/*
* 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');
/**
* 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} userFeatures
* @param {module:eclairjs.RDD} productFeatures
* @class
* @memberof module:eclairjs/mllib/recommendation
*/
var MatrixFactorizationModel = function () {
this.logger = Logger.getLogger("MatrixFactorizationModel_js");
var jvmObject = arguments[0];
if (arguments.length > 1) {
var userFeatures = Utils.unwrapObject(arguments[1]);
var productFeatures = Utils.unwrapObject(arguments[2]);
jvmObject = new org.apache.spark.mllib.recommendation.MatrixFactorizationModel(arguments[0], userFeatures, productFeatures);
}
JavaWrapper.call(this, jvmObject);
};
MatrixFactorizationModel.prototype = Object.create(JavaWrapper.prototype);
MatrixFactorizationModel.prototype.constructor = MatrixFactorizationModel;
/**
* @param {integer | module:eclairjs.RDD} user
* @param {integer} [product] Required if argument one is integer
* @returns {module:eclairjs.RDD | number}
*/
MatrixFactorizationModel.prototype.predict = function (user, product) {
if (product) {
return this.getJavaObject().predict(user, product);
} else {
var usersProducts_uw = Utils.unwrapObject(user);
var rdd = usersProducts_uw.rdd();
var javaObject = this.getJavaObject().predict(rdd);
return Utils.javaToJs(javaObject.toJavaRDD());
}
};
/**
* 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[]} [[module:eclairjs/mllib/recommendation.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 javaObject = this.getJavaObject().recommendProducts(user,num);
// return Utils.javaToJs(javaObject);
};
/**
* 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[]} [[module:eclairjs/mllib/recommendation.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 javaObject = this.getJavaObject().recommendUsers(product,num);
// return Utils.javaToJs(javaObject);
};
/**
* 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.
* If the directory already exists, this method throws an exception.
* @param {boolean} [overwrite] if true overwrites the model, defaults to false;
*/
MatrixFactorizationModel.prototype.save = function (sc, path, overwrite) {
if (overwrite) {
Utils.deleteHadoopFsPath(path);
}
var sc_uw = Utils.unwrapObject(sc);
this.getJavaObject().save(sc_uw.sc(), path);
};
/**
* 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} [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of
*/
MatrixFactorizationModel.prototype.recommendProductsForUsers = function (num) {
var javaObject = this.getJavaObject().recommendProductsForUsers(num);
return Utils.javaToJs(javaObject.toJavaRDD());
};
/**
* 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} [(Int, Array[Rating])] objects, where every tuple contains a productID and an array
*/
MatrixFactorizationModel.prototype.recommendUsersForProducts = function (num) {
throw "not implemented by ElairJS";
// var javaObject = this.getJavaObject().recommendUsersForProducts(num);
// return new RDD(javaObject);
};
/**
*
* @returns {module:eclairjs.RDD}
*/
MatrixFactorizationModel.prototype.userFeatures = function () {
var javaObject = this.getJavaObject().userFeatures();
return Utils.javaToJs(javaObject.toJavaRDD());
};
/**
*
* @returns {module:eclairjs.RDD}
*/
MatrixFactorizationModel.prototype.productFeatures = function () {
var javaObject = this.getJavaObject().productFeatures();
return Utils.javaToJs(javaObject.toJavaRDD());
};
//
// 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 sc_uw = Utils.unwrapObject(sc);
var javaObject = org.apache.spark.mllib.recommendation.MatrixFactorizationModel.load(sc_uw.sc(), path);
return new MatrixFactorizationModel(javaObject);
};
module.exports = MatrixFactorizationModel;
})();