/*
* 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');
/**
* Decision tree model for classification or regression.
* This model stores the decision tree structure and parameters.
* @param topNode root node
* @param algo algorithm type -- classification or regression
* @classdesc
*/
/**
* @param {Node} topNode
* @param {Algo} algo
* @class
* @memberof module:eclairjs/mllib/tree/model
*/
var DecisionTreeModel = function (topNode, algo) {
var jvmObject;
if (topNode instanceof org.apache.spark.mllib.tree.model.DecisionTreeModel) {
jvmObject = topNode;
}/* if (topeNode instanceof Node {
jvmObject = new org.apache.spark.mllib.tree.model.DecisionTreeModel(topNode,algo);
} */ else {
throw "DecisionTreeModel invalid constructor parameter"
}
this.logger = Logger.getLogger("DecisionTreeModel_js");
JavaWrapper.call(this, jvmObject);
};
DecisionTreeModel.prototype = Object.create(JavaWrapper.prototype);
DecisionTreeModel.prototype.constructor = DecisionTreeModel;
/**
* Predict values for a single data point using the model trained.
*
* @param {@module:eclairjs/mllib/linalg.Vector | module:eclairjs.RDD} features Vector or RDD representing a single data point
* @returns {float | module:eclairjs.RDD} float or RDD prediction from the trained model
*/
DecisionTreeModel.prototype.predict = function (features) {
var features_uw = Utils.unwrapObject(features);
return Utils.javaToJs(this.getJavaObject().predict(features_uw));
};
/**
* Get number of nodes in tree, including leaf nodes.
* @returns {integer}
*/
DecisionTreeModel.prototype.numNodes = function () {
return this.getJavaObject().numNodes();
};
/**
* Get depth of tree.
* E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes.
* @returns {integer}
*/
DecisionTreeModel.prototype.depth = function () {
return this.getJavaObject().depth();
};
/**
* Print a summary of the model.
* @returns {string}
*/
DecisionTreeModel.prototype.toString = function () {
return this.getJavaObject().toString();
};
/**
* Print the full model to a string.
* @returns {string}
*/
DecisionTreeModel.prototype.toDebugString = function () {
return this.getJavaObject().toDebugString();
};
/**
* @param {module:eclairjs.SparkContext} sc Spark context used to save model data.
* @param {string} path Path specifying the directory in which to save this model.
* If the directory already exists, this method throws an exception.
*/
DecisionTreeModel.prototype.save = function (sc, path) {
var sc_uw = Utils.unwrapObject(sc);
this.getJavaObject().save(sc_uw.sc(), path);
};
//
// static methods
//
/**
*
* @param {module:eclairjs.SparkContext} sc Spark context used for loading model files.
* @param {string} path Path specifying the directory to which the model was saved.
* @returns {module:eclairjs/mllib/tree/model.DecisionTreeModel} Model instance
*/
DecisionTreeModel.load = function (sc, path) {
var sc_uw = Utils.unwrapObject(sc);
var javaObject = org.apache.spark.mllib.tree.model.DecisionTreeModel.load(sc_uw.sc(), path);
return new DecisionTreeModel(javaObject);
};
module.exports = DecisionTreeModel;
})();