Movie Recommender System project - Scala - Spark - MovieLens dataset

Class Project - CS-449 - Systems for Data Science - EPFL

We built a movie recommender system in Scala during a class project at EPFL.
We started from the template at this following link: https://gitlab.epfl.ch/sacs/cs-449-sds-public/project/cs449-template-m2-2022
Our homemade fonctions are mostly in the src/main/scala/shared/predictions.scala file (and we edited the tests files with assertions in src/test/scala and others files in src/main/scala)
Based on Spark with Scala language and Breeze library. The recommender is based on kNN with cosine similarity.

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Initial template README.md:

Description

Milestone-2.pdf

Note: Section 'Updates' lists the updates since the original release of the Milestone.

Mu has prepared a report template for your convenience here: Report Template.

Dependencies

    sbt >= 1.4.7
    openjdk@8

Should be available by default on iccluster028.iccluster.epfl.ch. Otherwise, refer to each project installation instructions. Prefer working locally on your own machine, you will have less interference in your measurements from other students.

If you work on iccluster028.iccluster.epfl.ch, you need to modify the PATH by default by adding the following line in ~/.bashrc:

    export PATH=$PATH:/opt/sbt/sbt/bin

If you have multiple installations of openjdk, you need to specify the one to use as JAVA_HOME, e.g. on OSX with openjdk@8 installed through Homebrew, you would do:

    export JAVA_HOME="/usr/local/Cellar/openjdk@8/1.8.0+282";

Dataset

Download data-m2.zip.

Unzip:

> unzip data-m2.zip

It should unzip into data/ by default. If not, manually move ml-100k and ml-1m into data/.

Repository Structure

src/main/scala/shared/predictions.scala: All the functionalities of your code for all questions should be defined there. This code should then be used in the following applications and tests.

Applications

1. ````src/main/scala/optimizing/Optimizing.scala````: Output answers to questions **BR.X**.
2. ````src/main/scala/distributed/Exact.scala````: Output answers to questions **EK.X**.
3. ````src/main/scala/distributed/Approximate.scala````: Output answers to questions **AK.X**.
4. ````src/main/scala/economics/Economics.scala````: Output answers to questions **E.X**

Applications are separate from tests to make it easier to test with different inputs and permit outputting your answers and timings in JSON format for easier grading.

Unit Tests

Corresponding unit tests for each application (except Economics.scala):

    src/test/scala/optimizing/OptimizingTests.scala
    src/test/scala/distributed/ExactTests.scala
    src/test/scala/distributed/ApproximateTests.scala

Your tests should demonstrate how to call your code to obtain the answers of the applications, and should make exactly the same calls as for the applications above. This structure intentionally encourages you to put as little as possible functionality in the application. This also gives the TA a clear and regular structure to check its correctness.

Usage

Execute unit tests

     sbt "testOnly test.AllTests"

You should fill all tests and ensure they all succeed prior to submission.

Run applications

Optimizing

sbt "runMain scaling.Optimizing --train data/ml-100k/u2.base --test data/ml-100k/u2.test --json optimizing-100k.json --master local[1] --users 943 --movies 1682"

Parallel Exact KNN

sbt "runMain distributed.Exact --train data/ml-100k/u2.base --test data/ml-100k/u2.test --json exact-100k-4.json --k 10 --master local[4] --users 943 --movies 1682"

Approximate KNN

sbt "runMain distributed.Approximate --train data/ml-100k/u2.base --test data/ml-100k/u2.test --json approximate-100k-4-k10-r2.json --k 10 --master local[4] --users 943 --movies 1682 --partitions 10 --replication 2"

Economics

sbt "runMain economics.Economics --json economics.json"

Time applications

For all the previous applications, you can set the number of measurements for timings by adding the following option --num_measurements X where X is an integer. The default value is 0.

IC Cluster

Test your application locally as much as possible and only test on the iccluster once everything works, to keep the cluster and the driver node maximally available for other students.

Assemble Application for Spark Submit

sbt clean: clean up temporary files and previous assembly packages.

sbt assembly: create a new jar target/scala-2.11/m2_yourid-assembly-1.0.jar that can be used with spark-submit.

Prefer packaging your application locally and upload the tar archive of your application before running on cluster.

Upload jar on Cluster

    scp target/scala-2.11/m2_yourid-assembly-1.0.jar <username>@iccluster028.iccluster.epfl.ch:~

Run on Cluster

See config.sh for HDFS paths to pre-uploaded train and test datasets to replace TRAIN and TEST, like in the example commands below:

When using ML-100k

spark-submit --class distributed.Exact --master yarn --conf "spark.dynamicAllocation.enabled=false" --num-executors 1 m2_yourid-assembly-1.0.jar --json exact-100k-1.json --train $ML100Ku2base --test $ML100Ku2test

When using ML-1m

spark-submit --class distributed.Exact --master yarn --conf "spark.dynamicAllocation.enabled=false" --num-executors 1 m2_yourid-assembly-1.0.jar --json exact-1m-1.json --train $ML1Mrbtrain --test $ML1Mrbtest --separator :: --k 300 --users 6040 --movies 3952

In order to keep results obtained with different parameters in different .json files, simply modify the corresponding parameter ("--json") passed and the values. For instance, with --num-executors 4 : --json exact-1m-4.json. Note that when changing from ML-100k to ML-1M, the parameter --separator :: should be added, and the number of users and movies should be modified.

Grading scripts

We will use the following scripts to grade your submission:

1. ````./test.sh````: Run all unit tests.
2. ````./run.sh````: Run all applications without timing measurements.
3. ````./time.sh````: Run all timing measurements. 

All scripts will produce execution logs in the logs directory, including answers produced in the JSON format. Logs directories are in the format logs/<scriptname>-<datetime>-<machine>/ and include at least an execution log log.txt as well as possible JSON outputs from applications.

Ensure all scripts run correctly locally before submitting.

Submission

Steps:

1. Create a zip archive with all your code within  ````src/````, as well as your report: ````zip sciper1-sciper2.zip -r src/ report.pdf````
2. Submit ````sciper1-sciper2.zip```` the TA for grading on
   https://cs449-submissions.epfl.ch:8083/m2 using the passcode you have previously received by email.

References

Essential sbt: https://www.scalawilliam.com/essential-sbt/

Explore Spark Interactively (supports autocompletion with tabs!): https://spark.apache.org/docs/latest/quick-start.html

Scallop Argument Parsing: https://github.com/scallop/scallop/wiki

Spark Resilient Distributed Dataset (RDD): https://spark.apache.org/docs/3.0.1/api/scala/org/apache/spark/rdd/RDD.html

Credits

Erick Lavoie (Design, Implementation, Tests)

Athanasios Xygkis (Requirements, Tests)