ICETransformer implements ICETransformer More...

Inheritance diagram for Synapse.ML.Explainers.ICETransformer:

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Collaboration diagram for Synapse.ML.Explainers.ICETransformer:

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Public Member Functions
	ICETransformer ()
	Creates a ICETransformer without any parameters. More...

	ICETransformer (string uid)
	Creates a ICETransformer with a UID that is used to give the ICETransformer a unique ID. More...

ICETransformer	SetCategoricalFeatures (ICECategoricalFeature[] value)
	Sets value for categoricalFeatures More...

ICETransformer	SetDependenceNameCol (string value)
	Sets value for dependenceNameCol More...

ICETransformer	SetFeatureNameCol (string value)
	Sets value for featureNameCol More...

ICETransformer	SetKind (string value)
	Sets value for kind More...

ICETransformer	SetModel (JavaTransformer value)
	Sets value for model More...

ICETransformer	SetNumSamples (int value)
	Sets value for numSamples More...

ICETransformer	SetNumericFeatures (ICENumericFeature[] value)
	Sets value for numericFeatures More...

ICETransformer	SetTargetClasses (int[] value)
	Sets value for targetClasses More...

ICETransformer	SetTargetClassesCol (string value)
	Sets value for targetClassesCol More...

ICETransformer	SetTargetCol (string value)
	Sets value for targetCol More...

ICECategoricalFeature[]	GetCategoricalFeatures ()
	Gets categoricalFeatures value More...

string	GetDependenceNameCol ()
	Gets dependenceNameCol value More...

string	GetFeatureNameCol ()
	Gets featureNameCol value More...

string	GetKind ()
	Gets kind value More...

JavaTransformer	GetModel ()
	Gets model value More...

int	GetNumSamples ()
	Gets numSamples value More...

ICENumericFeature[]	GetNumericFeatures ()
	Gets numericFeatures value More...

int[]	GetTargetClasses ()
	Gets targetClasses value More...

string	GetTargetClassesCol ()
	Gets targetClassesCol value More...

string	GetTargetCol ()
	Gets targetCol value More...

void	Save (string path)
	Saves the object so that it can be loaded later using Load. Note that these objects can be shared with Scala by Loading or Saving in Scala. More...

JavaMLWriter	Write ()
	Returns a JavaMLWriter instance for this ML instance.

JavaMLReader< ICETransformer >	Read ()
	Get the corresponding JavaMLReader instance. More...

Static Public Member Functions
static ICETransformer	Load (string path)
	Loads the ICETransformer that was previously saved using Save(string). More...

Detailed Description

ICETransformer implements ICETransformer

Constructor & Destructor Documentation

◆ ICETransformer() [1/2]

Synapse.ML.Explainers.ICETransformer.ICETransformer ( )

inline

Creates a ICETransformer without any parameters.

◆ ICETransformer() [2/2]

Synapse.ML.Explainers.ICETransformer.ICETransformer ( string uid )

inline

Creates a ICETransformer with a UID that is used to give the ICETransformer a unique ID.

Parameters

uid	An immutable unique ID for the object and its derivatives.

Member Function Documentation

◆ GetCategoricalFeatures()

ICECategoricalFeature [] Synapse.ML.Explainers.ICETransformer.GetCategoricalFeatures ( )

Gets categoricalFeatures value

Returns: categoricalFeatures: The list of categorical features to explain.

◆ GetDependenceNameCol()

string Synapse.ML.Explainers.ICETransformer.GetDependenceNameCol ( )

Gets dependenceNameCol value

Returns: dependenceNameCol: Output column name which corresponds to dependence values of PDP-based-feature-importance option (kind == feature)

◆ GetFeatureNameCol()

string Synapse.ML.Explainers.ICETransformer.GetFeatureNameCol ( )

Gets featureNameCol value

Returns: featureNameCol: Output column name which corresponds to names of the features used in calculation of PDP-based-feature-importance option (kind == feature)

◆ GetKind()

string Synapse.ML.Explainers.ICETransformer.GetKind ( )

Gets kind value

Returns: kind: Whether to return the partial dependence plot (PDP) averaged across all the samples in the dataset or individual feature importance (ICE) per sample. Allowed values are "average" for PDP, "individual" for ICE and "feature" for PDP-based feature importance.

◆ GetModel()

JavaTransformer Synapse.ML.Explainers.ICETransformer.GetModel ( )

inline

Gets model value

Returns: model: The model to be interpreted.

◆ GetNumericFeatures()

ICENumericFeature [] Synapse.ML.Explainers.ICETransformer.GetNumericFeatures ( )

Gets numericFeatures value

Returns: numericFeatures: The list of numeric features to explain.

◆ GetNumSamples()

int Synapse.ML.Explainers.ICETransformer.GetNumSamples ( )

Gets numSamples value

Returns: numSamples: Number of samples to generate.

◆ GetTargetClasses()

int [] Synapse.ML.Explainers.ICETransformer.GetTargetClasses ( )

Gets targetClasses value

Returns: targetClasses: The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.

◆ GetTargetClassesCol()

string Synapse.ML.Explainers.ICETransformer.GetTargetClassesCol ( )

Gets targetClassesCol value

Returns: targetClassesCol: The name of the column that specifies the indices of the classes for multinomial classification models.

◆ GetTargetCol()

string Synapse.ML.Explainers.ICETransformer.GetTargetCol ( )

Gets targetCol value

Returns: targetCol: The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability

◆ Load()

static ICETransformer Synapse.ML.Explainers.ICETransformer.Load ( string path )

static

Loads the ICETransformer that was previously saved using Save(string).

Parameters

path	The path the previous ICETransformer was saved to

Returns: New ICETransformer object, loaded from path.

◆ Read()

JavaMLReader<ICETransformer> Synapse.ML.Explainers.ICETransformer.Read ( )

Get the corresponding JavaMLReader instance.

Returns: an JavaMLReader<ICETransformer> instance for this ML instance.

◆ Save()

void Synapse.ML.Explainers.ICETransformer.Save ( string path )

Saves the object so that it can be loaded later using Load. Note that these objects can be shared with Scala by Loading or Saving in Scala.

Parameters

path	The path to save the object to

◆ SetCategoricalFeatures()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetCategoricalFeatures ( ICECategoricalFeature[] value )

Sets value for categoricalFeatures

Parameters

value The list of categorical features to explain.

Returns: New ICETransformer object

◆ SetDependenceNameCol()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetDependenceNameCol ( string value )

Sets value for dependenceNameCol

Parameters

value Output column name which corresponds to dependence values of PDP-based-feature-importance option (kind == feature)

Returns: New ICETransformer object

◆ SetFeatureNameCol()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetFeatureNameCol ( string value )

Sets value for featureNameCol

Parameters

value Output column name which corresponds to names of the features used in calculation of PDP-based-feature-importance option (kind == feature)

Returns: New ICETransformer object

◆ SetKind()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetKind ( string value )

Sets value for kind

Parameters

value Whether to return the partial dependence plot (PDP) averaged across all the samples in the dataset or individual feature importance (ICE) per sample. Allowed values are "average" for PDP, "individual" for ICE and "feature" for PDP-based feature importance.

Returns: New ICETransformer object

◆ SetModel()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetModel ( JavaTransformer value )

Sets value for model

Parameters

value The model to be interpreted.

Returns: New ICETransformer object

◆ SetNumericFeatures()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetNumericFeatures ( ICENumericFeature[] value )

Sets value for numericFeatures

Parameters

value The list of numeric features to explain.

Returns: New ICETransformer object

◆ SetNumSamples()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetNumSamples ( int value )

Sets value for numSamples

Parameters

value Number of samples to generate.

Returns: New ICETransformer object

◆ SetTargetClasses()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetTargetClasses ( int[] value )

Sets value for targetClasses

Parameters

value The indices of the classes for multinomial classification models. Default: 0.For regression models this parameter is ignored.

Returns: New ICETransformer object

◆ SetTargetClassesCol()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetTargetClassesCol ( string value )

Sets value for targetClassesCol

Parameters

value The name of the column that specifies the indices of the classes for multinomial classification models.

Returns: New ICETransformer object

◆ SetTargetCol()

ICETransformer Synapse.ML.Explainers.ICETransformer.SetTargetCol ( string value )

Sets value for targetCol

Parameters

value The column name of the prediction target to explain (i.e. the response variable). This is usually set to "prediction" for regression models and "probability" for probabilistic classification models. Default value: probability

Returns: New ICETransformer object

The documentation for this class was generated from the following file:

synapse/ml/explainers/ICETransformer.cs

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ ICETransformer() [1/2]

◆ ICETransformer() [2/2]

Member Function Documentation

◆ GetCategoricalFeatures()

◆ GetDependenceNameCol()

◆ GetFeatureNameCol()

◆ GetKind()

◆ GetModel()

◆ GetNumericFeatures()

◆ GetNumSamples()

◆ GetTargetClasses()

◆ GetTargetClassesCol()

◆ GetTargetCol()

◆ Load()

◆ Read()

◆ Save()

◆ SetCategoricalFeatures()

◆ SetDependenceNameCol()

◆ SetFeatureNameCol()

◆ SetKind()

◆ SetModel()

◆ SetNumericFeatures()

◆ SetNumSamples()

◆ SetTargetClasses()

◆ SetTargetClassesCol()

◆ SetTargetCol()