Synapseml  0.10.0
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Synapse.ML.Recommendation.RankingTrainValidationSplit Class Reference

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< RankingTrainValidationSplitRead ()
 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
uidAn 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
datasetThe 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
pathThe 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
pathThe path to save the object to

◆ SetAlpha()

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

Sets value for alpha

Parameters
valuealpha for implicit preference
Returns
New RankingTrainValidationSplit object

◆ SetBlockSize()

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

Sets value for blockSize

Parameters
valueblock 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
valueset 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
valuestrategy 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
valueestimator 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
valueparam maps for the estimator
Returns
New RankingTrainValidationSplit object

◆ SetEvaluator()

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

Sets value for evaluator

Parameters
valueevaluator 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
valueStorageLevel for ALS model factors.
Returns
New RankingTrainValidationSplit object

◆ SetImplicitPrefs()

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

Sets value for implicitPrefs

Parameters
valuewhether to use implicit preference
Returns
New RankingTrainValidationSplit object

◆ SetIntermediateStorageLevel()

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

Sets value for intermediateStorageLevel

Parameters
valueStorageLevel for intermediate datasets. Cannot be 'NONE'.
Returns
New RankingTrainValidationSplit object

◆ SetItemCol()

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

Sets value for itemCol

Parameters
valuecolumn 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
valuemaximum number of iterations (>= 0)
Returns
New RankingTrainValidationSplit object

◆ SetMinRatingsI()

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

Sets value for minRatingsI

Parameters
valuemin ratings for items > 0
Returns
New RankingTrainValidationSplit object

◆ SetMinRatingsU()

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

Sets value for minRatingsU

Parameters
valuemin ratings for users > 0
Returns
New RankingTrainValidationSplit object

◆ SetNonnegative()

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

Sets value for nonnegative

Parameters
valuewhether 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
valuenumber of item blocks
Returns
New RankingTrainValidationSplit object

◆ SetNumUserBlocks()

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

Sets value for numUserBlocks

Parameters
valuenumber of user blocks
Returns
New RankingTrainValidationSplit object

◆ SetParallelism()

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

Sets value for parallelism

Parameters
valuethe 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
valueprediction column name
Returns
New RankingTrainValidationSplit object

◆ SetRank()

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

Sets value for rank

Parameters
valuerank of the factorization
Returns
New RankingTrainValidationSplit object

◆ SetRatingCol()

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

Sets value for ratingCol

Parameters
valuecolumn name for ratings
Returns
New RankingTrainValidationSplit object

◆ SetRegParam()

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

Sets value for regParam

Parameters
valueregularization parameter (>= 0)
Returns
New RankingTrainValidationSplit object

◆ SetSeed()

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

Sets value for seed

Parameters
valuerandom seed
Returns
New RankingTrainValidationSplit object

◆ SetTrainRatio()

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

Sets value for trainRatio

Parameters
valueratio 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
valuecolumn 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: