Synapseml  0.10.0
Public Member Functions | Static Public Member Functions | List of all members
Synapse.ML.Isolationforest.IsolationForestModel Class Reference

IsolationForestModel implements IsolationForestModel More...

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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< IsolationForestModelRead ()
 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
uidAn 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
pathThe 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
pathThe path to save the object to

◆ SetBootstrap()

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

Sets value for bootstrap

Parameters
valueIf 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
valueThe 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
valueThe 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
valueThe feature vector.
Returns
New IsolationForestModel object

◆ SetInnerModel()

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

Sets value for innerModel

Parameters
valuethe fit isolation forrest instance
Returns
New IsolationForestModel object

◆ SetMaxFeatures()

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

Sets value for maxFeatures

Parameters
valueThe 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
valueThe 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
valueThe number of trees in the ensemble.
Returns
New IsolationForestModel object

◆ SetPredictionCol()

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

Sets value for predictionCol

Parameters
valueThe predicted label.
Returns
New IsolationForestModel object

◆ SetRandomSeed()

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

Sets value for randomSeed

Parameters
valueThe 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
valueThe outlier score.
Returns
New IsolationForestModel object

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