MLFlowTracker

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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   protected[java.lang]

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6. def convertInferenceConfigToDataFrame(config: InferenceMainConfig): DataFrame

   

   Seems a bit counter-intuitive to do this, but this allows for cloud-agnostic storage of the config.

   Seems a bit counter-intuitive to do this, but this allows for cloud-agnostic storage of the config. Otherwise, a configuration would need to be created to manage which cloud this is operating on and handle native SDK object writers. Instead of re-inventing the wheel here, a DataFrame can be serialized to any cloud-native storage medium with very little issue.

   config

   The inference configuration generated for a particular modeling run

   returns

   A DataFrame consisting of a single row and a single field. Cell 1:1 contains the json string.

   Definition Classes

   InferenceTools

7. def convertInferenceConfigToJson(config: InferenceMainConfig): InferenceJsonReturn

   

   Handler method for converting the InferenceMainConfig object to a serializable Json String with correct scala-compatible data structures.

   Handler method for converting the InferenceMainConfig object to a serializable Json String with correct scala-compatible data structures.

   config

   instance of InferenceMainConfig

   returns

   [InferenceJsonReturn] consisting of compact form (for logging) and prettyprint form (human readable)

   Definition Classes

   InferenceTools

8. def convertJsonConfigToClass(jsonConfig: String): InferenceMainConfig

   

   Handler method for converting a read-in json config String to an instance of InferenceMainConfig

   Handler method for converting a read-in json config String to an instance of InferenceMainConfig

   jsonConfig

   the config as a Json-formatted String

   returns

   config as InstanceOf[InferenceMainConfig]

   Definition Classes

   InferenceTools

9. def convertMainConfigToJson(config: MainConfig): MainJsonReturn

   

   Definition Classes

   InferenceTools

10. def createHostedMlFlowClient(): MlflowClient

    

11. def createInferencePayload(dataFrame: DataFrame, modelingColumnsPayload: Array[String], allColumnsPayload: Array[String]): InferencePayload

    
    Definition Classes

    InferenceTools

12. def deleteCustomTags(client: MlflowClient, runId: String, tagKeys: Seq[String]): Unit

    

13. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

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14. def equals(arg0: Any): Boolean

    

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15. def extractInferenceConfigFromDataFrame(configDataFrame: DataFrame): InferenceMainConfig

    

    Extract the InferenceMainConfig from a stored DataFrame containing the string-encoded json in row 1, column 1

    Extract the InferenceMainConfig from a stored DataFrame containing the string-encoded json in row 1, column 1

    configDataFrame

    A Dataframe that contains the configuration for the Inference run.

    returns

    an instance of InferenceMainConfig

    Definition Classes

    InferenceTools

16. def extractInferenceJsonFromDataFrame(configDataFrame: DataFrame): String

    

    From a supplied DataFrame that contains the configuration in cell 1:1, get the json string

    From a supplied DataFrame that contains the configuration in cell 1:1, get the json string

    configDataFrame

    A Dataframe that contains the configuration for the Inference run.

    returns

    The string-encoded json payload for InferenceMainConfig

    Definition Classes

    InferenceTools

17. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

18. def generateMlFlowRunId(): String

    

19. def getArtifactLogSetting: Boolean

    

20. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

21. def getMLFlowClient: MlflowClient

    

    Get a single MLFlow Client for the instance of the object.

    Get a single MLFlow Client for the instance of the object. Reduce garbage collection by not creating a version each time the object is called. As of 0.7.1

22. def getMlFlowBestSuffix: String

    

23. def getMlFlowCustomRunTags: Map[String, String]

    

24. def getMlFlowExperimentName: String

    

25. def getMlFlowLoggingMode: String

    

26. def getMlFlowTrackingURI: String

    

27. def getModelSaveDirectory: String

    

28. def getOrCreateExperimentId(client: MlflowClient, experimentName: String = _mlFlowExperimentName): String

    

    Method for either getting an existing experiment by name, or creating a new one by name and returning the id

    Method for either getting an existing experiment by name, or creating a new one by name and returning the id

    returns

    the experiment id from either an existing run or the newly created one.

29. def hashCode(): Int

    

    Definition Classes

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30. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

31. def logArtifactsOff(): MLFlowTracker.this.type

    

32. def logArtifactsOn(): MLFlowTracker.this.type

    

33. def logCustomTags(client: MlflowClient, runId: String, tags: Map[String, String]): Unit

    

34. def logMlFlowDataAndModels(runData: Array[GenericModelReturn], modelFamily: String, modelType: String, inferenceSaveLocation: String, optimizationStrategy: String): MLFlowReportStructure

    

    Public method for logging a model, parameters, and metrics to MlFlow

    Public method for logging a model, parameters, and metrics to MlFlow

    runData

    Full collection parameters, results, and models for the autoML experiment

    modelFamily

    Type of Model Family used (e.g. "RandomForest")

    modelType

    Type of Model used (e.g. "regression")

35. def logMlFlowForPipeline(mlFlowRunId: String, runData: Array[GenericModelReturn], modelFamily: String, modelType: String, optimizationStrategy: String): MLFlowReportStructure

    

    This method does not save any artifacts or inference configs.

    This method does not save any artifacts or inference configs. For the Best Model logging mode, it logs params and metrics to a given mlFlowRunId For the tuning logging mode, it logs params and metrics to separate mlFlowRunIds

36. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

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37. final def notify(): Unit

    

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38. final def notifyAll(): Unit

    

    Definition Classes

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39. def removeArrayOfColumns(payload: InferencePayload, removalArray: Array[String]): InferencePayload

    
    Definition Classes

    InferenceTools

40. lazy val sc: SparkContext

    

    Definition Classes

    SparkSessionWrapper

41. def setMainConfig(value: MainConfig): MLFlowTracker.this.type

    

42. def setMlFlowBestSuffix(value: String): MLFlowTracker.this.type

    

43. def setMlFlowCustomRunTags(value: Map[String, String]): MLFlowTracker.this.type

    

44. def setMlFlowExperimentName(value: String): MLFlowTracker.this.type

    

45. def setMlFlowHostedAPIToken(value: String): MLFlowTracker.this.type

    

46. def setMlFlowLoggingMode(value: String): MLFlowTracker.this.type

    

47. def setMlFlowTrackingURI(value: String): MLFlowTracker.this.type

    

48. def setModelSaveDirectory(value: String): MLFlowTracker.this.type

    

49. lazy val spark: SparkSession

    

    Definition Classes

    SparkSessionWrapper

50. final def synchronized[T0](arg0: ⇒ T0): T0

    

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51. def toString(): String

    

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52. final def wait(): Unit

    

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    @throws( ... )

53. final def wait(arg0: Long, arg1: Int): Unit

    

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54. final def wait(arg0: Long): Unit

    

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    @throws( ... )