{"id":104964,"date":"2021-09-30T11:00:00","date_gmt":"2021-09-30T08:00:00","guid":{"rendered":"https:\/\/examples.javacodegeeks.com\/?p=104964"},"modified":"2021-09-28T12:23:15","modified_gmt":"2021-09-28T09:23:15","slug":"apache-hadoop-mahout-tutorial","status":"publish","type":"post","link":"https:\/\/examples.javacodegeeks.com\/apache-hadoop-mahout-tutorial\/","title":{"rendered":"Apache Hadoop Mahout Tutorial"},"content":{"rendered":"

1. Introduction<\/h2>\n

This is an in-depth article related to the Apache Hadoop Mahout. It is used in Machine Learning solutions with Hadoop. It was developed by Facebook. Hadoop and Mahout are Apache Opensource projects now. Apache Mahout was part of the Lucene project in 2008. It became an independent project in 2010.<\/p>\n

2. Apache Hadoop Mahout<\/h2>\n

2.1 Prerequisites<\/h3>\n

Java 7 or 8 is required on the Linux, windows, or mac operating system. Maven 3.6.1 is required. Apache Hadoop 2.9.1 and Mahout 0.9 are used in this example.<\/p>\n

2.2 Download<\/h3>\n

You can download Java 8 can be downloaded from the Oracle web site<\/a> . Apache Maven 3.6.1 can be downloaded from Apache site<\/a>. Apache Hadoop 2.9.1 can be downloaded from Hadoop Website<\/a>. You can download Apache Mahout 0.9 from the Apache Mahout website<\/a>.<\/p>\n

2.3 Setup<\/h3>\n

You can set the environment variables for JAVA_HOME and PATH. They can be set as shown below:<\/p>\n

Setup<\/em><\/span><\/p>\n

JAVA_HOME=\"\/desktop\/jdk1.8.0_73\"\nexport JAVA_HOME\nPATH=$JAVA_HOME\/bin:$PATH\nexport PATH\n<\/pre>\n
The environment variables for maven are set as below:<\/p>\n
Maven Environment<\/em><\/span><\/p>\n
JAVA_HOME=\u201d\/jboss\/jdk1.8.0_73\u2033\nexport M2_HOME=\/users\/bhagvan.kommadi\/Desktop\/apache-maven-3.6.1\nexport M2=$M2_HOME\/bin\nexport PATH=$M2:$PATH\n<\/pre>\n
2.4 How to download and install Hadoop and Mahout<\/h3>\n
After downloading the zip files of Hadoop and Mahout they can be extracted to different folders. The libraries in the libs folder are set in the CLASSPATH variable.<\/p>\n
2.5 Apache Mahout<\/h3>\n
 Mahout comes from the word “elephant”. Apache Mahout is used for developing solutions involving ML algorithms like Recommendation, Classification, and Clustering. Mahout has features such as data mining framework, data analysis, clustering implementation, classification implementation, evolutionary programming techniques, and matrix and vector libraries. Social media companies like facebook, yahoo, linkedin, and foursquare use mahout. Mahout ecommerce framework has recomender engine which helps in identifying places on Foursquare. Twitter has chosen Mahout for user inteterest modelling and yahoo uses it for pattern matching.<\/p>\n
2.6 Apache Hadoop  Configuration<\/h3>\n
You need to configure HADOOP_HOME<\/code> as below:<\/p>\n
Setup<\/em><\/span><\/p>\n
export HADOOP_HOME=\/users\/bhagvan.kommadi\/desktop\/hadoop-2.9.1\/\n<\/pre>\n
You need to configure $HADOOP_HOME\/etc\/hadoop\/core-site.xml<\/code> as below:<\/p>\n
Core Site XML file<\/em><\/span><\/p>\n
<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<?xml-stylesheet type=\"text\/xsl\" href=\"configuration.xsl\"?>\n<!--\n  Licensed under the Apache License, Version 2.0 (the \"License\");\n  you may not use this file except in compliance with the License.\n  You may obtain a copy of the License at\n\n    http:\/\/www.apache.org\/licenses\/LICENSE-2.0\n\n  Unless required by applicable law or agreed to in writing, software\n  distributed under the License is distributed on an \"AS IS\" BASIS,\n  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n  See the License for the specific language governing permissions and\n  limitations under the License. See accompanying LICENSE file.\n-->\n\n<!-- Put site-specific property overrides in this file. -->\n\n<configuration>\n\n<property>\n <name>fs.defaultFS<\/name>\n <value>hdfs:\/\/apples-MacBook-Air.local:8020<\/value>\n<\/property>\n\n<\/configuration>\n\n<\/pre>\n
You need to start running Hadoop by using the command below:
 
<\/div> <\/div><\/p>\n
Hadoop Execution<\/em><\/span><\/p>\n
cd hadoop-2.9.1\/\ncd sbin\n.\/start-dfs.sh\n<\/pre>\n
The output of the commands is shown below:<\/p>\n
Hadoop Execution<\/em><\/span><\/p>\n
apples-MacBook-Air:sbin bhagvan.kommadi$ .\/start-dfs.sh\n20\/09\/14 20:26:23 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform\u2026 using builtin-java classes where applicable\nStarting namenodes on [apples-MacBook-Air.local]\napples-MacBook-Air.local: Warning: Permanently added the ECDSA host key for IP address 'fe80::4e9:963f:5cc3:a000%en0' to the list of known hosts.\nPassword:\napples-MacBook-Air.local: starting namenode, logging to \/Users\/bhagvan.kommadi\/desktop\/hadoop-2.9.1\/logs\/hadoop-bhagvan.kommadi-namenode-apples-MacBook-Air.local.out\nPassword:\nlocalhost: starting datanode, logging to \/Users\/bhagvan.kommadi\/desktop\/hadoop-2.9.1\/logs\/hadoop-bhagvan.kommadi-datanode-apples-MacBook-Air.local.out\nStarting secondary namenodes [0.0.0.0]\nPassword:\n0.0.0.0: starting secondarynamenode, logging to \/Users\/bhagvan.kommadi\/desktop\/hadoop-2.9.1\/logs\/hadoop-bhagvan.kommadi-secondarynamenode-apples-MacBook-Air.local.out\n20\/09\/14 20:27:07 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform\u2026 using builtin-java classes where applicable\n<\/pre>\n
2.7 Apache Hadoop  Mahout<\/h3>\n
Let us start with basic usecase for Mahout – Recommendation Engine.  First step is to create a data model. Below is the data model.<\/p>\n
Hadoop Execution<\/em><\/span><\/p>\n
1,11,2.0\n1,12,5.0\n1,13,5.0\n1,14,5.0\n1,15,4.0\n1,16,5.0\n1,17,1.0\n1,18,5.0\n2,10,1.0\n2,11,2.0\n2,15,5.0\n2,16,4.5\n2,17,1.0\n2,18,5.0\n3,11,2.5\n3,12,4.5\n3,13,4.0\n3,14,3.0\n3,15,3.5\n3,16,4.5\n3,17,4.0\n3,18,5.0\n4,10,5.0\n4,11,5.0\n4,12,5.0\n4,13,0.0\n4,14,2.0\n4,15,3.0\n4,16,1.0\n4,17,4.0\n4,18,1.0\n<\/pre>\n
The PearsonCorrelationSimilarity<\/code> class is used to create usersimilarity. It takes the userdata model in the constructor. The data model file has the User, Item, and Preference columns related to the product.  The data model file is passed as file in the FileDataModel<\/code> constructor.<\/p>\n
Data Model<\/em><\/span><\/p>\n
DataModel usermodel = new FileDataModel(new File(\"userdata.txt\")); \n<\/pre>\n
The next step is to create UserSimilarity<\/code> by using PearsonCorrelationSimilarity<\/code> as shown below in the RecommenderBuilder<\/code> class.<\/p>\n
User Similarity<\/em><\/span><\/p>\n
RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {\n\t\t\tpublic Recommender buildRecommender(DataModel model) throws TasteException {\n\t\t\t\tUserSimilarity similarity = new PearsonCorrelationSimilarity(model);\n    }\n}\n<\/pre>\n
ThresholdUserNeighborhood<\/code> is used for the UserNeighborhood<\/code> class. ThresholdUserNeighborhood<\/code> is a neighborhood for all the users whose similarity to the given user meets or exceeds a certain threshold. In the below code, threshold is set as 2.0.<\/p>\n
User Similarity<\/em><\/span><\/p>\n
RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {\n\t\t\tpublic Recommender buildRecommender(DataModel model) throws TasteException {\n\t\t\t\tUserSimilarity similarity = new PearsonCorrelationSimilarity(model);\n                                UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);\n    }\n}\n<\/pre>\n
The next step is to create GenericUserbasedRecomender<\/code> as shown below:<\/p>\n
User Recommender<\/em><\/span><\/p>\n
RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {\n\t\t\tpublic Recommender buildRecommender(DataModel model) throws TasteException {\n\t\t\t\tUserSimilarity similarity = new PearsonCorrelationSimilarity(model);\n                                UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);\n            return new GenericUserBasedRecommender(model, neighborhood, similarity);\n    }\n}\n<\/pre>\n
The next step is to invoke  the recommend()<\/code> method of Recommender<\/code> interface. Method  parameters are the user id and the number of recommendations. Below code shows the implementation:<\/p>\n
User Recommender<\/em><\/span><\/p>\n
Recommender recommender = recommenderBuilder.buildRecommender(usermodel);\n\t\tList recommendations = recommender.recommend(3, 1);\t\n         System.out.println(\"Recommendations \"+recommendations);\t\t\n         for (RecommendedItem recommendationItem : recommendations) {\n            System.out.println(recommendationItem);\n         }\n      \n      }catch(Exception exception){\n\n            exception.printStackTrace();\n\n       }\n\n<\/pre>\n
Below is the complete class which shows the implementation  of RecommendationEngine<\/code>.<\/p>\n
Recommendation Engine<\/em><\/span><\/p>\n
import java.io.File;\nimport java.util.List;\n\nimport org.apache.mahout.cf.taste.eval.RecommenderBuilder;\nimport org.apache.mahout.cf.taste.impl.model.file.FileDataModel;\nimport org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;\nimport org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;\nimport org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;\n\nimport org.apache.mahout.cf.taste.model.DataModel;\nimport org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;\n\nimport org.apache.mahout.cf.taste.recommender.RecommendedItem;\nimport org.apache.mahout.cf.taste.recommender.UserBasedRecommender;\nimport org.apache.mahout.cf.taste.recommender.Recommender;\nimport org.apache.mahout.cf.taste.common.TasteException;\nimport org.apache.mahout.cf.taste.similarity.UserSimilarity;\nimport org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;\npublic class RecommendationEngine {\n   public static void main(String args[]){\n      try{\n\n         DataModel usermodel = new FileDataModel(new File(\"userdata.txt\")); \n\n         System.out.println(usermodel);     \nRecommenderBuilder recommenderBuilder = new RecommenderBuilder() {\n\t\t\tpublic Recommender buildRecommender(DataModel model) throws TasteException {\n\t\t\t\tUserSimilarity similarity = new PearsonCorrelationSimilarity(model);\n\n\t\t\t\tUserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);\n\t\t\t\treturn new GenericUserBasedRecommender(model, neighborhood, similarity);\n\t\t\t}\n\t\t}; \n         \n        Recommender recommender = recommenderBuilder.buildRecommender(usermodel);\n\t\tList recommendations = recommender.recommend(3, 1);\t\n         System.out.println(\"Recommendations \"+recommendations);\t\t\n         for (RecommendedItem recommendationItem : recommendations) {\n            System.out.println(recommendationItem);\n         }\n      \n      }catch(Exception exception){\n\n            exception.printStackTrace();\n\n       }\n\n     }\n     \n} \n\n<\/pre>\n
To compile the code above, you can use the command below:<\/p>\n
Recommendation Engine<\/em><\/span><\/p>\n
 javac -cp \"\/Users\/bhagvan.kommadi\/desktop\/mahout-distribution-0.9\/*\" RecommendationEngine.java \n<\/pre>\n
To execute the code, the command below is used.<\/p>\n
Recommendation Engine<\/em><\/span><\/p>\n
java -cp \"\/Users\/bhagvan.kommadi\/desktop\/mahout-distribution-0.9\/*:.:\/Users\/bhagvan.kommadi\/desktop\/mahout-distribution-0.9\/lib\/*\" RecommendationEngine\n<\/pre>\n
The output when the above command is executed is shown below.<\/p>\n
Recommendation Engine<\/em><\/span><\/p>\n
apples-MacBook-Air:apache_mahout bhagvan.kommadi$ java -cp \"\/Users\/bhagvan.kommadi\/desktop\/mahout-distribution-0.9\/*:.:\/Users\/bhagvan.kommadi\/desktop\/mahout-distribution-0.9\/lib\/*\" RecommendationEngine\nlog4j:WARN No appenders could be found for logger (org.apache.mahout.cf.taste.impl.model.file.FileDataModel).\nlog4j:WARN Please initialize the log4j system properly.\nlog4j:WARN See http:\/\/logging.apache.org\/log4j\/1.2\/faq.html#noconfig for more info.\nFileDataModel[dataFile:\/Users\/bhagvan.kommadi\/Desktop\/JavacodeGeeks\/Code\/apache_mahout\/userdata.txt]\nRecommendations [RecommendedItem[item:10, value:1.0]]\nRecommendedItem[item:10, value:1.0]\n<\/pre>\n
3. Download the Source Code<\/h2>\n
Download<\/strong>
\nYou can download the full source code of this example here: Apache Hadoop Mahout Tutorial<\/strong><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"
1. Introduction This is an in-depth article related to the Apache Hadoop Mahout. It is used in Machine Learning solutions with Hadoop. It was developed by Facebook. Hadoop and Mahout are Apache Opensource projects now. Apache Mahout was part of the Lucene project in 2008. It became an independent project in 2010. 2. Apache Hadoop …<\/p>\n","protected":false},"author":31,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1255],"tags":[1270],"class_list":["post-104964","post","type-post","status-publish","format-standard","hentry","category-apache-hadoop","tag-apache-hadoop"],"yoast_head":"\nApache Hadoop Mahout Tutorial - Java Code Geeks<\/title>\n<meta name=\"description\" content=\"Hadoop & Mahout are Apache Opensource projects now. Mahout was part of the Lucene project in 2008. It became an independent project in 2010.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/examples.javacodegeeks.com\/apache-hadoop-mahout-tutorial\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apache Hadoop Mahout Tutorial - Java Code Geeks\" \/>\n<meta property=\"og:description\" content=\"Hadoop & Mahout are Apache Opensource projects now. Mahout was part of the Lucene project in 2008. 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