/*
* 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.
*/
(function () {
var ProbabilisticClassifier = require(EclairJS_Globals.NAMESPACE + '/ml/classification/ProbabilisticClassifier');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* @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]
*/
var LogisticRegression = function(uid) {
this.logger = Logger.getLogger("ml_classification_LogisticRegression_js");
var jvmObject;
if (uid) {
if (uid instanceof org.apache.spark.ml.classification.LogisticRegression) {
jvmObject = uid;
} else {
jvmObject = new org.apache.spark.ml.classification.LogisticRegression(uid);
}
} else {
jvmObject = new org.apache.spark.ml.classification.LogisticRegression();
}
ProbabilisticClassifier.call(this, jvmObject);
};
LogisticRegression.prototype = Object.create(ProbabilisticClassifier.prototype);
LogisticRegression.prototype.constructor = LogisticRegression;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {string}
*/
LogisticRegression.prototype.uid = function () {
return this.getJavaObject().uid();
};
/**
* Set the regularization parameter.
* Default is 0.0.
* @param {float} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setRegParam = function(value) {
var javaObject = this.getJavaObject().setRegParam(value);
return new LogisticRegression(javaObject);
};
/**
* @returns {module:eclairjs/ml/param.Param}
*/
LogisticRegression.prototype.regParam = function() {
return Utils.javaToJs(this.getJavaObject().regParam());
};
/**
* 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 javaObject = this.getJavaObject().setElasticNetParam(value);
return new LogisticRegression(javaObject);
};
/**
* Set the maximum number of iterations.
* Default is 100.
* @param {integer} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setMaxIter = function(value) {
var javaObject = this.getJavaObject().setMaxIter(value);
return new LogisticRegression(javaObject);
};
/**
* @returns {module:eclairjs/ml/param.Param}
*/
LogisticRegression.prototype.maxIter = function() {
return Utils.javaToJs(this.getJavaObject().maxIter());
};
/**
* 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 javaObject = this.getJavaObject().setTol(value);
return new LogisticRegression(javaObject);
};
/**
* Whether to fit an intercept term.
* Default is true.
* @param {boolean} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setFitIntercept = function(value) {
var javaObject = this.getJavaObject().setFitIntercept(value);
return new LogisticRegression(javaObject);
};
/**
* 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 javaObject = this.getJavaObject().setStandardization(value);
return new LogisticRegression(javaObject);
};
/**
* @param {float} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setThreshold = function(value) {
var javaObject = this.getJavaObject().setThreshold(value);
return new LogisticRegression(javaObject);
};
/**
* @returns {float}
*/
LogisticRegression.prototype.getThreshold = function() {
return this.getJavaObject().getThreshold();
};
/**
* @returns {module:eclairjs/ml/param.Param}
*/
LogisticRegression.prototype.threshold = function() {
return Utils.javaToJs(this.getJavaObject().threshold());
};
/**
* Whether to over-/under-sample training instances according to the given weights in weightCol.
* If empty, all instances are treated equally (weight 1.0).
* Default is empty, so all instances have weight one.
* @param {string} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setWeightCol = function(value) {
var javaObject = this.getJavaObject().setWeightCol(value);
return new LogisticRegression(javaObject);
};
/**
* @param {float[]} value
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.setThresholds = function(value) {
var javaObject = this.getJavaObject().setThresholds(value);
return new LogisticRegression(javaObject);
};
/**
* @returns {float[]}
*/
LogisticRegression.prototype.getThresholds = function() {
return this.getJavaObject().getThresholds();
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.prototype.copy = function(extra) {
var extra_uw = Utils.unwrapObject(extra);
var javaObject = this.getJavaObject().copy(extra_uw);
return new LogisticRegression(javaObject);
};
/**
* FIXME from Param
* @returns {string}
*/
LogisticRegression.prototype.explainParams = function() {
return this.getJavaObject().explainParams();
};
//
// static methods
//
/**
* @param {string} path
* @returns {module:eclairjs/ml/classification.LogisticRegression}
*/
LogisticRegression.load = function(path) {
var javaObject = org.apache.spark.ml.classification.LogisticRegression.load(path);
return new LogisticRegression(javaObject);
};
module.exports = LogisticRegression;
})();