Parameters of H2OWord2Vec

Affected Class

  • ai.h2o.sparkling.ml.features.H2OWord2Vec

Parameters

  • Each parameter has also a corresponding getter and setter method. (E.g.: label -> getLabel() , setLabel(...) )

outputCol

Output column name

Default value: "H2OWord2Vec_965a7e7503c9__output"

Also available on the trained model.

columnsToCategorical

List of columns to convert to categorical before modelling

Scala default value: Array() ; Python default value: []

convertInvalidNumbersToNa

If set to ‘true’, the model converts invalid numbers to NA during making predictions.

Scala default value: false ; Python default value: False

Also available on the trained model.

convertUnknownCategoricalLevelsToNa

If set to ‘true’, the model converts unknown categorical levels to NA during making predictions.

Scala default value: false ; Python default value: False

Also available on the trained model.

dataFrameSerializer

A full name of a serializer used for serialization and deserialization of Spark DataFrames to a JSON value within NullableDataFrameParam.

Default value: "ai.h2o.sparkling.utils.JSONDataFrameSerializer"

Also available on the trained model.

epochs

Number of training iterations to run.

Default value: 5

Also available on the trained model.

exportCheckpointsDir

Automatically export generated models to this directory.

Scala default value: null ; Python default value: None

Also available on the trained model.

initLearningRate

Set the starting learning rate.

Scala default value: 0.025f ; Python default value: 0.025

Also available on the trained model.

inputCol

Input column name

Default value: "No default value"

Also available on the trained model.

keepBinaryModels

If set to true, all binary models created during execution of the fit method will be kept in DKV of H2O-3 cluster.

Scala default value: false ; Python default value: False

maxRuntimeSecs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Default value: 0.0

Also available on the trained model.

minWordFreq

This will discard words that appear less than <int> times.

Default value: 5

Also available on the trained model.

modelId

Destination id for this model; auto-generated if not specified.

Scala default value: null ; Python default value: None

normModel

Use Hierarchical Softmax. Possible values are "HSM".

Default value: "HSM"

Also available on the trained model.

sentSampleRate
Set threshold for occurrence of words. Those that appear with higher frequency in the training data

will be randomly down-sampled; useful range is (0, 1e-5).

Scala default value: 0.001f ; Python default value: 0.001

Also available on the trained model.

splitRatio

Accepts values in range [0, 1.0] which determine how large part of dataset is used for training and for validation. For example, 0.8 -> 80% training 20% validation. This parameter is ignored when validationDataFrame is set.

Default value: 1.0

validationDataFrame

A data frame dedicated for a validation of the trained model. If the parameters is not set,a validation frame created via the ‘splitRatio’ parameter. The parameter is not serializable!

Scala default value: null ; Python default value: None

vecSize

Set size of word vectors.

Default value: 100

Also available on the trained model.

windowSize

Set max skip length between words.

Default value: 5

Also available on the trained model.

wordModel

The word model to use (SkipGram or CBOW). Possible values are "SkipGram", "CBOW".

Default value: "SkipGram"

Also available on the trained model.