IsolationForest implements IsolationForest More...

Inheritance diagram for Synapse.ML.Isolationforest.IsolationForest:

Collaboration diagram for Synapse.ML.Isolationforest.IsolationForest:

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

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

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

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

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

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

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

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

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

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

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

IsolationForest	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...

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...

override IsolationForestModel	Fit (DataFrame dataset)
	Fits a model to the input data. 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< IsolationForest >	Read ()
	Get the corresponding JavaMLReader instance. More...

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

Detailed Description

IsolationForest implements IsolationForest

Constructor & Destructor Documentation

◆ IsolationForest() [1/2]

Synapse.ML.Isolationforest.IsolationForest.IsolationForest ( )

inline

Creates a IsolationForest without any parameters.

◆ IsolationForest() [2/2]

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

inline

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

Parameters

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

Member Function Documentation

◆ Fit()

override IsolationForestModel Synapse.ML.Isolationforest.IsolationForest.Fit ( DataFrame dataset )

Fits a model to the input data.

Parameters

dataset The DataFrame to fit the model to.

Returns: IsolationForestModel

◆ GetBootstrap()

bool Synapse.ML.Isolationforest.IsolationForest.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.IsolationForest.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.IsolationForest.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.IsolationForest.GetFeaturesCol ( )

Gets featuresCol value

Returns: featuresCol: The feature vector.

◆ GetMaxFeatures()

double Synapse.ML.Isolationforest.IsolationForest.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.IsolationForest.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.IsolationForest.GetNumEstimators ( )

Gets numEstimators value

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

◆ GetPredictionCol()

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

Gets predictionCol value

Returns: predictionCol: The predicted label.

◆ GetRandomSeed()

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

Gets randomSeed value

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

◆ GetScoreCol()

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

Gets scoreCol value

Returns: scoreCol: The outlier score.

◆ Load()

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

static

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

Parameters

path	The path the previous IsolationForest was saved to

Returns: New IsolationForest object, loaded from path.

◆ Read()

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

Get the corresponding JavaMLReader instance.

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

◆ Save()

void Synapse.ML.Isolationforest.IsolationForest.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()

IsolationForest Synapse.ML.Isolationforest.IsolationForest.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 IsolationForest object

◆ SetContamination()

IsolationForest Synapse.ML.Isolationforest.IsolationForest.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 IsolationForest object

◆ SetContaminationError()

IsolationForest Synapse.ML.Isolationforest.IsolationForest.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 IsolationForest object

◆ SetFeaturesCol()

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

Sets value for featuresCol

Parameters

value The feature vector.

Returns: New IsolationForest object

◆ SetMaxFeatures()

IsolationForest Synapse.ML.Isolationforest.IsolationForest.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 IsolationForest object

◆ SetMaxSamples()

IsolationForest Synapse.ML.Isolationforest.IsolationForest.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 IsolationForest object

◆ SetNumEstimators()

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

Sets value for numEstimators

Parameters

value The number of trees in the ensemble.

Returns: New IsolationForest object

◆ SetPredictionCol()

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

Sets value for predictionCol

Parameters

value The predicted label.

Returns: New IsolationForest object

◆ SetRandomSeed()

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

Sets value for randomSeed

Parameters

value The seed used for the random number generator.

Returns: New IsolationForest object

◆ SetScoreCol()

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

Sets value for scoreCol

Parameters

value The outlier score.

Returns: New IsolationForest object

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

synapse/ml/isolationforest/IsolationForest.cs

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ IsolationForest() [1/2]

◆ IsolationForest() [2/2]

Member Function Documentation

◆ Fit()

◆ GetBootstrap()

◆ GetContamination()

◆ GetContaminationError()

◆ GetFeaturesCol()

◆ GetMaxFeatures()

◆ GetMaxSamples()

◆ GetNumEstimators()

◆ GetPredictionCol()

◆ GetRandomSeed()

◆ GetScoreCol()

◆ Load()

◆ Read()

◆ Save()

◆ SetBootstrap()

◆ SetContamination()

◆ SetContaminationError()

◆ SetFeaturesCol()

◆ SetMaxFeatures()

◆ SetMaxSamples()

◆ SetNumEstimators()

◆ SetPredictionCol()

◆ SetRandomSeed()

◆ SetScoreCol()