DatasetFactory.createSimpleDataset with a cache<BinaryObject, BinaryObject) returns null

[maheshkr76private]

Hi,

I am trying to create a dataset with a SQL table Cache as follows

The sql table was created as follows
CREATE TABLE Person_Table_Test (id LONG, name VARCHAR(64), cityId DOUBLE,
country VARCHAR(64), pinCode LONG, age LONG, date DATE, time TIMESTAMP,
PRIMARY KEY (id)) WITH "template=SQLTABLE_CACHE_TEMPLATE , backups=2"

The dataset is created as follows
PersonTable<Binary, Binary> type

 try {
      dataSet = DatasetFactory.createSimpleDataset(ignite, personTable, new
TableFeatureExtractor(features));
    } catch (Exception e) {
      e.printStackTrace();
    }

TableFeatureExtractor is as follows

public class TableFeatureExtractor implements IgniteBiFunction<BinaryObject,
BinaryObject, Vector> {

  @Override
  public Vector apply(BinaryObject k, BinaryObject v) {
  }
}


The dataset seems to be created from logs, but all its operations always
return null.

Quesiton: is this model of creating a dataset supported in Ignite 2.7?

regards
Mahesh




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[maheshkr76private]

In other words, for ML functions,

can I use IgniteBiFunction with Key and Value as *BinaryObject*, and at the
same time KEY_TYPE and VALUE_TYPE is not specified while creating the SQL
table cache?

Unfortunately, in our product, we have data in SQL tables, and from these we
intend to create datasets and develop some ML functions with it. These SQL
tables are generated dynamically by users.





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[Yury Babak]

Mahesh,

Unfortunately no. We start to support model training over caches with binary objects from Ignite 2.8.

This support was added by ticket https://issues.apache.org/jira/browse/IGNITE-10700

So, for now, this option is available only for builds from the master branch.

вс, 20 янв. 2019 г. в 16:02, mahesh76private <[hidden email]>:

In other words, for ML functions,

can I use IgniteBiFunction with Key and Value as *BinaryObject*, and at the
same time KEY_TYPE and VALUE_TYPE is not specified while creating the SQL
table cache?

Unfortunately, in our product, we have data in SQL tables, and from these we
intend to create datasets and develop some ML functions with it. These SQL
tables are generated dynamically by users.





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[maheshkr76private]

Yes. Yuri. Got it. We will wait for 2.8 release.



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[zaleslaw]

Hi, what kind of ML algorithms are you going to use?



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[mehdi sey]

i think classification and clustering is most important

On Wed, Jan 23, 2019 at 2:09 PM zaleslaw <[hidden email]> wrote:

Hi, what kind of ML algorithms are you going to use?



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[maheshkr76private]

classification and clustering....
Importantly, critical for us to ensure, we have all the hooks to write our
algorithms from datasets.
Will provide more feedback in the next 1-2 months.




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[zaleslaw]

Ok, will wait your feedback, also we are going to add example to master "how
to work with SQL cache from ML module"



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[maheshkr76private]

that would be awesome....




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