/*
* 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');
/**
* @classdesc
* Linear regression results evaluated on a dataset.
* @class
* @memberof module:eclairjs/ml/regression
*/
var LinearRegressionSummary = function (jvmObject) {
this.logger = Logger.getLogger("ml_regression_LinearRegressionSummary_js");
JavaWrapper.call(this, jvmObject);
};
LinearRegressionSummary.prototype = Object.create(JavaWrapper.prototype);
LinearRegressionSummary.prototype.constructor = LinearRegressionSummary;
/**
*
* @returns {module:eclairjs/sql.DataFrame}
*/
LinearRegressionSummary.prototype.predictions = function () {
return Utils.javaToJs(this.getJavaObject().predictions());
};
/**
*
* @returns {string}
*/
LinearRegressionSummary.prototype.predictionCol = function () {
return this.getJavaObject().predictionCol();
};
/**
*
* @returns {string}
*/
LinearRegressionSummary.prototype.labelCol = function () {
return this.getJavaObject().labelCol();
};
/**
*
* @returns {module:eclairjs/ml/regression.LinearRegressionModel}
*/
LinearRegressionSummary.prototype.model = function () {
return Utils.javaToJs(this.getJavaObject().model());
};
/**
*
* @returns {float}
*/
LinearRegressionSummary.prototype.explainedVariance = function () {
return this.getJavaObject().explainedVariance();
};
/**
* Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
* Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
* @returns {float}
*/
LinearRegressionSummary.prototype.meanAbsoluteError = function () {
return this.getJavaObject().meanAbsoluteError();
};
/**
* Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
* Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
* @returns {float}
*/
LinearRegressionSummary.prototype.meanSquaredError = function () {
return this.getJavaObject().meanSquaredError();
};
/**
* Returns the root mean squared error, which is defined as the square root of the mean squared error.
* Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
* @returns {float}
*/
LinearRegressionSummary.prototype.rootMeanSquaredError = function () {
return this.getJavaObject().rootMeanSquaredError();
};
/**
* Returns R^2^, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination
* Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
* @returns {float}
*/
LinearRegressionSummary.prototype.r2 = function () {
return this.getJavaObject().r2();
};
/**
* Residuals (label - predicted value)
* @returns {module:eclairjs/sql.DataFrame}
*/
LinearRegressionSummary.prototype.residuals = function () {
return Utils.javaToJs(this.getJavaObject().residuals());
};
/**
* Number of instances in DataFrame predictions
* @returns {integer}
*/
LinearRegressionSummary.prototype.numInstances = function () {
return this.getJavaObject().numInstances();
};
/**
* The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
* @returns {float[]}
*/
LinearRegressionSummary.prototype.devianceResiduals = function () {
return Utils.javaToJs(this.getJavaObject().devianceResiduals());
};
/**
* Standard error of estimated coefficients and intercept.
* @returns {float[]}
*/
LinearRegressionSummary.prototype.coefficientStandardErrors = function () {
return Utils.javaToJs(this.getJavaObject().coefficientStandardErrors());
};
/**
* T-statistic of estimated coefficients and intercept.
* @returns {float[]}
*/
LinearRegressionSummary.prototype.tValues = function () {
return Utils.javaToJs(this.getJavaObject().tValues());
};
/**
* Two-sided p-value of estimated coefficients and intercept.
* @returns {float[]}
*/
LinearRegressionSummary.prototype.pValues = function () {
return Utils.javaToJs(this.getJavaObject().pValues());
};
module.exports = LinearRegressionSummary;
})();