3218fa795f
Adding algorithm for implicit feedback data to ALS This PR adds the commonly used "implicit feedack" variant to ALS. The implementation is based in part on Mahout's implementation, which is in turn based on [Collaborative Filtering for Implicit Feedback Datasets](http://research.yahoo.com/pub/2433). It has been adapted for the blocked approach used in MLlib. I have tested this implementation against the MovieLens 100k, 1m and 10m datasets, and confirmed that it produces the same RMSE score as Mahout, as well as my own port of Mahout's implicit ALS implementation to Spark (not that RMSE is necessarily the best metric to judge by for implicit feedback, but it provides a consistent metric for comparison). It turned out to be more straightforward than I had thought to add this. The main additions are: 1. Adding `implicitPrefs` boolean flag and `alpha` parameter 2. Added the `computeYtY` method. In each least-squares step, the algorithm requires the computation of `YtY`, where `Y` is the {user, item} factor matrix. Since the factors are already block-distributed in an `RDD`, this is quite straightforward to compute but does add an extra operation over the explicit version (but only twice per iteration) 3. Finally the actual solve step in `updateBlock` boils down to: * a multiplication of the `XtX` matrix by `alpha * rating` * a multiplication of the `Xty` vector by `1 + alpha * rating` * when solving for the factor vector, the implicit variant adds the `YtY` matrix to the LHS 4. Added `trainImplicit` methods in the `ALS` object 5. Added test cases for both Scala and Java - based on achieving a confidence-weighted RMSE score < 0.4 (this is taken from Mahout's test cases) It would be great to get some feedback on this and have people test things out against some datasets (MovieLens and others and perhaps proprietary datasets) both locally and on a cluster if possible. I have not yet tested on a cluster but will try to do that soon. I have tried to make things as efficient as possible but if there are potential improvements let me know. The results of a run against ml-1m are below (note the vanilla RMSE scores will be very different from the explicit variant): **INPUTS** ``` iterations=10 factors=10 lambda=0.01 alpha=1 implicitPrefs=true ``` **RESULTS** ``` Spark MLlib 0.8.0-SNAPSHOT RMSE = 3.1544 Time: 24.834 sec ``` ``` My own port of Mahout's ALS to Spark (updated to 0.8.0-SNAPSHOT) RMSE = 3.1543 Time: 58.708 sec ``` ``` Mahout 0.8 time ./factorize-movielens-1M.sh /path/to/ratings/ml-1m/ratings.dat real 3m48.648s user 6m39.254s sys 0m14.505s RMSE = 3.1539 ``` Results of a run against ml-10m ``` Spark MLlib RMSE = 3.1200 Time: 162.348 sec ``` ``` Mahout 0.8 real 23m2.220s user 43m39.185s sys 0m25.316s RMSE = 3.1187 ``` |
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| assembly | ||
| bagel | ||
| bin | ||
| conf | ||
| core | ||
| docs | ||
| ec2 | ||
| examples | ||
| mllib | ||
| project | ||
| python | ||
| repl | ||
| repl-bin | ||
| sbt | ||
| streaming | ||
| tools | ||
| yarn | ||
| .gitignore | ||
| kmeans_data.txt | ||
| LICENSE | ||
| lr_data.txt | ||
| make-distribution.sh | ||
| NOTICE | ||
| pagerank_data.txt | ||
| pom.xml | ||
| pyspark | ||
| pyspark.cmd | ||
| pyspark2.cmd | ||
| README.md | ||
| run-example | ||
| run-example.cmd | ||
| run-example2.cmd | ||
| spark-class | ||
| spark-class.cmd | ||
| spark-class2.cmd | ||
| spark-executor | ||
| spark-shell | ||
| spark-shell.cmd | ||
Apache Spark
Lightning-Fast Cluster Computing - http://spark.incubator.apache.org/
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.incubator.apache.org/documentation.html. This README file only contains basic setup instructions.
Building
Spark requires Scala 2.9.3 (Scala 2.10 is not yet supported). The project is built using Simple Build Tool (SBT), which is packaged with it. To build Spark and its example programs, run:
sbt/sbt assembly
Once you've built Spark, the easiest way to start using it is the shell:
./spark-shell
Or, for the Python API, the Python shell (./pyspark).
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./run-example <class> <params>. For example:
./run-example org.apache.spark.examples.SparkLR local[2]
will run the Logistic Regression example locally on 2 CPUs.
Each of the example programs prints usage help if no params are given.
All of the Spark samples take a <master> parameter that is the cluster URL
to connect to. This can be a mesos:// or spark:// URL, or "local" to run
locally with one thread, or "local[N]" to run locally with N threads.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
storage systems. Because the protocols have changed in different versions of
Hadoop, you must build Spark against the same version that your cluster runs.
You can change the version by setting the SPARK_HADOOP_VERSION environment
when building Spark.
For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:
# Apache Hadoop 1.2.1
$ SPARK_HADOOP_VERSION=1.2.1 sbt/sbt assembly
# Cloudera CDH 4.2.0 with MapReduce v1
$ SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 sbt/sbt assembly
For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions
with YARN, also set SPARK_YARN=true:
# Apache Hadoop 2.0.5-alpha
$ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
# Cloudera CDH 4.2.0 with MapReduce v2
$ SPARK_HADOOP_VERSION=2.0.0-cdh4.2.0 SPARK_YARN=true sbt/sbt assembly
For convenience, these variables may also be set through the conf/spark-env.sh file
described below.
When developing a Spark application, specify the Hadoop version by adding the
"hadoop-client" artifact to your project's dependencies. For example, if you're
using Hadoop 1.0.1 and build your application using SBT, add this entry to
libraryDependencies:
"org.apache.hadoop" % "hadoop-client" % "1.2.1"
If your project is built with Maven, add this to your POM file's <dependencies> section:
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>1.2.1</version>
</dependency>
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.
Apache Incubator Notice
Apache Spark is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
Contributing to Spark
Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project's open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project's open source license and warrant that you have the legal authority to do so.