n as a Tuple of values x and y. We then convert Matrix m to an RDD of IJV values, an RDD of CSV values, a DataFrame, and a two-dimensional Double Array, and we display the values in each of these data structures.
{% endhighlight %}
scala> val scr = dml(s).out(“m”, “n”); scr: org.apache.sysml.api.mlcontext.Script = Inputs: None
Outputs: [1] m [2] n
scala> val res = ml.execute(scr) res: org.apache.sysml.api.mlcontext.MLResults = [1] (Matrix) m: Matrix: scratch_space//_p12059_9.31.117.12//_t0/temp26_14, [2 x 2, nnz=4, blocks (1000 x 1000)], binaryblock, dirty [2] (Double) n: 110.0
scala> val (x, y) = res.getTupleMatrix, Double x: org.apache.sysml.api.mlcontext.Matrix = Matrix: scratch_space//_p12059_9.31.117.12//_t0/temp26_14, [2 x 2, nnz=4, blocks (1000 x 1000)], binaryblock, dirty y: Double = 110.0
scala> x.toRDDStringIJV.collect.foreach(println) 1 1 11.0 1 2 22.0 2 1 33.0 2 2 44.0
scala> x.toRDDStringCSV.collect.foreach(println) 11.0,22.0 33.0,44.0
scala> x.toDF.collect.foreach(println) [0.0,11.0,22.0] [1.0,33.0,44.0]
scala> x.to2DDoubleArray res10: Array[Array[Double]] = Array(Array(11.0, 22.0), Array(33.0, 44.0))
{% endhighlight %}
Univariate Statistics on Haberman Data
Our next example will involve Haberman's Survival Data Set in CSV format from the Center for Machine Learning and Intelligent Systems. We will run the SystemML Univariate Statistics (“Univar-Stats.dml”) script on this data.
We‘ll pull the data from a URL and convert it to an RDD, habermanRDD. Next, we’ll create metadata, habermanMetadata, stating that the matrix consists of 306 rows and 4 columns.
As we can see from the comments in the script here, the script requires a ‘TYPES’ input matrix that lists the types of the features (1 for scale, 2 for nominal, 3 for ordinal), so we create a typesRDD matrix consisting of 1 row and 4 columns, with corresponding metadata, typesMetadata.
Next, we create the DML script object called uni using ScriptFactory‘s dmlFromUrl method, specifying the GitHub URL where the DML script is located. We bind the habermanRDD matrix to the A variable in Univar-Stats.dml, and we bind the typesRDD matrix to the K variable. In addition, we supply a $CONSOLE_OUTPUT parameter with a Boolean value of true, which indicates that we’d like to output labeled results to the console. We'll explain why we bind to the A and K variables in the Input Variables vs Input Parameters section below.
{% endhighlight %}
scala> val habermanList = scala.io.Source.fromURL(habermanUrl).mkString.split(“\n”) habermanList: Array[String] = Array(30,64,1,1, 30,62,3,1, 30,65,0,1, 31,59,2,1, 31,65,4,1, 33,58,10,1, 33,60,0,1, 34,59,0,2, 34,66,9,2, 34,58,30,1, 34,60,1,1, 34,61,10,1, 34,67,7,1, 34,60,0,1, 35,64,13,1, 35,63,0,1, 36,60,1,1, 36,69,0,1, 37,60,0,1, 37,63,0,1, 37,58,0,1, 37,59,6,1, 37,60,15,1, 37,63,0,1, 38,69,21,2, 38,59,2,1, 38,60,0,1, 38,60,0,1, 38,62,3,1, 38,64,1,1, 38,66,0,1, 38,66,11,1, 38,60,1,1, 38,67,5,1, 39,66,0,2, 39,63,0,1, 39,67,0,1, 39,58,0,1, 39,59,2,1, 39,63,4,1, 40,58,2,1, 40,58,0,1, 40,65,0,1, 41,60,23,2, 41,64,0,2, 41,67,0,2, 41,58,0,1, 41,59,8,1, 41,59,0,1, 41,64,0,1, 41,69,8,1, 41,65,0,1, 41,65,0,1, 42,69,1,2, 42,59,0,2, 42,58,0,1, 42,60,1,1, 42,59,2,1, 42,61,4,1, 42,62,20,1, 42,65,0,1, 42,63,1,1, 43,58,52,2, 43,59,2,2, 43,64,0,2, 43,64,0,2, 43,63,14,1, 43,64,2,1, 43... scala> val habermanRDD = sc.parallelize(habermanList) habermanRDD: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[159] at parallelize at :43
scala> val habermanMetadata = new MatrixMetadata(306, 4) habermanMetadata: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 306, columns: 4, non-zeros: None, rows per block: None, columns per block: None
scala> val typesRDD = sc.parallelize(Array(“1.0,1.0,1.0,2.0”)) typesRDD: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[160] at parallelize at :39
scala> val typesMetadata = new MatrixMetadata(1, 4) typesMetadata: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 1, columns: 4, non-zeros: None, rows per block: None, columns per block: None
scala> val scriptUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” scriptUrl: String = https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml
scala> val uni = dmlFromUrl(scriptUrl).in(“A”, habermanRDD, habermanMetadata).in(“K”, typesRDD, typesMetadata).in(“$CONSOLE_OUTPUT”, true) uni: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) A: ParallelCollectionRDD[159] at parallelize at :43 [2] (RDD) K: ParallelCollectionRDD[160] at parallelize at :39 [3] (Boolean) $CONSOLE_OUTPUT: true
Outputs: None
scala> ml.execute(uni) ...
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 res23: org.apache.sysml.api.mlcontext.MLResults = None
{% endhighlight %}
Alternatively, we could supply a java.net.URL to the Script in method. Note that if the URL matrix data is in IJV format, metadata needs to be supplied for the matrix.
scala> val typesRDD = sc.parallelize(Array(“1.0,1.0,1.0,2.0”)) typesRDD: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[50] at parallelize at :33
scala> val scriptUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” scriptUrl: String = https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml
scala> val uni = dmlFromUrl(scriptUrl).in(“A”, new java.net.URL(habermanUrl)).in(“K”, typesRDD).in(“$CONSOLE_OUTPUT”, true) uni: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (URL) A: http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data [2] (RDD) K: ParallelCollectionRDD[50] at parallelize at :33 [3] (Boolean) $CONSOLE_OUTPUT: true
Outputs: None
scala> ml.execute(uni) ...
(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 [1]: 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 [2]: 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 [3]: Scale
Feature [4]: Categorical (Nominal) (15) Num of categories | 2 (16) Mode | 1 (17) Num of modes | 1 res5: org.apache.sysml.api.mlcontext.MLResults = None
{% endhighlight %}
Input Variables vs Input Parameters
If we examine the Univar-Stats.dml file, we see in the comments that it can take 4 input parameters, $X, $TYPES, $CONSOLE_OUTPUT, and $STATS. Input parameters are typically useful when executing SystemML in Standalone mode, Spark batch mode, or Hadoop batch mode. For example, $X specifies the location in the file system where the input data matrix is located, $TYPES specifies the location in the file system where the input types matrix is located, $CONSOLE_OUTPUT specifies whether or not labeled statistics should be output to the console, and $STATS specifies the location in the file system where the output matrix should be written.
{% highlight r %} ...
INPUT PARAMETERS:
-------------------------------------------------------------------------------------------------
NAME TYPE DEFAULT MEANING
-------------------------------------------------------------------------------------------------
X String --- Location of INPUT data matrix
TYPES String --- Location of INPUT matrix that lists the types of the features:
1 for scale, 2 for nominal, 3 for ordinal
CONSOLE_OUTPUT Boolean FALSE If TRUE, print summary statistics to console
STATS String --- Location of OUTPUT matrix with summary statistics computed for
all features (17 statistics - 14 scale, 3 categorical)
-------------------------------------------------------------------------------------------------
OUTPUT: Matrix of summary statistics
... consoleOutput = ifdef($CONSOLE_OUTPUT, FALSE); A = read($X); # data file K = read($TYPES); # attribute kind file ... write(baseStats, $STATS); ... {% endhighlight %}
Because MLContext is a programmatic interface, it offers more flexibility. You can still use input parameters and files in the file system, such as this example that specifies file paths to the input matrices and the output matrix:
{% highlight scala %} val script = dmlFromFile(“scripts/algorithms/Univar-Stats.dml”).in(“$X”, “data/haberman.data”).in(“$TYPES”, “data/types.csv”).in(“$STATS”, “data/univarOut.mtx”).in(“$CONSOLE_OUTPUT”, true) ml.execute(script) {% endhighlight %}
Using the MLContext API, rather than relying solely on input parameters, we can bind to the variables associated with the read and write statements. In the fragment of Univar-Stats.dml above, notice that the matrix at path $X is read to variable A, $TYPES is read to variable K, and baseStats is written to path $STATS. Therefore, we can bind the Haberman input data matrix to the A variable, the input types matrix to the K variable, and the output matrix to the baseStats variable.
Outputs: [1] baseStats
scala> val baseStats = ml.execute(uni).getMatrix(“baseStats”) ... baseStats: org.apache.sysml.api.mlcontext.Matrix = Matrix: scratch_space/_p12059_9.31.117.12/parfor/4_resultmerge1, [17 x 4, nnz=44, blocks (1000 x 1000)], binaryblock, dirty
scala> baseStats.toRDDStringIJV.collect.slice(0,9).foreach(println) 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 3 1 53.0
{% endhighlight %}
Script Information
The info method on a Script object can provide useful information about a DML or PyDML script, such as the inputs, output, symbol table, script string, and the script execution string that is passed to the internals of SystemML.
scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...
Outputs: [1] minOut [2] maxOut [3] meanOut
scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”) min: Double = 1.4149740823476975E-7 max: Double = 0.9999999956646207 mean: Double = 0.5000954668004209
scala> println(minMaxMeanScript.info) Script Type: DML
Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...
Outputs: [1] (Double) minOut: 1.4149740823476975E-7 [2] (Double) maxOut: 0.9999999956646207 [3] (Double) meanOut: 0.5000954668004209
Input Parameters: None
Input Variables: [1] Xin
Output Variables: [1] minOut [2] maxOut [3] meanOut
Symbol Table: [1] (Double) meanOut: 0.5000954668004209 [2] (Double) maxOut: 0.9999999956646207 [3] (Double) minOut: 1.4149740823476975E-7 [4] (Matrix) Xin: Matrix: scratch_space/temp_1166464711339222, [10000 x 100, nnz=1000000, blocks (1000 x 1000)], binaryblock, not-dirty
Script String:
minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin)
Script Execution String: Xin = read('');
minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin) write(minOut, ''); write(maxOut, ''); write(meanOut, '');
{% endhighlight %}
Clearing Scripts and MLContext
Dealing with large matrices can require a significant amount of memory. To deal help deal with this, you can call a Script object's clearAll method to clear the inputs, outputs, symbol table, and script string. In terms of memory, the symbol table is most important because it holds references to matrices.
In this example, we display the symbol table of the minMaxMeanScript, call clearAll on the script, and then display the symbol table, which is empty.
{% endhighlight %}
scala> minMaxMeanScript.clearAll
scala> println(minMaxMeanScript.displaySymbolTable) Symbol Table: None
{% endhighlight %}
The MLContext object holds references to the scripts that have been executed. Calling clear on the MLContext clears all scripts that it has references to and then removes the references to these scripts.
{% highlight scala %} ml.clear {% endhighlight %}
Statistics
Statistics about script executions can be output to the console by calling MLContext's setStatistics method with a value of true.
{% endhighlight %}
scala> val minMaxMean = | """ | minOut = min(Xin) | maxOut = max(Xin) | meanOut = mean(Xin) | """ minMaxMean: String = " minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin) "
scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...
Outputs: [1] minOut [2] maxOut [3] meanOut
scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”) SystemML Statistics: Total elapsed time: 0.000 sec. Total compilation time: 0.000 sec. Total execution time: 0.000 sec. Number of compiled Spark inst: 0. Number of executed Spark inst: 0. Cache hits (Mem, WB, FS, HDFS): 2/0/0/1. Cache writes (WB, FS, HDFS): 1/0/0. Cache times (ACQr/m, RLS, EXP): 3.137/0.000/0.001/0.000 sec. HOP DAGs recompiled (PRED, SB): 0/0. HOP DAGs recompile time: 0.000 sec. Spark ctx create time (lazy): 0.000 sec. Spark trans counts (par,bc,col):0/0/2. Spark trans times (par,bc,col): 0.000/0.000/6.434 secs. Total JIT compile time: 112.372 sec. Total JVM GC count: 54. Total JVM GC time: 9.664 sec. Heavy hitter instructions (name, time, count): -- 1) uamin 3.150 sec 1 -- 2) uamean 0.021 sec 1 -- 3) uamax 0.017 sec 1 -- 4) rmvar 0.000 sec 3 -- 5) assignvar 0.000 sec 3
min: Double = 2.4982850344024143E-8 max: Double = 0.9999997007231808 mean: Double = 0.5002109404821844
{% endhighlight %}
Explain
A DML or PyDML script is converted into a SystemML program during script execution. Information about this program can be displayed by calling MLContext's setExplain method with a value of true.
{% endhighlight %}
scala> val minMaxMean = | """ | minOut = min(Xin) | maxOut = max(Xin) | meanOut = mean(Xin) | """ minMaxMean: String = " minOut = min(Xin) maxOut = max(Xin) meanOut = mean(Xin) "
scala> val mm = new MatrixMetadata(numRows, numCols) mm: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 10000, columns: 100, non-zeros: None, rows per block: None, columns per block: None
scala> val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) minMaxMeanScript: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (DataFrame) Xin: [C0: double, C1: double, C2: double, C3: double, C4: double, C5: double, C6: double, C7: double, ...
Outputs: [1] minOut [2] maxOut [3] meanOut
scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”)
PROGRAM --MAIN PROGRAM ----GENERIC (lines 1-8) [recompile=false] ------(12) TRead Xin [10000,100,1000,1000,1000000] [0,0,76 -> 76MB] [chkpt], CP ------(13) ua(minRC) (12) [0,0,-1,-1,-1] [76,0,0 -> 76MB], CP ------(21) TWrite minOut (13) [0,0,-1,-1,-1] [0,0,0 -> 0MB], CP ------(14) ua(maxRC) (12) [0,0,-1,-1,-1] [76,0,0 -> 76MB], CP ------(27) TWrite maxOut (14) [0,0,-1,-1,-1] [0,0,0 -> 0MB], CP ------(15) ua(meanRC) (12) [0,0,-1,-1,-1] [76,0,0 -> 76MB], CP ------(33) TWrite meanOut (15) [0,0,-1,-1,-1] [0,0,0 -> 0MB], CP
min: Double = 5.16651366133658E-9 max: Double = 0.9999999368927975 mean: Double = 0.5001096515241128
{% endhighlight %}
Different explain levels can be set. The explain levels are NONE, HOPS, RUNTIME, RECOMPILE_HOPS, and RECOMPILE_RUNTIME.
scala> val (min, max, mean) = ml.execute(minMaxMeanScript).getTuple[Double, Double, Double](“minOut”, “maxOut”, “meanOut”)
PROGRAM ( size CP/SP = 9/0 ) --MAIN PROGRAM ----GENERIC (lines 1-8) [recompile=false] ------CP uamin Xin.MATRIX.DOUBLE _Var8.SCALAR.DOUBLE 8 ------CP uamax Xin.MATRIX.DOUBLE _Var9.SCALAR.DOUBLE 8 ------CP uamean Xin.MATRIX.DOUBLE _Var10.SCALAR.DOUBLE 8 ------CP assignvar _Var8.SCALAR.DOUBLE.false minOut.SCALAR.DOUBLE ------CP assignvar _Var9.SCALAR.DOUBLE.false maxOut.SCALAR.DOUBLE ------CP assignvar _Var10.SCALAR.DOUBLE.false meanOut.SCALAR.DOUBLE ------CP rmvar _Var8 ------CP rmvar _Var9 ------CP rmvar _Var10
min: Double = 5.16651366133658E-9 max: Double = 0.9999999368927975 mean: Double = 0.5001096515241128
{% endhighlight %}
Script Creation and ScriptFactory
Script objects can be created using standard Script constructors. A Script can be of two types: DML (R-based syntax) and PYDML (Python-based syntax). If no ScriptType is specified, the default Script type is DML.
scala> val script = new Script(ScriptType.PYDML); ... scala> println(script.getScriptType) PYDML
{% endhighlight %}
The ScriptFactory class offers convenient methods for creating DML and PYDML scripts from a variety of sources. ScriptFactory can create a script object from a String, File, URL, or InputStream.
Script from URL:
Here we create Script object s1 by reading Univar-Stats.dml from a URL.
{% highlight scala %} val uniUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” val s1 = ScriptFactory.dmlFromUrl(scriptUrl) {% endhighlight %}
Script from String:
We create Script objects s2 and s3 from Strings using ScriptFactory's dml and dmlFromString methods. Both methods perform the same action. This example reads an algorithm at a URL to String uniString and then creates two script objects based on this String.
{% highlight scala %} val uniUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” val uniString = scala.io.Source.fromURL(uniUrl).mkString val s2 = ScriptFactory.dml(uniString) val s3 = ScriptFactory.dmlFromString(uniString) {% endhighlight %}
Script from File:
We create Script object s4 based on a path to a file using ScriptFactory's dmlFromFile method. This example reads a URL to a String, writes this String to a file, and then uses the path to the file to create a Script object.
{% highlight scala %} val uniUrl = “https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/algorithms/Univar-Stats.dml” val uniString = scala.io.Source.fromURL(uniUrl).mkString scala.tools.nsc.io.File(“uni.dml”).writeAll(uniString) val s4 = ScriptFactory.dmlFromFile(“uni.dml”) {% endhighlight %}
Script from InputStream:
The SystemML jar file contains all the primary algorithm scripts. We can read one of these scripts as an InputStream and use this to create a Script object.
{% highlight scala %} val inputStream = getClass.getResourceAsStream(“/scripts/algorithms/Univar-Stats.dml”) val s5 = ScriptFactory.dmlFromInputStream(inputStream) {% endhighlight %}
Script from Resource:
As mentioned, the SystemML jar file contains all the primary algorithm script files. For convenience, we can read these script files or other script files on the classpath using ScriptFactory's dmlFromResource and pydmlFromResource methods.
{% highlight scala %} val s6 = ScriptFactory.dmlFromResource(“/scripts/algorithms/Univar-Stats.dml”); {% endhighlight %}
ScriptExecutor
A Script is executed by a ScriptExecutor. If no ScriptExecutor is specified, a default ScriptExecutor will be created to execute a Script. Script execution consists of several steps, as detailed in SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. Additional information can be found in the Javadocs for ScriptExecutor.
Advanced users may find it useful to be able to specify their own execution or to override ScriptExecutor methods by subclassing ScriptExecutor.
In this example, we override the parseScript and validateScript methods to display messages to the console during these execution steps.
{% endhighlight %}
scala> val helloScript = dml(“print(‘hello world’)”) helloScript: org.apache.sysml.api.mlcontext.Script = Inputs: None
Outputs: None
scala> ml.execute(helloScript, new MyScriptExecutor) Parsing script Validating script hello world res63: org.apache.sysml.api.mlcontext.MLResults = None
{% endhighlight %}
MatrixMetadata
When supplying matrix data to Apache SystemML using the MLContext API, matrix metadata can be supplied using a MatrixMetadata object. Supplying characteristics about a matrix can significantly improve performance. For some types of input matrices, supplying metadata is mandatory. Metadata at a minimum typically consists of the number of rows and columns in a matrix. The number of non-zeros can also be supplied.
Additionally, the number of rows and columns per block can be supplied, although in typical usage it's probably fine to use the default values used by SystemML (1,000 rows and 1,000 columns per block). SystemML handles a matrix internally by splitting the matrix into chunks, or blocks. The number of rows and columns per block refers to the size of these matrix blocks.
CSV RDD with No Metadata:
Here we see an example of inputting an RDD of Strings in CSV format with no metadata. Note that in general it is recommended that metadata is supplied. We output the sum and mean of the cells in the matrix.
{% endhighlight %}
scala> val sumAndMean = dml(“sum = sum(m); mean = mean(m)”).in(“m”, rddCSV).out(“sum”, “mean”) sumAndMean: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m: ParallelCollectionRDD[190] at parallelize at :38
Outputs: [1] sum [2] mean
scala> ml.execute(sumAndMean) res20: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) sum: 10.0 [2] (Double) mean: 2.5
{% endhighlight %}
IJV RDD with Metadata:
Next, we‘ll supply an RDD in IJV format. IJV is a sparse format where each line has three space-separated values. The first value indicates the row number, the second value indicates the column number, and the third value indicates the cell value. Since the total numbers of rows and columns can’t be determined from these IJV rows, we need to supply metadata describing the matrix size.
Here, we specify that our matrix has 3 rows and 3 columns.
{% endhighlight %}
scala> val mm3x3 = new MatrixMetadata(MatrixFormat.IJV, 3, 3) mm3x3: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 3, columns: 3, non-zeros: None, rows per block: None, columns per block: None
scala> val sumAndMean = dml(“sum = sum(m); mean = mean(m)”).in(“m”, rddIJV, mm3x3).out(“sum”, “mean”) sumAndMean: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m: ParallelCollectionRDD[202] at parallelize at :38
Outputs: [1] sum [2] mean
scala> ml.execute(sumAndMean) res21: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) sum: 10.0 [2] (Double) mean: 1.1111111111111112
{% endhighlight %}
Next, we‘ll run the same DML, but this time we’ll specify that the input matrix is 4x4 instead of 3x3.
{% endhighlight %}
scala> val mm4x4 = new MatrixMetadata(MatrixFormat.IJV, 4, 4) mm4x4: org.apache.sysml.api.mlcontext.MatrixMetadata = rows: 4, columns: 4, non-zeros: None, rows per block: None, columns per block: None
scala> val sumAndMean = dml(“sum = sum(m); mean = mean(m)”).in(“m”, rddIJV, mm4x4).out(“sum”, “mean”) sumAndMean: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) m: ParallelCollectionRDD[210] at parallelize at :38
Outputs: [1] sum [2] mean
scala> ml.execute(sumAndMean) res22: org.apache.sysml.api.mlcontext.MLResults = [1] (Double) sum: 10.0 [2] (Double) mean: 0.625
{% endhighlight %}
Matrix Data Conversions and Performance
Internally, Apache SystemML uses a binary-block matrix representation, where a matrix is represented as a grouping of blocks. Each block is equal in size to the other blocks in the matrix and consists of a number of rows and columns. The default block size is 1,000 rows by 1,000 columns.
Conversion of a large set of data to a SystemML matrix representation can potentially be time-consuming. Therefore, if you use a set of data multiple times, one way to potentially improve performance is to convert it to a SystemML matrix representation and then use this representation rather than performing the data conversion each time.
There are currently two mechanisms for this in SystemML: (1) BinaryBlockMatrix and (2) Matrix.
BinaryBlockMatrix:
If you have an input DataFrame, it can be converted to a BinaryBlockMatrix, and this BinaryBlockMatrix can be passed as an input rather than passing in the DataFrame as an input.
For example, suppose we had a 10000x100 matrix represented as a DataFrame, as we saw in an earlier example. Now suppose we create two Script objects with the DataFrame as an input, as shown below. In the Spark Shell, when executing this code, you can see that each of the two Script object creations requires the time-consuming data conversion step.
{% highlight scala %} import org.apache.spark.sql._ import org.apache.spark.sql.types.{StructType,StructField,DoubleType} import scala.util.Random val numRows = 10000 val numCols = 100 val data = sc.parallelize(0 to numRows-1).map { _ => Row.fromSeq(Seq.fill(numCols)(Random.nextDouble)) } val schema = StructType((0 to numCols-1).map { i => StructField(“C” + i, DoubleType, true) } ) val df = spark.createDataFrame(data, schema) val mm = new MatrixMetadata(numRows, numCols) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, df, mm).out(“minOut”, “maxOut”, “meanOut”) {% endhighlight %}
Rather than passing in a DataFrame each time to the Script object creation, let's instead create a BinaryBlockMatrix object based on the DataFrame and pass this BinaryBlockMatrix to the Script object creation. If we run the code below in the Spark Shell, we see that the data conversion step occurs when the BinaryBlockMatrix object is created. However, when we create a Script object twice, we see that no conversion penalty occurs, since this conversion occurred when the BinaryBlockMatrix was created.
{% highlight scala %} import org.apache.spark.sql._ import org.apache.spark.sql.types.{StructType,StructField,DoubleType} import scala.util.Random val numRows = 10000 val numCols = 100 val data = sc.parallelize(0 to numRows-1).map { _ => Row.fromSeq(Seq.fill(numCols)(Random.nextDouble)) } val schema = StructType((0 to numCols-1).map { i => StructField(“C” + i, DoubleType, true) } ) val df = spark.createDataFrame(data, schema) val mm = new MatrixMetadata(numRows, numCols) val bbm = new BinaryBlockMatrix(df, mm) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, bbm).out(“minOut”, “maxOut”, “meanOut”) val minMaxMeanScript = dml(minMaxMean).in(“Xin”, bbm).out(“minOut”, “maxOut”, “meanOut”) {% endhighlight %}
Matrix:
When a matrix is returned as an output, it is returned as a Matrix object, which is a wrapper around a SystemML MatrixObject. As a result, an output Matrix is already in a SystemML representation, meaning that it can be passed as an input with no data conversion penalty.
As an example, here we read in matrix x as an RDD in CSV format. We create a Script that adds one to all values in the matrix. We obtain the resulting matrix y as a Matrix. We execute the script five times, feeding the output matrix as the input matrix for the next script execution.
{% endhighlight %}
scala> val add = dml(“y = x + 1”).in(“x”, rddCSV).out(“y”) add: org.apache.sysml.api.mlcontext.Script = Inputs: [1] (RDD) x: ParallelCollectionRDD[341] at parallelize at :53
Outputs: [1] y
scala> for (i <- 1 to 5) { | println(“#” + i + “:”); | val m = ml.execute(add).getMatrix(“y”) | m.toRDDStringCSV.collect.foreach(println) | add.in(“x”, m) | } #1: 2.0,3.0 4.0,5.0 #2: 3.0,4.0 5.0,6.0 #3: 4.0,5.0 6.0,7.0 #4: 5.0,6.0 7.0,8.0 #5: 6.0,7.0 8.0,9.0
{% endhighlight %}
Project Information
SystemML project information such as version and build time can be obtained through the MLContext API. The project version can be obtained by ml.version. The build time can be obtained by ml.buildTime. The contents of the project manifest can be displayed using ml.info. Individual properties can be obtained using the ml.info.property method, as shown below.
scala> print(ml.info.property(“Main-Class”)) org.apache.sysml.api.DMLScript
{% endhighlight %}
Jupyter (PySpark) Notebook Example - Poisson Nonnegative Matrix Factorization
Similar to the Scala API, SystemML also provides a Python MLContext API. Before usage, you'll need to install it first.
Here, we'll explore the use of SystemML via PySpark in a Jupyter notebook. This Jupyter notebook example can be nicely viewed in a rendered state on GitHub, and can be downloaded here to a directory of your choice.
From the directory with the downloaded notebook, start Jupyter with PySpark:
This will open Jupyter in a browser:
We can then open up the SystemML-PySpark-Recommendation-Demo notebook.
Set up the notebook and download the data
{% highlight python %} %load_ext autoreload %autoreload 2 %matplotlib inline
import numpy as np import matplotlib.pyplot as plt from systemml import MLContext, dml # pip install systeml plt.rcParams[‘figure.figsize’] = (10, 6) {% endhighlight %}
{% highlight python %} %%sh
Download dataset
curl -O http://snap.stanford.edu/data/amazon0601.txt.gz gunzip amazon0601.txt.gz {% endhighlight %}
Use PySpark to load the data in as a Spark DataFrame
{% highlight python %}
Load data
import pyspark.sql.functions as F dataPath = “amazon0601.txt”
X_train = (sc.textFile(dataPath) .filter(lambda l: not l.startswith(“#”)) .map(lambda l: l.split(“\t”)) .map(lambda prods: (int(prods[0]), int(prods[1]), 1.0)) .toDF((“prod_i”, “prod_j”, “x_ij”)) .filter(“prod_i < 500 AND prod_j < 500”) # Filter for memory constraints .cache())
max_prod_i = X_train.select(F.max(“prod_i”)).first()[0] max_prod_j = X_train.select(F.max(“prod_j”)).first()[0] numProducts = max(max_prod_i, max_prod_j) + 1 # 0-based indexing print(“Total number of products: {}”.format(numProducts)) {% endhighlight %}
Create a SystemML MLContext object
{% highlight python %}
Create SystemML MLContext
ml = MLContext(sc) {% endhighlight %}
Define a kernel for Poisson nonnegative matrix factorization (PNMF) in DML
{% highlight python %}
Define PNMF kernel in SystemML's DSL using the R-like syntax for PNMF
pnmf = """
data & args
X = X+1 # change product IDs to be 1-based, rather than 0-based V = table(X[,1], X[,2]) size = ifdef($size, -1) if(size > -1) { V = V[1:size,1:size] }
n = nrow(V) m = ncol(V) range = 0.01 W = Rand(rows=n, cols=rank, min=0, max=range, pdf=“uniform”) H = Rand(rows=rank, cols=m, min=0, max=range, pdf=“uniform”) losses = matrix(0, rows=max_iter, cols=1)
run PNMF
i=1 while(i <= max_iter) {
update params
H = (H * (t(W) %% (V/(W%%H))))/t(colSums(W)) W = (W * ((V/(W%%H)) %% t(H)))/t(rowSums(H))
compute loss
losses[i,] = -1 * (sum(Vlog(W%%H)) - as.scalar(colSums(W)%*%rowSums(H))) i = i + 1; } """ {% endhighlight %}
Execute the algorithm
{% highlight python %}
Run the PNMF script on SystemML with Spark
script = dml(pnmf).input(X=X_train, max_iter=100, rank=10).output(“W”, “H”, “losses”) W, H, losses = ml.execute(script).get(“W”, “H”, “losses”) {% endhighlight %}
Retrieve the losses during training and plot them
{% highlight python %}
Plot training loss over time
xy = losses.toDF().sort(“__INDEX”).map(lambda r: (r[0], r[1])).collect() x, y = zip(*xy) plt.plot(x, y) plt.xlabel(‘Iteration’) plt.ylabel(‘Loss’) plt.title(‘PNMF Training Loss’) {% endhighlight %}
Recommended Spark Configuration Settings
For best performance, we recommend setting the following flags when running SystemML with Spark: --conf spark.driver.maxResultSize=0 --conf spark.akka.frameSize=128.