Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5

Batch Processing

LASSO algorithm follows the general workflow described in Usage Model: Training and Prediction.

Training

For a description of common input and output parameters, refer to Usage Model: Training and Prediction. The LASSO algorithm has the following input parameters in addition to the common input parameters:

Input ID

Input

weights

Optional input.

Pointer to the 1 x n numeric table with weights of samples. The input can be an object of any class derived from NumericTable except for PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

By default, all weights are equal to 1.

gramMatrix

Optional input.

Pointer to the p x p numeric table with pre-computed Gram Matrix. The input can be an object of any class derived from NumericTable except for CSRNumericTable.

By default, the table is set to an empty numeric table. It is used only when the number of features is less than the number of observations.

The LASSO batch training algorithm has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

The computation method used by the LASSO regression. The only training method supported so far is the default dense method.

interceptFlag

True

A flag that indicates whether or not to compute

lassoParameters

Numeric table of size 1 x 1 that contains the default LASSO parameter equal to 0.1.

L 1 coefficients:

A numeric table of size 1 x k (where k is the number of dependent variables) or 1 x 1. The contents of the table depend on its size:

  • For the table of size 1 x k, use the values of LASSO parameters for j = 1, ..., k

  • For the table of size 1 x 1, use the value of LASSO parameter for each dependant variable

This parameter can be an object of any class derived from NumericTable, except for PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

optimizationSolver

Coordinate Descent solver

Optimization procedure used at the training stage.

optResultToCompute

0

The 64-bit integer flag that specifies which extra characteristics of the LASSO regression to compute.

Provide the following value to request a characteristic:

  • computeGramMatrix for Computation Gram matrix

dataUseInComputation

doNotUse

A flag that indicates a permission to overwrite input data. Provide the following value to restrict or allow modification of input data:

  • doNotUse – restricts modification

  • doUse – allows modification

In additional to linear regression result LASSO algorithm has the following optional results:

Result ID

Result

gramMatrix

Pointer to the computed Gram Matrix with size p x p.

Prediction

For a description of the input and output, refer to Usage Model: Training and Prediction.

At the prediction stage, LASSO algorithm has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

Default performance-oriented computation method, the only method supported by the regression-based prediction.

Examples

C++:

Java*:

Python*: