/*
* 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 JavaWrapper = require(EclairJS_Globals.NAMESPACE + '/JavaWrapper');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
//var GeneralizedLinearRegressionSummary = Java.type('org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary');
/**
* @classdesc
* :: Experimental ::
* Summary of {@link GeneralizedLinearRegression} model and predictions.
*
* @param dataset Dataset to be summarized.
* @param origModel Model to be summarized. This is copied to create an internal
* model which cannot be modified from outside.
* @class
* @memberof module:eclairjs/ml/regression
*/
var GeneralizedLinearRegressionSummary = function(jvmObject) {
this.logger = Logger.getLogger("ml_regression_GeneralizedLinearRegressionSummary_js");
JavaWrapper.call(this, jvmObject);
};
GeneralizedLinearRegressionSummary.prototype = Object.create(JavaWrapper.prototype);
GeneralizedLinearRegressionSummary.prototype.constructor = GeneralizedLinearRegressionSummary;
/**
*
* Akaike Information Criterion (AIC) for the fitted model.
*
* @returns {double}
*/
GeneralizedLinearRegressionSummary.prototype.aic = function () {
return Utils.javaToJs(this.getJavaObject().aic());
};
/**
*
* The deviance for the fitted model.
*
* @returns {double}
*/
GeneralizedLinearRegressionSummary.prototype.deviance = function () {
return Utils.javaToJs(this.getJavaObject().deviance());
};
/**
*
* Degrees of freedom.
*
* @returns {double}
*/
GeneralizedLinearRegressionSummary.prototype.degreesOfFreedom = function () {
return Utils.javaToJs(this.getJavaObject().degreesOfFreedom());
};
/**
*
* The dispersion of the fitted model.
*
* @returns {double}
*/
GeneralizedLinearRegressionSummary.prototype.dispersion = function () {
return Utils.javaToJs(this.getJavaObject().dispersion());
};
/**
*
* The deviance for the null model.
*
* @returns {double}
*/
GeneralizedLinearRegressionSummary.prototype.nullDeviance = function () {
return Utils.javaToJs(this.getJavaObject().nullDeviance());
};
/**
*
* The residual degrees of freedom.
*
* @returns {int}
*/
GeneralizedLinearRegressionSummary.prototype.residualDegreeOfFreedom = function () {
return Utils.javaToJs(this.getJavaObject().residualDegreeOfFreedom());
};
/**
*
* The residual degrees of freedom for the null model.
*
* @returns {int}
*/
GeneralizedLinearRegressionSummary.prototype.residualDegreeOfFreedomNull = function () {
return Utils.javaToJs(this.getJavaObject().residualDegreeOfFreedomNull());
};
/**
*
* Predictions output by the model's `transform` method.
*
* @returns {module:eclairjs/sql.Dataset}
*/
GeneralizedLinearRegressionSummary.prototype.predictions = function () {
return Utils.javaToJs(this.getJavaObject().predictions());
};
/**
* Get the residuals of the fitted model by type.
*
* @param {string} [residualsType] The type of residuals which should be returned.
* Supported options: deviance, pearson, working and response.
* @returns {module:eclairjs/sql.Dataset}
* @function
* @name module:eclairjs/ml/regression.GeneralizedLinearRegressionSummary#residuals
*/
GeneralizedLinearRegressionSummary.prototype.residuals = function(residualsType) {
if (arguments[0]) {
return Utils.javaToJs(this.getJavaObject().residuals(residualsType));
} else {
return Utils.javaToJs(this.getJavaObject().residuals());
}
};
module.exports = GeneralizedLinearRegressionSummary;
})();