/*
* 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.
*/
module.exports = function(kernelP) {
return (function() {
var Utils = require('../../utils.js');
var RDD = require('../../rdd/RDD.js');
var gKernelP = kernelP;
/**
* Model representing the result of matrix factorization.
*
* Note: If you create the model directly using constructor, please be aware that fast prediction
* requires cached user/product features and their associated partitioners.
*
* @param rank Rank for the features in this model.
* @param userFeatures RDD of tuples where each tuple represents the userId and
* the features computed for this user.
* @param productFeatures RDD of tuples where each tuple represents the productId
* and the features computed for this product.
* @classdesc
*/
/**
* @param {number} rank
* @param {module:eclairjs/rdd.RDD} userFeatures
* @param {module:eclairjs/rdd.RDD} productFeatures
* @class
* @memberof module:eclairjs/mllib/recommendation
*/
function MatrixFactorizationModel() {
Utils.handleConstructor(this, arguments, gKernelP);
}
/**
* @param {number|RDD} user
* @param {number} Optional product
* @returns {Promise.<number>|RDD}
*/
MatrixFactorizationModel.prototype.predict = function(user, product) {
var args = {
target: this,
method: 'predict',
args: Utils.wrapArguments(arguments),
returnType: product ? Number : RDD
};
return Utils.generate(args);
};
/**
* Recommends products to a user.
*
* @param {number} user the user to recommend products to
* @param {number} num how many products to return. The number returned may be less than this.
* "score" in the rating field. Each represents one recommended product, and they are sorted
* by score, decreasing. The first returned is the one predicted to be most strongly
* recommended to the user. The score is an opaque value that indicates how strongly
* recommended the product is.
* @returns {module:eclairjs/mllib/recommendation.Rating[]} [[Rating]] objects, each of which contains the given user ID, a product ID, and a
*/
MatrixFactorizationModel.prototype.recommendProducts = function(user,num) {
throw "not implemented by ElairJS";
// var args ={
// target: this,
// method: 'recommendProducts',
// args: [
// { value: user, type: 'number' },
// { value: num, type: 'number' }
// ],
// returnType: [Rating]
//
// };
//
// return Utils.generate(args);
};
/**
* Recommends users to a product. That is, this returns users who are most likely to be
* interested in a product.
*
* @param {number} product the product to recommend users to
* @param {number} num how many users to return. The number returned may be less than this.
* "score" in the rating field. Each represents one recommended user, and they are sorted
* by score, decreasing. The first returned is the one predicted to be most strongly
* recommended to the product. The score is an opaque value that indicates how strongly
* recommended the user is.
* @returns {module:eclairjs/mllib/recommendation.Rating[]} [[Rating]] objects, each of which contains a user ID, the given product ID, and a
*/
MatrixFactorizationModel.prototype.recommendUsers = function(product,num) {
throw "not implemented by ElairJS";
// var args ={
// target: this,
// method: 'recommendUsers',
// args: [
// { value: product, type: 'number' },
// { value: num, type: 'number' }
// ],
// returnType: [Rating]
//
// };
//
// return Utils.generate(args);
};
/**
* Save this model to the given path.
*
* This saves:
* - human-readable (JSON) model metadata to path/metadata/
* - Parquet formatted data to path/data/
*
* The model may be loaded using {@link load}.
*
* @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.
* @param {boolean} [overwrite] overwrites the directory, defaults to false.
* @returns {Promise.<Void>} A Promise that resolves to nothing.
*/
MatrixFactorizationModel.prototype.save = function(sc,path,overwrite) {
var args ={
target: this,
method: 'save',
args: [
{ value: sc, type: 'SparkContext' },
{ value: path, type: 'string' },
{ value: overwrite, type: 'booolean' }
],
returnType: null
};
return Utils.generate(args);
};
/**
* Recommends topK products for all users.
*
* @param {number} num how many products to return for every user.
* rating objects which contains the same userId, recommended productID and a "score" in the
* rating field. Semantics of score is same as recommendProducts API
* @returns {module:eclairjs/rdd.RDD} [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of
*/
MatrixFactorizationModel.prototype.recommendProductsForUsers = function(num) {
var args = {
target: this,
method: 'recommendProductsForUsers',
args: Utils.wrapArguments(arguments),
returnType: RDD
};
return Utils.generate(args);
};
/**
* Recommends topK users for all products.
*
* @param {number} num how many users to return for every product.
* of rating objects which contains the recommended userId, same productID and a "score" in the
* rating field. Semantics of score is same as recommendUsers API
* @returns {module:eclairjs/rdd.RDD} [(Int, Array[Rating])] objects, where every tuple contains a productID and an array
*/
MatrixFactorizationModel.prototype.recommendUsersForProducts = function(num) {
throw "not implemented by ElairJS";
// var args ={
// target: this,
// method: 'recommendUsersForProducts',
// args: [
// { value: num, type: 'number' }
// ],
// returnType: RDD
//
// };
//
// return Utils.generate(args);
};
/**
*
* @returns {module:eclairjs/rdd.RDD}
*/
MatrixFactorizationModel.prototype.userFeatures = function() {
var args = {
target: this,
method: 'userFeatures',
returnType: RDD
};
return Utils.generate(args);
};
/**
*
* @returns {module:eclairjs/rdd.RDD}
*/
MatrixFactorizationModel.prototype.productFeatures = function() {
var args = {
target: this,
method: 'productFeatures',
returnType: RDD
};
return Utils.generate(args);
};
//
// static methods
//
/**
* Load a model from the given path.
*
* The model should have been saved by {@link save}.
*
* @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/recommendation.MatrixFactorizationModel} Model instance
*/
MatrixFactorizationModel.load = function(sc,path) {
var args ={
target: MatrixFactorizationModel,
method: 'load',
static: true,
kernelP: gKernelP,
args: [
{ value: sc, type: 'SparkContext' },
{ value: path, type: 'string' }
],
returnType: MatrixFactorizationModel,
};
return Utils.generate(args);
};
MatrixFactorizationModel.moduleLocation = '/mllib/recommendation/MatrixFactorizationModel';
return MatrixFactorizationModel;
})();
};