Declarative Machine Learning (DML) Language Reference
Table of Contents
 Introduction
 Variables
 Expressions
 Statements
 Variable Scoping
 CommandLine Arguments
 Builtin Functions
 Matrix Construction, Manipulation, and Aggregation BuiltIn Functions
 Matrix and/or Scalar Comparison BuiltIn Functions
 Casting BuiltIn Functions
 Statistical BuiltIn Functions
 Mathematical and Trigonometric BuiltIn Functions
 Linear Algebra BuiltIn Functions
 Read/Write BuiltIn Functions
 Data PreProcessing BuiltIn Functions
 Other BuiltIn Functions
 Frames
 Modules
 Reserved Keywords
Introduction
SystemML compiles scripts written in Declarative Machine Learning (or DML for short) into mixed driver and distributed jobs. DML’s syntax closely follows R, thereby minimizing the learning curve to use SystemML. Before getting into detail, let’s start with a simple Hello World program in DML. Assuming that Spark is installed on your machine or cluster, place SystemML.jar
into your directory. Now, create a text file hello.dml
containing following code:
print("Hello World");
To run this program on your machine, use following command:
sparksubmit SystemML.jar f hello.dml
The option f
in the above command refers to the path to the DML script. A detailed list of the
available options can be found running sparksubmit SystemML.jar help
.
Variables
Identifier Names
Identifiers are casesensitive (e.g., var1
, Var1
, and VAR1
are different identifier names), must start with either an uppercase or lowercase letter, and may contain any alphanumeric character including underscore after the first letter. The reserved keywords described later cannot be used as identifier names. Though it is allowed, but not recommended to use builtin functions as an identifier. The only exceptions to this rule are five builtin functions: ‘as.scalar’, ‘as.matrix’, ‘as.double’, ‘as.integer’ and ‘as.logical’.
Examples
A # valid variable name
_A # invalid variable name  starts with underscore
1_A # invalid variable name  starts with number
A_1 # valid variable name
min = 10 # valid but deprecated
Before, proceeding ahead let’s run the Hello World program using variable:
helloStr = "Hello World"
print(helloStr)
As seen in above example, there is no formal declaration of a variable. A variable is created when first assigned a value, and its type is inferred.
Data Types
Three data types (frame, matrix and scalar) and four value types (double, integer, string, and boolean) are supported. Matrices are 2dimensional, and support the double value type (i.e., the cells in a matrix are of type double). The frame data type denotes the tabular data, potentially containing columns of value type numeric, string, and boolean. Frame functions are described in Frames and Data PreProcessing BuiltIn Functions. SystemML supports type polymorphism for both data type (primarily, matrix and scalar types) and value type during evaluation. For example:
# Spoiler alert: matrix() is a builtin function to
# create matrix, which will be discussed later
A = matrix(0, rows=10, cols=10)
B = 10
C = B + sum(A)
print( "B:" + B + ", C:" + C + ", A[1,1]:" + as.scalar(A[1,1]))
In the above script, we create three variables: A
, B
and C
of type matrix
, scalar integer
and scalar double
respectively. Since A
is a matrix
, it has to be converted to scalar using a builtin function as.scalar
. In the above script the operator +
used inside print()
function, performs string concatenation. Hence, the output of above script is as follows:
B:10, C:10.0, A[1,1]:0.0
If instead of as.scalar(A[1,1])
we would have used A[1,1]
, then we will get an compilation error print statement can only print scalars
.
Comments
Two forms of commenting are supported: line and block comments. A line comment is indicated using a hash (#
), and everything to the right of the hash is commented out. A block comment is indicated using “/*
” to start the comment block and “*/
” to end it.
Examples
# this is an example of a line comment
/* this is an example of a
multiline block comment
*/
Expressions
Now that we have familiarized ourselves with variables and data type, let’s understand how to use them in expressions.
Operators
SystemML follows same associativity and precedence order as R as described in below table. The dimensions of the input matrices need to match the operator semantics, otherwise an exception will be raised at compile time. When one of the operands is a matrix and the other operand is a scalar value, the operation is performed cellwise on the matrix using the scalar operand.
Table 1: Operators
Operator  Input  Output  Details 

^  Matrix or Scalar  Matrix or Scalar^{1, 2}  Exponentiation (right associativity) – Highest precedence 
 +  Matrix or Scalar  Matrix or Scalar^{1}  Unary plus, minus 
%*%  Matrix  Matrix  Matrix multiplication 
%/% %%  Matrix or Scalar  Matrix or Scalar^{1, 2}  Integer division and Modulus operator 
/ *  Matrix or Scalar  Matrix or Scalar^{1, 2}  Multiplication and Division 
+   Matrix or Scalar  Matrix or Scalar^{1, 2}  Addition (or string concatenation) and Subtraction 
< > == != <= >=  Matrix or Scalar (any value type)  Matrix or Scalar^{1, 2} (boolean type)  Relational operators 
&  !  Scalar  Scalar  Boolean operators (Note: operators && and  are not supported) 
=      Assignment (Lowest precendence). Note: associativity of assignment “a = b = 3” is not supported 
1 If one of the operands is a matrix, output is matrix; otherwise it is scalar.
2 Support for Matrixvector operations
Example
A = matrix(1, rows=2,cols=2)
B = matrix(3, rows=2,cols=2)
C = 10
D = A %*% B + C * 2.1
print( "D[1,1]:" + as.scalar(D[1,1]))
Since matrix multiplication has higher precedence than scalar multiplication, which in turns has higher precedence than addition, the first cell of matrix D
is evaluated as ((1*3)+(1*3))+(10*2.1) = 27.0
.
MatrixVector Operations
Arithmetic and relational operations described in above table support matrixvector operations. This allows efficient cellwise operations with either row or a column vector.
Syntax
Input_Matrix operation Input_Vector
Example
M + V or M > V, where M is a matrix and V is either row matrix or a column matrix.
MatrixVector operation avoids need for creating replicated matrix for certain subset of operations. For example: to compute class conditional probabilities in NaïveBayes, without support for matrixvector operations, one might write below given inefficient script that creates unnecessary and possibly huge replicatedClassSums.
ones = matrix(1, rows=1, cols=numFeatures)
repClassSums = classSums %*% ones
class_conditionals = (classFeatureCounts + laplace_correction) / repClassSums
With support of matrixvector operations, the above script becomes much more efficient as well as concise:
class_conditionals = (classFeatureCounts + laplace_correction) / classSums
Matrix Indexing
Each matrix has a specified number of rows and columns. A 1x1 matrix is not equivalent to a scalar double. The first index for both row and columns in a matrix is 1. For example, a matrix with 10 rows and 10 columns would have rows numbered 1 to 10, and columns numbered 1 to 10.
The elements of the matrix can be accessed by matrix indexing, with both row and column indices required. The indices must either be an expression evaluating to a positive numeric (integer or double) scalar value, or blank. To select the entire row or column of a matrix, leave the appropriate index blank. If a double value is used for indexing, the index value is implicitly cast to an integer with floor (value+eps) in order to account for double inaccuracy (see IEEE754, double precision, eps=pow(2,53)).
Examples
X[1,4] # access cell in row 1, column 4 of matrix X
X[i,j] # access cell in row i, column j of X.
X[1,] # access the 1st row of X
X[,2] # access the 2nd column of X
X[,] # access all rows and columns of X
Range indexing is supported to access a contiguous block of rows and columns in the matrix. The grammar for rangebased indexing is below. The constraint is that lowerrow < upperrow, and lowercolumn < uppercolumn.
[Matrix name][lowerrow : upperrow],[lowercolumn : uppercolumn]
Examples
X[1:4, 1:4] # access the 4 x 4 submatrix comprising columns 1 – 4 of rows 1 – 4 of X
X[1:4, ] # select the first 4 rows of X
X[1:, ] # incorrect format
Statements
A script is a sequence of statements with the default computation semantics being sequential evaluation of the individual statements. The use of a semicolon at the end of a statement is optional. The types of statements supported are
 assignment,
 control structures (while, if, for), and
 userdefined function declaration.
Assignment Statement
An assignment statement consists of an expression, the result of which is assigned to a variable. The variable gets the appropriate data type (matrix or scalar) and value type (double, int, string, boolean) depending on the type of the variable output by the expression.
Examples
# max_iteration is of type integer
max_iteration = 3;
# V has data type matrix and value type double.
V = W %*% H;
Control Statements
While Statement
The syntax for a while statement is as follows:
while (predicate){
statement1
statement2
...
}
The statements in the while statement body are evaluated repeatedly until the predicate evaluates to true. The while statement body must be surrounded by braces, even if the body only has a single statement. The predicate in the while statement consist of operations on scalar variables and literals. The body of a while statement may contain any sequence of statements.
Example
while( (i < 20) & (!converge) ) {
H = H * (t(W) %*% V) / ( t(W) %*% W %*% H);
W = W * (V %*% t(H) / (W %*% H %*% t(H));
i = i + 1;
}
If Statement
The syntax for an if statement is as follows:
if (predicate1) {
statement1
statement2
...
} [ else if (predicate2){
statement1
statement2
...
} ] [ else {
statement1
statement2
...
} ]
The If statement has three bodies: the if
body (evaluated if predicate1 evaluates to true), the optional else if
body (evaluated if predicate2 evaluates to true) and the optional else
body (evaluated otherwise). There can be multiple else if
bodies with different predicates but at most one else
body. The bodies may contain any sequence of statements. If only a single statement is enclosed in a body, the braces surrounding the statement can be omitted.
Examples
# example of if statement
if( i < 20 ) {
converge = false;
} else {
converge = true;
}
# example of nested control structures
while( !converge ) {
H = H * (t(W) %*% V) / ( t(W) %*% W %*% H);
W = W * (V %*% t(H) / (W %*% H %*% t(H));
i = i + 1;
zerror = sum(z  W %*% H);
if (zerror < maxError) {
converge = true;
} else {
converge = false;
}
}
For Statement
The syntax for a for statement is as follows.
for (var in <for_predicate> ) {
<statement>*
}
<for_predicate> ::= [lower]:[upper]  seq ([lower], [upper], [increment])
var is an integer scalar variable. lower, upper, and increment are integer expressions.
Similarly, seq([lower],[upper],[increment])
defines a sequence of numbers: {lower, lower + increment, lower + 2(increment), … }. For each element in the sequence, var is assigned the value, and statements in the for loop body are executed.
The for loop body may contain any sequence of statements. The statements in the for statement body must be surrounded by braces, even if the body only has a single statement.
Example
# example for statement
A = 5;
for (i in 1:20) {
A = A + 1;
}
ParFor Statement
The syntax and semantics of a parfor
(parallel for
) statement are equivalent to a for
statement except for the different keyword and a list of optional parameters.
parfor (var in <for_predicate> <parfor_paramslist> ) {
<statement>*
}
<parfor_paramslist> ::= <,<parfor_parameter>>*
<parfor_parameter> ::
= check = <dependency_analysis>
 = par = <degree_of_parallelism>
 = mode = <execution_mode>
 = taskpartitioner = <task_partitioning_algorithm>
 = tasksize = <task_size>
 = datapartitioner = <data_partitioning_mode>
 = resultmerge = <result_merge_mode>
 = opt = <optimization_mode>
 = log = <log_level>
 = profile = <monitor>
<dependency_analysis> 0 1
<degree_of_parallelism> arbitrary integer number
<execution_mode> LOCAL REMOTE_MR REMOTE_MR_DP REMOTE_SPARK REMOTE_SPARK_DP
<task_partitioning_algorithm> FIXED NAIVE STATIC FACTORING FACTORING_CMIN FACTORING_CMAX
<task_size> arbitrary integer number
<data_partitioning_mode> NONE LOCAL REMOTE_MR REMOTE_SPARK
<result_merge_mode> LOCAL_MEM LOCAL_FILE LOCAL_AUTOMATIC REMOTE_MR REMOTE_SPARK
<optimization_mode> NONE RULEBASED CONSTRAINED HEURISTIC GREEDY FULL_DP
<log_level> ALL TRACE DEBUG INFO WARN ERROR FATAL OFF
<monitor> 0 1
If any of these parameters is not specified, the following respective defaults are used:
Table 2: Parfor default parameter values
Parameter Name  Default Value 

check  1 
par  [number of virtual processors on master node] 
mode  LOCAL 
taskpartitioner  FIXED 
tasksize  1 
datapartitioner  NONE 
resultmerge  LOCAL_AUTOMATIC 
opt  RULEBASED 
log  INFO 
profile  0 
Of particular note is the check
parameter. SystemML’s parfor
statement by default (check = 1
) performs dependency analysis in an
attempt to guarantee result correctness for parallel execution. For example, the following parfor
statement is incorrect because
the iterations do not act independently, so they are not parallelizable. The iterations incorrectly try to increment the same sum
variable.
sum = 0
parfor(i in 1:3) {
sum = sum + i; # not parallelizable  generates error
}
print(sum)
SystemML’s parfor
dependency analysis can occasionally result in false positives, as in the following example. This example creates a 2x30
matrix. It then utilizes a parfor
loop to write 10 2x3 matrices into the 2x30 matrix. This parfor
statement is parallelizable and correct,
but the dependency analysis generates a false positive dependency error for the variable ms
.
ms = matrix(0, rows=2, cols=3*10)
parfor (v in 1:10) { # parallelizable  false positive
mv = matrix(v, rows=2, cols=3)
ms[,(v1)*3+1:v*3] = mv
}
If a false positive arises but you are certain that the parfor
is parallelizable, the parfor
dependency check can be disabled via
the check = 0
option.
ms = matrix(0, rows=2, cols=3*10)
parfor (v in 1:10, check=0) { # parallelizable
mv = matrix(v, rows=2, cols=3)
ms[,(v1)*3+1:v*3] = mv
}
While developing DML scripts or debugging, it can be useful to turn off parfor
parallelization. This can be accomplished in the following
three ways:
 Replace
parfor()
withfor()
. Sinceparfor
is derived fromfor
, you can always usefor
wherever you can useparfor
. parfor(opt = NONE, par = 1, ...)
. This disables optimization, uses defaults, and overwrites the specified parameters.parfor(opt = CONSTRAINED, par = 1, ...)
. This optimizes using the specified parameters.
UserDefined Function (UDF)
The UDF function declaration statement provides the function signature, which defines the formal parameters used to call the function and return values for the function. The function definition specifies the function implementation, and can either be a sequence of statements or external packages / libraries. If the UDF is implemented in a SystemML script, then UDF declaration and definition occur together.
The syntax for the UDF function declaration is given as follows. The function definition is stored as a list of statements in the function body. The explanation of the parameters is given below. Any statement can be placed inside a UDF definition except UDF function declaration statements. The variables specified in the return clause will be returned, and no explicit return statement within the function body is required.
functionName = function([ <DataType>? <ValueType> <var>, ]* )
return ([ <DataType>? <ValueType> <var>,]*) {
# function body definition in DML
statement1
statement2
...
}
The syntax for the UDF function declaration for functions defined in external packages/ ibraries is given as follows. The parameters are explained below. The main difference is that a user must specify the appropriate collection of userParam=value pairs for the given external package. Also, one of the userParam should be ’classname’.
functionName = externalFunction(
[<DataType>? <ValueType> <var>, ]* )
return ([<DataType>? <ValueType> <var>,]*)
implemented in ([userParam=value]*)
Table 3: Parameters for UDF Function Definition Statements
Parameter Name  Description  Optional  Permissible Values 

functionName  Name of the function.  No  Any nonkeyword string 
DataType  The data type of the identifier for a formal parameter or return value.  If the value value is scalar or object, then DataType is optional  matrix, scalar, object (capitalization does not matter) 
ValueType  The value type of the identifier for a formal parameter or return value.  No. The value type object can only use used with data type object.  double, integer, string, boolean, object 
Var  The identifier for a formal parameter or return value.  No  Any nonkeyword sting 
userParam=value  Userdefined parameter to invoke the package.  Yes  Any nonkeyword string 
Examples
# example of a UDF defined in DML
mean = function (matrix[double] A) return (double m) {
m = sum(A)/nrow(A)
}
# example of a UDF defined in DML with multiple return values
minMax = function( matrix[double] M) return (double minVal, double maxVal) {
minVal = min(M);
maxVal = max(M);
}
# example of an external UDF
time = externalFunction(Integer i) return (Double B)
implemented in (classname="org.apache.sysml.udf.lib.TimeWrapper", exectype="mem");
t = time(1);
print("Time: " + t);
A UDF invocation specifies the function identifier, variable identifiers for calling parameters, and the variables to be populated by the returned values from the function. The syntax for function calls is as follows.
returnVal = functionName( param1, param2, ….)
[returnVal1, returnVal2, ...] = functionName(param1, param2, ….)
Examples
# DML script with a function call
B = matrix(0, rows = 10,cols = 10);
C = matrix(0, rows = 100, cols = 100);
D = addEach(1, C);
index = 0;
while (index < 5) {
[minD, maxD] = minMax(D);
index = index + 1
}
Variable Scoping
DML supports following two types of scoping:
 Default: All the variables are bound to global unbounded scope.
 Function scope: Only the variables specified in the function declaration can be accessed inside function.
Note: The commandline parameters are treated as constants which are introduced during parsetime.
Example of Default Scope
if(1!=0) {
A = 1;
}
print("A:" + A);
This will result in parser warning, but the program will run to completion. If the expression in the “if” predicate would have evaluated to false, it would have resulted in runtime error. Also, functions need not be defined prior to its call. That is: following code will work without parser warning:
A = 2;
C = foo(1, A)
print("C:" + C);
foo = function(double A, double B) return (double C) {
C = A + B;
}
Example of Function Scope
A = 2;
D = 1;
foo = function(double A, double B) return (double C) {
A = 3.0; # value of global A won’t change since it is pass by value
C = A + B # Note: C = A + D will result in compilation error
}
C = foo(A, 1)
print("C:" + C + " A:" + A);
The above code will output: C:4.0 A:2
CommandLine Arguments
Since most algorithms require arguments to be passed from command line, DML supports commandline arguments. The command line parameters are treated as constants (similar to arguments passed to main function of a java program). The command line parameters can be passed in two ways:

As named arguments (recommended):
nvargs param1=7 param2="abc" param3=3.14

As positional arguments (deprecated):
args 7 "abc" 3.14
The named arguments can be accessed by adding “\$” before the parameter name, i.e. \$param1. On the other hand, the positional parameter are accessible by adding “\$” before their positions (starting from index 1), i.e. \$1. A string parameter can be passed without quote. For example, param2=abc
is valid argument, but it is not recommend.
Sometimes the user would want to support default values in case user does not explicitly pass the corresponding command line parameter (in below example: $nbrRows
). To do so, we use the ifdef
function which assigns either command line parameter or the default value to the local parameter.
local_variable = ifdef(command line variable, default value)
Example: Script in file test.dml
localVar_nbrRows=ifdef($nbrRows , 10)
M = rand (rows = localVar_nbrRows, cols = $nbrCols)
write (M, $fname, format="csv")
print("Done creating and writing random matrix in " + $fname)
In above script, ifdef(\$nbrRows, 10)
function is a shorthand for “ifdef(\$nbrRows) then \$nbrRows else 10
”.
Let’s assume that the above script is invoked using following the command line values:
sparksubmit SystemML.jar f test.dml nvargs fname=test.mtx nbrRows=5 nbrCols=5
In this case, the script will create a random matrix M with 5 rows and 5 columns and write it to the file “text.mtx” in csv format. After that it will print the message “Done creating and writing random matrix in test.mtx” on the standard output.
If however, the above script is invoked from the command line using named arguments:
sparksubmit SystemML.jar f test.dml nvargs fname=test.mtx nbrCols=5
Then, the script will instead create a random matrix M with 10 rows (i.e. default value provided in the script) and 5 columns.
It is important to note that the placeholder variables should be treated like constants that are initialized once, either via command linearguments or via default values at the beginning of the script.
Each argValue passed from the commandline has a scalar data type, and the value type for argValue is inferred using the following logic:
if (argValue can be cast as Integer)
Assign argValue integer value type
else if (argValue can be cast as Double)
Assign argValue double value type
else if (argValue can be cast as Boolean)
Assign argValue boolean value type
else
Assign argValue string value type
In above example, the placeholder variable \$nbrCols
will be treated as integer in the script. If however, the command line arguments were “nbrCols=5.0
”, then it would be treated as a double.
NOTE: argName must be a valid identifier. NOTE: If argValue contains spaces, it must be enclosed in doublequotes. NOTE: The values passed from the commandline are passed as literal values which replace the placeholders in the DML script, and are not interpreted as DML.
BuiltIn Functions
Builtin functions are categorized in:
 Matrix Construction, Manipulation, and Aggregation BuiltIn Functions
 Matrix and/or Scalar Comparison BuiltIn Functions
 Casting BuiltIn Functions
 Statistical BuiltIn Functions
 Mathematical and Trigonometric BuiltIn Functions
 Linear Algebra BuiltIn Functions
 Other BuiltIn Functions
The tables below list the supported builtin functions. For example, consider the following expressions:
s = sum(A);
B = rowSums(A);
C = colSums(A);
D = rowSums(C);
diff = s – as.scalar(D);
The builtin function sum
operates on a matrix (say A of dimensionality (m x n)) and returns a scalar value corresponding to the sum of all values in the matrix. The builtin functions rowSums
and colSums
, on the other hand, aggregate values on a perrow and percolumn basis respectively. They output matrices of dimensionality (m x 1) and 1xn, respectively. Therefore, B is a m x 1 matrix and C is a 1 x n matrix. Applying rowSums
on matrix C, we obtain matrix D as a 1 x 1 matrix. A 1 x 1 matrix is different from a scalar; to treat D as a scalar, an explicit as.scalar
operation is invoked in the final statement. The difference between s and as.scalar(D)
should be 0.
Matrix Construction, Manipulation, and Aggregation BuiltIn Functions
Table 4: Matrix Construction, Manipulation, and Aggregation BuiltIn Functions
Function  Description  Parameters  Example 

cbind()  Columnwise matrix concatenation. Concatenates the second matrix as additional columns to the first matrix  Input: (X <matrix>, Y <matrix>) Output: <matrix> X and Y are matrices, where the number of rows in X and the number of rows in Y are the same. 
A = matrix(1, rows=2,cols=3) B = matrix(2, rows=2,cols=3) C = cbind(A,B) print(“Dimensions of C: “ + nrow(C) + “ X “ + ncol(C)) Output: Dimensions of C: 2 X 6 
matrix()  Matrix constructor (assigning all the cells to numeric literals).  Input: (<init>, rows=<value>, cols=<value>) init: numeric literal; rows/cols: number of rows/cols (expression) Output: matrix 
# 10x10 matrix initialized to 0 A = matrix (0, rows=10, cols=10) 
Matrix constructor (reshaping an existing matrix).  Input: (<existing matrix>, rows=<value>, cols=<value>, byrow=TRUE) Output: matrix 
A = matrix (0, rows=10, cols=10) B = matrix (A, rows=100, cols=1) 

Matrix constructor (initializing using string).  Input: (<initialization string>, rows=<value>, cols=<value>) Output: matrix 
A = matrix(“4 3 2 5 7 8”, rows=3, cols=2) Creates a matrix: [ [4, 3], [2, 5], [7, 8] ] 

min() max() 
Return the minimum/maximum cell value in matrix  Input: matrix Output: scalar 
min(X) max(Y) 
min() max() 
Return the minimum/maximum cell values of two matrices, matrix and scalar, or scalar value of two scalars.  Input: matrices or scalars Output: matrix or scalar 
With x,y, z as scalars, and X, Y, Z as matrices: Z = min (X, Y) Z = min (X, y) z = min(x,y) 
nrow(), ncol(), length() 
Return the number of rows, number of columns, or number of cells in matrix or frame respectively.  Input: matrix or frame Output: scalar 
nrow(X) ncol(F) length(X) 
prod()  Return the product of all cells in matrix  Input: matrix Output: scalarj 
prod(X) 
rand()  Generates a random matrix  Input: (rows=<value>, cols=<value>, min=<value>, max=<value>, sparsity=<value>, pdf=<string>, seed=<value>) rows/cols: Number of rows/cols (expression) min/max: Min/max value for cells (either constant value, or variable that evaluates to constant value) sparsity: fraction of nonzero cells (constant value) pdf: “uniform” (min, max) distribution, or “normal” (0,1) distribution; or “poisson” (lambda=1) distribution. string; default value is “uniform”. Note that, for the Poisson distribution, users can provide the mean/lambda parameter as follows: rand(rows=1000,cols=1000, pdf=”poisson”, lambda=2.5). The default value for lambda is 1. seed: Every invocation of rand() internally generates a random seed with which the cell values are generated. One can optionally provide a seed when repeatability is desired. Output: matrix 
X = rand(rows=10, cols=20, min=0, max=1, pdf=”uniform”, sparsity=0.2) The example generates a 10 x 20 matrix, with cell values uniformly chosen at random between 0 and 1, and approximately 20% of cells will have nonzero values. 
rbind()  Rowwise matrix concatenation. Concatenates the second matrix as additional rows to the first matrix  Input: (X <matrix>, Y <matrix>) Output: <matrix> X and Y are matrices, where the number of columns in X and the number of columns in Y are the same. 
A = matrix(1, rows=2,cols=3) B = matrix(2, rows=2,cols=3) C = rbind(A,B) print(“Dimensions of C: “ + nrow(C) + “ X “ + ncol(C)) Output: Dimensions of C: 4 X 3 
removeEmpty()  Removes all empty rows or columns from the input matrix target X according to the specified margin. Also, allows to apply a filter F before removing the empty rows/cols.  Input : (target= X <matrix>, margin=”…”, select=F) Output : <matrix> Valid values for margin are “rows” or “cols”. 
A = removeEmpty(target=X, margin=”rows”, select=F) 
replace()  Creates a copy of input matrix X, where all values that are equal to the scalar pattern s1 are replaced with the scalar replacement s2.  Input : (target= X <matrix>, pattern=<scalar>, replacement=<scalar>) Output : <matrix> If s1 is NaN, then all NaN values of X are treated as equal and hence replaced with s2. Positive and negative infinity are treated as different values. 
A = replace(target=X, pattern=s1, replacement=s2) 
rev()  Reverses the rows in a matrix  Input : (<matrix>) Output : <matrix> 
A = matrix(“1 2 3 4”, rows=2, cols=2) B = matrix(“1 2 3 4”, rows=4, cols=1) C = matrix(“1 2 3 4”, rows=1, cols=4) revA = rev(A) revB = rev(B) revC = rev(C) Matrix revA: [[3, 4], [1, 2]] Matrix revB: [[4], [3], [2], [1]] Matrix revC: [[1, 2, 3, 4]] 
seq()  Creates a single column vector with values starting from <from>, to <to>, in increments of <increment>  Input: (<from>, <to>, <increment>) Output: <matrix> 
S = seq (10, 200, 10) 
sum()  Sum of all cells in matrix  Input: matrix Output: scalar 
sum(X) 
Matrix and/or Scalar Comparison BuiltIn Functions
Table 5: Matrix and/or Scalar Comparison BuiltIn Functions
Function  Description  Parameters  Example 

pmin() pmax() 
“parallel min/max”. Return cellwise minimum/maximum. If the second input is a scalar then it is compared against all cells in the first input. 
Input: (<matrix>, <matrix>), or (<matrix>, <scalar>) Output: matrix 
pmin(X,Y) pmax(X,y) 
rowIndexMax()  Rowwise computation – for each row, find the max value, and return its column index.  Input: (matrix) Output: (n x 1) matrix 
rowIndexMax(X) 
rowIndexMin()  Rowwise computation – for each row, find the minimum value, and return its column index.  Input: (matrix) Output: (n x 1) matrix 
rowIndexMin(X) 
ppred()  “parallel predicate”. The relational operator specified in the third argument is cellwise applied to input matrices. If the second argument is a scalar, then it is used against all cells in the first argument. NOTE: ppred() has been replaced by the relational operators, so its use is discouraged. 
Input: (<matrix>, <matrix>, <string with relational operator>), or (<matrix>, <scalar>, <string with relational operator>) Output: matrix 
ppred(X,Y,”<”) ppred(X,y,”<”) 
Casting BuiltIn Functions
Table 6: Casting BuiltIn Functions
Function  Description  Parameters  Example 

as.scalar(), as.matrix() 
A 1x1 matrix is cast as scalar (value type preserving), and a scalar is cast as 1x1 matrix with value type double  Input: (<matrix>), or (<scalar>) Output: <scalar>, or <matrix> 
as.scalar(X) as.matrix(x) 
as.double(), as.integer(), as.logical() 
A variable is cast as the respective value type, data type preserving. as.integer() performs a safe cast. For numerical inputs, as.logical() returns false if the input value is 0 or 0.0, and true otherwise.  Input: (<scalar>) Output: <scalar> 
as.double(X) as.integer(x) as.logical(y) 
Statistical BuiltIn Functions
Table 7: Statistical BuiltIn Functions
Function  Description  Parameters  Example 

mean() avg() 
Return the mean value of all cells in matrix  Input: matrix Output: scalar 
mean(X) 
var() sd() 
Return the variance/stdDev value of all cells in matrix  Input: matrix Output: scalar 
var(X) sd(X) 
moment()  Returns the kth central moment of values in a column matrix V, where k = 2, 3, or 4. It can be used to compute statistical measures like Variance, Kurtosis, and Skewness. This function also takes an optional weights parameter W.  Input: (X <(n x 1) matrix>, [W <(n x 1) matrix>),] k <scalar>) Output: <scalar> 
A = rand(rows=100000,cols=1, pdf=”normal”) print(“Variance from our (standard normal) random generator is approximately “ + moment(A,2)) 
colSums() colMeans() colVars() colSds() colMaxs() colMins() 
Columnwise computations – for each column, compute the sum/mean/variance/stdDev/max/min of cell values  Input: matrix Output: (1 x n) matrix 
colSums(X) colMeans(X) colVars(X) colSds(X) colMaxs(X) colMins(X) 
cov()  Returns the covariance between two 1dimensional column matrices X and Y. The function takes an optional weights parameter W. All column matrices X, Y, and W (when specified) must have the exact same dimension.  Input: (X <(n x 1) matrix>, Y <(n x 1) matrix> [, W <(n x 1) matrix>)]) Output: <scalar> 
cov(X,Y) cov(X,Y,W) 
table()  Returns the contingency table of two vectors A and B. The resulting table F consists of max(A) rows and max(B) columns. More precisely, F[i,j] = { k  A[k] = i and B[k] = j, 1 ≤ k ≤ n }, where A and B are two ndimensional vectors. This function supports multiple other variants, which can be found below, at the end of this Table 7. 
Input: (<(n x 1) matrix>, <(n x 1) matrix>), [<(n x 1) matrix>]) Output: <matrix> 
F = table(A, B) F = table(A, B, C) And, several other forms (see below Table 7.) 
cdf() pnorm() pexp() pchisq() pf() pt() icdf() qnorm() qexp() qchisq() qf() qt() 
p=cdf(target=q, …) returns the cumulative probability P[X <= q]. q=icdf(target=p, …) returns the inverse cumulative probability i.e., it returns q such that the given target p = P[X<=q]. For more details, please see the section “Probability Distribution Functions” below Table 7. 
Input: (target=<scalar>, dist=”…”, …) Output: <scalar> 
p = cdf(target=q, dist=”normal”, mean=1.5, sd=2); is same as p=pnorm(target=q, mean=1.5, sd=2); q=icdf(target=p, dist=”normal”) is same as q=qnorm(target=p, mean=0,sd=1) More examples can be found in the section “Probability Distribution Functions” below Table 7. 
aggregate()  Splits/groups the values from X according to the corresponding values from G, and then applies the function fn on each group. The result F is a column matrix, in which each row contains the value computed from a distinct group in G. More specifically, F[k,1] = fn( {X[i,1]  1<=i<=n and G[i,1] = k} ), where n = nrow(X) = nrow(G). Note that the distinct values in G are used as row indexes in the result matrix F. Therefore, nrow(F) = max(G). It is thus recommended that the values in G are consecutive and start from 1. This function supports multiple other variants, which can be found below, at the end of this Table 7. 
Input: (target = X <(n x 1) matrix, or matrix>, groups = G <(n x 1) matrix>, fn= “…” [,weights= W<(n x 1) matrix>] [,ngroups=N] ) Output: F <matrix> Note: X is a (n x 1) matrix unless ngroups is specified with no weights, in which case X is a regular (n x m) matrix. The parameter fn takes one of the following functions: “count”, “sum”, “mean”, “variance”, “centralmoment”. In the case of central moment, one must also provide the order of the moment that need to be computed (see example). 
F = aggregate(target=X, groups=G, fn= “…” [,weights = W]) F = aggregate(target=X, groups=G1, fn= “sum”); F = aggregate(target=Y, groups=G2, fn= “mean”, weights=W); F = aggregate(target=Z, groups=G3, fn= “centralmoment”, order= “2”); And, several other forms (see below Table 7.) 
interQuartileMean()  Returns the mean of all x in X such that x>quantile(X, 0.25) and x<=quantile(X, 0.75). X, W are column matrices (vectors) of the same size. W contains the weights for data in X.  Input: (X <(n x 1) matrix> [, W <(n x 1) matrix>)]) Output: <scalar> 
interQuartileMean(X) interQuartileMean(X, W) 
quantile ()  The pquantile for a random variable X is the value x such that Pr[X<x] <= p and Pr[X<= x] >= p let n=nrow(X), i=ceiling(p*n), quantile() will return X[i]. p is a scalar (0<p<1) that specifies the quantile to be computed. Optionally, a weight vector may be provided for X. 
Input: (X <(n x 1) matrix>, [W <(n x 1) matrix>),] p <scalar>) Output: <scalar> 
quantile(X, p) quantile(X, W, p) 
quantile ()  Returns a column matrix with list of all quantiles requested in P.  Input: (X <(n x 1) matrix>, [W <(n x 1) matrix>),] P <(q x 1) matrix>) Output: matrix 
quantile(X, P) quantile(X, W, P) 
median()  Computes the median in a given column matrix of values  Input: (X <(n x 1) matrix>, [W <(n x 1) matrix>),]) Output: <scalar> 
median(X) median(X,W) 
rowSums() rowMeans() rowVars() rowSds() rowMaxs() rowMins() 
Rowwise computations – for each row, compute the sum/mean/variance/stdDev/max/min of cell value  Input: matrix Output: (n x 1) matrix 
rowSums(X) rowMeans(X) rowVars(X) rowSds(X) rowMaxs(X) rowMins(X) 
cumsum()  Column prefixsum (For rowprefix sum, use cumsum(t(X))  Input: matrix Output: matrix of the same dimensions 
A = matrix(“1 2 3 4 5 6”, rows=3, cols=2) B = cumsum(A) The output matrix B = [[1, 2], [4, 6], [9, 12]] 
cumprod()  Column prefixprod (For rowprefix prod, use cumprod(t(X))  Input: matrix Output: matrix of the same dimensions 
A = matrix(“1 2 3 4 5 6”, rows=3, cols=2) B = cumprod(A) The output matrix B = [[1, 2], [3, 8], [15, 48]] 
cummin()  Column prefixmin (For rowprefix min, use cummin(t(X))  Input: matrix Output: matrix of the same dimensions 
A = matrix(“3 4 1 6 5 2”, rows=3, cols=2) B = cummin(A) The output matrix B = [[3, 4], [1, 4], [1, 2]] 
cummax()  Column prefixmax (For rowprefix min, use cummax(t(X))  Input: matrix Output: matrix of the same dimensions 
A = matrix(“3 4 1 6 5 2”, rows=3, cols=2) B = cummax(A) The output matrix B = [[3, 4], [3, 6], [5, 6]] 
sample(range, size, replacement, seed)  Sample returns a column vector of length size, containing uniform random numbers from [1, range]  Input: range: integer size: integer replacement: boolean (Optional, default: FALSE) seed: integer (Optional) Output: Matrix dimensions are size x 1 
sample(100, 5) sample(100, 5, TRUE) sample(100, 120, TRUE) sample(100, 5, 1234) # 1234 is the seed sample(100, 5, TRUE, 1234) 
outer(vector1, vector2, “op”)  Applies element wise binary operation “op” (for example: “<”, “==”, “>=”, “”, “min”) on the all combination of vector. Note: Using “”, we get outer product of two vectors. 
Input: vectors of same size d, string Output: matrix of size d X d 
A = matrix(“1 4”, rows = 2, cols = 1) B = matrix(“3 6”, rows = 1, cols = 2) C = outer(A, B, “<”) D = outer(A, B, “*”) The output matrix C = [[1, 1], [0, 1]] The output matrix D = [[3, 6], [12, 24]] 
Alternative forms of table()
The builtin function table() supports different types of input parameters. These variations are described below:
 Basic form:
F=table(A,B)
As described above in Table 7.  Weighted form:
F=table(A,B,W)
Users can provide an optional third parameter C with the same dimensions as of A and B. In this case, the output F[i,j] = ∑kC[k], where A[k] = i and B[k] = j (1 ≤ k ≤ n).  Scalar form
In basic and weighted forms, both B and W are one dimensional matrices with same number of rows/columns as in A. Instead, one can also passin scalar values in the place of B and W. For example, F=table(A,1) is same as the basic form where B is a matrix with all 1’s. Similarly,
F=table(A,B,3)
is identical to the following two DML statements.
m3 = matrix(3,rows=nrow(A),cols=1);
F = table(A,B,m3);
 Specified Output Size
In the above forms, the dimensions of the matrix produced this function is known only after its execution is complete. Users can precisely control the size of the output matrix via two additional arguments, odim1 and odim2, as shown below:
F = table(A,B,odim1,odim2);
The output F will have exactlyodim1
rows andodim2
columns. F may be a truncated or padded (with zeros) version of the output produced bytable(A,B)
– depending on the values ofmax(A)
andmax(B)
. For example, ifmax(A) < odim1
then the last (odim1max(A)
) rows will have zeros.
Alternative forms of aggregate()
The builtin function aggregate() supports different types of input parameters. These variations are described below:
 Basic form:
F=aggregate(target=X, groups=G, fn="sum")
As described above in Table 7.  Weighted form:
F=aggregate(target=X, groups=G, weights=W, fn="sum")
Users can provide an optional parameter W with the same dimensions as of A and B. In this case, fn computes the weighted statistics over values from X, which are grouped by values from G.  Specified Output Size
As noted in Table 7, the number of rows in the output matrix F is equal to the maximum value in the grouping matrix G. Therefore, the dimensions of F are known only after its execution is complete. When needed, users can precisely control the size of the output matrix via an additional argument,
ngroups
, as shown below:
F = aggregate(target=X, groups=G, fn="sum", ngroups=10);
The output F will have exactly 10 rows and 1 column. F may be a truncated or padded (with zeros) version of the output produced byaggregate(target=X, groups=G, fn="sum")
– depending on the values ofngroups
andmax(G)
. For example, ifmax(G) < ngroups
then the last (ngroupsmax(G)
) rows will have zeros.
Probability Distribution Functions
p = cdf(target=q, dist=fn, ..., lower.tail=TRUE)
This computes the cumulative probability at the given quantile i.e., P[X<=q], where X is random variable whose distribution is specified via string argument fn.
target
: input quantile at which cumulative probability P[X<=q] is computed, where X is random variable whose distribution is specified via string argument fn. This is a mandatory argument.dist
: name of the distribution specified as a string. Valid values are “normal” (for Normal or Gaussian distribution), “f” (for F distribution), “t” (for Student tdistribution), “chisq” (for Chi Squared distribution), and “exp” (for Exponential distribution). This is a mandatory argument....
: parameters of the distribution For
dist="normal"
, valid parameters are mean and sd that specify the mean and standard deviation of the normal distribution. The default values for mean and sd are 0.0 and 1.0, respectively.  For
dist="f"
, valid parameters are df1 and df2 that specify two degrees of freedom. Both these parameters are mandatory.  For
dist="t"
, and dist=”chisq”, valid parameter is df that specifies the degrees of freedom. This parameter is mandatory.  For
dist="exp"
, valid parameter is rate that specifies the rate at which events occur. Note that the mean of exponential distribution is 1.0/rate. The default value is 1.0.
 For
Lower.tail
: a Boolean value with default set to TRUE. cdf() computes P[X<=q] when lower.tail=TRUE and it computes P[X>q] when lower.tail=FALSE. In other words, a complement of the cumulative distribution is computed when lower.tail=FALSE.
q = icdf(target=p, dist=fn, ...)
This computes the inverse cumulative probability i.e., it computes a quantile q such that the given probability p = P[X<=q], where X is random variable whose distribution is specified via string argument fn.
target
: a mandatory argument that specifies the input probability.dist
: name of the distribution specified as a string. Same as that in cdf()....
: parameters of the distribution. Same as those in cdf().
Alternative to cdf()
and icdf()
, users can also use distributionspecific functions. The functions pnorm()
, pf()
, pt()
, pchisq()
, and pexp()
computes the cumulative probabilities for Normal, F, t, Chi Squared, and Exponential distributions, respectively. Appropriate distribution parameters must be provided for each function. Similarly, qnorm()
, qf()
, qt()
, qchisq()
, and qexp()
compute the inverse cumulative probabilities for Normal, F, t, Chi Squared, and Exponential distributions.
Following pairs of DML statements are equivalent.
p = cdf(target=q, dist="normal", mean=1.5, sd=2);
is same as
p=pnorm(target=q, mean=1.5, sd=2);
p = cdf(target=q, dist="exp", rate=5);
is same as
pexp(target=q,rate=5);
p = cdf(target=q, dist="chisq", df=100);
is same as
pchisq(target=q, df=100)
p = cdf(target=q, dist="f", df1=100, df2=200);
is same as
pf(target=q, df1=100, df2=200);
p = cdf(target=q, dist="t", df=100);
is same as
pt(target=q, df=100)
p = cdf(target=q, dist="normal", lower.tail=FALSE);
is same as
p=pnorm(target=q, lower.tail=FALSE);
is same as
p=pnorm(target=q, mean=0, sd=1.0, lower.tail=FALSE);
is same as
p=pnorm(target=q, sd=1.0, lower.tail=FALSE);
Examples of icdf():
q=icdf(target=p, dist="normal");
is same as
q=qnorm(target=p, mean=0,sd=1);
q=icdf(target=p, dist="exp");
is same as
q=qexp(target=p, rate=1);
q=icdf(target=p, dist="chisq", df=50);
is same as
qchisq(target=p, df=50);
q=icdf(target=p, dist="f", df1=50, df2=25);
is same as
qf(target=p, , df1=50, df2=25);
q=icdf(target=p, dist="t", df=50);
is same as
qt(target=p, df=50);
Mathematical and Trigonometric BuiltIn Functions
Table 8: Mathematical and Trigonometric BuiltIn Functions
Function  Description  Parameters  Example 

exp(), log(), abs(), sqrt(), round(), floor(), ceil()  Apply mathematical function on input (cell wise if input is matrix)  Input: (<matrix>), or (<scalar>) Output: <matrix>, or <scalar> 
sqrt(X) log(X,y) round(X) floor(X) ceil(X) 
sin(), cos(), tan(), asin(), acos(), atan()  Apply trigonometric function on input (cell wise if input is matrix)  Input: (<matrix>), or (<scalar>) Output: <matrix>, or <scalar> 
sin(X) 
sign()  Returns a matrix representing the signs of the input matrix elements, where 1 represents positive, 0 represents zero, and 1 represents negative  Input : (A <matrix>) Output : <matrix> 
A = matrix(“5 0 3 3”, rows=2, cols=2) signA = sign(A) Matrix signA: [[1, 0], [1, 1]] 
Linear Algebra BuiltIn Functions
Table 9: Linear Algebra BuiltIn Functions
Function  Description  Parameters  Example 

cholesky()  Computes the Cholesky decomposition of symmetric input matrix A  Input: (A <matrix>) Output: <matrix> 
A = matrix(“4 12 16 12 37 43 16 43 98”, rows=3, cols=3) B = cholesky(A) Matrix B: [[2, 0, 0], [6, 1, 0], [8, 5, 3]] 
diag()  Create diagonal matrix from (n x 1) matrix, or take diagonal from square matrix  Input: (n x 1) matrix, or (n x n) matrix Output: (n x n) matrix, or (n x 1) matrix 
D = diag(matrix(1.0, rows=3, cols=1)) E = diag(matrix(1.0, rows=3, cols=3)) 
eigen()  Computes Eigen decomposition of input matrix A. The Eigen decomposition consists of two matrices V and w such that A = V %*% diag(w) %*% t(V). The columns of V are the eigenvectors of the original matrix A. And, the eigen values are given by w. It is important to note that this function can operate only on smalltomedium sized input matrix that can fit in the main memory. For larger matrices, an outofmemory exception is raised. 
Input : (A <matrix>) Output : [w <(m x 1) matrix>, V <matrix>] A is a square symmetric matrix with dimensions (m x m). This function returns two matrices w and V, where w is (m x 1) and V is of size (m x m). 
[w, V] = eigen(A) 
lu()  Computes Pivoted LU decomposition of input matrix A. The LU decomposition consists of three matrices P, L, and U such that P %*% A = L %*% U, where P is a permutation matrix that is used to rearrange the rows in A before the decomposition can be computed. L is a lowertriangular matrix whereas U is an uppertriangular matrix. It is important to note that this function can operate only on smalltomedium sized input matrix that can fit in the main memory. For larger matrices, an outofmemory exception is raised. 
Input : (A <matrix>) Output : [<matrix>, <matrix>, <matrix>] A is a square matrix with dimensions m x m. This function returns three matrices P, L, and U, all of which are of size m x m. 
[P, L, U] = lu(A) 
qr()  Computes QR decomposition of input matrix A using Householder reflectors. The QR decomposition of A consists of two matrices Q and R such that A = Q%*%R where Q is an orthogonal matrix (i.e., Q%*%t(Q) = t(Q)%*%Q = I, identity matrix) and R is an upper triangular matrix. For efficiency purposes, this function returns the matrix of Householder reflector vectors H instead of Q (which is a large m x m potentially dense matrix). The Q matrix can be explicitly computed from H, if needed. In most applications of QR, one is interested in calculating Q %*% B or t(Q) %*% B – and, both can be computed directly using H instead of explicitly constructing the large Q matrix. It is important to note that this function can operate only on smalltomedium sized input matrix that can fit in the main memory. For larger matrices, an outofmemory exception is raised. 
Input : (A <matrix>) Output : [<matrix>, <matrix>] A is a (m x n) matrix, which can either be a square matrix (m=n) or a rectangular matrix (m != n). This function returns two matrices H and R of size (m x n) i.e., same size as of the input matrix A. 
[H, R] = qr(A) 
solve()  Computes the least squares solution for system of linear equations A %*% x = b i.e., it finds x such that A%*%x – b is minimized. The solution vector x is computed using a QR decomposition of A. It is important to note that this function can operate only on smalltomedium sized input matrix that can fit in the main memory. For larger matrices, an outofmemory exception is raised. 
Input : (A <(m x n) matrix>, b <(m x 1) matrix>) Output : <matrix> A is a matrix of size (m x n) and b is a 1D matrix of size m x 1. This function returns a 1D matrix x of size n x 1. 
x = solve(A,b) 
t()  Transpose matrix  Input: matrix Output: matrix 
t(X) 
trace()  Return the sum of the cells of the main diagonal square matrix  Input: matrix Output: scalar 
trace(X) 
Read/Write BuiltIn Functions
The read
and write
functions support the reading and writing of matrices and scalars from/to the file system
(local or HDFS). Typically, associated with each data file is a JSONformatted metadata file (MTD) that stores
metadata information about the content of the data file, such as the number of rows and columns.
For data files written by SystemML, an MTD file will automatically be generated. The name of the
MTD file associated with <filename>
must be <filename.mtd>
. In general, it is highly recommended
that users provide MTD files for their own data as well.
Note: Metadata can also be passed as parameters to read
and write
function calls.
File formats and MTD files
SystemML supports 4 file formats:
 CSV (delimited)
 Matrix Market (coordinate)
 Text (i,j,v)
 Binary
The CSV format is a standard textbased format where columns are separated by delimiter characters, typically commas, and rows are represented on separate lines.
SystemML supports the Matrix Market coordinate format, which is a textbased, spaceseparated format used to represent sparse matrices. Additional information about the Matrix Market format can be found at http://math.nist.gov/MatrixMarket/formats.html#MMformat. SystemML does not currently support the Matrix Market array format for dense matrices. In the Matrix Market coordinate format, metadata (the number of rows, the number of columns, and the number of nonzero values) are included in the data file. Rows and columns index from 1. Matrix Market data must be in a single file, whereas the (i,j,v) text format can span multiple part files on HDFS. Therefore, for scalability reasons, the use of the (i,j,v) text and binary formats is encouraged when scaling to big data.
The (i,j,v) format is a textbased sparse format in which the cell values of a matrix are serialized in spaceseparated triplets of rowId, columnId, and cellValue, with the rowId and columnId indices being 1based. This is similar to the Matrix Market coordinate format, except metadata is stored in a separate file rather than in the data file itself, and the (i,j,v) text format can span multiple part files.
The binary format can only be read and written by SystemML.
Let’s look at a matrix and examples of its data represented in the supported formats with corresponding metadata. In the table below, we have a matrix consisting of 4 rows and 3 columns.
Table 10: Matrix
1.0  2.0  3.0 
0  0  0 
7.0  8.0  9.0 
0  0  0 
Below, we have examples of this matrix in the CSV, Matrix Market, IJV, and Binary formats, along with corresponding metadata.
1.0,2.0,3.0
0,0,0
7.0,8.0,9.0
0,0,0
{
"data_type": "matrix",
"value_type": "double",
"rows": 4,
"cols": 3,
"nnz": 6,
"format": "csv",
"header": false,
"sep": ",",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
%%MatrixMarket matrix coordinate real general
4 3 6
1 1 1.0
1 2 2.0
1 3 3.0
3 1 7.0
3 2 8.0
3 3 9.0
1 1 1.0
1 2 2.0
1 3 3.0
3 1 7.0
3 2 8.0
3 3 9.0
{
"data_type": "matrix",
"value_type": "double",
"rows": 4,
"cols": 3,
"nnz": 6,
"format": "text",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
Binary is not a textbased format.
{
"data_type": "matrix",
"value_type": "double",
"rows": 4,
"cols": 3,
"rows_in_block": 1000,
"cols_in_block": 1000,
"nnz": 6,
"format": "binary",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
As another example, here we see the content of the MTD file scalar.mtd
associated with a scalar data file scalar
that contains the scalar value 2.0.
{
"data_type": "scalar",
"value_type": "double",
"format": "text",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
Metadata is represented as an MTD file that contains a single JSON object with the attributes described below.
Table 11: MTD attributes
Parameter Name  Description  Optional  Permissible values  Data type valid for 

data_type 
Indicates the data type of the data  Yes. Default value is matrix if not specified 
matrix , scalar 
matrix , scalar 
value_type 
Indicates the value type of the data  Yes. Default value is double if not specified 
double , int , string , boolean . Must be double when data_type is matrix 
matrix , scalar 
rows 
Number of rows in matrix 
Yes – only when format is csv 
any integer > 0 
matrix 
cols 
Number of columns in matrix 
Yes – only when format is csv 
any integer > 0 
matrix 
rows_in_block , cols_in_block 
Valid only for binary format. Indicates dimensions of blocks 
No. Only valid if matrix is in binary format 
any integer > 0 
matrix in binary format. Valid only when binary format 
nnz 
Number of nonzero values  Yes  any integer > 0 
matrix 
format 
Data file format  Yes. Default value is text 
csv , mm , text , binary 
matrix , scalar . Formats csv and mm are applicable only to matrices 
description 
Description of the data  Yes  Any valid JSON string or object  matrix , scalar 
author 
User that created the metadata file, defaults to SystemML 
N/A  N/A  N/A 
created 
Date/time when metadata file was written  N/A  N/A  N/A 
In addition, when reading or writing CSV files, the metadata may contain one or more of the following five attributes.
Note that this metadata can be specified as parameters to the read
and write
function calls.
Table 12: Additional MTD attributes when reading/writing CSV files
Parameter Name  Description  Optional  Permissible values  Data type valid for 

header 
Specifies whether the data file has a header. If the header exists, it must be the first line in the file.  Yes, default value is false . 
true /false (TRUE /FALSE in DML) 
matrix 
sep 
Specifies the separator (delimiter) used in the data file. Note that using a delimiter composed of just numeric values or a period (decimal point) can be ambiguous and may lead to unexpected results.  Yes, default value is “, ” (comma) 
string  matrix 
fill 
Only valid when reading CSV files. It specifies whether or not to fill the empty fields in the input file. Empty fields are denoted by consecutive separators (delimiters). If fill is true then every empty field is filled with the value specified by the “default” attribute. An exception is raised if fill is false and the input file has one or more empty fields. 
Yes, default is true . 
true /false (TRUE /FALSE in DML) 
matrix 
default 
Only valid when reading CSV files and fill is true . It specifies the special value with which all empty values are filled while reading the input matrix. 
Yes, default value is 0 
any double  matrix 
sparse 
Only valid when writing CSV files. It specifies whether or not to explicitly output zero (0 ) values. Zero values are written out only when sparse=FALSE . 
Yes, default value is FALSE . 
TRUE /FALSE in DML 
matrix 
Furthermore, the following additional notes apply when reading and writing CSV files.
 Every line in the input file must have the same number of fields or values.
 The input file can only contain numeric values separated by the delimiter (as specified by
sep
).  While writing CSV files, if
header=TRUE
is specified as a parameter to thewrite
function, then the header line is formed as a concatenated string of column names separated by delimiters. Columns are of the formC<column_id>
. For a matrix with 5 columns, the header line would look like:C1,C2,C3,C4,C5
(assumingsep=","
).
Read BuiltIn Function
The syntax of the read
statement is as follows:
read("inputfile", [additional parameters])
where "inputfile"
(also known as iofilename
) is the path to the data file in the file system. The list of parameters is the same as the metadata attributes
provided in MTD files. For the "inputfile"
parameter, the user can use constant string concatenation to give the full path of the
file, where “+” is used as the concatenation operator. However, the file path must evaluate to a constant string at compile time. For
example, "/my/dir" + "filename.mtx"
is valid parameter but "/my/dir" + "filename" + i + ".mtx"
is not (where i
is a variable).
The user has the option of specifying each parameter value in the MTD file, the read
function invocation, or in both locations.
However, parameter values specified in both the read
invocation and the MTD file must have the same value. Also, if a scalar value is being read,
then format
cannot be specified.
The read
invocation in SystemML is parameterized as follows during compilation.
 Default values are assigned to parameters.
 Parameters provided in
read()
either fill in values or override defaults.  SystemML will look for the MTD file at compile time in the specified location (at the same path as the data file, where the filename of the MTD file is the same name as the data file with the extension
.mtd
).  If all nonoptional parameters aren’t specified or conflicting values are detected, then an exception is thrown.
Examples
# Read a matrix with path "in/v.ijv".
# Defaults for data_type and value_type are used.
V = read("in/v.ijv", rows=10, cols=8, format="text");
# Read a matrix with path "in/v.ijv".
# The user specifies "in/" as the directory and "v" as
# the file name and uses constant string concatenation.
dir = "in/";
file = "v.ijv";
V = read(dir + file, rows=10, cols=8, format="text");
# Read a matrix data file with an MTD file available
# (MTD file path: in/data.ijv.mtd)
V = read("in/data.ijv");
# Read a csv matrix data file with no header, comma as
# separator, 3 rows, and 3 columns.
V = read("m.csv", format="csv", header=FALSE, sep=",", rows=3, cols=3);
# Read a csv matrix data file with an MTD file available
# (MTD file: m.csv.mtd)
V = read("m.csv");
# Read a scalar integer value from file "in/scalar"
V = read("in/scalar", data_type="scalar", value_type="int");
Additionally, readMM()
and read.csv()
are supported and can be used instead of specifying format="mm"
or format="csv"
in the read()
function.
Write BuiltIn Function
The write
method is used to persist scalar
and matrix
data to files in the local file system or HDFS. The syntax of write
is shown below.
The parameters are described in Table 13. Note that the set of supported parameters for write
is NOT the same as for read
.
SystemML writes an MTD file for the written data.
write(identifier, "outputfile", [additional parameters])
The user can use constant string concatenation in the "outputfile"
parameter to give the full path of the file, where +
is used as the concatenation operator.
Table 13: Parameters for write()
method
Parameter Name  Description  Optional  Permissible Values 

identifier 
Variable whose data is to be written to a file. Data can be matrix or scalar . 
No  Any variable name 
"outputfile" 
The path to the data file in the file system  No  Any valid filename 
[additional parameters] 
See Tables 11 and 12 
Examples
Write V
matrix to file out/file.ijv
in the default text
format. This also creates the metadata file out/file.ijv.mtd
.
write(V, "out/file.ijv");
Example content of out/file.ijv.mtd
:
{
"data_type": "matrix",
"value_type": "double",
"rows": 10,
"cols": 8,
"nnz": 4,
"format": "text",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
Write V
to out/file
in binary
format:
write(V, "out/file", format="binary");
Example content of out/file.mtd
:
{
"data_type": "matrix",
"value_type": "double",
"rows": 10,
"cols": 8,
"nnz": 4,
"rows_in_block": 1000,
"cols_in_block": 1000,
"format": "binary",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
Write V
to n.csv
in csv
format with column headers, ";"
as delimiter, and zero values are not written.
write(V, "n.csv", format="csv", header=TRUE, sep=";", sparse=TRUE);
Example content of n.csv.mtd
:
{
"data_type": "matrix",
"value_type": "double",
"rows": 3,
"cols": 3,
"nnz": 9,
"format": "csv",
"header": true,
"sep": ";",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
Write x
integer value to file out/scalar_i
write(x, "out/scalar_i");
Example content of out/scalar_i.mtd
:
{
"data_type": "scalar",
"value_type": "int",
"format": "text",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
Unlike read
, the write
function does not need a constant string expression, so the following example will work:
A = rand(rows=10, cols=2);
dir = "tmp/";
i = 1;
file = "A" + i + ".mtx";
write(A, dir + file, format="csv");
The description
parameter can be used to attach a description to the metadata:
A = matrix("1 2 3 4", rows=2, cols=2)
write(A, "mymatrix.csv", format="csv", description="my matrix")
This will generate the following mymatrix.csv.mtd
metadata file:
{
"data_type": "matrix",
"value_type": "double",
"rows": 2,
"cols": 2,
"nnz": 4,
"format": "csv",
"header": false,
"sep": ",",
"description": "my matrix",
"author": "SystemML",
"created": "20170101 00:00:01 PST"
}
Data PreProcessing BuiltIn Functions
The data preprocessing builtin transform()
function is used to transform a given tabular input data set (with data type frame
) in CSV format into a matrix
. The transform()
function supports the following six columnlevel data transformations:
 Omitting: Given a list of columns, this transformation removes all rows which contain missing values for at least one of the specified columns.
 Missing Value Imputation: This replaces missing data in individual columns with valid values, depending on the specific imputation method. There are three supported imputation methods –
global_mean
that replaces a missing value in a numeric/scale column with the mean of all nonmissing entries in the column;global_mode
that replaces a missing value in a categorical column with the mode of all nonmissing entries in the column; andconstant
that replaces missing values in a scale/categorical column with the specified constant.  Recoding: This is applicable for categorical columns. It maps all distinct categories (potentially, strings and booleans) in the column into consecutive numbers, starting from 1. For example, a
direction
column with four distinct values (east, west, north, south) into a column with four numeric values 1.0, 2.0, 3.0, and 4.0.  Binning: This procedure is used to group a number of continuous values (i.e., discretize) into a small number of bins. For example, a column with
age
values can be discretized into a small number of age intervals. The only method that is currently supported isequiwidth
binning.  Dummycoding: This procedure transforms a categorical column into multiple columns of zeros and ones, which collectively capture the full information about the categorical variable. The number of resulting columns is equal to the number of distinct values in the input column. In the example of the
direction
variable mentioned above, this procedure replaces the original column with four new columns with zeros and ones –direction_east
,direction_west
,direction_north
, anddirection_south
.  Scaling: This centers and/or scales the values in a given numeric/continuous column. The two supported methods are
meansubtraction
that centers each value by subtracting the mean, andzscore
that scales mean subtracted values by dividing them with the respective columnwise standard deviation.
The transformations are specified to operate on individual columns. The set of all required transformations across all the columns in the input data must be provided via a specification file in JSON format. Furthermore, the notation indicating missing values must be specified using the na.strings
property in the mtd
file associated with the input CSV data, along with other properties such as header
and sep
(the delimiter). Note that the delimiter cannot be part of any value. For example, if a “,” (comma) is part of any value, then it cannot be used a delimiter. Users must choose a different sep
value (e.g., a tab “\t”).
The following table indicates which transformations can be used simultaneously on a single column.
Table 14: Data transformations that can be used simultaneously.
OMIT  MVI  RCD  BIN  DCD  SCL  

OMIT    x  *  *  *  * 
MVI  x    *  *  *  * 
RCD  *  *    x  *  x 
BIN  *  *  x    *  x 
DCD  *  *  *  *    x 
SCL  *  *  x  x  x   
Key  Meaning 

OMIT  Missing value handling by omitting 
MVI  Missing value handling by imputation 
RCD  Recoding 
BIN  Binning 
DCD  Dummycoding 
SCL  Scaling 
Key  Meaning 

*  Combination is allowed 
x  Combination is invalid 
  Combination is not applicable 
The transform()
function signature is shown here:
output = transform(target = input,
transformSpec = "/path/to/transformation/specification",
transformPath = "/path/to/transformation/metadata",
applyTransformPath = "/path/to/transformation/metadata")
The target
parameter points to the input tabular data that needs to be transformed, the transformSpec
parameter refers to the transformation specification JSON file indicating the list of transformations that must be performed, and transformPath
denotes the output directory at which all the resulting metadata constructed during the transformation process is stored. Examples of such metadata include the number of distinct values in a categorical column, the list of distinct values and associated recoded IDs, the bin definitions (number of bins, bin widths), etc. This metadata can subsequently be utilized to transform new incoming data, for example, the test set in a predictive modeling exercise. The parameter applyTransformPath
refers to existing transformation metadata which was generated by some earlier invocation of the transform()
function. Therefore, in any invocation of transform()
, only transformSpec
or applyTransformPath
can be specified. The transformation metadata is generated when transformSpec
is specified, and it is used and applied when applyTransformPath
is specified. On the other hand, the transformPath
always refers to a location where the resulting transformation metadata is stored.
The transform()
function returns the actual transformed data in the form of a matrix, containing only numeric values.
As an example of the transform()
function, consider the following data.csv
file that represents a sample of homes data.
Table 15: The data.csv
homes data set
zipcode  district  sqft  numbedrooms  numbathrooms  floors  view  saleprice  askingprice 

95141  south  3002  6  3  2  FALSE  929  934 
NA  west  1373  1  3  FALSE  695  698  
91312  south  NA  6  2  2  FALSE  902  
94555  NA  1835  3  3  888  892  
95141  west  2770  5  2.5  TRUE  812  816  
95141  east  2833  6  2.5  2  TRUE  927  
96334  NA  1339  6  3  1  FALSE  672  675 
96334  south  2742  6  2.5  2  FALSE  872  876 
96334  north  2195  5  2.5  2  FALSE  799  803 
Note that the missing values are denoted either by an empty value or as a String
“NA”. This information must be captured via the na.strings
property in the metadata file associated with the input data. In this example, the data is stored in CSV format with “,” as the delimiter (the sep
property). Recall that the delimiter cannot be part of any value. The metadata file data.csv.mtd
looks as follows:
{
"data_type": "frame",
"format": "csv",
"sep": ",",
"header": true,
"na.strings": [ "NA", "" ]
}
An example transformation specification file data.spec.json
is given below:
{
"omit": [ "zipcode" ]
,"impute":
[ { "name": "district" , "method": "constant", "value": "south" }
,{ "name": "numbedrooms" , "method": "constant", "value": 2 }
,{ "name": "numbathrooms", "method": "constant", "value": 1 }
,{ "name": "floors" , "method": "constant", "value": 1 }
,{ "name": "view" , "method": "global_mode" }
,{ "name": "askingprice" , "method": "global_mean" }
,{ "name": "sqft" , "method": "global_mean" }
]
,"recode":
[ "zipcode", "district", "numbedrooms", "numbathrooms", "floors", "view" ]
,"bin":
[ { "name": "saleprice" , "method": "equiwidth", "numbins": 3 }
,{ "name": "sqft" , "method": "equiwidth", "numbins": 4 }
]
,"dummycode":
[ "district", "numbathrooms", "floors", "view", "saleprice", "sqft" ]
}
The following DML utilizes the transform()
function.
D = read("/user/ml/data.csv");
tfD = transform(target=D,
transformSpec="/user/ml/data.spec.json",
transformPath="/user/ml/datatransformation");
s = sum(tfD);
print("Sum = " + s);
The transformation specification file can also utilize column numbers rather than than column names by setting
the ids
property to true.
The following data.spec2.json
specification file is the equivalent of the
aforementioned data.spec.json
file but with column numbers
rather than column names.
{
"ids": true
,"omit" : [ 1 ]
,"impute":
[ { "id": 2, "method": "constant", "value": "south" }
,{ "id": 4, "method": "constant", "value": 2 }
,{ "id": 5, "method": "constant", "value": 1 }
,{ "id": 6, "method": "constant", "value": 1 }
,{ "id": 7, "method": "global_mode" }
,{ "id": 9, "method": "global_mean" }
,{ "id": 3, "method": "global_mean" }
]
,"recode": [ 1, 2, 4, 5, 6, 7 ]
,"bin":
[ { "id": 8, "method": "equiwidth", "numbins": 3 }
,{ "id": 3, "method": "equiwidth", "numbins": 4 }
]
,"dummycode": [ 2, 5, 6, 7, 8, 3 ]
}
As a further JSON transformation specification example, the following data.spec3.json
file specifies scaling transformations on three columns.
{
"omit": [ "zipcode" ]
,"impute":
[ { "name": "district" , "method": "constant", "value": "south" }
,{ "name": "numbedrooms" , "method": "constant", "value": 2 }
,{ "name": "numbathrooms", "method": "constant", "value": 1 }
,{ "name": "floors" , "method": "constant", "value": 1 }
,{ "name": "view" , "method": "global_mode" }
,{ "name": "askingprice" , "method": "global_mean" }
,{ "name": "sqft" , "method": "global_mean" }
]
,"recode":
[ "zipcode", "district", "numbedrooms", "numbathrooms", "floors", "view" ]
,"dummycode":
[ "district", "numbathrooms", "floors", "view" ]
,"scale":
[ { "name": "sqft", "method": "meansubtraction" }
,{ "name": "saleprice", "method": "zscore" }
,{ "name": "askingprice", "method": "zscore" }
]
}
The following code snippet shows an example scenario of transforming a training data set and subsequently testing the data set.
Training Phase
Train = read("/user/ml/trainset.csv");
trainD = transform(target=Train,
transformSpec="/user/ml/tf.spec.json",
transformPath="/user/ml/train_tf_metadata");
# Build a predictive model using trainD
...
Testing Phase
Test = read("/user/ml/testset.csv");
testD = transform(target=Test,
transformPath="/user/ml/test_tf_metadata",
applyTransformPath="/user/ml/train_tf_metdata");
# Test the model using testD
...
Note that the metadata generated during the training phase (located at /user/ml/train_tf_metadata
) is used to apply the list of transformations (that were carried out on training data set) on the test data set. Since the second invocation of transform()
does not really generate any new metadata data, the given metadata (/user/ml/train_tf_metdata
) is copied to the target location (/user/ml/test_tf_metdata
). Even though such a behavior creates redundant copies of transformation metadata, it is preferred as it allows the association of every data set with the corresponding transformation metadata.
Other BuiltIn Functions
Table 16: Other BuiltIn Functions
Function  Description  Parameters  Example 

append()  Append a string to another string separated by “\n” Limitation: The string may grow up to 1 MByte. 
Input: (<string>, <string>) Output: <string> 
s = “iter=” + i i = i + 1 s = append(s, “iter=” + i) write(s, “s.out”) 
toString()  Formats a Matrix or Frame object into a string. “rows” & “cols” : number of rows and columns to print “decimal” : number of digits after the decimal “sparse” : set to true to print Matrix object in sparse format, i.e. RowIndex ColIndex Value “sep” and “linesep” : interelement separator and the line separator strings 
Input : (<matrix> or <frame>, rows=100, cols=100, decimal=3, sparse=FALSE, sep=” “, linesep=”\n”) Output: <string> 
X = matrix(seq(1, 9), rows=3, cols=3) str = toString(X, sep=”  “) F = as.frame(X) print(toString(F, rows=2, cols=2)) 
print()  Prints a scalar variable. The print() function allows printfstyle formatting by optionally allowing multiple arguments, where the first argument is the string that specifies the formatting and the additional arguments are the arguments to format.  Input: <scalar> or <string, args…> 
print(“hello”) print(“hello” + “world”) print(“value of x is “ + x ) a=’hello’; b=3; c=4.5; d=TRUE; print(‘%s %d %f %b’, a, b, c, d); a=’hello’; b=’goodbye’; c=4; d=3; e=3.0; f=5.0; g=FALSE; print(‘%s %d %f %b’, (a+b), (cd), (e*f), !g); 
stop()  Halts the execution of DML program by printing the message that is passed in as the argument. Note that the use of stop() is not allowed inside a parfor loop. 
Input: (<scalar>)  stop(“Inputs to DML program are invalid”) stop(“Class labels must be either 1 or +1”) 
order()  Sort a column of the matrix X in decreasing/increasing order and return either index (index.return=TRUE) or data (index.return=FALSE).  Input: (target=X, by=column, decreasing, index.return)  order(X, by=1, decreasing=FALSE, index.return=FALSE) 
Frames
The frame
data type represents tabular data. In contrast to a matrix
, whose element values are of type double
, a frame
can be associated with a schema to specify additional value types. Frames can be read from and written to files and support both left and right indexing. Builtin functions are provided to convert between frames and matrices. Advanced transform operations can also be applied. Note that frames are only supported for standalone and spark modes.
Creating Frames
To create a frame
, specify data_type="frame"
when reading data from a file. Input formats csv, text, and binary are supported.
A = read("fileA", data_type="frame", rows=10, cols=8);
B = read("dataB", data_type="frame", rows=3, cols=3, format="csv");
A schema can be specified when creating a frame
where the schema is a string containing a value type per column. The supported value types for a schema are string
, double
, int
, boolean
. Note schema=""
resolves to a string schema and if no schema is specified, the default is ""
.
This example shows creating a frame with schema="string,double,int,boolean"
since the data has four columns (one of each supported value type).
tableSchema = "string,double,int,boolean";
C = read("tableC", data_type="frame", schema=tableSchema, rows=1600, cols=4, format="csv");
Note: the header line in frame CSV files is sensitive to white spaces.
For example, CSV1 with header ID,FirstName,LastName
results in three columns with tokens between separators. In contrast, CSV2 with header ID, FirstName,LastName
also results in three columns but the second column has a space preceding FirstName
. This extra space is significant when referencing the second column by name in transform specifications as described in Transforming Frames.
ID,FirstName,LastName
1,FirstName1,LastName1
2,FirstName2,LastName2
ID, FirstName,LastName
1,FirstName1,LastName1
2,FirstName2,LastName2
{
"data_type": "frame",
"format": "csv",
"header": true
}
Appending Frames
BuiltIn functions cbind()
and rbind()
are supported for frames to add columns or rows to an existing frame.
Table F1: Frame Append BuiltIn Functions
Function  Description  Parameters  Example 

cbind()  Columnwise frame concatenation. Concatenates the second frame as additional columns to the first frame.  Input: (X <frame>, Y <frame>) Output: <frame> X and Y are frames, where the number of rows in X and the number of rows in Y are the same. 
A = read(“file1”, data_type=”frame”, rows=2, cols=3, format=”binary”) B = read(“file2”, data_type=”frame”, rows=2, cols=3, format=”binary”) C = cbind(A, B) # Dimensions of C: 2 X 6 
rbind()  Rowwise frame concatenation. Concatenates the second frame as additional rows to the first frame.  Input: (X <fame>, Y <frame>) Output: <frame> X and Y are frames, where the number of columns in X and the number of columns in Y are the same. 
A = read(“file1”, data_type=”frame”, rows=2, cols=3, format=”binary”) B = read(“file2”, data_type=”frame”, rows=2, cols=3, format=”binary”) C = rbind(A, B) # Dimensions of C: 4 X 3 
Indexing Frames
Similar to matrices, frames support both right and left indexing. Note for left indexing, the right hand side frame size and selected left hand side frame slice must match.
# = [right indexing]
A = read("inputA", data_type="frame", rows=10, cols=10, format="binary")
B = A[4:5, 6:7]
C = A[1, ]
D = A[, 3]
E = A[, 1:2]
# [left indexing] =
F = read("inputF", data_type="frame", rows=10, cols=10, format="binary")
F[4:5, 6:7] = B
F[1, ] = C
F[, 3] = D
F[, 1:2] = E
Casting Frames
Frames support converting between matrices and scalars using as.frame(), as.matrix()
and as.scalar()
. Casting a frame to a matrix is a best effort operation, which tries to parse doubles. If there are strings that cannot be parsed, the as.frame()
operation produces errors. For example, a java.lang.NumberFormatException
may occur for invalid data since Java’s Double.parseDouble()
is used internally for parsing.
Table F2: Casting BuiltIn Functions
Function  Description  Parameters  Example 

as.frame(<matrix>)  Matrix is cast to frame.  Input: (<matrix>) Output: <frame> 
A = read(“inputMatrixDataFile”) B = as.frame(A) write(B, “outputFrameDataFile”, format=”binary”) 
as.frame(<scalar>)  Scalar is cast to 1x1 frame.  Input: (<scalar>) Output: <frame> 
A = read(“inputScalarData”, data_type=”scalar”, value_type=”string”) B = as.frame(A) write(B, “outputFrameData”) 
as.matrix(<frame>)  Frame is cast to matrix.  Input: (<frame>) Output: <matrix> 
B = read(“inputFrameDataFile”) C = as.matrix(B) write(C, “outputMatrixDataFile”, format=”binary”) 
as.scalar(<frame>)  1x1 Frame is cast to scalar.  Input: (<frame>) Output: <scalar> 
B = read(“inputFrameData”, data_type=”frame”, schema=”string”, rows=1, cols=1) C = as.scalar(B) write(C, “outputScalarData”) 
Note: as.frame(matrix)
produces a double schema, and as.scalar(frame)
produces of scalar of value type given by the frame schema.
Transforming Frames
Frames support additional Data PreProcessing BuiltIn Functions as shown below.
Function  Description  Parameters  Example 

transformencode()  Transforms a frame into a matrix using specification. Builds and applies frame metadata. 
Input: target = <frame> spec = <json specification> Outputs: <matrix>, <frame> 
transformencode 
transformdecode()  Transforms a matrix into a frame using specification. Valid only for specific transformation types. 
Input: target = <matrix> spec = <json specification> meta = <frame> Output: <frame> 
transformdecode 
transformapply()  Transforms a frame into a matrix using specification. Applies existing frame metadata. 
Input: target = <frame> spec = <json specification> meta = <frame> Output: <matrix> 
transformapply 
The following table summarizes the supported transformations for transformencode(), transformdecode(), transformapply()
. Note only recoding, dummy coding and passthrough are reversible, i.e., subject to transformdecode()
, whereas binning, missing value imputation, and omit are not.
Table F3: Frame data transformation types.
encode  decode  apply  

RCD  *  *  * 
DCD  *  *  * 
BIN  *  x  * 
MVI  *  x  * 
OMIT  *  x  * 
Key  Meaning 

RCD  Recoding 
DCD  Dummycoding 
BIN  Binning 
MVI  Missing value handling by imputation 
OMIT  Missing value handling by omitting 
Key  Meaning 

*  Supported 
x  Not supported 
The following examples use homes.csv
data set.
Table F4: The homes.csv
data set
zipcode  district  sqft  numbedrooms  numbathrooms  floors  view  saleprice  askingprice 

95141  west  1373  7  1  3  FALSE  695  698 
91312  south  3261  6  2  2  FALSE  902  906 
94555  north  1835  3  3  3  TRUE  888  892 
95141  east  2833  6  2.5  2  TRUE  927  932 
96334  south  2742  6  2.5  2  FALSE  872  876 
96334  north  2195  5  2.5  2  FALSE  799  803 
98755  north  3469  7  2.5  2  FALSE  958  963 
96334  west  1685  7  1.5  2  TRUE  757  760 
95141  west  2238  4  3  3  FALSE  894  899 
91312  west  1245  4  1  1  FALSE  547  549 
98755  south  3702  7  3  1  FALSE  959  964 
98755  north  1865  7  1  2  TRUE  742  745 
94555  north  3837  3  1  1  FALSE  839  842 
91312  west  2139  3  1  3  TRUE  820  824 
95141  north  3824  4  3  1  FALSE  954  958 
98755  east  2858  5  1.5  1  FALSE  759  762 
91312  south  1827  7  3  1  FALSE  735  738 
91312  south  3557  2  2.5  1  FALSE  888  892 
91312  south  2553  2  2.5  2  TRUE  884  889 
96334  west  1682  3  1.5  1  FALSE  625  628 
The metadata file homes.csv.mtd
looks as follows:
{
"data_type": "frame",
"format": "csv",
"header": true,
}
transformencode
The transformencode()
function takes a frame and outputs a matrix based on defined transformation specification. In addition, the corresponding metadata is output as a frame
.
Note: the metadata output is simply a frame so all frame operations (including read/write) can also be applied to the metadata.
This example replaces values in specific columns to create a recoded matrix with associated frame identifying the mapping between original and substituted values. An example transformation specification file homes.tfspec_recode2.json
is given below:
{
"recode": [ "zipcode", "district", "view" ]
}
The following DML utilizes the transformencode()
function.
F1 = read("/user/ml/homes.csv", data_type="frame", format="csv");
jspec = read("/user/ml/homes.tfspec_recode2.json", data_type="scalar", value_type="string");
[X, M] = transformencode(target=F1, spec=jspec);
print(toString(X));
if(1==1){}
print(toString(M));
The transformed matrix X and output M are as follows.
1.000 1.000 1373.000 7.000 1.000 3.000 1.000 695.000 698.000
2.000 2.000 3261.000 6.000 2.000 2.000 1.000 902.000 906.000
3.000 3.000 1835.000 3.000 3.000 3.000 2.000 888.000 892.000
1.000 4.000 2833.000 6.000 2.500 2.000 2.000 927.000 932.000
4.000 2.000 2742.000 6.000 2.500 2.000 1.000 872.000 876.000
4.000 3.000 2195.000 5.000 2.500 2.000 1.000 799.000 803.000
5.000 3.000 3469.000 7.000 2.500 2.000 1.000 958.000 963.000
4.000 1.000 1685.000 7.000 1.500 2.000 2.000 757.000 760.000
1.000 1.000 2238.000 4.000 3.000 3.000 1.000 894.000 899.000
2.000 1.000 1245.000 4.000 1.000 1.000 1.000 547.000 549.000
5.000 2.000 3702.000 7.000 3.000 1.000 1.000 959.000 964.000
5.000 3.000 1865.000 7.000 1.000 2.000 2.000 742.000 745.000
3.000 3.000 3837.000 3.000 1.000 1.000 1.000 839.000 842.000
2.000 1.000 2139.000 3.000 1.000 3.000 2.000 820.000 824.000
1.000 3.000 3824.000 4.000 3.000 1.000 1.000 954.000 958.000
5.000 4.000 2858.000 5.000 1.500 1.000 1.000 759.000 762.000
2.000 2.000 1827.000 7.000 3.000 1.000 1.000 735.000 738.000
2.000 2.000 3557.000 2.000 2.500 1.000 1.000 888.000 892.000
2.000 2.000 2553.000 2.000 2.500 2.000 2.000 884.000 889.000
4.000 1.000 1682.000 3.000 1.500 1.000 1.000 625.000 628.000
# FRAME: nrow = 5, ncol = 9
# zipcode district sqft numbedrooms numbathrooms floors view saleprice askingprice
# STRING STRING STRING STRING STRING STRING STRING STRING STRING
96334·4 south·2 FALSE·1
95141·1 east·4 TRUE·2
98755·5 north·3
94555·3 west·1
91312·2
As mentioned in Creating Frames, the header line in frame CSV files is sensitive to white space. The tabs below show compatible transform specifications for the given CSV header. Note the extra (possibly inadvertent) space before the district
column in CSV2 impacts the transform specification. More specifically, transform spec1 does not match the header in CSV2. To match, either remove the extra space before district
in CSV2 or use spec2 which quotes the district
token name to include the extra space.
zipcode,district,sqft,numbedrooms,numbathrooms,floors,view,saleprice,askingprice
95141,west,1373,7,1,3,FALSE,695,698
91312,south,3261,6,2,2,FALSE,902,906
zipcode, district,sqft,numbedrooms,numbathrooms,floors,view,saleprice,askingprice
95141,west,1373,7,1,3,FALSE,695,698
91312,south,3261,6,2,2,FALSE,902,906
{
ids:false, recode: [ zipcode, district, view ]
}
{
ids:false, recode: [ zipcode, " district", view ]
}
{
"data_type": "frame",
"format": "csv",
"header": true
}
transformdecode
The transformdecode()
function can be used to transform a matrix
back into a frame
. Only recoding, dummy coding and passthrough transformations are reversible and can be used with transformdecode()
. The transformations binning, missing value imputation, and omit are not reversible and cannot be used with transformdecode()
.
The next example takes the outputs from the transformencode example and reconstructs the original data using the same transformation specification.
F1 = read("/user/ml/homes.csv", data_type="frame", format="csv");
jspec = read("/user/ml/homes.tfspec_recode2.json", data_type="scalar", value_type="string");
[X, M] = transformencode(target=F1, spec=jspec);
F2 = transformdecode(target=X, spec=jspec, meta=M);
print(toString(F2));
# FRAME: nrow = 20, ncol = 9
# C1 C2 C3 C4 C5 C6 C7 C8 C9
# STRING STRING DOUBLE DOUBLE DOUBLE DOUBLE STRING DOUBLE DOUBLE
95141 west 1373.000 7.000 1.000 3.000 FALSE 695.000 698.000
91312 south 3261.000 6.000 2.000 2.000 FALSE 902.000 906.000
94555 north 1835.000 3.000 3.000 3.000 TRUE 888.000 892.000
95141 east 2833.000 6.000 2.500 2.000 TRUE 927.000 932.000
96334 south 2742.000 6.000 2.500 2.000 FALSE 872.000 876.000
96334 north 2195.000 5.000 2.500 2.000 FALSE 799.000 803.000
98755 north 3469.000 7.000 2.500 2.000 FALSE 958.000 963.000
96334 west 1685.000 7.000 1.500 2.000 TRUE 757.000 760.000
95141 west 2238.000 4.000 3.000 3.000 FALSE 894.000 899.000
91312 west 1245.000 4.000 1.000 1.000 FALSE 547.000 549.000
98755 south 3702.000 7.000 3.000 1.000 FALSE 959.000 964.000
98755 north 1865.000 7.000 1.000 2.000 TRUE 742.000 745.000
94555 north 3837.000 3.000 1.000 1.000 FALSE 839.000 842.000
91312 west 2139.000 3.000 1.000 3.000 TRUE 820.000 824.000
95141 north 3824.000 4.000 3.000 1.000 FALSE 954.000 958.000
98755 east 2858.000 5.000 1.500 1.000 FALSE 759.000 762.000
91312 south 1827.000 7.000 3.000 1.000 FALSE 735.000 738.000
91312 south 3557.000 2.000 2.500 1.000 FALSE 888.000 892.000
91312 south 2553.000 2.000 2.500 2.000 TRUE 884.000 889.000
96334 west 1682.000 3.000 1.500 1.000 FALSE 625.000 628.000
transformapply
In contrast to transformencode()
, which creates and applies frame metadata (transformencode := build+apply), transformapply()
applies existing metadata (transformapply := apply).
The following example uses transformapply()
with the input matrix and second output (i.e., existing frame metadata built with transformencode()
) from the transformencode example for the homes.tfspec_bin2.json
transformation specification.
{
"recode": [ zipcode, "district", "view" ], "bin": [
{ "name": "saleprice" , "method": "equiwidth", "numbins": 3 }
,{ "name": "sqft", "method": "equiwidth", "numbins": 4 }]
}
F1 = read("/user/ml/homes.csv", data_type="frame", format="csv");
jspec = read("/user/ml/homes.tfspec_bin2.json", data_type="scalar", value_type="string");
[X, M] = transformencode(target=F1, spec=jspec);
X2 = transformapply(target=F1, spec=jspec, meta=M);
print(toString(X2));
1.000 1.000 1.000 7.000 1.000 3.000 1.000 1.000 698.000
2.000 2.000 1.000 6.000 2.000 2.000 1.000 1.000 906.000
3.000 3.000 1.000 3.000 3.000 3.000 2.000 1.000 892.000
1.000 4.000 1.000 6.000 2.500 2.000 2.000 1.000 932.000
4.000 2.000 1.000 6.000 2.500 2.000 1.000 1.000 876.000
4.000 3.000 1.000 5.000 2.500 2.000 1.000 1.000 803.000
5.000 3.000 1.000 7.000 2.500 2.000 1.000 1.000 963.000
4.000 1.000 1.000 7.000 1.500 2.000 2.000 1.000 760.000
1.000 1.000 1.000 4.000 3.000 3.000 1.000 1.000 899.000
2.000 1.000 1.000 4.000 1.000 1.000 1.000 1.000 549.000
5.000 2.000 1.000 7.000 3.000 1.000 1.000 1.000 964.000
5.000 3.000 1.000 7.000 1.000 2.000 2.000 1.000 745.000
3.000 3.000 1.000 3.000 1.000 1.000 1.000 1.000 842.000
2.000 1.000 1.000 3.000 1.000 3.000 2.000 1.000 824.000
1.000 3.000 1.000 4.000 3.000 1.000 1.000 1.000 958.000
5.000 4.000 1.000 5.000 1.500 1.000 1.000 1.000 762.000
2.000 2.000 1.000 7.000 3.000 1.000 1.000 1.000 738.000
2.000 2.000 1.000 2.000 2.500 1.000 1.000 1.000 892.000
2.000 2.000 1.000 2.000 2.500 2.000 2.000 1.000 889.000
4.000 1.000 1.000 3.000 1.500 1.000 1.000 1.000 628.000
Modules
A module is a collection of UDF declarations. For calling a module, source(…) and setwd(…) are used to read and use a source file.
Syntax
setwd(<filepath>);
source(<DMLfilename>) as <namespacename>;
It is important to note that:
 setwd(…) and source(…) do not support $parameters.
 Nested namespaces are not supported.
 Namespace are required for source(…).
 Only UDFs are imported, not the statements.
 Path for input/output files is not affected by setwd.
 setwd is applicable only for local filesystem not HDFS.
 Spaces are not allowed between namespace and function name during call. For example: ns1::foo(…) is correct way to call the function.
 Like R, the path of source() is relative to where the calling java program is running.
Example
Assume the file a.dml contains:
#source("/home/ml/spark_test/b.dml") as ns1 # will work
#source("b.dml") as ns1 # will work
#source("./b.dml") as ns1 # will work
source("hdfs:/user/ml/nike/b.dml") as ns1
f1 = function() {
print("From a.dml's function()");
}
setwd("dir1")
source("c.dml") as ns2
tmp = ns2::f();
tmp1 = ns1::f();
tmp = f1();
The file b.dml contains:
f = function() {
print("From b.dml's function()");
}
The file c.dml contains:
f = function() {
print("From c.dml's function()");
}
The output after running a.dml is as follows:
From c.dml's function()
From b.dml's function()
From a.dml's function()
Reserved Keywords
Reserved keywords cannot be used as variable names.
All reserved keywords are casesensitive.
as
boolean
Boolean
double
Double
else
externalFunction
for
function
FALSE
if
ifdef
implemented
in
int
integer
Int
Integer
parfor
return
setwd
source
string
String
TRUE
while