.. _parameters_H2OGLRM: Parameters of H2OGLRM --------------------- Affected Class ############## - ``ai.h2o.sparkling.ml.features.H2OGLRM`` Parameters ########## - *Each parameter has also a corresponding getter and setter method.* *(E.g.:* ``label`` *->* ``getLabel()`` *,* ``setLabel(...)`` *)* maxScoringIterations The maximum number of iterations used in MOJO scoring to update X *Default value:* ``100`` *Also available on the trained model.* reconstructedCol Reconstructed column name. This column contains reconstructed input values (A_hat=X*Y instead of just X). *Default value:* ``"H2OGLRM_c36ff076a8bc__reconstructed"`` *Also available on the trained model.* withReconstructedCol A flag identifying whether a column with reconstructed input values will be produced or not. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* lossByColNames Columns names for which loss function will be overridden by the 'lossByCol' parameter *Scala default value:* ``null`` *; Python default value:* ``None`` outputCol Output column name *Default value:* ``"H2OGLRM_c36ff076a8bc__output"`` *Also available on the trained model.* userX User-specified initial matrix X. *Scala default value:* ``null`` *; Python default value:* ``None`` userY User-specified initial matrix Y. *Scala default value:* ``null`` *; Python default value:* ``None`` 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.* expandUserY Expand categorical columns in user-specified initial Y. *Scala default value:* ``true`` *; Python default value:* ``True`` *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.* gammaX Regularization weight on X matrix. *Default value:* ``0.0`` *Also available on the trained model.* gammaY Regularization weight on Y matrix. *Default value:* ``0.0`` *Also available on the trained model.* ignoreConstCols Ignore constant columns. *Scala default value:* ``true`` *; Python default value:* ``True`` *Also available on the trained model.* ignoredCols Names of columns to ignore for training. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* imputeOriginal Reconstruct original training data by reversing transform. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* init Initialization mode. Possible values are ``"Random"``, ``"SVD"``, ``"PlusPlus"``, ``"User"``, ``"Power"``. *Default value:* ``"PlusPlus"`` *Also available on the trained model.* initStepSize Initial step size. *Default value:* ``1.0`` *Also available on the trained model.* inputCols The array of input columns *Scala default value:* ``Array()`` *; Python default value:* ``[]`` *Also available on the trained model.* k Rank of matrix approximation. *Default value:* ``1`` *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`` loadingName [Deprecated] Use representation_name instead. Frame key to save resulting X. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* loss Numeric loss function. Possible values are ``"Quadratic"``, ``"Absolute"``, ``"Huber"``, ``"Poisson"``, ``"Periodic(0)"``, ``"Logistic"``, ``"Hinge"``, ``"Categorical"``, ``"Ordinal"``. *Default value:* ``"Quadratic"`` *Also available on the trained model.* lossByCol Loss function by column (override). Possible values are ``"Quadratic"``, ``"Absolute"``, ``"Huber"``, ``"Poisson"``, ``"Periodic(0)"``, ``"Logistic"``, ``"Hinge"``, ``"Categorical"``, ``"Ordinal"``. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* maxIterations Maximum number of iterations. *Default value:* ``1000`` *Also available on the trained model.* maxRuntimeSecs Maximum allowed runtime in seconds for model training. Use 0 to disable. *Default value:* ``0.0`` *Also available on the trained model.* maxUpdates Maximum number of updates, defaults to 2*max_iterations. *Default value:* ``2000`` *Also available on the trained model.* minStepSize Minimum step size. *Scala default value:* ``1.0e-4`` *; Python default value:* ``1.0E-4`` *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`` multiLoss Categorical loss function. Possible values are ``"Quadratic"``, ``"Absolute"``, ``"Huber"``, ``"Poisson"``, ``"Periodic(0)"``, ``"Logistic"``, ``"Hinge"``, ``"Categorical"``, ``"Ordinal"``. *Default value:* ``"Categorical"`` *Also available on the trained model.* period Length of period (only used with periodic loss function). *Default value:* ``1`` *Also available on the trained model.* recoverSvd Recover singular values and eigenvectors of XY. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* regularizationX Regularization function for X matrix. Possible values are ``"None"``, ``"Quadratic"``, ``"L2"``, ``"L1"``, ``"NonNegative"``, ``"OneSparse"``, ``"UnitOneSparse"``, ``"Simplex"``. *Default value:* ``"None"`` *Also available on the trained model.* regularizationY Regularization function for Y matrix. Possible values are ``"None"``, ``"Quadratic"``, ``"L2"``, ``"L1"``, ``"NonNegative"``, ``"OneSparse"``, ``"UnitOneSparse"``, ``"Simplex"``. *Default value:* ``"None"`` *Also available on the trained model.* representationName Frame key to save resulting X. *Scala default value:* ``null`` *; Python default value:* ``None`` *Also available on the trained model.* scoreEachIteration Whether to score during each iteration of model training. *Scala default value:* ``false`` *; Python default value:* ``False`` *Also available on the trained model.* seed RNG seed for initialization. *Scala default value:* ``-1L`` *; Python default value:* ``-1`` *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`` svdMethod Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable). Possible values are ``"GramSVD"``, ``"Power"``, ``"Randomized"``. *Default value:* ``"Randomized"`` *Also available on the trained model.* transform Transformation of training data. Possible values are ``"NONE"``, ``"STANDARDIZE"``, ``"NORMALIZE"``, ``"DEMEAN"``, ``"DESCALE"``. *Default value:* ``"NONE"`` *Also available on the trained model.* 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``