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 <debasish....@one.verizon.com>
Date: 2014-11-05T01:23:09Z
validate user/product on MovieLens dataset through user input and compute
map measure along with rmse
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