public class ModelParametersSchema<P extends Model.Parameters,S extends ModelParametersSchema<P,S>> extends Schema<P,S>
Schema.Meta
Modifier and Type | Field and Description |
---|---|
KeyV3.ModelKeyV3 |
checkpoint
A model key associated with a previously trained
model.
|
Model.Parameters.FoldAssignmentScheme |
fold_assignment |
FrameV3.ColSpecifierV3 |
fold_column |
boolean |
ignore_const_cols |
java.lang.String[] |
ignored_columns |
boolean |
keep_cross_validation_predictions |
KeyV3.ModelKeyV3 |
model_id |
int |
nfolds |
FrameV3.ColSpecifierV3 |
offset_column |
FrameV3.ColSpecifierV3 |
response_column |
boolean |
score_each_iteration |
ScoreKeeper.StoppingMetric |
stopping_metric
Metric to use for convergence checking, only for _stopping_rounds > 0
|
int |
stopping_rounds
Early stopping based on convergence of stopping_metric.
|
double |
stopping_tolerance |
KeyV3.FrameKeyV3 |
training_frame |
KeyV3.FrameKeyV3 |
validation_frame |
FrameV3.ColSpecifierV3 |
weights_column |
Constructor and Description |
---|
ModelParametersSchema() |
Modifier and Type | Method and Description |
---|---|
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.
|
P |
fillImpl(P impl)
Fill an impl object and any children from this schema and its children.
|
static AutoBuffer |
writeParametersJSON(AutoBuffer ab,
ModelParametersSchema parameters,
ModelParametersSchema default_parameters)
Write the parameters, including their metadata, into an AutoBuffer.
|
createAndFillImpl, createImpl, fillFromParms, fillFromParms, get__meta, getExperimentalVersion, getHighestSupportedVersion, getImplClass, getImplClass, getLatestVersion, getSchemaVersion, markdown, markdown, markdown, newInstance, newInstance, registerAllSchemasIfNecessary, schema, schema, schemaClass, schemas, setField
clone, frozenType, read_impl, read, readExternal, readJSON_impl, readJSON, toJsonString, write_impl, write, writeExternal, writeJSON_impl, writeJSON
@API(help="Destination id for this model; auto-generated if not specified", required=false, direction=INOUT) public KeyV3.ModelKeyV3 model_id
@API(help="Training frame", direction=INOUT) public KeyV3.FrameKeyV3 training_frame
@API(help="Validation frame", direction=INOUT, gridable=true) public KeyV3.FrameKeyV3 validation_frame
@API(help="Number of folds for N-fold cross-validation", level=critical, direction=INOUT) public int nfolds
@API(help="Keep cross-validation model predictions", level=expert, direction=INOUT) public boolean keep_cross_validation_predictions
@API(help="Response column", is_member_of_frames={"training_frame","validation_frame"}, is_mutually_exclusive_with="ignored_columns", direction=INOUT, gridable=true) public FrameV3.ColSpecifierV3 response_column
@API(help="Column with observation weights", level=secondary, is_member_of_frames={"training_frame","validation_frame"}, is_mutually_exclusive_with={"ignored_columns","response_column"}, direction=INOUT) public FrameV3.ColSpecifierV3 weights_column
@API(help="Offset column", level=secondary, is_member_of_frames={"training_frame","validation_frame"}, is_mutually_exclusive_with={"ignored_columns","response_column","weights_column"}, direction=INOUT) public FrameV3.ColSpecifierV3 offset_column
@API(help="Column with cross-validation fold index assignment per observation", level=secondary, is_member_of_frames="training_frame", is_mutually_exclusive_with={"ignored_columns","response_column","weights_column","offset_column"}, direction=INOUT) public FrameV3.ColSpecifierV3 fold_column
@API(help="Cross-validation fold assignment scheme, if fold_column is not specified", values={"AUTO","Random","Modulo","Stratified"}, level=secondary, direction=INOUT) public Model.Parameters.FoldAssignmentScheme fold_assignment
@API(help="Ignored columns", is_member_of_frames={"training_frame","validation_frame"}, direction=INOUT) public java.lang.String[] ignored_columns
@API(help="Ignore constant columns", direction=INOUT) public boolean ignore_const_cols
@API(help="Whether to score during each iteration of model training", direction=INOUT, level=secondary) public boolean score_each_iteration
@API(help="Model checkpoint to resume training with", level=secondary, direction=INOUT) 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="Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)", values={"AUTO","deviance","logloss","MSE","AUC","r2","misclassification"}, level=secondary, direction=INOUT, gridable=true) public ScoreKeeper.StoppingMetric stopping_metric
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 ModelParametersSchema<P,S>>
public P fillImpl(P impl)
Schema
fillImpl
in class Schema<P extends Model.Parameters,S extends ModelParametersSchema<P,S>>
public static final AutoBuffer writeParametersJSON(AutoBuffer ab, ModelParametersSchema parameters, ModelParametersSchema default_parameters)