Github user mengxr commented on the pull request:
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@debasish83 Do you mind me sending you an update?
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Github user debasish83 commented on the pull request:
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@mengxr please go ahead...
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Github user debasish83 commented on the pull request:
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was very last few weeks...update it in next few days...
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@debasish83 Do you have time to update the PR?
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@debasish83 In RankingMetrics, the label set doesn't have an ordering. It
is either hit or miss at each position.
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Github user srowen commented on the pull request:
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@debasish83 do you mean RMSE? it is well-defined but not very useful. MAP
is the useful metric. I think that only a rank-dependent metric makes sense.
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Github user debasish83 commented on the pull request:
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I meant MAP...what's the MAP on netflix dataset you have seen before and
with what lambda ? I am running MAP experiments with various factorization
formulations
Github user srowen commented on the pull request:
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I don't remember from the last time I tried the Netflix dataset but in
general MAP is low, like 0.05-0.1, even when recommendations are good
according to RMSE.
Github user debasish83 commented on the pull request:
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agreed with the implicit MAP calculationFor netflix dataset, I got
0.014...May be I need to use a better regularization...was that 0.05-0.1 number
from using
Github user srowen commented on the pull request:
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That might be a fine score. I remember them being MAP being quite low in
general and that 0.1 would be very good. I don't recall the number for
Netflix, so don't take
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -167,23 +169,66 @@ object MovieLensALS {
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-89697236
@srowen For netflix dataset what's the MAP you have seen before...I started
experiments on Netflix dataset...lambda is 0.065 for netflix as well right ?
For MovieLens
Github user debasish83 commented on the pull request:
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@coderxiang @mengxr If I have a dataset with implicit (click or 0) then MAP
is not that well defined right since in label set everything is 1.0 and so
there is no
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -138,14 +141,122 @@ class
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--- Diff:
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---
@@ -138,14 +141,122 @@ class
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---
@@ -138,14 +141,122 @@ class
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--- Diff:
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---
@@ -138,14 +141,122 @@ class
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -138,14 +141,122 @@ class
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -138,14 +141,122 @@ class
Github user mengxr commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -70,26 +71,28 @@ class MatrixFactorizationModel(
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--- Diff:
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@@ -167,23 +169,66 @@ object MovieLensALS {
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -167,23 +169,66 @@ object MovieLensALS {
Github user mengxr commented on a diff in the pull request:
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--- Diff:
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---
@@ -70,26 +71,28 @@ class MatrixFactorizationModel(
Github user mengxr commented on a diff in the pull request:
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--- Diff:
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@@ -167,23 +169,66 @@ object MovieLensALS {
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -167,23 +169,66 @@ object MovieLensALS {
Github user mengxr commented on a diff in the pull request:
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -167,23 +169,66 @@ object MovieLensALS {
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-89083630
@debasish83 The code style still need fixes. On the high level, I think
having variable `num` is not necessary. Let's do a global num in this PR and
see whether users ask
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -138,14 +141,122 @@ class
Github user mengxr commented on a diff in the pull request:
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--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -103,13 +109,106 @@ class MatrixFactorizationModel
Github user mengxr commented on a diff in the pull request:
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--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -39,14 +39,15 @@ import org.apache.spark.rdd.RDD
object
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I reran the map computation on MovieLens with varying ranks:
Example run:
./bin/spark-submit --master spark://TUSCA09LMLVT00C.local:7077 --class
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@mengxr could you please do another passI might have missed the JavaRDD
compatibility issue but fixed rest of your comments...
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@@ -103,13 +109,106 @@ class
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--- Diff:
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@@ -74,6 +75,9 @@ object MovieLensALS {
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@@ -171,18 +175,62 @@ object MovieLensALS {
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@@ -171,18 +175,62 @@ object MovieLensALS {
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@@ -171,18 +175,62 @@ object MovieLensALS {
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@@ -171,18 +175,62 @@ object MovieLensALS {
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@@ -171,18 +175,62 @@ object MovieLensALS {
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@@ -171,18 +175,62 @@ object MovieLensALS {
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@@ -171,18 +175,62 @@ object MovieLensALS {
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@@ -35,33 +41,33 @@ import
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@@ -35,33 +41,33 @@ import
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---
@@ -17,14 +17,20 @@
package
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@@ -17,14 +17,20 @@
package
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@@ -103,13 +109,106 @@ class
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@@ -103,13 +109,106 @@ class
Github user debasish83 commented on the pull request:
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@mengxr I also added 2 test-cases for batch predict APIs. These features
are useful if users are interested in computing MAP measures...Let me know if I
move the
Github user debasish83 commented on the pull request:
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If we move computeRankingMetrics and computeRMSE to a better place, I can
guard it through tests...
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@@ -18,14 +18,14 @@
package org.apache.spark.examples.mllib
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package org.apache.spark.examples.mllib
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package
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package
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@@ -39,14 +39,15 @@ import org.apache.spark.rdd.RDD
object
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Github user debasish83 commented on the pull request:
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@mengxr can we please review and merge this ? It will greatly help in
reporting results from PLSA and other variants of matrix factorization
results...should I update
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More details about the API added and experiments are on the JIRA
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-62670769
@mengxr added recommendAll API to MatrixFactorizationModel and right now
the catesian based topK finding is also in the code for validation...
Example run:
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-62670877
More performance tests undergoing for internal datasets where the cartesian
code is really slow (due to groupByKey)...For MovieLens there is no substantial
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-62675915
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Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-62675921
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