Could you post your code? It sounds like a bug. One thing to check is
that wheher you set regType, which is None by default. -Xiangrui
On Tue, Dec 23, 2014 at 3:36 PM, Thomas Kwan thomas.k...@manage.com wrote:
Hi there
We are on mllib 1.1.1, and trying different regularization parameters. We
--
Skype: boci13, Hangout: boci.b...@gmail.com
On Tue, Dec 23, 2014 at 1:35 AM, Xiangrui Meng men...@gmail.com wrote:
How big is the dataset you want to use in prediction? -Xiangrui
On Mon, Dec 22, 2014 at 1:47 PM, boci
There is an SVD++ implementation in GraphX. It would be nice if you
can compare its performance vs. Mahout. -Xiangrui
On Wed, Dec 24, 2014 at 6:46 AM, Prafulla Wani prafulla.w...@gmail.com wrote:
hi ,
Is there any plan to add SVDPlusPlus based recommender to MLLib ? It is
implemented in
We have streaming linear regression (since v1.1) and k-means (v1.2) in
MLlib. You can check the user guide:
http://spark.apache.org/docs/latest/mllib-linear-methods.html#streaming-linear-regression
http://spark.apache.org/docs/latest/mllib-clustering.html#streaming-clustering
-Xiangrui
On Tue,
Sean's PR may be relevant to this issue
(https://github.com/apache/spark/pull/3702). As a workaround, you can
try to truncate the raw scores to 4 digits (e.g., 0.5643215 - 0.5643)
before sending it to BinaryClassificationMetrics. This may not work
well if he score distribution is very skewed. See
Dear Spark users and developers,
I’m happy to announce Spark Packages (http://spark-packages.org), a
community package index to track the growing number of open source
packages and libraries that work with Apache Spark. Spark Packages
makes it easy for users to find, discuss, rate, and install
Did you check the indices in the LIBSVM data and the master file? Do
they match? -Xiangrui
On Sat, Dec 20, 2014 at 8:13 AM, Sameer Tilak ssti...@live.com wrote:
Hi All,
I use LIBSVM format to specify my input feature vector, which used 1-based
index. When I run regression the o/p is 0-indexed
How big is the dataset you want to use in prediction? -Xiangrui
On Mon, Dec 22, 2014 at 1:47 PM, boci boci.b...@gmail.com wrote:
Hi!
I want to try out spark mllib in my spark project, but I got a little
problem. I have training data (external file), but the real data com from
another rdd.
Hi Jay,
Please try increasing executor memory (if the available memory is more
than 2GB) and reduce numBlocks in ALS. The current implementation
stores all subproblems in memory and hence the memory requirement is
significant when k is large. You can also try reducing k and see
whether the
you can use the default toString method to get the string
representation. if you want to customized, check the indices/values
fields. -Xiangrui
On Fri, Dec 5, 2014 at 7:32 AM, debbie debbielarso...@hotmail.com wrote:
Basic question:
What is the best way to loop through one of these and print
If you want to train offline and predict online, you can use the
current LR implementation to train a model and then apply
model.predict on the dstream. -Xiangrui
On Sun, Dec 7, 2014 at 6:30 PM, Nasir Khan nasirkhan.onl...@gmail.com wrote:
I am new to spark.
Lets say i want to develop a machine
Is it possible that after filtering the feature dimension changed?
This may happen if you use LIBSVM format but didn't specify the number
of features. -Xiangrui
On Tue, Dec 9, 2014 at 4:54 AM, Sameer Tilak ssti...@live.com wrote:
Hi All,
I was able to run LinearRegressionwithSGD for a largeer
Please check the number of partitions after sc.textFile. Use
sc.textFile('...', 8) to have at least 8 partitions. -Xiangrui
On Tue, Dec 9, 2014 at 4:58 AM, DB Tsai dbt...@dbtsai.com wrote:
You just need to use the latest master code without any configuration
to get performance improvement from
Could you post the full stacktrace? It seems to be some recursive call
in parsing. -Xiangrui
On Tue, Dec 9, 2014 at 7:44 PM, jishnu.prat...@wipro.com wrote:
Hi
I am getting Stack overflow Error
Exception in main java.lang.stackoverflowerror
On Sun, Dec 14, 2014 at 3:06 AM, Saurabh Agrawal
saurabh.agra...@markit.com wrote:
Hi,
I am a new bee in spark and scala world
I have been trying to implement Collaborative filtering using MlLib supplied
out of the box with Spark and Scala
I have 2 problems
1. The best
of item blocks. And
yes, I've been following the JIRA for the new ALS implementation. I'll try
it out when it's ready for testing. .
On Wed, Dec 3, 2014 at 4:24 AM, Xiangrui Meng men...@gmail.com wrote:
Hi Bharath,
You can try setting a small item blocks in this case. 1200 is
definitely too large
Hi Bharath,
You can try setting a small item blocks in this case. 1200 is
definitely too large for ALS. Please try 30 or even smaller. I'm not
sure whether this could solve the problem because you have 100 items
connected with 10^8 users. There is a JIRA for this issue:
The training RMSE may increase due to regularization. Squared loss
only represents part of the global loss. If you watch the sum of the
squared loss and the regularization, it should be non-increasing.
-Xiangrui
On Wed, Nov 26, 2014 at 9:53 AM, Sean Owen so...@cloudera.com wrote:
I also modified
In 1.2, we added streaming k-means:
https://github.com/apache/spark/pull/2942 . -Xiangrui
On Mon, Nov 24, 2014 at 5:25 PM, Joanne Contact joannenetw...@gmail.com wrote:
Thank you Tobias!
On Mon, Nov 24, 2014 at 5:13 PM, Tobias Pfeiffer t...@preferred.jp wrote:
Hi,
On Tue, Nov 25, 2014 at
There is a simple example here:
https://github.com/apache/spark/blob/master/examples/src/main/python/kmeans.py
. You can take advantage of sparsity by computing the distance via
inner products:
http://spark-summit.org/2014/talk/sparse-data-support-in-mllib-2
-Xiangrui
On Tue, Nov 25, 2014 at 2:39
It is data-dependent, and hence needs hyper-parameter tuning, e.g.,
grid search. The first batch is certainly expensive. But after you
figure out a small range for each parameter that fits your data,
following batches should be not that expensive. There is an example
from AMPCamp:
Besides API stability concerns, models constructed directly from users
rather than returned by ALS may not work well. The userFeatures and
productFeatures are both with partitioners so we can perform quick
lookup for prediction. If you save userFeatures and productFeatures
and load them back, it
The data is in LIBSVM format. So this line won't work:
values = [float(s) for s in line.split(' ')]
Please use the util function in MLUtils to load it as an RDD of LabeledPoint.
http://spark.apache.org/docs/latest/mllib-data-types.html#labeled-point
from pyspark.mllib.util import MLUtils
Try building Spark with -Pnetlib-lgpl, which includes the JNI library
in the Spark assembly jar. This is the simplest approach. If you want
to include it as part of your project, make sure the library is inside
the assembly jar or you specify it via `--jars` with spark-submit.
-Xiangrui
On Mon,
How many features and how many partitions? You set kmeans_clusters to
1. If the feature dimension is large, it would be really
expensive. You can check the WebUI and see task failures there. The
stack trace you posted is from the driver. Btw, the total memory you
have is 64GB * 10, so you can
If Spark is not installed on the client side, you won't be able to
deserialize the model. Instead of serializing the model object, you
may serialize the model weights array and implement predict on the
client side. -Xiangrui
On Fri, Nov 14, 2014 at 2:54 PM, xiaoyan yu xiaoyan...@gmail.com wrote:
This is a bug. Could you make a JIRA? -Xiangrui
On Sat, Nov 15, 2014 at 3:27 AM, lev kat...@gmail.com wrote:
Hi,
I'm having trouble using both zipWithIndex and repartition. When I use them
both, the following action will get stuck and won't return.
I'm using spark 1.1.0.
Those 2 lines
I think I understand where the bug is now. I created a JIRA
(https://issues.apache.org/jira/browse/SPARK-4433) and will make a PR
soon. -Xiangrui
On Sat, Nov 15, 2014 at 7:39 PM, Xiangrui Meng men...@gmail.com wrote:
This is a bug. Could you make a JIRA? -Xiangrui
On Sat, Nov 15, 2014 at 3:27
PR: https://github.com/apache/spark/pull/3291 . For now, here is a workaround:
val a = sc.parallelize(1 to 10).zipWithIndex()
a.partitions // call .partitions explicitly
a.repartition(10).count()
Thanks for reporting the bug! -Xiangrui
On Sat, Nov 15, 2014 at 8:38 PM, Xiangrui Meng men
If you use Kryo serialier, you need to register mutable.BitSet and Rating:
https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala#L102
The JIRA was marked resolved because chill resolved the problem in
v0.4.0 and we have this
That means the -Pnetlib-lgpl option didn't work. Could you use sbt
to build the assembly jar and see whether the .so file is inside the
assembly jar? Which system and Java version are you using? -Xiangrui
On Wed, Nov 12, 2014 at 2:22 PM, jpl jlefe...@soe.ucsc.edu wrote:
Hi Xiangrui, thank you
You need to use maven to include python files. See
https://github.com/apache/spark/pull/1223 . -Xiangrui
On Wed, Nov 12, 2014 at 4:48 PM, jamborta jambo...@gmail.com wrote:
I have figured out that building the fat jar with sbt does not seem to
included the pyspark scripts using the following
-evaluator are some
amazing systems for this, but they fundamentally use PMML as the model
representation here.
I have read some JIRA tickets that Xiangrui Meng is interested in getting
PMML implemented to export MLLib models, is that happening? Further, would
something like Manish Amde's
Could you provide more information? For example, spark version,
dataset size (number of instances/number of features), cluster size,
error messages from both the drive and the executor. -Xiangrui
On Mon, Nov 10, 2014 at 11:28 AM, tsj tsj...@gmail.com wrote:
Hello all,
I have some text data
I think you need a Java bean class instead of a normal class. See
example here: http://spark.apache.org/docs/1.1.0/sql-programming-guide.html
(switch to the java tab). -Xiangrui
On Tue, Nov 11, 2014 at 7:18 AM, Naveen Kumar Pokala
npok...@spcapitaliq.com wrote:
Hi,
This is my Instrument java
Could you try jar tf on the assembly jar and grep
netlib-native_system-linux-x86_64.so? -Xiangrui
On Tue, Nov 11, 2014 at 7:11 PM, jpl jlefe...@soe.ucsc.edu wrote:
Hi,
I am having trouble using the BLAS libs with the MLLib functions. I am
using org.apache.spark.mllib.clustering.KMeans (on a
a issue...
Any idea how to optimize this so that we can calculate MAP statistics on
large samples of data ?
On Thu, Nov 6, 2014 at 4:41 PM, Xiangrui Meng men...@gmail.com wrote:
ALS model contains RDDs. So you cannot put `model.recommendProducts`
inside a RDD closure `userProductsRDD.map
local matrix-matrix multiplication or distributed?
On Tue, Nov 4, 2014 at 11:58 PM, ll duy.huynh@gmail.com wrote:
what is the best way to implement a sparse x sparse matrix multiplication
with spark?
--
View this message in context:
We are working on distributed block matrices. The main JIRA is at:
https://issues.apache.org/jira/browse/SPARK-3434
The goal is to support basic distributed linear algebra, (dense first
and then sparse).
-Xiangrui
On Wed, Nov 5, 2014 at 12:23 AM, ll duy.huynh@gmail.com wrote:
@sowen.. i
You can use breeze for local sparse-sparse matrix multiplication and
then define an RDD of sub-matrices
RDD[(Int, Int, CSCMatrix[Double])] (blockRowId, blockColId, sub-matrix)
and then use join and aggregateByKey to implement this feature, which
is the same as in MapReduce.
-Xiangrui
Which Spark version did you use? Could you check the WebUI and attach
the error message on executors? -Xiangrui
On Wed, Nov 5, 2014 at 8:23 AM, rok rokros...@gmail.com wrote:
yes, the training set is fine, I've verified it.
--
View this message in context:
The proposed new set of APIs (SPARK-3573, SPARK-3530) will address
this issue. We carry over extra columns with training and prediction
and then leverage on Spark SQL's execution plan optimization to decide
which columns are really needed. For the current set of APIs, we can
add `predictOnValues`
Many ML algorithms are sequential because they were not designed to be
parallel. However, ML is not driven by algorithms in practice, but by
data and applications. As datasets getting bigger and bigger, some
algorithms got revised to work in parallel, like SGD and matrix
factorization. MLlib tries
This operation requires two transformers:
1) Indexer, which maps string features into categorical features
2) OneHotEncoder, which flatten categorical features into binary features
We are working on the new dataset implementation, so we can easily
express those transformations. Sorry for late!
DId you cache the data and check the load balancing? How many
features? Which API are you using, Scala, Java, or Python? -Xiangrui
On Thu, Oct 30, 2014 at 9:13 AM, Jimmy ji...@sellpoints.com wrote:
Watch the app manager it should tell you what's running and taking awhile...
My guess it's a
You can remove 0.5 from all non-zeros. -Xiangrui
On Wed, Oct 29, 2014 at 9:20 PM, Sameer Tilak ssti...@live.com wrote:
Hi All,
I have my sparse data in libsvm format.
val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc,
mllib/data/sample_libsvm_data.txt)
I am running Linear
trainErr = labelAndPreds.filter(r = r._1 != r._2).count.toDouble /
testParsedData.count
// println(Training Error = + trainErr)
println(Calendar.getInstance().getTime())
}
}
Thanks,
Best,
Peng
On Thu, Oct 30, 2014 at 1:23 PM, Xiangrui Meng men...@gmail.com wrote:
DId you cache the data
FYI, there is a PR to make mllib.rdd.RDDFunctions public:
https://github.com/apache/spark/pull/2907 -Xiangrui
On Tue, Oct 28, 2014 at 5:18 AM, Yanbo Liang yanboha...@gmail.com wrote:
Yes, it can import org.apache.spark.mllib.rdd.RDDFunctions but you can not
use any method in this class or even
=97FPVIfyCsbgsASL94CoDQusg=AFQjCNEQ6gUlwpr6KzlcZVd0sQeCSdjQgQsig2=Ne7pL_Z94wN4g9BwSutsXQ
-Ilya Ganelin
On Mon, Oct 27, 2014 at 6:12 PM, Xiangrui Meng men...@gmail.com wrote:
Could you save the data before ALS and try to reproduce the problem?
You might try reducing the number of partitions and not using
We are working on the pipeline features, which would make this
procedure much easier in MLlib. This is still a WIP and the main JIRA
is at:
https://issues.apache.org/jira/browse/SPARK-1856
Best,
Xiangrui
On Mon, Oct 27, 2014 at 8:56 AM, chirag lakhani
chirag.lakh...@gmail.com wrote:
Hello,
I
Could you save the data before ALS and try to reproduce the problem?
You might try reducing the number of partitions and not using Kryo
serialization, just to narrow down the issue. -Xiangrui
On Mon, Oct 27, 2014 at 1:29 PM, Ilya Ganelin ilgan...@gmail.com wrote:
Hi Burak.
I always see this
If your file is not very large, try
sc.wholeTextFiles(...).values.flatMap(_.split(\n).grouped(4).map(_.mkString(\n)))
-Xiangrui
On Sat, Oct 25, 2014 at 12:57 AM, Parthus peng.wei@gmail.com wrote:
Hi,
It might be a naive question, but I still wish that somebody could help me
handle it.
Yes. where the indices are one-based and **in ascending order**. -Xiangrui
On Tue, Oct 21, 2014 at 1:10 PM, Sameer Tilak ssti...@live.com wrote:
Hi All,
I have a question regarding the ordering of indices. The document says that
the indices indices are one-based and in ascending order.
Please check out the example code:
https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/mllib/TallSkinnySVD.scala
-Xiangrui
On Tue, Oct 21, 2014 at 5:34 AM, viola viola.wiersc...@siemens.com wrote:
Hi,
I am VERY new to spark and mllib and ran into a
computePrincipalComponents returns a local matrix X, whose columns are
the principal components (ordered), while those column vectors are in
the same feature space as the input feature vectors. -Xiangrui
On Thu, Oct 16, 2014 at 2:39 AM, al123 ant.lay...@hotmail.co.uk wrote:
Hi,
I don't think
You cannot recover the document from the TF-IDF vector, because
HashingTF is not reversible. You can assign each document a unique ID,
and join back the result after training. HasingTF can transform
individual record:
val docs: RDD[(String, Seq[String])] = ...
val tf = new HashingTF()
val
What is the feature dimension? I saw you used 100 partitions. How many
cores does your cluster have? -Xiangrui
On Tue, Oct 14, 2014 at 1:51 PM, Ray ray-w...@outlook.com wrote:
Hi guys,
An interesting thing, for the input dataset which has 1.5 million vectors,
if set the KMeans's k_value = 100
LBFGS is better. If you data is easily separable, LR might return
values very close or equal to either 0.0 or 1.0. It is rare but it may
happen. -Xiangrui
On Tue, Oct 14, 2014 at 3:18 PM, Aris arisofala...@gmail.com wrote:
Wow...I just tried LogisticRegressionWithLBFGS, and using
Just ran a test on mnist8m (8m x 784) with k = 100 and numIter = 50.
It worked fine. Ray, the error log you posted is after cluster
termination, which is not the root cause. Could you search your log
and find the real cause? On the executor tab screenshot, I saw only
200MB is used. Did you cache
I used k-means||, which is the default. And it took less than 1
minute to finish. 50 iterations took less than 25 minutes on a cluster
of 9 m3.2xlarge EC2 nodes. Which deploy mode did you use? Is it
yarn-client? -Xiangrui
On Tue, Oct 14, 2014 at 6:03 PM, Ray ray-w...@outlook.com wrote:
Hi
1. No.
2. The seed per partition is fixed. So it should generate
non-overlapping subsets.
3. There was a bug in 1.0, which was fixed in 1.0.1 and 1.1.
Best,
Xiangrui
On Thu, Oct 9, 2014 at 11:05 AM, Nan Zhu zhunanmcg...@gmail.com wrote:
Hi, all
When we use MLUtils.kfold to generate training
please re-try with --driver-memory 10g . The default is 256m. -Xiangrui
On Thu, Oct 9, 2014 at 2:33 AM, Clive Cox clive@rummble.com wrote:
Hi,
I'm trying out the DIMSUM item similarity from github master commit
69c3f441a9b6e942d6c08afecd59a0349d61cc7b . My matrix is:
Num items : 8860
The proper step size partially depends on the Lipschitz constant of
the objective. You should let the machine try different combinations
of parameters and select the best. We are working with people from
AMPLab to make hyperparameter tunning easier in MLlib 1.2. For the
theory, Nesterov's book
Did you test different regularization parameters and step sizes? In
the combination that works, I don't see A + D. Did you test that
combination? Are there any linear dependency between A's columns and
D's columns? -Xiangrui
On Tue, Oct 7, 2014 at 1:56 PM, Sameer Tilak ssti...@live.com wrote:
Did you add a different version of breeze to the classpath? In Spark
1.0, we use breeze 0.7, and in Spark 1.1 we use 0.9. If the breeze
version you used is different from the one comes with Spark, you might
see class not found. -Xiangrui
On Fri, Oct 3, 2014 at 4:22 AM, Priya Ch
The current impl of ALS constructs least squares subproblems in
memory. So for rank 100, the total memory it requires is about 480,189
* 100^2 / 2 * 8 bytes ~ 20GB, divided by the number of blocks. For
rank 1000, this number goes up to 2TB, unfortunately. There is a JIRA
for optimizing ALS:
It would be really helpful if you can help test the scalability of the
new ALS impl:
https://github.com/mengxr/spark-als/blob/master/src/main/scala/org/apache/spark/ml/SimpleALS.scala
. It should be faster and more scalable, but the code is messy now.
Best,
Xiangrui
On Fri, Oct 3, 2014 at 11:57
ALS still needs to load and deserialize the in/out blocks (one by one)
from disk and then construct least squares subproblems. All happen in
RAM. The final model is also stored in memory. -Xiangrui
On Wed, Oct 1, 2014 at 4:36 AM, Alex T chiorts...@gmail.com wrote:
Hi, thanks for the reply.
I
Yes, the bigram in that demo only has two characters, which could
separate different character sets. -Xiangrui
On Wed, Oct 1, 2014 at 2:54 PM, Liquan Pei liquan...@gmail.com wrote:
The program computes hashing bi-gram frequency normalized by total number of
bigrams then filter out zero values.
The cost depends on the feature dimension, number of instances, number
of classes, and number of partitions. Do you mind sharing those
numbers? -Xiangrui
On Wed, Oct 1, 2014 at 6:31 PM, Mike Bernico mike.bern...@gmail.com wrote:
Hi Everyone,
I'm working on training mllib's Naive Bayes to
Which Spark version are you using? It works in 1.1.0 but not in 1.0.0.
-Xiangrui
On Wed, Oct 1, 2014 at 2:13 PM, Jimmy McErlain ji...@sellpoints.com wrote:
So I am trying to print the model output from MLlib however I am only
getting things like the following:
You may need a cluster with more memory. The current ALS
implementation constructs all subproblems in memory. With rank=10,
that means (6.5M + 2.5M) * 10^2 / 2 * 8 bytes = 3.5GB. The ratings
need 2GB, not counting the overhead. ALS creates in/out blocks to
optimize the computation, which takes
We don't handle missing value imputation in the current version of
MLlib. In future releases, we can store feature information in the
dataset metadata, which may store the default value to replace missing
values. But no one is committed to work on this feature. For now, you
can filter out examples
Hi Krishna,
Some planned features for MLlib 1.2 can be found via Spark JIRA:
http://bit.ly/1ywotkm , though this list is not fixed. The feature
freeze will happen by the end of Oct. Then we will cut branch-1.2 and
start QA. I don't recommend using branch-1.2 for hands-on tutorial
around Oct 29th
The test accuracy doesn't mean the total loss. All points between (-1,
1) can separate points -1 and +1 and give you 1.0 accuracy, but their
coressponding loss are different. -Xiangrui
On Sun, Sep 28, 2014 at 2:48 AM, Yanbo Liang yanboha...@gmail.com wrote:
Hi
We have used LogisticRegression
We removed commons-math3 from dependencies to avoid version conflict
with hadoop-common. hadoop-common-2.3+ depends on commons-math3-3.1.1,
while breeze depends on commons-math3-3.3. 3.3 is not backward
compatible with 3.1.1. So we removed it because the breeze functions
we use do not touch
7000x7000 is not tall-and-skinny matrix. Storing the dense matrix
requires 784MB. The driver needs more storage for collecting result
from executors as well as making a copy for LAPACK's dgesvd. So you
need more memory. Do you need the full SVD? If not, try to use a small
k, e.g, 50. -Xiangrui
On
For the vectorizer, what's the output feature dimension and are you
creating sparse vectors or dense vectors? The model on the driver
consists of numClasses * numFeatures doubles. However, the driver
needs more memory in order to receive the task result (of the same
size) from executors. So you
Please also check the load balance of the RDD on YARN. How many
partitions are you using? Does it match the number of CPU cores?
-Xiangrui
On Thu, Sep 25, 2014 at 12:28 PM, bhusted brian.hus...@gmail.com wrote:
What is the size of your vector mine is set to 20? I am seeing slow results
as well
You dataset is small. NaiveBayes should work under the default
settings, even in local mode. Could you try local mode first without
changing any Spark settings? Since your dataset is small, could you
save the vectorized data (RDD[LabeledPoint]) and send me a sample? I
want to take a look at the
Does feature size 43839 equal to the number of terms? Check the output
dimension of your feature vectorizer and reduce number of partitions
to match the number of physical cores. I saw you set
spark.storage.memoryFaction to 0.0. Maybe it is better to keep the
default. Also please confirm the
The importance should be based on some statistics, for example, the
standard deviation of the feature column and the magnitude of the
weight. If the columns are scaled to unit standard deviation (using
StandardScaler), you can tell the importance by the absolute value of
the weight. But there are
Did you cache `features`? Without caching it is slow because we need
O(k) iterations. The storage requirement on the driver is about 2 * n
* k = 2 * 3 million * 200 ~= 9GB, not considering any overhead.
Computing U is also an expensive task in your case. We should use some
randomized SVD
We don't support kernels because it doesn't scale well. Please check
When to use LIBLINEAR but not LIBSVM on
http://www.csie.ntu.edu.tw/~cjlin/liblinear/index.html . I like Jey's
suggestion on expanding features. -Xiangrui
On Thu, Sep 18, 2014 at 12:29 PM, Jey Kottalam j...@cs.berkeley.edu wrote:
Could you create a JIRA for it? We can either remove special
characters or encode with alphanumerics. -Xiangrui
On Thu, Sep 18, 2014 at 3:50 PM, SK skrishna...@gmail.com wrote:
Hi,
The default log files for the Mllib examples use a rather long naming
convention that includes special
Please check the colStats method defined under mllib.stat.Statistics. -Xiangrui
On Mon, Sep 15, 2014 at 1:00 PM, jamborta jambo...@gmail.com wrote:
Hi all,
I have an RDD that contains around 50 columns. I need to sum each column,
which I am doing by running it through a for loop, creating an
Spark doesn't support MultipleOutput at this time. You can cache the
parent RDD. Then create RDDs from it and save them separately.
-Xiangrui
On Fri, Sep 12, 2014 at 7:45 AM, Guillermo Ortiz konstt2...@gmail.com wrote:
I would like to define the names of my output in Spark, I have a process
Thanks for the update! -Xiangrui
On Sun, Sep 14, 2014 at 11:33 PM, jatinpreet jatinpr...@gmail.com wrote:
Hi,
I have been able to get the same accuracy with MLlib as Mahout's. The
pre-processing phase of Mahout was the reason behind the accuracy mismatch.
After studying and applying the
Or you can use the factory method `Vectors.sparse`:
val sv = Vectors.sparse(numProducts, productIds.map(x = (x, 1.0)))
where numProducts should be the largest product id plus one.
Best,
Xiangrui
On Mon, Sep 15, 2014 at 12:46 PM, Chris Gore cdg...@cdgore.com wrote:
Hi Sameer,
MLLib uses
I wrote an input format for Redshift's tables unloaded UNLOAD the
ESCAPE option: https://github.com/mengxr/redshift-input-format , which
can recognize multi-line records.
Redshift puts a backslash before any in-record `\\`, `\r`, `\n`, and
the delimiter character. You can apply the same escaping
If you are using the Mahout's Multinomial Naive Bayes, it should be
the same as MLlib's. I tried MLlib with news20.scale downloaded from
http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html
and the test accuracy is 82.4%. -Xiangrui
On Tue, Sep 9, 2014 at 4:58 AM, jatinpreet
You can try LinearRegression with sparse input. It converges the least
squares solution if the linear system is over-determined, while the
convergence rate depends on the condition number. Applying standard
scaling is popular heuristic to reduce the condition number.
If you are interested in
There is an undocumented configuration to put users jars in front of
spark jar. But I'm not very certain that it works as expected (and
this is why it is undocumented). Please try turning on
spark.yarn.user.classpath.first . -Xiangrui
On Sat, Sep 6, 2014 at 5:13 PM, Victor Tso-Guillen
When you submit the job to yarn with spark-submit, set --conf
spark.yarn.user.classpath.first=true .
On Mon, Sep 8, 2014 at 10:46 AM, Penny Espinoza
pesp...@societyconsulting.com wrote:
I don't understand what you mean. Can you be more specific?
From: Victor
Could you attach the driver log? -Xiangrui
On Mon, Sep 8, 2014 at 7:23 AM, Hui Li hli161...@gmail.com wrote:
I am running a very simple example using the SVMWithSGD on Amazon EMR. I
haven't got any result after one hour long.
My instance-type is: m3.large
instance-count is: 3
Dataset
to partition
the problem cleanly and don't need consensus step (which is what I
implemented in the code)
Thanks
Deb
On Sep 7, 2014 11:35 PM, Xiangrui Meng men...@gmail.com wrote:
You can try LinearRegression with sparse input. It converges the least
squares solution if the linear system is over
This is not supported in MLlib. Hopefully, we will add support for
weighted examples in v1.2. If you want to train weighted instances
with the current tree implementation, please try importance sampling
first to adjust the weights. For instance, an example with weight 0.3
is sampled with
We have a pending PR (https://github.com/apache/spark/pull/216) for
discretization but it has performance issues. We will try to spend
more time to improve it. -Xiangrui
On Tue, Sep 2, 2014 at 2:56 AM, filipus floe...@gmail.com wrote:
i guess i found it
I think they are the same. If you have hub (https://hub.github.com/)
installed, you can run
hub checkout https://github.com/apache/spark/pull/216
and then `sbt/sbt assembly`
-Xiangrui
On Wed, Sep 3, 2014 at 12:03 AM, filipus floe...@gmail.com wrote:
howto install? just clone by git clone
There is a sliding method implemented in MLlib
(https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/rdd/SlidingRDD.scala),
which is used in computing Area Under Curve:
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