GLRMParametersV3 (h2o-java-rest-bindings version 3.8.0.5 API)

java.lang.Object
- water.bindings.pojos.Schema
- - water.bindings.pojos.ModelParametersSchema
  - - water.bindings.pojos.GLRMParametersV3

public class GLRMParametersV3
extends ModelParametersSchema

Field Summary

Fields
Modifier and Type	Field and Description
`java.lang.String`	`checkpoint` Model checkpoint to resume training with
`boolean`	`expand_user_y` Expand categorical columns in user-specified initial Y
`FoldAssignmentScheme`	`fold_assignment` Cross-validation fold assignment scheme, if fold_column is not specified
`ColSpecifierV3`	`fold_column` Column with cross-validation fold index assignment per observation
`double`	`gamma_x` Regularization weight on X matrix
`double`	`gamma_y` Regularization weight on Y matrix
`boolean`	`ignore_const_cols` Ignore constant columns
`java.lang.String[]`	`ignored_columns` Ignored columns
`boolean`	`impute_original` Reconstruct original training data by reversing transform
`Initialization`	`init` Initialization mode
`double`	`init_step_size` Initial step size
`int`	`k` Rank of matrix approximation
`boolean`	`keep_cross_validation_predictions` Keep cross-validation model predictions
`java.lang.String`	`loading_name` Frame key to save resulting X
`Loss`	`loss` Numeric loss function
`Loss[]`	`loss_by_col` Loss function by column (override)
`int[]`	`loss_by_col_idx` Loss function by column index (override)
`int`	`max_iterations` Maximum number of iterations
`double`	`max_runtime_secs` Maximum allowed runtime in seconds for model training.
`int`	`max_updates` Maximum number of updates
`double`	`min_step_size` Minimum step size
`java.lang.String`	`model_id` Destination id for this model; auto-generated if not specified
`Loss`	`multi_loss` Categorical loss function
`int`	`nfolds` Number of folds for N-fold cross-validation
`ColSpecifierV3`	`offset_column` Offset column
`boolean`	`parallelize_cross_validation` Allow parallel training of cross-validation models
`int`	`period` Length of period (only used with periodic loss function)
`boolean`	`recover_svd` Recover singular values and eigenvectors of XY
`Regularizer`	`regularization_x` Regularization function for X matrix
`Regularizer`	`regularization_y` Regularization function for Y matrix
`ColSpecifierV3`	`response_column` Response column
`boolean`	`score_each_iteration` Whether to score during each iteration of model training
`long`	`seed` RNG seed for initialization
`StoppingMetric`	`stopping_metric` Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)
`int`	`stopping_rounds` Early stopping based on convergence of stopping_metric.
`double`	`stopping_tolerance` Relative tolerance for metric-based stopping criterion Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
`Method`	`svd_method` Method for computing SVD during initialization (Caution: Power and Randomized are currently experimental and unstable)
`java.lang.String`	`training_frame` Training frame
`TransformType`	`transform` Transformation of training data
`java.lang.String`	`user_x` User-specified initial X
`java.lang.String`	`user_y` User-specified initial Y
`java.lang.String`	`validation_frame` Validation frame
`ColSpecifierV3`	`weights_column` Column with observation weights

Constructor Summary

Constructors
Constructor and Description

GLRMParametersV3()

Constructors
Constructor and Description
`GLRMParametersV3()`

Method Summary
- Methods inherited from class java.lang.Object
  clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - transform
```
public TransformType transform
```
    Transformation of training data
  - k
```
public int k
```
    Rank of matrix approximation
  - loss
```
public Loss loss
```
    Numeric loss function
  - multi_loss
```
public Loss multi_loss
```
    Categorical loss function
  - loss_by_col
```
public Loss[] loss_by_col
```
    Loss function by column (override)
  - loss_by_col_idx
```
public int[] loss_by_col_idx
```
    Loss function by column index (override)
  - period
```
public int period
```
    Length of period (only used with periodic loss function)
  - regularization_x
```
public Regularizer regularization_x
```
    Regularization function for X matrix
  - regularization_y
```
public Regularizer regularization_y
```
    Regularization function for Y matrix
  - gamma_x
```
public double gamma_x
```
    Regularization weight on X matrix
  - gamma_y
```
public double gamma_y
```
    Regularization weight on Y matrix
  - max_iterations
```
public int max_iterations
```
    Maximum number of iterations
  - max_updates
```
public int max_updates
```
    Maximum number of updates
  - init_step_size
```
public double init_step_size
```
    Initial step size
  - min_step_size
```
public double min_step_size
```
    Minimum step size
  - seed
```
public long seed
```
    RNG seed for initialization
  - init
```
public Initialization init
```
    Initialization mode
  - svd_method
```
public Method svd_method
```
    Method for computing SVD during initialization (Caution: Power and Randomized are currently experimental and unstable)
  - user_y
```
public java.lang.String user_y
```
    User-specified initial Y
  - user_x
```
public java.lang.String user_x
```
    User-specified initial X
  - loading_name
```
public java.lang.String loading_name
```
    Frame key to save resulting X
  - expand_user_y
```
public boolean expand_user_y
```
    Expand categorical columns in user-specified initial Y
  - impute_original
```
public boolean impute_original
```
    Reconstruct original training data by reversing transform
  - recover_svd
```
public boolean recover_svd
```
    Recover singular values and eigenvectors of XY
  - model_id
```
public java.lang.String model_id
```
    Destination id for this model; auto-generated if not specified
  - training_frame
```
public java.lang.String training_frame
```
    Training frame
  - validation_frame
```
public java.lang.String validation_frame
```
    Validation frame
  - nfolds
```
public int nfolds
```
    Number of folds for N-fold cross-validation
  - keep_cross_validation_predictions
```
public boolean keep_cross_validation_predictions
```
    Keep cross-validation model predictions
  - parallelize_cross_validation
```
public boolean parallelize_cross_validation
```
    Allow parallel training of cross-validation models
  - response_column
```
public ColSpecifierV3 response_column
```
    Response column
  - weights_column
```
public ColSpecifierV3 weights_column
```
    Column with observation weights
  - offset_column
```
public ColSpecifierV3 offset_column
```
    Offset column
  - fold_column
```
public ColSpecifierV3 fold_column
```
    Column with cross-validation fold index assignment per observation
  - fold_assignment
```
public FoldAssignmentScheme fold_assignment
```
    Cross-validation fold assignment scheme, if fold_column is not specified
  - ignored_columns
```
public java.lang.String[] ignored_columns
```
    Ignored columns
  - ignore_const_cols
```
public boolean ignore_const_cols
```
    Ignore constant columns
  - score_each_iteration
```
public boolean score_each_iteration
```
    Whether to score during each iteration of model training
  - checkpoint
```
public java.lang.String checkpoint
```
    Model checkpoint to resume training with
  - stopping_rounds
```
public int stopping_rounds
```
    Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
  - max_runtime_secs
```
public double max_runtime_secs
```
    Maximum allowed runtime in seconds for model training. Use 0 to disable.
  - stopping_metric
```
public StoppingMetric stopping_metric
```
    Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)
  - stopping_tolerance
```
public double stopping_tolerance
```
    Relative tolerance for metric-based stopping criterion Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
- Constructor Detail
  - GLRMParametersV3
```
public GLRMParametersV3()
```

Class GLRMParametersV3

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

transform

k

loss

multi_loss

loss_by_col

loss_by_col_idx

period

regularization_x

regularization_y

gamma_x

gamma_y

max_iterations

max_updates

init_step_size

min_step_size

seed

init

svd_method

user_y

user_x

loading_name

expand_user_y

impute_original

recover_svd

model_id

training_frame

validation_frame

nfolds

keep_cross_validation_predictions

parallelize_cross_validation

response_column

weights_column

offset_column

fold_column

fold_assignment

ignored_columns

ignore_const_cols

score_each_iteration

checkpoint

stopping_rounds

max_runtime_secs

stopping_metric

stopping_tolerance

Constructor Detail

GLRMParametersV3