IsolationForestModel implements IsolationForestModel More...

Inheritance diagram for Synapse.ML.Isolationforest.IsolationForestModel:

Collaboration diagram for Synapse.ML.Isolationforest.IsolationForestModel:

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

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

IsolationForestModel	SetBootstrap (bool value)
	Sets value for bootstrap More...

IsolationForestModel	SetContamination (double value)
	Sets value for contamination More...

IsolationForestModel	SetContaminationError (double value)
	Sets value for contaminationError More...

IsolationForestModel	SetFeaturesCol (string value)
	Sets value for featuresCol More...

IsolationForestModel	SetInnerModel (JavaTransformer value)
	Sets value for innerModel More...

IsolationForestModel	SetMaxFeatures (double value)
	Sets value for maxFeatures More...

IsolationForestModel	SetMaxSamples (double value)
	Sets value for maxSamples More...

IsolationForestModel	SetNumEstimators (int value)
	Sets value for numEstimators More...

IsolationForestModel	SetPredictionCol (string value)
	Sets value for predictionCol More...

IsolationForestModel	SetRandomSeed (long value)
	Sets value for randomSeed More...

IsolationForestModel	SetScoreCol (string value)
	Sets value for scoreCol More...

bool	GetBootstrap ()
	Gets bootstrap value More...

double	GetContamination ()
	Gets contamination value More...

double	GetContaminationError ()
	Gets contaminationError value More...

string	GetFeaturesCol ()
	Gets featuresCol value More...

JavaTransformer	GetInnerModel ()
	Gets innerModel value More...

double	GetMaxFeatures ()
	Gets maxFeatures value More...

double	GetMaxSamples ()
	Gets maxSamples value More...

int	GetNumEstimators ()
	Gets numEstimators value More...

string	GetPredictionCol ()
	Gets predictionCol value More...

long	GetRandomSeed ()
	Gets randomSeed value More...

string	GetScoreCol ()
	Gets scoreCol 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< IsolationForestModel >	Read ()
	Get the corresponding JavaMLReader instance. More...

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

Detailed Description

IsolationForestModel implements IsolationForestModel

Constructor & Destructor Documentation

◆ IsolationForestModel() [1/2]

Synapse.ML.Isolationforest.IsolationForestModel.IsolationForestModel ( )

inline

Creates a IsolationForestModel without any parameters.

◆ IsolationForestModel() [2/2]

Synapse.ML.Isolationforest.IsolationForestModel.IsolationForestModel ( string uid )

inline

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

Parameters

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

Member Function Documentation

◆ GetBootstrap()

bool Synapse.ML.Isolationforest.IsolationForestModel.GetBootstrap ( )

Gets bootstrap value

Returns: bootstrap: If true, draw sample for each tree with replacement. If false, do not sample with replacement.

◆ GetContamination()

double Synapse.ML.Isolationforest.IsolationForestModel.GetContamination ( )

Gets contamination value

Returns: contamination: The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter.

◆ GetContaminationError()

double Synapse.ML.Isolationforest.IsolationForestModel.GetContaminationError ( )

Gets contaminationError value

Returns: contaminationError: The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value.

◆ GetFeaturesCol()

string Synapse.ML.Isolationforest.IsolationForestModel.GetFeaturesCol ( )

Gets featuresCol value

Returns: featuresCol: The feature vector.

◆ GetInnerModel()

JavaTransformer Synapse.ML.Isolationforest.IsolationForestModel.GetInnerModel ( )

inline

Gets innerModel value

Returns: innerModel: the fit isolation forrest instance

◆ GetMaxFeatures()

double Synapse.ML.Isolationforest.IsolationForestModel.GetMaxFeatures ( )

Gets maxFeatures value

Returns: maxFeatures: The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count.

◆ GetMaxSamples()

double Synapse.ML.Isolationforest.IsolationForestModel.GetMaxSamples ( )

Gets maxSamples value

Returns: maxSamples: The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count.

◆ GetNumEstimators()

int Synapse.ML.Isolationforest.IsolationForestModel.GetNumEstimators ( )

Gets numEstimators value

Returns: numEstimators: The number of trees in the ensemble.

◆ GetPredictionCol()

string Synapse.ML.Isolationforest.IsolationForestModel.GetPredictionCol ( )

Gets predictionCol value

Returns: predictionCol: The predicted label.

◆ GetRandomSeed()

long Synapse.ML.Isolationforest.IsolationForestModel.GetRandomSeed ( )

Gets randomSeed value

Returns: randomSeed: The seed used for the random number generator.

◆ GetScoreCol()

string Synapse.ML.Isolationforest.IsolationForestModel.GetScoreCol ( )

Gets scoreCol value

Returns: scoreCol: The outlier score.

◆ Load()

static IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.Load ( string path )

static

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

Parameters

path	The path the previous IsolationForestModel was saved to

Returns: New IsolationForestModel object, loaded from path.

◆ Read()

JavaMLReader<IsolationForestModel> Synapse.ML.Isolationforest.IsolationForestModel.Read ( )

Get the corresponding JavaMLReader instance.

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

◆ Save()

void Synapse.ML.Isolationforest.IsolationForestModel.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

◆ SetBootstrap()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetBootstrap ( bool value )

Sets value for bootstrap

Parameters

value If true, draw sample for each tree with replacement. If false, do not sample with replacement.

Returns: New IsolationForestModel object

◆ SetContamination()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetContamination ( double value )

Sets value for contamination

Parameters

value The fraction of outliers in the training data set. If this is set to 0.0, it speeds up the training and all predicted labels will be false. The model and outlier scores are otherwise unaffected by this parameter.

Returns: New IsolationForestModel object

◆ SetContaminationError()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetContaminationError ( double value )

Sets value for contaminationError

Parameters

value The error allowed when calculating the threshold required to achieve the specified contamination fraction. The default is 0.0, which forces an exact calculation of the threshold. The exact calculation is slow and can fail for large datasets. If there are issues with the exact calculation, a good choice for this parameter is often 1% of the specified contamination value.

Returns: New IsolationForestModel object

◆ SetFeaturesCol()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetFeaturesCol ( string value )

Sets value for featuresCol

Parameters

value The feature vector.

Returns: New IsolationForestModel object

◆ SetInnerModel()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetInnerModel ( JavaTransformer value )

Sets value for innerModel

Parameters

value the fit isolation forrest instance

Returns: New IsolationForestModel object

◆ SetMaxFeatures()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetMaxFeatures ( double value )

Sets value for maxFeatures

Parameters

value The number of features used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count.

Returns: New IsolationForestModel object

◆ SetMaxSamples()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetMaxSamples ( double value )

Sets value for maxSamples

Parameters

value The number of samples used to train each tree. If this value is between 0.0 and 1.0, then it is treated as a fraction. If it is >1.0, then it is treated as a count.

Returns: New IsolationForestModel object

◆ SetNumEstimators()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetNumEstimators ( int value )

Sets value for numEstimators

Parameters

value The number of trees in the ensemble.

Returns: New IsolationForestModel object

◆ SetPredictionCol()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetPredictionCol ( string value )

Sets value for predictionCol

Parameters

value The predicted label.

Returns: New IsolationForestModel object

◆ SetRandomSeed()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetRandomSeed ( long value )

Sets value for randomSeed

Parameters

value The seed used for the random number generator.

Returns: New IsolationForestModel object

◆ SetScoreCol()

IsolationForestModel Synapse.ML.Isolationforest.IsolationForestModel.SetScoreCol ( string value )

Sets value for scoreCol

Parameters

value The outlier score.

Returns: New IsolationForestModel object

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

synapse/ml/isolationforest/IsolationForestModel.cs

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ IsolationForestModel() [1/2]

◆ IsolationForestModel() [2/2]

Member Function Documentation

◆ GetBootstrap()

◆ GetContamination()

◆ GetContaminationError()

◆ GetFeaturesCol()

◆ GetInnerModel()

◆ GetMaxFeatures()

◆ GetMaxSamples()

◆ GetNumEstimators()

◆ GetPredictionCol()

◆ GetRandomSeed()

◆ GetScoreCol()

◆ Load()

◆ Read()

◆ Save()

◆ SetBootstrap()

◆ SetContamination()

◆ SetContaminationError()

◆ SetFeaturesCol()

◆ SetInnerModel()

◆ SetMaxFeatures()

◆ SetMaxSamples()

◆ SetNumEstimators()

◆ SetPredictionCol()

◆ SetRandomSeed()

◆ SetScoreCol()