public static final class GLRMV3.GLRMParametersV3 extends water.api.schemas3.ModelParametersSchemaV3<GLRMModel.GLRMParameters,GLRMV3.GLRMParametersV3>
Modifier and Type | Field and Description |
---|---|
boolean |
expand_user_y |
static java.lang.String[] |
fields |
double |
gamma_x |
double |
gamma_y |
boolean |
impute_original |
hex.genmodel.algos.glrm.GlrmInitialization |
init |
double |
init_step_size |
int |
k |
java.lang.String |
loading_name |
hex.genmodel.algos.glrm.GlrmLoss |
loss |
hex.genmodel.algos.glrm.GlrmLoss[] |
loss_by_col |
int[] |
loss_by_col_idx |
int |
max_iterations |
int |
max_updates |
double |
min_step_size |
hex.genmodel.algos.glrm.GlrmLoss |
multi_loss |
int |
period |
boolean |
recover_svd |
hex.genmodel.algos.glrm.GlrmRegularizer |
regularization_x |
hex.genmodel.algos.glrm.GlrmRegularizer |
regularization_y |
long |
seed |
SVDModel.SVDParameters.Method |
svd_method |
DataInfo.TransformType |
transform |
water.api.schemas3.KeyV3.FrameKeyV3 |
user_x |
water.api.schemas3.KeyV3.FrameKeyV3 |
user_y |
categorical_encoding, checkpoint, distribution, fold_assignment, fold_column, huber_alpha, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, keep_cross_validation_predictions, max_categorical_levels, max_runtime_secs, model_id, nfolds, offset_column, parallelize_cross_validation, quantile_alpha, response_column, score_each_iteration, stopping_metric, stopping_rounds, stopping_tolerance, training_frame, tweedie_power, validation_frame, weights_column
Constructor and Description |
---|
GLRMV3.GLRMParametersV3() |
append_field_arrays, fields, fillFromImpl, fillImpl, writeParametersJSON
createAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall
public static java.lang.String[] fields
@API(help="Transformation of training data", values={"NONE","STANDARDIZE","NORMALIZE","DEMEAN","DESCALE"}, gridable=true) public DataInfo.TransformType transform
@API(help="Rank of matrix approximation", required=true, gridable=true) public int k
@API(help="Numeric loss function", values={"Quadratic","Absolute","Huber","Poisson","Hinge","Logistic","Periodic"}, gridable=true) public hex.genmodel.algos.glrm.GlrmLoss loss
@API(help="Categorical loss function", values={"Categorical","Ordinal"}, gridable=true) public hex.genmodel.algos.glrm.GlrmLoss multi_loss
@API(help="Loss function by column (override)", values={"Quadratic","Absolute","Huber","Poisson","Hinge","Logistic","Periodic","Categorical","Ordinal"}, gridable=true) public hex.genmodel.algos.glrm.GlrmLoss[] loss_by_col
@API(help="Loss function by column index (override)") public int[] loss_by_col_idx
@API(help="Length of period (only used with periodic loss function)", gridable=true) public int period
@API(help="Regularization function for X matrix", values={"None","Quadratic","L2","L1","NonNegative","OneSparse","UnitOneSparse","Simplex"}, gridable=true) public hex.genmodel.algos.glrm.GlrmRegularizer regularization_x
@API(help="Regularization function for Y matrix", values={"None","Quadratic","L2","L1","NonNegative","OneSparse","UnitOneSparse","Simplex"}, gridable=true) public hex.genmodel.algos.glrm.GlrmRegularizer regularization_y
@API(help="Regularization weight on X matrix", gridable=true) public double gamma_x
@API(help="Regularization weight on Y matrix", gridable=true) public double gamma_y
@API(help="Maximum number of iterations", gridable=true) public int max_iterations
@API(help="Maximum number of updates, defaults to 2*max_iterations", gridable=true) public int max_updates
@API(help="Initial step size", gridable=true) public double init_step_size
@API(help="Minimum step size", gridable=true) public double min_step_size
@API(help="RNG seed for initialization", gridable=true) public long seed
@API(help="Initialization mode", values={"Random","SVD","PlusPlus","User"}, gridable=true) public hex.genmodel.algos.glrm.GlrmInitialization init
@API(help="Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable)", values={"GramSVD","Power","Randomized"}, gridable=true) public SVDModel.SVDParameters.Method svd_method
@API(help="User-specified initial Y") public water.api.schemas3.KeyV3.FrameKeyV3 user_y
@API(help="User-specified initial X") public water.api.schemas3.KeyV3.FrameKeyV3 user_x
@API(help="Frame key to save resulting X") public java.lang.String loading_name
@API(help="Expand categorical columns in user-specified initial Y") public boolean expand_user_y
@API(help="Reconstruct original training data by reversing transform") public boolean impute_original
@API(help="Recover singular values and eigenvectors of XY") public boolean recover_svd