index
int64 | repo_id
string | file_path
string | content
string |
|---|---|---|---|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/LeaderboardsHandler.java
|
package water.automl.api;
import hex.leaderboard.Leaderboard;
import water.*;
import water.api.Handler;
import water.api.schemas3.TwoDimTableV3;
import water.automl.api.schemas3.LeaderboardV99;
import water.automl.api.schemas3.LeaderboardsV99;
import water.exceptions.H2OKeyNotFoundArgumentException;
import water.exceptions.H2OKeyWrongTypeArgumentException;
public class LeaderboardsHandler extends Handler {
/** Class which contains the internal representation of the leaderboards list and params. */
public static final class Leaderboards extends Iced {
public Leaderboard[] leaderboards;
public static Leaderboard[] fetchAll() {
final Key<Leaderboard>[] leaderboardKeys = KeySnapshot.globalSnapshot().filter(new KeySnapshot.KVFilter() {
@Override
public boolean filter(KeySnapshot.KeyInfo k) {
return Value.isSubclassOf(k._type, Leaderboard.class);
}
}).keys();
Leaderboard[] leaderboards = new Leaderboard[leaderboardKeys.length];
for (int i = 0; i < leaderboardKeys.length; i++) {
Leaderboard leaderboard = getFromDKV(leaderboardKeys[i]);
leaderboards[i] = leaderboard;
}
return leaderboards;
}
} // public class Leaderboards
/** Return all the Leaderboards. */
@SuppressWarnings("unused") // called through reflection by RequestServer
public water.automl.api.schemas3.LeaderboardsV99 list(int version, water.automl.api.schemas3.LeaderboardsV99 s) {
Leaderboards m = s.createAndFillImpl();
m.leaderboards = Leaderboards.fetchAll();
return s.fillFromImpl(m);
}
@SuppressWarnings("unused") // called through reflection by RequestServer
public LeaderboardV99 fetch(int version, LeaderboardsV99 input) {
if (null == input.project_name)
throw new H2OKeyNotFoundArgumentException("Client must specify a project_name.");
Leaderboard leaderboard = getFromDKV(Key.make(Leaderboard.idForProject(input.project_name)), "project_name");
LeaderboardV99 lb = new LeaderboardV99().fillFromImpl(leaderboard);
if (input.extensions != null) {
lb.table = new TwoDimTableV3().fillFromImpl(leaderboard.toTwoDimTable(input.extensions));
}
return lb;
}
private static Leaderboard getFromDKV(Key key) {
return getFromDKV(key, "(none)");
}
private static Leaderboard getFromDKV(Key key, String argName) {
Value v = DKV.get(key);
if (v == null)
throw new H2OKeyNotFoundArgumentException(key.toString());
Iced ice = v.get();
if (! (ice instanceof Leaderboard))
throw new H2OKeyWrongTypeArgumentException(argName, key.toString(), Leaderboard.class, ice.getClass());
return (Leaderboard) ice;
}
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/AutoMLBuildSpecV99.java
|
package water.automl.api.schemas3;
import ai.h2o.automl.Algo;
import ai.h2o.automl.AutoMLBuildSpec;
import ai.h2o.automl.AutoMLBuildSpec.AutoMLStoppingCriteria;
import hex.KeyValue;
import hex.ScoreKeeper.StoppingMetric;
import hex.genmodel.utils.DistributionFamily;
import water.Iced;
import water.api.API;
import water.api.EnumValuesProvider;
import water.api.Schema;
import water.api.ValuesProvider;
import water.api.schemas3.*;
import water.util.*;
import java.util.Arrays;
// TODO: this is about to change from SchemaV3 to RequestSchemaV3:
public class AutoMLBuildSpecV99 extends SchemaV3<AutoMLBuildSpec, AutoMLBuildSpecV99> {
//////////////////////////////////////////////////////
// Input and output classes used by the build process.
//////////////////////////////////////////////////////
/**
* The specification of overall build parameters for the AutoML process.
* TODO: this should have all the standard early-stopping functionality like Grid does.
*/
public static final class AutoMLBuildControlV99 extends SchemaV3<AutoMLBuildSpec.AutoMLBuildControl, AutoMLBuildControlV99> {
@API(help="Optional project name used to group models from multiple AutoML runs into a single Leaderboard; derived from the training data name if not specified.",
direction = API.Direction.INOUT)
public String project_name;
@API(help="Model performance based stopping criteria for the AutoML run.")
public AutoMLStoppingCriteriaV99 stopping_criteria;
@API(help="Number of folds for k-fold cross-validation (defaults to -1 (AUTO), otherwise it must be >=2 or use 0 to disable). Disabling prevents Stacked Ensembles from being built.",
level = API.Level.secondary)
public int nfolds;
@API(help = "Balance training data class counts via over/under-sampling (for imbalanced data).",
level = API.Level.secondary)
public boolean balance_classes;
@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 = API.Level.expert)
public float[] class_sampling_factors;
@API(help = "Maximum relative size of the training data after balancing class counts (defaults to 5.0 and can be less than 1.0). Requires balance_classes.",
level = API.Level.expert)
public float max_after_balance_size;
@API(help="Whether to keep the predictions of the cross-validation predictions. "
+ "This needs to be set to TRUE if running the same AutoML object for repeated runs because CV predictions are required to build additional Stacked Ensemble models in AutoML.",
level = API.Level.expert)
public boolean keep_cross_validation_predictions;
@API(help="Whether to keep the cross-validated models. Keeping cross-validation models may consume significantly more memory in the H2O cluster.",
level = API.Level.expert)
public boolean keep_cross_validation_models;
@API(help="Whether to keep cross-validation assignments.",
level = API.Level.expert)
public boolean keep_cross_validation_fold_assignment;
@API(help = "Path to a directory where every generated model will be stored.",
level = API.Level.expert)
public String export_checkpoints_dir;
@API(help="Distribution function used by algorithms that support it; other algorithms use their defaults.",
direction=API.Direction.INOUT, values = { "AUTO", "bernoulli", //"fractionalbinomial", "quasibinomial", "ordinal",
"multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber", "custom" })
public DistributionFamily distribution;
@API(direction = API.Direction.INPUT,
help = "Tweedie power for Tweedie regression, must be between 1 and 2.")
public double tweedie_power;
@API(direction = API.Direction.INPUT,
help = "Desired quantile for Quantile regression, must be between 0 and 1.")
public double quantile_alpha;
@API(direction = API.Direction.INPUT,
help = "Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).")
public double huber_alpha;
@API(help = "Reference to custom evaluation function, format: `language:keyName=funcName`", level = API.Level.secondary, direction=API.Direction.INOUT, gridable = false)
public String custom_metric_func;
@API(help = "Reference to custom distribution, format: `language:keyName=funcName`", direction=API.Direction.INOUT)
public String custom_distribution_func;
} // class AutoMLBuildControlV99
/**
* The specification of the datasets to be used for the AutoML process.
* The user can specify a directory path, a file path (including HDFS, s3 or the like),
* or the ID of an already-parsed Frame in the H2O cluster. Only one of these may be specified;
* if more than one is specified the server will return a 412. Paths are processed
* as usual in H2O.
* <p>
* The user also specifies the response column and, optionally, an array of columns to ignore.
*/
public static final class AutoMLInputV99 extends SchemaV3<AutoMLBuildSpec.AutoMLInput, AutoMLInputV99> {
@API(help = "ID of the training data frame.")
public KeyV3.FrameKeyV3 training_frame;
@API(help = "Response column",
is_member_of_frames = {"training_frame", "validation_frame", "leaderboard_frame", "blending_frame"},
is_mutually_exclusive_with = {"ignored_columns", "fold_column", "weights_column"}
)
public FrameV3.ColSpecifierV3 response_column;
@API(help = "ID of the validation data frame (used for early stopping in grid searches and for early stopping of the AutoML process itself).")
public KeyV3.FrameKeyV3 validation_frame;
@API(help = "ID of the H2OFrame used to train the the metalearning algorithm in Stacked Ensembles (instead of relying on cross-validated predicted values)."
+ " When provided, it is also recommended to disable cross validation by setting `nfolds=0` and to provide a leaderboard frame for scoring purposes.")
public KeyV3.FrameKeyV3 blending_frame;
@API(help = "ID of the leaderboard data frame (used to score models and rank them on the AutoML Leaderboard).")
public KeyV3.FrameKeyV3 leaderboard_frame;
@API(help = "Fold column (contains fold IDs) in the training frame. These assignments are used to create the folds for cross-validation of the models.",
level = API.Level.secondary,
is_member_of_frames = {"training_frame", "validation_frame", "leaderboard_frame"},
is_mutually_exclusive_with = {"ignored_columns", "response_column", "weights_column"}
)
public FrameV3.ColSpecifierV3 fold_column;
@API(help = "Weights column in the training frame, which specifies the row weights used in model training.",
level = API.Level.secondary,
is_member_of_frames = {"training_frame", "validation_frame", "leaderboard_frame"},
is_mutually_exclusive_with = {"ignored_columns", "response_column", "fold_column"}
)
public FrameV3.ColSpecifierV3 weights_column;
@API(help = "Names of columns to ignore in the training frame when building models.",
level = API.Level.secondary,
is_member_of_frames = {"training_frame", "validation_frame", "leaderboard_frame", "blending_frame"},
is_mutually_exclusive_with = {"response_column", "fold_column", "weights_column"}
)
public String[] ignored_columns;
@API(help="Metric used to sort leaderboard",
valuesProvider = AutoMLMetricProvider.class,
level = API.Level.secondary)
public String sort_metric;
} // class AutoMLInputV99
public static final class AutoMLStoppingCriteriaV99 extends SchemaV3<AutoMLStoppingCriteria, AutoMLStoppingCriteriaV99> {
@API(help = "Seed for random number generator; set to a value other than -1 for reproducibility.",
level = API.Level.secondary)
public long seed;
@API(help = "Maximum number of models to build (optional)." +
" Always set this parameter to ensure AutoML reproducibility: all models are then trained until convergence and none is constrained by a time budget.",
level = API.Level.secondary)
public int max_models;
@API(help = "This argument specifies the maximum time that the AutoML process will run for." +
" If both max_runtime_secs and max_models are specified, then the AutoML run will stop as soon as it hits either of these limits." +
" If neither max_runtime_secs nor max_models are specified, then max_runtime_secs defaults to 3600 seconds (1 hour).",
level = API.Level.secondary)
public double max_runtime_secs;
@API(help = "Maximum time to spend on each individual model (optional)." +
" Note that models constrained by a time budget are not guaranteed reproducible.",
level = API.Level.secondary)
public double max_runtime_secs_per_model;
@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 = API.Level.secondary)
public int stopping_rounds;
@API(help = "Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)",
valuesProvider = AutoMLMetricProvider.class,
level = API.Level.secondary)
public StoppingMetric stopping_metric;
@API(help = "Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)",
level = API.Level.secondary)
public double stopping_tolerance;
@Override
public AutoMLStoppingCriteria fillImpl(AutoMLStoppingCriteria impl) {
AutoMLStoppingCriteria filled = super.fillImpl(impl, new String[] {"_searchCriteria"});
PojoUtils.copyProperties(filled.getSearchCriteria(), this, PojoUtils.FieldNaming.DEST_HAS_UNDERSCORES, new String[] {"_stoppingCriteria"});
PojoUtils.copyProperties(filled.getSearchCriteria().stoppingCriteria(), this, PojoUtils.FieldNaming.DEST_HAS_UNDERSCORES, new String[] {"max_runtime_secs_per_model"});
return filled;
}
@Override
public AutoMLStoppingCriteriaV99 fillFromImpl(AutoMLStoppingCriteria impl) {
AutoMLStoppingCriteriaV99 schema = super.fillFromImpl(impl, new String[]{"_searchCriteria"});
PojoUtils.copyProperties(schema, impl.getSearchCriteria(), PojoUtils.FieldNaming.ORIGIN_HAS_UNDERSCORES, new String[] {"_stoppingCriteria"});
PojoUtils.copyProperties(schema, impl.getSearchCriteria().stoppingCriteria(), PojoUtils.FieldNaming.ORIGIN_HAS_UNDERSCORES, new String[] {"max_runtime_secs_per_model"});
return schema;
}
}
public static final class AlgoProvider extends EnumValuesProvider<Algo> {
public AlgoProvider() {
super(Algo.class);
}
}
public static final class AutoMLMetricProvider extends EnumValuesProvider<StoppingMetric> {
public AutoMLMetricProvider() {
// list all metrics currently supported in leaderboard, and by all algos used in AutoML, incl. corresponding gris searches.
super(StoppingMetric.class, e -> Arrays.asList(
StoppingMetric.AUTO,
StoppingMetric.AUC,
StoppingMetric.AUCPR,
StoppingMetric.deviance,
StoppingMetric.lift_top_group,
StoppingMetric.logloss,
StoppingMetric.MAE,
StoppingMetric.mean_per_class_error,
StoppingMetric.misclassification,
StoppingMetric.MSE,
StoppingMetric.RMSE,
StoppingMetric.RMSLE
).contains(e));
}
}
public static final class ScopeProvider implements ValuesProvider {
private static final String ANY_ALGO = "any";
private static final String[] SCOPES = ArrayUtils.append(new String[]{ANY_ALGO}, new AlgoProvider().values());
@Override
public String[] values() {
return SCOPES;
}
}
public static final class AutoMLCustomParameterV99<V> extends Schema<Iced, AutoMLCustomParameterV99<V>> {
@API(help="Scope of application of the parameter (specific algo, or any algo).",
valuesProvider=ScopeProvider.class)
public String scope;
@API(help="Name of the model parameter.")
public String name;
@API(help="Value of the model parameter.")
public JSONValue value;
@SuppressWarnings("unchecked")
V getFormattedValue() {
switch (name) {
case "monotone_constraints":
return (V)value.valueAsArray(KeyValue[].class, KeyValueV3[].class);
default:
return (V)value.value();
}
}
}
public static final class AutoMLBuildModelsV99 extends SchemaV3<AutoMLBuildSpec.AutoMLBuildModels, AutoMLBuildModelsV99> {
@API(help="A list of algorithms to skip during the model-building phase.",
valuesProvider=AlgoProvider.class,
level = API.Level.secondary)
public Algo[] exclude_algos;
@API(help="A list of algorithms to restrict to during the model-building phase.",
valuesProvider=AlgoProvider.class,
level = API.Level.secondary)
public Algo[] include_algos;
@API(help="The budget ratio (between 0 and 1) dedicated to the exploitation (vs exploration) phase.",
level = API.Level.secondary)
public double exploitation_ratio;
@API(help="The list of modeling steps to be used by the AutoML engine (they may not all get executed, depending on other constraints).",
level = API.Level.expert)
public StepDefinitionV99[] modeling_plan;
@API(help="The list of preprocessing steps to run. Only 'target_encoding' is currently supported.",
level = API.Level.secondary)
public PreprocessingStepDefinitionV99[] preprocessing;
@API(help="Custom algorithm parameters.",
level = API.Level.expert)
public AutoMLCustomParameterV99[] algo_parameters;
@API(help = "A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint.",
level = API.Level.secondary)
public KeyValueV3[] monotone_constraints;
@Override
public AutoMLBuildSpec.AutoMLBuildModels fillImpl(AutoMLBuildSpec.AutoMLBuildModels impl) {
super.fillImpl(impl, new String[]{"algo_parameters"});
if (monotone_constraints != null) {
AutoMLCustomParameterV99 mc = new AutoMLCustomParameterV99();
mc.scope = ScopeProvider.ANY_ALGO;
mc.name = "monotone_constraints";
mc.value = JSONValue.fromValue(monotone_constraints);
if (algo_parameters == null) {
algo_parameters = new AutoMLCustomParameterV99[] {mc};
} else {
algo_parameters = ArrayUtils.append(algo_parameters, mc);
}
}
if (algo_parameters != null) {
AutoMLBuildSpec.AutoMLCustomParameters.Builder builder = AutoMLBuildSpec.AutoMLCustomParameters.create();
for (AutoMLCustomParameterV99 param : algo_parameters) {
if (ScopeProvider.ANY_ALGO.equals(param.scope)) {
builder.add(param.name, param.getFormattedValue());
} else {
Algo algo = EnumUtils.valueOf(Algo.class, param.scope);
builder.add(algo, param.name, param.getFormattedValue());
}
}
impl.algo_parameters = builder.build();
}
return impl;
}
@Override
public AutoMLBuildModelsV99 fillFromImpl(AutoMLBuildSpec.AutoMLBuildModels impl) {
return super.fillFromImpl(impl, new String[]{"algo_parameters"});
}
} // class AutoMLBuildModels
////////////////
// Input fields
@API(help="Specification of overall controls for the AutoML build process.")
public AutoMLBuildControlV99 build_control;
@API(help="Specification of the input data for the AutoML build process.")
public AutoMLInputV99 input_spec;
@API(help="If present, specifies details of how to train models.")
public AutoMLBuildModelsV99 build_models;
////////////////
// Output fields
@API(help="The AutoML Job key",
direction=API.Direction.OUTPUT)
public JobV3 job;
@Override
public AutoMLBuildSpec fillImpl(AutoMLBuildSpec impl) {
return super.fillImpl(impl, new String[] {"job"});
}
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/AutoMLV99.java
|
package water.automl.api.schemas3;
import ai.h2o.automl.AutoML;
import ai.h2o.automl.events.EventLog;
import ai.h2o.automl.StepDefinition;
import ai.h2o.automl.events.EventLogEntry;
import hex.leaderboard.Leaderboard;
import water.Iced;
import water.Key;
import water.api.API;
import water.api.schemas3.KeyV3;
import water.api.schemas3.SchemaV3;
import water.api.schemas3.TwoDimTableV3;
import water.logging.LoggingLevel;
// TODO: this is about to change from SchemaV3 to RequestSchemaV3:
public class AutoMLV99 extends SchemaV3<AutoML,AutoMLV99> {
public static class AutoMLKeyV3 extends KeyV3<Iced, AutoMLKeyV3, AutoML> {
public AutoMLKeyV3() { }
public AutoMLKeyV3(Key<AutoML> key) {
super(key);
}
}
@API(help="Optional AutoML run ID; omitting this returns all runs", direction=API.Direction.INPUT)
public AutoMLKeyV3 automl_id;
@API(help="Verbosity level of the returned event log", direction=API.Direction.INOUT,
valuesProvider= EventLogEntryV99.LevelProvider.class)
public LoggingLevel verbosity;
@API(help="ID of the actual training frame for this AutoML run after any automatic splitting", direction=API.Direction.OUTPUT)
public KeyV3.FrameKeyV3 training_frame;
@API(help="ID of the actual validation frame for this AutoML run after any automatic splitting", direction=API.Direction.OUTPUT)
public KeyV3.FrameKeyV3 validation_frame;
@API(help="ID of the actual blending frame used to train the Stacked Ensembles in blending mode", direction = API.Direction.OUTPUT)
public KeyV3.FrameKeyV3 blending_frame;
@API(help="ID of the actual leaderboard frame for this AutoML run after any automatic splitting", direction=API.Direction.OUTPUT)
public KeyV3.FrameKeyV3 leaderboard_frame;
/**
* Identifier for models that should be grouped together in the leaderboard
* (e.g., "airlines" and "iris").
*/
@API(help="Identifier for models that should be grouped together in the same leaderboard", direction=API.Direction.INOUT)
public String project_name;
@API(help="The leaderboard for this project, potentially including models from other AutoML runs", direction=API.Direction.OUTPUT)
public LeaderboardV99 leaderboard;
@API(help="The leaderboard for this project, potentially including models from other AutoML runs, for easy rendering", direction=API.Direction.OUTPUT)
public TwoDimTableV3 leaderboard_table;
@API(help="Event log of this AutoML run", direction=API.Direction.OUTPUT)
public EventLogV99 event_log;
@API(help="Event log of this AutoML run, for easy rendering", direction=API.Direction.OUTPUT)
public TwoDimTableV3 event_log_table;
@API(help="Metric used to sort leaderboard", direction=API.Direction.INPUT)
public String sort_metric;
@API(help="The list of modeling steps effectively used during the AutoML run", direction=API.Direction.OUTPUT)
public StepDefinitionV99[] modeling_steps;
@Override public AutoMLV99 fillFromImpl(AutoML autoML) {
super.fillFromImpl(autoML, new String[] { "leaderboard", "event_log", "leaderboard_table", "event_log_table", "sort_metric", "modeling_steps" });
if (null == autoML) return this;
project_name = autoML.projectName();
if (null != autoML._key) {
automl_id = new AutoMLKeyV3(autoML._key);
}
if (null != autoML.getTrainingFrame()) {
training_frame = new KeyV3.FrameKeyV3(autoML.getTrainingFrame()._key);
}
if (null != autoML.getValidationFrame()) {
validation_frame = new KeyV3.FrameKeyV3(autoML.getValidationFrame()._key);
}
if (null != autoML.getBlendingFrame()) {
blending_frame = new KeyV3.FrameKeyV3(autoML.getBlendingFrame()._key);
}
if (null != autoML.getLeaderboardFrame()) {
leaderboard_frame = new KeyV3.FrameKeyV3(autoML.getLeaderboardFrame()._key);
}
// NOTE: don't return nulls; return an empty leaderboard/eventLog, to ease life for the client
EventLog eventLog = autoML.eventLog();
if (null == eventLog) {
eventLog = new EventLog(autoML._key);
}
event_log = new EventLogV99();
event_log.verbosity = verbosity;
event_log.fillFromImpl(eventLog);
event_log_table = event_log.table; // for backwards compatibility
Leaderboard lb = autoML.leaderboard();
if (null == lb) {
lb = new Leaderboard(project_name, eventLog.asLogger(EventLogEntry.Stage.ModelTraining), autoML.getLeaderboardFrame(), sort_metric, Leaderboard.ScoreData.auto);
}
leaderboard = new LeaderboardV99().fillFromImpl(lb);
leaderboard_table = leaderboard.table; //for backwards compatibility
if (autoML.getActualModelingSteps() != null) {
modeling_steps = new StepDefinitionV99[autoML.getActualModelingSteps().length];
int i = 0;
for (StepDefinition stepDef : autoML.getActualModelingSteps()) {
modeling_steps[i++] = new StepDefinitionV99().fillFromImpl(stepDef);
}
}
return this;
}
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/EventLogEntryV99.java
|
package water.automl.api.schemas3;
import ai.h2o.automl.events.EventLogEntry;
import ai.h2o.automl.events.EventLogEntry.Stage;
import water.api.API;
import water.api.EnumValuesProvider;
import water.api.schemas3.SchemaV3;
import water.logging.LoggingLevel;
import java.util.Objects;
public class EventLogEntryV99 extends SchemaV3<EventLogEntry, EventLogEntryV99> {
@API(help="Timestamp for this event, in milliseconds since Jan 1, 1970", direction=API.Direction.OUTPUT)
public long timestamp;
@API(help="Importance of this log event", valuesProvider = LevelProvider.class, direction=API.Direction.OUTPUT)
public LoggingLevel level;
@API(help="Stage of the AutoML process for this log event", valuesProvider = StageProvider.class, direction=API.Direction.OUTPUT)
public Stage stage;
@API(help="Message for this event", direction=API.Direction.OUTPUT)
public String message;
@API(help="String identifier associated to this entry", direction=API.Direction.OUTPUT)
public String name;
@API(help="Value associated to this entry", direction=API.Direction.OUTPUT)
public String value;
public static final class LevelProvider extends EnumValuesProvider<LoggingLevel> {
public LevelProvider() { super(LoggingLevel.class); }
}
public static final class StageProvider extends EnumValuesProvider<Stage> {
public StageProvider() { super(Stage.class); }
}
@Override
public EventLogEntryV99 fillFromImpl(EventLogEntry impl) {
super.fillFromImpl(impl, new String[] { "value", "valueFormatter" });
this.value = impl.getValueFormatter() == null ? Objects.toString(impl.getValue(), "")
: impl.getValueFormatter().format(impl.getValue());
return this;
}
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/EventLogV99.java
|
package water.automl.api.schemas3;
import ai.h2o.automl.events.EventLog;
import ai.h2o.automl.events.EventLogEntry;
import water.api.API;
import water.api.Schema;
import water.api.schemas3.TwoDimTableV3;
import water.logging.LoggingLevel;
import java.util.function.Predicate;
import java.util.stream.Stream;
public class EventLogV99 extends Schema<EventLog, EventLogV99> {
@API(help="ID of the AutoML run for which the event log was recorded", direction=API.Direction.INOUT)
public AutoMLV99.AutoMLKeyV3 automl_id;
@API(help="List of events produced during the AutoML run", direction=API.Direction.OUTPUT)
public EventLogEntryV99[] events;
@API(help="A table representation of this event log, for easy rendering", direction=API.Direction.OUTPUT)
public TwoDimTableV3 table;
@API(help="Verbosity level of the returned event log", direction=API.Direction.INOUT,
valuesProvider= EventLogEntryV99.LevelProvider.class)
public LoggingLevel verbosity;
@Override public EventLogV99 fillFromImpl(EventLog eventLog) {
super.fillFromImpl(eventLog, new String[] { "events" });
Predicate<EventLogEntry> predicate = (e) -> verbosity == null || e.getLevel().ordinal() >= verbosity.ordinal();
if (null != eventLog._events) {
events = Stream.of(eventLog._events.clone())
.filter(predicate)
.map(e -> new EventLogEntryV99().fillFromImpl(e))
.toArray(EventLogEntryV99[]::new);
}
table = new TwoDimTableV3().fillFromImpl(eventLog.toTwoDimTable(predicate));
return this;
}
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/LeaderboardV99.java
|
package water.automl.api.schemas3;
import hex.leaderboard.Leaderboard;
import water.api.API;
import water.api.schemas3.KeyV3;
import water.api.schemas3.SchemaV3;
import water.api.schemas3.TwoDimTableV3;
import java.util.stream.Stream;
public class LeaderboardV99 extends SchemaV3<Leaderboard, LeaderboardV99> {
/**
* Identifier for models that should be grouped together in the leaderboard
* (e.g., "airlines" and "iris").
*/
@API(help="Identifier for models that should be grouped together in the leaderboard", direction=API.Direction.INOUT)
public final String project_name = "<default>";
/**
* List of models for this leaderboard, sorted by metric so that the best is first
* according to the standard metric for the given model type.
*/
@API(help="List of models for this leaderboard, sorted by metric so that the best is first", direction=API.Direction.OUTPUT)
public KeyV3.ModelKeyV3[] models;
/**
* Frame for which the metrics have been computed for this leaderboard.
*/
@API(help="Frame for this leaderboard", direction=API.Direction.OUTPUT)
public KeyV3.FrameKeyV3 leaderboard_frame;
/**
* Checksum for the Frame for which the metrics have been computed for this leaderboard.
*/
@API(help="Checksum for the Frame for this leaderboard", direction=API.Direction.OUTPUT)
public long leaderboard_frame_checksum;
/**
* Sort metrics for the models in this leaderboard, in the same order as the models.
*/
@API(help="Sort metrics for the models in this leaderboard, in the same order as the models", direction=API.Direction.OUTPUT)
public double[] sort_metrics;
/**
* Metric used to sort this leaderboard.
*/
@API(help="Metric used to sort this leaderboard", direction=API.Direction.INOUT)
public String sort_metric;
/**
* Metric direction used in the sort.
*/
@API(help="Metric direction used in the sort", direction=API.Direction.INOUT)
public boolean sort_decreasing;
@API(help="A table representation of this leaderboard, for easy rendering", direction=API.Direction.OUTPUT)
public TwoDimTableV3 table;
@Override
public LeaderboardV99 fillFromImpl(Leaderboard leaderboard) {
super.fillFromImpl(leaderboard, new String[] { "models", "leaderboard_frame", "sort_metrics", "sort_decreasing" });
models = Stream.of(leaderboard.getModelKeys())
.map(KeyV3.ModelKeyV3::new)
.toArray(KeyV3.ModelKeyV3[]::new);
if (leaderboard.leaderboardFrame() != null)
leaderboard_frame = new KeyV3.FrameKeyV3(leaderboard.leaderboardFrame()._key);
sort_metrics = leaderboard.getSortMetricValues();
sort_decreasing = !Leaderboard.isLossFunction(sort_metric);
table = new TwoDimTableV3().fillFromImpl(leaderboard.toTwoDimTable());
return this;
}
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/LeaderboardsV99.java
|
package water.automl.api.schemas3;
import water.api.API;
import water.api.schemas3.RequestSchemaV3;
import water.automl.api.LeaderboardsHandler;
public class LeaderboardsV99 extends RequestSchemaV3<LeaderboardsHandler.Leaderboards, LeaderboardsV99> {
// Input fields
@API(help="Name of project of interest", json=false)
public String project_name;
@API(help="List of extension columns to add to leaderboard", direction= API.Direction.INPUT)
public String[] extensions;
// Output fields
@API(help="Leaderboards", direction=API.Direction.OUTPUT)
public LeaderboardV99[] leaderboards;
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/PreprocessingStepDefinitionV99.java
|
package water.automl.api.schemas3;
import ai.h2o.automl.preprocessing.PreprocessingStepDefinition;
import water.api.API;
import water.api.EnumValuesProvider;
import water.api.Schema;
import static ai.h2o.automl.preprocessing.PreprocessingStepDefinition.*;
public final class PreprocessingStepDefinitionV99 extends Schema<PreprocessingStepDefinition, PreprocessingStepDefinitionV99> {
public static final class TypeProvider extends EnumValuesProvider<Type> {
public TypeProvider() {
super(Type.class);
}
}
@API(help="A type representing the preprocessing step to be executed.", valuesProvider=TypeProvider.class, direction=API.Direction.INOUT)
public Type type;
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/SchemaExtensions.java
|
package water.automl.api.schemas3;
import ai.h2o.automl.Models;
import water.Iced;
import water.Key;
import water.api.schemas3.KeyV3;
public final class SchemaExtensions {
public static class ModelsKeyV3 extends KeyV3<Iced, SchemaExtensions.ModelsKeyV3, Models> {
public ModelsKeyV3() {}
public ModelsKeyV3(Key<Models> key) { super(key); }
}
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/automl/api/schemas3/StepDefinitionV99.java
|
package water.automl.api.schemas3;
import ai.h2o.automl.StepDefinition;
import ai.h2o.automl.StepDefinition.Alias;
import ai.h2o.automl.StepDefinition.Step;
import water.api.API;
import water.api.EnumValuesProvider;
import water.api.Schema;
public final class StepDefinitionV99 extends Schema<StepDefinition, StepDefinitionV99> {
public static final class StepV99 extends Schema<Step, StepV99> {
@API(help="The id of the step (must be unique per step provider).", direction=API.Direction.INOUT)
public String id;
@API(help="The group of execution of the given step (groups are executed in ascending order of priority)." +
"Steps with group=0 are skipped. Defaults to -1 to use the default group assigned to the step id.",
direction=API.Direction.INOUT)
public int group = Step.DEFAULT_GROUP;
@API(help="The relative weight for the given step (can impact time and/or number of models allocated for this step). " +
"Steps with weight=0 are skipped. Defaults to -1 to use the default weight assigned to the step id.",
direction=API.Direction.INOUT)
public int weight = Step.DEFAULT_WEIGHT;
}
public static final class AliasProvider extends EnumValuesProvider<Alias> {
public AliasProvider() {
super(Alias.class);
}
}
@API(help="Name of the step provider (usually, this is also the name of an algorithm).", direction=API.Direction.INOUT)
public String name;
@API(help="An alias representing a predefined list of steps to be executed.", valuesProvider=AliasProvider.class, direction=API.Direction.INOUT)
public Alias alias;
@API(help="The list of steps to be executed (Mutually exclusive with alias).", direction=API.Direction.INOUT)
public StepV99[] steps;
}
|
0
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water
|
java-sources/ai/h2o/h2o-automl/3.46.0.7/water/exceptions/H2OAutoMLException.java
|
package water.exceptions;
import water.H2OError;
public class H2OAutoMLException extends H2OAbstractRuntimeException {
private final Throwable _rootCause;
private final int _httpResponse;
public H2OAutoMLException(String msg) {
this(msg, null);
}
public H2OAutoMLException(String msg, Throwable rootException) {
this(msg, rootException, 0);
}
public H2OAutoMLException(String msg, Throwable rootException, int httpResponse) {
super(msg, msg);
_rootCause = rootException;
_httpResponse = httpResponse > 0 ? httpResponse : super.HTTP_RESPONSE_CODE();
}
@Override
protected int HTTP_RESPONSE_CODE() {
return _httpResponse;
}
@Override
public H2OError toH2OError(String error_url) {
H2OError err;
String rootMessage = _rootCause== null ? null : _rootCause.getMessage().trim();
if (_rootCause instanceof H2OAbstractRuntimeException) {
err = ((H2OAbstractRuntimeException) _rootCause).toH2OError(error_url);
} else {
err = new H2OError(timestamp, error_url, getMessage(), dev_message, HTTP_RESPONSE_CODE(), values, _rootCause);
}
StringBuilder msg = new StringBuilder(getMessage().trim());
if (msg.charAt(msg.length()-1) != '.') msg.append('.');
if (rootMessage != null) {
msg.append(' ');
msg.append("Root cause: ");
msg.append(rootMessage);
err._exception_msg = msg.toString();
}
return err;
}
}
|
0
|
java-sources/ai/h2o/h2o-avro-parser/3.46.0.7/water/parser
|
java-sources/ai/h2o/h2o-avro-parser/3.46.0.7/water/parser/avro/AvroParser.java
|
package water.parser.avro;
import org.apache.avro.Schema;
import org.apache.avro.file.DataFileReader;
import org.apache.avro.file.DataFileStream;
import org.apache.avro.file.SeekableByteArrayInput;
import org.apache.avro.file.SeekableInput;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericDatumReader;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.io.DatumReader;
import org.apache.avro.util.Utf8;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.util.Arrays;
import java.util.List;
import water.H2O;
import water.Job;
import water.Key;
import water.fvec.Vec;
import water.parser.BufferedString;
import water.parser.ParseReader;
import water.parser.ParseSetup;
import water.parser.ParseWriter;
import water.parser.Parser;
import water.util.ArrayUtils;
import water.util.Log;
import static water.parser.avro.AvroUtil.*;
/**
* AVRO parser for H2O distributed parsing subsystem.
*/
public class AvroParser extends Parser {
/** Avro header */
private final byte[] header;
AvroParser(ParseSetup setup, Key<Job> jobKey) {
super(setup, jobKey);
this.header = ((AvroParser.AvroParseSetup) setup).header;
}
@Override
protected final ParseWriter parseChunk(int cidx, ParseReader din, ParseWriter dout) {
// We will read GenericRecord and load them based on schema
final DatumReader<GenericRecord> datumReader = new GenericDatumReader<>();
final H2OSeekableInputAdaptor sbai = new H2OSeekableInputAdaptor(cidx, din);
DataFileReader<GenericRecord> dataFileReader = null;
int cnt = 0;
try {
// Reconstruct Avro header
DataFileStream.Header
fakeHeader = new DataFileReader<>(new SeekableByteArrayInput(this.header), datumReader).getHeader();
dataFileReader = DataFileReader.openReader(sbai, datumReader, fakeHeader, true);
Schema schema = dataFileReader.getSchema();
GenericRecord gr = new GenericData.Record(schema);
Schema.Field[] flatSchema = flatSchema(schema);
long sync = dataFileReader.previousSync();
if (sbai.chunkCnt == 0) { // Find data in first chunk
while (dataFileReader.hasNext() && dataFileReader.previousSync() == sync) {
gr = dataFileReader.next(gr);
// Write values to the output
// FIXME: what if user change input names, or ignore an input column?
write2frame(gr, _setup.getColumnNames(), flatSchema, _setup.getColumnTypes(), dout);
cnt++;
}
} // else first chunk does not contain synchronization block, so give up and let another reader to use it
} catch (IOException e) {
throw new IllegalStateException("Failed to read AVRO.", e);
}
Log.trace(String.format("Avro: ChunkIdx: %d read %d records, start at %d off, block count: %d, block size: %d", cidx, cnt, din.getChunkDataStart(cidx), dataFileReader.getBlockCount(), dataFileReader.getBlockSize()));
return dout;
}
/** A simple adaptor for Avro Seekable Input.
*
* It implements lazy loading of chunks from ParseReader and track how many chunks
* were loaded.
*
* Warning: This is not designed to be accessed by multiple threads!
*/
private static class H2OSeekableInputAdaptor implements SeekableInput {
private final ParseReader din;
private final int startCidx;
protected int pos;
protected int mark;
private byte[] data;
// Additional chunks loaded
protected int chunkCnt;
public H2OSeekableInputAdaptor(int cidx, ParseReader din) {
this.din = din;
this.startCidx = cidx;
this.data = din.getChunkData(cidx);
this.chunkCnt = 0;
this.mark = din.getChunkDataStart(cidx) > 0 ? din.getChunkDataStart(cidx) : 0;
this.pos = mark;
}
@Override
public void seek(long p) throws IOException {
this.reset();
this.skip(p);
}
@Override
public long tell() throws IOException {
return this.pos;
}
@Override
public long length() throws IOException {
return -1;
}
@Override
public int read(byte[] b, int off, int len) throws IOException {
if (b == null) {
throw new NullPointerException();
} else if (off < 0 || len < 0 || len > b.length - off) {
throw new IndexOutOfBoundsException();
}
needData(len);
if (pos >= count()) {
return -1;
}
int avail = count() - pos;
if (len > avail) {
len = avail;
}
if (len <= 0) {
return 0;
}
// FIXME drop read data
System.arraycopy(data, pos, b, off, len);
pos += len;
return len;
}
@Override
public void close() throws IOException {
data = null;
}
public void reset() {
pos = 0;
}
public long skip(long n) {
long remain = 0;
while ((remain = count() - pos) < n && loadNextData()) ;
if (n < remain) {
remain = n < 0 ? 0 : n;
}
pos += remain;
return remain;
}
private int count() {
return data.length;
}
private boolean needData(int len) {
boolean loaded = false;
while ((count() - pos) < len && (loaded = loadNextData())) ;
return loaded;
}
private boolean loadNextData() {
// FIXME: just replace data
byte[] nextChunk = this.din.getChunkData(this.startCidx + chunkCnt + 1);
if (nextChunk != null && nextChunk.length > 0) {
this.data = ArrayUtils.append(this.data, nextChunk);
this.chunkCnt++;
Log.trace(String.format("Avro stream wrapper - loading another chunk: StartChunkIdx: %d, LoadedChunkCnt: %d", startCidx, chunkCnt));
return true;
} else {
return false;
}
}
}
/**
* The main method transforming Avro record into a row in H2O frame.
*
* @param gr Avro generic record
* @param columnNames Column names prepared by parser setup
* @param inSchema Flattenized Avro schema which corresponds to passed column names
* @param columnTypes Target H2O types
* @param dout Parser writer
*/
private static void write2frame(GenericRecord gr, String[] columnNames, Schema.Field[] inSchema, byte[] columnTypes, ParseWriter dout) {
assert inSchema.length == columnTypes.length : "AVRO field flatenized schema has to match to parser setup";
BufferedString bs = new BufferedString();
for (int cIdx = 0; cIdx < columnNames.length; cIdx++) {
int inputFieldIdx = inSchema[cIdx].pos();
Schema.Type inputType = toPrimitiveType(inSchema[cIdx].schema());
byte targetType = columnTypes[cIdx]; // FIXME: support target conversions
Object value = gr.get(inputFieldIdx);
if (value == null) {
dout.addInvalidCol(cIdx);
} else {
switch (inputType) {
case BOOLEAN:
dout.addNumCol(cIdx, ((Boolean) value) ? 1 : 0);
break;
case INT:
dout.addNumCol(cIdx, ((Integer) value), 0);
break;
case LONG:
dout.addNumCol(cIdx, ((Long) value), 0);
break;
case FLOAT:
dout.addNumCol(cIdx, (Float) value);
break;
case DOUBLE:
dout.addNumCol(cIdx, (Double) value);
break;
case ENUM:
// Note: this code expects ordering of categoricals provided by Avro remain same
// as in H2O!!!
GenericData.EnumSymbol es = (GenericData.EnumSymbol) value;
dout.addNumCol(cIdx, es.getSchema().getEnumOrdinal(es.toString()));
break;
case BYTES:
dout.addStrCol(cIdx, bs.set(((ByteBuffer) value).array()));
break;
case STRING:
dout.addStrCol(cIdx, bs.set(((Utf8) value).getBytes()));
break;
case NULL:
dout.addInvalidCol(cIdx);
break;
}
}
}
}
public static class AvroParseSetup extends ParseSetup {
final byte[] header;
final long blockSize;
public AvroParseSetup(int ncols,
String[] columnNames,
byte[] ctypes,
String[][] domains,
String[][] naStrings,
String[][] data,
byte[] header,
long blockSize) {
super(AvroParserProvider.AVRO_INFO, (byte) '|', true, HAS_HEADER , ncols, columnNames, ctypes, domains, naStrings, data, false);
this.header = header;
this.blockSize = blockSize;
this.setChunkSize((int) blockSize);
}
public AvroParseSetup(ParseSetup ps, byte[] header, long blockSize, String[][] domains) {
super(ps);
this.header = header;
this.blockSize = blockSize;
this.setDomains(domains);
this.setChunkSize((int) blockSize);
}
@Override
protected Parser parser(Key jobKey) {
return new AvroParser(this, jobKey);
}
}
public static ParseSetup guessSetup(byte[] bits) {
try {
return runOnPreview(bits, new AvroPreviewProcessor<ParseSetup>() {
@Override
public ParseSetup process(byte[] header, GenericRecord gr, long blockCount,
long blockSize) {
return deriveParseSetup(header, gr, blockCount, blockSize);
}
});
} catch (IOException e) {
throw new RuntimeException("Avro format was not recognized", e);
}
}
static AvroInfo extractAvroInfo(byte[] bits, final ParseSetup requiredSetup) throws IOException {
return runOnPreview(bits, new AvroPreviewProcessor<AvroInfo>() {
@Override
public AvroInfo process(byte[] header, GenericRecord gr, long blockCount,
long blockSize) {
Schema recordSchema = gr.getSchema();
List<Schema.Field> fields = recordSchema.getFields();
int supportedFieldCnt = 0 ;
for (Schema.Field f : fields) if (isSupportedSchema(f.schema())) supportedFieldCnt++;
assert supportedFieldCnt == requiredSetup.getColumnNames().length : "User-driven changes are not not supported in Avro format";
String[][] domains = new String[supportedFieldCnt][];
int i = 0;
for (Schema.Field f : fields) {
Schema schema = f.schema();
if (isSupportedSchema(schema)) {
byte type = schemaToColumnType(schema);
if (type == Vec.T_CAT) {
domains[i] = getDomain(schema);
}
i++;
}
}
return new AvroInfo(header, blockCount, blockSize, domains);
}
});
}
static <T> T runOnPreview(byte[] bits, AvroPreviewProcessor<T> processor) throws IOException {
DatumReader<GenericRecord> datumReader = new GenericDatumReader<GenericRecord>();
SeekableByteArrayInput sbai = new SeekableByteArrayInput(bits);
DataFileReader<GenericRecord> dataFileReader = null;
try {
dataFileReader = new DataFileReader<>(sbai, datumReader);
int headerLen = (int) dataFileReader.previousSync();
byte[] header = Arrays.copyOf(bits, headerLen);
if (dataFileReader.hasNext()) {
GenericRecord gr = dataFileReader.next();
return processor.process(header, gr, dataFileReader.getBlockCount(), dataFileReader.getBlockSize());
} else {
throw new RuntimeException("Empty Avro file - cannot run preview! ");
}
} finally {
try { if (dataFileReader!=null) dataFileReader.close(); } catch (IOException safeToIgnore) {}
}
}
private static ParseSetup deriveParseSetup(byte[] header, GenericRecord gr,
long blockCount, long blockSize) {
// Expect flat structure
Schema recordSchema = gr.getSchema();
List<Schema.Field> fields = recordSchema.getFields();
int supportedFieldCnt = 0 ;
for (Schema.Field f : fields) if (isSupportedSchema(f.schema())) supportedFieldCnt++;
String[] names = new String[supportedFieldCnt];
byte[] types = new byte[supportedFieldCnt];
String[][] domains = new String[supportedFieldCnt][];
String[] dataPreview = new String[supportedFieldCnt];
int i = 0;
for (Schema.Field f : fields) {
Schema schema = f.schema();
if (isSupportedSchema(schema)) {
names[i] = f.name();
types[i] = schemaToColumnType(schema);
if (types[i] == Vec.T_CAT) {
domains[i] = getDomain(schema);
}
dataPreview[i] = gr.get(f.name()) != null ? gr.get(f.name()).toString() : "null";
i++;
} else {
Log.warn("Skipping field: " + f.name() + " because of unsupported type: " + schema.getType() + " schema: " + schema);
}
}
AvroParseSetup ps = new AvroParseSetup(
supportedFieldCnt,
names,
types,
domains,
null,
new String[][] { dataPreview },
header,
blockSize
);
return ps;
}
/** Helper to represent Avro header
* and size of 1st block of data.
*/
static class AvroInfo {
public AvroInfo(byte[] header, long firstBlockCount, long firstBlockSize, String[][] domains) {
this.header = header;
this.firstBlockCount = firstBlockCount;
this.firstBlockSize = firstBlockSize;
this.domains = domains;
}
byte[] header;
long firstBlockCount;
long firstBlockSize;
String[][] domains;
}
private interface AvroPreviewProcessor<R> {
R process(byte[] header, GenericRecord gr, long blockCount, long blockSize);
}
}
|
0
|
java-sources/ai/h2o/h2o-avro-parser/3.46.0.7/water/parser
|
java-sources/ai/h2o/h2o-avro-parser/3.46.0.7/water/parser/avro/AvroParserProvider.java
|
package water.parser.avro;
import water.DKV;
import water.Iced;
import water.Job;
import water.Key;
import water.exceptions.H2OIllegalArgumentException;
import water.fvec.ByteVec;
import water.fvec.Frame;
import water.parser.DefaultParserProviders;
import water.parser.ParseSetup;
import water.parser.Parser;
import water.parser.ParserInfo;
import water.parser.ParserProvider;
/**
* Avro parser provider.
*/
public class AvroParserProvider extends ParserProvider {
/* Setup for this parser */
static ParserInfo AVRO_INFO = new ParserInfo("AVRO", DefaultParserProviders.MAX_CORE_PRIO + 10, true, true);
@Override
public ParserInfo info() {
return AVRO_INFO;
}
@Override
public Parser createParser(ParseSetup setup, Key<Job> jobKey) {
return new AvroParser(setup, jobKey);
}
@Override
public ParseSetup guessSetup(ByteVec bv, byte[] bits, byte sep, int ncols, boolean singleQuotes,
int checkHeader, String[] columnNames, byte[] columnTypes,
String[][] domains, String[][] naStrings) {
return AvroParser.guessSetup(bits);
}
@Override
public ParseSetup createParserSetup(Key[] inputs, ParseSetup requiredSetup) {
// We need to get header of Avro file to configure the Avro parser.
// The code expects that inputs are consistent and extract only header
// from the first file.
// Also expect that files are not compressed
assert inputs != null && inputs.length > 0 : "Inputs cannot be empty!";
Key firstInput = inputs[0];
Iced ice = DKV.getGet(firstInput);
if (ice == null) throw new H2OIllegalArgumentException("Missing data", "Did not find any data under key " + firstInput);
ByteVec bv = (ByteVec)(ice instanceof ByteVec ? ice : ((Frame)ice).vecs()[0]);
byte [] bits = bv.getFirstBytes();
try {
AvroParser.AvroInfo avroInfo = AvroParser.extractAvroInfo(bits, requiredSetup);
return new AvroParser.AvroParseSetup(requiredSetup, avroInfo.header, avroInfo.firstBlockSize, avroInfo.domains);
} catch (Throwable e) {
throw new H2OIllegalArgumentException("Wrong data", "Cannot find Avro header in input file: " + firstInput, e);
}
}
}
|
0
|
java-sources/ai/h2o/h2o-avro-parser/3.46.0.7/water/parser
|
java-sources/ai/h2o/h2o-avro-parser/3.46.0.7/water/parser/avro/AvroUtil.java
|
package water.parser.avro;
import org.apache.avro.Schema;
import java.util.Arrays;
import java.util.List;
import water.fvec.Vec;
/**
* Utilities to work with Avro schema.
*/
public final class AvroUtil {
/** Return true if the given schema can be transformed
* into h2o type.
*
* @param s avro field schema
* @return true if the schema can be transformed into H2O type
*/
public static boolean isSupportedSchema(Schema s) {
Schema.Type typ = s.getType();
switch (typ) {
case BOOLEAN:
case INT:
case LONG:
case FLOAT:
case DOUBLE:
case ENUM:
case STRING:
case NULL:
case BYTES:
return true;
case UNION: // Flattenize the union
List<Schema> unionSchemas = s.getTypes();
if (unionSchemas.size() == 1) {
return isSupportedSchema(unionSchemas.get(0));
} else if (unionSchemas.size() == 2) {
Schema s1 = unionSchemas.get(0);
Schema s2 = unionSchemas.get(1);
return s1.getType().equals(Schema.Type.NULL) && isSupportedSchema(s2)
|| s2.getType().equals(Schema.Type.NULL) && isSupportedSchema(s1);
}
default:
return false;
}
}
/**
* Transform Avro schema into H2O type.
*
* @param s avro schema
* @return a byte representing H2O column type
* @throws IllegalArgumentException if schema is not supported
*/
public static byte schemaToColumnType(Schema s) {
Schema.Type typ = s.getType();
switch (typ) {
case BOOLEAN:
case INT:
case LONG:
case FLOAT:
case DOUBLE:
return Vec.T_NUM;
case ENUM:
return Vec.T_CAT;
case STRING:
return Vec.T_STR;
case NULL:
return Vec.T_BAD;
case BYTES:
return Vec.T_STR;
case UNION: // Flattenize the union
List<Schema> unionSchemas = s.getTypes();
if (unionSchemas.size() == 1) {
return schemaToColumnType(unionSchemas.get(0));
} else if (unionSchemas.size() == 2) {
Schema s1 = unionSchemas.get(0);
Schema s2 = unionSchemas.get(1);
if (s1.getType().equals(Schema.Type.NULL)) return schemaToColumnType(s2);
else if (s2.getType().equals(Schema.Type.NULL)) return schemaToColumnType(s1);
}
default:
throw new IllegalArgumentException("Unsupported Avro schema type: " + s);
}
}
static String[] getDomain(Schema fieldSchema) {
if (fieldSchema.getType() == Schema.Type.ENUM) {
return fieldSchema.getEnumSymbols().toArray(new String[] {});
} else if (fieldSchema.getType() == Schema.Type.UNION) {
List<Schema> unionSchemas = fieldSchema.getTypes();
if (unionSchemas.size() == 1) {
return getDomain(unionSchemas.get(0));
} else if (unionSchemas.size() == 2) {
Schema s1 = unionSchemas.get(0);
Schema s2 = unionSchemas.get(1);
if (s1.getType() == Schema.Type.NULL) return getDomain(s2);
else if (s2.getType() == Schema.Type.NULL) return getDomain(s1);
}
}
throw new IllegalArgumentException("Cannot get domain from field: " + fieldSchema);
}
/**
* Transform Avro schema into its primitive representation.
*
* @param s avro schema
* @return primitive type as a result of transformation
* @throws IllegalArgumentException if the schema has no primitive transformation
*/
public static Schema.Type toPrimitiveType(Schema s) {
Schema.Type typ = s.getType();
switch(typ) {
case BOOLEAN:
case INT:
case LONG:
case FLOAT:
case DOUBLE:
case ENUM:
case STRING:
case NULL:
case BYTES:
return typ;
case UNION:
List<Schema> unionSchemas = s.getTypes();
if (unionSchemas.size() == 1) {
return toPrimitiveType(unionSchemas.get(0));
} else if (unionSchemas.size() == 2) {
Schema s1 = unionSchemas.get(0);
Schema s2 = unionSchemas.get(1);
if (s1.getType().equals(Schema.Type.NULL)) return toPrimitiveType(s2);
else if (s2.getType().equals(Schema.Type.NULL)) return toPrimitiveType(s1);
}
default:
throw new IllegalArgumentException("Unsupported Avro schema type: " + s);
}
}
/**
* The method "flattenize" the given Avro schema.
* @param s Avro schema
* @return List of supported fields which were extracted from original Schema
*/
public static Schema.Field[] flatSchema(Schema s) {
List<Schema.Field> fields = s.getFields();
Schema.Field[] flatSchema = new Schema.Field[fields.size()];
int cnt = 0;
for (Schema.Field f : fields) {
if (isSupportedSchema(f.schema())) {
flatSchema[cnt] = f;
cnt++;
}
}
// Return resized array
return cnt != flatSchema.length ? Arrays.copyOf(flatSchema, cnt) : flatSchema;
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/H2oApi.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings;
import water.bindings.pojos.*;
import water.bindings.proxies.retrofit.*;
import retrofit2.*;
import retrofit2.converter.gson.GsonConverterFactory;
import com.google.gson.*;
import com.google.gson.reflect.TypeToken;
import com.google.gson.stream.JsonReader;
import com.google.gson.stream.JsonWriter;
import okhttp3.OkHttpClient;
import java.io.IOException;
import java.util.concurrent.TimeUnit;
import java.lang.reflect.Array;
import java.lang.reflect.Field;
import java.lang.reflect.InvocationTargetException;
import java.lang.reflect.Type;
@SuppressWarnings("unused")
public class H2oApi {
public static String DEFAULT_URL = "http://localhost:54321/";
public H2oApi() {
this(DEFAULT_URL);
}
public H2oApi(String url) {
_url = url;
}
public H2oApi setUrl(String url) {
_url = url;
retrofit = null;
return this;
}
public H2oApi setTimeout(int t) {
timeout_s = t;
retrofit = null;
return this;
}
/**
* Set time interval for job polling in {@link #waitForJobCompletion(JobKeyV3)}.
* @param millis time interval, in milliseconds
*/
public H2oApi setJobPollInterval(int millis) {
pollInterval_ms = millis;
return this;
}
/**
* Continuously poll server for the status of the given job, until it completes.
* @param jobKey job to query
* @return the finished job
*/
public JobV3 waitForJobCompletion(JobKeyV3 jobKey) {
return waitForJobCompletion(keyToString(jobKey));
}
public JobV3 waitForJobCompletion(String jobId) {
Jobs jobService = getService(Jobs.class);
Response<JobsV3> jobsResponse = null;
int retries = 3;
JobsV3 jobs = null;
do {
try {
Thread.sleep(pollInterval_ms);
jobsResponse = jobService.fetch(jobId).execute();
} catch (IOException e) {
System.err.println("Caught exception: " + e);
} catch (InterruptedException e) { /* pass */ }
if (jobsResponse == null || !jobsResponse.isSuccessful())
if (retries-- > 0)
continue;
else
throw new RuntimeException("/3/Jobs/" + jobId + " failed 3 times.");
jobs = jobsResponse.body();
if (jobs.jobs == null || jobs.jobs.length != 1)
throw new RuntimeException("Failed to find Job: " + jobId);
} while (jobs != null && jobs.jobs[0].status.equals("RUNNING"));
return jobs == null? null : jobs.jobs[0];
}
/**
* Train a XGBoost model.
*/
public XGBoostV3 train_xgboost() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainXgboost().execute().body();
}
public XGBoostV3 train_xgboost(XGBoostParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainXgboost(
params.ntrees,
params.maxDepth,
params.minRows,
params.minChildWeight,
params.learnRate,
params.eta,
params.sampleRate,
params.subsample,
params.colSampleRate,
params.colsampleBylevel,
params.colSampleRatePerTree,
params.colsampleBytree,
params.colsampleBynode,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.maxDeltaStep,
params.scoreTreeInterval,
params.seed,
params.minSplitImprovement,
params.gamma,
params.nthread,
params.buildTreeOneNode,
params.saveMatrixDirectory,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.maxBins,
params.maxLeaves,
params.treeMethod,
params.growPolicy,
params.booster,
params.regLambda,
params.regAlpha,
params.quietMode,
params.sampleType,
params.normalizeType,
params.rateDrop,
params.oneDrop,
params.skipDrop,
params.dmatrixType,
params.backend,
params.gpuId,
params.interactionConstraints,
params.scalePosWeight,
params.evalMetric,
params.scoreEvalMetricOnly,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of XGBoost model builder parameters.
*/
public XGBoostV3 validate_xgboost() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersXgboost().execute().body();
}
public XGBoostV3 validate_xgboost(XGBoostParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersXgboost(
params.ntrees,
params.maxDepth,
params.minRows,
params.minChildWeight,
params.learnRate,
params.eta,
params.sampleRate,
params.subsample,
params.colSampleRate,
params.colsampleBylevel,
params.colSampleRatePerTree,
params.colsampleBytree,
params.colsampleBynode,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.maxDeltaStep,
params.scoreTreeInterval,
params.seed,
params.minSplitImprovement,
params.gamma,
params.nthread,
params.buildTreeOneNode,
params.saveMatrixDirectory,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.maxBins,
params.maxLeaves,
params.treeMethod,
params.growPolicy,
params.booster,
params.regLambda,
params.regAlpha,
params.quietMode,
params.sampleType,
params.normalizeType,
params.rateDrop,
params.oneDrop,
params.skipDrop,
params.dmatrixType,
params.backend,
params.gpuId,
params.interactionConstraints,
params.scalePosWeight,
params.evalMetric,
params.scoreEvalMetricOnly,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for XGBoost model.
*/
public XGBoostV3 grid_search_xgboost() throws IOException {
Grid s = getService(Grid.class);
return s.trainXgboost().execute().body();
}
public XGBoostV3 grid_search_xgboost(XGBoostParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainXgboost(
params.ntrees,
params.maxDepth,
params.minRows,
params.minChildWeight,
params.learnRate,
params.eta,
params.sampleRate,
params.subsample,
params.colSampleRate,
params.colsampleBylevel,
params.colSampleRatePerTree,
params.colsampleBytree,
params.colsampleBynode,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.maxDeltaStep,
params.scoreTreeInterval,
params.seed,
params.minSplitImprovement,
params.gamma,
params.nthread,
params.buildTreeOneNode,
params.saveMatrixDirectory,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.maxBins,
params.maxLeaves,
params.treeMethod,
params.growPolicy,
params.booster,
params.regLambda,
params.regAlpha,
params.quietMode,
params.sampleType,
params.normalizeType,
params.rateDrop,
params.oneDrop,
params.skipDrop,
params.dmatrixType,
params.backend,
params.gpuId,
params.interactionConstraints,
params.scalePosWeight,
params.evalMetric,
params.scoreEvalMetricOnly,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for XGBoost model.
*/
public XGBoostV3 grid_search_xgboost_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeXgboost().execute().body();
}
public XGBoostV3 grid_search_xgboost_resume(XGBoostParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeXgboost(
params.ntrees,
params.maxDepth,
params.minRows,
params.minChildWeight,
params.learnRate,
params.eta,
params.sampleRate,
params.subsample,
params.colSampleRate,
params.colsampleBylevel,
params.colSampleRatePerTree,
params.colsampleBytree,
params.colsampleBynode,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.maxDeltaStep,
params.scoreTreeInterval,
params.seed,
params.minSplitImprovement,
params.gamma,
params.nthread,
params.buildTreeOneNode,
params.saveMatrixDirectory,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.maxBins,
params.maxLeaves,
params.treeMethod,
params.growPolicy,
params.booster,
params.regLambda,
params.regAlpha,
params.quietMode,
params.sampleType,
params.normalizeType,
params.rateDrop,
params.oneDrop,
params.skipDrop,
params.dmatrixType,
params.backend,
params.gpuId,
params.interactionConstraints,
params.scalePosWeight,
params.evalMetric,
params.scoreEvalMetricOnly,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Remote XGBoost execution - init
*/
public XGBoostExecRespV3 remote_xgb_init() throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.init().execute().body();
}
public XGBoostExecRespV3 remote_xgb_init(KeyV3 key, String data) throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.init(keyToString(key), data).execute().body();
}
/**
* Remote XGBoost execution - setup
*/
public StreamingSchema remote_xgb_setup() throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.setup().execute().body();
}
public StreamingSchema remote_xgb_setup(KeyV3 key, String data) throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.setup(keyToString(key), data).execute().body();
}
/**
* Remote XGBoost execution - update
*/
public XGBoostExecRespV3 remote_xgb_update() throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.update().execute().body();
}
public XGBoostExecRespV3 remote_xgb_update(KeyV3 key, String data) throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.update(keyToString(key), data).execute().body();
}
/**
* Remote XGBoost execution - getEvalMetric
*/
public XGBoostExecRespV3 remote_xgb_metric() throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.getEvalMetric().execute().body();
}
public XGBoostExecRespV3 remote_xgb_metric(KeyV3 key, String data) throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.getEvalMetric(keyToString(key), data).execute().body();
}
/**
* Remote XGBoost execution - get booster
*/
public StreamingSchema remote_xgb_booster() throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.getBooster().execute().body();
}
public StreamingSchema remote_xgb_booster(KeyV3 key, String data) throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.getBooster(keyToString(key), data).execute().body();
}
/**
* Remote XGBoost execution - cleanup
*/
public XGBoostExecRespV3 remote_xgb_cleanup() throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.cleanup().execute().body();
}
public XGBoostExecRespV3 remote_xgb_cleanup(KeyV3 key, String data) throws IOException {
XGBoostExecutor s = getService(XGBoostExecutor.class);
return s.cleanup(keyToString(key), data).execute().body();
}
/**
* Generate a MOJO 2 pipeline artifact from the Assembly
*/
public StreamingSchema _assembly_fetch_mojo_pipeline(String assemblyId, String fileName) throws IOException {
Assembly s = getService(Assembly.class);
return s.fetchMojoPipeline(assemblyId, fileName).execute().body();
}
public StreamingSchema _assembly_fetch_mojo_pipeline(AssemblyV99 params) throws IOException {
Assembly s = getService(Assembly.class);
return s.fetchMojoPipeline(
params.assemblyId,
params.fileName,
params.steps,
keyToString(params.frame),
params._excludeFields
).execute().body();
}
/**
* Set Amazon S3 credentials (Secret Key ID, Secret Access Key)
*/
public PersistS3CredentialsV3 set_s3_credentials(String secretKeyId, String secretAccessKey) throws IOException {
PersistS3 s = getService(PersistS3.class);
return s.setS3Credentials(secretKeyId, secretAccessKey).execute().body();
}
public PersistS3CredentialsV3 set_s3_credentials(String secretKeyId, String secretAccessKey, String sessionToken) throws IOException {
PersistS3 s = getService(PersistS3.class);
return s.setS3Credentials(secretKeyId, secretAccessKey, sessionToken).execute().body();
}
/**
* Remove store Amazon S3 credentials
*/
public PersistS3CredentialsV3 remove_s3_credentials(String secretKeyId, String secretAccessKey) throws IOException {
PersistS3 s = getService(PersistS3.class);
return s.removeS3Credentials(secretKeyId, secretAccessKey).execute().body();
}
public PersistS3CredentialsV3 remove_s3_credentials(String secretKeyId, String secretAccessKey, String sessionToken) throws IOException {
PersistS3 s = getService(PersistS3.class);
return s.removeS3Credentials(secretKeyId, secretAccessKey, sessionToken).execute().body();
}
/**
* Return all the AutoML leaderboards.
*/
public LeaderboardsV99 leaderboards() throws IOException {
Leaderboards s = getService(Leaderboards.class);
return s.list().execute().body();
}
public LeaderboardsV99 leaderboards(String projectName, String[] extensions) throws IOException {
Leaderboards s = getService(Leaderboards.class);
return s.list(projectName, extensions, "").execute().body();
}
public LeaderboardsV99 leaderboards(String projectName, String[] extensions, String _excludeFields) throws IOException {
Leaderboards s = getService(Leaderboards.class);
return s.list(projectName, extensions, _excludeFields).execute().body();
}
/**
* Return the AutoML leaderboard for the given project.
*/
public LeaderboardV99 leaderboard(String projectName) throws IOException {
Leaderboards s = getService(Leaderboards.class);
return s.fetch(projectName).execute().body();
}
public LeaderboardV99 leaderboard(String projectName, String[] extensions) throws IOException {
Leaderboards s = getService(Leaderboards.class);
return s.fetch(projectName, extensions, "").execute().body();
}
public LeaderboardV99 leaderboard(String projectName, String[] extensions, String _excludeFields) throws IOException {
Leaderboards s = getService(Leaderboards.class);
return s.fetch(projectName, extensions, _excludeFields).execute().body();
}
/**
* Train a Infogram model.
*/
public InfogramV3 train_infogram() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainInfogram().execute().body();
}
public InfogramV3 train_infogram(InfogramParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainInfogram(
params.seed,
params.standardize,
keyToString(params.plugValues),
params.maxIterations,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.algorithm,
params.algorithmParams,
params.protectedColumns,
params.totalInformationThreshold,
params.netInformationThreshold,
params.relevanceIndexThreshold,
params.safetyIndexThreshold,
params.dataFraction,
params.topNFeatures,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of Infogram model builder parameters.
*/
public InfogramV3 validate_infogram() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersInfogram().execute().body();
}
public InfogramV3 validate_infogram(InfogramParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersInfogram(
params.seed,
params.standardize,
keyToString(params.plugValues),
params.maxIterations,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.algorithm,
params.algorithmParams,
params.protectedColumns,
params.totalInformationThreshold,
params.netInformationThreshold,
params.relevanceIndexThreshold,
params.safetyIndexThreshold,
params.dataFraction,
params.topNFeatures,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for Infogram model.
*/
public InfogramV3 grid_search_infogram() throws IOException {
Grid s = getService(Grid.class);
return s.trainInfogram().execute().body();
}
public InfogramV3 grid_search_infogram(InfogramParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainInfogram(
params.seed,
params.standardize,
keyToString(params.plugValues),
params.maxIterations,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.algorithm,
params.algorithmParams,
params.protectedColumns,
params.totalInformationThreshold,
params.netInformationThreshold,
params.relevanceIndexThreshold,
params.safetyIndexThreshold,
params.dataFraction,
params.topNFeatures,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for Infogram model.
*/
public InfogramV3 grid_search_infogram_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeInfogram().execute().body();
}
public InfogramV3 grid_search_infogram_resume(InfogramParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeInfogram(
params.seed,
params.standardize,
keyToString(params.plugValues),
params.maxIterations,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.algorithm,
params.algorithmParams,
params.protectedColumns,
params.totalInformationThreshold,
params.netInformationThreshold,
params.relevanceIndexThreshold,
params.safetyIndexThreshold,
params.dataFraction,
params.topNFeatures,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a TargetEncoder model.
*/
public TargetEncoderV3 train_targetencoder() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainTargetencoder().execute().body();
}
public TargetEncoderV3 train_targetencoder(TargetEncoderParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainTargetencoder(
params.columnsToEncode,
params.keepOriginalCategoricalColumns,
params.blending,
params.inflectionPoint,
params.smoothing,
params.dataLeakageHandling,
params.noise,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of TargetEncoder model builder parameters.
*/
public TargetEncoderV3 validate_targetencoder() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersTargetencoder().execute().body();
}
public TargetEncoderV3 validate_targetencoder(TargetEncoderParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersTargetencoder(
params.columnsToEncode,
params.keepOriginalCategoricalColumns,
params.blending,
params.inflectionPoint,
params.smoothing,
params.dataLeakageHandling,
params.noise,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for TargetEncoder model.
*/
public TargetEncoderV3 grid_search_targetencoder() throws IOException {
Grid s = getService(Grid.class);
return s.trainTargetencoder().execute().body();
}
public TargetEncoderV3 grid_search_targetencoder(TargetEncoderParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainTargetencoder(
params.columnsToEncode,
params.keepOriginalCategoricalColumns,
params.blending,
params.inflectionPoint,
params.smoothing,
params.dataLeakageHandling,
params.noise,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for TargetEncoder model.
*/
public TargetEncoderV3 grid_search_targetencoder_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeTargetencoder().execute().body();
}
public TargetEncoderV3 grid_search_targetencoder_resume(TargetEncoderParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeTargetencoder(
params.columnsToEncode,
params.keepOriginalCategoricalColumns,
params.blending,
params.inflectionPoint,
params.smoothing,
params.dataLeakageHandling,
params.noise,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Transform using give TargetEncoderModel
*/
public FrameKeyV3 target_encoder_transform() throws IOException {
TargetEncoderTransform s = getService(TargetEncoderTransform.class);
return s.transform().execute().body();
}
public FrameKeyV3 target_encoder_transform(TargetEncoderTransformParametersV3 params) throws IOException {
TargetEncoderTransform s = getService(TargetEncoderTransform.class);
return s.transform(
keyToString(params.model),
keyToString(params.frame),
params.asTraining,
params.blending,
params.inflectionPoint,
params.smoothing,
params.noise
).execute().body();
}
/**
* Train a DeepLearning model.
*/
public DeepLearningV3 train_deeplearning() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainDeeplearning().execute().body();
}
public DeepLearningV3 train_deeplearning(DeepLearningParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainDeeplearning(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.activation,
params.hidden,
params.epochs,
params.trainSamplesPerIteration,
params.targetRatioCommToComp,
params.seed,
params.adaptiveRate,
params.rho,
params.epsilon,
params.rate,
params.rateAnnealing,
params.rateDecay,
params.momentumStart,
params.momentumRamp,
params.momentumStable,
params.nesterovAcceleratedGradient,
params.inputDropoutRatio,
params.hiddenDropoutRatios,
params.l1,
params.l2,
params.maxW2,
params.initialWeightDistribution,
params.initialWeightScale,
keyArrayToStringArray(params.initialWeights),
keyArrayToStringArray(params.initialBiases),
params.loss,
params.scoreInterval,
params.scoreTrainingSamples,
params.scoreValidationSamples,
params.scoreDutyCycle,
params.classificationStop,
params.regressionStop,
params.quietMode,
params.scoreValidationSampling,
params.overwriteWithBestModel,
params.autoencoder,
params.useAllFactorLevels,
params.standardize,
params.diagnostics,
params.variableImportances,
params.fastMode,
params.forceLoadBalance,
params.replicateTrainingData,
params.singleNodeMode,
params.shuffleTrainingData,
params.missingValuesHandling,
params.sparse,
params.colMajor,
params.averageActivation,
params.sparsityBeta,
params.maxCategoricalFeatures,
params.reproducible,
params.exportWeightsAndBiases,
params.miniBatchSize,
params.elasticAveraging,
params.elasticAveragingMovingRate,
params.elasticAveragingRegularization,
keyToString(params.pretrainedAutoencoder),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of DeepLearning model builder parameters.
*/
public DeepLearningV3 validate_deeplearning() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersDeeplearning().execute().body();
}
public DeepLearningV3 validate_deeplearning(DeepLearningParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersDeeplearning(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.activation,
params.hidden,
params.epochs,
params.trainSamplesPerIteration,
params.targetRatioCommToComp,
params.seed,
params.adaptiveRate,
params.rho,
params.epsilon,
params.rate,
params.rateAnnealing,
params.rateDecay,
params.momentumStart,
params.momentumRamp,
params.momentumStable,
params.nesterovAcceleratedGradient,
params.inputDropoutRatio,
params.hiddenDropoutRatios,
params.l1,
params.l2,
params.maxW2,
params.initialWeightDistribution,
params.initialWeightScale,
keyArrayToStringArray(params.initialWeights),
keyArrayToStringArray(params.initialBiases),
params.loss,
params.scoreInterval,
params.scoreTrainingSamples,
params.scoreValidationSamples,
params.scoreDutyCycle,
params.classificationStop,
params.regressionStop,
params.quietMode,
params.scoreValidationSampling,
params.overwriteWithBestModel,
params.autoencoder,
params.useAllFactorLevels,
params.standardize,
params.diagnostics,
params.variableImportances,
params.fastMode,
params.forceLoadBalance,
params.replicateTrainingData,
params.singleNodeMode,
params.shuffleTrainingData,
params.missingValuesHandling,
params.sparse,
params.colMajor,
params.averageActivation,
params.sparsityBeta,
params.maxCategoricalFeatures,
params.reproducible,
params.exportWeightsAndBiases,
params.miniBatchSize,
params.elasticAveraging,
params.elasticAveragingMovingRate,
params.elasticAveragingRegularization,
keyToString(params.pretrainedAutoencoder),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for DeepLearning model.
*/
public DeepLearningV3 grid_search_deeplearning() throws IOException {
Grid s = getService(Grid.class);
return s.trainDeeplearning().execute().body();
}
public DeepLearningV3 grid_search_deeplearning(DeepLearningParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainDeeplearning(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.activation,
params.hidden,
params.epochs,
params.trainSamplesPerIteration,
params.targetRatioCommToComp,
params.seed,
params.adaptiveRate,
params.rho,
params.epsilon,
params.rate,
params.rateAnnealing,
params.rateDecay,
params.momentumStart,
params.momentumRamp,
params.momentumStable,
params.nesterovAcceleratedGradient,
params.inputDropoutRatio,
params.hiddenDropoutRatios,
params.l1,
params.l2,
params.maxW2,
params.initialWeightDistribution,
params.initialWeightScale,
keyArrayToStringArray(params.initialWeights),
keyArrayToStringArray(params.initialBiases),
params.loss,
params.scoreInterval,
params.scoreTrainingSamples,
params.scoreValidationSamples,
params.scoreDutyCycle,
params.classificationStop,
params.regressionStop,
params.quietMode,
params.scoreValidationSampling,
params.overwriteWithBestModel,
params.autoencoder,
params.useAllFactorLevels,
params.standardize,
params.diagnostics,
params.variableImportances,
params.fastMode,
params.forceLoadBalance,
params.replicateTrainingData,
params.singleNodeMode,
params.shuffleTrainingData,
params.missingValuesHandling,
params.sparse,
params.colMajor,
params.averageActivation,
params.sparsityBeta,
params.maxCategoricalFeatures,
params.reproducible,
params.exportWeightsAndBiases,
params.miniBatchSize,
params.elasticAveraging,
params.elasticAveragingMovingRate,
params.elasticAveragingRegularization,
keyToString(params.pretrainedAutoencoder),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for DeepLearning model.
*/
public DeepLearningV3 grid_search_deeplearning_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeDeeplearning().execute().body();
}
public DeepLearningV3 grid_search_deeplearning_resume(DeepLearningParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeDeeplearning(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.activation,
params.hidden,
params.epochs,
params.trainSamplesPerIteration,
params.targetRatioCommToComp,
params.seed,
params.adaptiveRate,
params.rho,
params.epsilon,
params.rate,
params.rateAnnealing,
params.rateDecay,
params.momentumStart,
params.momentumRamp,
params.momentumStable,
params.nesterovAcceleratedGradient,
params.inputDropoutRatio,
params.hiddenDropoutRatios,
params.l1,
params.l2,
params.maxW2,
params.initialWeightDistribution,
params.initialWeightScale,
keyArrayToStringArray(params.initialWeights),
keyArrayToStringArray(params.initialBiases),
params.loss,
params.scoreInterval,
params.scoreTrainingSamples,
params.scoreValidationSamples,
params.scoreDutyCycle,
params.classificationStop,
params.regressionStop,
params.quietMode,
params.scoreValidationSampling,
params.overwriteWithBestModel,
params.autoencoder,
params.useAllFactorLevels,
params.standardize,
params.diagnostics,
params.variableImportances,
params.fastMode,
params.forceLoadBalance,
params.replicateTrainingData,
params.singleNodeMode,
params.shuffleTrainingData,
params.missingValuesHandling,
params.sparse,
params.colMajor,
params.averageActivation,
params.sparsityBeta,
params.maxCategoricalFeatures,
params.reproducible,
params.exportWeightsAndBiases,
params.miniBatchSize,
params.elasticAveraging,
params.elasticAveragingMovingRate,
params.elasticAveragingRegularization,
keyToString(params.pretrainedAutoencoder),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a GLM model.
*/
public GLMV3 train_glm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGlm().execute().body();
}
public GLMV3 train_glm(GLMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGlm(
params.seed,
params.family,
params.tweedieVariancePower,
params.dispersionLearningRate,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
params.influence,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.dispersionParameterMethod,
params.startval,
params.calcLike,
params.generateVariableInflationFactors,
params.intercept,
params.buildNullModel,
params.fixDispersionParameter,
params.initDispersionParameter,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
keyToString(params.linearConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.fixTweedieVariancePower,
params.removeCollinearColumns,
params.dispersionEpsilon,
params.tweedieEpsilon,
params.maxIterationsDispersion,
params.generateScoringHistory,
params.initOptimalGlm,
params.separateLinearBeta,
params.constraintEta0,
params.constraintTau,
params.constraintAlpha,
params.constraintBeta,
params.constraintC0,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of GLM model builder parameters.
*/
public GLMV3 validate_glm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGlm().execute().body();
}
public GLMV3 validate_glm(GLMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGlm(
params.seed,
params.family,
params.tweedieVariancePower,
params.dispersionLearningRate,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
params.influence,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.dispersionParameterMethod,
params.startval,
params.calcLike,
params.generateVariableInflationFactors,
params.intercept,
params.buildNullModel,
params.fixDispersionParameter,
params.initDispersionParameter,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
keyToString(params.linearConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.fixTweedieVariancePower,
params.removeCollinearColumns,
params.dispersionEpsilon,
params.tweedieEpsilon,
params.maxIterationsDispersion,
params.generateScoringHistory,
params.initOptimalGlm,
params.separateLinearBeta,
params.constraintEta0,
params.constraintTau,
params.constraintAlpha,
params.constraintBeta,
params.constraintC0,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for GLM model.
*/
public GLMV3 grid_search_glm() throws IOException {
Grid s = getService(Grid.class);
return s.trainGlm().execute().body();
}
public GLMV3 grid_search_glm(GLMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainGlm(
params.seed,
params.family,
params.tweedieVariancePower,
params.dispersionLearningRate,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
params.influence,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.dispersionParameterMethod,
params.startval,
params.calcLike,
params.generateVariableInflationFactors,
params.intercept,
params.buildNullModel,
params.fixDispersionParameter,
params.initDispersionParameter,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
keyToString(params.linearConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.fixTweedieVariancePower,
params.removeCollinearColumns,
params.dispersionEpsilon,
params.tweedieEpsilon,
params.maxIterationsDispersion,
params.generateScoringHistory,
params.initOptimalGlm,
params.separateLinearBeta,
params.constraintEta0,
params.constraintTau,
params.constraintAlpha,
params.constraintBeta,
params.constraintC0,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for GLM model.
*/
public GLMV3 grid_search_glm_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeGlm().execute().body();
}
public GLMV3 grid_search_glm_resume(GLMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeGlm(
params.seed,
params.family,
params.tweedieVariancePower,
params.dispersionLearningRate,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
params.influence,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.dispersionParameterMethod,
params.startval,
params.calcLike,
params.generateVariableInflationFactors,
params.intercept,
params.buildNullModel,
params.fixDispersionParameter,
params.initDispersionParameter,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
keyToString(params.linearConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.fixTweedieVariancePower,
params.removeCollinearColumns,
params.dispersionEpsilon,
params.tweedieEpsilon,
params.maxIterationsDispersion,
params.generateScoringHistory,
params.initOptimalGlm,
params.separateLinearBeta,
params.constraintEta0,
params.constraintTau,
params.constraintAlpha,
params.constraintBeta,
params.constraintC0,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a GLRM model.
*/
public GLRMV3 train_glrm(int k) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGlrm(k).execute().body();
}
public GLRMV3 train_glrm(GLRMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGlrm(
params.transform,
params.k,
params.loss,
params.multiLoss,
params.lossByCol,
params.lossByColIdx,
params.period,
params.regularizationX,
params.regularizationY,
params.gammaX,
params.gammaY,
params.maxIterations,
params.maxUpdates,
params.initStepSize,
params.minStepSize,
params.seed,
params.init,
params.svdMethod,
keyToString(params.userY),
keyToString(params.userX),
params.loadingName,
params.representationName,
params.expandUserY,
params.imputeOriginal,
params.recoverSvd,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of GLRM model builder parameters.
*/
public GLRMV3 validate_glrm(int k) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGlrm(k).execute().body();
}
public GLRMV3 validate_glrm(GLRMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGlrm(
params.transform,
params.k,
params.loss,
params.multiLoss,
params.lossByCol,
params.lossByColIdx,
params.period,
params.regularizationX,
params.regularizationY,
params.gammaX,
params.gammaY,
params.maxIterations,
params.maxUpdates,
params.initStepSize,
params.minStepSize,
params.seed,
params.init,
params.svdMethod,
keyToString(params.userY),
keyToString(params.userX),
params.loadingName,
params.representationName,
params.expandUserY,
params.imputeOriginal,
params.recoverSvd,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for GLRM model.
*/
public GLRMV3 grid_search_glrm(int k) throws IOException {
Grid s = getService(Grid.class);
return s.trainGlrm(k).execute().body();
}
public GLRMV3 grid_search_glrm(GLRMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainGlrm(
params.transform,
params.k,
params.loss,
params.multiLoss,
params.lossByCol,
params.lossByColIdx,
params.period,
params.regularizationX,
params.regularizationY,
params.gammaX,
params.gammaY,
params.maxIterations,
params.maxUpdates,
params.initStepSize,
params.minStepSize,
params.seed,
params.init,
params.svdMethod,
keyToString(params.userY),
keyToString(params.userX),
params.loadingName,
params.representationName,
params.expandUserY,
params.imputeOriginal,
params.recoverSvd,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for GLRM model.
*/
public GLRMV3 grid_search_glrm_resume(int k) throws IOException {
Grid s = getService(Grid.class);
return s.resumeGlrm(k).execute().body();
}
public GLRMV3 grid_search_glrm_resume(GLRMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeGlrm(
params.transform,
params.k,
params.loss,
params.multiLoss,
params.lossByCol,
params.lossByColIdx,
params.period,
params.regularizationX,
params.regularizationY,
params.gammaX,
params.gammaY,
params.maxIterations,
params.maxUpdates,
params.initStepSize,
params.minStepSize,
params.seed,
params.init,
params.svdMethod,
keyToString(params.userY),
keyToString(params.userX),
params.loadingName,
params.representationName,
params.expandUserY,
params.imputeOriginal,
params.recoverSvd,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a KMeans model.
*/
public KMeansV3 train_kmeans() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainKmeans().execute().body();
}
public KMeansV3 train_kmeans(KMeansParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainKmeans(
keyToString(params.userPoints),
params.maxIterations,
params.standardize,
params.seed,
params.init,
params.estimateK,
params.clusterSizeConstraints,
params.k,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of KMeans model builder parameters.
*/
public KMeansV3 validate_kmeans() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersKmeans().execute().body();
}
public KMeansV3 validate_kmeans(KMeansParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersKmeans(
keyToString(params.userPoints),
params.maxIterations,
params.standardize,
params.seed,
params.init,
params.estimateK,
params.clusterSizeConstraints,
params.k,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for KMeans model.
*/
public KMeansV3 grid_search_kmeans() throws IOException {
Grid s = getService(Grid.class);
return s.trainKmeans().execute().body();
}
public KMeansV3 grid_search_kmeans(KMeansParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainKmeans(
keyToString(params.userPoints),
params.maxIterations,
params.standardize,
params.seed,
params.init,
params.estimateK,
params.clusterSizeConstraints,
params.k,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for KMeans model.
*/
public KMeansV3 grid_search_kmeans_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeKmeans().execute().body();
}
public KMeansV3 grid_search_kmeans_resume(KMeansParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeKmeans(
keyToString(params.userPoints),
params.maxIterations,
params.standardize,
params.seed,
params.init,
params.estimateK,
params.clusterSizeConstraints,
params.k,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a NaiveBayes model.
*/
public NaiveBayesV3 train_naivebayes() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainNaivebayes().execute().body();
}
public NaiveBayesV3 train_naivebayes(NaiveBayesParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainNaivebayes(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.laplace,
params.minSdev,
params.epsSdev,
params.minProb,
params.epsProb,
params.computeMetrics,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of NaiveBayes model builder parameters.
*/
public NaiveBayesV3 validate_naivebayes() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersNaivebayes().execute().body();
}
public NaiveBayesV3 validate_naivebayes(NaiveBayesParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersNaivebayes(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.laplace,
params.minSdev,
params.epsSdev,
params.minProb,
params.epsProb,
params.computeMetrics,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for NaiveBayes model.
*/
public NaiveBayesV3 grid_search_naivebayes() throws IOException {
Grid s = getService(Grid.class);
return s.trainNaivebayes().execute().body();
}
public NaiveBayesV3 grid_search_naivebayes(NaiveBayesParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainNaivebayes(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.laplace,
params.minSdev,
params.epsSdev,
params.minProb,
params.epsProb,
params.computeMetrics,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for NaiveBayes model.
*/
public NaiveBayesV3 grid_search_naivebayes_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeNaivebayes().execute().body();
}
public NaiveBayesV3 grid_search_naivebayes_resume(NaiveBayesParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeNaivebayes(
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.laplace,
params.minSdev,
params.epsSdev,
params.minProb,
params.epsProb,
params.computeMetrics,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a PCA model.
*/
public PCAV3 train_pca(int k) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainPca(k).execute().body();
}
public PCAV3 train_pca(PCAParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainPca(
params.transform,
params.pcaMethod,
params.pcaImpl,
params.k,
params.maxIterations,
params.seed,
params.useAllFactorLevels,
params.computeMetrics,
params.imputeMissing,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of PCA model builder parameters.
*/
public PCAV3 validate_pca(int k) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersPca(k).execute().body();
}
public PCAV3 validate_pca(PCAParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersPca(
params.transform,
params.pcaMethod,
params.pcaImpl,
params.k,
params.maxIterations,
params.seed,
params.useAllFactorLevels,
params.computeMetrics,
params.imputeMissing,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for PCA model.
*/
public PCAV3 grid_search_pca(int k) throws IOException {
Grid s = getService(Grid.class);
return s.trainPca(k).execute().body();
}
public PCAV3 grid_search_pca(PCAParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainPca(
params.transform,
params.pcaMethod,
params.pcaImpl,
params.k,
params.maxIterations,
params.seed,
params.useAllFactorLevels,
params.computeMetrics,
params.imputeMissing,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for PCA model.
*/
public PCAV3 grid_search_pca_resume(int k) throws IOException {
Grid s = getService(Grid.class);
return s.resumePca(k).execute().body();
}
public PCAV3 grid_search_pca_resume(PCAParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumePca(
params.transform,
params.pcaMethod,
params.pcaImpl,
params.k,
params.maxIterations,
params.seed,
params.useAllFactorLevels,
params.computeMetrics,
params.imputeMissing,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a SVD model.
*/
public SVDV99 train_svd() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainSvd().execute().body();
}
public SVDV99 train_svd(SVDParametersV99 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainSvd(
params.transform,
params.svdMethod,
params.nv,
params.maxIterations,
params.seed,
params.keepU,
params.uName,
params.useAllFactorLevels,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of SVD model builder parameters.
*/
public SVDV99 validate_svd() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersSvd().execute().body();
}
public SVDV99 validate_svd(SVDParametersV99 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersSvd(
params.transform,
params.svdMethod,
params.nv,
params.maxIterations,
params.seed,
params.keepU,
params.uName,
params.useAllFactorLevels,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for SVD model.
*/
public SVDV99 grid_search_svd() throws IOException {
Grid s = getService(Grid.class);
return s.trainSvd().execute().body();
}
public SVDV99 grid_search_svd(SVDParametersV99 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainSvd(
params.transform,
params.svdMethod,
params.nv,
params.maxIterations,
params.seed,
params.keepU,
params.uName,
params.useAllFactorLevels,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for SVD model.
*/
public SVDV99 grid_search_svd_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeSvd().execute().body();
}
public SVDV99 grid_search_svd_resume(SVDParametersV99 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeSvd(
params.transform,
params.svdMethod,
params.nv,
params.maxIterations,
params.seed,
params.keepU,
params.uName,
params.useAllFactorLevels,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a DRF model.
*/
public DRFV3 train_drf() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainDrf().execute().body();
}
public DRFV3 train_drf(DRFParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainDrf(
params.mtries,
params.binomialDoubleTrees,
params.sampleRate,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of DRF model builder parameters.
*/
public DRFV3 validate_drf() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersDrf().execute().body();
}
public DRFV3 validate_drf(DRFParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersDrf(
params.mtries,
params.binomialDoubleTrees,
params.sampleRate,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for DRF model.
*/
public DRFV3 grid_search_drf() throws IOException {
Grid s = getService(Grid.class);
return s.trainDrf().execute().body();
}
public DRFV3 grid_search_drf(DRFParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainDrf(
params.mtries,
params.binomialDoubleTrees,
params.sampleRate,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for DRF model.
*/
public DRFV3 grid_search_drf_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeDrf().execute().body();
}
public DRFV3 grid_search_drf_resume(DRFParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeDrf(
params.mtries,
params.binomialDoubleTrees,
params.sampleRate,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a GBM model.
*/
public GBMV3 train_gbm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGbm().execute().body();
}
public GBMV3 train_gbm(GBMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGbm(
params.learnRate,
params.learnRateAnnealing,
params.sampleRate,
params.colSampleRate,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.predNoiseBandwidth,
params.interactionConstraints,
params.autoRebalance,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of GBM model builder parameters.
*/
public GBMV3 validate_gbm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGbm().execute().body();
}
public GBMV3 validate_gbm(GBMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGbm(
params.learnRate,
params.learnRateAnnealing,
params.sampleRate,
params.colSampleRate,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.predNoiseBandwidth,
params.interactionConstraints,
params.autoRebalance,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for GBM model.
*/
public GBMV3 grid_search_gbm() throws IOException {
Grid s = getService(Grid.class);
return s.trainGbm().execute().body();
}
public GBMV3 grid_search_gbm(GBMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainGbm(
params.learnRate,
params.learnRateAnnealing,
params.sampleRate,
params.colSampleRate,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.predNoiseBandwidth,
params.interactionConstraints,
params.autoRebalance,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for GBM model.
*/
public GBMV3 grid_search_gbm_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeGbm().execute().body();
}
public GBMV3 grid_search_gbm_resume(GBMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeGbm(
params.learnRate,
params.learnRateAnnealing,
params.sampleRate,
params.colSampleRate,
params.monotoneConstraints,
params.maxAbsLeafnodePred,
params.predNoiseBandwidth,
params.interactionConstraints,
params.autoRebalance,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a IsolationForest model.
*/
public IsolationForestV3 train_isolationforest() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainIsolationforest().execute().body();
}
public IsolationForestV3 train_isolationforest(IsolationForestParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainIsolationforest(
params.sampleSize,
params.sampleRate,
params.mtries,
params.contamination,
colToString(params.validationResponseColumn),
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of IsolationForest model builder parameters.
*/
public IsolationForestV3 validate_isolationforest() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersIsolationforest().execute().body();
}
public IsolationForestV3 validate_isolationforest(IsolationForestParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersIsolationforest(
params.sampleSize,
params.sampleRate,
params.mtries,
params.contamination,
colToString(params.validationResponseColumn),
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for IsolationForest model.
*/
public IsolationForestV3 grid_search_isolationforest() throws IOException {
Grid s = getService(Grid.class);
return s.trainIsolationforest().execute().body();
}
public IsolationForestV3 grid_search_isolationforest(IsolationForestParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainIsolationforest(
params.sampleSize,
params.sampleRate,
params.mtries,
params.contamination,
colToString(params.validationResponseColumn),
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for IsolationForest model.
*/
public IsolationForestV3 grid_search_isolationforest_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeIsolationforest().execute().body();
}
public IsolationForestV3 grid_search_isolationforest_resume(IsolationForestParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeIsolationforest(
params.sampleSize,
params.sampleRate,
params.mtries,
params.contamination,
colToString(params.validationResponseColumn),
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a ExtendedIsolationForest model.
*/
public ExtendedIsolationForestV3 train_extendedisolationforest() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainExtendedisolationforest().execute().body();
}
public ExtendedIsolationForestV3 train_extendedisolationforest(ExtendedIsolationForestParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainExtendedisolationforest(
params.ntrees,
params.sampleSize,
params.extensionLevel,
params.seed,
params.scoreTreeInterval,
params.disableTrainingMetrics,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of ExtendedIsolationForest model builder parameters.
*/
public ExtendedIsolationForestV3 validate_extendedisolationforest() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersExtendedisolationforest().execute().body();
}
public ExtendedIsolationForestV3 validate_extendedisolationforest(ExtendedIsolationForestParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersExtendedisolationforest(
params.ntrees,
params.sampleSize,
params.extensionLevel,
params.seed,
params.scoreTreeInterval,
params.disableTrainingMetrics,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for ExtendedIsolationForest model.
*/
public ExtendedIsolationForestV3 grid_search_extendedisolationforest() throws IOException {
Grid s = getService(Grid.class);
return s.trainExtendedisolationforest().execute().body();
}
public ExtendedIsolationForestV3 grid_search_extendedisolationforest(ExtendedIsolationForestParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainExtendedisolationforest(
params.ntrees,
params.sampleSize,
params.extensionLevel,
params.seed,
params.scoreTreeInterval,
params.disableTrainingMetrics,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for ExtendedIsolationForest model.
*/
public ExtendedIsolationForestV3 grid_search_extendedisolationforest_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeExtendedisolationforest().execute().body();
}
public ExtendedIsolationForestV3 grid_search_extendedisolationforest_resume(ExtendedIsolationForestParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeExtendedisolationforest(
params.ntrees,
params.sampleSize,
params.extensionLevel,
params.seed,
params.scoreTreeInterval,
params.disableTrainingMetrics,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a Aggregator model.
*/
public AggregatorV99 train_aggregator() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainAggregator().execute().body();
}
public AggregatorV99 train_aggregator(AggregatorParametersV99 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainAggregator(
params.transform,
params.pcaMethod,
params.k,
params.maxIterations,
params.targetNumExemplars,
params.relTolNumExemplars,
params.seed,
params.useAllFactorLevels,
params.saveMappingFrame,
params.numIterationWithoutNewExemplar,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of Aggregator model builder parameters.
*/
public AggregatorV99 validate_aggregator() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersAggregator().execute().body();
}
public AggregatorV99 validate_aggregator(AggregatorParametersV99 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersAggregator(
params.transform,
params.pcaMethod,
params.k,
params.maxIterations,
params.targetNumExemplars,
params.relTolNumExemplars,
params.seed,
params.useAllFactorLevels,
params.saveMappingFrame,
params.numIterationWithoutNewExemplar,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for Aggregator model.
*/
public AggregatorV99 grid_search_aggregator() throws IOException {
Grid s = getService(Grid.class);
return s.trainAggregator().execute().body();
}
public AggregatorV99 grid_search_aggregator(AggregatorParametersV99 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainAggregator(
params.transform,
params.pcaMethod,
params.k,
params.maxIterations,
params.targetNumExemplars,
params.relTolNumExemplars,
params.seed,
params.useAllFactorLevels,
params.saveMappingFrame,
params.numIterationWithoutNewExemplar,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for Aggregator model.
*/
public AggregatorV99 grid_search_aggregator_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeAggregator().execute().body();
}
public AggregatorV99 grid_search_aggregator_resume(AggregatorParametersV99 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeAggregator(
params.transform,
params.pcaMethod,
params.k,
params.maxIterations,
params.targetNumExemplars,
params.relTolNumExemplars,
params.seed,
params.useAllFactorLevels,
params.saveMappingFrame,
params.numIterationWithoutNewExemplar,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a Word2Vec model.
*/
public Word2VecV3 train_word2vec() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainWord2vec().execute().body();
}
public Word2VecV3 train_word2vec(Word2VecParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainWord2vec(
params.vecSize,
params.windowSize,
params.sentSampleRate,
params.normModel,
params.epochs,
params.minWordFreq,
params.initLearningRate,
params.wordModel,
keyToString(params.preTrained),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of Word2Vec model builder parameters.
*/
public Word2VecV3 validate_word2vec() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersWord2vec().execute().body();
}
public Word2VecV3 validate_word2vec(Word2VecParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersWord2vec(
params.vecSize,
params.windowSize,
params.sentSampleRate,
params.normModel,
params.epochs,
params.minWordFreq,
params.initLearningRate,
params.wordModel,
keyToString(params.preTrained),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for Word2Vec model.
*/
public Word2VecV3 grid_search_word2vec() throws IOException {
Grid s = getService(Grid.class);
return s.trainWord2vec().execute().body();
}
public Word2VecV3 grid_search_word2vec(Word2VecParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainWord2vec(
params.vecSize,
params.windowSize,
params.sentSampleRate,
params.normModel,
params.epochs,
params.minWordFreq,
params.initLearningRate,
params.wordModel,
keyToString(params.preTrained),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for Word2Vec model.
*/
public Word2VecV3 grid_search_word2vec_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeWord2vec().execute().body();
}
public Word2VecV3 grid_search_word2vec_resume(Word2VecParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeWord2vec(
params.vecSize,
params.windowSize,
params.sentSampleRate,
params.normModel,
params.epochs,
params.minWordFreq,
params.initLearningRate,
params.wordModel,
keyToString(params.preTrained),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a StackedEnsemble model.
*/
public StackedEnsembleV99 train_stackedensemble(KeyV3[] baseModels) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainStackedensemble(keyArrayToStringArray(baseModels)).execute().body();
}
public StackedEnsembleV99 train_stackedensemble(StackedEnsembleParametersV99 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainStackedensemble(
keyArrayToStringArray(params.baseModels),
params.metalearnerAlgorithm,
params.metalearnerNfolds,
params.metalearnerFoldAssignment,
colToString(params.metalearnerFoldColumn),
params.metalearnerTransform,
params.keepLeveloneFrame,
params.metalearnerParams,
keyToString(params.blendingFrame),
params.seed,
params.scoreTrainingSamples,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of StackedEnsemble model builder parameters.
*/
public StackedEnsembleV99 validate_stackedensemble(KeyV3[] baseModels) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersStackedensemble(keyArrayToStringArray(baseModels)).execute().body();
}
public StackedEnsembleV99 validate_stackedensemble(StackedEnsembleParametersV99 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersStackedensemble(
keyArrayToStringArray(params.baseModels),
params.metalearnerAlgorithm,
params.metalearnerNfolds,
params.metalearnerFoldAssignment,
colToString(params.metalearnerFoldColumn),
params.metalearnerTransform,
params.keepLeveloneFrame,
params.metalearnerParams,
keyToString(params.blendingFrame),
params.seed,
params.scoreTrainingSamples,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for StackedEnsemble model.
*/
public StackedEnsembleV99 grid_search_stackedensemble(KeyV3[] baseModels) throws IOException {
Grid s = getService(Grid.class);
return s.trainStackedensemble(keyArrayToStringArray(baseModels)).execute().body();
}
public StackedEnsembleV99 grid_search_stackedensemble(StackedEnsembleParametersV99 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainStackedensemble(
keyArrayToStringArray(params.baseModels),
params.metalearnerAlgorithm,
params.metalearnerNfolds,
params.metalearnerFoldAssignment,
colToString(params.metalearnerFoldColumn),
params.metalearnerTransform,
params.keepLeveloneFrame,
params.metalearnerParams,
keyToString(params.blendingFrame),
params.seed,
params.scoreTrainingSamples,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for StackedEnsemble model.
*/
public StackedEnsembleV99 grid_search_stackedensemble_resume(KeyV3[] baseModels) throws IOException {
Grid s = getService(Grid.class);
return s.resumeStackedensemble(keyArrayToStringArray(baseModels)).execute().body();
}
public StackedEnsembleV99 grid_search_stackedensemble_resume(StackedEnsembleParametersV99 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeStackedensemble(
keyArrayToStringArray(params.baseModels),
params.metalearnerAlgorithm,
params.metalearnerNfolds,
params.metalearnerFoldAssignment,
colToString(params.metalearnerFoldColumn),
params.metalearnerTransform,
params.keepLeveloneFrame,
params.metalearnerParams,
keyToString(params.blendingFrame),
params.seed,
params.scoreTrainingSamples,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a CoxPH model.
*/
public CoxPHV3 train_coxph() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainCoxph().execute().body();
}
public CoxPHV3 train_coxph(CoxPHParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainCoxph(
colToString(params.startColumn),
colToString(params.stopColumn),
params.stratifyBy,
params.ties,
params.init,
params.lreMin,
params.maxIterations,
params.interactionsOnly,
params.interactions,
params.interactionPairs,
params.useAllFactorLevels,
params.singleNodeMode,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of CoxPH model builder parameters.
*/
public CoxPHV3 validate_coxph() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersCoxph().execute().body();
}
public CoxPHV3 validate_coxph(CoxPHParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersCoxph(
colToString(params.startColumn),
colToString(params.stopColumn),
params.stratifyBy,
params.ties,
params.init,
params.lreMin,
params.maxIterations,
params.interactionsOnly,
params.interactions,
params.interactionPairs,
params.useAllFactorLevels,
params.singleNodeMode,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for CoxPH model.
*/
public CoxPHV3 grid_search_coxph() throws IOException {
Grid s = getService(Grid.class);
return s.trainCoxph().execute().body();
}
public CoxPHV3 grid_search_coxph(CoxPHParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainCoxph(
colToString(params.startColumn),
colToString(params.stopColumn),
params.stratifyBy,
params.ties,
params.init,
params.lreMin,
params.maxIterations,
params.interactionsOnly,
params.interactions,
params.interactionPairs,
params.useAllFactorLevels,
params.singleNodeMode,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for CoxPH model.
*/
public CoxPHV3 grid_search_coxph_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeCoxph().execute().body();
}
public CoxPHV3 grid_search_coxph_resume(CoxPHParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeCoxph(
colToString(params.startColumn),
colToString(params.stopColumn),
params.stratifyBy,
params.ties,
params.init,
params.lreMin,
params.maxIterations,
params.interactionsOnly,
params.interactions,
params.interactionPairs,
params.useAllFactorLevels,
params.singleNodeMode,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a Generic model.
*/
public GenericV3 train_generic() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGeneric().execute().body();
}
public GenericV3 train_generic(GenericParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGeneric(
params.path,
keyToString(params.modelKey),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of Generic model builder parameters.
*/
public GenericV3 validate_generic() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGeneric().execute().body();
}
public GenericV3 validate_generic(GenericParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGeneric(
params.path,
keyToString(params.modelKey),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for Generic model.
*/
public GenericV3 grid_search_generic() throws IOException {
Grid s = getService(Grid.class);
return s.trainGeneric().execute().body();
}
public GenericV3 grid_search_generic(GenericParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainGeneric(
params.path,
keyToString(params.modelKey),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for Generic model.
*/
public GenericV3 grid_search_generic_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeGeneric().execute().body();
}
public GenericV3 grid_search_generic_resume(GenericParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeGeneric(
params.path,
keyToString(params.modelKey),
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a GAM model.
*/
public GAMV3 train_gam(String[][] gamColumns) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGam(gamColumns).execute().body();
}
public GAMV3 train_gam(GAMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainGam(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.startval,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.standardize,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.intercept,
params.prior,
params.coldStart,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.storeKnotLocations,
params.numKnots,
params.splineOrders,
params.splinesNonNegative,
params.gamColumns,
params.scale,
params.bs,
params.keepGamCols,
params.standardizeTpGamCols,
params.scaleTpPenaltyMat,
params.knotIds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of GAM model builder parameters.
*/
public GAMV3 validate_gam(String[][] gamColumns) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGam(gamColumns).execute().body();
}
public GAMV3 validate_gam(GAMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersGam(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.startval,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.standardize,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.intercept,
params.prior,
params.coldStart,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.storeKnotLocations,
params.numKnots,
params.splineOrders,
params.splinesNonNegative,
params.gamColumns,
params.scale,
params.bs,
params.keepGamCols,
params.standardizeTpGamCols,
params.scaleTpPenaltyMat,
params.knotIds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for GAM model.
*/
public GAMV3 grid_search_gam(String[][] gamColumns) throws IOException {
Grid s = getService(Grid.class);
return s.trainGam(gamColumns).execute().body();
}
public GAMV3 grid_search_gam(GAMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainGam(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.startval,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.standardize,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.intercept,
params.prior,
params.coldStart,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.storeKnotLocations,
params.numKnots,
params.splineOrders,
params.splinesNonNegative,
params.gamColumns,
params.scale,
params.bs,
params.keepGamCols,
params.standardizeTpGamCols,
params.scaleTpPenaltyMat,
params.knotIds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for GAM model.
*/
public GAMV3 grid_search_gam_resume(String[][] gamColumns) throws IOException {
Grid s = getService(Grid.class);
return s.resumeGam(gamColumns).execute().body();
}
public GAMV3 grid_search_gam_resume(GAMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeGam(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.startval,
params.lambdaSearch,
params.earlyStopping,
params.nlambdas,
params.standardize,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.intercept,
params.prior,
params.coldStart,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.interactions,
params.interactionPairs,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.storeKnotLocations,
params.numKnots,
params.splineOrders,
params.splinesNonNegative,
params.gamColumns,
params.scale,
params.bs,
params.keepGamCols,
params.standardizeTpGamCols,
params.scaleTpPenaltyMat,
params.knotIds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a ANOVAGLM model.
*/
public ANOVAGLMV3 train_anovaglm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainAnovaglm().execute().body();
}
public ANOVAGLMV3 train_anovaglm(ANOVAGLMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainAnovaglm(
params.seed,
params.standardize,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.alpha,
params.lambda,
params.lambdaSearch,
params.solver,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.computePValues,
params.maxIterations,
params.link,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.highestInteractionTerm,
params.type,
params.earlyStopping,
params.saveTransformedFramekeys,
params.nparallelism,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of ANOVAGLM model builder parameters.
*/
public ANOVAGLMV3 validate_anovaglm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersAnovaglm().execute().body();
}
public ANOVAGLMV3 validate_anovaglm(ANOVAGLMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersAnovaglm(
params.seed,
params.standardize,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.alpha,
params.lambda,
params.lambdaSearch,
params.solver,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.computePValues,
params.maxIterations,
params.link,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.highestInteractionTerm,
params.type,
params.earlyStopping,
params.saveTransformedFramekeys,
params.nparallelism,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for ANOVAGLM model.
*/
public ANOVAGLMV3 grid_search_anovaglm() throws IOException {
Grid s = getService(Grid.class);
return s.trainAnovaglm().execute().body();
}
public ANOVAGLMV3 grid_search_anovaglm(ANOVAGLMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainAnovaglm(
params.seed,
params.standardize,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.alpha,
params.lambda,
params.lambdaSearch,
params.solver,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.computePValues,
params.maxIterations,
params.link,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.highestInteractionTerm,
params.type,
params.earlyStopping,
params.saveTransformedFramekeys,
params.nparallelism,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for ANOVAGLM model.
*/
public ANOVAGLMV3 grid_search_anovaglm_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeAnovaglm().execute().body();
}
public ANOVAGLMV3 grid_search_anovaglm_resume(ANOVAGLMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeAnovaglm(
params.seed,
params.standardize,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.alpha,
params.lambda,
params.lambdaSearch,
params.solver,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.computePValues,
params.maxIterations,
params.link,
params.prior,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.highestInteractionTerm,
params.type,
params.earlyStopping,
params.saveTransformedFramekeys,
params.nparallelism,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a PSVM model.
*/
public PSVMV3 train_psvm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainPsvm().execute().body();
}
public PSVMV3 train_psvm(PSVMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainPsvm(
params.hyperParam,
params.kernelType,
params.gamma,
params.rankRatio,
params.positiveWeight,
params.negativeWeight,
params.disableTrainingMetrics,
params.svThreshold,
params.maxIterations,
params.factThreshold,
params.feasibleThreshold,
params.surrogateGapThreshold,
params.muFactor,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of PSVM model builder parameters.
*/
public PSVMV3 validate_psvm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersPsvm().execute().body();
}
public PSVMV3 validate_psvm(PSVMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersPsvm(
params.hyperParam,
params.kernelType,
params.gamma,
params.rankRatio,
params.positiveWeight,
params.negativeWeight,
params.disableTrainingMetrics,
params.svThreshold,
params.maxIterations,
params.factThreshold,
params.feasibleThreshold,
params.surrogateGapThreshold,
params.muFactor,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for PSVM model.
*/
public PSVMV3 grid_search_psvm() throws IOException {
Grid s = getService(Grid.class);
return s.trainPsvm().execute().body();
}
public PSVMV3 grid_search_psvm(PSVMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainPsvm(
params.hyperParam,
params.kernelType,
params.gamma,
params.rankRatio,
params.positiveWeight,
params.negativeWeight,
params.disableTrainingMetrics,
params.svThreshold,
params.maxIterations,
params.factThreshold,
params.feasibleThreshold,
params.surrogateGapThreshold,
params.muFactor,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for PSVM model.
*/
public PSVMV3 grid_search_psvm_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumePsvm().execute().body();
}
public PSVMV3 grid_search_psvm_resume(PSVMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumePsvm(
params.hyperParam,
params.kernelType,
params.gamma,
params.rankRatio,
params.positiveWeight,
params.negativeWeight,
params.disableTrainingMetrics,
params.svThreshold,
params.maxIterations,
params.factThreshold,
params.feasibleThreshold,
params.surrogateGapThreshold,
params.muFactor,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a RuleFit model.
*/
public RuleFitV3 train_rulefit() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainRulefit().execute().body();
}
public RuleFitV3 train_rulefit(RuleFitParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainRulefit(
params.seed,
params.algorithm,
params.minRuleLength,
params.maxRuleLength,
params.maxNumRules,
params.modelType,
params.ruleGenerationNtrees,
params.removeDuplicates,
params.lambda,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of RuleFit model builder parameters.
*/
public RuleFitV3 validate_rulefit() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersRulefit().execute().body();
}
public RuleFitV3 validate_rulefit(RuleFitParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersRulefit(
params.seed,
params.algorithm,
params.minRuleLength,
params.maxRuleLength,
params.maxNumRules,
params.modelType,
params.ruleGenerationNtrees,
params.removeDuplicates,
params.lambda,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for RuleFit model.
*/
public RuleFitV3 grid_search_rulefit() throws IOException {
Grid s = getService(Grid.class);
return s.trainRulefit().execute().body();
}
public RuleFitV3 grid_search_rulefit(RuleFitParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainRulefit(
params.seed,
params.algorithm,
params.minRuleLength,
params.maxRuleLength,
params.maxNumRules,
params.modelType,
params.ruleGenerationNtrees,
params.removeDuplicates,
params.lambda,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for RuleFit model.
*/
public RuleFitV3 grid_search_rulefit_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeRulefit().execute().body();
}
public RuleFitV3 grid_search_rulefit_resume(RuleFitParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeRulefit(
params.seed,
params.algorithm,
params.minRuleLength,
params.maxRuleLength,
params.maxNumRules,
params.modelType,
params.ruleGenerationNtrees,
params.removeDuplicates,
params.lambda,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a UpliftDRF model.
*/
public UpliftDRFV3 train_upliftdrf(String treatmentColumn) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainUpliftdrf(treatmentColumn).execute().body();
}
public UpliftDRFV3 train_upliftdrf(UpliftDRFParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainUpliftdrf(
params.mtries,
params.sampleRate,
params.treatmentColumn,
params.upliftMetric,
params.auucType,
params.auucNbins,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of UpliftDRF model builder parameters.
*/
public UpliftDRFV3 validate_upliftdrf(String treatmentColumn) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersUpliftdrf(treatmentColumn).execute().body();
}
public UpliftDRFV3 validate_upliftdrf(UpliftDRFParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersUpliftdrf(
params.mtries,
params.sampleRate,
params.treatmentColumn,
params.upliftMetric,
params.auucType,
params.auucNbins,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for UpliftDRF model.
*/
public UpliftDRFV3 grid_search_upliftdrf(String treatmentColumn) throws IOException {
Grid s = getService(Grid.class);
return s.trainUpliftdrf(treatmentColumn).execute().body();
}
public UpliftDRFV3 grid_search_upliftdrf(UpliftDRFParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainUpliftdrf(
params.mtries,
params.sampleRate,
params.treatmentColumn,
params.upliftMetric,
params.auucType,
params.auucNbins,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for UpliftDRF model.
*/
public UpliftDRFV3 grid_search_upliftdrf_resume(String treatmentColumn) throws IOException {
Grid s = getService(Grid.class);
return s.resumeUpliftdrf(treatmentColumn).execute().body();
}
public UpliftDRFV3 grid_search_upliftdrf_resume(UpliftDRFParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeUpliftdrf(
params.mtries,
params.sampleRate,
params.treatmentColumn,
params.upliftMetric,
params.auucType,
params.auucNbins,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.ntrees,
params.maxDepth,
params.minRows,
params.nbins,
params.nbinsTopLevel,
params.nbinsCats,
params.r2Stopping,
params.seed,
params.buildTreeOneNode,
params.sampleRatePerClass,
params.colSampleRatePerTree,
params.colSampleRateChangePerLevel,
params.scoreTreeInterval,
params.minSplitImprovement,
params.histogramType,
params.calibrateModel,
keyToString(params.calibrationFrame),
params.calibrationMethod,
params.checkConstantResponse,
params.inTrainingCheckpointsDir,
params.inTrainingCheckpointsTreeInterval,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a ModelSelection model.
*/
public ModelSelectionV3 train_modelselection() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainModelselection().execute().body();
}
public ModelSelectionV3 train_modelselection(ModelSelectionParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainModelselection(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.multinodeMode,
params.buildGlmModel,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.startval,
params.calcLike,
params.mode,
params.intercept,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.maxPredictorNumber,
params.minPredictorNumber,
params.nparallelism,
params.pValuesThreshold,
params.influence,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of ModelSelection model builder parameters.
*/
public ModelSelectionV3 validate_modelselection() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersModelselection().execute().body();
}
public ModelSelectionV3 validate_modelselection(ModelSelectionParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersModelselection(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.multinodeMode,
params.buildGlmModel,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.startval,
params.calcLike,
params.mode,
params.intercept,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.maxPredictorNumber,
params.minPredictorNumber,
params.nparallelism,
params.pValuesThreshold,
params.influence,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for ModelSelection model.
*/
public ModelSelectionV3 grid_search_modelselection() throws IOException {
Grid s = getService(Grid.class);
return s.trainModelselection().execute().body();
}
public ModelSelectionV3 grid_search_modelselection(ModelSelectionParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainModelselection(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.multinodeMode,
params.buildGlmModel,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.startval,
params.calcLike,
params.mode,
params.intercept,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.maxPredictorNumber,
params.minPredictorNumber,
params.nparallelism,
params.pValuesThreshold,
params.influence,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for ModelSelection model.
*/
public ModelSelectionV3 grid_search_modelselection_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeModelselection().execute().body();
}
public ModelSelectionV3 grid_search_modelselection_resume(ModelSelectionParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeModelselection(
params.seed,
params.family,
params.tweedieVariancePower,
params.tweedieLinkPower,
params.theta,
params.solver,
params.alpha,
params.lambda,
params.lambdaSearch,
params.multinodeMode,
params.buildGlmModel,
params.earlyStopping,
params.nlambdas,
params.scoreIterationInterval,
params.standardize,
params.coldStart,
params.missingValuesHandling,
keyToString(params.plugValues),
params.nonNegative,
params.maxIterations,
params.betaEpsilon,
params.objectiveEpsilon,
params.gradientEpsilon,
params.objReg,
params.link,
params.startval,
params.calcLike,
params.mode,
params.intercept,
params.prior,
params.lambdaMinRatio,
keyToString(params.betaConstraints),
params.maxActivePredictors,
params.balanceClasses,
params.classSamplingFactors,
params.maxAfterBalanceSize,
params.maxConfusionMatrixSize,
params.computePValues,
params.removeCollinearColumns,
params.maxPredictorNumber,
params.minPredictorNumber,
params.nparallelism,
params.pValuesThreshold,
params.influence,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a IsotonicRegression model.
*/
public IsotonicRegressionV3 train_isotonicregression() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainIsotonicregression().execute().body();
}
public IsotonicRegressionV3 train_isotonicregression(IsotonicRegressionParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainIsotonicregression(
params.outOfBounds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of IsotonicRegression model builder parameters.
*/
public IsotonicRegressionV3 validate_isotonicregression() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersIsotonicregression().execute().body();
}
public IsotonicRegressionV3 validate_isotonicregression(IsotonicRegressionParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersIsotonicregression(
params.outOfBounds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for IsotonicRegression model.
*/
public IsotonicRegressionV3 grid_search_isotonicregression() throws IOException {
Grid s = getService(Grid.class);
return s.trainIsotonicregression().execute().body();
}
public IsotonicRegressionV3 grid_search_isotonicregression(IsotonicRegressionParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainIsotonicregression(
params.outOfBounds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for IsotonicRegression model.
*/
public IsotonicRegressionV3 grid_search_isotonicregression_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeIsotonicregression().execute().body();
}
public IsotonicRegressionV3 grid_search_isotonicregression_resume(IsotonicRegressionParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeIsotonicregression(
params.outOfBounds,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a DT model.
*/
public DTV3 train_dt() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainDt().execute().body();
}
public DTV3 train_dt(DTParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainDt(
params.seed,
params.maxDepth,
params.minRows,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of DT model builder parameters.
*/
public DTV3 validate_dt() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersDt().execute().body();
}
public DTV3 validate_dt(DTParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersDt(
params.seed,
params.maxDepth,
params.minRows,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for DT model.
*/
public DTV3 grid_search_dt() throws IOException {
Grid s = getService(Grid.class);
return s.trainDt().execute().body();
}
public DTV3 grid_search_dt(DTParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainDt(
params.seed,
params.maxDepth,
params.minRows,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for DT model.
*/
public DTV3 grid_search_dt_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeDt().execute().body();
}
public DTV3 grid_search_dt_resume(DTParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeDt(
params.seed,
params.maxDepth,
params.minRows,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a HGLM model.
*/
public HGLMV3 train_hglm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainHglm().execute().body();
}
public HGLMV3 train_hglm(HGLMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainHglm(
params.scoreIterationInterval,
params.seed,
params.missingValuesHandling,
keyToString(params.plugValues),
params.family,
params.randFamily,
params.maxIterations,
params.initialFixedEffects,
keyToString(params.initialRandomEffects),
keyToString(params.initialTMatrix),
params.tauUVarInit,
params.tauEVarInit,
params.randomColumns,
params.method,
params.emEpsilon,
params.randomIntercept,
params.groupColumn,
params.genSynData,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of HGLM model builder parameters.
*/
public HGLMV3 validate_hglm() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersHglm().execute().body();
}
public HGLMV3 validate_hglm(HGLMParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersHglm(
params.scoreIterationInterval,
params.seed,
params.missingValuesHandling,
keyToString(params.plugValues),
params.family,
params.randFamily,
params.maxIterations,
params.initialFixedEffects,
keyToString(params.initialRandomEffects),
keyToString(params.initialTMatrix),
params.tauUVarInit,
params.tauEVarInit,
params.randomColumns,
params.method,
params.emEpsilon,
params.randomIntercept,
params.groupColumn,
params.genSynData,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for HGLM model.
*/
public HGLMV3 grid_search_hglm() throws IOException {
Grid s = getService(Grid.class);
return s.trainHglm().execute().body();
}
public HGLMV3 grid_search_hglm(HGLMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainHglm(
params.scoreIterationInterval,
params.seed,
params.missingValuesHandling,
keyToString(params.plugValues),
params.family,
params.randFamily,
params.maxIterations,
params.initialFixedEffects,
keyToString(params.initialRandomEffects),
keyToString(params.initialTMatrix),
params.tauUVarInit,
params.tauEVarInit,
params.randomColumns,
params.method,
params.emEpsilon,
params.randomIntercept,
params.groupColumn,
params.genSynData,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for HGLM model.
*/
public HGLMV3 grid_search_hglm_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeHglm().execute().body();
}
public HGLMV3 grid_search_hglm_resume(HGLMParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeHglm(
params.scoreIterationInterval,
params.seed,
params.missingValuesHandling,
keyToString(params.plugValues),
params.family,
params.randFamily,
params.maxIterations,
params.initialFixedEffects,
keyToString(params.initialRandomEffects),
keyToString(params.initialTMatrix),
params.tauUVarInit,
params.tauEVarInit,
params.randomColumns,
params.method,
params.emEpsilon,
params.randomIntercept,
params.groupColumn,
params.genSynData,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Train a AdaBoost model.
*/
public AdaBoostV3 train_adaboost() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainAdaboost().execute().body();
}
public AdaBoostV3 train_adaboost(AdaBoostParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.trainAdaboost(
params.nlearners,
params.weakLearner,
params.learnRate,
params.weakLearnerParams,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Validate a set of AdaBoost model builder parameters.
*/
public AdaBoostV3 validate_adaboost() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersAdaboost().execute().body();
}
public AdaBoostV3 validate_adaboost(AdaBoostParametersV3 params) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.validate_parametersAdaboost(
params.nlearners,
params.weakLearner,
params.learnRate,
params.weakLearnerParams,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Run grid search for AdaBoost model.
*/
public AdaBoostV3 grid_search_adaboost() throws IOException {
Grid s = getService(Grid.class);
return s.trainAdaboost().execute().body();
}
public AdaBoostV3 grid_search_adaboost(AdaBoostParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.trainAdaboost(
params.nlearners,
params.weakLearner,
params.learnRate,
params.weakLearnerParams,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Resume grid search for AdaBoost model.
*/
public AdaBoostV3 grid_search_adaboost_resume() throws IOException {
Grid s = getService(Grid.class);
return s.resumeAdaboost().execute().body();
}
public AdaBoostV3 grid_search_adaboost_resume(AdaBoostParametersV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.resumeAdaboost(
params.nlearners,
params.weakLearner,
params.learnRate,
params.weakLearnerParams,
params.seed,
keyToString(params.modelId),
keyToString(params.trainingFrame),
keyToString(params.validationFrame),
params.nfolds,
params.keepCrossValidationModels,
params.keepCrossValidationPredictions,
params.keepCrossValidationFoldAssignment,
params.parallelizeCrossValidation,
params.distribution,
params.tweediePower,
params.quantileAlpha,
params.huberAlpha,
colToString(params.responseColumn),
colToString(params.weightsColumn),
colToString(params.offsetColumn),
colToString(params.foldColumn),
params.foldAssignment,
params.categoricalEncoding,
params.maxCategoricalLevels,
params.ignoredColumns,
params.ignoreConstCols,
params.scoreEachIteration,
keyToString(params.checkpoint),
params.stoppingRounds,
params.maxRuntimeSecs,
params.stoppingMetric,
params.stoppingTolerance,
params.gainsliftBins,
params.customMetricFunc,
params.customDistributionFunc,
params.exportCheckpointsDir,
params.aucType
).execute().body();
}
/**
* Make a new GLM model based on existing one
*/
public GLMModelV3 make_glm_model(ModelKeyV3 model, String[] names, double[] beta) throws IOException {
MakeGLMModel s = getService(MakeGLMModel.class);
return s.make_model(keyToString(model), names, beta).execute().body();
}
public GLMModelV3 make_glm_model(MakeGLMModelV3 params) throws IOException {
MakeGLMModel s = getService(MakeGLMModel.class);
return s.make_model(
keyToString(params.model),
keyToString(params.dest),
params.names,
params.beta,
params.threshold
).execute().body();
}
/**
* Get full regularization path
*/
public GLMRegularizationPathV3 glm_regularization_path(ModelKeyV3 model) throws IOException {
GetGLMRegPath s = getService(GetGLMRegPath.class);
return s.extractRegularizationPath(keyToString(model)).execute().body();
}
public GLMRegularizationPathV3 glm_regularization_path(GLMRegularizationPathV3 params) throws IOException {
GetGLMRegPath s = getService(GetGLMRegPath.class);
return s.extractRegularizationPath(
keyToString(params.model),
params.lambdas,
params.alphas,
params.explainedDevianceTrain,
params.explainedDevianceValid,
params.coefficients,
params.coefficientsStd,
params.coefficientNames,
params.zValues,
params.pValues,
params.stdErrs
).execute().body();
}
/**
* Get weighted gram matrix
*/
public GramV3 weighted_gram_matrix(FrameKeyV3 x) throws IOException {
ComputeGram s = getService(ComputeGram.class);
return s.computeGram(keyToString(x)).execute().body();
}
public GramV3 weighted_gram_matrix(GramV3 params) throws IOException {
ComputeGram s = getService(ComputeGram.class);
return s.computeGram(
keyToString(params.x),
colToString(params.w),
params.useAllFactorLevels,
params.standardize,
params.skipMissing
).execute().body();
}
/**
* Find synonyms using a word2vec model
*/
public Word2VecSynonymsV3 word2vec_synonyms(ModelKeyV3 model, String word, int count) throws IOException {
Word2VecSynonyms s = getService(Word2VecSynonyms.class);
return s.findSynonyms(keyToString(model), word, count).execute().body();
}
public Word2VecSynonymsV3 word2vec_synonyms(Word2VecSynonymsV3 params) throws IOException {
Word2VecSynonyms s = getService(Word2VecSynonyms.class);
return s.findSynonyms(
keyToString(params.model),
params.word,
params.count,
params.synonyms,
params.scores
).execute().body();
}
/**
* Transform words to vectors using a word2vec model
*/
public Word2VecTransformV3 word2vec_transform(ModelKeyV3 model, FrameKeyV3 wordsFrame) throws IOException {
Word2VecTransform s = getService(Word2VecTransform.class);
return s.transform(keyToString(model), keyToString(wordsFrame)).execute().body();
}
public Word2VecTransformV3 word2vec_transform(ModelKeyV3 model, FrameKeyV3 wordsFrame, Word2VecModelAggregateMethod aggregateMethod) throws IOException {
Word2VecTransform s = getService(Word2VecTransform.class);
return s.transform(keyToString(model), keyToString(wordsFrame), aggregateMethod).execute().body();
}
/**
* Test only
*/
public DataInfoFrameV3 glm_datainfo_frame() throws IOException {
DataInfoFrame s = getService(DataInfoFrame.class);
return s.getDataInfoFrame().execute().body();
}
public DataInfoFrameV3 glm_datainfo_frame(DataInfoFrameV3 params) throws IOException {
DataInfoFrame s = getService(DataInfoFrame.class);
return s.getDataInfoFrame(
keyToString(params.frame),
params.interactions,
params.useAll,
params.standardize,
params.interactionsOnly
).execute().body();
}
/**
* Obtain a traverseable representation of a specific tree
*/
public TreeV3 get_tree(ModelKeyV3 model, int treeNumber) throws IOException {
Tree s = getService(Tree.class);
return s.getTree(keyToString(model), treeNumber).execute().body();
}
public TreeV3 get_tree(TreeV3 params) throws IOException {
Tree s = getService(Tree.class);
return s.getTree(
keyToString(params.model),
params.treeNumber,
params.treeClass,
params.plainLanguageRules
).execute().body();
}
/**
* Create a synthetic H2O Frame with random data. You can specify the number of rows/columns, as well as column types:
* integer, real, boolean, time, string, categorical. The frame may also have a dedicated "response" column, and some
* of the entries in the dataset may be created as missing.
*/
public JobV3 createFrame() throws IOException {
CreateFrame s = getService(CreateFrame.class);
return s.run().execute().body();
}
public JobV3 createFrame(CreateFrameV3 params) throws IOException {
CreateFrame s = getService(CreateFrame.class);
return s.run(
keyToString(params.dest),
params.rows,
params.cols,
params.seed,
params.seedForColumnTypes,
params.randomize,
params.value,
params.realRange,
params.categoricalFraction,
params.factors,
params.integerFraction,
params.integerRange,
params.binaryFraction,
params.binaryOnesFraction,
params.timeFraction,
params.stringFraction,
params.missingFraction,
params.hasResponse,
params.responseFactors,
params.positiveResponse,
params._excludeFields
).execute().body();
}
/**
* Split an H2O Frame.
*/
public SplitFrameV3 splitFrame() throws IOException {
SplitFrame s = getService(SplitFrame.class);
return s.run().execute().body();
}
public SplitFrameV3 splitFrame(SplitFrameV3 params) throws IOException {
SplitFrame s = getService(SplitFrame.class);
return s.run(
keyToString(params.key),
keyToString(params.dataset),
params.ratios,
keyArrayToStringArray(params.destinationFrames)
).execute().body();
}
/**
* Create interactions between categorical columns.
*/
public JobV3 generateInteractions(int maxFactors) throws IOException {
Interaction s = getService(Interaction.class);
return s.run(maxFactors).execute().body();
}
public JobV3 generateInteractions(InteractionV3 params) throws IOException {
Interaction s = getService(Interaction.class);
return s.run(
keyToString(params.dest),
keyToString(params.sourceFrame),
params.factorColumns,
params.pairwise,
params.maxFactors,
params.minOccurrence,
params._excludeFields
).execute().body();
}
/**
* Insert missing values.
*/
public JobV3 _missingInserter_run(FrameKeyV3 dataset, double fraction) throws IOException {
MissingInserter s = getService(MissingInserter.class);
return s.run(keyToString(dataset), fraction).execute().body();
}
public JobV3 _missingInserter_run(MissingInserterV3 params) throws IOException {
MissingInserter s = getService(MissingInserter.class);
return s.run(
keyToString(params.dataset),
params.fraction,
params.seed,
params._excludeFields
).execute().body();
}
/**
* Row-by-row discrete cosine transforms in 1D, 2D and 3D.
*/
public JobV3 _dctTransformer_run(FrameKeyV3 dataset, int[] dimensions) throws IOException {
DCTTransformer s = getService(DCTTransformer.class);
return s.run(keyToString(dataset), dimensions).execute().body();
}
public JobV3 _dctTransformer_run(DCTTransformerV3 params) throws IOException {
DCTTransformer s = getService(DCTTransformer.class);
return s.run(
keyToString(params.dataset),
keyToString(params.destinationFrame),
params.dimensions,
params.inverse,
params._excludeFields
).execute().body();
}
/**
* Tabulate one column vs another.
*/
public TabulateV3 _tabulate_run(FrameKeyV3 dataset, ColSpecifierV3 predictor, ColSpecifierV3 response) throws IOException {
Tabulate s = getService(Tabulate.class);
return s.run(keyToString(dataset), colToString(predictor), colToString(response)).execute().body();
}
public TabulateV3 _tabulate_run(TabulateV3 params) throws IOException {
Tabulate s = getService(Tabulate.class);
return s.run(
keyToString(params.dataset),
colToString(params.predictor),
colToString(params.response),
colToString(params.weight),
params.nbinsPredictor,
params.nbinsResponse
).execute().body();
}
/**
* Import raw data files into a single-column H2O Frame.
*/
public ImportFilesV3 importFiles(String path) throws IOException {
ImportFiles s = getService(ImportFiles.class);
return s.importFiles(path).execute().body();
}
public ImportFilesV3 importFiles(String path, String pattern) throws IOException {
ImportFiles s = getService(ImportFiles.class);
return s.importFiles(path, pattern, "").execute().body();
}
public ImportFilesV3 importFiles(String path, String pattern, String _excludeFields) throws IOException {
ImportFiles s = getService(ImportFiles.class);
return s.importFiles(path, pattern, _excludeFields).execute().body();
}
/**
* Import raw data files from multiple directories (or different data sources) into a single-column H2O Frame.
*/
public ImportFilesMultiV3 importFilesMulti(String[] paths) throws IOException {
ImportFilesMulti s = getService(ImportFilesMulti.class);
return s.importFilesMulti(paths).execute().body();
}
public ImportFilesMultiV3 importFilesMulti(String[] paths, String pattern) throws IOException {
ImportFilesMulti s = getService(ImportFilesMulti.class);
return s.importFilesMulti(paths, pattern, "").execute().body();
}
public ImportFilesMultiV3 importFilesMulti(String[] paths, String pattern, String _excludeFields) throws IOException {
ImportFilesMulti s = getService(ImportFilesMulti.class);
return s.importFilesMulti(paths, pattern, _excludeFields).execute().body();
}
/**
* Import SQL table into an H2O Frame.
*/
public JobV3 importSqlTable(String connectionUrl, String username, String password) throws IOException {
ImportSQLTable s = getService(ImportSQLTable.class);
return s.importSQLTable(connectionUrl, username, password).execute().body();
}
public JobV3 importSqlTable(ImportSQLTableV99 params) throws IOException {
ImportSQLTable s = getService(ImportSQLTable.class);
return s.importSQLTable(
params.connectionUrl,
params.table,
params.selectQuery,
params.useTempTable,
params.tempTableName,
params.username,
params.password,
params.columns,
params.fetchMode,
params.numChunksHint,
params._excludeFields
).execute().body();
}
/**
* Import Hive table into an H2O Frame.
*/
public JobV3 importHiveTable(String table) throws IOException {
ImportHiveTable s = getService(ImportHiveTable.class);
return s.importHiveTable(table).execute().body();
}
public JobV3 importHiveTable(ImportHiveTableV3 params) throws IOException {
ImportHiveTable s = getService(ImportHiveTable.class);
return s.importHiveTable(
params.database,
params.table,
params.partitions,
params.allowMultiFormat,
params._excludeFields
).execute().body();
}
/**
* Save an H2O Frame contents into a Hive table.
*/
public SaveToHiveTableV3 saveToHiveTable(FrameKeyV3 frameId, String jdbcUrl, String tableName) throws IOException {
SaveToHiveTable s = getService(SaveToHiveTable.class);
return s.saveToHiveTable(keyToString(frameId), jdbcUrl, tableName).execute().body();
}
public SaveToHiveTableV3 saveToHiveTable(SaveToHiveTableV3 params) throws IOException {
SaveToHiveTable s = getService(SaveToHiveTable.class);
return s.saveToHiveTable(
keyToString(params.frameId),
params.jdbcUrl,
params.tableName,
params.tablePath,
params.format,
params.tmpPath,
params._excludeFields
).execute().body();
}
/**
* Guess the parameters for parsing raw byte-oriented data into an H2O Frame.
*/
public ParseSetupV3 guessParseSetup(FrameKeyV3[] sourceFrames) throws IOException {
ParseSetup s = getService(ParseSetup.class);
return s.guessSetup(keyArrayToStringArray(sourceFrames)).execute().body();
}
public ParseSetupV3 guessParseSetup(ParseSetupV3 params) throws IOException {
ParseSetup s = getService(ParseSetup.class);
return s.guessSetup(
keyArrayToStringArray(params.sourceFrames),
params.parseType,
params.separator,
params.singleQuotes,
params.checkHeader,
params.columnNames,
params.skippedColumns,
params.columnTypes,
params.naStrings,
params.columnNameFilter,
params.columnOffset,
params.columnCount,
params.totalFilteredColumnCount,
params.customNonDataLineMarkers,
keyToString(params.decryptTool),
params.partitionBy,
params.escapechar,
params.forceColTypes,
params.tzAdjustToLocal,
params._excludeFields
).execute().body();
}
/**
* Parse a raw byte-oriented Frame into a useful columnar data Frame.
*/
public ParseV3 parse(ParseV3 params) throws IOException {
Parse s = getService(Parse.class);
return s.parse(
keyToString(params.destinationFrame),
keyArrayToStringArray(params.sourceFrames),
params.parseType,
params.separator,
params.singleQuotes,
params.checkHeader,
params.numberColumns,
params.columnNames,
params.columnTypes,
params.skippedColumns,
params.forceColTypes,
params.domains,
params.naStrings,
params.chunkSize,
params.deleteOnDone,
params.blocking,
keyToString(params.decryptTool),
params.customNonDataLineMarkers,
params.partitionBy,
params.escapechar,
params.tzAdjustToLocal,
params._excludeFields
).execute().body();
}
/**
* Install a decryption tool for parsing of encrypted data.
*/
public DecryptionSetupV3 setupDecryption() throws IOException {
DecryptionSetup s = getService(DecryptionSetup.class);
return s.setupDecryption().execute().body();
}
public DecryptionSetupV3 setupDecryption(DecryptionSetupV3 params) throws IOException {
DecryptionSetup s = getService(DecryptionSetup.class);
return s.setupDecryption(
keyToString(params.decryptToolId),
params.decryptImpl,
keyToString(params.keystoreId),
params.keystoreType,
params.keyAlias,
params.password,
params.cipherSpec,
params._excludeFields
).execute().body();
}
/**
* Parse a raw byte-oriented Frame into a useful columnar data Frame.
*/
public JobV3 parseSvmLight(FrameKeyV3[] sourceFrames) throws IOException {
ParseSVMLight s = getService(ParseSVMLight.class);
return s.parseSVMLight(keyArrayToStringArray(sourceFrames)).execute().body();
}
public JobV3 parseSvmLight(FrameKeyV3 destinationFrame, FrameKeyV3[] sourceFrames) throws IOException {
ParseSVMLight s = getService(ParseSVMLight.class);
return s.parseSVMLight(keyToString(destinationFrame), keyArrayToStringArray(sourceFrames), "").execute().body();
}
public JobV3 parseSvmLight(FrameKeyV3 destinationFrame, FrameKeyV3[] sourceFrames, String _excludeFields) throws IOException {
ParseSVMLight s = getService(ParseSVMLight.class);
return s.parseSVMLight(keyToString(destinationFrame), keyArrayToStringArray(sourceFrames), _excludeFields).execute().body();
}
/**
* The endpoint used to let H2O know from external services that it should keep running.
*/
public PingV3 ping() throws IOException {
Ping s = getService(Ping.class);
return s.ping().execute().body();
}
public PingV3 ping(String _excludeFields) throws IOException {
Ping s = getService(Ping.class);
return s.ping(_excludeFields).execute().body();
}
/**
* Determine the status of the nodes in the H2O cloud.
*/
public CloudV3 cloudStatus() throws IOException {
Cloud s = getService(Cloud.class);
return s.status().execute().body();
}
public CloudV3 cloudStatus(boolean skipTicks) throws IOException {
Cloud s = getService(Cloud.class);
return s.status(skipTicks, "").execute().body();
}
public CloudV3 cloudStatus(boolean skipTicks, String _excludeFields) throws IOException {
Cloud s = getService(Cloud.class);
return s.status(skipTicks, _excludeFields).execute().body();
}
/**
* Determine the status of the nodes in the H2O cloud.
*/
public CloudV3 cloudStatusMinimal() throws IOException {
Cloud s = getService(Cloud.class);
return s.head().execute().body();
}
public CloudV3 cloudStatusMinimal(boolean skipTicks) throws IOException {
Cloud s = getService(Cloud.class);
return s.head(skipTicks, "").execute().body();
}
public CloudV3 cloudStatusMinimal(boolean skipTicks, String _excludeFields) throws IOException {
Cloud s = getService(Cloud.class);
return s.head(skipTicks, _excludeFields).execute().body();
}
/**
* Lock the cloud.
*/
public CloudLockV3 cloudLock() throws IOException {
CloudLock s = getService(CloudLock.class);
return s.lock().execute().body();
}
public CloudLockV3 cloudLock(String reason) throws IOException {
CloudLock s = getService(CloudLock.class);
return s.lock(reason, "").execute().body();
}
public CloudLockV3 cloudLock(String reason, String _excludeFields) throws IOException {
CloudLock s = getService(CloudLock.class);
return s.lock(reason, _excludeFields).execute().body();
}
/**
* Get a list of all the H2O Jobs (long-running actions).
*/
public JobsV3 jobs() throws IOException {
Jobs s = getService(Jobs.class);
return s.list().execute().body();
}
public JobsV3 jobs(JobKeyV3 jobId) throws IOException {
Jobs s = getService(Jobs.class);
return s.list(keyToString(jobId), "").execute().body();
}
public JobsV3 jobs(JobKeyV3 jobId, String _excludeFields) throws IOException {
Jobs s = getService(Jobs.class);
return s.list(keyToString(jobId), _excludeFields).execute().body();
}
/**
* Debugging tool that provides information on current communication between nodes.
*/
public TimelineV3 timeline() throws IOException {
Timeline s = getService(Timeline.class);
return s.fetch().execute().body();
}
public TimelineV3 timeline(String _excludeFields) throws IOException {
Timeline s = getService(Timeline.class);
return s.fetch(_excludeFields).execute().body();
}
/**
* Report real-time profiling information for all nodes (sorted, aggregated stack traces).
*/
public ProfilerV3 profiler(int depth) throws IOException {
Profiler s = getService(Profiler.class);
return s.fetch(depth).execute().body();
}
public ProfilerV3 profiler(int depth, String _excludeFields) throws IOException {
Profiler s = getService(Profiler.class);
return s.fetch(depth, _excludeFields).execute().body();
}
/**
* Report stack traces for all threads on all nodes.
*/
public JStackV3 stacktraces() throws IOException {
JStack s = getService(JStack.class);
return s.fetch().execute().body();
}
public JStackV3 stacktraces(String _excludeFields) throws IOException {
JStack s = getService(JStack.class);
return s.fetch(_excludeFields).execute().body();
}
/**
* Run a network test to measure the performance of the cluster interconnect.
*/
public NetworkTestV3 testNetwork() throws IOException {
NetworkTest s = getService(NetworkTest.class);
return s.fetch().execute().body();
}
public NetworkTestV3 testNetwork(String _excludeFields) throws IOException {
NetworkTest s = getService(NetworkTest.class);
return s.fetch(_excludeFields).execute().body();
}
/**
* Unlock all keys in the H2O distributed K/V store, to attempt to recover from a crash.
*/
public UnlockKeysV3 unlockAllKeys() throws IOException {
UnlockKeys s = getService(UnlockKeys.class);
return s.unlock().execute().body();
}
public UnlockKeysV3 unlockAllKeys(String _excludeFields) throws IOException {
UnlockKeys s = getService(UnlockKeys.class);
return s.unlock(_excludeFields).execute().body();
}
/**
* Shut down the cluster.
*/
public ShutdownV3 shutdownCluster() throws IOException {
Shutdown s = getService(Shutdown.class);
return s.shutdown().execute().body();
}
public ShutdownV3 shutdownCluster(String _excludeFields) throws IOException {
Shutdown s = getService(Shutdown.class);
return s.shutdown(_excludeFields).execute().body();
}
/**
* Return information about this H2O cluster.
*/
public AboutV3 about() throws IOException {
About s = getService(About.class);
return s.get().execute().body();
}
public AboutV3 about(String _excludeFields) throws IOException {
About s = getService(About.class);
return s.get(_excludeFields).execute().body();
}
/**
* Return the list of (almost) all REST API endpoints.
*/
public MetadataV3 endpoints() throws IOException {
Metadata s = getService(Metadata.class);
return s.listRoutes().execute().body();
}
public MetadataV3 endpoints(MetadataV3 params) throws IOException {
Metadata s = getService(Metadata.class);
return s.listRoutes(
params.num,
params.httpMethod,
params.path,
params.classname,
params.schemaname,
params._excludeFields
).execute().body();
}
/**
* Return the REST API endpoint metadata, including documentation, for the endpoint specified by path or index.
*/
public MetadataV3 endpoint(String path) throws IOException {
Metadata s = getService(Metadata.class);
return s.fetchRoute(path).execute().body();
}
public MetadataV3 endpoint(MetadataV3 params) throws IOException {
Metadata s = getService(Metadata.class);
return s.fetchRoute(
params.path,
params.num,
params.httpMethod,
params.classname,
params.schemaname,
params._excludeFields
).execute().body();
}
/**
* Return the REST API schema metadata for specified schema class.
*/
public MetadataV3 schemaForClass(String classname) throws IOException {
Metadata s = getService(Metadata.class);
return s.fetchSchemaMetadataByClass(classname).execute().body();
}
public MetadataV3 schemaForClass(MetadataV3 params) throws IOException {
Metadata s = getService(Metadata.class);
return s.fetchSchemaMetadataByClass(
params.classname,
params.num,
params.httpMethod,
params.path,
params.schemaname,
params._excludeFields
).execute().body();
}
/**
* Return the REST API schema metadata for specified schema.
*/
public MetadataV3 schema(String schemaname) throws IOException {
Metadata s = getService(Metadata.class);
return s.fetchSchemaMetadata(schemaname).execute().body();
}
public MetadataV3 schema(MetadataV3 params) throws IOException {
Metadata s = getService(Metadata.class);
return s.fetchSchemaMetadata(
params.schemaname,
params.num,
params.httpMethod,
params.path,
params.classname,
params._excludeFields
).execute().body();
}
/**
* Return list of all REST API schemas.
*/
public MetadataV3 schemas() throws IOException {
Metadata s = getService(Metadata.class);
return s.listSchemas().execute().body();
}
public MetadataV3 schemas(MetadataV3 params) throws IOException {
Metadata s = getService(Metadata.class);
return s.listSchemas(
params.num,
params.httpMethod,
params.path,
params.classname,
params.schemaname,
params._excludeFields
).execute().body();
}
/**
* Typeahead hander for filename completion.
*/
public TypeaheadV3 typeaheadFileSuggestions(String src) throws IOException {
Typeahead s = getService(Typeahead.class);
return s.files(src).execute().body();
}
public TypeaheadV3 typeaheadFileSuggestions(String src, int limit) throws IOException {
Typeahead s = getService(Typeahead.class);
return s.files(src, limit, "").execute().body();
}
public TypeaheadV3 typeaheadFileSuggestions(String src, int limit, String _excludeFields) throws IOException {
Typeahead s = getService(Typeahead.class);
return s.files(src, limit, _excludeFields).execute().body();
}
/**
* Get the status of the given H2O Job (long-running action).
*/
public JobsV3 job(JobKeyV3 jobId) throws IOException {
Jobs s = getService(Jobs.class);
return s.fetch(keyToString(jobId)).execute().body();
}
public JobsV3 job(JobKeyV3 jobId, String _excludeFields) throws IOException {
Jobs s = getService(Jobs.class);
return s.fetch(keyToString(jobId), _excludeFields).execute().body();
}
/**
* Cancel a running job.
*/
public JobsV3 cancelJob(JobKeyV3 jobId) throws IOException {
Jobs s = getService(Jobs.class);
return s.cancel(keyToString(jobId)).execute().body();
}
public JobsV3 cancelJob(JobKeyV3 jobId, String _excludeFields) throws IOException {
Jobs s = getService(Jobs.class);
return s.cancel(keyToString(jobId), _excludeFields).execute().body();
}
/**
* Find a value within a Frame.
*/
public FindV3 findInFrame(FrameV3 key, long row) throws IOException {
Find s = getService(Find.class);
return s.find(key, row).execute().body();
}
public FindV3 findInFrame(FindV3 params) throws IOException {
Find s = getService(Find.class);
return s.find(
params.key,
params.column,
params.row,
params.match,
params._excludeFields
).execute().body();
}
/**
* Export a Frame to the given path with optional overwrite.
*/
public FramesV3 exportFrame(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.export(keyToString(frameId)).execute().body();
}
public FramesV3 exportFrame(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.export(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Save frame data to the given path.
*/
public FrameSaveV3 saveFrame(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.save(keyToString(frameId)).execute().body();
}
public FrameSaveV3 saveFrame(FrameSaveV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.save(
keyToString(params.frameId),
params.dir,
params.force,
params._excludeFields
).execute().body();
}
/**
* Load a frame from data on given path.
*/
public FrameLoadV3 loadFrame() throws IOException {
Frames s = getService(Frames.class);
return s.load().execute().body();
}
public FrameLoadV3 loadFrame(FrameLoadV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.load(
keyToString(params.frameId),
params.dir,
params.force,
params._excludeFields
).execute().body();
}
/**
* Return the summary metrics for a column, e.g. min, max, mean, sigma, percentiles, etc.
*/
public FramesV3 frameColumnSummary(FrameKeyV3 frameId, String column) throws IOException {
Frames s = getService(Frames.class);
return s.columnSummary(keyToString(frameId), column).execute().body();
}
public FramesV3 frameColumnSummary(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.columnSummary(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return the domains for the specified categorical column ("null" if the column is not a categorical).
*/
public FramesV3 frameColumnDomain(FrameKeyV3 frameId, String column) throws IOException {
Frames s = getService(Frames.class);
return s.columnDomain(keyToString(frameId), column).execute().body();
}
public FramesV3 frameColumnDomain(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.columnDomain(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return the specified column from a Frame.
*/
public FramesV3 frameColumn(FrameKeyV3 frameId, String column) throws IOException {
Frames s = getService(Frames.class);
return s.column(keyToString(frameId), column).execute().body();
}
public FramesV3 frameColumn(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.column(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return all the columns from a Frame.
*/
public FramesV3 frameColumns(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.columns(keyToString(frameId)).execute().body();
}
public FramesV3 frameColumns(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.columns(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return a Frame, including the histograms, after forcing computation of rollups.
*/
public FramesV3 frameSummary(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.summary(keyToString(frameId)).execute().body();
}
public FramesV3 frameSummary(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.summary(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return a basic info about Frame to fill client Rapid expression cache.
*/
public FramesV3 lightFrame(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.fetchLight(keyToString(frameId)).execute().body();
}
public FramesV3 lightFrame(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.fetchLight(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return the specified Frame.
*/
public FramesV3 frame(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.fetch(keyToString(frameId)).execute().body();
}
public FramesV3 frame(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.fetch(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return all Frames in the H2O distributed K/V store.
*/
public FramesListV3 frames() throws IOException {
Frames s = getService(Frames.class);
return s.list().execute().body();
}
public FramesListV3 frames(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.list(keyToString(frameId), "").execute().body();
}
public FramesListV3 frames(FrameKeyV3 frameId, String _excludeFields) throws IOException {
Frames s = getService(Frames.class);
return s.list(keyToString(frameId), _excludeFields).execute().body();
}
/**
* Delete the specified Frame from the H2O distributed K/V store.
*/
public FramesV3 deleteFrame(FrameKeyV3 frameId) throws IOException {
Frames s = getService(Frames.class);
return s.delete(keyToString(frameId)).execute().body();
}
public FramesV3 deleteFrame(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.delete(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Delete all Frames from the H2O distributed K/V store.
*/
public FramesV3 deleteAllFrames() throws IOException {
Frames s = getService(Frames.class);
return s.deleteAll().execute().body();
}
public FramesV3 deleteAllFrames(FramesV3 params) throws IOException {
Frames s = getService(Frames.class);
return s.deleteAll(
keyToString(params.frameId),
params.column,
params.rowOffset,
params.rowCount,
params.columnOffset,
params.fullColumnCount,
params.columnCount,
params.findCompatibleModels,
params.path,
params.force,
params.numParts,
params.parallel,
params.format,
params.compression,
params.writeChecksum,
params.tzAdjustFromLocal,
params.separator,
params.header,
params.quoteHeader,
params._excludeFields
).execute().body();
}
/**
* Return information about chunks for a given frame.
*/
public FrameChunksV3 frameChunks(FrameKeyV3 frameId) throws IOException {
FrameChunks s = getService(FrameChunks.class);
return s.fetch(keyToString(frameId)).execute().body();
}
/**
* Return the specified Model from the H2O distributed K/V store, optionally with the list of compatible Frames.
*/
public ModelsV3 model(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.fetch(keyToString(modelId)).execute().body();
}
public ModelsV3 model(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.fetch(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Return all Models from the H2O distributed K/V store.
*/
public ModelsV3 models() throws IOException {
Models s = getService(Models.class);
return s.list().execute().body();
}
public ModelsV3 models(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.list(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Delete the specified Model from the H2O distributed K/V store.
*/
public ModelsV3 deleteModel(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.delete(keyToString(modelId)).execute().body();
}
public ModelsV3 deleteModel(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.delete(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Delete all Models from the H2O distributed K/V store.
*/
public ModelsV3 deleteAllModels() throws IOException {
Models s = getService(Models.class);
return s.deleteAll().execute().body();
}
public ModelsV3 deleteAllModels(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.deleteAll(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Return potentially abridged model suitable for viewing in a browser (currently only used for java model code).
*/
public StreamingSchema modelPreview(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.fetchPreview(keyToString(modelId)).execute().body();
}
public StreamingSchema modelPreview(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.fetchPreview(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* [DEPRECATED] Return the stream containing model implementation in Java code.
*/
public StreamingSchema modelJavaCode(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.fetchJavaCode(keyToString(modelId)).execute().body();
}
public StreamingSchema modelJavaCode(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.fetchJavaCode(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Return the model in the MOJO format. This format can then be interpreted by gen_model.jar in order to perform
* prediction / scoring. Currently works for GBM and DRF algos only.
*/
public StreamingSchema modelMojo(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.fetchMojo(keyToString(modelId)).execute().body();
}
public StreamingSchema modelMojo(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.fetchMojo(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Return the model in the binary format.
*/
public StreamingSchema modelBinary(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.fetchBinaryModel(keyToString(modelId)).execute().body();
}
public StreamingSchema modelBinary(ModelsV3 params) throws IOException {
Models s = getService(Models.class);
return s.fetchBinaryModel(
keyToString(params.modelId),
params.preview,
params.findCompatibleFrames,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Create data for partial dependence plot(s) for the specified model and frame.
*/
public JobV3 makePDP() throws IOException {
PartialDependence s = getService(PartialDependence.class);
return s.makePartialDependence().execute().body();
}
public JobV3 makePDP(PartialDependenceV3 params) throws IOException {
PartialDependence s = getService(PartialDependence.class);
return s.makePartialDependence(
keyToString(params.modelId),
keyToString(params.frameId),
params.rowIndex,
params.cols,
params.weightColumnIndex,
params.addMissingNa,
params.nbins,
params.userSplits,
params.userCols,
params.numUserSplits,
params.colPairs2dpdp,
keyToString(params.destinationKey),
params.targets
).execute().body();
}
/**
* Fetch feature interaction data
*/
public FeatureInteractionV3 makeFI(int maxInteractionDepth, int maxTreeDepth, int maxDeepening) throws IOException {
FeatureInteraction s = getService(FeatureInteraction.class);
return s.makeFeatureInteraction(maxInteractionDepth, maxTreeDepth, maxDeepening).execute().body();
}
public FeatureInteractionV3 makeFI(FeatureInteractionV3 params) throws IOException {
FeatureInteraction s = getService(FeatureInteraction.class);
return s.makeFeatureInteraction(
keyToString(params.modelId),
params.maxInteractionDepth,
params.maxTreeDepth,
params.maxDeepening,
params._excludeFields
).execute().body();
}
/**
* Fetch Friedman Popescus H.
*/
public FriedmanPopescusHV3 makeH(FrameV3 frame, String[] variables) throws IOException {
FriedmansPopescusH s = getService(FriedmansPopescusH.class);
return s.makeFriedmansPopescusH(frame, variables).execute().body();
}
public FriedmanPopescusHV3 makeH(FriedmanPopescusHV3 params) throws IOException {
FriedmansPopescusH s = getService(FriedmansPopescusH.class);
return s.makeFriedmansPopescusH(
keyToString(params.modelId),
params.frame,
params.variables,
params.h,
params._excludeFields
).execute().body();
}
/**
* Fetch significant rules table.
*/
public SignificantRulesV3 makeRules() throws IOException {
SignificantRules s = getService(SignificantRules.class);
return s.makeSignificantRulesTable().execute().body();
}
public SignificantRulesV3 makeRules(ModelKeyV3 modelId) throws IOException {
SignificantRules s = getService(SignificantRules.class);
return s.makeSignificantRulesTable(keyToString(modelId), "").execute().body();
}
public SignificantRulesV3 makeRules(ModelKeyV3 modelId, String _excludeFields) throws IOException {
SignificantRules s = getService(SignificantRules.class);
return s.makeSignificantRulesTable(keyToString(modelId), _excludeFields).execute().body();
}
/**
* Fetch partial dependence data.
*/
public PartialDependenceV3 fetchPDP(String name) throws IOException {
PartialDependence s = getService(PartialDependence.class);
return s.fetchPartialDependence(name).execute().body();
}
public PartialDependenceV3 fetchPDP(String name, String type, String url) throws IOException {
PartialDependence s = getService(PartialDependence.class);
return s.fetchPartialDependence(name, type, url).execute().body();
}
/**
* Import given binary model into H2O.
*/
public ModelsV3 importModel(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.importModel(keyToString(modelId)).execute().body();
}
public ModelsV3 importModel(ModelImportV3 params) throws IOException {
Models s = getService(Models.class);
return s.importModel(
keyToString(params.modelId),
params.dir,
params.force,
params._excludeFields
).execute().body();
}
/**
* Export given model.
*/
public ModelExportV3 exportModel(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.exportModel(keyToString(modelId)).execute().body();
}
public ModelExportV3 exportModel(ModelExportV3 params) throws IOException {
Models s = getService(Models.class);
return s.exportModel(
keyToString(params.modelId),
params.dir,
params.force,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Upload given binary model into H2O.
*/
public ModelsV3 uploadModel(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.uploadModel(keyToString(modelId)).execute().body();
}
public ModelsV3 uploadModel(ModelImportV3 params) throws IOException {
Models s = getService(Models.class);
return s.uploadModel(
keyToString(params.modelId),
params.dir,
params.force,
params._excludeFields
).execute().body();
}
/**
* Export given model as Mojo.
*/
public ModelExportV3 exportMojo(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.exportMojo(keyToString(modelId)).execute().body();
}
public ModelExportV3 exportMojo(ModelExportV3 params) throws IOException {
Models s = getService(Models.class);
return s.exportMojo(
keyToString(params.modelId),
params.dir,
params.force,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Export given model details in json format.
*/
public ModelExportV3 exportModelDetails(ModelKeyV3 modelId) throws IOException {
Models s = getService(Models.class);
return s.exportModelDetails(keyToString(modelId)).execute().body();
}
public ModelExportV3 exportModelDetails(ModelExportV3 params) throws IOException {
Models s = getService(Models.class);
return s.exportModelDetails(
keyToString(params.modelId),
params.dir,
params.force,
params.exportCrossValidationPredictions,
params._excludeFields
).execute().body();
}
/**
* Return the specified grid search result.
*/
public GridSchemaV99 grid(GridKeyV3 gridId) throws IOException {
Grids s = getService(Grids.class);
return s.fetch(keyToString(gridId)).execute().body();
}
public GridSchemaV99 grid(GridSchemaV99 params) throws IOException {
Grids s = getService(Grids.class);
return s.fetch(
keyToString(params.gridId),
params.sortBy,
params.decreasing,
keyArrayToStringArray(params.modelIds)
).execute().body();
}
/**
* Return all grids from H2O distributed K/V store.
*/
public GridsV99 grids() throws IOException {
Grids s = getService(Grids.class);
return s.list().execute().body();
}
/**
* Return a new unique model_id for the specified algorithm.
*/
public ModelIdV3 newModelId(String algo) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.calcModelId(algo).execute().body();
}
public ModelIdV3 newModelId(String algo, String _excludeFields) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.calcModelId(algo, _excludeFields).execute().body();
}
/**
* Return the Model Builder metadata for the specified algorithm.
*/
public ModelBuildersV3 modelBuilder(String algo) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.fetch(algo).execute().body();
}
public ModelBuildersV3 modelBuilder(String algo, String _excludeFields) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.fetch(algo, _excludeFields).execute().body();
}
/**
* Return the Model Builder metadata for all available algorithms.
*/
public ModelBuildersV3 modelBuilders() throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.list().execute().body();
}
public ModelBuildersV3 modelBuilders(String algo) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.list(algo, "").execute().body();
}
public ModelBuildersV3 modelBuilders(String algo, String _excludeFields) throws IOException {
ModelBuilders s = getService(ModelBuilders.class);
return s.list(algo, _excludeFields).execute().body();
}
/**
* Return the saved scoring metrics for the specified Model and Frame.
*/
public ModelMetricsListSchemaV3 _mmFetch1(ModelKeyV3 model, FrameKeyV3 frame) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.fetch(keyToString(model), keyToString(frame)).execute().body();
}
public ModelMetricsListSchemaV3 _mmFetch1(ModelMetricsListSchemaV3 params) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.fetch(
keyToString(params.model),
keyToString(params.frame),
keyToString(params.predictionsFrame),
keyToString(params.deviancesFrame),
params.reconstructionError,
params.reconstructionErrorPerFeature,
params.deepFeaturesHiddenLayer,
params.deepFeaturesHiddenLayerName,
params.reconstructTrain,
params.projectArchetypes,
params.reverseTransform,
params.leafNodeAssignment,
params.leafNodeAssignmentType,
params.predictStagedProba,
params.predictContributions,
params.rowToTreeAssignment,
params.predictContributionsOutputFormat,
params.topN,
params.bottomN,
params.compareAbs,
params.featureFrequencies,
params.exemplarIndex,
params.deviances,
params.customMetricFunc,
params.aucType,
params.auucType,
params.customAuucThresholds,
params.auucNbins,
keyToString(params.backgroundFrame),
params.outputSpace,
params.outputPerReference,
params._excludeFields
).execute().body();
}
/**
* Return the saved scoring metrics for the specified Model and Frame.
*/
public ModelMetricsListSchemaV3 _mmDelete1(ModelKeyV3 model, FrameKeyV3 frame) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.delete(keyToString(model), keyToString(frame)).execute().body();
}
public ModelMetricsListSchemaV3 _mmDelete1(ModelMetricsListSchemaV3 params) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.delete(
keyToString(params.model),
keyToString(params.frame),
keyToString(params.predictionsFrame),
keyToString(params.deviancesFrame),
params.reconstructionError,
params.reconstructionErrorPerFeature,
params.deepFeaturesHiddenLayer,
params.deepFeaturesHiddenLayerName,
params.reconstructTrain,
params.projectArchetypes,
params.reverseTransform,
params.leafNodeAssignment,
params.leafNodeAssignmentType,
params.predictStagedProba,
params.predictContributions,
params.rowToTreeAssignment,
params.predictContributionsOutputFormat,
params.topN,
params.bottomN,
params.compareAbs,
params.featureFrequencies,
params.exemplarIndex,
params.deviances,
params.customMetricFunc,
params.aucType,
params.auucType,
params.customAuucThresholds,
params.auucNbins,
keyToString(params.backgroundFrame),
params.outputSpace,
params.outputPerReference,
params._excludeFields
).execute().body();
}
/**
* Return the scoring metrics for the specified Frame with the specified Model. If the Frame has already been scored
* with the Model then cached results will be returned; otherwise predictions for all rows in the Frame will be
* generated and the metrics will be returned.
*/
public ModelMetricsListSchemaV3 score(ModelKeyV3 model, FrameKeyV3 frame) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.score(keyToString(model), keyToString(frame)).execute().body();
}
public ModelMetricsListSchemaV3 score(ModelMetricsListSchemaV3 params) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.score(
keyToString(params.model),
keyToString(params.frame),
keyToString(params.predictionsFrame),
keyToString(params.deviancesFrame),
params.reconstructionError,
params.reconstructionErrorPerFeature,
params.deepFeaturesHiddenLayer,
params.deepFeaturesHiddenLayerName,
params.reconstructTrain,
params.projectArchetypes,
params.reverseTransform,
params.leafNodeAssignment,
params.leafNodeAssignmentType,
params.predictStagedProba,
params.predictContributions,
params.rowToTreeAssignment,
params.predictContributionsOutputFormat,
params.topN,
params.bottomN,
params.compareAbs,
params.featureFrequencies,
params.exemplarIndex,
params.deviances,
params.customMetricFunc,
params.aucType,
params.auucType,
params.customAuucThresholds,
params.auucNbins,
keyToString(params.backgroundFrame),
params.outputSpace,
params.outputPerReference,
params._excludeFields
).execute().body();
}
/**
* Score (generate predictions) for the specified Frame with the specified Model. Both the Frame of predictions and
* the metrics will be returned.
*/
public ModelMetricsListSchemaV3 predict(ModelKeyV3 model, FrameKeyV3 frame) throws IOException {
Predictions s = getService(Predictions.class);
return s.predict(keyToString(model), keyToString(frame)).execute().body();
}
public ModelMetricsListSchemaV3 predict(ModelMetricsListSchemaV3 params) throws IOException {
Predictions s = getService(Predictions.class);
return s.predict(
keyToString(params.model),
keyToString(params.frame),
keyToString(params.predictionsFrame),
keyToString(params.deviancesFrame),
params.reconstructionError,
params.reconstructionErrorPerFeature,
params.deepFeaturesHiddenLayer,
params.deepFeaturesHiddenLayerName,
params.reconstructTrain,
params.projectArchetypes,
params.reverseTransform,
params.leafNodeAssignment,
params.leafNodeAssignmentType,
params.predictStagedProba,
params.predictContributions,
params.rowToTreeAssignment,
params.predictContributionsOutputFormat,
params.topN,
params.bottomN,
params.compareAbs,
params.featureFrequencies,
params.exemplarIndex,
params.deviances,
params.customMetricFunc,
params.aucType,
params.auucType,
params.customAuucThresholds,
params.auucNbins,
keyToString(params.backgroundFrame),
params.outputSpace,
params.outputPerReference,
params._excludeFields
).execute().body();
}
/**
* Score (generate predictions) for the specified Frame with the specified Model. Both the Frame of predictions and
* the metrics will be returned.
*/
public JobV3 predict_async(ModelKeyV3 model, FrameKeyV3 frame) throws IOException {
Predictions s = getService(Predictions.class);
return s.predictAsync(keyToString(model), keyToString(frame)).execute().body();
}
public JobV3 predict_async(ModelMetricsListSchemaV3 params) throws IOException {
Predictions s = getService(Predictions.class);
return s.predictAsync(
keyToString(params.model),
keyToString(params.frame),
keyToString(params.predictionsFrame),
keyToString(params.deviancesFrame),
params.reconstructionError,
params.reconstructionErrorPerFeature,
params.deepFeaturesHiddenLayer,
params.deepFeaturesHiddenLayerName,
params.reconstructTrain,
params.projectArchetypes,
params.reverseTransform,
params.leafNodeAssignment,
params.leafNodeAssignmentType,
params.predictStagedProba,
params.predictContributions,
params.rowToTreeAssignment,
params.predictContributionsOutputFormat,
params.topN,
params.bottomN,
params.compareAbs,
params.featureFrequencies,
params.exemplarIndex,
params.deviances,
params.customMetricFunc,
params.aucType,
params.auucType,
params.customAuucThresholds,
params.auucNbins,
keyToString(params.backgroundFrame),
params.outputSpace,
params.outputPerReference,
params._excludeFields
).execute().body();
}
/**
* Create a ModelMetrics object from the predicted and actual values, and a domain for classification problems or a
* distribution family for regression problems.
*/
public ModelMetricsMakerSchemaV3 makeMetrics(String predictionsFrame, String actualsFrame) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.make(predictionsFrame, actualsFrame).execute().body();
}
public ModelMetricsMakerSchemaV3 makeMetrics(ModelMetricsMakerSchemaV3 params) throws IOException {
ModelMetrics s = getService(ModelMetrics.class);
return s.make(
params.predictionsFrame,
params.actualsFrame,
params.weightsFrame,
params.treatmentFrame,
params.domain,
params.distribution,
params.aucType,
params.auucType,
params.auucNbins,
params.customAuucThresholds
).execute().body();
}
/**
* Return a CPU usage snapshot of all cores of all nodes in the H2O cluster.
*/
public WaterMeterCpuTicksV3 waterMeterCpuTicks(int nodeidx) throws IOException {
WaterMeterCpuTicks s = getService(WaterMeterCpuTicks.class);
return s.fetch(nodeidx).execute().body();
}
public WaterMeterCpuTicksV3 waterMeterCpuTicks(int nodeidx, String _excludeFields) throws IOException {
WaterMeterCpuTicks s = getService(WaterMeterCpuTicks.class);
return s.fetch(nodeidx, _excludeFields).execute().body();
}
/**
* Return IO usage snapshot of all nodes in the H2O cluster.
*/
public WaterMeterIoV3 waterMeterIoForNode(int nodeidx) throws IOException {
WaterMeterIo s = getService(WaterMeterIo.class);
return s.fetch(nodeidx).execute().body();
}
public WaterMeterIoV3 waterMeterIoForNode(int nodeidx, String _excludeFields) throws IOException {
WaterMeterIo s = getService(WaterMeterIo.class);
return s.fetch(nodeidx, _excludeFields).execute().body();
}
/**
* Return IO usage snapshot of all nodes in the H2O cluster.
*/
public WaterMeterIoV3 waterMeterIoForCluster() throws IOException {
WaterMeterIo s = getService(WaterMeterIo.class);
return s.fetch_all().execute().body();
}
public WaterMeterIoV3 waterMeterIoForCluster(int nodeidx) throws IOException {
WaterMeterIo s = getService(WaterMeterIo.class);
return s.fetch_all(nodeidx, "").execute().body();
}
public WaterMeterIoV3 waterMeterIoForCluster(int nodeidx, String _excludeFields) throws IOException {
WaterMeterIo s = getService(WaterMeterIo.class);
return s.fetch_all(nodeidx, _excludeFields).execute().body();
}
/**
* Return true or false.
*/
public NodePersistentStorageV3 npsContains(String category, String name) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.exists(category, name).execute().body();
}
public NodePersistentStorageV3 npsContains(NodePersistentStorageV3 params) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.exists(
params.category,
params.name,
params.value,
params._excludeFields
).execute().body();
}
/**
* Return true or false.
*/
public NodePersistentStorageV3 npsEnabled() throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.configured().execute().body();
}
public NodePersistentStorageV3 npsEnabled(NodePersistentStorageV3 params) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.configured(
params.category,
params.name,
params.value,
params._excludeFields
).execute().body();
}
/**
* Store a named value.
*/
public NodePersistentStorageV3 npsPut(String category, String name) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.put_with_name(category, name).execute().body();
}
public NodePersistentStorageV3 npsPut(NodePersistentStorageV3 params) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.put_with_name(
params.category,
params.name,
params.value,
params._excludeFields
).execute().body();
}
/**
* Return value for a given name.
*/
public NodePersistentStorageV3 npsGet(String category, String name) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.get_as_string(category, name).execute().body();
}
public NodePersistentStorageV3 npsGet(NodePersistentStorageV3 params) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.get_as_string(
params.category,
params.name,
params.value,
params._excludeFields
).execute().body();
}
/**
* Delete a key.
*/
public NodePersistentStorageV3 npsRemove(String category, String name) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.delete(category, name).execute().body();
}
public NodePersistentStorageV3 npsRemove(NodePersistentStorageV3 params) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.delete(
params.category,
params.name,
params.value,
params._excludeFields
).execute().body();
}
/**
* Store a value.
*/
public NodePersistentStorageV3 npsCreateCategory(String category) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.put(category).execute().body();
}
public NodePersistentStorageV3 npsCreateCategory(NodePersistentStorageV3 params) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.put(
params.category,
params.name,
params.value,
params._excludeFields
).execute().body();
}
/**
* Return all keys stored for a given category.
*/
public NodePersistentStorageV3 npsKeys(String category) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.list(category).execute().body();
}
public NodePersistentStorageV3 npsKeys(NodePersistentStorageV3 params) throws IOException {
NodePersistentStorage s = getService(NodePersistentStorage.class);
return s.list(
params.category,
params.name,
params.value,
params._excludeFields
).execute().body();
}
/**
* Get named log file for a node.
*/
public LogsV3 logs(String nodeidx, String name) throws IOException {
Logs s = getService(Logs.class);
return s.fetch(nodeidx, name).execute().body();
}
public LogsV3 logs(String nodeidx, String name, String _excludeFields) throws IOException {
Logs s = getService(Logs.class);
return s.fetch(nodeidx, name, _excludeFields).execute().body();
}
/**
* Kill minus 3 on *this* node
*/
public KillMinus3V3 logThreadDump() throws IOException {
KillMinus3 s = getService(KillMinus3.class);
return s.killm3().execute().body();
}
public KillMinus3V3 logThreadDump(String _excludeFields) throws IOException {
KillMinus3 s = getService(KillMinus3.class);
return s.killm3(_excludeFields).execute().body();
}
/**
* Execute an Rapids AstRoot.
*/
public RapidsSchemaV3 rapidsExec(String ast) throws IOException {
Rapids s = getService(Rapids.class);
return s.rapidsExec(ast).execute().body();
}
public RapidsSchemaV3 rapidsExec(RapidsSchemaV3 params) throws IOException {
Rapids s = getService(Rapids.class);
return s.rapidsExec(
params.ast,
params.sessionId,
params.id,
params._excludeFields
).execute().body();
}
/**
* Generate a Java POJO from the Assembly
*/
public AssemblyV99 _assembly_toJava(String assemblyId, String fileName) throws IOException {
Assembly s = getService(Assembly.class);
return s.toJava(assemblyId, fileName).execute().body();
}
public AssemblyV99 _assembly_toJava(AssemblyV99 params) throws IOException {
Assembly s = getService(Assembly.class);
return s.toJava(
params.assemblyId,
params.fileName,
params.steps,
keyToString(params.frame),
params._excludeFields
).execute().body();
}
/**
* Fit an assembly to an input frame
*/
public AssemblyV99 _assembly_fit() throws IOException {
Assembly s = getService(Assembly.class);
return s.fit().execute().body();
}
public AssemblyV99 _assembly_fit(AssemblyV99 params) throws IOException {
Assembly s = getService(Assembly.class);
return s.fit(
params.steps,
keyToString(params.frame),
params.fileName,
params.assemblyId,
params._excludeFields
).execute().body();
}
/**
* Download dataset as a CSV.
*/
public DownloadDataV3 _downloadDataset_fetch(FrameKeyV3 frameId) throws IOException {
DownloadDataset s = getService(DownloadDataset.class);
return s.fetch(keyToString(frameId)).execute().body();
}
public DownloadDataV3 _downloadDataset_fetch(FrameKeyV3 frameId, boolean hexString) throws IOException {
DownloadDataset s = getService(DownloadDataset.class);
return s.fetch(keyToString(frameId), hexString, "").execute().body();
}
public DownloadDataV3 _downloadDataset_fetch(FrameKeyV3 frameId, boolean hexString, String _excludeFields) throws IOException {
DownloadDataset s = getService(DownloadDataset.class);
return s.fetch(keyToString(frameId), hexString, _excludeFields).execute().body();
}
/**
* Download dataset as a CSV.
*/
public DownloadDataV3 _downloadDataset_fetchStreaming(FrameKeyV3 frameId) throws IOException {
DownloadDataset s = getService(DownloadDataset.class);
return s.fetchStreaming(keyToString(frameId)).execute().body();
}
public DownloadDataV3 _downloadDataset_fetchStreaming(FrameKeyV3 frameId, boolean hexString) throws IOException {
DownloadDataset s = getService(DownloadDataset.class);
return s.fetchStreaming(keyToString(frameId), hexString, "").execute().body();
}
public DownloadDataV3 _downloadDataset_fetchStreaming(FrameKeyV3 frameId, boolean hexString, String _excludeFields) throws IOException {
DownloadDataset s = getService(DownloadDataset.class);
return s.fetchStreaming(keyToString(frameId), hexString, _excludeFields).execute().body();
}
/**
* Remove an arbitrary key from the H2O distributed K/V store.
*/
public RemoveV3 deleteKey(KeyV3 key) throws IOException {
DKV s = getService(DKV.class);
return s.remove(keyToString(key)).execute().body();
}
public RemoveV3 deleteKey(KeyV3 key, boolean cascade) throws IOException {
DKV s = getService(DKV.class);
return s.remove(keyToString(key), cascade, "").execute().body();
}
public RemoveV3 deleteKey(KeyV3 key, boolean cascade, String _excludeFields) throws IOException {
DKV s = getService(DKV.class);
return s.remove(keyToString(key), cascade, _excludeFields).execute().body();
}
/**
* Remove all keys from the H2O distributed K/V store.
*/
public RemoveAllV3 deleteAllKeys() throws IOException {
DKV s = getService(DKV.class);
return s.removeAll().execute().body();
}
public RemoveAllV3 deleteAllKeys(KeyV3[] retainedKeys) throws IOException {
DKV s = getService(DKV.class);
return s.removeAll(keyArrayToStringArray(retainedKeys), "").execute().body();
}
public RemoveAllV3 deleteAllKeys(KeyV3[] retainedKeys, String _excludeFields) throws IOException {
DKV s = getService(DKV.class);
return s.removeAll(keyArrayToStringArray(retainedKeys), _excludeFields).execute().body();
}
/**
* Save a message to the H2O logfile.
*/
public LogAndEchoV3 logAndEcho() throws IOException {
LogAndEcho s = getService(LogAndEcho.class);
return s.echo().execute().body();
}
public LogAndEchoV3 logAndEcho(String message) throws IOException {
LogAndEcho s = getService(LogAndEcho.class);
return s.echo(message, "").execute().body();
}
public LogAndEchoV3 logAndEcho(String message, String _excludeFields) throws IOException {
LogAndEcho s = getService(LogAndEcho.class);
return s.echo(message, _excludeFields).execute().body();
}
/**
* Issue a new session ID.
*/
public InitIDV3 newSession() throws IOException {
InitID s = getService(InitID.class);
return s.startSession().execute().body();
}
public InitIDV3 newSession(String sessionKey) throws IOException {
InitID s = getService(InitID.class);
return s.startSession(sessionKey, "").execute().body();
}
public InitIDV3 newSession(String sessionKey, String _excludeFields) throws IOException {
InitID s = getService(InitID.class);
return s.startSession(sessionKey, _excludeFields).execute().body();
}
/**
* End a session.
*/
public InitIDV3 endSession() throws IOException {
InitID s = getService(InitID.class);
return s.endSession().execute().body();
}
public InitIDV3 endSession(String sessionKey) throws IOException {
InitID s = getService(InitID.class);
return s.endSession(sessionKey, "").execute().body();
}
public InitIDV3 endSession(String sessionKey, String _excludeFields) throws IOException {
InitID s = getService(InitID.class);
return s.endSession(sessionKey, _excludeFields).execute().body();
}
/**
* Set session property.
*/
public SessionPropertyV3 setSessionProperty() throws IOException {
SessionProperties s = getService(SessionProperties.class);
return s.setSessionProperty().execute().body();
}
public SessionPropertyV3 setSessionProperty(SessionPropertyV3 params) throws IOException {
SessionProperties s = getService(SessionProperties.class);
return s.setSessionProperty(
params.sessionKey,
params.key,
params.value,
params._excludeFields
).execute().body();
}
/**
* Get session property.
*/
public SessionPropertyV3 getSessionProperty() throws IOException {
SessionProperties s = getService(SessionProperties.class);
return s.getSessionProperty().execute().body();
}
public SessionPropertyV3 getSessionProperty(SessionPropertyV3 params) throws IOException {
SessionProperties s = getService(SessionProperties.class);
return s.getSessionProperty(
params.sessionKey,
params.key,
params.value,
params._excludeFields
).execute().body();
}
/**
* Explicitly call System.gc().
*/
public GarbageCollectV3 garbageCollect() throws IOException {
GarbageCollect s = getService(GarbageCollect.class);
return s.gc().execute().body();
}
/**
* Example of an experimental endpoint. Call via /EXPERIMENTAL/Sample. Experimental endpoints can change at any
* moment.
*/
public CloudV3 _sample_status() throws IOException {
Sample s = getService(Sample.class);
return s.status().execute().body();
}
public CloudV3 _sample_status(boolean skipTicks) throws IOException {
Sample s = getService(Sample.class);
return s.status(skipTicks, "").execute().body();
}
public CloudV3 _sample_status(boolean skipTicks, String _excludeFields) throws IOException {
Sample s = getService(Sample.class);
return s.status(skipTicks, _excludeFields).execute().body();
}
/**
* Produce help for Rapids AstRoot language.
*/
public RapidsHelpV3 rapids_help() throws IOException {
Rapids s = getService(Rapids.class);
return s.genHelp().execute().body();
}
/**
* Get metrics for Steam from H2O.
*/
public SteamMetricsV3 steamMetrics() throws IOException {
SteamMetrics s = getService(SteamMetrics.class);
return s.fetch().execute().body();
}
public SteamMetricsV3 steamMetrics(String _excludeFields) throws IOException {
SteamMetrics s = getService(SteamMetrics.class);
return s.fetch(_excludeFields).execute().body();
}
/**
* List of all registered capabilities
*/
public CapabilitiesV3 list_all_capabilities() throws IOException {
Capabilities s = getService(Capabilities.class);
return s.listAll().execute().body();
}
public CapabilitiesV3 list_all_capabilities(String _excludeFields) throws IOException {
Capabilities s = getService(Capabilities.class);
return s.listAll(_excludeFields).execute().body();
}
/**
* List registered core capabilities
*/
public CapabilitiesV3 list_core_capabilities() throws IOException {
Capabilities s = getService(Capabilities.class);
return s.listCore().execute().body();
}
public CapabilitiesV3 list_core_capabilities(String _excludeFields) throws IOException {
Capabilities s = getService(Capabilities.class);
return s.listCore(_excludeFields).execute().body();
}
/**
* List of all registered Rest API capabilities
*/
public CapabilitiesV3 list_rest_capabilities() throws IOException {
Capabilities s = getService(Capabilities.class);
return s.listRest().execute().body();
}
public CapabilitiesV3 list_rest_capabilities(String _excludeFields) throws IOException {
Capabilities s = getService(Capabilities.class);
return s.listRest(_excludeFields).execute().body();
}
/**
* Import previously saved grid model
*/
public GridKeyV3 import_grid(String gridPath) throws IOException {
Grid s = getService(Grid.class);
return s.importGrid(gridPath).execute().body();
}
public GridKeyV3 import_grid(String gridPath, boolean loadParamsReferences) throws IOException {
Grid s = getService(Grid.class);
return s.importGrid(gridPath, loadParamsReferences).execute().body();
}
/**
* Export a Grid and its models.
*/
public GridKeyV3 export_grid(String gridId, String gridDirectory) throws IOException {
Grid s = getService(Grid.class);
return s.exportGrid(gridId, gridDirectory).execute().body();
}
public GridKeyV3 export_grid(GridExportV3 params) throws IOException {
Grid s = getService(Grid.class);
return s.exportGrid(
params.gridId,
params.gridDirectory,
params.saveParamsReferences,
params.exportCrossValidationPredictions
).execute().body();
}
/**
* Recover stored state and resume interrupted job.
*/
public ResumeV3 recovery_resume() throws IOException {
Recovery s = getService(Recovery.class);
return s.resume().execute().body();
}
public ResumeV3 recovery_resume(String recoveryDir) throws IOException {
Recovery s = getService(Recovery.class);
return s.resume(recoveryDir).execute().body();
}
/**
* Returns the list of all REST API (v4) endpoints.
*/
public EndpointsListV4 endpoints4() throws IOException {
Endpoints s = getService(Endpoints.class);
return s.listRoutes4().execute().body();
}
public EndpointsListV4 endpoints4(String __schema) throws IOException {
Endpoints s = getService(Endpoints.class);
return s.listRoutes4(__schema).execute().body();
}
/**
* Start a new Rapids session, and return the session id.
*/
public SessionIdV4 newSession4() throws IOException {
Sessions s = getService(Sessions.class);
return s.newSession4().execute().body();
}
public SessionIdV4 newSession4(String _fields) throws IOException {
Sessions s = getService(Sessions.class);
return s.newSession4(_fields).execute().body();
}
/**
* Close the Rapids session.
*/
public InitIDV3 endSession4(String sessionKey) throws IOException {
Sessions s = getService(Sessions.class);
return s.endSession(sessionKey).execute().body();
}
public InitIDV3 endSession4(String sessionKey, String _excludeFields) throws IOException {
Sessions s = getService(Sessions.class);
return s.endSession(sessionKey, _excludeFields).execute().body();
}
/**
* Return basic information about all models available to train.
*/
public ModelsInfoV4 modelsInfo() throws IOException {
Modelsinfo s = getService(Modelsinfo.class);
return s.modelsInfo().execute().body();
}
public ModelsInfoV4 modelsInfo(String __schema) throws IOException {
Modelsinfo s = getService(Modelsinfo.class);
return s.modelsInfo(__schema).execute().body();
}
/**
* Create frame with random (uniformly distributed) data. You can specify how many columns of each type to make; and
* what the desired range for each column type.
*/
public JobV4 createSimpleFrame() throws IOException {
Frames s = getService(Frames.class);
return s.createSimpleFrame().execute().body();
}
public JobV4 createSimpleFrame(CreateFrameSimpleIV4 params) throws IOException {
Frames s = getService(Frames.class);
return s.createSimpleFrame(
keyToString(params.dest),
params.seed,
params.nrows,
params.ncolsReal,
params.ncolsInt,
params.ncolsEnum,
params.ncolsBool,
params.ncolsStr,
params.ncolsTime,
params.realLb,
params.realUb,
params.intLb,
params.intUb,
params.enumNlevels,
params.boolP,
params.timeLb,
params.timeUb,
params.strLength,
params.missingFraction,
params.responseType,
params.responseLb,
params.responseUb,
params.responseP,
params.responseNlevels,
params._fields
).execute().body();
}
/**
* Retrieve information about the current state of a job.
*/
public JobV4 getJob4(String jobId) throws IOException {
Jobs s = getService(Jobs.class);
return s.getJob4(jobId).execute().body();
}
public JobV4 getJob4(String jobId, String _fields) throws IOException {
Jobs s = getService(Jobs.class);
return s.getJob4(jobId, _fields).execute().body();
}
//--------- PRIVATE --------------------------------------------------------------------------------------------------
private Retrofit retrofit;
private String _url = DEFAULT_URL;
private int timeout_s = 60;
private int pollInterval_ms = 1000;
private void initializeRetrofit() {
Gson gson = new GsonBuilder()
.registerTypeAdapterFactory(new ModelV3TypeAdapter())
.registerTypeAdapter(KeyV3.class, new KeySerializer())
.registerTypeAdapter(ColSpecifierV3.class, new ColSerializer())
.registerTypeAdapter(ModelBuilderSchema.class, new ModelDeserializer())
.registerTypeAdapter(ModelSchemaBaseV3.class, new ModelSchemaDeserializer())
.registerTypeAdapter(ModelOutputSchemaV3.class, new ModelOutputDeserializer())
.registerTypeAdapter(ModelParametersSchemaV3.class, new ModelParametersDeserializer())
.create();
OkHttpClient client = new OkHttpClient.Builder()
.connectTimeout(timeout_s, TimeUnit.SECONDS)
.writeTimeout(timeout_s, TimeUnit.SECONDS)
.readTimeout(timeout_s, TimeUnit.SECONDS)
.build();
this.retrofit = new Retrofit.Builder()
.client(client)
.baseUrl(_url)
.addConverterFactory(GsonConverterFactory.create(gson))
.build();
}
private Retrofit getRetrofit() {
if (retrofit == null) initializeRetrofit();
return retrofit;
}
private <T> T getService(Class<T> clazz) {
return getRetrofit().create(clazz);
}
/**
* Keys get sent as Strings and returned as objects also containing the type and URL,
* so they need a custom GSON serializer.
*/
private static class KeySerializer implements JsonSerializer<KeyV3>, JsonDeserializer<KeyV3> {
@Override
public JsonElement serialize(KeyV3 key, Type typeOfKey, JsonSerializationContext context) {
return new JsonPrimitive(key.name);
}
@Override
public KeyV3 deserialize(JsonElement json, Type typeOfT, JsonDeserializationContext context) {
if (json.isJsonNull()) return null;
JsonObject jobj = json.getAsJsonObject();
String type = jobj.get("type").getAsString();
switch (type) {
// TODO: dynamically generate all possible cases
case "Key<Model>": return context.deserialize(jobj, ModelKeyV3.class);
case "Key<Job>": return context.deserialize(jobj, JobKeyV3.class);
case "Key<Grid>": return context.deserialize(jobj, GridKeyV3.class);
case "Key<Frame>": return context.deserialize(jobj, FrameKeyV3.class);
default: throw new JsonParseException("Unable to deserialize key of type " + type);
}
}
}
private static class ColSerializer implements JsonSerializer<ColSpecifierV3> {
@Override
public JsonElement serialize(ColSpecifierV3 col, Type typeOfCol, JsonSerializationContext context) {
return new JsonPrimitive(col.columnName);
}
}
/**
* Factory method for parsing a ModelBuilderSchema json object into an instance of the model-specific subclass.
*/
private static class ModelDeserializer implements JsonDeserializer<ModelBuilderSchema> {
@Override
public ModelBuilderSchema deserialize(JsonElement json, Type typeOfT, JsonDeserializationContext context)
throws JsonParseException {
if (json.isJsonNull()) return null;
if (json.isJsonObject()) {
JsonObject jobj = json.getAsJsonObject();
if (jobj.has("algo")) {
String algo = jobj.get("algo").getAsJsonPrimitive().getAsString().toLowerCase();
switch (algo) {
case "xgboost": return context.deserialize(json, XGBoostV3.class);
case "infogram": return context.deserialize(json, InfogramV3.class);
case "targetencoder": return context.deserialize(json, TargetEncoderV3.class);
case "deeplearning": return context.deserialize(json, DeepLearningV3.class);
case "glm": return context.deserialize(json, GLMV3.class);
case "glrm": return context.deserialize(json, GLRMV3.class);
case "kmeans": return context.deserialize(json, KMeansV3.class);
case "naivebayes": return context.deserialize(json, NaiveBayesV3.class);
case "pca": return context.deserialize(json, PCAV3.class);
case "svd": return context.deserialize(json, SVDV99.class);
case "drf": return context.deserialize(json, DRFV3.class);
case "gbm": return context.deserialize(json, GBMV3.class);
case "isolationforest": return context.deserialize(json, IsolationForestV3.class);
case "extendedisolationforest": return context.deserialize(json, ExtendedIsolationForestV3.class);
case "aggregator": return context.deserialize(json, AggregatorV99.class);
case "word2vec": return context.deserialize(json, Word2VecV3.class);
case "stackedensemble": return context.deserialize(json, StackedEnsembleV99.class);
case "coxph": return context.deserialize(json, CoxPHV3.class);
case "generic": return context.deserialize(json, GenericV3.class);
case "gam": return context.deserialize(json, GAMV3.class);
case "anovaglm": return context.deserialize(json, ANOVAGLMV3.class);
case "psvm": return context.deserialize(json, PSVMV3.class);
case "rulefit": return context.deserialize(json, RuleFitV3.class);
case "upliftdrf": return context.deserialize(json, UpliftDRFV3.class);
case "modelselection": return context.deserialize(json, ModelSelectionV3.class);
case "isotonicregression": return context.deserialize(json, IsotonicRegressionV3.class);
case "dt": return context.deserialize(json, DTV3.class);
case "hglm": return context.deserialize(json, HGLMV3.class);
case "adaboost": return context.deserialize(json, AdaBoostV3.class);
default:
throw new JsonParseException("Unable to deserialize model of type " + algo);
}
}
}
throw new JsonParseException("Invalid ModelBuilderSchema element " + json.toString());
}
}
/**
* Factory method for parsing a ModelSchemaBaseV3 json object into an instance of the model-specific subclass.
*/
private static class ModelSchemaDeserializer implements JsonDeserializer<ModelSchemaBaseV3> {
@Override
public ModelSchemaBaseV3 deserialize(JsonElement json, Type typeOfT, JsonDeserializationContext context)
throws JsonParseException {
if (json.isJsonNull()) return null;
if (json.isJsonObject()) {
JsonObject jobj = json.getAsJsonObject();
if (jobj.has("algo")) {
String algo = jobj.get("algo").getAsJsonPrimitive().getAsString().toLowerCase();
switch (algo) {
case "xgboost": return context.deserialize(json, XGBoostModelV3.class);
case "infogram": return context.deserialize(json, InfogramModelV3.class);
case "targetencoder": return context.deserialize(json, TargetEncoderModelV3.class);
case "deeplearning": return context.deserialize(json, DeepLearningModelV3.class);
case "glm": return context.deserialize(json, GLMModelV3.class);
case "glrm": return context.deserialize(json, GLRMModelV3.class);
case "kmeans": return context.deserialize(json, KMeansModelV3.class);
case "naivebayes": return context.deserialize(json, NaiveBayesModelV3.class);
case "pca": return context.deserialize(json, PCAModelV3.class);
case "svd": return context.deserialize(json, SVDModelV99.class);
case "drf": return context.deserialize(json, DRFModelV3.class);
case "gbm": return context.deserialize(json, GBMModelV3.class);
case "isolationforest": return context.deserialize(json, IsolationForestModelV3.class);
case "extendedisolationforest": return context.deserialize(json, ExtendedIsolationForestModelV3.class);
case "aggregator": return context.deserialize(json, AggregatorModelV99.class);
case "word2vec": return context.deserialize(json, Word2VecModelV3.class);
case "stackedensemble": return context.deserialize(json, StackedEnsembleModelV99.class);
case "coxph": return context.deserialize(json, CoxPHModelV3.class);
case "generic": return context.deserialize(json, GenericModelV3.class);
case "gam": return context.deserialize(json, GAMModelV3.class);
case "anovaglm": return context.deserialize(json, ANOVAGLMModelV3.class);
case "psvm": return context.deserialize(json, PSVMModelV3.class);
case "rulefit": return context.deserialize(json, RuleFitModelV3.class);
case "upliftdrf": return context.deserialize(json, UpliftDRFModelV3.class);
case "modelselection": return context.deserialize(json, ModelSelectionModelV3.class);
case "isotonicregression": return context.deserialize(json, IsotonicRegressionModelV3.class);
case "dt": return context.deserialize(json, DTModelV3.class);
case "hglm": return context.deserialize(json, HGLMModelV3.class);
case "adaboost": return context.deserialize(json, AdaBoostModelV3.class);
default:
throw new JsonParseException("Unable to deserialize model of type " + algo);
}
}
}
throw new JsonParseException("Invalid ModelSchemaBaseV3 element " + json.toString());
}
}
/**
* Factory method for parsing a ModelOutputSchemaV3 json object into an instance of the model-specific subclass.
*/
private static class ModelOutputDeserializer implements JsonDeserializer<ModelOutputSchemaV3> {
@Override
public ModelOutputSchemaV3 deserialize(JsonElement json, Type typeOfT, JsonDeserializationContext context)
throws JsonParseException {
if (json.isJsonNull()) return null;
if (json.isJsonObject()) {
JsonObject jobj = json.getAsJsonObject();
if (jobj.has("algo")) {
String algo = jobj.get("algo").getAsJsonPrimitive().getAsString().toLowerCase();
switch (algo) {
case "xgboost": return context.deserialize(json, XGBoostModelOutputV3.class);
case "infogram": return context.deserialize(json, InfogramModelOutputV3.class);
case "targetencoder": return context.deserialize(json, TargetEncoderModelOutputV3.class);
case "deeplearning": return context.deserialize(json, DeepLearningModelOutputV3.class);
case "glm": return context.deserialize(json, GLMModelOutputV3.class);
case "glrm": return context.deserialize(json, GLRMModelOutputV3.class);
case "kmeans": return context.deserialize(json, KMeansModelOutputV3.class);
case "naivebayes": return context.deserialize(json, NaiveBayesModelOutputV3.class);
case "pca": return context.deserialize(json, PCAModelOutputV3.class);
case "svd": return context.deserialize(json, SVDModelOutputV99.class);
case "drf": return context.deserialize(json, DRFModelOutputV3.class);
case "gbm": return context.deserialize(json, GBMModelOutputV3.class);
case "isolationforest": return context.deserialize(json, IsolationForestModelOutputV3.class);
case "extendedisolationforest": return context.deserialize(json, ExtendedIsolationForestModelOutputV3.class);
case "aggregator": return context.deserialize(json, AggregatorModelOutputV99.class);
case "word2vec": return context.deserialize(json, Word2VecModelOutputV3.class);
case "stackedensemble": return context.deserialize(json, StackedEnsembleModelOutputV99.class);
case "coxph": return context.deserialize(json, CoxPHModelOutputV3.class);
case "generic": return context.deserialize(json, GenericModelOutputV3.class);
case "gam": return context.deserialize(json, GAMModelOutputV3.class);
case "anovaglm": return context.deserialize(json, ANOVAGLMModelOutputV3.class);
case "psvm": return context.deserialize(json, PSVMModelOutputV3.class);
case "rulefit": return context.deserialize(json, RuleFitModelOutputV3.class);
case "upliftdrf": return context.deserialize(json, UpliftDRFModelOutputV3.class);
case "modelselection": return context.deserialize(json, ModelSelectionModelOutputV3.class);
case "isotonicregression": return context.deserialize(json, IsotonicRegressionModelOutputV3.class);
case "dt": return context.deserialize(json, DTModelOutputV3.class);
case "hglm": return context.deserialize(json, HGLMModelOutputV3.class);
case "adaboost": return context.deserialize(json, AdaBoostModelOutputV3.class);
default:
throw new JsonParseException("Unable to deserialize model of type " + algo);
}
}
}
throw new JsonParseException("Invalid ModelOutputSchemaV3 element " + json.toString());
}
}
/**
* Factory method for parsing a ModelParametersSchemaV3 json object into an instance of the model-specific subclass.
*/
private static class ModelParametersDeserializer implements JsonDeserializer<ModelParametersSchemaV3> {
@Override
public ModelParametersSchemaV3 deserialize(JsonElement json, Type typeOfT, JsonDeserializationContext context)
throws JsonParseException {
if (json.isJsonNull()) return null;
if (json.isJsonObject()) {
JsonObject jobj = json.getAsJsonObject();
if (jobj.has("algo")) {
String algo = jobj.get("algo").getAsJsonPrimitive().getAsString().toLowerCase();
switch (algo) {
case "xgboost": return context.deserialize(json, XGBoostParametersV3.class);
case "infogram": return context.deserialize(json, InfogramParametersV3.class);
case "targetencoder": return context.deserialize(json, TargetEncoderParametersV3.class);
case "deeplearning": return context.deserialize(json, DeepLearningParametersV3.class);
case "glm": return context.deserialize(json, GLMParametersV3.class);
case "glrm": return context.deserialize(json, GLRMParametersV3.class);
case "kmeans": return context.deserialize(json, KMeansParametersV3.class);
case "naivebayes": return context.deserialize(json, NaiveBayesParametersV3.class);
case "pca": return context.deserialize(json, PCAParametersV3.class);
case "svd": return context.deserialize(json, SVDParametersV99.class);
case "drf": return context.deserialize(json, DRFParametersV3.class);
case "gbm": return context.deserialize(json, GBMParametersV3.class);
case "isolationforest": return context.deserialize(json, IsolationForestParametersV3.class);
case "extendedisolationforest": return context.deserialize(json, ExtendedIsolationForestParametersV3.class);
case "aggregator": return context.deserialize(json, AggregatorParametersV99.class);
case "word2vec": return context.deserialize(json, Word2VecParametersV3.class);
case "stackedensemble": return context.deserialize(json, StackedEnsembleParametersV99.class);
case "coxph": return context.deserialize(json, CoxPHParametersV3.class);
case "generic": return context.deserialize(json, GenericParametersV3.class);
case "gam": return context.deserialize(json, GAMParametersV3.class);
case "anovaglm": return context.deserialize(json, ANOVAGLMParametersV3.class);
case "psvm": return context.deserialize(json, PSVMParametersV3.class);
case "rulefit": return context.deserialize(json, RuleFitParametersV3.class);
case "upliftdrf": return context.deserialize(json, UpliftDRFParametersV3.class);
case "modelselection": return context.deserialize(json, ModelSelectionParametersV3.class);
case "isotonicregression": return context.deserialize(json, IsotonicRegressionParametersV3.class);
case "dt": return context.deserialize(json, DTParametersV3.class);
case "hglm": return context.deserialize(json, HGLMParametersV3.class);
case "adaboost": return context.deserialize(json, AdaBoostParametersV3.class);
default:
throw new JsonParseException("Unable to deserialize model of type " + algo);
}
}
}
throw new JsonParseException("Invalid ModelParametersSchemaV3 element " + json.toString());
}
}
private static class ModelV3TypeAdapter implements TypeAdapterFactory {
@Override
public <T> TypeAdapter<T> create(Gson gson, TypeToken<T> type) {
final Class<? super T> rawType = type.getRawType();
if (!ModelBuilderSchema.class.isAssignableFrom(rawType) &&
!ModelSchemaBaseV3.class.isAssignableFrom(rawType)) return null;
final TypeAdapter<T> delegate = gson.getDelegateAdapter(this, type);
return new TypeAdapter<T>() {
@Override
public void write(JsonWriter out, T value) throws IOException {
delegate.write(out, value);
}
@Override
public T read(JsonReader in) throws IOException {
JsonObject jobj = new JsonParser().parse(in).getAsJsonObject();
if (jobj.has("parameters") && jobj.get("parameters").isJsonArray()) {
JsonArray jarr = jobj.get("parameters").getAsJsonArray();
JsonObject paramsNew = new JsonObject();
for (JsonElement item : jarr) {
JsonObject itemObj = item.getAsJsonObject();
paramsNew.add(itemObj.get("name").getAsString(), itemObj.get("actual_value"));
}
jobj.add("parameters", paramsNew);
}
// noinspection unchecked
return (T) new Gson().fromJson(jobj, rawType);
}
};
}
}
/**
* Return an array of Strings for an array of keys.
*/
public static String[] keyArrayToStringArray(KeyV3[] keys) {
if (keys == null) return null;
String[] ids = new String[keys.length];
int i = 0;
for (KeyV3 key : keys) ids[i++] = key.name;
return ids;
}
/**
* Return an array of keys from an array of Strings.
* @param ids array of string ids to convert to KeyV3's
* @param clz class of key objects to create. Since we have JobKeyV3, FrameKeyV3, ModelKeyV3, etc -- this
* method needs to know which of these keys you want to create
*/
public static <T extends KeyV3> T[] stringArrayToKeyArray(String[] ids, Class<T> clz) {
if (ids == null) return null;
// noinspection unchecked
T[] keys = (T[]) Array.newInstance(clz, ids.length);
String keyType = clz.getSimpleName();
if (keyType.endsWith("KeyV3")) keyType = keyType.substring(0, keyType.length()-5);
try {
int i = 0;
for (String id: ids) {
keys[i] = clz.getConstructor().newInstance();
keys[i].name = id;
keys[i].type = keyType;
i++;
}
}
catch (Exception e) {
e.printStackTrace();
}
return keys;
}
/**
*
*/
public static String keyToString(KeyV3 key) {
return key == null? null : key.name;
}
/**
*
*/
public static FrameKeyV3 stringToFrameKey(String key) {
if (key == null) return null;
FrameKeyV3 k = new FrameKeyV3();
k.name = key;
return k;
}
/**
*
*/
private static String colToString(ColSpecifierV3 col) {
return col == null? null : col.columnName;
}
/**
*
*/
public static String stringToCol(String col) {
if (col == null) return null;
ColSpecifierV3 c = new ColSpecifierV3();
c.columnName = col;
return col;
}
public static void copyFields(Object to, Object from) {
Field[] fromFields = from.getClass().getDeclaredFields();
Field[] toFields = to.getClass().getDeclaredFields();
for (Field fromField : fromFields){
Field toField;
try {
toField = to.getClass().getDeclaredField(fromField.getName());
fromField.setAccessible(true);
toField.setAccessible(true);
toField.set(to, fromField.get(from));
}
catch (Exception ignored) {
// NoSuchField is the normal case
}
}
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/examples/Example.java
|
package water.bindings.examples;
import com.google.gson.*;
import retrofit2.Response;
import retrofit2.Retrofit;
import retrofit2.converter.gson.GsonConverterFactory;
import water.bindings.pojos.*;
import water.bindings.proxies.retrofit.*;
import java.io.IOException;
import java.lang.reflect.Type;
import java.util.ArrayList;
import java.util.List;
/**
* Example of using generated Java bindings for REST API.
*
* Before use, please, make sure you generated the bindings
* via Gradle: `gradlew
*/
public class Example {
/**
* Keys get sent as Strings and returned as objects also containing the type and URL,
* so they need a custom GSON serializer.
*/
private static class KeySerializer implements JsonSerializer<KeyV3> {
public JsonElement serialize(KeyV3 key, Type typeOfKey, JsonSerializationContext context) {
return new JsonPrimitive(key.name);
}
}
/**
* KeysColSpecifiers get sent as Strings and returned as objects also containing a list of Frames that the col must be a member of,
* so they need a custom GSON serializer.
private static class ColSpecifierSerializer implements JsonSerializer<ColSpecifierV3> {
public JsonElement serialize(ColSpecifierV3 cs, Type t, JsonSerializationContext context) {
return new JsonPrimitive(cs.column_name);
}
}
*/
public static JobV3 poll(Retrofit retrofit, String job_id) {
Jobs jobsService = retrofit.create(Jobs.class);
Response<JobsV3> jobsResponse = null;
int retries = 3;
JobsV3 jobs = null;
do {
try {
jobsResponse = jobsService.fetch(job_id).execute();
}
catch (IOException e) {
System.err.println("Caught exception: " + e);
}
if (! jobsResponse.isSuccessful())
if (retries-- > 0)
continue;
else
throw new RuntimeException("/3/Jobs/{job_id} failed 3 times.");
jobs = jobsResponse.body();
if (null == jobs.jobs || jobs.jobs.length != 1)
throw new RuntimeException("Failed to find Job: " + job_id);
if (! "RUNNING".equals(jobs.jobs[0].status)) try { Thread.sleep(100); } catch (InterruptedException e) {} // wait 100mS
} while ("RUNNING".equals(jobs.jobs[0].status));
return jobs.jobs[0];
}
public static void gbm_example_flow() {
GsonBuilder builder = new GsonBuilder();
builder.registerTypeAdapter(KeyV3.class, new KeySerializer());
// builder.registerTypeAdapter(ColSpecifierV3.class, new ColSpecifierSerializer());
Gson gson = builder.create();
Retrofit retrofit = new Retrofit.Builder()
.baseUrl("http://localhost:54321/") // note trailing slash for Retrofit 2
.addConverterFactory(GsonConverterFactory.create(gson))
.build();
ImportFiles importService = retrofit.create(ImportFiles.class);
ParseSetup parseSetupService = retrofit.create(ParseSetup.class);
Parse parseService = retrofit.create(Parse.class);
Frames framesService = retrofit.create(Frames.class);
Models modelsService = retrofit.create(Models.class);
ModelBuilders modelBuildersService = retrofit.create(ModelBuilders.class);
Predictions predictionsService = retrofit.create(Predictions.class);
JobV3 job = null;
try {
// STEP 1: import raw file
ImportFilesV3 importBody = importService.importFiles("http://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/arrhythmia.csv.gz", null, null).execute().body();
System.out.println("import: " + importBody);
// STEP 2: parse setup
ParseSetupV3 parseSetupBody = parseSetupService.guessSetup(importBody.destinationFrames,
ApiParseTypeValuesProvider.GUESS,
(byte)',',
false,
-1,
null,
null,
null,
null,
null,
0,
0,
0,
null,
null,
null,
(byte)'\\',
false,
false,
null).execute().body();
System.out.println("parseSetupBody: " + parseSetupBody);
// STEP 3: parse into columnar Frame
List<String> source_frames = new ArrayList<>();
for (FrameKeyV3 frame : parseSetupBody.sourceFrames)
source_frames.add(frame.name);
ParseV3 parseBody = parseService.parse("arrhythmia.hex",
source_frames.toArray(new String[0]),
parseSetupBody.parseType,
parseSetupBody.separator,
parseSetupBody.singleQuotes,
parseSetupBody.checkHeader,
parseSetupBody.numberColumns,
parseSetupBody.columnNames,
parseSetupBody.columnTypes,
parseSetupBody.skippedColumns,
parseSetupBody.forceColTypes,
null, // domains
parseSetupBody.naStrings,
parseSetupBody.chunkSize,
true,
true,
null,
null,
null,
parseSetupBody.escapechar,
false,
null).execute().body();
System.out.println("parseBody: " + parseBody);
// STEP 5: Train the model (NOTE: step 4 is polling, which we don't require because we specified blocking for the parse above)
GBMParametersV3 gbmParms = new GBMParametersV3();
FrameKeyV3 trainingFrame = new FrameKeyV3();
trainingFrame.name = "arrhythmia.hex";
gbmParms.trainingFrame = trainingFrame;
ColSpecifierV3 responseColumn = new ColSpecifierV3();
responseColumn.columnName = "C1";
gbmParms.responseColumn = responseColumn;
System.out.println("About to train GBM. . .");
GBMV3 gbmBody = (GBMV3)ModelBuilders.Helper.trainGbm(modelBuildersService, gbmParms).execute().body();
System.out.println("gbmBody: " + gbmBody);
// STEP 6: poll for completion
job = gbmBody.job;
if (null == job || null == job.key)
throw new RuntimeException("train_gbm returned a bad Job: " + job);
job = poll(retrofit, job.key.name);
System.out.println("GBM build done.");
// STEP 7: fetch the model
// TODO: Retrofit seems to be only deserializing the base class. What to do?
KeyV3 modelKey = job.dest;
ModelsV3 models = modelsService.fetch(modelKey.name).execute().body();
System.out.println("models: " + models);
// GBMModelV3 model = (GBMModelV3)models.models[0];
// System.out.println("new GBM model: " + model);
System.out.println("new GBM model: " + models.models[0]);
// STEP 8: predict!
ModelMetricsListSchemaV3 predictions = predictionsService.predict(modelKey.name,
trainingFrame.name,
"predictions",
null,
false, false, -1, null,
false, false, false, false, null,
false, false, false, null, -1, -1, false, false, -1, false, null, null, null, null, -1, null, false, false, null).execute().body();
System.out.println("predictions: " + predictions);
}
catch (IOException e) {
System.err.println("Caught exception: " + e);
}
}
public static void simple_example() {
Gson gson = new GsonBuilder().registerTypeAdapter(KeyV3.class, new KeySerializer()).create();
Retrofit retrofit = new Retrofit.Builder()
.baseUrl("http://localhost:54321/") // note trailing slash for Retrofit 2
.addConverterFactory(GsonConverterFactory.create(gson))
.build();
CreateFrame createFrameService = retrofit.create(CreateFrame.class);
Frames framesService = retrofit.create(Frames.class);
Models modelsService = retrofit.create(Models.class);
try {
// NOTE: the Call objects returned by the service can't be reused, but they can be cloned.
Response<FramesListV3> all_frames_response = framesService.list().execute();
Response<ModelsV3> all_models_response = modelsService.list().execute();
if (all_frames_response.isSuccessful()) {
FramesListV3 all_frames = all_frames_response.body();
System.out.println("All Frames: ");
System.out.println(all_frames);
} else {
System.err.println("framesService.list() failed");
}
if (all_models_response.isSuccessful()) {
ModelsV3 all_models = all_models_response.body();
System.out.println("All Models: ");
System.out.println(all_models);
} else {
System.err.println("modelsService.list() failed");
}
Response<JobV3> create_frame_response = createFrameService.run(null, 1000, 100, 42, 42, true, 0, 100000, 0.2, 100, 0.2, 32767, 0.2, 0.5, 0.2, 0, 0.2, true, 2, true, null).execute();
if (create_frame_response.isSuccessful()) {
JobV3 job = create_frame_response.body();
if (null == job || null == job.key)
throw new RuntimeException("CreateFrame returned a bad Job: " + job);
job = poll(retrofit, job.key.name);
KeyV3 new_frame = job.dest;
System.out.println("Created frame: " + new_frame);
all_frames_response = framesService.list().execute();
if (all_frames_response.isSuccessful()) {
FramesListV3 all_frames = all_frames_response.body();
System.out.println("All Frames (after createFrame): ");
System.out.println(all_frames);
} else {
System.err.println("framesService.list() failed");
}
Response<FramesV3> one_frame_response = framesService.fetch(new_frame.name).execute();
if (one_frame_response.isSuccessful()) {
FramesV3 one_frames = one_frame_response.body();
System.out.println("One Frame (after createFrame): ");
System.out.println(one_frames);
} else {
System.err.println("framesService.fetch() failed");
}
} else {
System.err.println("createFrameService.run() failed");
}
}
catch (IOException e) {
System.err.println("Caught exception: " + e);
}
} // simple_example()
public static void main (String[] args) {
gbm_example_flow();
simple_example();
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/examples
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/examples/retrofit/GBM_Example.java
|
package water.bindings.examples.retrofit;
import water.bindings.H2oApi;
import water.bindings.pojos.*;
import java.io.File;
import java.io.IOException;
import java.util.UUID;
public class GBM_Example {
public static void gbmExampleFlow(String url) throws IOException {
H2oApi h2o = url != null ? new H2oApi(url) : new H2oApi();
// Utility var:
JobV3 job = null;
// STEP 0: init a session
String sessionId = h2o.newSession().sessionKey;
// STEP 1: import raw file
ImportFilesV3 importBody = h2o.importFiles(
"http://s3.amazonaws.com/h2o-public-test-data/smalldata/flow_examples/arrhythmia.csv.gz", null
);
System.out.println("import: " + importBody);
// STEP 2: parse setup
ParseSetupV3 parseSetupBody = h2o.guessParseSetup(H2oApi.stringArrayToKeyArray(importBody.destinationFrames, FrameKeyV3.class));
System.out.println("parseSetupBody: " + parseSetupBody);
// STEP 3: parse into columnar Frame
ParseV3 parseParms = new ParseV3();
H2oApi.copyFields(parseParms, parseSetupBody);
parseParms.destinationFrame = H2oApi.stringToFrameKey("arrhythmia.hex");
parseParms.blocking = true; // alternately, call h2o.waitForJobCompletion(parseSetupBody.job)
ParseV3 parseBody = h2o.parse(parseParms);
System.out.println("parseBody: " + parseBody);
// STEP 4: Split into test and train datasets
String tmpVec = "tmp_" + UUID.randomUUID().toString();
String splitExpr =
"(, " +
" (tmp= " + tmpVec + " (h2o.runif arrhythmia.hex 906317))" +
" (assign train " +
" (rows arrhythmia.hex (<= " + tmpVec + " 0.75)))" +
" (assign test " +
" (rows arrhythmia.hex (> " + tmpVec + " 0.75)))" +
" (rm " + tmpVec + "))";
RapidsSchemaV3 rapidsParms = new RapidsSchemaV3();
rapidsParms.sessionId = sessionId;
rapidsParms.ast = splitExpr;
h2o.rapidsExec(rapidsParms);
// STEP 5: Train the model (NOTE: step 4 is polling, which we don't require because we specified blocking for the parse above)
GBMParametersV3 gbmParms = new GBMParametersV3();
// gbmParms.trainingFrame = H2oApi.stringToFrameKey("arrhythmia.hex");
gbmParms.trainingFrame = H2oApi.stringToFrameKey("train");
gbmParms.validationFrame = H2oApi.stringToFrameKey("test");
ColSpecifierV3 responseColumn = new ColSpecifierV3();
responseColumn.columnName = "C1";
gbmParms.responseColumn = responseColumn;
System.out.println("About to train GBM. . .");
GBMV3 gbmBody = h2o.train_gbm(gbmParms);
System.out.println("gbmBody: " + gbmBody);
// STEP 6: poll for completion
job = h2o.waitForJobCompletion(gbmBody.job.key);
System.out.println("GBM build done.");
// STEP 7: fetch the model
ModelKeyV3 model_key = (ModelKeyV3)job.dest;
ModelsV3 models = h2o.model(model_key);
System.out.println("models: " + models);
GBMModelV3 model = (GBMModelV3)models.models[0];
System.out.println("new GBM model: " + model);
// System.out.println("new GBM model: " + models.models[0]);
assert model.getClass() == GBMModelV3.class;
assert model.output.getClass() == GBMModelOutputV3.class;
assert model.parameters.getClass() == GBMParametersV3.class;
// STEP 9 (optional): export model as binary
ModelExportV3 modelExport = new ModelExportV3();
modelExport.modelId = model_key;
File binaryModelFile = File.createTempFile("model", ".h2o");
modelExport.dir = File.createTempFile("model", ".h2o").getPath();
binaryModelFile.deleteOnExit();
// STEP 8: predict!
ModelMetricsListSchemaV3 predict_params = new ModelMetricsListSchemaV3();
predict_params.model = model_key;
predict_params.frame = gbmParms.trainingFrame;
predict_params.predictionsFrame = H2oApi.stringToFrameKey("predictions");
ModelMetricsListSchemaV3 predictions = h2o.predict(predict_params);
System.out.println("predictions: " + predictions);
// STEP 99: end the session
h2o.endSession();
}
public static void gbmExampleFlow() throws IOException {
gbmExampleFlow(null);
}
public static void main (String[] args) throws IOException {
gbmExampleFlow();
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/examples
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/examples/retrofit/ImportPatternExample.java
|
package water.bindings.examples.retrofit;
import water.bindings.H2oApi;
import water.bindings.pojos.*;
import java.io.IOException;
import java.util.Arrays;
public class ImportPatternExample {
public static void importPatternExample(String url) throws IOException {
H2oApi h2o = url != null ? new H2oApi(url) : new H2oApi();
//Set url
if (url != null) {
h2o.setUrl(url);
}
//Util var
JobV3 job = null;
//Init h2o session
String sessionId = h2o.newSession().sessionKey;
//Import and parse files based on regex pattern
{ //prostate dataset (Single file)
String pattern = "prostate_0.*"; //Regex pattern of file to import
ImportFilesV3 importBody = h2o.importFiles("../smalldata/junit/parse_folder", pattern);
ParseSetupV3 parseSetupParams = new ParseSetupV3();
parseSetupParams.sourceFrames = H2oApi.stringArrayToKeyArray(importBody.destinationFrames, FrameKeyV3.class);
parseSetupParams.checkHeader = 1;
ParseSetupV3 parseSetupBody = h2o.guessParseSetup(parseSetupParams);
ParseV3 parseParms = new ParseV3();
H2oApi.copyFields(parseParms, parseSetupBody);
parseParms.destinationFrame = H2oApi.stringToFrameKey("prostate");
parseParms.blocking = true;
ParseV3 parseBody = h2o.parse(parseParms);
assert importBody.files.length == 1;
String[] parsedFiles = new String[importBody.files.length];
for(int i = 0; i < importBody.files.length; i ++){
parsedFiles[i] = importBody.files[i].substring(importBody.files[0].lastIndexOf("/")+1);
}
String[] result = {"prostate_0.csv"};
assert parseBody.numberColumns == 9;
assert parseBody.rows == 10;
String[] colNames = {"ID", "CAPSULE", "AGE", "RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"};
assert Arrays.equals(parseBody.columnNames,colNames);
}
{ //iris dataset (Single file)
String pattern = "iris_.*_correct.*"; //Regex pattern of file to import
ImportFilesV3 importBody = h2o.importFiles("../smalldata/iris", pattern);
ParseSetupV3 parseSetupParams = new ParseSetupV3();
parseSetupParams.sourceFrames = H2oApi.stringArrayToKeyArray(importBody.destinationFrames, FrameKeyV3.class);
parseSetupParams.checkHeader = 1;
ParseSetupV3 parseSetupBody = h2o.guessParseSetup(parseSetupParams);
ParseV3 parseParms = new ParseV3();
H2oApi.copyFields(parseParms, parseSetupBody);
parseParms.destinationFrame = H2oApi.stringToFrameKey("iris");
parseParms.blocking = true;
ParseV3 parseBody = h2o.parse(parseParms);
assert importBody.files.length == 1;
String[] parsedFiles = new String[importBody.files.length];
for(int i = 0; i < importBody.files.length; i ++){
parsedFiles[i] = importBody.files[i].substring(importBody.files[i].lastIndexOf("/")+1);
}
String[] result = {"iris_wheader_correct.csv"};
assert Arrays.equals(parsedFiles,result);
assert parseBody.numberColumns == 5;
assert parseBody.rows == 150;
String[] colNames = {"sepal_length", "sepal_width", "petal_length", "petal_width", "species"};
assert Arrays.equals(parseBody.columnNames,colNames);
}
{ //GBM datasets (Multiple files)
String pattern = "50_.*"; //Regex pattern of files to import
ImportFilesV3 importBody = h2o.importFiles("../smalldata/gbm_test", pattern);
ParseSetupV3 parseSetupParams = new ParseSetupV3();
parseSetupParams.sourceFrames = H2oApi.stringArrayToKeyArray(importBody.destinationFrames, FrameKeyV3.class);
parseSetupParams.checkHeader = 1;
ParseSetupV3 parseSetupBody = h2o.guessParseSetup(parseSetupParams);
ParseV3 parseParms = new ParseV3();
H2oApi.copyFields(parseParms, parseSetupBody);
parseParms.destinationFrame = H2oApi.stringToFrameKey("50cat");
parseParms.blocking = true;
ParseV3 parseBody = h2o.parse(parseParms);
assert importBody.files.length == 2;
String[] parsedFiles = new String[importBody.files.length];
for(int i = 0; i < importBody.files.length; i ++){
parsedFiles[i] = importBody.files[i].substring(importBody.files[i].lastIndexOf("/")+1);
}
String[] result = {"50_cattest_train.csv","50_cattest_test.csv"};
Arrays.sort(result);
Arrays.sort(parsedFiles);
assert Arrays.equals(parsedFiles,result);
assert parseBody.numberColumns == 3;
assert parseBody.rows == 5000;
String[] colNames = {"x1","x2","y"};
assert Arrays.equals(parseBody.columnNames,colNames);
}
// STEP 99: end the session
h2o.endSession();
}
public static void importPatternExample() throws IOException {
importPatternExample(null);
}
public static void main (String[] args) throws IOException {
importPatternExample();
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/examples
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/examples/retrofit/Merge_Example.java
|
package water.bindings.examples.retrofit;
import water.bindings.H2oApi;
import water.bindings.pojos.*;
import water.bindings.proxies.retrofit.*;
import java.io.IOException;
import java.util.UUID;
public class Merge_Example {
public static void mergeExample(String url) throws IOException {
H2oApi h2o = url != null ? new H2oApi(url) : new H2oApi();
// Set url if specified
if (url != null) {
h2o.setUrl(url);
}
// Utility var:
JobV3 job = null;
// init a session
String sessionId = h2o.newSession().sessionKey;
// import and parse files
{ // tourism
ImportFilesV3 importBody = h2o.importFiles("../smalldata/merge/tourism.csv", null);
ParseSetupV3 parseSetupParams = new ParseSetupV3();
parseSetupParams.sourceFrames = H2oApi.stringArrayToKeyArray(importBody.destinationFrames, FrameKeyV3.class);
parseSetupParams.checkHeader = 1;
ParseSetupV3 parseSetupBody = h2o.guessParseSetup(parseSetupParams);
ParseV3 parseParms = new ParseV3();
H2oApi.copyFields(parseParms, parseSetupBody);
parseParms.destinationFrame = H2oApi.stringToFrameKey("tourism");
parseParms.blocking = true;
ParseV3 parseBody = h2o.parse(parseParms);
}
{ // heart
ImportFilesV3 importBody = h2o.importFiles("../smalldata/merge/heart.csv", null);
ParseSetupV3 parseSetupParams = new ParseSetupV3();
parseSetupParams.sourceFrames = H2oApi.stringArrayToKeyArray(importBody.destinationFrames, FrameKeyV3.class);
parseSetupParams.checkHeader = 1;
ParseSetupV3 parseSetupBody = h2o.guessParseSetup(parseSetupParams);
ParseV3 parseParms = new ParseV3();
H2oApi.copyFields(parseParms, parseSetupBody);
parseParms.destinationFrame = H2oApi.stringToFrameKey("heart");
parseParms.checkHeader = 1;
parseParms.blocking = true;
ParseV3 parseBody = h2o.parse(parseParms);
}
// convert heart.geotime to categorical / factor
RapidsSchemaV3 rapidsParms = new RapidsSchemaV3();
rapidsParms.sessionId = sessionId;
rapidsParms.ast = "(assign heart (:= heart (as.factor (cols heart 1)) 1 [0:263]))";
h2o.rapidsExec(rapidsParms);
// merge datasets
rapidsParms.ast = String.format("(assign mergedframe (merge %s %s TRUE FALSE [] [] \"auto\") )",
"tourism",
"heart");
h2o.rapidsExec(rapidsParms);
// STEP 99: end the session
h2o.endSession();
}
public static void mergeExample() throws IOException {
mergeExample(null);
}
public static void main (String[] args) throws IOException {
mergeExample();
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ANOVAGLMModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ANOVAGLMModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Table of Coefficients
*/
@SerializedName("coefficients_table")
public TwoDimTableV3[] coefficientsTable;
/**
* AnovaGLM transformed predictor frame key. For debugging purposes only
*/
@SerializedName("transformed_columns_key")
public FrameKeyV3 transformedColumnsKey;
/**
* ANOVA table frame key containing Type III SS calculation, degree of freedom, F-statistics and p-values. This
* frame content is repeated in the model summary.
*/
@SerializedName("result_frame_key")
public FrameKeyV3 resultFrameKey;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public ANOVAGLMModelOutputV3() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ANOVAGLMModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ANOVAGLMModelV3 extends ModelSchemaV3<ANOVAGLMParametersV3, ANOVAGLMModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public ANOVAGLMParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public ANOVAGLMModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public ANOVAGLMModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ANOVAGLMParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ANOVAGLMParametersV3 extends ModelParametersSchemaV3 {
/**
* Seed for pseudo random number generator (if applicable)
*/
public long seed;
/**
* Standardize numeric columns to have zero mean and unit variance
*/
public boolean standardize;
/**
* Family. Use binomial for classification with logistic regression, others are for regression problems.
*/
public GLMFamily family;
/**
* Tweedie variance power
*/
@SerializedName("tweedie_variance_power")
public double tweedieVariancePower;
/**
* Tweedie link power
*/
@SerializedName("tweedie_link_power")
public double tweedieLinkPower;
/**
* Theta
*/
public double theta;
/**
* 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.
*/
public double[] alpha;
/**
* Regularization strength
*/
public double[] lambda;
/**
* Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
*/
@SerializedName("lambda_search")
public boolean lambdaSearch;
/**
* 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.
*/
public GLMSolver solver;
/**
* Handling of missing values. Either MeanImputation, Skip or PlugValues.
*/
@SerializedName("missing_values_handling")
public GLMMissingValuesHandling missingValuesHandling;
/**
* 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)
*/
@SerializedName("plug_values")
public FrameKeyV3 plugValues;
/**
* Restrict coefficients (not intercept) to be non-negative
*/
@SerializedName("non_negative")
public boolean nonNegative;
/**
* Request p-values computation, p-values work only with IRLSM solver and no regularization
*/
@SerializedName("compute_p_values")
public boolean computePValues;
/**
* Maximum number of iterations
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Link function.
*/
public GLMLink link;
/**
* 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.
*/
public double prior;
/**
* Balance training data class counts via over/under-sampling (for imbalanced data).
*/
@SerializedName("balance_classes")
public boolean balanceClasses;
/**
* 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.
*/
@SerializedName("class_sampling_factors")
public float[] classSamplingFactors;
/**
* Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
* balance_classes.
*/
@SerializedName("max_after_balance_size")
public float maxAfterBalanceSize;
/**
* Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so
* on... Default to 2.
*/
@SerializedName("highest_interaction_term")
public int highestInteractionTerm;
/**
* Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3
*/
public int type;
/**
* Stop early when there is no more relative improvement on train or validation (if provided).
*/
@SerializedName("early_stopping")
public boolean earlyStopping;
/**
* true to save the keys of transformed predictors and interaction column.
*/
@SerializedName("save_transformed_framekeys")
public boolean saveTransformedFramekeys;
/**
* Number of models to build in parallel. Default to 4. Adjust according to your system.
*/
public int nparallelism;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public ANOVAGLMParametersV3() {
seed = -1L;
standardize = true;
family = GLMFamily.AUTO;
tweedieVariancePower = 0.0;
tweedieLinkPower = 1.0;
theta = 0.0;
lambda = new double[]{0.0};
lambdaSearch = false;
solver = GLMSolver.IRLSM;
missingValuesHandling = GLMMissingValuesHandling.MeanImputation;
nonNegative = false;
computePValues = true;
maxIterations = 0;
link = GLMLink.family_default;
prior = 0.0;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
highestInteractionTerm = 0;
type = 0;
earlyStopping = false;
saveTransformedFramekeys = false;
nparallelism = 4;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ANOVAGLMV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ANOVAGLMV3 extends ModelBuilderSchema<ANOVAGLMParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public ANOVAGLMParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public ANOVAGLMV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/APIDirection.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum APIDirection {
INOUT,
INPUT,
OUTPUT,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/APILevel.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum APILevel {
critical,
expert,
secondary,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AUUCType.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum AUUCType {
AUTO,
gain,
lift,
qini,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AboutEntryV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AboutEntryV3 extends SchemaV3 {
/**
* Property name
*/
public String name;
/**
* Property value
*/
public String value;
/**
* Public constructor
*/
public AboutEntryV3() {
name = "";
value = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AboutV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AboutV3 extends RequestSchemaV3 {
/**
* List of properties about this running H2O instance
*/
public AboutEntryV3[] entries;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public AboutV3() {
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AdaBoostModelAlgorithm.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum AdaBoostModelAlgorithm {
AUTO,
DEEP_LEARNING,
DRF,
GBM,
GLM,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AdaBoostModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AdaBoostModelOutputV3 extends ModelOutputSchemaV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public AdaBoostModelOutputV3() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AdaBoostModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AdaBoostModelV3 extends ModelSchemaV3<AdaBoostParametersV3, AdaBoostModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public AdaBoostParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public AdaBoostModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public AdaBoostModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AdaBoostParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AdaBoostParametersV3 extends ModelParametersSchemaV3 {
/**
* Number of AdaBoost weak learners.
*/
public int nlearners;
/**
* Choose a weak learner type. Defaults to AUTO, which means DRF.
*/
@SerializedName("weak_learner")
public AdaBoostModelAlgorithm weakLearner;
/**
* Learning rate (from 0.0 to 1.0)
*/
@SerializedName("learn_rate")
public double learnRate;
/**
* Customized parameters for the weak_learner algorithm.
*/
@SerializedName("weak_learner_params")
public String weakLearnerParams;
/**
* Seed for pseudo random number generator (if applicable)
*/
public long seed;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public AdaBoostParametersV3() {
nlearners = 50;
weakLearner = AdaBoostModelAlgorithm.AUTO;
learnRate = 0.5;
weakLearnerParams = "";
seed = -1L;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AdaBoostV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AdaBoostV3 extends ModelBuilderSchema<AdaBoostParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public AdaBoostParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public AdaBoostV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AggregatorModelOutputV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AggregatorModelOutputV99 extends ModelOutputSchemaV3 {
/**
* Aggregated Frame of Exemplars
*/
@SerializedName("output_frame")
public FrameKeyV3 outputFrame;
/**
* Aggregated Frame mapping to the rows in the original data
*/
@SerializedName("mapping_frame")
public FrameKeyV3 mappingFrame;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public AggregatorModelOutputV99() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AggregatorModelV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AggregatorModelV99 extends ModelSchemaV3<AggregatorParametersV99, AggregatorModelOutputV99> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public AggregatorParametersV99 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public AggregatorModelOutputV99 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public AggregatorModelV99() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AggregatorParametersV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AggregatorParametersV99 extends ModelParametersSchemaV3 {
/**
* Transformation of training data
*/
public DataInfoTransformType transform;
/**
* Method for computing PCA (Caution: GLRM is currently experimental and unstable)
*/
@SerializedName("pca_method")
public PCAMethod pcaMethod;
/**
* Rank of matrix approximation
*/
public int k;
/**
* Maximum number of iterations for PCA
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* Targeted number of exemplars
*/
@SerializedName("target_num_exemplars")
public int targetNumExemplars;
/**
* Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents)
*/
@SerializedName("rel_tol_num_exemplars")
public double relTolNumExemplars;
/**
* RNG seed for initialization
*/
public long seed;
/**
* Whether first factor level is included in each categorical expansion
*/
@SerializedName("use_all_factor_levels")
public boolean useAllFactorLevels;
/**
* Whether to export the mapping of the aggregated frame
*/
@SerializedName("save_mapping_frame")
public boolean saveMappingFrame;
/**
* The number of iterations to run before aggregator exits if the number of exemplars collected didn't change
*/
@SerializedName("num_iteration_without_new_exemplar")
public int numIterationWithoutNewExemplar;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public AggregatorParametersV99() {
transform = DataInfoTransformType.NORMALIZE;
pcaMethod = PCAMethod.Power;
k = 1;
maxIterations = 0;
targetNumExemplars = 5000;
relTolNumExemplars = 0.5;
seed = -1L;
useAllFactorLevels = false;
saveMappingFrame = false;
numIterationWithoutNewExemplar = 500;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AggregatorV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AggregatorV99 extends ModelBuilderSchema<AggregatorParametersV99> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public AggregatorParametersV99 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public AggregatorV99() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ApiParseTypeValuesProvider.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum ApiParseTypeValuesProvider {
ARFF,
AVRO,
CSV,
GUESS,
PARQUET,
SVMLight,
XLS,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ApiSaveToHiveTableHandlerHiveFrameSaverFormat.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum ApiSaveToHiveTableHandlerHiveFrameSaverFormat {
CSV,
PARQUET,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AssemblyKeyV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AssemblyKeyV3 extends KeyV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Name (string representation) for this Key.
public String name;
// Name (string representation) for the type of Keyed this Key points to.
public String type;
// URL for the resource that this Key points to, if one exists.
public String url;
*/
/**
* Public constructor
*/
public AssemblyKeyV3() {
name = "";
type = "";
url = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/AssemblyV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class AssemblyV99 extends RequestSchemaV3 {
/**
* A list of steps describing the assembly line.
*/
public String[] steps;
/**
* Input Frame for the assembly.
*/
public FrameKeyV3 frame;
/**
* The name of the file (and generated class in case of pojo)
*/
@SerializedName("file_name")
public String fileName;
/**
* The key of the Assembly object to retrieve from the DKV.
*/
@SerializedName("assembly_id")
public String assemblyId;
/**
* Output of the assembly line.
*/
public FrameKeyV3 result;
/**
* A Key to the fit Assembly data structure
*/
public AssemblyKeyV3 assembly;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public AssemblyV99() {
fileName = "";
assemblyId = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/Automlapischemas3AutoMLBuildSpecAutoMLMetricProvider.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum Automlapischemas3AutoMLBuildSpecAutoMLMetricProvider {
AUC,
AUCPR,
AUTO,
MAE,
MSE,
RMSE,
RMSLE,
deviance,
lift_top_group,
logloss,
mean_per_class_error,
misclassification,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/Automlapischemas3AutoMLBuildSpecScopeProvider.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum Automlapischemas3AutoMLBuildSpecScopeProvider {
DRF,
DeepLearning,
GBM,
GLM,
StackedEnsemble,
XGBoost,
any,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CapabilitiesV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CapabilitiesV3 extends RequestSchemaV3 {
/**
* List of H2O capabilities
*/
public CapabilityEntryV3[] capabilities;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public CapabilitiesV3() {
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CapabilityEntryV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CapabilityEntryV3 extends SchemaV3 {
/**
* Extension name
*/
public String name;
/**
* Public constructor
*/
public CapabilityEntryV3() {
name = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CartesianSearchCriteriaV99.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CartesianSearchCriteriaV99 extends HyperSpaceSearchCriteriaV99 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Hyperparameter space search strategy.
public GridHyperSpaceSearchCriteriaStrategy strategy;
*/
/**
* Public constructor
*/
public CartesianSearchCriteriaV99() {
strategy = GridHyperSpaceSearchCriteriaStrategy.Cartesian;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CloudLockV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CloudLockV3 extends RequestSchemaV3 {
/**
* reason
*/
public String reason;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public CloudLockV3() {
reason = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CloudV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CloudV3 extends RequestSchemaV3 {
/**
* skip_ticks
*/
@SerializedName("skip_ticks")
public boolean skipTicks;
/**
* version
*/
public String version;
/**
* branch_name
*/
@SerializedName("branch_name")
public String branchName;
/**
* last_commit_hash
*/
@SerializedName("last_commit_hash")
public String lastCommitHash;
/**
* describe
*/
public String describe;
/**
* compiled_by
*/
@SerializedName("compiled_by")
public String compiledBy;
/**
* compiled_on
*/
@SerializedName("compiled_on")
public String compiledOn;
/**
* build_number
*/
@SerializedName("build_number")
public String buildNumber;
/**
* build_age
*/
@SerializedName("build_age")
public String buildAge;
/**
* build_too_old
*/
@SerializedName("build_too_old")
public boolean buildTooOld;
/**
* Node index number cloud status is collected from (zero-based)
*/
@SerializedName("node_idx")
public int nodeIdx;
/**
* cloud_name
*/
@SerializedName("cloud_name")
public String cloudName;
/**
* cloud_size
*/
@SerializedName("cloud_size")
public int cloudSize;
/**
* cloud_uptime_millis
*/
@SerializedName("cloud_uptime_millis")
public long cloudUptimeMillis;
/**
* Cloud internal timezone
*/
@SerializedName("cloud_internal_timezone")
public String cloudInternalTimezone;
/**
* Timezone used for parsing datetime columns
*/
@SerializedName("datafile_parser_timezone")
public String datafileParserTimezone;
/**
* cloud_healthy
*/
@SerializedName("cloud_healthy")
public boolean cloudHealthy;
/**
* Nodes reporting unhealthy
*/
@SerializedName("bad_nodes")
public int badNodes;
/**
* Cloud voting is stable
*/
public boolean consensus;
/**
* Cloud is accepting new members or not
*/
public boolean locked;
/**
* Cloud is in client mode.
*/
@SerializedName("is_client")
public boolean isClient;
/**
* nodes
*/
public NodeV3[] nodes;
/**
* internal_security_enabled
*/
@SerializedName("internal_security_enabled")
public boolean internalSecurityEnabled;
/**
* leader_idx
*/
@SerializedName("leader_idx")
public int leaderIdx;
/**
* web_ip
*/
@SerializedName("web_ip")
public String webIp;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public CloudV3() {
skipTicks = false;
version = "";
branchName = "";
lastCommitHash = "";
describe = "";
compiledBy = "";
compiledOn = "";
buildNumber = "";
buildAge = "";
buildTooOld = false;
nodeIdx = 0;
cloudName = "";
cloudSize = 0;
cloudUptimeMillis = 0L;
cloudInternalTimezone = "";
datafileParserTimezone = "";
cloudHealthy = false;
badNodes = 0;
consensus = false;
locked = false;
isClient = false;
internalSecurityEnabled = false;
leaderIdx = -1;
webIp = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ClusteringModelBuilderSchema.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ClusteringModelBuilderSchema extends ModelBuilderSchema<ModelParametersSchemaV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public ModelParametersSchemaV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public ClusteringModelBuilderSchema() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ClusteringModelParametersSchemaV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ClusteringModelParametersSchemaV3 extends ModelParametersSchemaV3 {
/**
* The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find
* up to k centroids.
*/
public int k;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public ClusteringModelParametersSchemaV3() {
k = 0;
nfolds = 0;
keepCrossValidationModels = false;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = false;
tweediePower = 0.0;
quantileAlpha = 0.0;
huberAlpha = 0.0;
maxCategoricalLevels = 0;
ignoreConstCols = false;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingTolerance = 0.0;
gainsliftBins = 0;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ColSpecifierV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ColSpecifierV3 extends SchemaV3 {
/**
* Name of the column
*/
@SerializedName("column_name")
public String columnName;
/**
* List of fields which specify columns that must contain this column
*/
@SerializedName("is_member_of_frames")
public String[] isMemberOfFrames;
/**
* Public constructor
*/
public ColSpecifierV3() {
columnName = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ColV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ColV3 extends SchemaV3 {
/**
* label
*/
public String label;
/**
* missing
*/
@SerializedName("missing_count")
public long missingCount;
/**
* zeros
*/
@SerializedName("zero_count")
public long zeroCount;
/**
* positive infinities
*/
@SerializedName("positive_infinity_count")
public long positiveInfinityCount;
/**
* negative infinities
*/
@SerializedName("negative_infinity_count")
public long negativeInfinityCount;
/**
* mins
*/
public double[] mins;
/**
* maxs
*/
public double[] maxs;
/**
* mean
*/
public double mean;
/**
* sigma
*/
public double sigma;
/**
* datatype: {enum, string, int, real, time, uuid}
*/
public String type;
/**
* domain; not-null for categorical columns only
*/
public String[] domain;
/**
* cardinality of this column's domain; not-null for categorical columns only
*/
@SerializedName("domain_cardinality")
public int domainCardinality;
/**
* data
*/
public double[] data;
/**
* string data
*/
@SerializedName("string_data")
public String[] stringData;
/**
* decimal precision, -1 for all digits
*/
public byte precision;
/**
* Histogram bins; null if not computed
*/
@SerializedName("histogram_bins")
public long[] histogramBins;
/**
* Start of histogram bin zero
*/
@SerializedName("histogram_base")
public double histogramBase;
/**
* Stride per bin
*/
@SerializedName("histogram_stride")
public double histogramStride;
/**
* Percentile values, matching the default percentiles
*/
public double[] percentiles;
/**
* Public constructor
*/
public ColV3() {
label = "";
missingCount = 0L;
zeroCount = 0L;
positiveInfinityCount = 0L;
negativeInfinityCount = 0L;
mean = 0.0;
sigma = 0.0;
type = "";
domainCardinality = 0;
precision = 0;
histogramBase = 0.0;
histogramStride = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ColumnSpecsBase.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ColumnSpecsBase extends SchemaV3 {
/**
* Column Name
*/
public String name;
/**
* Column Type
*/
public String type;
/**
* Column Format (printf)
*/
public String format;
/**
* Column Description
*/
public String description;
/**
* Public constructor
*/
public ColumnSpecsBase() {
name = "";
type = "";
format = "";
description = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ColumnsMappingV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ColumnsMappingV3 extends SchemaV3 {
/**
* Input column(s) from the same encoding group.
*/
public String[] from;
/**
* Output column(s) generated by the application of target encoding to the `from` group.
*/
public String[] to;
/**
* Public constructor
*/
public ColumnsMappingV3() {
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ConfusionMatrixV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ConfusionMatrixV3 extends SchemaV3 {
/**
* Annotated confusion matrix
*/
public TwoDimTableV3 table;
/**
* Public constructor
*/
public ConfusionMatrixV3() {
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CoxPHModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CoxPHModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Table of Coefficients
*/
@SerializedName("coefficients_table")
public TwoDimTableV3 coefficientsTable;
/**
* var(coefficients)
*/
@SerializedName("var_coef")
public double[][] varCoef;
/**
* null log-likelihood
*/
@SerializedName("null_loglik")
public double nullLoglik;
/**
* log-likelihood
*/
public double loglik;
/**
* log-likelihood test stat
*/
@SerializedName("loglik_test")
public double loglikTest;
/**
* Wald test stat
*/
@SerializedName("wald_test")
public double waldTest;
/**
* Score test stat
*/
@SerializedName("score_test")
public double scoreTest;
/**
* R-square
*/
public double rsq;
/**
* Maximum R-square
*/
public double maxrsq;
/**
* log relative error
*/
public double lre;
/**
* number of iterations
*/
public int iter;
/**
* x weighted mean vector for categorical variables
*/
@SerializedName("x_mean_cat")
public double[][] xMeanCat;
/**
* x weighted mean vector for numeric variables
*/
@SerializedName("x_mean_num")
public double[][] xMeanNum;
/**
* unweighted mean vector for numeric offsets
*/
@SerializedName("mean_offset")
public double[] meanOffset;
/**
* names of offsets
*/
@SerializedName("offset_names")
public String[] offsetNames;
/**
* n
*/
public long n;
/**
* number of rows with missing values
*/
@SerializedName("n_missing")
public long nMissing;
/**
* total events
*/
@SerializedName("total_event")
public long totalEvent;
/**
* time
*/
public double[] time;
/**
* number at risk
*/
@SerializedName("n_risk")
public double[] nRisk;
/**
* number of events
*/
@SerializedName("n_event")
public double[] nEvent;
/**
* number of censored obs
*/
@SerializedName("n_censor")
public double[] nCensor;
/**
* baseline cumulative hazard
*/
@SerializedName("cumhaz_0")
public double[] cumhaz0;
/**
* component of var(cumhaz)
*/
@SerializedName("var_cumhaz_1")
public double[] varCumhaz1;
/**
* component of var(cumhaz)
*/
@SerializedName("var_cumhaz_2")
public FrameKeyV3 varCumhaz2;
/**
* Baseline Hazard
*/
@SerializedName("baseline_hazard")
public FrameKeyV3 baselineHazard;
/**
* Baseline Survival
*/
@SerializedName("baseline_survival")
public FrameKeyV3 baselineSurvival;
/**
* formula
*/
public String formula;
/**
* ties
*/
public CoxPHTies ties;
/**
* concordance
*/
public double concordance;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public CoxPHModelOutputV3() {
nullLoglik = 0.0;
loglik = 0.0;
loglikTest = 0.0;
waldTest = 0.0;
scoreTest = 0.0;
rsq = 0.0;
maxrsq = 0.0;
lre = 0.0;
iter = 0;
n = 0L;
nMissing = 0L;
totalEvent = 0L;
formula = "";
concordance = 0.0;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CoxPHModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CoxPHModelV3 extends ModelSchemaV3<CoxPHParametersV3, CoxPHModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public CoxPHParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public CoxPHModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public CoxPHModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CoxPHParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CoxPHParametersV3 extends ModelParametersSchemaV3 {
/**
* Start Time Column.
*/
@SerializedName("start_column")
public ColSpecifierV3 startColumn;
/**
* Stop Time Column.
*/
@SerializedName("stop_column")
public ColSpecifierV3 stopColumn;
/**
* List of columns to use for stratification.
*/
@SerializedName("stratify_by")
public String[] stratifyBy;
/**
* Method for Handling Ties.
*/
public CoxPHTies ties;
/**
* Coefficient starting value.
*/
public double init;
/**
* Minimum log-relative error.
*/
@SerializedName("lre_min")
public double lreMin;
/**
* Maximum number of iterations.
*/
@SerializedName("max_iterations")
public int maxIterations;
/**
* A list of columns that should only be used to create interactions but should not itself participate in model
* training.
*/
@SerializedName("interactions_only")
public String[] interactionsOnly;
/**
* A list of predictor column indices to interact. All pairwise combinations will be computed for the list.
*/
public String[] interactions;
/**
* A list of pairwise (first order) column interactions.
*/
@SerializedName("interaction_pairs")
public StringPairV3[] interactionPairs;
/**
* (Internal. For development only!) Indicates whether to use all factor levels.
*/
@SerializedName("use_all_factor_levels")
public boolean useAllFactorLevels;
/**
* Run on a single node to reduce the effect of network overhead (for smaller datasets)
*/
@SerializedName("single_node_mode")
public boolean singleNodeMode;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public CoxPHParametersV3() {
ties = CoxPHTies.efron;
init = 0.0;
lreMin = 9.0;
maxIterations = 20;
useAllFactorLevels = false;
singleNodeMode = false;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CoxPHTies.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum CoxPHTies {
breslow,
efron,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CoxPHV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CoxPHV3 extends ModelBuilderSchema<CoxPHParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public CoxPHParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public CoxPHV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CreateFrameOriginalIV4.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CreateFrameOriginalIV4 extends InputSchemaV4 {
/**
* destination key
*/
public FrameKeyV3 dest;
/**
* Number of rows
*/
public int rows;
/**
* Number of data columns (in addition to the first response column)
*/
public int cols;
/**
* Random number seed that determines the random values
*/
public long seed;
/**
* Whether frame should be randomized
*/
public boolean randomize;
/**
* Constant value (for randomize=false)
*/
public long value;
/**
* Range for real variables (-range ... range)
*/
@SerializedName("real_range")
public double realRange;
/**
* Fraction of categorical columns (for randomize=true)
*/
@SerializedName("categorical_fraction")
public double categoricalFraction;
/**
* Factor levels for categorical variables
*/
public int factors;
/**
* Fraction of integer columns (for randomize=true)
*/
@SerializedName("integer_fraction")
public double integerFraction;
/**
* Range for integer variables (-range ... range)
*/
@SerializedName("integer_range")
public int integerRange;
/**
* Fraction of binary columns (for randomize=true)
*/
@SerializedName("binary_fraction")
public double binaryFraction;
/**
* Fraction of 1's in binary columns
*/
@SerializedName("binary_ones_fraction")
public double binaryOnesFraction;
/**
* Fraction of date/time columns (for randomize=true)
*/
@SerializedName("time_fraction")
public double timeFraction;
/**
* Fraction of string columns (for randomize=true)
*/
@SerializedName("string_fraction")
public double stringFraction;
/**
* Fraction of missing values
*/
@SerializedName("missing_fraction")
public double missingFraction;
/**
* Whether an additional response column should be generated
*/
@SerializedName("has_response")
public boolean hasResponse;
/**
* Number of factor levels of the first column (1=real, 2=binomial, N=multinomial)
*/
@SerializedName("response_factors")
public int responseFactors;
/**
* For real-valued response variable: Whether the response should be positive only.
*/
@SerializedName("positive_response")
public boolean positiveResponse;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Filter on the set of output fields: if you set _fields="foo,bar,baz", then only those fields will be included in
// the output; or you can specify _fields="-goo,gee" to include all fields except goo and gee. If the result
// contains nested data structures, then you can refer to the fields within those structures as well. For example if
// you specify _fields="foo(oof),bar(-rab)", then only fields foo and bar will be included, and within foo there
// will be only field oof, whereas within bar all fields except rab will be reported.
public String _fields;
*/
/**
* Public constructor
*/
public CreateFrameOriginalIV4() {
rows = 10000;
cols = 10;
seed = -1L;
randomize = true;
value = 0L;
realRange = 100.0;
categoricalFraction = 0.2;
factors = 100;
integerFraction = 0.2;
integerRange = 100;
binaryFraction = 0.1;
binaryOnesFraction = 0.02;
timeFraction = 0.0;
stringFraction = 0.0;
missingFraction = 0.01;
hasResponse = false;
responseFactors = 2;
positiveResponse = false;
_fields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CreateFrameSimpleIV4.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CreateFrameSimpleIV4 extends InputSchemaV4 {
/**
* Id for the frame to be created.
*/
public FrameKeyV3 dest;
/**
* Random number seed that determines the random values.
*/
public long seed;
/**
* Number of rows.
*/
public int nrows;
/**
* Number of real-valued columns. Values in these columns will be uniformly distributed between real_lb and real_ub.
*/
@SerializedName("ncols_real")
public int ncolsReal;
/**
* Number of integer columns.
*/
@SerializedName("ncols_int")
public int ncolsInt;
/**
* Number of enum (categorical) columns.
*/
@SerializedName("ncols_enum")
public int ncolsEnum;
/**
* Number of boolean (binary) columns.
*/
@SerializedName("ncols_bool")
public int ncolsBool;
/**
* Number of string columns.
*/
@SerializedName("ncols_str")
public int ncolsStr;
/**
* Number of time columns.
*/
@SerializedName("ncols_time")
public int ncolsTime;
/**
* Lower bound for the range of the real-valued columns.
*/
@SerializedName("real_lb")
public double realLb;
/**
* Upper bound for the range of the real-valued columns.
*/
@SerializedName("real_ub")
public double realUb;
/**
* Lower bound for the range of integer columns.
*/
@SerializedName("int_lb")
public int intLb;
/**
* Upper bound for the range of integer columns.
*/
@SerializedName("int_ub")
public int intUb;
/**
* Number of levels (categories) for the enum columns.
*/
@SerializedName("enum_nlevels")
public int enumNlevels;
/**
* Fraction of ones in each boolean (binary) column.
*/
@SerializedName("bool_p")
public double boolP;
/**
* Lower bound for the range of time columns (in ms since the epoch).
*/
@SerializedName("time_lb")
public long timeLb;
/**
* Upper bound for the range of time columns (in ms since the epoch).
*/
@SerializedName("time_ub")
public long timeUb;
/**
* Length of generated strings in string columns.
*/
@SerializedName("str_length")
public int strLength;
/**
* Fraction of missing values.
*/
@SerializedName("missing_fraction")
public double missingFraction;
/**
* Type of the response column to add.
*/
@SerializedName("response_type")
public SimpleRecipeResponseType responseType;
/**
* Lower bound for the response variable (real/int/time types).
*/
@SerializedName("response_lb")
public double responseLb;
/**
* Upper bound for the response variable (real/int/time types).
*/
@SerializedName("response_ub")
public double responseUb;
/**
* Frequency of 1s for the bool (binary) response column.
*/
@SerializedName("response_p")
public double responseP;
/**
* Number of categorical levels for the enum response column.
*/
@SerializedName("response_nlevels")
public int responseNlevels;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Filter on the set of output fields: if you set _fields="foo,bar,baz", then only those fields will be included in
// the output; or you can specify _fields="-goo,gee" to include all fields except goo and gee. If the result
// contains nested data structures, then you can refer to the fields within those structures as well. For example if
// you specify _fields="foo(oof),bar(-rab)", then only fields foo and bar will be included, and within foo there
// will be only field oof, whereas within bar all fields except rab will be reported.
public String _fields;
*/
/**
* Public constructor
*/
public CreateFrameSimpleIV4() {
seed = -1L;
nrows = 100;
ncolsReal = 0;
ncolsInt = 0;
ncolsEnum = 0;
ncolsBool = 0;
ncolsStr = 0;
ncolsTime = 0;
realLb = -100.0;
realUb = 100.0;
intLb = -100;
intUb = 100;
enumNlevels = 10;
boolP = 0.3;
timeLb = 946080000000L;
timeUb = 1576800000000L;
strLength = 8;
missingFraction = 0.0;
responseType = SimpleRecipeResponseType.NONE;
responseLb = 0.0;
responseUb = 10.0;
responseP = 0.6;
responseNlevels = 25;
_fields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/CreateFrameV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class CreateFrameV3 extends RequestSchemaV3 {
/**
* destination key
*/
public FrameKeyV3 dest;
/**
* Number of rows
*/
public long rows;
/**
* Number of data columns (in addition to the first response column)
*/
public int cols;
/**
* Random number seed that determines the random values
*/
public long seed;
/**
* Random number seed for setting the column types
*/
@SerializedName("seed_for_column_types")
public long seedForColumnTypes;
/**
* Whether frame should be randomized
*/
public boolean randomize;
/**
* Constant value (for randomize=false)
*/
public long value;
/**
* Range for real variables (-range ... range)
*/
@SerializedName("real_range")
public long realRange;
/**
* Fraction of categorical columns (for randomize=true)
*/
@SerializedName("categorical_fraction")
public double categoricalFraction;
/**
* Factor levels for categorical variables
*/
public int factors;
/**
* Fraction of integer columns (for randomize=true)
*/
@SerializedName("integer_fraction")
public double integerFraction;
/**
* Range for integer variables (-range ... range)
*/
@SerializedName("integer_range")
public long integerRange;
/**
* Fraction of binary columns (for randomize=true)
*/
@SerializedName("binary_fraction")
public double binaryFraction;
/**
* Fraction of 1's in binary columns
*/
@SerializedName("binary_ones_fraction")
public double binaryOnesFraction;
/**
* Fraction of date/time columns (for randomize=true)
*/
@SerializedName("time_fraction")
public double timeFraction;
/**
* Fraction of string columns (for randomize=true)
*/
@SerializedName("string_fraction")
public double stringFraction;
/**
* Fraction of missing values
*/
@SerializedName("missing_fraction")
public double missingFraction;
/**
* Whether an additional response column should be generated
*/
@SerializedName("has_response")
public boolean hasResponse;
/**
* Number of factor levels of the first column (1=real, 2=binomial, N=multinomial or ordinal)
*/
@SerializedName("response_factors")
public int responseFactors;
/**
* For real-valued response variable: Whether the response should be positive only.
*/
@SerializedName("positive_response")
public boolean positiveResponse;
/**
* Job Key
*/
public JobKeyV3 key;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public CreateFrameV3() {
rows = 10000L;
cols = 10;
seed = -1L;
seedForColumnTypes = -1L;
randomize = true;
value = 0L;
realRange = 100L;
categoricalFraction = 0.2;
factors = 100;
integerFraction = 0.2;
integerRange = 100L;
binaryFraction = 0.1;
binaryOnesFraction = 0.02;
timeFraction = 0.0;
stringFraction = 0.0;
missingFraction = 0.01;
hasResponse = false;
responseFactors = 2;
positiveResponse = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DCTTransformerV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DCTTransformerV3 extends RequestSchemaV3 {
/**
* Dataset
*/
public FrameKeyV3 dataset;
/**
* Destination Frame ID
*/
@SerializedName("destination_frame")
public FrameKeyV3 destinationFrame;
/**
* Dimensions of the input array: Height, Width, Depth (Nx1x1 for 1D, NxMx1 for 2D)
*/
public int[] dimensions;
/**
* Whether to do the inverse transform
*/
public boolean inverse;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public DCTTransformerV3() {
inverse = false;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DRFModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DRFModelOutputV3 extends SharedTreeModelOutputV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Variable Importances
public TwoDimTableV3 variableImportances;
// The Intercept term, the initial model function value to which trees make adjustments
public double initF;
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public DRFModelOutputV3() {
initF = 0.0;
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DRFModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DRFModelV3 extends SharedTreeModelV3<DRFParametersV3, DRFModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public DRFParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public DRFModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public DRFModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DRFParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DRFParametersV3 extends SharedTreeParametersV3 {
/**
* Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for
* classification and p/3 for regression (where p is the # of predictors
*/
public int mtries;
/**
* For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.
*/
@SerializedName("binomial_double_trees")
public boolean binomialDoubleTrees;
/**
* Row sample rate per tree (from 0.0 to 1.0)
*/
@SerializedName("sample_rate")
public double sampleRate;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Balance training data class counts via over/under-sampling (for imbalanced data).
public boolean balanceClasses;
// 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.
public float[] classSamplingFactors;
// Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
// balance_classes.
public float maxAfterBalanceSize;
// [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
public int maxConfusionMatrixSize;
// Number of trees.
public int ntrees;
// Maximum tree depth (0 for unlimited).
public int maxDepth;
// Fewest allowed (weighted) observations in a leaf.
public double minRows;
// For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
public int nbins;
// For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease
// by factor of two per level
public int nbinsTopLevel;
// For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher
// values can lead to more overfitting.
public int nbinsCats;
// r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and
// stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds
// this
public double r2Stopping;
// Seed for pseudo random number generator (if applicable)
public long seed;
// Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
public boolean buildTreeOneNode;
// A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree
public double[] sampleRatePerClass;
// Column sample rate per tree (from 0.0 to 1.0)
public double colSampleRatePerTree;
// Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)
public double colSampleRateChangePerLevel;
// Score the model after every so many trees. Disabled if set to 0.
public int scoreTreeInterval;
// Minimum relative improvement in squared error reduction for a split to happen
public double minSplitImprovement;
// What type of histogram to use for finding optimal split points
public TreeSharedTreeModelSharedTreeParametersHistogramType histogramType;
// Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can
// provide more accurate estimates of class probabilities.
public boolean calibrateModel;
// Data for model calibration
public FrameKeyV3 calibrationFrame;
// Calibration method to use
public TreeCalibrationHelperCalibrationMethod calibrationMethod;
// Check if response column is constant. If enabled, then an exception is thrown if the response column is a
// constant value.If disabled, then model will train regardless of the response column being a constant value or
// not.
public boolean checkConstantResponse;
// Create checkpoints into defined directory while training process is still running. In case of cluster shutdown,
// this checkpoint can be used to restart training.
public String inTrainingCheckpointsDir;
// Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is
// defined
public int inTrainingCheckpointsTreeInterval;
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public DRFParametersV3() {
mtries = -1;
binomialDoubleTrees = false;
sampleRate = 0.632;
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
ntrees = 50;
maxDepth = 20;
minRows = 1.0;
nbins = 20;
nbinsTopLevel = 1024;
nbinsCats = 1024;
r2Stopping = 1.7976931348623157e+308;
seed = -1L;
buildTreeOneNode = false;
colSampleRatePerTree = 1.0;
colSampleRateChangePerLevel = 1.0;
scoreTreeInterval = 0;
minSplitImprovement = 1e-05;
histogramType = TreeSharedTreeModelSharedTreeParametersHistogramType.AUTO;
calibrateModel = false;
calibrationMethod = TreeCalibrationHelperCalibrationMethod.AUTO;
checkConstantResponse = true;
inTrainingCheckpointsDir = "";
inTrainingCheckpointsTreeInterval = 1;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DRFV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DRFV3 extends SharedTreeV3<DRFParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public DRFParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public DRFV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DStackTraceV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DStackTraceV3 extends SchemaV3 {
/**
* Node name
*/
public String node;
/**
* Unix epoch time
*/
public long time;
/**
* One trace per thread
*/
@SerializedName("thread_traces")
public String[] threadTraces;
/**
* Public constructor
*/
public DStackTraceV3() {
node = "";
time = 0L;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DTModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DTModelOutputV3 extends ModelOutputSchemaV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public DTModelOutputV3() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DTModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DTModelV3 extends ModelSchemaV3<DTParametersV3, DTModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public DTParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public DTModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public DTModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DTParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DTParametersV3 extends ModelParametersSchemaV3 {
/**
* Seed for random numbers (affects sampling)
*/
public long seed;
/**
* Max depth of tree.
*/
@SerializedName("max_depth")
public int maxDepth;
/**
* Fewest allowed (weighted) observations in a leaf.
*/
@SerializedName("min_rows")
public int minRows;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public DTParametersV3() {
seed = -1L;
maxDepth = 20;
minRows = 10;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DTV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DTV3 extends ModelBuilderSchema<DTParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public DTParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public DTV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DataInfoFrameV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DataInfoFrameV3 extends SchemaV3 {
/**
* input frame
*/
public FrameKeyV3 frame;
/**
* interactions
*/
public String[] interactions;
/**
* use all factor levels
*/
@SerializedName("use_all")
public boolean useAll;
/**
* standardize
*/
public boolean standardize;
/**
* interactions only returned
*/
@SerializedName("interactions_only")
public boolean interactionsOnly;
/**
* output frame
*/
public FrameKeyV3 result;
/**
* Public constructor
*/
public DataInfoFrameV3() {
useAll = false;
standardize = false;
interactionsOnly = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DataInfoTransformType.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum DataInfoTransformType {
DEMEAN,
DESCALE,
NONE,
NORMALIZE,
STANDARDIZE,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DecryptionSetupV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DecryptionSetupV3 extends RequestSchemaV3 {
/**
* Target key for the Decryption Tool
*/
@SerializedName("decrypt_tool_id")
public DecryptionToolKeyV3 decryptToolId;
/**
* Implementation of the Decryption Tool
*/
@SerializedName("decrypt_impl")
public String decryptImpl;
/**
* Location of Java Keystore
*/
@SerializedName("keystore_id")
public FrameKeyV3 keystoreId;
/**
* Keystore type
*/
@SerializedName("keystore_type")
public String keystoreType;
/**
* Key alias
*/
@SerializedName("key_alias")
public String keyAlias;
/**
* Key password
*/
public String password;
/**
* Specification of the cipher (and padding)
*/
@SerializedName("cipher_spec")
public String cipherSpec;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public DecryptionSetupV3() {
decryptImpl = "water.parser.GenericDecryptionTool";
keystoreType = "";
keyAlias = "";
password = "";
cipherSpec = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DecryptionToolKeyV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DecryptionToolKeyV3 extends KeyV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Name (string representation) for this Key.
public String name;
// Name (string representation) for the type of Keyed this Key points to.
public String type;
// URL for the resource that this Key points to, if one exists.
public String url;
*/
/**
* Public constructor
*/
public DecryptionToolKeyV3() {
name = "";
type = "";
url = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningActivation.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum DeepLearningActivation {
Maxout,
MaxoutWithDropout,
Rectifier,
RectifierWithDropout,
Tanh,
TanhWithDropout,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningClassSamplingMethod.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum DeepLearningClassSamplingMethod {
Stratified,
Uniform,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningInitialWeightDistribution.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum DeepLearningInitialWeightDistribution {
Normal,
Uniform,
UniformAdaptive,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningLoss.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum DeepLearningLoss {
Absolute,
Automatic,
CrossEntropy,
Huber,
Quadratic,
Quantile,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningMissingValuesHandling.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum DeepLearningMissingValuesHandling {
MeanImputation,
Skip,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DeepLearningModelOutputV3 extends ModelOutputSchemaV3 {
/**
* Frame keys for weight matrices
*/
public FrameKeyV3[] weights;
/**
* Frame keys for bias vectors
*/
public FrameKeyV3[] biases;
/**
* Normalization/Standardization multipliers for numeric predictors
*/
public double[] normmul;
/**
* Normalization/Standardization offsets for numeric predictors
*/
public double[] normsub;
/**
* Normalization/Standardization multipliers for numeric response
*/
public double[] normrespmul;
/**
* Normalization/Standardization offsets for numeric response
*/
public double[] normrespsub;
/**
* Categorical offsets for one-hot encoding
*/
public int[] catoffsets;
/**
* Variable Importances
*/
@SerializedName("variable_importances")
public TwoDimTableV3 variableImportances;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public DeepLearningModelOutputV3() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DeepLearningModelV3 extends ModelSchemaV3<DeepLearningParametersV3, DeepLearningModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public DeepLearningParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public DeepLearningModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public DeepLearningModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DeepLearningParametersV3 extends ModelParametersSchemaV3 {
/**
* Balance training data class counts via over/under-sampling (for imbalanced data).
*/
@SerializedName("balance_classes")
public boolean balanceClasses;
/**
* 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.
*/
@SerializedName("class_sampling_factors")
public float[] classSamplingFactors;
/**
* Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires
* balance_classes.
*/
@SerializedName("max_after_balance_size")
public float maxAfterBalanceSize;
/**
* [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.
*/
@SerializedName("max_confusion_matrix_size")
public int maxConfusionMatrixSize;
/**
* Activation function.
*/
public DeepLearningActivation activation;
/**
* Hidden layer sizes (e.g. [100, 100]).
*/
public int[] hidden;
/**
* How many times the dataset should be iterated (streamed), can be fractional.
*/
public double epochs;
/**
* Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available
* data (e.g., replicated training data), -2: automatic.
*/
@SerializedName("train_samples_per_iteration")
public long trainSamplesPerIteration;
/**
* Target ratio of communication overhead to computation. Only for multi-node operation and
* train_samples_per_iteration = -2 (auto-tuning).
*/
@SerializedName("target_ratio_comm_to_comp")
public double targetRatioCommToComp;
/**
* Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded.
*/
public long seed;
/**
* Adaptive learning rate.
*/
@SerializedName("adaptive_rate")
public boolean adaptiveRate;
/**
* Adaptive learning rate time decay factor (similarity to prior updates).
*/
public double rho;
/**
* Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress).
*/
public double epsilon;
/**
* Learning rate (higher => less stable, lower => slower convergence).
*/
public double rate;
/**
* Learning rate annealing: rate / (1 + rate_annealing * samples).
*/
@SerializedName("rate_annealing")
public double rateAnnealing;
/**
* Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1).
*/
@SerializedName("rate_decay")
public double rateDecay;
/**
* Initial momentum at the beginning of training (try 0.5).
*/
@SerializedName("momentum_start")
public double momentumStart;
/**
* Number of training samples for which momentum increases.
*/
@SerializedName("momentum_ramp")
public double momentumRamp;
/**
* Final momentum after the ramp is over (try 0.99).
*/
@SerializedName("momentum_stable")
public double momentumStable;
/**
* Use Nesterov accelerated gradient (recommended).
*/
@SerializedName("nesterov_accelerated_gradient")
public boolean nesterovAcceleratedGradient;
/**
* Input layer dropout ratio (can improve generalization, try 0.1 or 0.2).
*/
@SerializedName("input_dropout_ratio")
public double inputDropoutRatio;
/**
* Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.
*/
@SerializedName("hidden_dropout_ratios")
public double[] hiddenDropoutRatios;
/**
* L1 regularization (can add stability and improve generalization, causes many weights to become 0).
*/
public double l1;
/**
* L2 regularization (can add stability and improve generalization, causes many weights to be small.
*/
public double l2;
/**
* Constraint for squared sum of incoming weights per unit (e.g. for Rectifier).
*/
@SerializedName("max_w2")
public float maxW2;
/**
* Initial weight distribution.
*/
@SerializedName("initial_weight_distribution")
public DeepLearningInitialWeightDistribution initialWeightDistribution;
/**
* Uniform: -value...value, Normal: stddev.
*/
@SerializedName("initial_weight_scale")
public double initialWeightScale;
/**
* A list of H2OFrame ids to initialize the weight matrices of this model with.
*/
@SerializedName("initial_weights")
public FrameKeyV3[] initialWeights;
/**
* A list of H2OFrame ids to initialize the bias vectors of this model with.
*/
@SerializedName("initial_biases")
public FrameKeyV3[] initialBiases;
/**
* Loss function.
*/
public DeepLearningLoss loss;
/**
* Shortest time interval (in seconds) between model scoring.
*/
@SerializedName("score_interval")
public double scoreInterval;
/**
* Number of training set samples for scoring (0 for all).
*/
@SerializedName("score_training_samples")
public long scoreTrainingSamples;
/**
* Number of validation set samples for scoring (0 for all).
*/
@SerializedName("score_validation_samples")
public long scoreValidationSamples;
/**
* Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring).
*/
@SerializedName("score_duty_cycle")
public double scoreDutyCycle;
/**
* Stopping criterion for classification error fraction on training data (-1 to disable).
*/
@SerializedName("classification_stop")
public double classificationStop;
/**
* Stopping criterion for regression error (MSE) on training data (-1 to disable).
*/
@SerializedName("regression_stop")
public double regressionStop;
/**
* Enable quiet mode for less output to standard output.
*/
@SerializedName("quiet_mode")
public boolean quietMode;
/**
* Method used to sample validation dataset for scoring.
*/
@SerializedName("score_validation_sampling")
public DeepLearningClassSamplingMethod scoreValidationSampling;
/**
* If enabled, override the final model with the best model found during training.
*/
@SerializedName("overwrite_with_best_model")
public boolean overwriteWithBestModel;
/**
* Auto-Encoder.
*/
public boolean autoencoder;
/**
* Use all factor levels of categorical variables. Otherwise, the first factor level is omitted (without loss of
* accuracy). Useful for variable importances and auto-enabled for autoencoder.
*/
@SerializedName("use_all_factor_levels")
public boolean useAllFactorLevels;
/**
* If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.
*/
public boolean standardize;
/**
* Enable diagnostics for hidden layers.
*/
public boolean diagnostics;
/**
* Compute variable importances for input features (Gedeon method) - can be slow for large networks.
*/
@SerializedName("variable_importances")
public boolean variableImportances;
/**
* Enable fast mode (minor approximation in back-propagation).
*/
@SerializedName("fast_mode")
public boolean fastMode;
/**
* Force extra load balancing to increase training speed for small datasets (to keep all cores busy).
*/
@SerializedName("force_load_balance")
public boolean forceLoadBalance;
/**
* Replicate the entire training dataset onto every node for faster training on small datasets.
*/
@SerializedName("replicate_training_data")
public boolean replicateTrainingData;
/**
* Run on a single node for fine-tuning of model parameters.
*/
@SerializedName("single_node_mode")
public boolean singleNodeMode;
/**
* Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is
* close to #nodes x #rows, of if using balance_classes).
*/
@SerializedName("shuffle_training_data")
public boolean shuffleTrainingData;
/**
* Handling of missing values. Either MeanImputation or Skip.
*/
@SerializedName("missing_values_handling")
public DeepLearningMissingValuesHandling missingValuesHandling;
/**
* Sparse data handling (more efficient for data with lots of 0 values).
*/
public boolean sparse;
/**
* #DEPRECATED Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow
* down backpropagation.
*/
@SerializedName("col_major")
public boolean colMajor;
/**
* Average activation for sparse auto-encoder. #Experimental
*/
@SerializedName("average_activation")
public double averageActivation;
/**
* Sparsity regularization. #Experimental
*/
@SerializedName("sparsity_beta")
public double sparsityBeta;
/**
* Max. number of categorical features, enforced via hashing. #Experimental
*/
@SerializedName("max_categorical_features")
public int maxCategoricalFeatures;
/**
* Force reproducibility on small data (will be slow - only uses 1 thread).
*/
public boolean reproducible;
/**
* Whether to export Neural Network weights and biases to H2O Frames.
*/
@SerializedName("export_weights_and_biases")
public boolean exportWeightsAndBiases;
/**
* Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).
*/
@SerializedName("mini_batch_size")
public int miniBatchSize;
/**
* Elastic averaging between compute nodes can improve distributed model convergence. #Experimental
*/
@SerializedName("elastic_averaging")
public boolean elasticAveraging;
/**
* Elastic averaging moving rate (only if elastic averaging is enabled).
*/
@SerializedName("elastic_averaging_moving_rate")
public double elasticAveragingMovingRate;
/**
* Elastic averaging regularization strength (only if elastic averaging is enabled).
*/
@SerializedName("elastic_averaging_regularization")
public double elasticAveragingRegularization;
/**
* Pretrained autoencoder model to initialize this model with.
*/
@SerializedName("pretrained_autoencoder")
public ModelKeyV3 pretrainedAutoencoder;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public DeepLearningParametersV3() {
balanceClasses = false;
maxAfterBalanceSize = 5.0f;
maxConfusionMatrixSize = 20;
activation = DeepLearningActivation.Rectifier;
hidden = new int[]{200, 200};
epochs = 10.0;
trainSamplesPerIteration = -2L;
targetRatioCommToComp = 0.05;
seed = -1L;
adaptiveRate = true;
rho = 0.99;
epsilon = 1e-08;
rate = 0.005;
rateAnnealing = 1e-06;
rateDecay = 1.0;
momentumStart = 0.0;
momentumRamp = 1000000.0;
momentumStable = 0.0;
nesterovAcceleratedGradient = true;
inputDropoutRatio = 0.0;
l1 = 0.0;
l2 = 0.0;
maxW2 = 3.4028235e+38f;
initialWeightDistribution = DeepLearningInitialWeightDistribution.UniformAdaptive;
initialWeightScale = 1.0;
loss = DeepLearningLoss.Automatic;
scoreInterval = 5.0;
scoreTrainingSamples = 10000L;
scoreValidationSamples = 0L;
scoreDutyCycle = 0.1;
classificationStop = 0.0;
regressionStop = 1e-06;
quietMode = false;
scoreValidationSampling = DeepLearningClassSamplingMethod.Uniform;
overwriteWithBestModel = true;
autoencoder = false;
useAllFactorLevels = true;
standardize = true;
diagnostics = true;
variableImportances = true;
fastMode = true;
forceLoadBalance = true;
replicateTrainingData = true;
singleNodeMode = false;
shuffleTrainingData = false;
missingValuesHandling = DeepLearningMissingValuesHandling.MeanImputation;
sparse = false;
colMajor = false;
averageActivation = 0.0;
sparsityBeta = 0.0;
maxCategoricalFeatures = 2147483647;
reproducible = false;
exportWeightsAndBiases = false;
miniBatchSize = 1;
elasticAveraging = false;
elasticAveragingMovingRate = 0.9;
elasticAveragingRegularization = 0.001;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 5;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.0;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DeepLearningV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DeepLearningV3 extends ModelBuilderSchema<DeepLearningParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public DeepLearningParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public DeepLearningV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/DownloadDataV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class DownloadDataV3 extends RequestSchemaV3 {
/**
* Frame to download
*/
@SerializedName("frame_id")
public FrameKeyV3 frameId;
/**
* Emit double values in a machine readable lossless format with Double.toHexString().
*/
@SerializedName("hex_string")
public boolean hexString;
/**
* CSV Stream
*/
public String csv;
/**
* Suggested Filename
*/
public String filename;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public DownloadDataV3() {
hexString = false;
csv = "";
filename = "";
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/EndpointV4.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class EndpointV4 extends OutputSchemaV4 {
/**
* Method+Url of the request; variable parts are enclosed in curly braces. For example: /4/schemas/{schema_name}
*/
public String url;
/**
* Short description of the functionality provided by the endpoint.
*/
public String description;
/**
* Unique name of the endpoint. These names can be used to look up the endpoint's info via GET /4/endpoints/{name}.
*/
public String name;
/**
* Input schema.
*/
@SerializedName("input_schema")
public String inputSchema;
/**
* Schema for the result returned by the endpoint.
*/
@SerializedName("output_schema")
public String outputSchema;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Url describing the schema of the current object.
public String __schema;
*/
/**
* Public constructor
*/
public EndpointV4() {
url = "null null";
description = "";
name = "";
inputSchema = "";
outputSchema = "";
__schema = "/4/schemas/EndpointV4";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/EndpointsListV4.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class EndpointsListV4 extends OutputSchemaV4 {
/**
* List of endpoints in H2O REST API (v4).
*/
public EndpointV4[] endpoints;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Url describing the schema of the current object.
public String __schema;
*/
/**
* Public constructor
*/
public EndpointsListV4() {
__schema = "/4/schemas/EndpointsListV4";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/EnsembleMetalearnerAlgorithm.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum EnsembleMetalearnerAlgorithm {
AUTO,
deeplearning,
drf,
gbm,
glm,
naivebayes,
xgboost,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/EnsembleStackedEnsembleModelStackedEnsembleParametersMetalearnerTransform.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum EnsembleStackedEnsembleModelStackedEnsembleParametersMetalearnerTransform {
Logit,
NONE,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/EnsembleStackedEnsembleModelStackingStrategy.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
public enum EnsembleStackedEnsembleModelStackingStrategy {
blending,
cross_validation,
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/EventV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class EventV3 extends SchemaV3 {
/**
* Time when the event was recorded. Format is hh:mm:ss:ms
*/
public String date;
/**
* Time in nanos
*/
public long nanos;
/**
* type of recorded event
*/
public TimelineEventEventType type;
/**
* Public constructor
*/
public EventV3() {
date = "";
nanos = -1L;
type = TimelineEventEventType.unknown;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ExtendedIsolationForestModelOutputV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ExtendedIsolationForestModelOutputV3 extends ModelOutputSchemaV3 {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Column names
public String[] names;
// Original column names
public String[] originalNames;
// Column types
public String[] columnTypes;
// Domains for categorical columns
public String[][] domains;
// Cross-validation models (model ids)
public ModelKeyV3[] crossValidationModels;
// Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id
// instead)
public FrameKeyV3[] crossValidationPredictions;
// Cross-validation holdout predictions (full out-of-sample predictions on training data)
public FrameKeyV3 crossValidationHoldoutPredictionsFrameId;
// Cross-validation fold assignment (each row is assigned to one holdout fold)
public FrameKeyV3 crossValidationFoldAssignmentFrameId;
// Category of the model (e.g., Binomial)
public ModelCategory modelCategory;
// Model summary
public TwoDimTableV3 modelSummary;
// Scoring history
public TwoDimTableV3 scoringHistory;
// Cross-Validation scoring history
public TwoDimTableV3[] cvScoringHistory;
// Model reproducibility information
public TwoDimTableV3[] reproducibilityInformationTable;
// Training data model metrics
public ModelMetricsBaseV3 trainingMetrics;
// Validation data model metrics
public ModelMetricsBaseV3 validationMetrics;
// Cross-validation model metrics
public ModelMetricsBaseV3 crossValidationMetrics;
// Cross-validation model metrics summary
public TwoDimTableV3 crossValidationMetricsSummary;
// Job status
public String status;
// Start time in milliseconds
public long startTime;
// End time in milliseconds
public long endTime;
// Runtime in milliseconds
public long runTime;
// Default threshold used for predictions
public double defaultThreshold;
// Help information for output fields
public Map<String,String> help;
*/
/**
* Public constructor
*/
public ExtendedIsolationForestModelOutputV3() {
status = "";
startTime = 0L;
endTime = 0L;
runTime = 0L;
defaultThreshold = 0.0;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ExtendedIsolationForestModelV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ExtendedIsolationForestModelV3 extends ModelSchemaV3<ExtendedIsolationForestParametersV3, ExtendedIsolationForestModelOutputV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// The build parameters for the model (e.g. K for KMeans).
public ExtendedIsolationForestParametersV3 parameters;
// The build output for the model (e.g. the cluster centers for KMeans).
public ExtendedIsolationForestModelOutputV3 output;
// Compatible frames, if requested
public String[] compatibleFrames;
// Checksum for all the things that go into building the Model.
public long checksum;
// Model key
public ModelKeyV3 modelId;
// The algo name for this Model.
public String algo;
// The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// The response column name for this Model (if applicable). Is null otherwise.
public String responseColumnName;
// The treatment column name for this Model (if applicable). Is null otherwise.
public String treatmentColumnName;
// The Model's training frame key
public FrameKeyV3 dataFrame;
// Timestamp for when this model was completed
public long timestamp;
// Indicator, whether export to POJO is available
public boolean havePojo;
// Indicator, whether export to MOJO is available
public boolean haveMojo;
*/
/**
* Public constructor
*/
public ExtendedIsolationForestModelV3() {
checksum = 0L;
algo = "";
algoFullName = "";
responseColumnName = "";
treatmentColumnName = "";
timestamp = 0L;
havePojo = false;
haveMojo = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ExtendedIsolationForestParametersV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ExtendedIsolationForestParametersV3 extends ModelParametersSchemaV3 {
/**
* Number of Extended Isolation Forest trees.
*/
public int ntrees;
/**
* Number of randomly sampled observations used to train each Extended Isolation Forest tree.
*/
@SerializedName("sample_size")
public int sampleSize;
/**
* Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with extension_Level = 0 behaves like
* Isolation Forest.
*/
@SerializedName("extension_level")
public int extensionLevel;
/**
* Seed for pseudo random number generator (if applicable)
*/
public long seed;
/**
* Score the model after every so many trees. Disabled if set to 0.
*/
@SerializedName("score_tree_interval")
public int scoreTreeInterval;
/**
* Disable calculating training metrics (expensive on large datasets)
*/
@SerializedName("disable_training_metrics")
public boolean disableTrainingMetrics;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Destination id for this model; auto-generated if not specified.
public ModelKeyV3 modelId;
// Id of the training data frame.
public FrameKeyV3 trainingFrame;
// Id of the validation data frame.
public FrameKeyV3 validationFrame;
// Number of folds for K-fold cross-validation (0 to disable or >= 2).
public int nfolds;
// Whether to keep the cross-validation models.
public boolean keepCrossValidationModels;
// Whether to keep the predictions of the cross-validation models.
public boolean keepCrossValidationPredictions;
// Whether to keep the cross-validation fold assignment.
public boolean keepCrossValidationFoldAssignment;
// Allow parallel training of cross-validation models
public boolean parallelizeCrossValidation;
// Distribution function
public GenmodelutilsDistributionFamily distribution;
// Tweedie power for Tweedie regression, must be between 1 and 2.
public double tweediePower;
// Desired quantile for Quantile regression, must be between 0 and 1.
public double quantileAlpha;
// Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).
public double huberAlpha;
// Response variable column.
public ColSpecifierV3 responseColumn;
// 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. If you set weight = 0
// for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate
// prediction, remove all rows with weight == 0.
public ColSpecifierV3 weightsColumn;
// Offset column. This will be added to the combination of columns before applying the link function.
public ColSpecifierV3 offsetColumn;
// Column with cross-validation fold index assignment per observation.
public ColSpecifierV3 foldColumn;
// 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 ModelParametersFoldAssignmentScheme foldAssignment;
// Encoding scheme for categorical features
public ModelParametersCategoricalEncodingScheme categoricalEncoding;
// For every categorical feature, only use this many most frequent categorical levels for model training. Only used
// for categorical_encoding == EnumLimited.
public int maxCategoricalLevels;
// Names of columns to ignore for training.
public String[] ignoredColumns;
// Ignore constant columns.
public boolean ignoreConstCols;
// Whether to score during each iteration of model training.
public boolean scoreEachIteration;
// Model checkpoint to resume training with.
public ModelKeyV3 checkpoint;
// 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)
public int stoppingRounds;
// Maximum allowed runtime in seconds for model training. Use 0 to disable.
public double maxRuntimeSecs;
// Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for
// Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client.
public ScoreKeeperStoppingMetric stoppingMetric;
// Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
public double stoppingTolerance;
// Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.
public int gainsliftBins;
// Reference to custom evaluation function, format: `language:keyName=funcName`
public String customMetricFunc;
// Reference to custom distribution, format: `language:keyName=funcName`
public String customDistributionFunc;
// Automatically export generated models to this directory.
public String exportCheckpointsDir;
// Set default multinomial AUC type.
public MultinomialAucType aucType;
*/
/**
* Public constructor
*/
public ExtendedIsolationForestParametersV3() {
ntrees = 100;
sampleSize = 256;
extensionLevel = 0;
seed = -1L;
scoreTreeInterval = 0;
disableTrainingMetrics = true;
nfolds = 0;
keepCrossValidationModels = true;
keepCrossValidationPredictions = false;
keepCrossValidationFoldAssignment = false;
parallelizeCrossValidation = true;
distribution = GenmodelutilsDistributionFamily.AUTO;
tweediePower = 1.5;
quantileAlpha = 0.5;
huberAlpha = 0.9;
foldAssignment = ModelParametersFoldAssignmentScheme.AUTO;
categoricalEncoding = ModelParametersCategoricalEncodingScheme.AUTO;
maxCategoricalLevels = 10;
ignoreConstCols = true;
scoreEachIteration = false;
stoppingRounds = 0;
maxRuntimeSecs = 0.0;
stoppingMetric = ScoreKeeperStoppingMetric.AUTO;
stoppingTolerance = 0.001;
gainsliftBins = -1;
customMetricFunc = "";
customDistributionFunc = "";
exportCheckpointsDir = "";
aucType = MultinomialAucType.AUTO;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/ExtendedIsolationForestV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class ExtendedIsolationForestV3 extends ModelBuilderSchema<ExtendedIsolationForestParametersV3> {
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Model builder parameters.
public ExtendedIsolationForestParametersV3 parameters;
// The algo name for this ModelBuilder.
public String algo;
// The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM).
public String algoFullName;
// Model categories this ModelBuilder can build.
public ModelCategory[] canBuild;
// Indicator whether the model is supervised or not.
public boolean supervised;
// Should the builder always be visible, be marked as beta, or only visible if the user starts up with the
// experimental flag?
public ModelBuilderBuilderVisibility visibility;
// Job Key
public JobV3 job;
// Parameter validation messages
public ValidationMessageV3[] messages;
// Count of parameter validation errors
public int errorCount;
// HTTP status to return for this build.
public int __httpStatus;
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public ExtendedIsolationForestV3() {
algo = "";
algoFullName = "";
supervised = false;
errorCount = 0;
__httpStatus = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FeatureInteractionV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FeatureInteractionV3 extends RequestSchemaV3 {
/**
* Model id of interest
*/
@SerializedName("model_id")
public ModelKeyV3 modelId;
/**
* Maximum interaction depth
*/
@SerializedName("max_interaction_depth")
public int maxInteractionDepth;
/**
* Maximum tree depth
*/
@SerializedName("max_tree_depth")
public int maxTreeDepth;
/**
* Maximum deepening
*/
@SerializedName("max_deepening")
public int maxDeepening;
/**
* Feature importance table
*/
@SerializedName("feature_interaction")
public TwoDimTableV3[] featureInteraction;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FeatureInteractionV3() {
maxInteractionDepth = 0;
maxTreeDepth = 0;
maxDeepening = 0;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FieldMetadataV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FieldMetadataV3 extends SchemaV3 {
/**
* Field name in the Schema
*/
public String name;
/**
* Type for this field
*/
public String type;
/**
* Type for this field is itself a Schema.
*/
@SerializedName("is_schema")
public boolean isSchema;
/**
* Schema name for this field, if it is_schema, or the name of the enum, if it's an enum.
*/
@SerializedName("schema_name")
public String schemaName;
/**
* Value for this field
*/
public Object value;
/**
* A short help description to appear alongside the field in a UI
*/
public String help;
/**
* The label that should be displayed for the field if the name is insufficient
*/
public String label;
/**
* Is this field required, or is the default value generally sufficient?
*/
public boolean required;
/**
* How important is this field? The web UI uses the level to do a slow reveal of the parameters
*/
public APILevel level;
/**
* Is this field an input, output or inout?
*/
public APIDirection direction;
/**
* Is the field inherited from the parent schema?
*/
@SerializedName("is_inherited")
public boolean isInherited;
/**
* If this field is inherited from a class higher in the hierarchy which one?
*/
@SerializedName("inherited_from")
public String inheritedFrom;
/**
* Is the field gridable (i.e., it can be used in grid call)
*/
@SerializedName("is_gridable")
public boolean isGridable;
/**
* For enum-type fields the allowed values are specified using the values annotation; this is used in UIs to tell
* the user the allowed values, and for validation
*/
public String[] values;
/**
* Should this field be rendered in the JSON representation?
*/
public boolean json;
/**
* For Vec-type fields this is the set of other Vec-type fields which must contain mutually exclusive values; for
* example, for a SupervisedModel the response_column must be mutually exclusive with the weights_column
*/
@SerializedName("is_member_of_frames")
public String[] isMemberOfFrames;
/**
* For Vec-type fields this is the set of Frame-type fields which must contain the named column; for example, for a
* SupervisedModel the response_column must be in both the training_frame and (if it's set) the validation_frame
*/
@SerializedName("is_mutually_exclusive_with")
public String[] isMutuallyExclusiveWith;
/**
* Public constructor
*/
public FieldMetadataV3() {
name = "";
type = "";
isSchema = false;
schemaName = "";
help = "";
label = "";
required = false;
isInherited = false;
inheritedFrom = "";
isGridable = false;
json = false;
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
0
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings
|
java-sources/ai/h2o/h2o-bindings/3.46.0.7/water/bindings/pojos/FindV3.java
|
/*
* This file is auto-generated by h2o-3/h2o-bindings/bin/gen_java.py
* Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details)
*/
package water.bindings.pojos;
import com.google.gson.Gson;
import com.google.gson.annotations.*;
public class FindV3 extends RequestSchemaV3 {
/**
* Frame to search
*/
public FrameV3 key;
/**
* Column, or null for all
*/
public String column;
/**
* Starting row for search
*/
public long row;
/**
* Value to search for; leave blank for a search for missing values
*/
public String match;
/**
* previous row with matching value, or -1
*/
public long prev;
/**
* next row with matching value, or -1
*/
public long next;
/*------------------------------------------------------------------------------------------------------------------
// INHERITED
//------------------------------------------------------------------------------------------------------------------
// Comma-separated list of JSON field paths to exclude from the result, used like:
// "/3/Frames?_exclude_fields=frames/frame_id/URL,__meta"
public String _excludeFields;
*/
/**
* Public constructor
*/
public FindV3() {
column = "";
row = 0L;
match = "";
prev = 0L;
next = 0L;
_excludeFields = "";
}
/**
* Return the contents of this object as a JSON String.
*/
@Override
public String toString() {
return new Gson().toJson(this);
}
}
|
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