/*
* Copyright 2016 IBM Corp.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
module.exports = function(kernelP) {
return (function() {
var Utils = require('../../utils.js');
var Predictor = require('../Predictor')();
var gKernelP = kernelP;
/**
* @classdesc
* :: Experimental ::
*
* Fit a Generalized Linear Model ([[https://en.wikipedia.org/wiki/Generalized_linear_model]])
* specified by giving a symbolic description of the linear predictor (link function) and
* a description of the error distribution (family).
* It supports "gaussian", "binomial", "poisson" and "gamma" as family.
* Valid link functions for each family is listed below. The first link function of each family
* is the default one.
* - "gaussian" -> "identity", "log", "inverse"
* - "binomial" -> "logit", "probit", "cloglog"
* - "poisson" -> "log", "identity", "sqrt"
* - "gamma" -> "inverse", "identity", "log"
* @class
* @memberof module:eclairjs/ml/regression
* @extends module:eclairjs/ml.Predictor
* @param {string} uid
*/
function GeneralizedLinearRegression() {
Utils.handleConstructor(this, arguments, gKernelP);
}
GeneralizedLinearRegression.prototype = Object.create(Predictor.prototype);
GeneralizedLinearRegression.prototype.constructor = GeneralizedLinearRegression;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {Promise.<string>}
*/
GeneralizedLinearRegression.prototype.uid = function () {
var args = {
target: this,
method: 'uid',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Sets the value of param {@link family}.
* Default is "gaussian".
*
* @param {string} value
* @returns {type}
*/
GeneralizedLinearRegression.prototype.setFamily = function(value) {
var args ={
target: this,
method: 'setFamily',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Sets the value of param {@link link}.
*
* @param {string} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setLink = function(value) {
var args ={
target: this,
method: 'setLink',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Set if we should fit the intercept
* Default is true.
* @param {boolean} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setFitIntercept = function(value) {
var args = {
target: this,
method: 'setFitIntercept',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Sets the maximum number of iterations (applicable for solver "irls").
* Default is 25.
*
* @param {number} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setMaxIter = function(value) {
var args = {
target: this,
method: 'setMaxIter',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Sets the convergence tolerance of iterations.
* Smaller value will lead to higher accuracy with the cost of more iterations.
* Default is 1E-6.
*
* @param {number} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setTol = function(value) {
var args ={
target: this,
method: 'setTol',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Sets the regularization parameter for L2 regularization.
* The regularization term is
* @example
* 0.5 * regParam * L2norm(coefficients)^2
*
* Default is 0.0.
*
* @param {number} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setRegParam = function(value) {
var args = {
target: this,
method: 'setRegParam',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Sets the value of param {@link weightCol}.
* If this is not set or empty, we treat all instance weights as 1.0.
* Default is not set, so all instances have weight one.
*
* @param {string} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setWeightCol = function(value) {
var args = {
target: this,
method: 'setWeightCol',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Sets the solver algorithm used for optimization.
* Currently only supports "irls" which is also the default solver.
*
* @param {string} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setSolver = function(value) {
var args = {
target: this,
method: 'setSolver',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* Sets the link prediction (linear predictor) column name.
*
* @param {string} value
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.setLinkPredictionCol = function(value) {
var args ={
target: this,
method: 'setLinkPredictionCol',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/sql.DataFrame} dataset
* @returns {module:eclairjs/ml/regression.LinearRegressionModel}
*/
GeneralizedLinearRegression.prototype.fit = function(dataset) {
var GeneralizedLinearRegressionModel = require('./GeneralizedLinearRegressionModel.js')();
var args = {
target: this,
method: 'fit',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegressionModel
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.prototype.copy = function(extra) {
var args = {
target: this,
method: 'copy',
args: Utils.wrapArguments(arguments),
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
//
// static methods
//
/**
* @param {string} path
* @returns {module:eclairjs/ml/regression.GeneralizedLinearRegression}
*/
GeneralizedLinearRegression.load = function(path) {
var args = {
target: GeneralizedLinearRegression,
method: 'load',
args: Utils.wrapArguments(arguments),
static: true,
kernelP: gKernelP,
returnType: GeneralizedLinearRegression
};
return Utils.generate(args);
};
GeneralizedLinearRegression.moduleLocation = '/ml/regression/GeneralizedLinearRegression';
return GeneralizedLinearRegression;
})();
};