基于Spark Mllib, 使用java api操作的电影推荐系统(spark1.5.2 jdk1.7)

深藏阁楼爱情的钟 2022-07-12 04:27 58阅读 0赞

最近在学习Spark Mllib，看了一些它的算法，但不知道算法怎么去应用，网上的实例大部分都是使用Scala语言写的，没有java的代码，从网上找到了一篇[基于Spark Mllib，SparkSQL的电影推荐系统][Spark Mllib_SparkSQL]  也是使用Scala语言（对其不是很了解，暂时也没有多少时间去学），还好他讲得特别细，关于测试数据他都有下载地址，在这就不多说了。直接把由其改写的java代码附上：

maven依赖如下（可能有的不需要，当时还看了spark的别的方面）：

<dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.10</artifactId>
        <version>1.5.2</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-hive_2.10</artifactId>
        <version>1.5.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-mllib_2.10</artifactId>
        <version>1.5.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>1.2.0</version>
    </dependency>

上面有的jar包可能直接下不下来，可以手动安装，或者连接vpn后再下载。

java代码如下：

import java.util.ArrayList;
    import java.util.List;
    
    import org.apache.spark.SparkConf;
    import org.apache.spark.api.java.JavaPairRDD;
    import org.apache.spark.api.java.JavaRDD;
    import org.apache.spark.api.java.JavaSparkContext;
    import org.apache.spark.api.java.function.DoubleFunction;
    import org.apache.spark.api.java.function.Function;
    import org.apache.spark.api.java.function.Function2;
    import org.apache.spark.api.java.function.VoidFunction;
    import org.apache.spark.mllib.recommendation.ALS;
    import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
    import org.apache.spark.mllib.recommendation.Rating;
    
    import scala.Tuple2;
    import scala.Tuple3;
    
    /**
     * Collaborative filtering 协同过滤 alternating least squares (ALS) (交替最小二乘法(ALS) )
     * Title. <br>
     * Description.
     * <p>
     * Version: 1.0
     * <p>
     */
    public class Test {
    	
    	public static void main(String[] args) {
    		// 创建入口对象
    		SparkConf conf = new SparkConf().setAppName("Collaborative Filtering Example").setMaster("local");
    		JavaSparkContext sc = new JavaSparkContext(conf);
    
    		// 加载评分数据
    		String path = "C:/Users/dulinan/Desktop/movie/ratings.dat";
    		JavaRDD<String> data = sc.textFile(path);
    		
    		// 所有评分数据，由于此数据要分三部分使用，60%用于训练，20%用于验证，最后20%用于测试。将时间戳%10可以得到近似的10等分，用于三部分数据切分
    		JavaRDD<Tuple2<Integer, Rating>> ratingsTrain_KV = data.map(new Function<String, Tuple2<Integer, Rating>>() {
    
    			@Override
                public Tuple2<Integer, Rating> call(String line) throws Exception {
    				String[] fields = line.split("::");
    				if (fields.length != 4) {
    					throw new IllegalArgumentException("每一行必须有且只有4个元素");
    				}
    				int userId = Integer.parseInt(fields[0]);
    				int movieId = Integer.parseInt(fields[1]);
    				double rating = Float.parseFloat(fields[2]);
    				int timestamp = (int) (Long.parseLong(fields[3])%10);
    	            return new Tuple2<Integer, Rating>(timestamp, new Rating(userId, movieId, rating));
                }
    
    		});
    
    		System.out.println("get " + ratingsTrain_KV.count() + " ratings from " + ratingsTrain_KV.distinct().count() + "users on " + ratingsTrain_KV.distinct().count() + "movies");
    		
    		// 加载我的评分数据
    		String mypath = "C:/Users/dulinan/Desktop/movie/test.dat";
    		JavaRDD<String> mydata = sc.textFile(mypath);
    		JavaRDD<Rating> myRatedData_Rating = mydata.map(new Function<String, Rating>() {
    
    			@Override
                public Rating call(String line) throws Exception {
    				String[] fields = line.split("::");
    				if (fields.length != 4) {
    					throw new IllegalArgumentException("每一行必须有且只有4个元素");
    				}
    				int userId = Integer.parseInt(fields[0]);
    				int movieId = Integer.parseInt(fields[1]);
    				double rating = Float.parseFloat(fields[2]);
    	            return new Rating(userId, movieId, rating);
                }
    		});
    		
    		//设置分区数
    		int numPartitions = 3;
    		
    		//将键值小于6（60%）的数据用于训练
    		JavaRDD<Rating> traningData_Rating = JavaPairRDD.fromJavaRDD(ratingsTrain_KV.filter(new Function<Tuple2<Integer,Rating>, Boolean>() {
    
    			@Override
                public Boolean call(Tuple2<Integer, Rating> v1) throws Exception {
    				
    	            return v1._1 < 6;
                }
    		})).values().union(myRatedData_Rating).repartition(numPartitions).cache();
    		
    		//将键值大于6小于8（20%）的数据用于验证
    		JavaRDD<Rating> validateData_Rating = JavaPairRDD.fromJavaRDD(ratingsTrain_KV.filter(new Function<Tuple2<Integer,Rating>, Boolean>() {
    			
    			@Override
    			public Boolean call(Tuple2<Integer, Rating> v1) throws Exception {
    				
    				return v1._1 >= 6 && v1._1 < 8;
    			}
    		})).values().repartition(numPartitions).cache();
    		
    		//将键值大于8（20%）的数据用于测试
    		JavaRDD<Rating> testData_Rating = JavaPairRDD.fromJavaRDD(ratingsTrain_KV.filter(new Function<Tuple2<Integer,Rating>, Boolean>() {
    			
    			@Override
    			public Boolean call(Tuple2<Integer, Rating> v1) throws Exception {
    				
    				return v1._1 >= 8;
    			}
    		})).values().cache();
    		
    		System.out.println("training data's num : " + traningData_Rating.count() + " validate data's num : " + validateData_Rating.count() + " test data's num : " + testData_Rating.count());
    		
    		// 为训练设置参数 每种参数设置2个值，三层for循环，一共进行8次训练
    		List<Integer> ranks = new ArrayList<Integer>();
    		ranks.add(8);
    		ranks.add(22);
    		
    		List<Double> lambdas = new ArrayList<Double>();
    		lambdas.add(0.1);
    		lambdas.add(10.0);
    		
    		List<Integer> iters = new ArrayList<Integer>();
    		iters.add(5);
    		iters.add(7);
    		
    		// 初始化最好的模型参数 
    		MatrixFactorizationModel bestModel = null;
    	    double bestValidateRnse = Double.MAX_VALUE;
    	    int bestRank = 0;
    	    double bestLambda = -1.0;
    	    int bestIter = -1;
    		
    		for (int i = 0; i < ranks.size(); i++) {
    			for (int j = 0; j < lambdas.size(); j++) {
    				for (int k = 0; k < iters.size(); k++) {
    					//训练获得模型
    					MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(traningData_Rating), ranks.get(i), iters.get(i), lambdas.get(i));
    					//通过校验集validateData_Rating获取方差，以便查看此模型的好坏，方差方法定义在最下面
    					double validateRnse = variance(model, validateData_Rating, validateData_Rating.count());
    					System.out.println("validation = " + validateRnse + " for the model trained with rank = " + ranks.get(i) + " lambda = " + lambdas.get(i) + " and numIter" + iters.get(i));
    					
    			        //将最好的模型训练结果所设置的参数进行保存
    					if (validateRnse < bestValidateRnse) {
    						bestModel = model;
    						bestValidateRnse = validateRnse;
    						bestRank = ranks.get(i);
    						bestLambda = lambdas.get(i);
    						bestIter = iters.get(i);
    					}
    				}
    			}
    		}
    		
    		//8次训练后获取最好的模型，根据最好的模型及训练集testData_Rating来获取此方差
    		double testDataRnse = variance(bestModel, testData_Rating, testData_Rating.count());
    		System.out.println("the best model was trained with rank = " + bestRank + " and lambda = " + bestLambda
    		                   + " and numIter = " + bestIter + " and Rnse on the test data is " + testDataRnse);
    		
    		// 获取测试数据中，分数的平均值
    		final double meanRating = traningData_Rating.union(validateData_Rating).mapToDouble(new DoubleFunction<Rating>() {
    			
    			@Override
    			public double call(Rating t) throws Exception {
    				return t.rating();
    			}
    		}).mean();
    		
    		// 根据平均值来计算旧的方差值
    		double baseLineRnse = Math.sqrt(testData_Rating.mapToDouble(new DoubleFunction<Rating>() {
    			
    			@Override
    			public double call(Rating t) throws Exception {
    				return (meanRating - t.rating()) * (meanRating - t.rating());
    			}
    		}).mean());
    		
    		// 通过模型，数据的拟合度提升了多少
    		double improvent = (baseLineRnse - testDataRnse) / baseLineRnse * 100;
    		System.out.println("the best model improves the baseline by " + improvent + "%");
    		
    		//加载电影数据
    		String moviepath = "C:/Users/dulinan/Desktop/movie/movies.dat";
    		JavaRDD<String> moviedata = sc.textFile(moviepath);
    		
    		// 将电影的id，标题，类型以三元组的形式保存
    		JavaRDD<Tuple3<Integer, String, String>> movieList_Tuple = moviedata.map(new Function<String, Tuple3<Integer, String, String>>() {
    
    			@Override
                public Tuple3<Integer, String, String> call(String line) throws Exception {
    				String[] fields = line.split("::");
    				if (fields.length != 3) {
    					throw new IllegalArgumentException("Each line must contain 3 fields");
    				}
    				int id = Integer.parseInt(fields[0]);
    				String title = fields[1];
    				String type = fields[2];
    	            return new Tuple3<Integer, String, String>(id, title, type);
                }
    		});
    		
    		// 将电影的id，标题以二元组的形式保存
    		JavaRDD<Tuple2<Integer, String>> movies_Map = movieList_Tuple.map(new Function<Tuple3<Integer,String,String>, Tuple2<Integer,String>>() {
    
    			@Override
                public Tuple2<Integer, String> call(Tuple3<Integer, String, String> v1) throws Exception {
    	            return new Tuple2<Integer, String>(v1._1(), v1._2());
                }
    		});
    		
    		System.out.println("movies recommond for you:");
    		
    		// 获取我所看过的电影ids
    		final List<Integer> movieIds = myRatedData_Rating.map(new Function<Rating, Integer>() {
    
    			@Override
                public Integer call(Rating v1) throws Exception {
    	            return v1.product();
                }
    		}).collect();
    		
    		// 从电影数据中去除我看过的电影数据
    		JavaRDD<Tuple2<Integer, String>> movieIdList  = movies_Map.filter(new Function<Tuple2<Integer,String>, Boolean>() {
    
    			@Override
                public Boolean call(Tuple2<Integer, String> v1) throws Exception {
    	            return !movieIds.contains(v1._1);
                }
    		});
    		
    		// 封装rating的参数形式，user为0，product为电影id进行封装
    		JavaPairRDD<Integer, Integer> recommondList = JavaPairRDD.fromJavaRDD(movieIdList.map(new Function<Tuple2<Integer,String>, Tuple2<Integer,Integer>>() {
    
    			@Override
                public Tuple2<Integer, Integer> call(Tuple2<Integer, String> v1) throws Exception {
    	            return new Tuple2<Integer, Integer>(0, v1._1);
                }
    		}));
    		
    		//通过模型预测出user为0的各product(电影id)的评分，并按照评分进行排序，获取前10个电影id
    		final List<Integer> list = bestModel.predict(recommondList).sortBy(new Function<Rating, Double>() {
    
    			@Override
                public Double call(Rating v1) throws Exception {
    	            return v1.rating();
                }
    		}, false, 1).map(new Function<Rating, Integer>() {
    
    			@Override
                public Integer call(Rating v1) throws Exception {
    	            return v1.product();
                }
    		}).take(10);
    		
    		if (list != null && !list.isEmpty()) {
    			//从电影数据中过滤出这10部电影，遍历打印
    			movieList_Tuple.filter(new Function<Tuple3<Integer,String,String>, Boolean>() {
    
    				@Override
                    public Boolean call(Tuple3<Integer, String, String> v1) throws Exception {
    	                return list.contains(v1._1());
                    }
    			}).foreach(new VoidFunction<Tuple3<Integer,String,String>>() {
    
    				@Override
                    public void call(Tuple3<Integer, String, String> t) throws Exception {
    	                System.out.println("nmovie name --> " + t._2() + " nmovie type --> " + t._3());
                    }
    			});
    		}
    		
    	}
    	
    	//方差的计算方法
    	public static double variance(MatrixFactorizationModel model, JavaRDD<Rating> predictionData, long n) {
    		//将predictionData转化成二元组型式，以便训练使用
    		JavaRDD<Tuple2<Object, Object>> userProducts = predictionData.map(new Function<Rating, Tuple2<Object, Object>>() {
    
    			public Tuple2<Object, Object> call(Rating r) {
    				return new Tuple2<Object, Object>(r.user(), r.product());
    			}
    		});
    		
    		//通过模型对数据进行预测
    		JavaPairRDD<Tuple2<Integer, Integer>, Double> prediction = JavaPairRDD.fromJavaRDD(model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
    
    	        public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r) {
    	        	//System.out.println(r.user()+"..."+r.product()+"..."+r.rating());
    		        return new Tuple2<Tuple2<Integer, Integer>, Double>(new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
    	        }
            }));
    		
    		//预测值和原值内连接
    		JavaRDD<Tuple2<Double, Double>> ratesAndPreds = JavaPairRDD.fromJavaRDD(predictionData.map(new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
    
    	        public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r) {
    	        	//System.out.println(r.user() + "..." + r.product() + "..." + r.rating());
    		        return new Tuple2<Tuple2<Integer, Integer>, Double>(new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
    	        }
            })).join(prediction).values();
    		
    		//计算方差并返回结果
    		Double dVar = ratesAndPreds.map(new Function<Tuple2<Double,Double>, Double>() {
    
    			@Override
                public Double call(Tuple2<Double, Double> v1) throws Exception {
    	            return (v1._1 - v1._2) * (v1._1 - v1._2);
                }
    		}).reduce(new Function2<Double, Double, Double>() {
    
    			@Override
                public Double call(Double v1, Double v2) throws Exception {
    	            return v1 + v2;
                }
    		});
    		
    		return Math.sqrt(dVar / n);
    	}
    	
    }

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[Spark Mllib_SparkSQL]: http://blog.csdn.net/qq1010885678/article/details/46052055
[Center]: /images/20220711/2bb56ac238344479ab07ccc60345758a.png