/*
* 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 ProbabilisticClassifier = require('./ProbabilisticClassifier')();
var gKernelP = kernelP;
/**
* @classdesc
* Logistic regression.
* Currently, this class only supports binary classification. It will support multiclass
* in the future.
* @class
* @extends module:eclairjs/ml/classification.ProbabilisticClassifier
* @memberof module:eclairjs/ml/classification
* @param {string} [uid]
*/
function LogisticRegression() {
Utils.handleConstructor(this, arguments, gKernelP);
}
LogisticRegression.prototype = Object.create(ProbabilisticClassifier.prototype);
LogisticRegression.prototype.constructor = LogisticRegression;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {Promise.<string>}
*/
LogisticRegression.prototype.uid = function () {
var args = {
target: this,
method: 'uid',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Set the regularization parameter.
* Default is 0.0.
* @param {float} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setRegParam = function(value) {
var args = {
target: this,
method: 'setRegParam',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* @returns {module:eclairjs/ml/param.Param}
*/
LogisticRegression.prototype.regParam = function() {
var Param = require('../param/Param')();
var args = {
target: this,
method: 'regParam',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* Set the ElasticNet mixing parameter.
* For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
* For 0 < alpha < 1, the penalty is a combination of L1 and L2.
* Default is 0.0 which is an L2 penalty.
* @param {float} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setElasticNetParam = function(value) {
var args = {
target: this,
method: 'setElasticNetParam',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* Set the maximum number of iterations.
* Default is 100.
* @param {integer} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setMaxIter = function(value) {
var args = {
target: this,
method: 'setMaxIter',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* @returns {module:eclairjs/ml/param.Param}
*/
LogisticRegression.prototype.maxIter = function() {
var Param = require('../param/Param')();
var args = {
target: this,
method: 'maxIter',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* Set the convergence tolerance of iterations.
* Smaller value will lead to higher accuracy with the cost of more iterations.
* Default is 1E-6.
* @param {float} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setTol = function(value) {
var args = {
target: this,
method: 'setTol',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* Whether to fit an intercept term.
* Default is true.
* @param {boolean} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setFitIntercept = function(value) {
var args = {
target: this,
method: 'setFitIntercept',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* Whether to standardize the training features before fitting the model.
* The coefficients of models will be always returned on the original scale,
* so it will be transparent for users. Note that with/without standardization,
* the models should be always converged to the same solution when no regularization
* is applied. In R's GLMNET package, the default behavior is true as well.
* Default is true.
* @param {boolean} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setStandardization = function(value) {
var args = {
target: this,
method: 'setStandardization',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* @param {float} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setThreshold = function(value) {
var args = {
target: this,
method: 'setThreshold',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* @returns {Promise.<float>}
*/
LogisticRegression.prototype.getThreshold = function() {
var args = {
target: this,
method: 'getThreshold',
args: Utils.wrapArguments(arguments),
returnType: Number
};
return Utils.generate(args);
};
/**
* @returns {module:eclairjs/ml/param.Param}
*/
LogisticRegression.prototype.threshold = function() {
var Param = require('../param/Param')();
var args = {
target: this,
method: 'threshold',
args: Utils.wrapArguments(arguments),
returnType: Param
};
return Utils.generate(args);
};
/**
* Whether to over-/under-sample training instances according to the given weights in weightCol.
* If not set or empty String, all instances are treated equally (weight 1.0).
* Default is not set, so all instances have weight one.
* @param {string} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setWeightCol = function(value) {
var args = {
target: this,
method: 'setWeightCol',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* @param {float[]} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setThresholds = function(value) {
var args = {
target: this,
method: 'setThresholds',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* @returns {Promise.<float[]>}
*/
LogisticRegression.prototype.getThresholds = function() {
var args = {
target: this,
method: 'getThresholds',
args: Utils.wrapArguments(arguments),
returnType: [Number]
};
return Utils.generate(args);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.copy = function(extra) {
var args = {
target: this,
method: 'copy',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args);
};
/**
* FIXME from Param
* @returns {Promise.<string>}
*/
LogisticRegression.prototype.explainParams = function() {
var args = {
target: this,
method: 'explainParams',
args: Utils.wrapArguments(arguments),
returnType: String
};
return Utils.generate(args);
};
/**
* Fits a model to the input data.
* @param {module:eclairjs/sql.DataFrame} dataset
* @param {module:eclairjs/ml/param.ParamMap} [paramMap] Parameter map.
* These values override any specified in this Estimator's embedded ParamMap.
* @returns {module:eclairjs/ml/classification.LogisticRegressionModel} fitted model
*/
LogisticRegression.prototype.fit = function(dataset, paramMap) {
var LogisticRegressionModel = require('./LogisticRegressionModel')();
var args = {
target: this,
method: 'fit',
args: Utils.wrapArguments(arguments),
returnType: LogisticRegressionModel
};
return Utils.generate(args);
};
//
// static methods
//
/**
* @param {string} path
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.load = function(path) {
var args = {
target: LogisticRegression,
method: 'load',
kernelP: gKernelP,
static: true,
args: Utils.wrapArguments(arguments),
returnType: LogisticRegression
};
return Utils.generate(args)
};
LogisticRegression.moduleLocation = '/ml/classification/LogisticRegression';
return LogisticRegression;
})();
};