Thank you very much for the answer. I'll try with customizing workflow.
There is a step where Seq of models is returned. My idea is to return model
and model parameters in this step. I'll let you know if it works.
Thanks,
Tihomie
On Feb 12, 2018 23:34, "Pat Ferrel" <p...@occamsmachete.com> wrote:
> This is an interesting question. As we make more mature full featured
> engines they will begin to employ hyper parameter search techniques or
> reinforcement params. This means that there is a new stage in the workflow
> or a feedback loop not already accounted for.
>
> Short answer is no, unless you want to re-write your engine.json after
> every train and probably keep the old one for safety. You must re-train to
> get the new params put into the metastore and therefor available to your
> engine.
>
> What we do for the Universal Recommender is have a special new workflow
> phase, call it a self-tuning phase, where we search for the right tuning of
> parameters. This it done with code that runs outside of pio and creates
> parameters that go into the engine.json. This can be done periodically to
> make sure the tuning is still optimal.
>
> Not sure whether feedback or hyper parameter search is the best
> architecture for you.
>
>
> From: Tihomir Lolić <tihomir.lo...@gmail.com> <tihomir.lo...@gmail.com>
> Reply: user@predictionio.apache.org <user@predictionio.apache.org>
> <user@predictionio.apache.org>
> Date: February 12, 2018 at 2:02:48 PM
> To: user@predictionio.apache.org <user@predictionio.apache.org>
> <user@predictionio.apache.org>
> Subject: Dynamically change parameter list
>
> Hi,
>
> I am trying to figure out how to dynamically update algorithm parameter
> list. After the train is finished only model is updated. The reason why I
> need this data to be updated is that I am creating data mapping based on
> the training data. Is there a way to update this data after the train is
> done?
>
> Here is the code that I am using. The variable that and should be updated
> after the train is marked *bold red.*
>
> import io.prediction.controller.{EmptyParams, EngineParams}
> import io.prediction.data.storage.EngineInstance
> import io.prediction.workflow.CreateWorkflow.WorkflowConfig
> import io.prediction.workflow._
> import org.apache.spark.ml.linalg.SparseVector
> import org.joda.time.DateTime
> import org.json4s.JsonAST._
>
> import scala.collection.mutable
>
> object TrainApp extends App {
>
> val envs = Map("FOO" -> "BAR")
>
> val sparkEnv = Map("spark.master" -> "local")
>
> val sparkConf = Map("spark.executor.extraClassPath" -> ".")
>
> val engineFactoryName = "LogisticRegressionEngine"
>
> val workflowConfig = WorkflowConfig(
> engineId = EngineConfig.engineId,
> engineVersion = EngineConfig.engineVersion,
> engineVariant = EngineConfig.engineVariantId,
> engineFactory = engineFactoryName
> )
>
> val workflowParams = WorkflowParams(
> verbose = workflowConfig.verbosity,
> skipSanityCheck = workflowConfig.skipSanityCheck,
> stopAfterRead = workflowConfig.stopAfterRead,
> stopAfterPrepare = workflowConfig.stopAfterPrepare,
> sparkEnv = WorkflowParams().sparkEnv ++ sparkEnv
> )
>
> WorkflowUtils.modifyLogging(workflowConfig.verbose)
>
> val dataSourceParams = DataSourceParams(sys.env.get("APP_NAME").get)
> val preparatorParams = EmptyParams()
>
> *val algorithmParamsList = Seq("Logistic" -> LogisticParams(columns =
> Array[String](),*
> * dataMapping
> = Map[String, Map[String, SparseVector]]()))*
> val servingParams = EmptyParams()
>
> val engineInstance = EngineInstance(
> id = "",
> status = "INIT",
> startTime = DateTime.now,
> endTime = DateTime.now,
> engineId = workflowConfig.engineId,
> engineVersion = workflowConfig.engineVersion,
> engineVariant = workflowConfig.engineVariant,
> engineFactory = workflowConfig.engineFactory,
> batch = workflowConfig.batch,
> env = envs,
> sparkConf = sparkConf,
> dataSourceParams = JsonExtractor.paramToJson(workflowConfig.jsonExtractor,
> workflowConfig.engineParamsKey -> dataSourceParams),
> preparatorParams = JsonExtractor.paramToJson(workflowConfig.jsonExtractor,
> workflowConfig.engineParamsKey -> preparatorParams),
> algorithmsParams =
> JsonExtractor.paramsToJson(workflowConfig.jsonExtractor,
> algorithmParamsList),
> servingParams = JsonExtractor.paramToJson(workflowConfig.jsonExtractor,
> workflowConfig.engineParamsKey -> servingParams)
> )
>
> val (engineLanguage, engineFactory) =
> WorkflowUtils.getEngine(engineInstance.engineFactory,
> getClass.getClassLoader)
>
> val engine = engineFactory()
>
> val engineParams = EngineParams(
> dataSourceParams = dataSourceParams,
> preparatorParams = preparatorParams,
> algorithmParamsList = algorithmParamsList,
> servingParams = servingParams
> )
>
> val engineInstanceId = CreateServer.engineInstances.
> insert(engineInstance)
>
> CoreWorkflow.runTrain(
> env = envs,
> params = workflowParams,
> engine = engine,
> engineParams = engineParams,
> engineInstance = engineInstance.copy(id = engineInstanceId)
> )
>
> CreateServer.actorSystem.shutdown()
> }
>
>
> Thank you,
> Tihomir
>
>
