XGBoost-Node APIs

Class

XGMatrix

XGModel

Result

Function

XGMatrix

matrix(data, row, col, missing = NaN) ⇒ XGMatrix

restoreMatrix(file) ⇒ XGMatrix

matrixFromCSC(data, indptr, indices, n = 0) ⇒ XGMatrix

matrixFromCSR(data, indptr, indices, n = 0) ⇒ XGMatrix

XGModel

XGModel(file) ⇒ XGModel

XGModel.predict(xgmatrix, mask = 0, ntree = 0) ⇒ Result

XGModel.predictAsync(xgmatrix, mask = 0, ntree = 0, cb: (err, res: Float32Array | null) => {})

Result

XGMatrix

Kind: object - input matrix for XGModel

Field	Type	Description
matrix	internal	readonly property
error	Error	error status

matrix(data, row, col, missing) ⇒ XGMatrix

Kind: global function Returns: XGMatrix - xgboost matrix

Param	Type	Description
data	Float32Array	input matrix with row-major order
row	Integer	matrix row
col	Integer	matrix col
missing	Number = NaN	missing value place holder

const xgboost = require('xgboost');
const input = new Float32Array([
  5.1,  3.5,  1.4,  0.2, // class 0
  6.6,  3. ,  4.4,  1.4, // class 1
  5.9,  3. ,  5.1,  1.8  // class 2
]);
const mat = new xgboost.matrix(input, 3, 4);

XGMatrix.col() ⇒ Result

Kind: member function Returns: Result - return matrix column size

mat.col();

XGMatrix.row() ⇒ Result

Kind: member function Returns: Result - return matrix row size

mat.row();

restoreMatrix(file) ⇒ XGMatrix

Kind: global function Returns: XGMatrix - xgboost matrix

Param	Type	Description
file	string	input matrix file path

const matFromFile = xgboost.restoreMatrix('test/data/xgmatrix.bin');

matrixFromCSC(data, indptr, indices, n) ⇒ XGMatrix

Kind: global function Returns: XGMatrix - xgboost matrix

Param	Type	Description
data	Float32Array	input matrix
indptr	Uint32Array	pointer to col headers
indices	Uint32Array	findex
n	Integer = 0	number of rows; when it's set to 0, then guess from data

// [
//   1, 2, 3, 1,
//   0, 1, 2, 3,
//   0, 1, 1, 1,
// ]
const sparseCSC = xgboost.matrixFromCSC(
  new Float32Array([1, 2, 1, 1, 3, 2, 1, 1, 3, 1]),
  new Uint32Array([0, 1, 4, 7, 10]),
  new Uint32Array([0, 0, 1, 2, 0, 1, 2, 0, 1, 2]),
  0);

matrixFromCSR(data, indptr, indices, n) ⇒ XGMatrix

Kind: global function Returns: XGMatrix - xgboost matrix

Param	Type	Description
data	Float32Array	input matrix
indptr	Uint32Array	pointer to row headers
indices	Uint32Array	findex
n	Integer = 0	number of columns; when it's set to 0, then guess from data

// [
//   1, 2, 3, 1,
//   0, 1, 2, 3,
//   0, 1, 1, 1,
// ]
const sparseCSR = xgboost.matrixFromCSR(
  new Float32Array([1, 2, 3, 1, 1, 2, 3, 1, 1, 1]),
  new Uint32Array([0, 4, 7, 10]),
  new Uint32Array([0, 1, 2, 3, 1, 2, 3, 1, 2, 3]),
  0);

XGModel

Kind: object - Trained XGModel

Field	Type	Description
model	internal	readonly property
error	Error	error status

XGModel(file) ⇒ XGModel

Kind: global function Returns: XGModel - xgboost model

Param	Type	Description
file	string	model file path

const model = xgboost.XGModel('test/data/iris.xg.model');

XGModel.predict(xgmatrix, mask = 0, ntree = 0) ⇒ Result

Kind: member function Returns: Result - prediction result with Float32Array

Param	Type	Description
matrix	XGMatrix	input matrix
mask	Integer = 0	options taken in prediction, possible values, 0:normal prediction, 1:output margin instead of transformed value, 2:output leaf index of trees instead of leaf value, note leaf index is unique per tree, 4:output feature contributions to individual predictions
ntree	Integer = 0	limit number of trees used for prediction, this is only valid for boosted trees when the parameter is set to 0, we will use all the trees

model.predict(mat);

XGModel.predictAsync(xgmatrix, mask = 0, ntree = 0, cb: (err, res: Float32Array | null) => {})

Kind: member function

Param	Type	Description
matrix	XGMatrix	input matrix
mask	Integer = 0	options taken in prediction, possible values, 0:normal prediction, 1:output margin instead of transformed value, 2:output leaf index of trees instead of leaf value, note leaf index is unique per tree, 4:output feature contributions to individual predictions
ntree	Integer = 0	limit number of trees used for prediction, this is only valid for boosted trees when the parameter is set to 0, we will use all the trees
cb	Function	callback function to accept error status and a Float32Array result

model.predictAsync(mat, 0, 0, (err, res) => {
  console.log(err);
  console.log(res);
});

Result

Kind: object

Field	Type	Description
value	Float32Array | number	prediction result or method result
error	Error	error status