Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17862#discussion_r115745818
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala ---
@@ -154,22 +159,23 @@ class LinearSVCSuite extends
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17862#discussion_r115741206
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala ---
@@ -154,22 +159,23 @@ class LinearSVCSuite extends
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17862#discussion_r115659479
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/LinearSVCSuite.scala ---
@@ -154,22 +159,23 @@ class LinearSVCSuite extends
Github user debasish83 commented on the issue:
https://github.com/apache/spark/pull/17862
@hhbyyh can we smooth the hinge-loss using soft-max (variant of ReLU) and
then use LBFGS ?
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Github user debasish83 commented on the issue:
https://github.com/apache/spark/pull/12574
test
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Github user debasish83 commented on the issue:
https://github.com/apache/spark/pull/14473
ADMM is already available as a breeze solver (BFGS, OWLQN,
NonlinearMinimizer) which is integrated with ml/mllib...It will be great if you
can look into it and let me know if you need pointers
Github user debasish83 commented on the issue:
https://github.com/apache/spark/pull/12574
Can we close it ? Looks like SPARK-18235 opened up recommendForAll
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Github user debasish83 commented on the issue:
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Github user debasish83 commented on the issue:
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I will take a pass at the PR as well..
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Github user debasish83 commented on the issue:
https://github.com/apache/spark/pull/12574
@MLnick I recently visited IBM STC but unfortunately missed you on the
meeting...we discussed about the ML/MLlib changes for matrix factorization...
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Github user debasish83 commented on the issue:
https://github.com/apache/spark/pull/458
ADMM is already implemented as part of Breeze proximal NonlinearMinimizer
where the ADMM solver stays in master and gradient calculator is used in
similar manner as how Breeze LBFGS/OWLQN has been
Github user debasish83 commented on the issue:
https://github.com/apache/spark/pull/1110
@mengxr say I have 20 nodes and 16 cores on each node, do you recommend
running treeReduce with 320 partitions and OpenBLAS with numThreads=1 on each
partition for SeqOp OR treeReduce with 20
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5869#issuecomment-162240882
@srowen actually I am not sure if MAP calculation got added in ML pipeline
or not...I will look into it and if someone else already added it, I will close
the PR
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-136503426
@rezazadeh got busy with spark streaming version of KNN :-) I will open up
2 PRs over the weekend as we discussed.
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5503#issuecomment-120658511
@dbtsai @mandar2812 I found the abstraction for kernel as explained in my
PR https://github.com/apache/spark/pull/6213 more generic in practical
use-cases compared
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-109654217
Internally we are using this code for euclidean/rbf driving PIC for
example...but sure we can focus on cosine first...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-107113056
@rezazadeh sure I will do thatCould you add a JIRA for 3 (Kernel
Clustering / PIC) so that we can add RBFKernel flow and implement PIC with
vector - matrix
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3536#issuecomment-105026856
Let's continue the validation discussion on
https://github.com/apache/spark/pull/6213. The PR introduces batch gemm based
similarity computati
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-104970859
Refactoring MatrixFactorizationModel.recommendForAll to a common place like
Vectors/Matrices will help users who have dense data with modest columns
(~1000-10K, most
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-104968079
Runtime comparison are posted on SPARK-4823 on MovieLens1m dataset, 8 core,
4 GB executor memory from my laptop.
Stage 24 - 35 is the row similarity flow
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-104936678
Internally vector flow in IndexedRowMatrix has helped us to do additional
optimization through user defined kernels and cut the computation which won't
happen
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-104934928
@mengxr I generalized MatrixFactorizationModel.recommendAll and use it for
similarUsers and similarProducts and use dgemm...In IndexedRowMatrix I only
exposed
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-103925316
For gemv it is not clear how to re-use the scratch space for result
vector...if we can't reuse the result vector over multiple calls to
kernel.compute we won&
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-103771669
Actually both for Euclidean and RBF it is possible as || x - y || can be
decomposed as ||x||2 + ||y||2 - 2*dot(x,y) where dot(x,y) can be computed
through dgemv
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-103615439
I am thinking more. May be EuclideanKernel can be decomposed using Matrix x
Vector as well
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-103614290
SparseMatrix x SparseVector got merged to Master today
https://github.com/apache/spark/pull/6209.
I will update the PR and separate the code path for
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-102843964
For CosineKernel and ProductKernel, we should be able to have a separate
code path with BLAS-2 once SparseMatrix x SparseVector merges and BLAS-3 once
SparseMatrix x
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6213#issuecomment-102841783
@mengxr the failures are related to yarn suite which does not look related
to my changes...tests I added ran fine...
[info] *** 1 TEST FAILED ***
[error
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/6209#issuecomment-102840355
Are there runtime comparisons posted with vector*vector operations for
these changes BLAS-1 vs BLAS-2 ? SparseMatrix * SparseVector compared to
Array[SparseVector] x
GitHub user debasish83 opened a pull request:
https://github.com/apache/spark/pull/6213
[MLLIB][SPARK-4675, SPARK-4823] RowSimilarity
@mengxr @srowen
For RowMatrix with 100K columns, colSimilarity with bruteforce/dimsum
sampling is used. This PR adds rowSimilarity to
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3536#issuecomment-99098372
@MLnick yes that's what I did...I have to convince users why use factor
vectors :-) For user->item recommendation, convincing is easy by showing the
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5869#issuecomment-98827606
@mengxr if you could please point to the ML pipeline module where I should
add it, I can do the change...
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GitHub user debasish83 reopened a pull request:
https://github.com/apache/spark/pull/5869
[SPARK-4231][MLLIB][Examples] MAP calculation added to examples.MovieLensALS
MAP calculation driver to MovieLensALS was not part of SPARK-3066 merge.
Added the driver in this PR
Github user debasish83 closed the pull request at:
https://github.com/apache/spark/pull/5869
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5869#issuecomment-98504679
RMSE is similar in my old runs..so the ALS core did not change...the MAP
driver code is also same since I just migrated it from my PR.
TUSCA09LMLVT00C:spark
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5869#issuecomment-98504419
Implicit lambda should not affect the explicit resultsI will take a
closer look into the recommendForAll and compare with my old version..
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5869#issuecomment-98502996
Stats from my old run:
./bin/spark-submit --master spark://TUSCA09LMLVT00C.local:7077 --class
org.apache.spark.examples.mllib.MovieLensALS --jars
~/.m2
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5869#issuecomment-98491289
@srowen ideally we should move both the utilities to compute rmse and MAP
on a MatrixFactorizationModel to a common place from examples since they are
the APIs that
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3536#issuecomment-98425139
@MLnick @srowen I did an experiment where I computed brute force topK
similar items using cosine distance and compared the intersection with item
factor based brute
GitHub user debasish83 opened a pull request:
https://github.com/apache/spark/pull/5869
[SPARK-4231][MLLIB][Examples] MAP calculation added to examples.MovieLensALS
MAP calculation driver to MovieLensALS was not part of SPARK-3066 merge.
Added the driver in this PR
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3890#issuecomment-98190811
I mean for svm the formulation is over all rows right...the smooth max will
be done on every row and label...max(0, 1 - y_i a_i*x)...so only change will be
a diff
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3890#issuecomment-98189044
nope...logistic is feature space...svm is data space...the gradient
calculation / BFGS CostFun will change
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3890#issuecomment-98188550
@dlwh we should simply use your smooth max and make max(0, 1 - ya'x)
differentiable for the first version...that needs no change to breeze...and
then if needed w
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/5829#discussion_r29494261
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -137,20 +141,113 @@ class
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/5829#discussion_r29493623
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -137,20 +141,113 @@ class
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-98073840
Changed the title to add driver for recommendAll API once SPARK-3066 merges
to master...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3890#issuecomment-98073720
this is linear svm strictly in primal form...there are ways to fix it
through going to dual space but that needs a linear / nonlinear kernel
generation which might be
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3890#issuecomment-98073658
@loachli hinge loss in linear svm is max(0, 1 - y*a'x) right ? Just replace
max with a smooth max and you should be able to smooth hinge gradient and then
it c
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/5829#discussion_r29492840
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala ---
@@ -39,7 +39,7 @@ class MLPairRDDFunctions[K: ClassTag, V: ClassTag
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/5829#discussion_r29492705
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala ---
@@ -39,7 +39,7 @@ class MLPairRDDFunctions[K: ClassTag, V: ClassTag
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5829#issuecomment-98058780
@mengxr looks good to me...I will fix SPARK-4321 based on this merge...I
need blockify for rowSimilarities (tall skinny sparse matrices for row
similarities)...should
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-98021307
@mengxr please go ahead...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-96403986
was very last few weeks...update it in next few days...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-91869124
ohh sorry I don't know about requester pays...let me look into it
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-91710827
@jkbradley let me know if you need vzcloud access and I can create few
nodes for you...ec2 might be easier for other's to access it as well...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-91710700
@jkbradley we still could not access the wikipedia dataset on ec2...will it
be possible for you to upload the 1 Billion token dataset on EC2 ? I wanted to
do a sparse
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-90950074
if you look into breeze.optimize.proximal.Proximal, I added a library of
projection/proximal operators...in my experiments looks like projection based
algorithms (SPG
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-90950364
Application is topic modeling using Sparsity constraints like L1 and
probability simplex and supporting bounds in ALS
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-90942562
@tmyklebu do you have the original NNLS paper in english ? Breeze also has
a linear CG...I am thinking if it is possible to merge simple projections like
positivity
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-90753271
Sure...Let me do that and point you to the repo...most likely it will be a
breeze based branch and I will copy the mllib implementation over thr...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-90753041
@mengxr @josephk In my internal testing, I am finding the sparse
formulations useful for extracting genre/topic information out of
netflix/movielens dataset...The
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-89729777
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 debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-89729377
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
formula
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-89706247
@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
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 Movi
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27769592
--- 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/5005#issuecomment-89594722
@mengxr any insight on it ? the runtime issue is only in first iteration
and I think you can point out if there is any obvious issue in the way I call
the solver
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27712646
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -138,14 +141,122 @@ class
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-88347022
@mengxr could you please do another passI might have missed the JavaRDD
compatibility issue but fixed rest of your comments...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-88346990
I reran the map computation on MovieLens with varying ranks:
Example run:
./bin/spark-submit --master spark://TUSCA09LMLVT00C.local:7077 --class
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-88292172
If we move computeRankingMetrics and computeRMSE to a better place, I can
guard it through tests...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3098#issuecomment-88291470
@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 a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27535273
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -103,13 +109,106 @@ class
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27533769
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -103,13 +109,106 @@ class
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27529681
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -103,13 +109,106 @@ class
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27529308
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -35,33 +41,33 @@ import
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27529347
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -35,33 +41,33 @@ import
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27529231
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -17,14 +17,20 @@
package
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27529218
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -17,14 +17,20 @@
package
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27528991
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -171,18 +175,62 @@ object MovieLensALS
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27528959
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -171,18 +175,62 @@ object MovieLensALS
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27528238
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -171,18 +175,62 @@ object MovieLensALS
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27528198
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -171,18 +175,62 @@ object MovieLensALS
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27528120
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -171,18 +175,62 @@ object MovieLensALS
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27528071
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -171,18 +175,62 @@ object MovieLensALS
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27525568
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -171,18 +175,62 @@ object MovieLensALS
Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27525485
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala ---
@@ -74,6 +75,9 @@ object MovieLensALS {
opt[Unit
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-87342283
What are MiMa tests ? I am bit confused on it...
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-87276063
Updated the PR with breeze 0.11.2...Except first iteration, rest of them
are at par:
Breeze NNLS:
TUSCA09LMLVT00C:spark-brznnls v606014$ grep
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-87165211
I integrated with Breeze 0.11.2. Only visible difference is first iteration
Breeze QuadraticMinimizer:
TUSCA09LMLVT00C:spark-qp-als v606014$ grep
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-86950106
@mengxr any updates on it ? breeze 0.11.2 is now integrated with Spark...I
can clean up the PR for reviews
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-86949884
@mengxr any updates on it ? breeze 0.11.2 is now integrated with Spark
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Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-85814758
@mengxr I discussed with David and the only reason I can think of is that
inside the solvers I am using DenseMatrix and DenseVector in-place of primitive
arrays for
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-85351062
All the runtime enhancements are being added to Breeze in this PR:
https://github.com/scalanlp/breeze/pull/386
Please let me know if there are additional feedbacks
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/5005#issuecomment-85348266
All the runtime enhancements are being added to Breeze in this PR:
https://github.com/scalanlp/breeze/pull/386
Please let me know if there are additional feedbacks
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-85161041
@mengxr I added the optimization for lower triangular matrix and now they
are very close...Let me know what do you think and if there are any other
tricks you would
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-84827225
I looked more into it and I will open up an API in Breeze
QuadraticMinimizer where in-place of DenseMatrix gram, upper triangular gram
can be sent but the inner
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-84766375
Also for the ml.QuadraticSolver vs ml.CholeskySolver first iteration
runtime difference I am considering opening up an API in Breeze
QuadraticMinimizer which only
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-84765491
I added all the testcases for ml.QuadraticSolver...the driver is through
MovieLensALS right now where --userConstraint and --productConstraint can be
specified...For
Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-84708094
@witgo there are lot of useful building blocks in your RBM PR...are you
planning to consolidate them in this PR ?
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