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MLModel

docs/apis/MLModel_en.md

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MLModel

MLModel is the complete model of a machine-learning algorithm. It serves as the base class for all machine learning models on Angel. A complex machine-learning algorithm usually needs multiple PSModel to work together, and MLModel provides unified handling of these PSModels.

Functionality

MLModel is an abstract class and needs to be inherited in implementing a specific machine-learning algorithm. It is a container class that handles all the PSModels in the algorithm; all the PSModels in it will be loaded, trained and saved as an overall model. In addition, MLModel contains the logic of the predict method.

Core Methods

  • predict

    • Definition: def predict(storage: DataBlock[predictType]): DataBlock[PredictResult]
    • Functionality: get model predictions (given a trained model); procedures of this method need to be implemented in specific PSModel
    • Parameters: storage: DataBlock[predictType], data
    • Return value: DataBlock[PredictResult], prediction result on provided data

  • setSavePath

    • Definition: def setSavePath(conf: Configuration)
    • Functionality: set save path for model
    • Parameters: conf: Configuration, job configuration
    • Return value: none

  • setLoadPath

    • Definition: def setLoadPath(conf: Configuration)
    • Functionality: set load path for model
    • Parameters: conf: Configuration, job configuration
    • Return value: none

  • addPSModel

    • Definition: def addPSModel(name: String, psModel: PSModel[_])
    • Functionality: add a new PSModel to MLModel
    • Parameters:
      • name: String, PSModel name
      • psModel: PSModel[_], PSModel object
    • Return value: none

  • getPSModel

    • Definition: def getPSModel(name: String): PSModel[_]
    • Functionality: retrieve PSModel object with its name
    • Parameters: name: String, PSModel name
    • Return value: a PSModel object