hex.schemas

Class HGLMV3.HGLMParametersV3

java.lang.Object

water.Iced

water.api.Schema<I,S>

water.api.schemas3.SchemaV3<P,S>

water.api.schemas3.ModelParametersSchemaV3<HGLMModel.HGLMParameters,HGLMV3.HGLMParametersV3>

hex.schemas.HGLMV3.HGLMParametersV3

All Implemented Interfaces:

java.io.Externalizable, java.io.Serializable, java.lang.Cloneable, water.Freezable

Enclosing class:

HGLMV3

public static final class HGLMV3.HGLMParametersV3
extends water.api.schemas3.ModelParametersSchemaV3<HGLMModel.HGLMParameters,HGLMV3.HGLMParametersV3>

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class water.api.schemas3.SchemaV3

water.api.schemas3.SchemaV3.Meta

Nested classes/interfaces inherited from class water.api.Schema

water.api.Schema.AutoParseable

Field Summary

Fields

Modifier and Type	Field and Description
double	em_epsilon
GLMModel.GLMParameters.Family	family
static java.lang.String[]	fields
boolean	gen_syn_data
java.lang.String	group_column
double[]	initial_fixed_effects
water.api.schemas3.KeyV3.FrameKeyV3	initial_random_effects
water.api.schemas3.KeyV3.FrameKeyV3	initial_t_matrix
int	max_iterations
HGLMModel.HGLMParameters.Method	method
GLMModel.GLMParameters.MissingValuesHandling	missing_values_handling
water.api.schemas3.KeyV3.FrameKeyV3	plug_values
GLMModel.GLMParameters.Family	rand_family
java.lang.String[]	random_columns
boolean	random_intercept
int	score_iteration_interval
long	seed
double	tau_e_var_init
double	tau_u_var_init

Fields inherited from class water.api.schemas3.ModelParametersSchemaV3

auc_type, categorical_encoding, checkpoint, custom_distribution_func, custom_metric_func, distribution, export_checkpoints_dir, fold_assignment, fold_column, gainslift_bins, huber_alpha, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, keep_cross_validation_models, 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

Fields inherited from class water.api.schemas3.SchemaV3

__meta

Constructor Summary

Constructors

Constructor and Description
HGLMParametersV3()

Method Summary

Methods inherited from class water.api.schemas3.ModelParametersSchemaV3

append_field_arrays, extractDeclaredApiParameters, fields, fillFromImpl, fillImpl, getAdditionalParameters, writeParametersJSON

Methods inherited from class water.api.schemas3.SchemaV3

writeJSON

Methods inherited from class water.api.Schema

createAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromAny, fillFromBody, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall

Methods inherited from class water.Iced

asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonBytes, toJsonString, write, writeExternal

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

fields

public static final java.lang.String[] fields

score_iteration_interval

@API(help="Perform scoring for every score_iteration_interval iterations.",
     level=secondary)
public int score_iteration_interval

seed

@API(help="Seed for pseudo random number generator (if applicable).",
     gridable=true)
public long seed

missing_values_handling

@API(help="Handling of missing values. Either MeanImputation, Skip or PlugValues.",
     values={"MeanImputation","Skip","PlugValues"},
     level=expert,
     direction=INOUT,
     gridable=true)
public GLMModel.GLMParameters.MissingValuesHandling missing_values_handling

plug_values

@API(help="Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues).",
     direction=INPUT)
public water.api.schemas3.KeyV3.FrameKeyV3 plug_values

family

@API(help="Family. Only gaussian is supported now.",
     values="gaussian",
     level=critical)
public GLMModel.GLMParameters.Family family

rand_family

@API(help="Set distribution of random effects.  Only Gaussian is implemented now.",
     values="gaussian",
     level=critical)
public GLMModel.GLMParameters.Family rand_family

max_iterations

@API(help="Maximum number of iterations.  Value should >=1.  A value of 0 is only set when only the model coefficient names and model coefficient dimensions are needed.",
     level=secondary)
public int max_iterations

initial_fixed_effects

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="An array that contains initial values of the fixed effects coefficient.")
public double[] initial_fixed_effects

initial_random_effects

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="A H2OFrame id that contains initial values of the random effects coefficient.  The row names shouldbe the random coefficient names.  If you are not sure what the random coefficient names are, build HGLM model with max_iterations = 0 and checkout the model output field random_coefficient_names.  The number of rows of this frame should be the number of level 2 units.  Again, to figure this out, build HGLM model with max_iterations=0 and check out the model output field group_column_names.  The number of rows should equal the length of thegroup_column_names.")
public water.api.schemas3.KeyV3.FrameKeyV3 initial_random_effects

initial_t_matrix

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="A H2OFrame id that contains initial values of the T matrix.  It should be a positive symmetric matrix.")
public water.api.schemas3.KeyV3.FrameKeyV3 initial_t_matrix

tau_u_var_init

@API(help="Initial variance of random coefficient effects.  If set, should provide a value > 0.0.  If not set, will be randomly set in the model building process.",
     level=expert,
     gridable=true)
public double tau_u_var_init

tau_e_var_init

@API(help="Initial variance of random noise.  If set, should provide a value > 0.0.  If not set, will be randomly set in the model building process.",
     level=expert,
     gridable=true)
public double tau_e_var_init

random_columns

@API(help="Random columns indices for HGLM.",
     gridable=true)
public java.lang.String[] random_columns

method

@API(help="We only implemented EM as a method to obtain the fixed, random coefficients and the various variances.",
     values="EM",
     level=critical)
public HGLMModel.HGLMParameters.Method method

em_epsilon

@API(help="Converge if beta/ubeta/tmat/tauEVar changes less (using L-infinity norm) than em esilon. ONLY applies to EM method.",
     level=expert)
public double em_epsilon

random_intercept

@API(help="If true, will allow random component to the GLM coefficients.",
     direction=INPUT,
     gridable=true)
public boolean random_intercept

group_column

@API(help="Group column is the column that is categorical and used to generate the groups in HGLM",
     gridable=true)
public java.lang.String group_column

gen_syn_data

@API(help="If true, add gaussian noise with variance specified in parms._tau_e_var_init.",
     direction=INPUT,
     gridable=true)
public boolean gen_syn_data

Constructor Detail

HGLMParametersV3

public HGLMParametersV3()