Source: mllib/tree/model/DecisionTreeModel.js

/*
 * 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.
 */

var Utils = require('../../../utils.js');

var gKernelP;

/**
 * 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
 * @class
 * @memberof module:eclairjs/mllib/tree/model
 */

function DecisionTreeModel() {
  Utils.handleConstructor(this, arguments, gKernelP);
}

/**
 * Predict values for a single data point using the model trained.
 *
 * @param {module:eclairjs/mllib/linalg.Vector} features  array representing a single data point
 * @returns {Promise.<number>}  Double prediction from the trained model
 */
DecisionTreeModel.prototype.predict0 = function(features) {
  throw "not implemented by ElairJS";
//
// function _resolve(result, resolve, reject) {
// 	var returnValue=parseInt(result)
// 	resolve(returnValue);
// };
//   var args ={
//     target: this,
//     method: 'predict',
//     args: [
//       { value: features, type: 'Vector' }
//     ],
//     resolver: _resolve,
//     returnType: Number
//
//   };
//
//   return Utils.generate(args);
};


/**
 * Predict values for the given data set using the model trained.
 *
 * @param {module:eclairjs/rdd.RDD} features  RDD representing data points to be predicted
 * @returns {module:eclairjs/rdd.RDD}  RDD of predictions for each of the given data points
 */
DecisionTreeModel.prototype.predict1 = function(features) {
  throw "not implemented by ElairJS";
//   var args ={
//     target: this,
//     method: 'predict',
//     args: [
//       { value: features, type: 'RDD' }
//     ],
//     returnType: RDD
//
//   };
//
//   return Utils.generate(args);
};


/**
 * Predict values for the given data set using the model trained.
 *
 * @param {JavaRDD} features  JavaRDD representing data points to be predicted
 * @returns {JavaRDD}  JavaRDD of predictions for each of the given data points
 */
DecisionTreeModel.prototype.predict2 = function(features) {
  throw "not implemented by ElairJS";
//   var args ={
//     target: this,
//     method: 'predict',
//     args: [
//       { value: features, type: 'JavaRDD' }
//     ],
//     returnType: JavaRDD
//
//   };
//
//   return Utils.generate(args);
};


/**
 * Get number of nodes in tree, including leaf nodes.
 * @returns {Promise.<number>}
 */
DecisionTreeModel.prototype.numNodes = function() {
  throw "not implemented by ElairJS";
//
// function _resolve(result, resolve, reject) {
// 	var returnValue=parseInt(result)
// 	resolve(returnValue);
// };
//   var args ={
//     target: this,
//     method: 'numNodes',
//     resolver: _resolve,
//     returnType: Number
//
//   };
//
//   return Utils.generate(args);
};


/**
 * Get depth of tree.
 * E.g.: Depth 0 means 1 leaf node.  Depth 1 means 1 internal node and 2 leaf nodes.
 * @returns {Promise.<number>}
 */
DecisionTreeModel.prototype.depth = function() {
  throw "not implemented by ElairJS";
//
// function _resolve(result, resolve, reject) {
// 	var returnValue=parseInt(result)
// 	resolve(returnValue);
// };
//   var args ={
//     target: this,
//     method: 'depth',
//     resolver: _resolve,
//     returnType: Number
//
//   };
//
//   return Utils.generate(args);
};


/**
 * Print a summary of the model.
 * @returns {Promise.<string>}
 */
DecisionTreeModel.prototype.toString = function() {
  throw "not implemented by ElairJS";
//
// function _resolve(result, resolve, reject) {
// 	var returnValue=result
// 	resolve(returnValue);
// };
//   var args ={
//     target: this,
//     method: 'toString',
//     resolver: _resolve,
//     returnType: String
//
//   };
//
//   return Utils.generate(args);
};


/**
 * Print the full model to a string.
 * @returns {Promise.<string>}
 */
DecisionTreeModel.prototype.toDebugString = function() {
  throw "not implemented by ElairJS";
//
// function _resolve(result, resolve, reject) {
// 	var returnValue=result
// 	resolve(returnValue);
// };
//   var args ={
//     target: this,
//     method: 'toDebugString',
//     resolver: _resolve,
//     returnType: String
//
//   };
//
//   return Utils.generate(args);
};


/**
 * @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.
 * @returns {Promise.<Void>} A Promise that resolves to nothing.
 */
DecisionTreeModel.prototype.save = function(sc,path) {
  throw "not implemented by ElairJS";
//   var args ={
//     target: this,
//     method: 'save',
//     args: [
//       { value: sc, type: 'SparkContext' },
//       { value: path, type: 'string' }
//     ],
//     returnType: null
//
//   };
//
//   return Utils.generate(args);
};

//
// 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) {
  throw "not implemented by ElairJS";
//   var args ={
//     target: DecisionTreeModel,
//     method: 'load',
//     args: [
//       { value: sc, type: 'SparkContext' },
//       { value: path, type: 'string' }
//     ],
//     returnType: DecisionTreeModel
//
//   };
//
//   return Utils.generate(args);
};

DecisionTreeModel.moduleLocation = '/mllib/tree/model/DecisionTreeModel';

module.exports = function(kP) {
  if (kP) gKernelP = kP;

  return DecisionTreeModel;
};