RankingTrainValidationSplit implements RankingTrainValidationSplit More...

Inheritance diagram for Synapse.ML.Recommendation.RankingTrainValidationSplit:

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Collaboration diagram for Synapse.ML.Recommendation.RankingTrainValidationSplit:

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

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

RankingTrainValidationSplit	SetAlpha (double value)
	Sets value for alpha More...

RankingTrainValidationSplit	SetBlockSize (int value)
	Sets value for blockSize More...

RankingTrainValidationSplit	SetCheckpointInterval (int value)
	Sets value for checkpointInterval More...

RankingTrainValidationSplit	SetColdStartStrategy (string value)
	Sets value for coldStartStrategy More...

RankingTrainValidationSplit	SetEstimator< M > (JavaEstimator< M > value)
	Sets value for estimator More...

RankingTrainValidationSplit	SetEstimatorParamMaps (ParamMap[] value)
	Sets value for estimatorParamMaps More...

RankingTrainValidationSplit	SetEvaluator (JavaEvaluator value)
	Sets value for evaluator More...

RankingTrainValidationSplit	SetFinalStorageLevel (string value)
	Sets value for finalStorageLevel More...

RankingTrainValidationSplit	SetImplicitPrefs (bool value)
	Sets value for implicitPrefs More...

RankingTrainValidationSplit	SetIntermediateStorageLevel (string value)
	Sets value for intermediateStorageLevel More...

RankingTrainValidationSplit	SetItemCol (string value)
	Sets value for itemCol More...

RankingTrainValidationSplit	SetMaxIter (int value)
	Sets value for maxIter More...

RankingTrainValidationSplit	SetMinRatingsI (int value)
	Sets value for minRatingsI More...

RankingTrainValidationSplit	SetMinRatingsU (int value)
	Sets value for minRatingsU More...

RankingTrainValidationSplit	SetNonnegative (bool value)
	Sets value for nonnegative More...

RankingTrainValidationSplit	SetNumItemBlocks (int value)
	Sets value for numItemBlocks More...

RankingTrainValidationSplit	SetNumUserBlocks (int value)
	Sets value for numUserBlocks More...

RankingTrainValidationSplit	SetParallelism (int value)
	Sets value for parallelism More...

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

RankingTrainValidationSplit	SetRank (int value)
	Sets value for rank More...

RankingTrainValidationSplit	SetRatingCol (string value)
	Sets value for ratingCol More...

RankingTrainValidationSplit	SetRegParam (double value)
	Sets value for regParam More...

RankingTrainValidationSplit	SetSeed (long value)
	Sets value for seed More...

RankingTrainValidationSplit	SetTrainRatio (double value)
	Sets value for trainRatio More...

RankingTrainValidationSplit	SetUserCol (string value)
	Sets value for userCol More...

double	GetAlpha ()
	Gets alpha value More...

int	GetBlockSize ()
	Gets blockSize value More...

int	GetCheckpointInterval ()
	Gets checkpointInterval value More...

string	GetColdStartStrategy ()
	Gets coldStartStrategy value More...

IEstimator< object >	GetEstimator ()
	Gets estimator value More...

ParamMap[]	GetEstimatorParamMaps ()
	Gets estimatorParamMaps value More...

JavaEvaluator	GetEvaluator ()
	Gets evaluator value More...

string	GetFinalStorageLevel ()
	Gets finalStorageLevel value More...

bool	GetImplicitPrefs ()
	Gets implicitPrefs value More...

string	GetIntermediateStorageLevel ()
	Gets intermediateStorageLevel value More...

string	GetItemCol ()
	Gets itemCol value More...

int	GetMaxIter ()
	Gets maxIter value More...

int	GetMinRatingsI ()
	Gets minRatingsI value More...

int	GetMinRatingsU ()
	Gets minRatingsU value More...

bool	GetNonnegative ()
	Gets nonnegative value More...

int	GetNumItemBlocks ()
	Gets numItemBlocks value More...

int	GetNumUserBlocks ()
	Gets numUserBlocks value More...

int	GetParallelism ()
	Gets parallelism value More...

string	GetPredictionCol ()
	Gets predictionCol value More...

int	GetRank ()
	Gets rank value More...

string	GetRatingCol ()
	Gets ratingCol value More...

double	GetRegParam ()
	Gets regParam value More...

long	GetSeed ()
	Gets seed value More...

double	GetTrainRatio ()
	Gets trainRatio value More...

string	GetUserCol ()
	Gets userCol value More...

override RankingTrainValidationSplitModel	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< RankingTrainValidationSplit >	Read ()
	Get the corresponding JavaMLReader instance. More...

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

Detailed Description

RankingTrainValidationSplit implements RankingTrainValidationSplit

Constructor & Destructor Documentation

◆ RankingTrainValidationSplit() [1/2]

Synapse.ML.Recommendation.RankingTrainValidationSplit.RankingTrainValidationSplit ( )

inline

Creates a RankingTrainValidationSplit without any parameters.

◆ RankingTrainValidationSplit() [2/2]

Synapse.ML.Recommendation.RankingTrainValidationSplit.RankingTrainValidationSplit ( string uid )

inline

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

Parameters

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

Member Function Documentation

◆ Fit()

override RankingTrainValidationSplitModel Synapse.ML.Recommendation.RankingTrainValidationSplit.Fit ( DataFrame dataset )

Fits a model to the input data.

Parameters

dataset The DataFrame to fit the model to.

Returns: RankingTrainValidationSplitModel

◆ GetAlpha()

double Synapse.ML.Recommendation.RankingTrainValidationSplit.GetAlpha ( )

Gets alpha value

Returns: alpha: alpha for implicit preference

◆ GetBlockSize()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetBlockSize ( )

Gets blockSize value

Returns: blockSize: block size for stacking input data in matrices. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data.

◆ GetCheckpointInterval()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetCheckpointInterval ( )

Gets checkpointInterval value

Returns: checkpointInterval: set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext

◆ GetColdStartStrategy()

string Synapse.ML.Recommendation.RankingTrainValidationSplit.GetColdStartStrategy ( )

Gets coldStartStrategy value

Returns: coldStartStrategy: strategy for dealing with unknown or new users/items at prediction time. This may be useful in cross-validation or production scenarios, for handling user/item ids the model has not seen in the training data. Supported values: nan,drop.

◆ GetEstimator()

IEstimator<object> Synapse.ML.Recommendation.RankingTrainValidationSplit.GetEstimator ( )

inline

Gets estimator value

Returns: estimator: estimator for selection

◆ GetEstimatorParamMaps()

ParamMap [] Synapse.ML.Recommendation.RankingTrainValidationSplit.GetEstimatorParamMaps ( )

inline

Gets estimatorParamMaps value

Returns: estimatorParamMaps: param maps for the estimator

◆ GetEvaluator()

JavaEvaluator Synapse.ML.Recommendation.RankingTrainValidationSplit.GetEvaluator ( )

inline

Gets evaluator value

Returns: evaluator: evaluator used to select hyper-parameters that maximize the validated metric

◆ GetFinalStorageLevel()

string Synapse.ML.Recommendation.RankingTrainValidationSplit.GetFinalStorageLevel ( )

Gets finalStorageLevel value

Returns: finalStorageLevel: StorageLevel for ALS model factors.

◆ GetImplicitPrefs()

bool Synapse.ML.Recommendation.RankingTrainValidationSplit.GetImplicitPrefs ( )

Gets implicitPrefs value

Returns: implicitPrefs: whether to use implicit preference

◆ GetIntermediateStorageLevel()

string Synapse.ML.Recommendation.RankingTrainValidationSplit.GetIntermediateStorageLevel ( )

Gets intermediateStorageLevel value

Returns: intermediateStorageLevel: StorageLevel for intermediate datasets. Cannot be 'NONE'.

◆ GetItemCol()

string Synapse.ML.Recommendation.RankingTrainValidationSplit.GetItemCol ( )

Gets itemCol value

Returns: itemCol: column name for item ids. Ids must be within the integer value range.

◆ GetMaxIter()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetMaxIter ( )

Gets maxIter value

Returns: maxIter: maximum number of iterations (>= 0)

◆ GetMinRatingsI()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetMinRatingsI ( )

Gets minRatingsI value

Returns: minRatingsI: min ratings for items > 0

◆ GetMinRatingsU()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetMinRatingsU ( )

Gets minRatingsU value

Returns: minRatingsU: min ratings for users > 0

◆ GetNonnegative()

bool Synapse.ML.Recommendation.RankingTrainValidationSplit.GetNonnegative ( )

Gets nonnegative value

Returns: nonnegative: whether to use nonnegative constraint for least squares

◆ GetNumItemBlocks()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetNumItemBlocks ( )

Gets numItemBlocks value

Returns: numItemBlocks: number of item blocks

◆ GetNumUserBlocks()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetNumUserBlocks ( )

Gets numUserBlocks value

Returns: numUserBlocks: number of user blocks

◆ GetParallelism()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetParallelism ( )

Gets parallelism value

Returns: parallelism: the number of threads to use when running parallel algorithms

◆ GetPredictionCol()

string Synapse.ML.Recommendation.RankingTrainValidationSplit.GetPredictionCol ( )

Gets predictionCol value

Returns: predictionCol: prediction column name

◆ GetRank()

int Synapse.ML.Recommendation.RankingTrainValidationSplit.GetRank ( )

Gets rank value

Returns: rank: rank of the factorization

◆ GetRatingCol()

string Synapse.ML.Recommendation.RankingTrainValidationSplit.GetRatingCol ( )

Gets ratingCol value

Returns: ratingCol: column name for ratings

◆ GetRegParam()

double Synapse.ML.Recommendation.RankingTrainValidationSplit.GetRegParam ( )

Gets regParam value

Returns: regParam: regularization parameter (>= 0)

◆ GetSeed()

long Synapse.ML.Recommendation.RankingTrainValidationSplit.GetSeed ( )

Gets seed value

Returns: seed: random seed

◆ GetTrainRatio()

double Synapse.ML.Recommendation.RankingTrainValidationSplit.GetTrainRatio ( )

Gets trainRatio value

Returns: trainRatio: ratio between training set and validation set (>= 0 and <= 1)

◆ GetUserCol()

string Synapse.ML.Recommendation.RankingTrainValidationSplit.GetUserCol ( )

Gets userCol value

Returns: userCol: column name for user ids. Ids must be within the integer value range.

◆ Load()

static RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.Load ( string path )

static

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

Parameters

path	The path the previous RankingTrainValidationSplit was saved to

Returns: New RankingTrainValidationSplit object, loaded from path.

◆ Read()

JavaMLReader<RankingTrainValidationSplit> Synapse.ML.Recommendation.RankingTrainValidationSplit.Read ( )

Get the corresponding JavaMLReader instance.

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

◆ Save()

void Synapse.ML.Recommendation.RankingTrainValidationSplit.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

◆ SetAlpha()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetAlpha ( double value )

Sets value for alpha

Parameters

value alpha for implicit preference

Returns: New RankingTrainValidationSplit object

◆ SetBlockSize()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetBlockSize ( int value )

Sets value for blockSize

Parameters

value block size for stacking input data in matrices. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data.

Returns: New RankingTrainValidationSplit object

◆ SetCheckpointInterval()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetCheckpointInterval ( int value )

Sets value for checkpointInterval

Parameters

value set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext

Returns: New RankingTrainValidationSplit object

◆ SetColdStartStrategy()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetColdStartStrategy ( string value )

Sets value for coldStartStrategy

Parameters

value strategy for dealing with unknown or new users/items at prediction time. This may be useful in cross-validation or production scenarios, for handling user/item ids the model has not seen in the training data. Supported values: nan,drop.

Returns: New RankingTrainValidationSplit object

◆ SetEstimator< M >()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetEstimator< M > ( JavaEstimator< M > value )

Sets value for estimator

Parameters

value estimator for selection

Returns: New RankingTrainValidationSplit object

Type Constraints

M	:	JavaModel<M>
M	:	WrapAsRankingTrainValidationSplit
M	:	Reference.Invoke
M	:	setEstimator
M	:	object
M	:	value

◆ SetEstimatorParamMaps()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetEstimatorParamMaps ( ParamMap[] value )

Sets value for estimatorParamMaps

Parameters

value param maps for the estimator

Returns: New RankingTrainValidationSplit object

◆ SetEvaluator()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetEvaluator ( JavaEvaluator value )

Sets value for evaluator

Parameters

value evaluator used to select hyper-parameters that maximize the validated metric

Returns: New RankingTrainValidationSplit object

◆ SetFinalStorageLevel()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetFinalStorageLevel ( string value )

Sets value for finalStorageLevel

Parameters

value StorageLevel for ALS model factors.

Returns: New RankingTrainValidationSplit object

◆ SetImplicitPrefs()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetImplicitPrefs ( bool value )

Sets value for implicitPrefs

Parameters

value whether to use implicit preference

Returns: New RankingTrainValidationSplit object

◆ SetIntermediateStorageLevel()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetIntermediateStorageLevel ( string value )

Sets value for intermediateStorageLevel

Parameters

value StorageLevel for intermediate datasets. Cannot be 'NONE'.

Returns: New RankingTrainValidationSplit object

◆ SetItemCol()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetItemCol ( string value )

Sets value for itemCol

Parameters

value column name for item ids. Ids must be within the integer value range.

Returns: New RankingTrainValidationSplit object

◆ SetMaxIter()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetMaxIter ( int value )

Sets value for maxIter

Parameters

value maximum number of iterations (>= 0)

Returns: New RankingTrainValidationSplit object

◆ SetMinRatingsI()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetMinRatingsI ( int value )

Sets value for minRatingsI

Parameters

value min ratings for items > 0

Returns: New RankingTrainValidationSplit object

◆ SetMinRatingsU()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetMinRatingsU ( int value )

Sets value for minRatingsU

Parameters

value min ratings for users > 0

Returns: New RankingTrainValidationSplit object

◆ SetNonnegative()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetNonnegative ( bool value )

Sets value for nonnegative

Parameters

value whether to use nonnegative constraint for least squares

Returns: New RankingTrainValidationSplit object

◆ SetNumItemBlocks()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetNumItemBlocks ( int value )

Sets value for numItemBlocks

Parameters

value number of item blocks

Returns: New RankingTrainValidationSplit object

◆ SetNumUserBlocks()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetNumUserBlocks ( int value )

Sets value for numUserBlocks

Parameters

value number of user blocks

Returns: New RankingTrainValidationSplit object

◆ SetParallelism()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetParallelism ( int value )

Sets value for parallelism

Parameters

value the number of threads to use when running parallel algorithms

Returns: New RankingTrainValidationSplit object

◆ SetPredictionCol()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetPredictionCol ( string value )

Sets value for predictionCol

Parameters

value prediction column name

Returns: New RankingTrainValidationSplit object

◆ SetRank()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetRank ( int value )

Sets value for rank

Parameters

value rank of the factorization

Returns: New RankingTrainValidationSplit object

◆ SetRatingCol()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetRatingCol ( string value )

Sets value for ratingCol

Parameters

value column name for ratings

Returns: New RankingTrainValidationSplit object

◆ SetRegParam()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetRegParam ( double value )

Sets value for regParam

Parameters

value regularization parameter (>= 0)

Returns: New RankingTrainValidationSplit object

◆ SetSeed()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetSeed ( long value )

Sets value for seed

Parameters

value random seed

Returns: New RankingTrainValidationSplit object

◆ SetTrainRatio()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetTrainRatio ( double value )

Sets value for trainRatio

Parameters

value ratio between training set and validation set (>= 0 and <= 1)

Returns: New RankingTrainValidationSplit object

◆ SetUserCol()

RankingTrainValidationSplit Synapse.ML.Recommendation.RankingTrainValidationSplit.SetUserCol ( string value )

Sets value for userCol

Parameters

value column name for user ids. Ids must be within the integer value range.

Returns: New RankingTrainValidationSplit object

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

synapse/ml/recommendation/RankingTrainValidationSplit.cs

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ RankingTrainValidationSplit() [1/2]

◆ RankingTrainValidationSplit() [2/2]

Member Function Documentation

◆ Fit()

◆ GetAlpha()

◆ GetBlockSize()

◆ GetCheckpointInterval()

◆ GetColdStartStrategy()

◆ GetEstimator()

◆ GetEstimatorParamMaps()

◆ GetEvaluator()

◆ GetFinalStorageLevel()

◆ GetImplicitPrefs()

◆ GetIntermediateStorageLevel()

◆ GetItemCol()

◆ GetMaxIter()

◆ GetMinRatingsI()

◆ GetMinRatingsU()

◆ GetNonnegative()

◆ GetNumItemBlocks()

◆ GetNumUserBlocks()

◆ GetParallelism()

◆ GetPredictionCol()

◆ GetRank()

◆ GetRatingCol()

◆ GetRegParam()

◆ GetSeed()

◆ GetTrainRatio()

◆ GetUserCol()

◆ Load()

◆ Read()

◆ Save()

◆ SetAlpha()

◆ SetBlockSize()

◆ SetCheckpointInterval()

◆ SetColdStartStrategy()

◆ SetEstimator< M >()

◆ SetEstimatorParamMaps()

◆ SetEvaluator()

◆ SetFinalStorageLevel()

◆ SetImplicitPrefs()

◆ SetIntermediateStorageLevel()

◆ SetItemCol()

◆ SetMaxIter()

◆ SetMinRatingsI()

◆ SetMinRatingsU()

◆ SetNonnegative()

◆ SetNumItemBlocks()

◆ SetNumUserBlocks()

◆ SetParallelism()

◆ SetPredictionCol()

◆ SetRank()

◆ SetRatingCol()

◆ SetRegParam()

◆ SetSeed()

◆ SetTrainRatio()

◆ SetUserCol()