public class ModelParametersSchemaV3<P extends Model.Parameters,S extends ModelParametersSchemaV3<P,S>> extends SchemaV3<P,S>
SchemaV3.Meta
Schema.AutoParseable
Constructor and Description |
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ModelParametersSchemaV3() |
Modifier and Type | Method and Description |
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protected static java.lang.String[] |
append_field_arrays(java.lang.String[] first,
java.lang.String[] second) |
java.lang.String[] |
fields() |
S |
fillFromImpl(P impl)
Fill this Schema from the given implementation object.
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P |
fillImpl(P impl)
Fill an impl object and any children from this schema and its children.
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static AutoBuffer |
writeParametersJSON(AutoBuffer ab,
ModelParametersSchemaV3 parameters,
ModelParametersSchemaV3 default_parameters)
Write the parameters, including their metadata, into an AutoBuffer.
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createAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromBody, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall
asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonString, write, writeExternal
@API(level=critical, direction=INOUT, help="Destination id for this model; auto-generated if not specified.") public KeyV3.ModelKeyV3 model_id
@API(level=critical, direction=INOUT, help="Id of the training data frame.") public KeyV3.FrameKeyV3 training_frame
@API(level=critical, direction=INOUT, gridable=true, help="Id of the validation data frame.") public KeyV3.FrameKeyV3 validation_frame
@API(level=critical, direction=INOUT, help="Number of folds for K-fold cross-validation (0 to disable or >= 2).") public int nfolds
@API(level=expert, direction=INOUT, help="Whether to keep the cross-validation models.") public boolean keep_cross_validation_models
@API(level=expert, direction=INOUT, help="Whether to keep the predictions of the cross-validation models.") public boolean keep_cross_validation_predictions
@API(level=expert, direction=INOUT, help="Whether to keep the cross-validation fold assignment.") public boolean keep_cross_validation_fold_assignment
@API(help="Allow parallel training of cross-validation models", direction=INOUT, level=expert) public boolean parallelize_cross_validation
@API(help="Distribution function", values={"AUTO","bernoulli","quasibinomial","ordinal","multinomial","gaussian","poisson","gamma","tweedie","laplace","quantile","huber"}, level=secondary, gridable=true) public hex.genmodel.utils.DistributionFamily distribution
@API(level=secondary, direction=INPUT, gridable=true, help="Tweedie power for Tweedie regression, must be between 1 and 2.") public double tweedie_power
@API(level=secondary, direction=INPUT, gridable=true, help="Desired quantile for Quantile regression, must be between 0 and 1.") public double quantile_alpha
@API(help="Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).", level=secondary, direction=INPUT, gridable=true) public double huber_alpha
@API(level=critical, direction=INOUT, gridable=true, is_member_of_frames={"training_frame","validation_frame"}, is_mutually_exclusive_with="ignored_columns", help="Response variable column.") public FrameV3.ColSpecifierV3 response_column
@API(level=secondary, direction=INOUT, gridable=true, is_member_of_frames={"training_frame","validation_frame"}, is_mutually_exclusive_with={"ignored_columns","response_column"}, help="Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor.") public FrameV3.ColSpecifierV3 weights_column
@API(level=secondary, direction=INOUT, gridable=true, is_member_of_frames={"training_frame","validation_frame"}, is_mutually_exclusive_with={"ignored_columns","response_column","weights_column"}, help="Offset column. This will be added to the combination of columns before applying the link function.") public FrameV3.ColSpecifierV3 offset_column
@API(level=secondary, direction=INOUT, gridable=true, is_member_of_frames="training_frame", is_mutually_exclusive_with={"ignored_columns","response_column","weights_column","offset_column"}, help="Column with cross-validation fold index assignment per observation.") public FrameV3.ColSpecifierV3 fold_column
@API(level=secondary, direction=INOUT, gridable=true, values={"AUTO","Random","Modulo","Stratified"}, help="Cross-validation fold assignment scheme, if fold_column is not specified. The \'Stratified\' option will stratify the folds based on the response variable, for classification problems.") public Model.Parameters.FoldAssignmentScheme fold_assignment
@API(level=secondary, direction=INOUT, gridable=true, values={"AUTO","Enum","OneHotInternal","OneHotExplicit","Binary","Eigen","LabelEncoder","SortByResponse","EnumLimited"}, help="Encoding scheme for categorical features") public Model.Parameters.CategoricalEncodingScheme categorical_encoding
@API(level=secondary, direction=INPUT, gridable=true, help="For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited.") public int max_categorical_levels
@API(level=critical, direction=INOUT, is_member_of_frames={"training_frame","validation_frame"}, help="Names of columns to ignore for training.") public java.lang.String[] ignored_columns
@API(level=critical, direction=INOUT, help="Ignore constant columns.") public boolean ignore_const_cols
@API(level=secondary, direction=INOUT, help="Whether to score during each iteration of model training.") public boolean score_each_iteration
@API(level=secondary, direction=INOUT, help="Model checkpoint to resume training with.") public KeyV3.ModelKeyV3 checkpoint
@API(help="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)", level=secondary, direction=INOUT, gridable=true) public int stopping_rounds
@API(help="Maximum allowed runtime in seconds for model training. Use 0 to disable.", level=secondary, direction=INOUT, gridable=true) public double max_runtime_secs
@API(help="Metric to use for early stopping (AUTO: logloss for classification, deviance for regression). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.", values={"AUTO","deviance","logloss","MSE","RMSE","MAE","RMSLE","AUC","lift_top_group","misclassification","mean_per_class_error","custom","custom_increasing"}, level=secondary, direction=INOUT, gridable=true) public ScoreKeeper.StoppingMetric stopping_metric
@API(help="Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)", level=secondary, direction=INOUT, gridable=true) public double stopping_tolerance
@API(help="Reference to custom evaluation function, format: `language:keyName=funcName`", level=secondary, direction=INOUT, gridable=false) public java.lang.String custom_metric_func
public java.lang.String[] fields()
protected static java.lang.String[] append_field_arrays(java.lang.String[] first, java.lang.String[] second)
public S fillFromImpl(P impl)
Schema
fillFromImpl
in class Schema<P extends Model.Parameters,S extends ModelParametersSchemaV3<P,S>>
public P fillImpl(P impl)
Schema
fillImpl
in class Schema<P extends Model.Parameters,S extends ModelParametersSchemaV3<P,S>>
public static AutoBuffer writeParametersJSON(AutoBuffer ab, ModelParametersSchemaV3 parameters, ModelParametersSchemaV3 default_parameters)