Hi Prem,
Spark actually does somewhat support different algorithms in
CrossValidator, but it's not really obvious. You basically need to make a
Pipeline and build a ParamGrid with different algorithms as stages. Here
is an simple example:
val dt = new DecisionTreeClassifier()
.setLabelCol("label")
.setFeaturesCol("features")
val lr = new LogisticRegression()
.setLabelCol("label")
.setFeaturesCol("features")
val pipeline = new Pipeline()
val paramGrid = new ParamGridBuilder()
.addGrid(pipeline.stages, Array(Array[PipelineStage](dt),
Array[PipelineStage](lr)))
val cv = new CrossValidator()
.setEstimator(pipeline)
.setEstimatorParamMaps(paramGrid)
Although adding more params in the grid can get a little complicated - I
discuss in detail here https://bryancutler.github.io/cv-pipelines/
As Patrick McCarthy mentioned, you might want to follow SPARK-19071 ,
specifically https://issues.apache.org/jira/browse/SPARK-19357 which
parallelizes model evaluation.
Bryan
On Tue, Sep 5, 2017 at 8:02 AM, Yanbo Liang <[email protected]> wrote:
> You are right, native Spark MLlib CrossValidation can't run *different
> *algorithms
> in parallel.
>
> Thanks
> Yanbo
>
> On Tue, Sep 5, 2017 at 10:56 PM, Timsina, Prem <[email protected]>
> wrote:
>
>> Hi Yanboo,
>>
>> Thank You, I very much appreciate your help.
>>
>> For the current use case, the data can fit into a single node. So,
>> spark-sklearn seems to be good choice.
>>
>>
>>
>> *I have on question regarding this *
>>
>> *“If no, Spark MLlib provide CrossValidation which can run multiple
>> machine learning algorithms parallel on distributed dataset and do
>> parameter search.
>> FYI: https://spark.apache.org/docs/latest/ml-tuning.html#cross-validation
>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__spark.apache.org_docs_latest_ml-2Dtuning.html-23cross-2Dvalidation&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=lVvXRRGoh5uXJw-K246dNzogKEfb2yFYtxpTB9xxizo&e=>”*
>>
>> If I understand correctly, it can run parameter search for
>> cross-validation in parallel.
>>
>> However, currently Spark does not support running multiple algorithms
>> (like Naïve Bayes, Random Forest, etc.) in parallel. Am I correct?
>>
>> If not, could you please point me to some resources where they have run
>> multiple algorithms in parallel.
>>
>>
>>
>> Thank You very much. It is great help, I will try spark-sklearn.
>>
>> Prem
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> *From: *Yanbo Liang <[email protected]>
>> *Date: *Tuesday, September 5, 2017 at 10:40 AM
>> *To: *Patrick McCarthy <[email protected]>
>> *Cc: *"Timsina, Prem" <[email protected]>, "[email protected]" <
>> [email protected]>
>> *Subject: *Re: Apache Spark: Parallelization of Multiple Machine
>> Learning ALgorithm
>>
>>
>>
>> Hi Prem,
>>
>>
>>
>> How large is your dataset? Can it be fitted in a single node?
>>
>> If no, Spark MLlib provide CrossValidation which can run multiple machine
>> learning algorithms parallel on distributed dataset and do parameter
>> search. FYI: https://spark.apache.org/docs/latest/ml-tuning.html#cro
>> ss-validation
>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__spark.apache.org_docs_latest_ml-2Dtuning.html-23cross-2Dvalidation&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=lVvXRRGoh5uXJw-K246dNzogKEfb2yFYtxpTB9xxizo&e=>
>>
>> If yes, you can also try spark-sklearn, which can distribute multiple
>> model training(single node training with sklearn) across a distributed
>> cluster and do parameter search. FYI: https://github.com/databr
>> icks/spark-sklearn
>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_databricks_spark-2Dsklearn&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=JfciAow01oTIYYCjhy83Q_nF85fKW9ZI-qYxfUa0BUU&e=>
>>
>>
>>
>> Thanks
>>
>> Yanbo
>>
>>
>>
>> On Tue, Sep 5, 2017 at 9:56 PM, Patrick McCarthy <[email protected]>
>> wrote:
>>
>> You might benefit from watching this JIRA issue -
>> https://issues.apache.org/jira/browse/SPARK-19071
>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_SPARK-2D19071&d=DwMFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=wnzquyZN5LCZ2v6jPXe4F2nU9j4v9g_t24s63U3cYqE&m=FtsbdcfaOELxFW8EFphZgjTd7cl3Kc5oYsQ558EZb3A&s=hQZ6ldug0XZvo4q87r0BQatn55B6UtyVVs0Ge9UneW4&e=>
>>
>>
>>
>> On Sun, Sep 3, 2017 at 5:50 PM, Timsina, Prem <[email protected]>
>> wrote:
>>
>> Is there a way to parallelize multiple ML algorithms in Spark. My use
>> case is something like this:
>>
>> A) Run multiple machine learning algorithm (Naive Bayes, ANN, Random
>> Forest, etc.) in parallel.
>>
>> 1) Validate each algorithm using 10-fold cross-validation
>>
>> B) Feed the output of step A) in second layer machine learning algorithm.
>>
>> My question is:
>>
>> Can we run multiple machine learning algorithm in step A in parallel?
>>
>> Can we do cross-validation in parallel? Like, run 10 iterations of Naive
>> Bayes training in parallel?
>>
>>
>>
>> I was not able to find any way to run the different algorithm in
>> parallel. And it seems cross-validation also can not be done in parallel.
>>
>> I appreciate any suggestion to parallelize this use case.
>>
>>
>>
>> Prem
>>
>>
>>
>>
>>
>
>
