public class TargetEncoderModel extends hex.Model<TargetEncoderModel,TargetEncoderModel.TargetEncoderParameters,TargetEncoderModel.TargetEncoderOutput>
| Modifier and Type | Class and Description |
|---|---|
static class |
TargetEncoderModel.DataLeakageHandlingStrategy |
static class |
TargetEncoderModel.TargetEncoderOutput |
static class |
TargetEncoderModel.TargetEncoderParameters |
hex.Model.AdaptFrameParameters, hex.Model.BigScore, hex.Model.BigScoreChunkPredict, hex.Model.BigScorePredict, hex.Model.Contributions, hex.Model.DeepFeatures, hex.Model.ExemplarMembers, hex.Model.FeatureFrequencies, hex.Model.GetMostImportantFeatures, hex.Model.GetNTrees, hex.Model.GLRMArchetypes, hex.Model.GridSortBy, hex.Model.InteractionBuilder, hex.Model.InteractionPair, hex.Model.InteractionSpec, hex.Model.JavaModelStreamWriter, hex.Model.JavaScoringOptions, hex.Model.LeafNodeAssignment, hex.Model.Output, hex.Model.Parameters, hex.Model.PredictScoreResult, hex.Model.StagedPredictions, hex.Model.UpdateAuxTreeWeights| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
ALGO_NAME |
static int |
NO_FOLD |
| Constructor and Description |
|---|
TargetEncoderModel(water.Key<TargetEncoderModel> selfKey,
TargetEncoderModel.TargetEncoderParameters parms,
TargetEncoderModel.TargetEncoderOutput output)
Start TargetEncoderModel per se
|
| Modifier and Type | Method and Description |
|---|---|
TargetEncoderMojoWriter |
getMojo() |
hex.ModelMetrics.MetricBuilder |
makeMetricBuilder(java.lang.String[] domain) |
protected water.Futures |
remove_impl(water.Futures fs,
boolean cascade) |
water.fvec.Frame |
score(water.fvec.Frame fr,
java.lang.String destination_key,
water.Job j,
boolean computeMetrics,
water.udf.CFuncRef customMetricFunc)
Model.score(Frame) always encodes as if the data were new (ie. |
protected double[] |
score0(double[] data,
double[] preds) |
water.fvec.Frame |
transform(water.fvec.Frame fr) |
water.fvec.Frame |
transform(water.fvec.Frame fr,
BlendingParams blendingParams,
double noiseLevel) |
water.fvec.Frame |
transform(water.fvec.Frame fr,
boolean asTraining,
int outOfFold,
BlendingParams blendingParams,
double noiseLevel)
Applies target encoding to unseen data during training.
|
water.fvec.Frame |
transformTraining(water.fvec.Frame fr) |
water.fvec.Frame |
transformTraining(water.fvec.Frame fr,
int outOfFold) |
adaptTestForTrain, adaptTestForTrain, adaptTestForTrain, addMetrics, addModelMetrics, addWarning, auc, AUCPR, checksum_impl, classification_error, compareTo, computeDeviances, containsResponse, data, defaultThreshold, defaultThreshold, deleteCrossValidationFoldAssignment, deleteCrossValidationModels, deleteCrossValidationPreds, deviance, deviance, evaluateAutoModelParameters, exportBinaryModel, exportMojo, fetchAll, fillScoringInfo, getDefaultGridSortBy, getGenModelEncoding, getPojoInterfaces, getToEigenVec, haveMojo, havePojo, importBinaryModel, initActualParamValues, isDistributionHuber, isFeatureUsedInPredict, isFeatureUsedInPredict, isSupervised, last_scored, lift_top_group, likelihood, logloss, loss, mae, makeAdaptFrameParameters, makeBigScoreTask, makeInteraction, makeInteractions, makeInteractions, makeSchema, makeScoringDomains, makeScoringNames, makeScoringNames, mean_per_class_error, modelDescriptor, mse, needsPostProcess, postProcessPredictions, predictScoreImpl, r2, readAll_impl, resetThreshold, rmsle, score, score, score, score, score, score, score0, score0, score0, score0PostProcessSupervised, scoreMetrics, scoring_history, scoringDomains, setInputParms, setupBigScorePredict, testJavaScoring, testJavaScoring, testJavaScoring, testJavaScoring, testJavaScoring, toJava, toJava, toJava, toJavaCheckTooBig, toJavaInit, toJavaNCLASSES, toJavaPredictBody, toJavaPROB, toJavaSuper, toJavaTransform, toMojo, toString, uploadBinaryModel, writeAll_impl, writeTodelete_and_lock, delete_and_lock, delete_and_lock, delete, delete, delete, delete, read_lock, read_lock, read_lock, unlock_all, unlock, unlock, unlock, unlock, update, update, update, write_lock_to_read_lock, write_lock, write_lock, write_lockchecksum_impl, checksum, checksum, getKey, readAll, remove_impl, remove_self_key_impl, remove, remove, remove, remove, remove, remove, removeQuietly, writeAllpublic static final java.lang.String ALGO_NAME
public static final int NO_FOLD
public TargetEncoderModel(water.Key<TargetEncoderModel> selfKey, TargetEncoderModel.TargetEncoderParameters parms, TargetEncoderModel.TargetEncoderOutput output)
public hex.ModelMetrics.MetricBuilder makeMetricBuilder(java.lang.String[] domain)
makeMetricBuilder in class hex.Model<TargetEncoderModel,TargetEncoderModel.TargetEncoderParameters,TargetEncoderModel.TargetEncoderOutput>public water.fvec.Frame transformTraining(water.fvec.Frame fr)
public water.fvec.Frame transformTraining(water.fvec.Frame fr,
int outOfFold)
public water.fvec.Frame transform(water.fvec.Frame fr)
public water.fvec.Frame transform(water.fvec.Frame fr,
BlendingParams blendingParams,
double noiseLevel)
public water.fvec.Frame transform(water.fvec.Frame fr,
boolean asTraining,
int outOfFold,
BlendingParams blendingParams,
double noiseLevel)
transform(Frame, BlendingParams, double) should be used to encode new data.
Whereas transformTraining(Frame) should be used to encode training data.fr - Data to transformasTraining - true iff transforming training data.outOfFold - if provided (if not = -1), if asTraining=true, and if the model was trained with Kfold strategy,
then the frame will be encoded by aggregating encodings from all folds except this one.
This is mainly used during cross-validation.blendingParams - Parameters for blending. If null, blending parameters from models parameters are loaded.
If those are not set, DEFAULT_BLENDING_PARAMS from TargetEncoder class are used.noiseLevel - Level of noise applied (use -1 for default noise level, 0 to disable noise).Frame with transformed fr, registered in DKV.protected double[] score0(double[] data,
double[] preds)
score0 in class hex.Model<TargetEncoderModel,TargetEncoderModel.TargetEncoderParameters,TargetEncoderModel.TargetEncoderOutput>public water.fvec.Frame score(water.fvec.Frame fr,
java.lang.String destination_key,
water.Job j,
boolean computeMetrics,
water.udf.CFuncRef customMetricFunc)
throws java.lang.IllegalArgumentException
Model.score(Frame) always encodes as if the data were new (ie. not training data).score in class hex.Model<TargetEncoderModel,TargetEncoderModel.TargetEncoderParameters,TargetEncoderModel.TargetEncoderOutput>java.lang.IllegalArgumentExceptionpublic TargetEncoderMojoWriter getMojo()
getMojo in class hex.Model<TargetEncoderModel,TargetEncoderModel.TargetEncoderParameters,TargetEncoderModel.TargetEncoderOutput>protected water.Futures remove_impl(water.Futures fs,
boolean cascade)
remove_impl in class hex.Model<TargetEncoderModel,TargetEncoderModel.TargetEncoderParameters,TargetEncoderModel.TargetEncoderOutput>