- Introduction
- Variables
- Expressions
- Statements
- Variable Scoping
- Command-Line Arguments
- Built-in Functions
- Matrix Construction, Manipulation, and Aggregation Built-In Functions
- Matrix and/or Scalar Comparison Built-In Functions
- Casting Built-In Functions
- Statistical Built-In Functions
- Mathematical and Trigonometric Built-In Functions
- Linear Algebra Built-In Functions
- Read/Write Built-In Functions
- Data Pre-Processing Built-In Functions
- Other Built-In Functions

- Frames
- Modules
- Reserved Keywords
- Invocation of SystemML
- MLContext API

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:

spark-submit 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 `spark-submit SystemML.jar -help`

.

Identifiers are case-sensitive (e.g., `var1`

, `Var1`

, and `VAR1`

are different identifier names), must start with either an upper-case or lower-case 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 built-in functions as an identifier. The only exceptions to this rule are five built-in functions: ‘as.scalar’, ‘as.matrix’, ‘as.double’, ‘as.integer’ and ‘as.logical’.

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.

Three data types (frame, matrix and scalar) and four value types (double, integer, string, and boolean) are supported. Matrices are 2-dimensional, 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 Pre-Processing Built-In 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 built-in 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 built-in 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`

.

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.

# this is an example of a line comment /* this is an example of a multi-line block comment */

Now that we have familiarized ourselves with variables and data type, let’s understand how to use them in expressions.

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 cell-wise on the matrix using the scalar operand.

**Table 1**: Operators

Operator | Input | Output | Details |
---|---|---|---|

^ | Matrix or Scalar | Matrix or Scalar1, 2 | Exponentiation (right associativity) – Highest precedence |

- + | Matrix or Scalar | Matrix or Scalar1 | Unary plus, minus |

%*% | Matrix | Matrix | Matrix multiplication |

%/% %% | Matrix or Scalar | Matrix or Scalar1, 2 | Integer division and Modulus operator |

/ * | Matrix or Scalar | Matrix or Scalar1, 2 | Multiplication and Division |

+ - | Matrix or Scalar | Matrix or Scalar1, 2 | Addition (or string concatenation) and Subtraction |

< > == != <= >= | Matrix or Scalar (any value type) | Matrix or Scalar1, 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 Matrix-vector operations

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`

.

Arithmetic and relational operations described in above table support matrix-vector operations. This allows efficient cell-wise operations with either row or a column vector.

Input_Matrix operation Input_Vector

M + V or M > V, where M is a matrix and V is either row matrix or a column matrix.

Matrix-Vector operation avoids need for creating replicated matrix for certain subset of operations. For example: to compute class conditional probabilities in Naïve-Bayes, without support for matrix-vector 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 matrix-vector operations, the above script becomes much more efficient as well as concise:

class_conditionals = (classFeatureCounts + laplace_correction) / classSums

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)).

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 range-based indexing is below. The constraint is that lower-row < upper-row, and lower-column < upper-column.

[Matrix name][lower-row : upper-row],[lower-column : upper-column]

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

A script is a sequence of statements with the default computation semantics being sequential evaluation of the individual statements. The use of a semi-colon at the end of a statement is optional. The types of statements supported are

- assignment,
- control structures (while, if, for), and
- user-defined function declaration.

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.

# max_iteration is of type integer max_iteration = 3; # V has data type matrix and value type double. V = W %*% H;

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.

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; }

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.

# 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; } }

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.

[lower]:[upper] defines a sequence of numbers with increment 1: {lower, lower + 1, lower + 2, …, upper – 1, upper}.

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 for statement A = 5; for (i in 1:20) { A = A + 1; }

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> <dependency_analysis> is one of the following tokens: 0 1 <degree_of_parallelism> is an arbitrary integer number <execution_mode> is one of the following tokens: LOCAL REMOTE_MR <task_partitioning_algorithm> is one of the following tokens: FIXED NAIVE STATIC FACTORING FACTORING_CMIN FACTORING_CMAX <task_size> is an arbitrary integer number <data_partitioning_mode> is one of the following tokens: NONE LOCAL REMOTE_MR <result_merge_mode> is one of the following tokens: LOCAL_MEM LOCAL_FILE LOCAL_AUTOMATIC REMOTE_MR <optimization_mode> is one of the following tokens: NONE CONSTRAINED RULEBASED HEURISTIC GREEDY FULL_DP

If any of these parameters is not specified, the following respective defaults are used: `check = 1`

, `par = [number of virtual processors on master node]`

, `mode = LOCAL`

, `taskpartitioner = FIXED`

, `tasksize = 1`

, `datapartitioner = NONE`

, `resultmerge = LOCAL_AUTOMATIC`

, `opt = RULEBASED`

.

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 parallizable. The iterations incorrectly try to increment the same `sum`

variable.

sum = 0 parfor(i in 1:3) { sum = sum + i; # not parallizable - 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 parallizable 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) { # parallizable - false positive mv = matrix(v, rows=2, cols=3) ms[,(v-1)*3+1:v*3] = mv }

If a false positive arises but you are certain that the `parfor`

is parallizable, 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) { # parallizable mv = matrix(v, rows=2, cols=3) ms[,(v-1)*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()`

with`for()`

. Since`parfor`

is derived from`for`

, you can always use`for`

wherever you can use`parfor`

. `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.

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 2**: Parameters for UDF Function Definition Statements

Parameter Name | Description | Optional | Permissible Values |
---|---|---|---|

functionName | Name of the function. | No | Any non-keyword 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 non-keyword sting |

userParam=value | User-defined parameter to invoke the package. | Yes | Any non-keyword string |

# 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, ….)

# 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 }

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 command-line parameters are treated as constants which are introduced during parse-time.

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; }

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`

Since most algorithms require arguments to be passed from command line, DML supports command-line 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)

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 short-hand for “`ifdef(\$nbrRows) then \$nbrRows else 10`

”.

Let’s assume that the above script is invoked using following the command line values:

spark-submit 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:

spark-submit 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 line-arguments or via default values at the beginning of the script.

Each argValue passed from the command-line 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 double-quotes. NOTE: The values passed from the command-line are passed as literal values which replace the placeholders in the DML script, and are not interpreted as DML.

Built-in functions are categorized in:

- Matrix Construction, Manipulation, and Aggregation Built-In Functions
- Matrix and/or Scalar Comparison Built-In Functions
- Casting Built-In Functions
- Statistical Built-In Functions
- Mathematical and Trigonometric Built-In Functions
- Linear Algebra Built-In Functions
- Other Built-In Functions

The tables below list the supported built-in 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 built-in functions `rowSums`

and `colSums`

, on the other hand, aggregate values on a per-row and per-column 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.

**Table 3**: Matrix Construction, Manipulation, and Aggregation Built-In Functions

Function | Description | Parameters | Example |
---|---|---|---|

append() | Adds the second argument as additional columns to the first argument (note that the first argument is not over-written). Append is meant to be used in situations where one cannot use left-indexing. NOTE: append() has been replaced by cbind(), so its use is discouraged. | Input: (X <matrix>, Y <matrix>) Output: <matrix> X and Y are matrices (with possibly multiple columns), where the number of rows in X and Y must be the same. Output is a matrix with exactly the same number of rows as X and Y. Let n1 and n2 denote the number of columns of matrix X and Y, respectively. The returned matrix has n1+n2 columns, where the first n1 columns contain X and the last n2 columns contain Y. | A = matrix(1, rows=2,cols=5) B = matrix(1, rows=2,cols=3) C = append(A,B) print("Dimensions of C: " + nrow(C) + " X " + ncol(C)) The output of above example is: Dimensions of C: 2 X 8 |

cbind() | Column-wise 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 non-zero 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 non-zero values. |

rbind() | Row-wise 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. | Input : (target= X <matrix>, margin=“...”) Output : <matrix> Valid values for margin are “rows” or “cols”. | A = removeEmpty(target=X, margin=“rows”) |

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) |

**Table 4**: Matrix and/or Scalar Comparison Built-In Functions

Function | Description | Parameters | Example |
---|---|---|---|

pmin() pmax() | “parallel min/max”. Return cell-wise 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() | Row-wise computation -- for each row, find the max value, and return its column index. | Input: (matrix) Output: (n x 1) matrix | rowIndexMax(X) |

rowIndexMin() | Row-wise 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 cell-wise 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,“<”) |

**Table 5**: Casting Built-In 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) |

**Table 6**: Statistical Built-In 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() | Column-wise 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 1-dimensional 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 n-dimensional vectors. This function supports multiple other variants, which can be found below, at the end of this Table 6. | 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 6.) |

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 6. | 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 6. |

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 6. | 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 6.) |

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 p-quantile 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() | Row-wise 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 prefix-sum (For row-prefix 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 prefix-prod (For row-prefix 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 prefix-min (For row-prefix 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 prefix-max (For row-prefix 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]] |

The built-in function table() supports different types of input parameters. These variations are described below:

- Basic form:
`F=table(A,B)`

As described above in Table 6. - 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 pass-in 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 exactly`odim1`

rows and`odim2`

columns. F may be a truncated or padded (with zeros) version of the output produced by`table(A,B)`

-- depending on the values of`max(A)`

and`max(B)`

. For example, if`max(A) < odim1`

then the last (`odim1-max(A)`

) rows will have zeros.

The built-in 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 6. - 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 6, 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 by`aggregate(target=X, groups=G, fn="sum")`

– depending on the values of`ngroups`

and`max(G)`

. For example, if`max(G) < ngroups`

then the last (`ngroups-max(G)`

) rows will have zeros.

`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 t-distribution), “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 distribution-specific 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);`

**Table 7**: Mathematical and Trigonometric Built-In 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]] |

**Table 8**: Linear Algebra Built-In 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) or (1 x n) matrix, or take diagonal from square matrix | Input: (n x 1) or (1 x n) matrix, or (n x n) matrix Output: (n x n) matrix, or (n x 1) matrix | diag(X) |

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 small-to-medium sized input matrix that can fit in the main memory. For larger matrices, an out-of-memory 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 lower-triangular matrix whereas U is an upper-triangular matrix. It is important to note that this function can operate only on small-to-medium sized input matrix that can fit in the main memory. For larger matrices, an out-of-memory 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 small-to-medium sized input matrix that can fit in the main memory. For larger matrices, an out-of-memory 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 small-to-medium sized input matrix that can fit in the main memory. For larger matrices, an out-of-memory 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) |

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 JSON-formatted 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.*

SystemML supports 4 file formats:

- CSV (delimited)
- Matrix Market (coordinate)
- Text (i,j,v)
- Binary

The CSV format is a standard text-based 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 text-based, space-separated 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 non-zero 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 text-based sparse format in which the cell values of a matrix are serialized in space-separated triplets of rowId, columnId, and cellValue, with the rowId and columnId indices being 1-based. 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 Table 9, we have a matrix consisting of 4 rows and 3 columns.

**Table 9**: Matrix

Below, we have examples of this matrix in the CSV, Matrix Market, IJV, and Binary formats, along with corresponding metadata.

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", "description": { "author": "SystemML" } }

Metadata is represented as an MTD file that contains a single JSON object with the attributes described below.

**Table 10**: 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 non-zero 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` |

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 11**: 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 the`write`

function, then the header line is formed as a concatenated string of column names separated by delimiters. Columns are of the form`C<column_id>`

. For a matrix with 5 columns, the header line would look like:`C1,C2,C3,C4,C5`

(assuming`sep=","`

).

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 non-optional parameters aren't specified or conflicting values are detected, then an exception is thrown.

# 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.

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 12. 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 12**: 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 10 and 11 |

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", "description": { "author": "SystemML" } }

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", "description": { "author": "SystemML" } }

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": ";", "description": { "author": "SystemML" } }

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", "description": { "author": "SystemML" } }

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 data pre-processing built-in `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 column-level 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 non-missing entries in the column;`global_mode`

that replaces a missing value in a*categorical*column with the mode of all non-missing entries in the column; and`constant`

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 is`equi-width`

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`

, and`direction_south`

.*Scaling*: This centers and/or scales the values in a given numeric/continuous column. The two supported methods are`mean-subtraction`

that centers each value by subtracting the mean, and`z-score`

that scales mean subtracted values by dividing them with the respective column-wise 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 13**: Data transformations that can be used simultaneously.

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 14**: 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": "equi-width", "numbins": 3 } ,{ "name": "sqft" , "method": "equi-width", "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/data-transformation"); 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": "equi-width", "numbins": 3 } ,{ "id": 3, "method": "equi-width", "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": "mean-subtraction" } ,{ "name": "saleprice", "method": "z-score" } ,{ "name": "askingprice", "method": "z-score" } ] }

The following code snippet shows an example scenario of transforming a training data set and subsequently testing the data set.

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 ...

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.

**Table 15**: Other Built-In 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” : inter-element 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 the value of a scalar variable x. This built-in takes an optional string parameter. | Input: (<scalar>) | print(“hello”) print(“hello” + “world”) print("value of x is " + x ) |

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) |

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. Built-in 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.

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.

Built-In functions cbind() and rbind() are supported for frames to add columns or rows to an existing frame.

**Table F1**: Frame Append Built-In Functions

Function | Description | Parameters | Example |
---|---|---|---|

cbind() | Column-wise 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() | Row-wise 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 |

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

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 Built-In 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.*

Frames support additional Data Pre-Processing Built-In 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 pass-through are reversible, i.e., subject to transformdecode(), whereas binning, missing value imputation, and omit are not.

**Table F3**: Frame data transformation types.

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, }

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

The transformdecode() function can be used to transform a matrix back into a frame. Only recoding, dummy coding and pass-through 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

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": "equi-width", "numbins": 3 } ,{ "name": "sqft", "method": "equi-width", "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

A module is a collection of UDF declarations. For calling a module, source(...) and setwd(...) are used to read and use a source file.

setwd(<file-path>); source(<DML-filename>) as <namespace-name>;

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.

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 cannot be used as variable names.

All reserved keywords are case-sensitive.

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