Hi Mahesh, No you don't need to use Hadoop at any stage in this project. Everything you need is in Spark (regarding ML algorithms). You can also use Spark MLLibs methods to randomly split datasets.
Best regards. On Mon, Mar 14, 2016 at 1:28 PM, Mahesh Dananjaya <[email protected] > wrote: > Hi Maheshakya, > I am writing some java programs and try to break the dataset into several > pieces and train a model repeatedly with those data sets using Spark MLLib. > Do i have to do anything with Hadoop at this stage, because i am working > with a standalone mode.thank you. > BR, > Mahesh. > > On Sun, Mar 13, 2016 at 6:30 PM, Maheshakya Wijewardena < > [email protected]> wrote: > >> Hi Mahesh, >> >> You don't have to look into carbon-ml. >> >> Best regards. >> >> On Sun, Mar 13, 2016 at 5:49 PM, Mahesh Dananjaya < >> [email protected]> wrote: >> >>> Hi maheshakya, >>> i am working on some examples related to Spark and ML.is there anything >>> to do with carbon-ml. I think i dont need to look into that one.do i? >>> BR, >>> Mahesh >>> >>> On Tue, Mar 8, 2016 at 11:55 AM, Maheshakya Wijewardena < >>> [email protected]> wrote: >>> >>>> Hi Mahesh, >>>> >>>> does that Scala API is with your current product or repo? >>>> >>>> >>>> No, we don't have the Scala API included. What we want is to design the >>>> Java implementations of those algorithms to train with mini-batches of >>>> streaming data with the help of the aforementioned methods so that we can >>>> include in as a CEP extension. >>>> >>>> As to clarify, please try to write a simple Java program using Spark >>>> MLLib linear regression and k-means clustering with a sample data set (You >>>> can find alot of data sets from UCI repo[1]). You need to break the >>>> dataset into several pieces and train a model repeatedly with those. >>>> After each training run, save the model information (such as weights, >>>> intercepts for regression and cluster centers for clustering - please check >>>> the arguments of those methods I have mentioned and save the required >>>> information of the model) >>>> When training a model we a new piece of data, use those methods to >>>> initialize and put the save values for the arguments. This way you can >>>> start from where you stopped in the previous run. >>>> >>>> Let us know your observations and feel free to ask if you need to know >>>> anything more on this. >>>> >>>> We'll let you know what needs to be done to include this in CEP. >>>> >>>> Best regards. >>>> >>>> On Tue, Mar 8, 2016 at 10:59 AM, Mahesh Dananjaya < >>>> [email protected]> wrote: >>>> >>>>> Hi Maheshakya, >>>>> great.thank you.i already have ML and CEP and working more towards it. >>>>> does that Scala API is with your current product or repo?. thank you. >>>>> BR, >>>>> Mahesh. >>>>> >>>>> On Sun, Mar 6, 2016 at 5:49 PM, Maheshakya Wijewardena < >>>>> [email protected]> wrote: >>>>> >>>>>> Hi Mahesh, >>>>>> >>>>>> Please find the comments inline. >>>>>> >>>>>> does data stream is taken to ML as the event publisher's format >>>>>>> through event publisher. Or we can use direct traffic that comes to >>>>>>> event >>>>>>> receiver, or else as streams >>>>>>> >>>>>> We intend to use the direct data as even streams. >>>>>> >>>>>> 1.) Those data coming from wso2 DAS to ML are coming as streams? >>>>>>> >>>>>> No, WSO2 ML doesn't use any even stream. The data stored in tables in >>>>>> DAS is loaded into ML. >>>>>> >>>>>> 2.) Are there any incremental learning algorithms currently active in >>>>>>> ML?you mentioned that there are and they are with scala API. So there >>>>>>> is a >>>>>>> streaming support with that Scala API. In that API which format the >>>>>>> data is >>>>>>> aquired to ML? >>>>>>> >>>>>> No, there are no incremental learning algorithms in ML. The scala API >>>>>> is about Spark MLLib. MLLib supports streaming k-means and other >>>>>> generalized linear models (linear regression variants and logistic >>>>>> regression) with Scala API. What they basically do in those >>>>>> implementations >>>>>> is retraining the trained models with mini batches when data sequentially >>>>>> arrives. There, the breaking of streaming data into mini batches is done >>>>>> with the help of Spark Streaming. But we do not intend to use Spark >>>>>> streaming in our implementation. What we need to do is implement a >>>>>> similar >>>>>> behavior for event streams using the Java API. The Java API has the >>>>>> following methods: >>>>>> >>>>>> - *createModel >>>>>> >>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/regression/LinearRegressionWithSGD.html#createModel%28org.apache.spark.mllib.linalg.Vector,%20double%29>* >>>>>> (Vector >>>>>> >>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/linalg/Vector.html> >>>>>> weights, >>>>>> double intercept) - for GLMs >>>>>> - *setInitialModel >>>>>> >>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/clustering/KMeans.html#setInitialModel%28org.apache.spark.mllib.clustering.KMeansModel%29>* >>>>>> (KMeansModel >>>>>> >>>>>> <http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/clustering/KMeansModel.html> >>>>>> model) >>>>>> - for K means >>>>>> >>>>>> With the help of these methods, we can train models again with newly >>>>>> arriving data, keeping the characteristics learned with the previous >>>>>> data. >>>>>> When implementing this, we need to pay attention to other parameters of >>>>>> incremental learning such as data horizon and data obsolescence >>>>>> (indicated >>>>>> in the project ideas page). >>>>>> We need to discuss on how to add these with CEP event streams. I have >>>>>> added Suho into the thread for more clarification. >>>>>> >>>>>> Best regards. >>>>>> >>>>>> >>>>>> On Sat, Mar 5, 2016 at 5:15 PM, Mahesh Dananjaya < >>>>>> [email protected]> wrote: >>>>>> >>>>>>> Hi maheshakya, >>>>>>> as we concerned to use WSO2 CEP to handle streaming data and >>>>>>> implement the machine learning algorithms with Spark MLLib, does data >>>>>>> stream is taken to ML as the event publisher's format through event >>>>>>> publisher. Or we can use direct traffic that comes to event receiver, >>>>>>> or >>>>>>> else as streams. referring to >>>>>>> https://docs.wso2.com/display/CEP410/User+Guide >>>>>>> 1.) Those data coming from wso2 DAS to ML are coming as streams? >>>>>>> 2.) Are there any incremental learning algorithms currently >>>>>>> active in ML?you mentioned that there are and they are with scala API. >>>>>>> So >>>>>>> there is a streaming support with that Scala API. In that API which >>>>>>> format >>>>>>> the data is aquired to ML? >>>>>>> >>>>>>> thank you. >>>>>>> BR, >>>>>>> Mahesh. >>>>>>> >>>>>>> On Fri, Mar 4, 2016 at 2:03 PM, Maheshakya Wijewardena < >>>>>>> [email protected]> wrote: >>>>>>> >>>>>>>> Hi Mahesh, >>>>>>>> >>>>>>>> We had to modify a the project scope a little to suit best for the >>>>>>>> requirements. We will update the project idea with those concerns soon >>>>>>>> and >>>>>>>> let you know. >>>>>>>> >>>>>>>> We do not support streaming data in WSO2 Machine learner at the >>>>>>>> moment. The new concern is to use WSO2 CEP to handle streaming data and >>>>>>>> implement the machine learning algorithms with Spark MLLib. You can >>>>>>>> look at >>>>>>>> the streaming k-means and streaming linear regression implementations >>>>>>>> in >>>>>>>> MLLib. Currently, the API is only for scala. Our need is to get the >>>>>>>> Java >>>>>>>> APIs of k-means and generalized linear models to support incremental >>>>>>>> learning with streaming data. This has to be done as mini-batch >>>>>>>> learning >>>>>>>> since these algorithms operates as stochastic gradient descents so >>>>>>>> that any >>>>>>>> learning with new data can be done on top of the previously learned >>>>>>>> models. >>>>>>>> So please go through the those APIs[1][2][3] and try to get an idea. >>>>>>>> Also please try to understand how event streams work in WSO2 CEP >>>>>>>> [4][5]. >>>>>>>> >>>>>>>> Best regards. >>>>>>>> >>>>>>>> [1] >>>>>>>> http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/regression/LinearRegressionWithSGD.html >>>>>>>> [2] >>>>>>>> http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/clustering/KMeans.html >>>>>>>> [3] >>>>>>>> http://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/classification/LogisticRegressionWithSGD.html >>>>>>>> [4] https://docs.wso2.com/display/CEP310/Working+with+Event+Streams >>>>>>>> [5] >>>>>>>> https://docs.wso2.com/display/CEP310/Working+with+Execution+Plans >>>>>>>> >>>>>>>> On Fri, Mar 4, 2016 at 11:26 AM, Mahesh Dananjaya < >>>>>>>> [email protected]> wrote: >>>>>>>> >>>>>>>>> Hi maheshakya, >>>>>>>>> give me sometime to go through your ML package. Do current product >>>>>>>>> have any stream data support?. i did some university projects related >>>>>>>>> to >>>>>>>>> machine learning with regressions,modelling, factor analysis, cluster >>>>>>>>> analysis and classification problems (Discriminant Analysis) with SVM >>>>>>>>> (Support Vector machines), Neural networks, LS classification and >>>>>>>>> ML(Maximum likelihood). give me sometime to see how wso2 architecture >>>>>>>>> works.then i can come up with good architecture.thank you. >>>>>>>>> BR, >>>>>>>>> Mahesh. >>>>>>>>> >>>>>>>>> On Wed, Mar 2, 2016 at 2:41 PM, Mahesh Dananjaya < >>>>>>>>> [email protected]> wrote: >>>>>>>>> >>>>>>>>>> Hi Maheshakya, >>>>>>>>>> Thank you for the resources. I will go through this and looking >>>>>>>>>> forward to this proposed project.Thank you. >>>>>>>>>> BR, >>>>>>>>>> Mahesh. >>>>>>>>>> >>>>>>>>>> On Wed, Mar 2, 2016 at 1:52 PM, Maheshakya Wijewardena < >>>>>>>>>> [email protected]> wrote: >>>>>>>>>> >>>>>>>>>>> Hi Mahesh, >>>>>>>>>>> >>>>>>>>>>> Thank you for the interest for this project. >>>>>>>>>>> >>>>>>>>>>> We would like to know what type of similar projects you have >>>>>>>>>>> worked on. You may have seen that WSO2 Machine Learner supports >>>>>>>>>>> several >>>>>>>>>>> learning algorithms at the moment[1]. This project intends to >>>>>>>>>>> leverage the >>>>>>>>>>> existing algorithms in WSO2 Machine Learner to support streaming >>>>>>>>>>> data. As >>>>>>>>>>> an initiative, first you can get an idea about what WSO2 Machine >>>>>>>>>>> Learner >>>>>>>>>>> does and how it operates. You can download WSO2 Machine Learner from >>>>>>>>>>> product page[2] and the the source code [3]. ML is using Apache >>>>>>>>>>> Spark >>>>>>>>>>> MLLib[4] for its' algorithms so it's better to read and understand >>>>>>>>>>> what it >>>>>>>>>>> does as well. >>>>>>>>>>> >>>>>>>>>>> In order to get an idea about the deliverables and the scope of >>>>>>>>>>> this project, try to understand how Spark streaming[5] (see >>>>>>>>>>> examples) >>>>>>>>>>> handles streaming data. Also, have a look in the streaming >>>>>>>>>>> algorithms[6][7] >>>>>>>>>>> supported by MLLib. There are two approaches discussed to employ >>>>>>>>>>> incremental learning in ML in the project proposals page. These >>>>>>>>>>> streaming >>>>>>>>>>> algorithms can be directly used in the first approach. For the other >>>>>>>>>>> approach, the your implementation should contain a procedure to >>>>>>>>>>> create mini >>>>>>>>>>> batches from streaming data with relevant sizes (i.e. a moving >>>>>>>>>>> window) and >>>>>>>>>>> do periodic retraining of the same algorithm. >>>>>>>>>>> >>>>>>>>>>> To start with the project, you will need to come up with a >>>>>>>>>>> suitable plan and an architecture first. >>>>>>>>>>> >>>>>>>>>>> Please watch the video referenced in the proposal (reference: >>>>>>>>>>> 5). It will help you getting a better idea about machine learning >>>>>>>>>>> algorithms with streaming data. >>>>>>>>>>> >>>>>>>>>>> Let us know if you need any help with these. >>>>>>>>>>> >>>>>>>>>>> Best regards >>>>>>>>>>> >>>>>>>>>>> [1] >>>>>>>>>>> https://docs.wso2.com/display/ML110/Machine+Learner+Algorithms >>>>>>>>>>> [2] http://wso2.com/products/machine-learner/ >>>>>>>>>>> [3] >>>>>>>>>>> https://docs.wso2.com/display/ML110/Building+from+Source#BuildingfromSource-Downloadingthesourcecheckout >>>>>>>>>>> [4] https://spark.apache.org/docs/1.4.1/mllib-guide.html >>>>>>>>>>> [5] >>>>>>>>>>> https://spark.apache.org/docs/1.4.1/streaming-programming-guide.html >>>>>>>>>>> [6] >>>>>>>>>>> https://spark.apache.org/docs/1.4.1/mllib-linear-methods.html#streaming-linear-regression >>>>>>>>>>> [7] >>>>>>>>>>> https://spark.apache.org/docs/1.4.1/mllib-clustering.html#streaming-k-means >>>>>>>>>>> >>>>>>>>>>> On Wed, Mar 2, 2016 at 1:19 PM, Mahesh Dananjaya < >>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>> >>>>>>>>>>>> Hi all, >>>>>>>>>>>> I am interesting on contribute to proposal 6: "Predictive >>>>>>>>>>>> analytic with online data for WSO2 Machine Learner" for GSOC2 this >>>>>>>>>>>> time. >>>>>>>>>>>> Since i have been engaging with some similar projects i think it >>>>>>>>>>>> will be a >>>>>>>>>>>> great experience for me. Please let me know what you think and >>>>>>>>>>>> what you >>>>>>>>>>>> suggest. I have been going through your documents.thank you. >>>>>>>>>>>> regards, >>>>>>>>>>>> Mahesh Dananjaya. >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> _______________________________________________ >>>>>>>>>>>> Dev mailing list >>>>>>>>>>>> [email protected] >>>>>>>>>>>> http://wso2.org/cgi-bin/mailman/listinfo/dev >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> Pruthuvi Maheshakya Wijewardena >>>>>>>>>>> [email protected] >>>>>>>>>>> +94711228855 >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> Pruthuvi Maheshakya Wijewardena >>>>>>>> [email protected] >>>>>>>> +94711228855 >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Pruthuvi Maheshakya Wijewardena >>>>>> [email protected] >>>>>> +94711228855 >>>>>> >>>>>> >>>>>> >>>>> >>>> >>>> >>>> -- >>>> Pruthuvi Maheshakya Wijewardena >>>> [email protected] >>>> +94711228855 >>>> >>>> >>>> >>> >> >> >> -- >> Pruthuvi Maheshakya Wijewardena >> [email protected] >> +94711228855 >> >> >> > -- Pruthuvi Maheshakya Wijewardena [email protected] +94711228855
_______________________________________________ Dev mailing list [email protected] http://wso2.org/cgi-bin/mailman/listinfo/dev
