/*
* 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 FPGrowthModel = require(EclairJS_Globals.NAMESPACE + '/mllib/fpm/FPGrowthModel');
/**
* A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in
* [[http://dx.doi.org/10.1145/1454008.1454027 Li et al., PFP: Parallel FP-Growth for Query
* Recommendation]]. PFP distributes computation in such a way that each worker executes an
* independent group of mining tasks. The FP-Growth algorithm is described in
* [[http://dx.doi.org/10.1145/335191.335372 Han et al., Mining frequent patterns without candidate
* generation]].
*
* @param minSupport the minimal support level of the frequent pattern, any pattern appears
* more than (minSupport * size-of-the-dataset) times will be output
* @param numPartitions number of partitions used by parallel FP-growth
*
* @see [[http://en.wikipedia.org/wiki/Association_rule_learning Association rule learning
* (Wikipedia)]]
*
* @classdesc
*/
/**
* Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
* as the input data}.
*
* @class
* @memberof module:eclairjs/mllib/fpm
*/
var FPGrowth = function(obj) {
this.logger = Logger.getLogger("FPGrowth_js");
var jvmObject;
if (obj instanceof org.apache.spark.mllib.fpm.FPGrowth) {
jvmObject = obj;
} else {
jvmObject = new org.apache.spark.mllib.fpm.FPGrowth();
}
JavaWrapper.call(this, jvmObject);
};
FPGrowth.prototype = Object.create(JavaWrapper.prototype);
FPGrowth.prototype.constructor = FPGrowth;
/**
* Sets the minimal support level (default: `0.3`).
*
* @param {float} minSupport
* @returns {module:eclairjs/mllib/fpm.FPGrowth}
*/
FPGrowth.prototype.setMinSupport = function(minSupport) {
var javaObject = this.getJavaObject().setMinSupport(minSupport);
return new FPGrowth(javaObject);
};
/**
* Sets the number of partitions used by parallel FP-growth (default: same as input data).
*
* @param {integer} numPartitions
* @returns {module:eclairjs/mllib/fpm.FPGrowth}
*/
FPGrowth.prototype.setNumPartitions = function(numPartitions) {
var javaObject = this.getJavaObject().setNumPartitions(numPartitions);
return new FPGrowth(javaObject);
};
/**
* Computes an FP-Growth model that contains frequent itemsets.
* @param {module:eclairjs.RDD} data input data set, each element contains a transaction
*
* @returns {module:eclairjs/mllib/fpm.FPGrowthModel} an [[module:eclairjs/mllib/fpm.FPGrowthModel]]
*/
FPGrowth.prototype.run = function(data) {
var data_uw = Utils.unwrapObject(data);
var javaObject = this.getJavaObject().run(data_uw);
return new FPGrowthModel(javaObject);
};
module.exports = FPGrowth;
})();