RumbleDB ML

RumbleDB ML is a Machine Learning library built on top of the RumbleDB engine that makes it more productive and easier to perform ML tasks thanks to the abstraction layer provided by JSONiq.

The machine learning capabilities are exposed through JSONiq function items. The concepts of "estimator" and "transformer", which are core to Machine Learning, are naturally function items and fit seamlessly in the JSONiq data model.

Training sets, test sets, and validation sets, which contain features and labels, are exposed through JSONiq sequences of object items: the keys of these objects are the features and labels.

The names of the estimators and of the transformers, as well as the functionality they encapsulate, are directly inherited from the SparkML library which RumbleDB ML is based on: we chose not to reinvent the wheel.

Transformers

A transformer is a function item that maps a sequence of objects to a sequence of objects.

It is an abstraction that either performs a feature transformation or generates predictions based on trained models. For example:

• Tokenizer is a feature transformer that receives textual input data and splits it into individual terms (usually words), which are called tokens.

• KMeansModel is a trained model and a transformer that can read a dataset containing features and generate predictions as its output.

Estimators

An estimator is a function item that maps a sequence of objects to a transformer (yes, you got it right: that's a function item returned by a function item. This is why they are also called higher-order functions!).

Estimators abstract the concept of a Machine Learning algorithm or any algorithm that fits or trains on data. For example, a learning algorithm such as KMeans is implemented as an Estimator. Calling this estimator on data essentially trains a KMeansModel, which is a Model and hence a Transformer.

Parameters

Transformers and estimators are function items in the RumbleDB Data Model. Their first argument is the sequence of objects that represents, for example, the training set or test set. Parameters can be provided as their second argument. This second argument is expected to be an object item. The machine learning parameters form the fields of the said object item as key-value pairs.

Type Annotations

RumbleDB ML works on highly structured data, because it requires full type information for all the fields in the training set or test set. It is on our development plan to automate the detection of these types when the sequence of objects gets created in the fly.

RumbleDB supports a user-defined type system with which you can validate and annotate datasets against a JSound schema.

This annotation is required to be applied on any dataset that must be used as input to RumbleDB ML, but it is superfluous if the data was directly read from a structured input format such as Parquet, CSV, Avro, SVM or ROOT.

Examples

• Tokenizer Example:

declare type local:id-and-sentence as {
  "id": "integer",
  "sentence": "string"
};


let $local-data := (
    {"id": 1, "sentence": "Hi I heard about Spark"},
    {"id": 2, "sentence": "I wish Java could use case classes"},
    {"id": 3, "sentence": "Logistic regression models are neat"}
)
let $df-data := validate type local:id-and-sentence* { $local-data }

let $transformer := get-transformer("Tokenizer")
for $i in $transformer(
    $df-data,
    {"inputCol": "sentence", "outputCol": "output"}
)
return $i

// returns
// { "id" : 1, "sentence" : "Hi I heard about Spark", "output" : [ "hi", "i", "heard", "about", "spark" ] }
// { "id" : 2, "sentence" : "I wish Java could use case classes", "output" : [ "i", "wish", "java", "could", "use", "case", "classes" ] }
// { "id" : 3, "sentence" : "Logistic regression models are neat", "output" : [ "logistic", "regression", "models", "are", "neat" ] }

• KMeans Example:

declare type local:col-1-2-3 as {
  "id": "integer",
  "col1": "decimal",
  "col2": "decimal",
  "col3": "decimal"
};

let $vector-assembler := get-transformer("VectorAssembler")(
  ?,
  { "inputCols" : [ "col1", "col2", "col3" ], "outputCol" : "features" }
)

let $local-data := (
    {"id": 0, "col1": 0.0, "col2": 0.0, "col3": 0.0},
    {"id": 1, "col1": 0.1, "col2": 0.1, "col3": 0.1},
    {"id": 2, "col1": 0.2, "col2": 0.2, "col3": 0.2},
    {"id": 3, "col1": 9.0, "col2": 9.0, "col3": 9.0},
    {"id": 4, "col1": 9.1, "col2": 9.1, "col3": 9.1},
    {"id": 5, "col1": 9.2, "col2": 9.2, "col3": 9.2}
)
let $df-data := validate type local:col-1-2-3* {$local-data }
let $df-data := $vector-assembler($df-data)

let $est := get-estimator("KMeans")
let $tra := $est(
    $df-data,
    {"featuresCol": "features"}
)

for $i in $tra(
    $df-data,
    {"featuresCol": "features"}
)
return $i

// returns
// { "id" : 0, "col1" : 0, "col2" : 0, "col3" : 0, "prediction" : 0 }
// { "id" : 1, "col1" : 0.1, "col2" : 0.1, "col3" : 0.1, "prediction" : 0 }
// { "id" : 2, "col1" : 0.2, "col2" : 0.2, "col3" : 0.2, "prediction" : 0 }
// { "id" : 3, "col1" : 9, "col2" : 9, "col3" : 9, "prediction" : 1 }
// { "id" : 4, "col1" : 9.1, "col2" : 9.1, "col3" : 9.1, "prediction" : 1 }
// { "id" : 5, "col1" : 9.2, "col2" : 9.2, "col3" : 9.2, "prediction" : 1 }

RumbleDB ML Functionality Overview:

RumblDB eML - Catalogue of Estimators:

AFTSurvivalRegression

Parameters:

- aggregationDepth: integer
- censorCol: string
- featuresCol: string
- fitIntercept: boolean
- labelCol: string
- maxIter: integer
- predictionCol: string
- quantileProbabilities: array (of double)
- quantilesCol: string
- tol: double

ALS

Parameters:

- alpha: double
- checkpointInterval: integer
- coldStartStrategy: string
- finalStorageLevel: string
- implicitPrefs: boolean
- intermediateStorageLevel: string
- itemCol: string
- maxIter: integer
- nonnegative: boolean
- numBlocks: integer
- numItemBlocks: integer
- numUserBlocks: integer
- predictionCol: string
- rank: integer
- ratingCol: string
- regParam: double
- seed: double
- userCol: string

BisectingKMeans

Parameters:

- distanceMeasure: string
- featuresCol: string
- k: integer
- maxIter: integer
- minDivisibleClusterSize: double
- predictionCol: string
- seed: double

BucketedRandomProjectionLSH

Parameters:

- bucketLength: double
- inputCol: string
- numHashTables: integer
- outputCol: string
- seed: double

ChiSqSelector

Parameters:

- fdr: double
- featuresCol: string
- fpr: double
- fwe: double
- labelCol: string
- numTopFeatures: integer
- outputCol: string
- percentile: double
- selectorType: string

CountVectorizer

Parameters:

- binary: boolean
- inputCol: string
- maxDF: double
- minDF: double
- minTF: double
- outputCol: string
- vocabSize: integer

CrossValidator

Parameters:

- collectSubModels: boolean
- estimator: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- numFolds: integer
- parallelism: integer
- seed: double

DecisionTreeClassifier

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- impurity: string
- labelCol: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- seed: double
- thresholds: array (of double)

DecisionTreeRegressor

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- impurity: string
- labelCol: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- predictionCol: string
- seed: double
- varianceCol: string

FPGrowth

Parameters:

- itemsCol: string
- minConfidence: double
- minSupport: double
- numPartitions: integer
- predictionCol: string

GBTClassifier

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- labelCol: string
- lossType: string
- maxBins: integer
- maxDepth: integer
- maxIter: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- seed: double
- stepSize: double
- subsamplingRate: double
- thresholds: array (of double)
- validationIndicatorCol: string

GBTRegressor

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- labelCol: string
- lossType: string
- maxBins: integer
- maxDepth: integer
- maxIter: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- predictionCol: string
- seed: double
- stepSize: double
- subsamplingRate: double
- validationIndicatorCol: string

GaussianMixture

Parameters:

- featuresCol: string
- k: integer
- maxIter: integer
- predictionCol: string
- probabilityCol: string
- seed: double
- tol: double

GeneralizedLinearRegression

Parameters:

- family: string
- featuresCol: string
- fitIntercept: boolean
- labelCol: string
- link: string
- linkPower: double
- linkPredictionCol: string
- maxIter: integer
- offsetCol: string
- predictionCol: string
- regParam: double
- solver: string
- tol: double
- variancePower: double
- weightCol: string

IDF

Parameters:

- inputCol: string
- minDocFreq: integer
- outputCol: string

Imputer

Parameters:

- inputCols: array (of string)
- missingValue: double
- outputCols: array (of string)
- strategy: string

IsotonicRegression

Parameters:

- featureIndex: integer
- featuresCol: string
- isotonic: boolean
- labelCol: string
- predictionCol: string
- weightCol: string

KMeans

Parameters:

- distanceMeasure: string
- featuresCol: string
- initMode: string
- initSteps: integer
- k: integer
- maxIter: integer
- predictionCol: string
- seed: double
- tol: double

LDA

Parameters:

- checkpointInterval: integer
- docConcentration: double
- docConcentration: array (of double)
- featuresCol: string
- k: integer
- keepLastCheckpoint: boolean
- learningDecay: double
- learningOffset: double
- maxIter: integer
- optimizeDocConcentration: boolean
- optimizer: string
- seed: double
- subsamplingRate: double
- topicConcentration: double
- topicDistributionCol: string

LinearRegression

Parameters:

- aggregationDepth: integer
- elasticNetParam: double
- epsilon: double
- featuresCol: string
- fitIntercept: boolean
- labelCol: string
- loss: string
- maxIter: integer
- predictionCol: string
- regParam: double
- solver: string
- standardization: boolean
- tol: double
- weightCol: string

LinearSVC

Parameters:

- aggregationDepth: integer
- featuresCol: string
- fitIntercept: boolean
- labelCol: string
- maxIter: integer
- predictionCol: string
- rawPredictionCol: string
- regParam: double
- standardization: boolean
- threshold: double
- tol: double
- weightCol: string

LogisticRegression

Parameters:

- aggregationDepth: integer
- elasticNetParam: double
- family: string
- featuresCol: string
- fitIntercept: boolean
- labelCol: string
- lowerBoundsOnCoefficients: object (of object of double)
- lowerBoundsOnIntercepts: object (of double)
- maxIter: integer
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- regParam: double
- standardization: boolean
- threshold: double
- thresholds: array (of double)
- tol: double
- upperBoundsOnCoefficients: object (of object of double)
- upperBoundsOnIntercepts: object (of double)
- weightCol: string

MaxAbsScaler

Parameters:

- inputCol: string
- outputCol: string

MinHashLSH

Parameters:

- inputCol: string
- numHashTables: integer
- outputCol: string
- seed: double

MinMaxScaler

Parameters:

- inputCol: string
- max: double
- min: double
- outputCol: string

MultilayerPerceptronClassifier

Parameters:

- blockSize: integer
- featuresCol: string
- initialWeights: object (of double)
- labelCol: string
- layers: array (of integer)
- maxIter: integer
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- seed: double
- solver: string
- stepSize: double
- thresholds: array (of double)
- tol: double

NaiveBayes

Parameters:

- featuresCol: string
- labelCol: string
- modelType: string
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- smoothing: double
- thresholds: array (of double)
- weightCol: string

OneHotEncoder

Parameters:

- dropLast: boolean
- handleInvalid: string
- inputCols: array (of string)
- outputCols: array (of string)

OneVsRest

Parameters:

- featuresCol: string
- labelCol: string
- parallelism: integer
- predictionCol: string
- rawPredictionCol: string
- weightCol: string

PCA

Parameters:

- inputCol: string
- k: integer
- outputCol: string

Pipeline

Parameters:

QuantileDiscretizer

Parameters:

- handleInvalid: string
- inputCol: string
- inputCols: array (of string)
- numBuckets: integer
- numBucketsArray: array (of integer)
- outputCol: string
- outputCols: array (of string)
- relativeError: double

RFormula

Parameters:

- featuresCol: string
- forceIndexLabel: boolean
- formula: string
- handleInvalid: string
- labelCol: string
- stringIndexerOrderType: string

RandomForestClassifier

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- labelCol: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- numTrees: integer
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- seed: double
- subsamplingRate: double
- thresholds: array (of double)

RandomForestRegressor

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- labelCol: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- numTrees: integer
- predictionCol: string
- seed: double
- subsamplingRate: double

StandardScaler

Parameters:

- inputCol: string
- outputCol: string
- withMean: boolean
- withStd: boolean

StringIndexer

Parameters:

- handleInvalid: string
- inputCol: string
- outputCol: string
- stringOrderType: string

TrainValidationSplit

Parameters:

- collectSubModels: boolean
- estimator: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- parallelism: integer
- seed: double
- trainRatio: double

VectorIndexer

Parameters:

- handleInvalid: string
- inputCol: string
- maxCategories: integer
- outputCol: string

Word2Vec

Parameters:

- inputCol: string
- maxIter: integer
- maxSentenceLength: integer
- minCount: integer
- numPartitions: integer
- outputCol: string
- seed: double
- stepSize: double
- vectorSize: integer
- windowSize: integer

RumbleDB ML - Catalogue of Transformers:

AFTSurvivalRegressionModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- quantileProbabilities: array (of double)
- quantilesCol: string

ALSModel

Parameters:

- coldStartStrategy: string
- itemCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- userCol: string

Binarizer

Parameters:

- inputCol: string
- outputCol: string
- threshold: double

BisectingKMeansModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string

BucketedRandomProjectionLSHModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

Bucketizer

Parameters:

- handleInvalid: string
- inputCol: string
- inputCols: array (of string)
- outputCol: string
- outputCols: array (of string)
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- splits: array (of double)
- splitsArray: array (of array of double)

ChiSqSelectorModel

Parameters:

- featuresCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

ColumnPruner

Parameters:

CountVectorizerModel

Parameters:

- binary: boolean
- inputCol: string
- minTF: double
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

CrossValidatorModel

Parameters:

- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

DCT

Parameters:

- inputCol: string
- inverse: boolean
- outputCol: string

DecisionTreeClassificationModel

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- impurity: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- seed: double
- thresholds: array (of double)

DecisionTreeRegressionModel

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- impurity: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- seed: double
- varianceCol: string

DistributedLDAModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- seed: double
- topicDistributionCol: string

ElementwiseProduct

Parameters:

- inputCol: string
- outputCol: string
- scalingVec: object (of double)

FPGrowthModel

Parameters:

- itemsCol: string
- minConfidence: double
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string

FeatureHasher

Parameters:

- categoricalCols: array (of string)
- inputCols: array (of string)
- numFeatures: integer
- outputCol: string

GBTClassificationModel

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- maxBins: integer
- maxDepth: integer
- maxIter: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- seed: double
- stepSize: double
- subsamplingRate: double
- thresholds: array (of double)

GBTRegressionModel

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- maxBins: integer
- maxDepth: integer
- maxIter: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- seed: double
- stepSize: double
- subsamplingRate: double

GaussianMixtureModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- probabilityCol: string

GeneralizedLinearRegressionModel

Parameters:

- featuresCol: string
- linkPredictionCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string

HashingTF

Parameters:

- binary: boolean
- inputCol: string
- numFeatures: integer
- outputCol: string

IDFModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

ImputerModel

Parameters:

- inputCols: array (of string)
- outputCols: array (of string)
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

IndexToString

Parameters:

- inputCol: string
- labels: array (of string)
- outputCol: string

Interaction

Parameters:

- inputCols: array (of string)
- outputCol: string

IsotonicRegressionModel

Parameters:

- featureIndex: integer
- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string

KMeansModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string

LinearRegressionModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string

LinearSVCModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- rawPredictionCol: string
- threshold: double
- weightCol: double

LocalLDAModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- seed: double
- topicDistributionCol: string

LogisticRegressionModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- threshold: double
- thresholds: array (of double)

MaxAbsScalerModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

MinHashLSHModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

MinMaxScalerModel

Parameters:

- inputCol: string
- max: double
- min: double
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

MultilayerPerceptronClassificationModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- thresholds: array (of double)

NGram

Parameters:

- inputCol: string
- n: integer
- outputCol: string

NaiveBayesModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- thresholds: array (of double)

Normalizer

Parameters:

- inputCol: string
- outputCol: string
- p: double

OneHotEncoder

Parameters:

- dropLast: boolean
- inputCol: string
- outputCol: string

OneHotEncoderModel

Parameters:

- dropLast: boolean
- handleInvalid: string
- inputCols: array (of string)
- outputCols: array (of string)
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

OneVsRestModel

Parameters:

- featuresCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- rawPredictionCol: string

PCAModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

PipelineModel

Parameters:

- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

PolynomialExpansion

Parameters:

- degree: integer
- inputCol: string
- outputCol: string

RFormulaModel

Parameters:

- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

RandomForestClassificationModel

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- numTrees: integer
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- probabilityCol: string
- rawPredictionCol: string
- seed: double
- subsamplingRate: double
- thresholds: array (of double)

RandomForestRegressionModel

Parameters:

- cacheNodeIds: boolean
- checkpointInterval: integer
- featuresCol: string
- featureSubsetStrategy: string
- impurity: string
- maxBins: integer
- maxDepth: integer
- maxMemoryInMB: integer
- minInfoGain: double
- minInstancesPerNode: integer
- numTrees: integer
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)
- predictionCol: string
- seed: double
- subsamplingRate: double

RegexTokenizer

Parameters:

- gaps: boolean
- inputCol: string
- minTokenLength: integer
- outputCol: string
- pattern: string
- toLowercase: boolean

SQLTransformer

Parameters:

- statement: string

StandardScalerModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

StopWordsRemover

Parameters:

- caseSensitive: boolean
- inputCol: string
- locale: string
- outputCol: string
- stopWords: array (of string)

StringIndexerModel

Parameters:

- handleInvalid: string
- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

Tokenizer

Parameters:

- inputCol: string
- outputCol: string

TrainValidationSplitModel

Parameters:

- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

VectorAssembler

Parameters:

- handleInvalid: string
- inputCols: array (of string)
- outputCol: string

VectorAttributeRewriter

Parameters:

VectorIndexerModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)

VectorSizeHint

Parameters:

- handleInvalid: string
- inputCol: string
- size: integer

VectorSlicer

Parameters:

- indices: array (of integer)
- inputCol: string
- names: array (of string)
- outputCol: string

Word2VecModel

Parameters:

- inputCol: string
- outputCol: string
- parent: estimator (i.e., function(object*, object) as function(object*, object) as object*)