hex.schemas

Class ANOVAGLMV3.ANOVAGLMParametersV3

java.lang.Object

water.Iced

water.api.Schema<I,S>

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

water.api.schemas3.ModelParametersSchemaV3<ANOVAGLMModel.ANOVAGLMParameters,ANOVAGLMV3.ANOVAGLMParametersV3>

hex.schemas.ANOVAGLMV3.ANOVAGLMParametersV3

All Implemented Interfaces:

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

Enclosing class:

ANOVAGLMV3

public static final class ANOVAGLMV3.ANOVAGLMParametersV3
extends water.api.schemas3.ModelParametersSchemaV3<ANOVAGLMModel.ANOVAGLMParameters,ANOVAGLMV3.ANOVAGLMParametersV3>

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[]	alpha
boolean	balance_classes For imbalanced data, balance training data class counts via over/under-sampling.
float[]	class_sampling_factors Desired over/under-sampling ratios per class (lexicographic order).
boolean	compute_p_values
boolean	early_stopping
GLMModel.GLMParameters.Family	family
static java.lang.String[]	fields
int	highest_interaction_term
double[]	lambda
boolean	lambda_search
GLMModel.GLMParameters.Link	link
float	max_after_balance_size When classes are balanced, limit the resulting dataset size to the specified multiple of the original dataset size.
int	max_iterations
GLMModel.GLMParameters.MissingValuesHandling	missing_values_handling
boolean	non_negative
int	nparallelism
water.api.schemas3.KeyV3.FrameKeyV3	plug_values
double	prior
boolean	save_transformed_framekeys
long	seed
GLMModel.GLMParameters.Solver	solver
boolean	standardize
double	theta
double	tweedie_link_power
double	tweedie_variance_power
int	type

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
ANOVAGLMParametersV3()

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

seed

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

standardize

@API(help="Standardize numeric columns to have zero mean and unit variance",
     level=critical)
public boolean standardize

family

@API(help="Family. Use binomial for classification with logistic regression, others are for regression problems.",
     values={"AUTO","gaussian","binomial","fractionalbinomial","quasibinomial","poisson","gamma","tweedie","negativebinomial"},
     level=critical)
public GLMModel.GLMParameters.Family family

tweedie_variance_power

@API(help="Tweedie variance power",
     level=critical,
     gridable=true)
public double tweedie_variance_power

tweedie_link_power

@API(help="Tweedie link power",
     level=critical,
     gridable=true)
public double tweedie_link_power

theta

@API(help="Theta",
     level=critical,
     gridable=true)
public double theta

alpha

@API(help="Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = \'L-BFGS\'; 0.5 otherwise.",
     level=critical,
     gridable=true)
public double[] alpha

lambda

@API(help="Regularization strength",
     required=false,
     level=critical,
     gridable=true)
public double[] lambda

lambda_search

@API(help="Use lambda search starting at lambda max, given lambda is then interpreted as lambda min",
     level=critical)
public boolean lambda_search

solver

@API(help="AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.",
     values={"AUTO","IRLSM","L_BFGS","COORDINATE_DESCENT_NAIVE","COORDINATE_DESCENT","GRADIENT_DESCENT_LH","GRADIENT_DESCENT_SQERR"},
     level=critical)
public GLMModel.GLMParameters.Solver solver

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

non_negative

@API(help="Restrict coefficients (not intercept) to be non-negative")
public boolean non_negative

compute_p_values

@API(help="Request p-values computation, p-values work only with IRLSM solver and no regularization",
     level=secondary,
     direction=INPUT)
public boolean compute_p_values

max_iterations

@API(help="Maximum number of iterations",
     level=secondary)
public int max_iterations

link

@API(help="Link function.",
     level=secondary,
     values={"family_default","identity","logit","log","inverse","tweedie","ologit"})
public GLMModel.GLMParameters.Link link

prior

@API(help="Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.",
     level=expert)
public double prior

balance_classes

@API(help="Balance training data class counts via over/under-sampling (for imbalanced data).",
     level=secondary,
     direction=INOUT)
public boolean balance_classes

For imbalanced data, balance training data class counts via over/under-sampling. This can result in improved predictive accuracy.

class_sampling_factors

@API(help="Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.",
     level=expert,
     direction=INOUT)
public float[] class_sampling_factors

Desired over/under-sampling ratios per class (lexicographic order). Only when balance_classes is enabled. If not specified, they will be automatically computed to obtain class balance during training.

max_after_balance_size

@API(help="Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.",
     level=expert,
     direction=INOUT)
public float max_after_balance_size

When classes are balanced, limit the resulting dataset size to the specified multiple of the original dataset size.

highest_interaction_term

@API(help="Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on...  Default to 2.",
     level=critical)
public int highest_interaction_term

type

@API(help="Refer to the SS type 1, 2, 3, or 4.  We are currently only supporting 3",
     level=critical)
public int type

early_stopping

@API(help="Stop early when there is no more relative improvement on train or validation (if provided).")
public boolean early_stopping

save_transformed_framekeys

@API(help="true to save the keys of transformed predictors and interaction column.")
public boolean save_transformed_framekeys

nparallelism

@API(help="Number of models to build in parallel.  Default to 4.  Adjust according to your system.")
public int nparallelism

Constructor Detail

ANOVAGLMParametersV3

public ANOVAGLMParametersV3()