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Built-in Functions

DaphneDSL offers numerous built-in functions, which can be used in every DaphneDSL script without requiring any imports. The general syntax for calling a built-in function is func(param1, param2, ...) (see the DaphneDSL Language Reference).

This document provides an overview of the DaphneDSL built-in functions. Note that we are still extending this set of built-in functions. Furthermore, we also plan to create a library of higher-level ML primitives allowing users to productively implement integrated data analysis pipelines at a much higher level of abstraction. Those library functions will internally be implemented using the built-in functions described in this document.

We use the following notation (deviating from the DaphneDSL function syntax):

  • square brackets [] mean that a parameter is optional
  • ... stands for an arbitrary repetition of the previous parameter (including zero).
  • / means alternative options, e.g., matrix/frame means the parameter could be a matrix or a frame

List of categories

DaphneDSL's built-in functions can be categorized as follows:

  • Data generation
  • Matrix/frame dimensions
  • Elementwise unary
  • Elementwise binary
  • Outer binary (generalized outer product)
  • Aggregation and statistical
  • Reorganization
  • Matrix decomposition & co
  • Deep neural network
  • Other matrix operations
  • Extended relational algebra
  • Conversions and casts
  • Input/output
  • Data preprocessing
  • Measurements

Data generation

  • fill(value:scalar, numRows:size, numCols:size)

    Creates a (numRows x numCols) matrix and sets all elements to value.

  • createFrame(column:matrix, ...[, labels:str, ...])

    Creates a frame from an arbitrary number of column matrices. Optionally, a label can be specified for each column (the number of provided columns and labels must be equal).

  • diagMatrix(arg:matrix)

    Creates an (n x n) diagonal matrix by placing the elements of the given (n x 1) column-matrix arg on the diagonal of an otherwise empty (zero) square matrix.

  • rand(numRows:size, numCols:size, min:scalar, max:scalar, sparsity:double, seed:si64)

    Generates a (numRows x numCols) matrix of random values. The values are drawn uniformly from the range [min, max] (both inclusive). The sparsity can be chosen between 0.0 (all zeros) and 1.0 (all non-zeros). The seed can be set to -1 (randomly chooses a seed), or be provided explicitly to enable reproducible random values.

  • sample(range:scalar, size:size, withReplacement:bool, seed:si64)

    Generates a (size x 1) column-matrix of values drawn from the range [0, range - 1]. The parameter withReplacement determines if a value can be drawn multiple times (true) or not (false). The seed can be set to -1 (randomly chooses a seed), or be provided explicitly to enable reproducible random values.

  • seq(from:scalar, to:scalar[, inc:scalar])

    Generates a column matrix containing an arithmetic sequence of values starting at from, going through to, in increments of inc. Note that from may be greater than to, and inc may be negative. The scalar inc is an optional argument and defaults to 1.

Matrix/frame dimensions

The following built-in functions allow to find out the shape/dimensions of matrices and frames.

  • nrow(arg:matrix/frame)

    Returns the number of rows in arg.

  • ncol(arg:matrix/frame)

    Returns the number of columns in arg.

  • ncell(arg:matrix/frame)

    Returns the number of cells in arg. This is the product of the number of rows and the number of columns.

Elementwise unary

The following built-in functions all follow the same scheme:

  • unaryFunc(arg:scalar/matrix)

    Applies the respective unary function (see table below) to the given scalar arg or to each element of the given matrix arg.

Arithmetic/general math

function meaning
abs absolute value
sign signum (1 for positive, 0 for zero, -1 for negative)
exp exponentiation (e to the power of arg)
ln natural logarithm (logarithm of arg to the base of e)
sqrt square root

Trigonometric/Hyperbolic

arg unit must be radians (conversion: \(x^\circ * \frac{\pi}{180^\circ} = y\) radians)

function meaning
sin sine
cos cosine
tan tangent
asin arc sine (inverse of sine)
acos arc cosine (inverse of cosine)
atan arc tangent (inverse of tangent)
sinh hyperbolic sine \(\left( \frac{\exp(\text{arg}) \, - \, \exp(\text{ - arg})}{2} \right)\)
cosh hyperbolic cosine \(\left( \frac{\exp(\text{arg}) \, + \, \exp(\text{ - arg})}{2} \right)\)
tanh hyperbolic tangent \(\left( \frac{\text{sinh arg}}{\text{cosh arg}} \right)\)

Rounding

function meaning
round round to nearest
floor round down
ceil round up

Elementwise binary

DaphneDSL supports various elementwise binary operations. Some of those can be used through operators in infix notation, e.g., +; and some through built-in functions, e.g., log(). Some operations even support both, e.g., pow(a, b) and a^b have the same semantics.

The built-in functions all follow the same scheme:

  • binaryFunc(lhs:scalar/matrix, rhs:scalar/matrix)

    Applies the respective binary function (see table below) to the corresponding pairs of a value in the left-hand-side argument lhs and the right-hand-side argument rhs. Regarding the combinations of scalars and matrices, the same broadcasting semantics apply as for binary operations like +, *, etc. (see the DaphneDSL Language Reference).

Arithmetic

function operator meaning
+ addition
- subtraction
* multiplication
/ division
pow ^ exponentiation (lhs to the power of rhs)
log logarithm (logarithm of lhs to the base of rhs)
mod % modulo

Min/max

function operator meaning
min minimum
max maximum

Logical

function operator meaning
&& logical conjunction
|| logical disjunction

Strings

function operator meaning
concat + string concatenation

Comparison

function operator meaning
== equal
!= not equal
< less than
<= less or equal
> greater than
>= greater or equal

Outer binary (generalized outer product)

The following built-in functions all follow the same scheme:

  • outerBinaryFunc(lhs:matrix, rhs:matrix)

The argument lhs is expected to be a column (m x 1) matrix, and the argument rhs is expected to be a row (1 x n) matrix. The result is a (m x n) matrix, whereby the element at position (i, j) is calculated by applying the respective binary function (see the table below) to the i-th element in lhs and the j-th element in rhs. Schematically, this looks as follows (where ∘ is some binary operation): 

       |    b0    b1 ...    bn rhs
    ---+----------------------
lhs a0 | a0∘b0 a0∘b1 ... a0∘bn res
    a1 | a1∘b0 a1∘b1 ... a1∘bn
    .. | ..... .....     .....
    am | am∘b0 am∘b1 ... am∘bn

Arithmetic

function meaning
outerAdd addition
outerSub subtraction
outerMul multiplication (the well-known outer product)
outerDiv division
outerPow exponentiation (lhs to the power of rhs)
outerLog logarithm (logarithm of lhs to the base of rhs)
outerMod modulo

Min/max

function meaning
outerMin minimum
outerMax maximum

Logical

function meaning
outerAnd logical conjunction
outerOr logical disjunction
outerXor logical exclusive disjunction (not supported yet)

Strings

function meaning
outerConcat string concatenation (not supported yet)

Comparison

function meaning
outerEq equal
outerNeq not equal
outerLt less than
outerLe less or equal
outerGt greater than
outerGe greater or equal

Aggregation and statistical

Full/row/column aggregation

The following built-in functions all follow the same scheme:

  • agg(arg:matrix)

    Full aggregation over all elements of the matrix arg using aggregation function agg (see table below). Returns a scalar.

  • agg(arg:matrix, axis:si64)

    Row or column aggregation over a (n x m) matrix arg using aggregation function agg (see table below).

    • axis == 0: calculate one aggregate per row; the result is a (n x 1) (column) matrix
    • axis == 1: calculate one aggregate per column; the result is a (1 x m) (row) matrix
function meaning
sum summation
aggMin minimum
aggMax maximum
mean arithmetic mean
var variance
stddev standard deviation
idxMin argmin (the index of the minimum value, only for row/column-wise aggregation)
idxMax argmax (the index of the maximum value, only for row/column-wise aggregation)

Cumulative aggregation

The following built-in functions all follow the same scheme:

  • cumAgg(arg:matrix)

    Cumulative aggregation over each column of the matrix arg. Returns a matrix of the same shape as arg.

function meaning
cumSum cumulative sum
cumProd cumulative product
cumMin cumulative minimum
cumMax cumulative maximum

Reorganization

  • reshape(arg:matrix, numRows:size, numCols:size)

    Changes the shape of arg to (numRows x numCols). Note that the number of cells must be retained, i.e., the product of numRows and numCols must be equal to the product of the number of rows in arg and the number of columns in arg.

  • transpose/t(arg:matrix)

    Transposes the given matrix arg.

  • cbind(lhs:matrix/frame, rhs:matrix/frame)

    Concatenates two matrices or two frames horizontally. The two inputs must have the same number of rows.

  • rbind(lhs:matrix/frame, rhs:matrix/frame)

    Concatenates two matrices or two frames vertically. The two inputs must have the same number of columns.

  • reverse(arg:matrix)

    Reverses the rows in the given matrix arg.

  • order(arg:matrix/frame, colIdxs:size, ..., ascs:bool, ..., returnIndexes:bool)

    Sorts the given matrix or frame by an arbitrary number of columns. The columns are specified in terms of their indexes (counting starts at zero). Each column can be sorted either in ascending (true) or descending (false) order (as determined by parameter ascs). The provided number of columns and sort orders must match. The parameter returnIndexes determines whether to return the sorted data (false) or a column-matrix of positions representing the permutation applied by the sorting (true).

Matrix decomposition & co

We plan to support various matrix decompositions like eigen, lu, qr, and svd.

Deep neural network

Note that most of these operations only have a CUDNN-based kernel for GPU execution at the moment.

  • affine(inputData:matrix, weightData:matrix, biasData:matrix)

  • avg_pool2d(inputData:matrix, numImages:size, numChannels:size, imgHeight:size, imgWidth:size, poolHeight:size, poolWidth:size, strideHeight:size, strideWidth:size, paddingHeight:size, paddingWidth:size)

    Performs average pooling operation.

  • max_pool2d(inputData:matrix, numImages:size, numChannels:size, imgHeight:size, imgWidth:size, poolHeight:size, poolWidth:size, strideHeight:size, strideWidth:size, paddingHeight:size, paddingWidth:size)

    Performs max pooling operation.

  • batch_norm2d(inputData:matrix, gamma, beta, emaMean, emaVar, eps)

    Performs batch normalization operation.

  • biasAdd(input:matrix, bias:matrix)

    Adds the (1 x numChannels) row-matrix bias to the input with the given number of channels.

  • conv2d(input:matrix, filter:matrix, numImages:size, numChannels:size, imgHeight:size, imgWidth:size, filterHeight:size, filterWidth:size, strideHeight:size, strideWidth:size, paddingHeight:size, paddingWidth:size)

    2D convolution.

  • relu(inputData:matrix)

  • softmax(inputData:matrix)

Other matrix operations

  • diagVector(arg:matrix)

    Extracts the diagonal of the given (n x n) matrix arg as a (n x 1) column-matrix.

  • lowerTri(arg:matrix, diag:bool, values:bool)

    Extracts the lower triangle of the given square matrix arg by setting all elements in the upper triangle to zero. If diag is true, the elements on the diagonal are retained; otherwise, they are set to zero, too. If values is true, the non-zero elements in the lower triangle are retained; otherwise, they are set to one.

  • upperTri(arg:matrix, diag:bool, values:bool)

    Extracts the upper triangle of the given square matrix arg by setting all elements in the lower triangle to zero. If diag is true, the elements on the diagonal are retained; otherwise, they are set to zero, too. If values is true, the non-zero elements in the upper triangle are retained; otherwise, they are set to one.

  • solve(A:matrix, b:matrix)

    Solves the system of linear equations given by the (n x n) matrix A and the (n x 1) column-matrix b and returns the result as a (n x 1) column-matrix.

  • replace(arg:matrix, pattern:scalar, replacement:scalar)

    Replaces all occurrences of the element pattern in the matrix arg by the element replacement.

  • ctable(ys:matrix, xs:matrix[, weight:scalar][, numRows:int, numCols:int])

    Returns the contingency table of two (n x 1) column-matrices ys and xs. The resulting matrix res consists of max(ys) + 1 rows and max(xs) + 1 columns. More precisely, \(\text{res}[x, y] = \left| \{ k \bigm| \text{ys}[k, 0] = y \wedge \text{xs}[k, 0] = x, \; 0 \leq k \leq n-1 \} \right| * \text{weight} \quad \forall x \in \text{xs}, y \in \text{ys}\).

    In other words, starting with an all-zero result matrix, all pairs of values \(\{ (\text{xs}[k, 0],\text{ys}[k, 0]) \mid 0 \leq k \leq n-1 \}\) are used to index the result matrix and increase the corresponding value by weight.
    Note that ys and xs must not contain negative numbers.

    The scalar weight is an optional argument and defaults to 1.0. The weight also determines the value type of the result.

    Moreover, optionally, the result shape in terms of the number of rows and columns can be specified. If omited, it defaults to the smallest numbers required to accommodate all given y/x-coordinates, as expressed above. If specified, the result can be either cropped or padded with zeros to the desired shape. If a value less than zero is provided as the number of rows/columns, the respective dimension will also be determined from the input data.

    This built-in function can be called with 2, 3, 4, or 5 arguments, depending on which optional arguments are given.

  • syrk(A:martix)

    Calculates t(A) @ A by symmetric rank-k update operations.

  • gemv(A:matrix, x:matrix)

    Calcuates t(A) @ x for the given (n x m) matrix A and (n x 1) column-matrix x.

Extended relational algebra

DaphneDSL supports relational algebra on frames in two ways: On the one hand, entire SQL queries can be executed over previously registered views. This aspect is described in detail in a separate tutorial. On the other hand, built-in functions for individual operations of extended relational algebra can be used on frames in DaphneDSL.

Entire SQL query

  • registerView(viewName:str, arg:frame)

    Registers the frame arg to be accessible to SQL queries by the name viewName.

  • sql(query:str)

    Executes the SQL query query on the frames previously registered with registerView() and returns the result as a frame.

Set Operations

We will support set operations such as intersect, merge, and except.

Cartesian product and joins

  • cartesian(lhs:frame, rhs:frame)

    Calculates the cartesian (cross) product of the two input frames.

  • innerJoin(lhs:frame, rhs:frame, lhsOn:str, rhsOn:str)

    Performs an inner join of the two input frames on lhs.lhsOn == rhs.rhsOn.

  • semiJoin(lhs:frame, rhs:frame, lhsOn:str, rhsOn:str)

    Performs a semi join of the two input frames on lhs.lhsOn == rhs.rhsOn. Returns only the columns belonging to lhs.

  • groupJoin(lhs:frame, rhs:frame, lhsOn:str, rhsOn:str, rhsAgg:str)

    Group-join of lhs and rhs on lhs.lhsOn == rhs.rhsOn with summation of rhs.rhsAgg.

We will support more variants of joins, including (left/right) outer joins, theta joins, anti-joins, etc.

Frame label manipulation

  • setColLabels(arg:frame, labels:str, ...)

    Sets the column labels of the given frame arg to the given labels. There must be as many labels as columns in arg.

  • setColLabelsPrefix(arg:frame, predfix:str, ...)

    Prepends the given prefix to the labels of all columns in arg.

Conversions and casts

Note that DaphneDSL offers casts in form of the as.()-expression. See the DaphneDSL Language Reference for details.

  • quantize(arg:matrix<f32>, min:f32, max:f32)

    Performs a min/max quantization of the values in arg. The result matrix is of value type ui8.

Input/output

DAPHNE supports local file I/O for various file formats. The format is determined by the specified file name extension. Currently, the following formats are supported:

For both reading and writing, file names can be specified as absolute or relative paths.

For most formats, DAPHNE requires additional information on the data and value types as well as dimensions, when reading files. These must be provided in a separate .meta-file.

  • print(arg:scalar/matrix/frame[, newline:bool[, toStderr:bool]])

    Prints the given scalar, matrix, or frame arg to stdout. The parameter newline is optional; true (the default) means a new line is started after arg, false means no new line is started. The parameter toStderr is optional; true means the text is printed to stderr, false (the default) means it is printed to stdout.

  • readFrame(filename:str)

    Reads the file filename into a frame. Assumes that a .meta-file is present for the specified filename.

  • readMatrix(filename:str)

    Reads the file filename into a matrix. Assumes that a .meta-file is present for the specified filename.

  • write/writeFrame/writeMatrix(arg:matrix/frame, filename:str)

    Writes the given matrix or frame arg into the specified file filename. Note that the type of arg determines how to store the data; thus, it suffices to call write() (but writeFrame() and writeMatrix() can be used synonymously for consistency with reading). At the same time, this creates a .meta-file for the written file, so that it can be read again using readMatrix()/readFrame().

  • stop([message:str])

    Terminates the DaphneDSL script execution with the given optional message.

Data preprocessing

  • oneHot(arg:matrix, info:matrix<si64>)

    Applies one-hot-encoding to the given (n x m) matrix arg. The (1 x m) row-matrix info specifies the details (in the following, d[j] is short for info[0, j]):

    • If d[j] == -1, then the j-th column of arg will remain as it is.
    • If d[j] == 0, then the j-th column of arg will be omitted in the output.

    • If d[j] > 0, then the j-th column of arg will be encoded to a vector of length d[j].

      More precisely, if d[j] > 0 the j-th column of arg must contain only integral values in the range [0, d[j] - 1], and will be replaced by d[j] columns containing only zeros and ones. For each row i in arg, the value in the as.scalar(arg[i, j])-th of those columns is set to 1, while all others are set to 0.

  • recode(arg:matrix, orderPreserving:bool)

    Applies dictionary encoding to the given (n x 1) matrix arg, i.e., assigns an integral code to each distinct value in arg. The codes start at 0. Iff the parameter orderPreserving is true, then the distinct values are sorted in ascending order before assigning codes. That way, range predicates are possible on the encoded output. Otherwise, new codes are assigned to distinct values are they are encountered. That way, only point predicates are possible on the encoded output.

    There are two results:

    • The first result is the encoded data, a (n x 1) matrix of the codes for each element in the input arg.
    • The second result is the decoding dictionary, a (#distinct(arg) x 1) matrix of the distinct values in arg. The value at position i is the value that is mapped to the code i.

    The encoded data can be decoded by right indexing on the dictionary using the codes as positions.

    Example:

    data = [10, 5, 20, 5, 20];
codes, dict = recode(data, false);
print(codes);
print(dict);
decoded = dict[codes, ];
print(decoded);

    DenseMatrix(5x1, int64_t)
0
1
2
1
2
DenseMatrix(3x1, int64_t)
10
5
20
DenseMatrix(5x1, int64_t)
10
5
20
5
20

  • bin(arg:matrix, numBins:size[, min:scalar, max:scalar])

    Applies binning to the given matrix arg, i.e., each value is mapped to the number of its bin (counting starts at zero). The bin boundaries are determined by splitting the interval [min, max] into numBins equi-width bins. The number of bins numBins is required. Specifying min and max is optional; if they are omitted, they are automatically calculated, whereby NaNs in arg are not taken into account.

    Example:

    print(bin(t([10, 20, 30, 40, 50, 60, 70]), 3));
print(bin(t([10, 20, 30, 40, 50, 60, 70]), 3, 10, 70));
print(bin(t([5.0, 20.0, nan, 40.0, inf, 60.0, 100.0]), 3, 10.0, 70.0));

    DenseMatrix(1x7, int64_t)
0 0 0 1 1 2 2
DenseMatrix(1x7, int64_t)
0 0 0 1 1 2 2
DenseMatrix(1x7, double)
0 0 nan 1 2 2 2

Measurements

  • now()

    Returns the current time since the epoch in nano seconds.