/*
* 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 PredictionModel = require(EclairJS_Globals.NAMESPACE + '/ml/PredictionModel');
var Logger = require(EclairJS_Globals.NAMESPACE + '/Logger');
var Utils = require(EclairJS_Globals.NAMESPACE + '/Utils');
/**
* @classdesc
* [Random Forest]{@link http://en.wikipedia.org/wiki/Random_forest} model for regression.
* It supports both continuous and categorical features.
* @class
* @extends module:eclairjs/ml.PredictionModel
* @memberof module:eclairjs/ml/regression
*/
var RandomForestRegressionModel = function(jvmObject) {
this.logger = Logger.getLogger("ml_regression_RandomForestRegressionModel_js");
PredictionModel.call(this, jvmObject);
};
RandomForestRegressionModel.prototype = Object.create(PredictionModel.prototype);
RandomForestRegressionModel.prototype.constructor = RandomForestRegressionModel;
/**
* An immutable unique ID for the object and its derivatives.
* @returns {string}
*/
RandomForestRegressionModel.prototype.uid = function () {
return this.getJavaObject().uid();
};
/**
* @returns {DecisionTreeModel[]}
*/
RandomForestRegressionModel.prototype.trees = function() {
var javaObject = this.getJavaObject().trees();
return Utils.javaToJs(javaObject);
};
/**
* @returns {float[]}
*/
RandomForestRegressionModel.prototype.treeWeights = function() {
var javaObject = this.getJavaObject().treeWeights();
return Utils.javaToJs(javaObject);
};
/**
* @param {module:eclairjs/ml/param.ParamMap} extra
* @returns {module:eclairjs/ml/regression.RandomForestRegressionModel}
*/
RandomForestRegressionModel.prototype.copy = function(extra) {
var extra_uw = Utils.unwrapObject(extra);
var javaObject = this.getJavaObject().copy(extra_uw);
return new RandomForestRegressionModel(javaObject);
};
/**
* @returns {string}
*/
RandomForestRegressionModel.prototype.toString = function() {
return this.getJavaObject().toString();
};
/**
* @returns {string}
*/
RandomForestRegressionModel.prototype.toDebugString = function() {
return this.getJavaObject().toDebugString();
};
/**
* Estimate of the importance of each feature.
* This generalizes the idea of "Gini" importance to other losses, following the explanation of Gini importance
* from "Random Forests" documentation by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
* This feature importance is calculated as follows: - Average over trees: - importance(feature j) = sum (over nodes which split on feature j)
* of the gain, where gain is scaled by the number of instances passing through node - Normalize importances for
* tree based on total number of training instances used to build tree. - Normalize feature importance vector to sum to 1.
*
* @returns {module:eclairjs/mllib/linalg.Vector}
*/
RandomForestRegressionModel.prototype.featureImportances = function () {
return Utils.javaToJs(this.getJavaObject().featureImportances());
};
/**
* Validates and transforms the input schema with the provided param map.
* @param {module:eclairjs/sql/types.StructType} schema
* @param {boolean} fitting whether this is in fitting
* @param {module:eclairjs/sql/types.DataType} featuresDataType SQL DataType for FeaturesType.
* E.g., {@link module:eclairjs/sql/types.VectorUDT}for vector features
* @returns {module:eclairjs/sql/types.StructType}
*/
RandomForestRegressionModel.prototype.validateAndTransformSchema = function (schema, fitting, featuresDataType) {
var schema_uw = Utils.unwrapObject(schema);
var featuresDataType_uw = Utils.unwrapObject(featuresDataType);
var javaObject = this.getJavaObject().validateAndTransformSchema(schema_uw, fitting, featuresDataType_uw);
return Utils.javaToJs(javaObject);
};
/**
* Param for label column name.
* @returns {module:eclairjs/ml/param.Param}
*/
RandomForestRegressionModel.prototype.labelCol = function () {
var javaObject = this.getJavaObject().labelCol();
return Utils.javaToJs(javaObject);
};
/**
* @returns {string}
*/
RandomForestRegressionModel.prototype.getLabelCol = function () {
return this.getJavaObject().getLabelCol();
};
/**
* Param for features column name.
* @returns {module:eclairjs/ml/param.Param}
*/
RandomForestRegressionModel.prototype.featuresCol = function () {
var javaObject = this.getJavaObject().featuresCol();
return Utils.javaToJs(javaObject);
};
/**
* @returns {string}
*/
RandomForestRegressionModel.prototype.getFeaturesCol = function () {
return this.getJavaObject().getFeaturesCol();
};
/**
* Param for prediction column name.
* @returns {module:eclairjs/ml/param.Param}
*/
RandomForestRegressionModel.prototype.predictionCol = function () {
var javaObject = this.getJavaObject().predictionCol();
return Utils.javaToJs(javaObject);
};
/**
* @returns {string}
*/
RandomForestRegressionModel.prototype.getPredictionCol = function () {
return this.getJavaObject().getPredictionCol();
};
/**
* @returns {module:eclairjs/ml/util.MLWriter}
*/
RandomForestRegressionModel.prototype.write = function() {
var javaObject = this.getJavaObject().write();
return Utils.javaToJs(javaObject);
};
//
// static methods
//
/**
* @returns {module:eclairjs/ml/util.MLReader}
*/
RandomForestRegressionModel.read = function() {
var javaObject = org.apache.spark.ml.regression.RandomForestRegressionModel.read();
return Utils.javaToJs(javaObject);
};
/**
* @param {string} path
* @returns {module:eclairjs/ml/regression.RandomForestRegressionModel}
*/
RandomForestRegressionModel.load = function(path) {
var javaObject = org.apache.spark.ml.regression.RandomForestRegressionModel.load(path);
return new RandomForestRegressionModel(javaObject);
};
module.exports = RandomForestRegressionModel;
})();