GitHub user debasish83 opened a pull request:
https://github.com/apache/spark/pull/3098
[MLLIB] SPARK-4231: Add RankingMetrics to examples.MovieLensALS
@mengxr @srowen
To validate ALS enhancements as proposed in
https://issues.apache.org/jira/browse/SPARK-2426, RMSE along with the
RankingMetrics measures are important to look at.
This PR adds a flag --validateProducts to examples.MovieLensALS.
Default validateProducts is false and we compute RMSE and MAP for test set
related to product recommendation.
./bin/spark-submit --master
spark://tusca09lmlvt00c.uswin.ad.vzwcorp.com:7077 --jars
/Users/v606014/.m2/repository/com/github/scopt/scopt_2.10/3.2.0/scopt_2.10-3.2.0.jar
--total-executor-cores 4 --executor-memory 4g --driver-memory 1g --class
org.apache.spark.examples.mllib.MovieLensALS
./examples/target/spark-examples_2.10-1.2.0-SNAPSHOT.jar --kryo --lambda 0.065
hdfs://localhost:8020/sandbox/movielens/
2014-11-04 17:15:24.262 java[4568:1903] Unable to load realm mapping info
from SCDynamicStore
14/11/04 17:15:24 WARN NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable
Got 1000209 ratings from 6040 users on 3706 movies.
Training: 799926, test: 200283.
Test RMSE = 0.8965005871008247 MAP = 7.438473265235346.
--validateProducts will validate user recommendation for each product
./bin/spark-submit --master
spark://tusca09lmlvt00c.uswin.ad.vzwcorp.com:7077 --jars
/Users/v606014/.m2/repository/com/github/scopt/scopt_2.10/3.2.0/scopt_2.10-3.2.0.jar
--total-executor-cores 4 --executor-memory 4g --driver-memory 1g --class
org.apache.spark.examples.mllib.MovieLensALS
./examples/target/spark-examples_2.10-1.2.0-SNAPSHOT.jar --kryo --lambda 0.065
--validateProducts hdfs://localhost:8020/sandbox/movielens/
2014-11-04 17:16:18.652 java[4635:1903] Unable to load realm mapping info
from SCDynamicStore
14/11/04 17:16:18 WARN NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable
Got 1000209 ratings from 6040 users on 3706 movies.
Training: 800014, test: 200195.
Test RMSE = 0.8986539583457682 MAP = 12.243775391575324.
Sean,
Are we looking at the right numbers here ? MAP for Movielens dataset is
around 12.243. You did similar experiments for oryx/myrrix before...
We can perhaps make the test set generation more intelligent but I went
with random sampling for now since I was looking at MAP measure..
For prec@k I am not sure what's the right k number to choose at...I do a
sweep over k to choose sweet spot internally.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/debasish83/spark irmetrics
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/3098.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #3098
----
commit 9b3951f558e5673eb475c575f14876421b5a3abc
Author: Debasish Das <[email protected]>
Date: 2014-11-05T01:23:09Z
validate user/product on MovieLens dataset through user input and compute
map measure along with rmse
----
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
