GMM classifier with Spark Dataframes











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I have a Spark DataFrame (sdf) with 2 columns as [label, features]. The dataframe looks like this:



+-----+--------------------+

|label|            features|

+-----+--------------------+

|  1.0|[-0.0157564170658...|

|  1.0|[-0.0517730601131...|

|  1.0|[-0.1163185834884...|

|  2.0|[-0.0583032481372...|

|  2.0|[-0.0341501310467...|

+-----+--------------------+


I want to train a GMM classifier for each label parallelly. My doubts are as below:



a. How the create new dataframe for each label from the existing dataframes?



b. Can I create an RDD of dataframes (pandas or spark) and then map it to a gmm function?
I am trying to use BayesianGaussianMixture from sklearn.mixture for my model training.






apache-spark pyspark apache-spark-sql gmm







share|improve this question




























    up vote
    -1
    down vote

    favorite












    I have a Spark DataFrame (sdf) with 2 columns as [label, features]. The dataframe looks like this:



    +-----+--------------------+
    
|label|            features|
    
+-----+--------------------+
    
|  1.0|[-0.0157564170658...|
    
|  1.0|[-0.0517730601131...|
    
|  1.0|[-0.1163185834884...|
    
|  2.0|[-0.0583032481372...|
    
|  2.0|[-0.0341501310467...|
    
+-----+--------------------+


    I want to train a GMM classifier for each label parallelly. My doubts are as below:



    a. How the create new dataframe for each label from the existing dataframes?



    b. Can I create an RDD of dataframes (pandas or spark) and then map it to a gmm function?
    I am trying to use BayesianGaussianMixture from sklearn.mixture for my model training.






    apache-spark pyspark apache-spark-sql gmm







    share|improve this question


























      up vote
      -1
      down vote

      favorite









      up vote
      -1
      down vote

      favorite











      I have a Spark DataFrame (sdf) with 2 columns as [label, features]. The dataframe looks like this:



      +-----+--------------------+
      
|label|            features|
      
+-----+--------------------+
      
|  1.0|[-0.0157564170658...|
      
|  1.0|[-0.0517730601131...|
      
|  1.0|[-0.1163185834884...|
      
|  2.0|[-0.0583032481372...|
      
|  2.0|[-0.0341501310467...|
      
+-----+--------------------+


      I want to train a GMM classifier for each label parallelly. My doubts are as below:



      a. How the create new dataframe for each label from the existing dataframes?



      b. Can I create an RDD of dataframes (pandas or spark) and then map it to a gmm function?
      I am trying to use BayesianGaussianMixture from sklearn.mixture for my model training.






      apache-spark pyspark apache-spark-sql gmm







      share|improve this question















      I have a Spark DataFrame (sdf) with 2 columns as [label, features]. The dataframe looks like this:



      +-----+--------------------+
      
|label|            features|
      
+-----+--------------------+
      
|  1.0|[-0.0157564170658...|
      
|  1.0|[-0.0517730601131...|
      
|  1.0|[-0.1163185834884...|
      
|  2.0|[-0.0583032481372...|
      
|  2.0|[-0.0341501310467...|
      
+-----+--------------------+


      I want to train a GMM classifier for each label parallelly. My doubts are as below:



      a. How the create new dataframe for each label from the existing dataframes?



      b. Can I create an RDD of dataframes (pandas or spark) and then map it to a gmm function?
      I am trying to use BayesianGaussianMixture from sklearn.mixture for my model training.






      apache-spark pyspark apache-spark-sql gmm




      apache-spark pyspark apache-spark-sql gmm






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 14 at 7:43









      Ali AzG

      587515




      587515










      asked Nov 14 at 7:32









      Pradip Gupta

      579




      579





























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