ModelDetail Objects

This interface encapsulates the representation detail of a data mining model.

m.getAlgorithmName() : string

Returns the name of the model builder algorithm.

m.getApplicationName() : string

Returns the name of the model builder application.

m.getApplicationVersion() : string

Returns the version of the model builder application.

m.getBuildDate() : Date

Returns the date this model was built.

m.getCopyright() : string

Returns the model copyright.

m.getInputDataModel() : DataModel

Returns the input data model required by the model. Note that the ModelingRole for each field in the input data model is ModelingRole.IN, even for fields that had been specified as ModelingRole.BOTH for association or sequence models.

m.getModelID() : string

Returns the model ID. For models within a composite model, it is assumed that each model has a unique ID.

m.getModelType() : ModelType

Returns the type of the model.

m.getOutputDataModel() : DataModel

Returns the output data model produced by the model. Note that the ModelingRole for each field in the output data model is ModelingRole.OUT, even for fields that had been specified as ModelingRole.BOTH for association or sequence models.

It is also important to note that this is not same as the output column set produced by the ModelApplier that applies the model to data. The transformer output column set will usually include additional fields from the input and may include property settings that modify the number or type of outputs produced (for example, a transformer that applies a clustering model may produce the cluster ID as an integer or a string depending on the property settings).

m.getPMMLModelType() : PMMLModelType

For a model which is represented internally using PMML (one for which isPMMLModel() returns True) this returns the PMML model type from the underlying PMML that was generated by the modeling algorithm. For a model which is not represented using PMML this returns None.

m.getPMMLText() : string

For a model which is represented internally using PMML (one for which isPMMLModel() returns True) this returns a string representing the PMML generated from the model building algorithm. For non-PMML models this returns None. When this returns None you may be able to obtain an alternative representation of the model as PMML using the export function: TaskRunner.exportModelToFile().

m.getSplitColumnNames() : List

Returns the list of split column names if this is a split model or None for non-split models.

m.getSplitModelCount() : int

Returns the number of split models if this is a split model or 0 otherwise.

m.getSplitModelKey(index) : List

index (int) : the model index

Returns the values of the split fields at the specified index for split models or None for non-split models. When present, the values are in the order specified by the split output columns.

m.getSplitModelPMMLText(index) : string

index (int) : the model index

Returns the PMML representation of the split model at the specified index for split models or None for non-split models or if the split model is not represented as PMML.

m.getUserName() : string

Returns the name of the account used to build the model.

m.isOutputColumnAuxiliary(name) : boolean

name (string) : the column name

Returns True if the supplied column name is an auxiliary column in the output data model or False. An auxiliary output column is a column that provides additional information about the output of the model, for example, the confidence of the prediction or distance from the cluster center.

m.isPMMLModel() : boolean

Returns True if this model is represented using PMML, or False otherwise.

m.isSplitModel() : boolean

Returns True if this model is a split model, False otherwise.