hex.genmodel.easy

Class EasyPredictModelWrapper

java.lang.Object

hex.genmodel.easy.EasyPredictModelWrapper

All Implemented Interfaces:

java.io.Serializable

public class EasyPredictModelWrapper
extends java.lang.Object
implements java.io.Serializable

An easy-to-use prediction wrapper for generated models. Instantiate as follows. The following two are equivalent. EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel); EasyPredictModelWrapper model = new EasyPredictModelWrapper( new EasyPredictModelWrapper.Config() .setModel(rawModel) .setConvertUnknownCategoricalLevelsToNa(false)); Note that for any given model, you must use the exact one correct predict method below based on the model category. By default, unknown categorical levels result in a thrown PredictUnknownCategoricalLevelException. The API was designed with this default to make the simplest possible setup inform the user if there are concerns with the data quality. An alternate behavior is to automatically convert unknown categorical levels to N/A. To do this, use setConvertUnknownCategoricalLevelsToNa(true) instead. Detection of unknown categoricals may be observed by registering an implementation of EasyPredictModelWrapper.ErrorConsumer in the process of EasyPredictModelWrapper.Config creation. Deprecation note: Total number of unknown categorical variables is newly accessible by registering CountingErrorConsumer.

See the top-of-tree master version of this file here on github.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	EasyPredictModelWrapper.Config Configuration builder for instantiating a Wrapper.
static class	EasyPredictModelWrapper.ErrorConsumer Observer interface with methods corresponding to errors during the prediction.

Field Summary

Fields

Modifier and Type	Field and Description
java.util.HashMap<java.lang.Integer,java.util.HashMap<java.lang.String,java.lang.Integer>>	domainMap
GenModel	m

Constructor Summary

Constructors

Constructor and Description
EasyPredictModelWrapper(EasyPredictModelWrapper.Config config) Create a wrapper for a generated model.
EasyPredictModelWrapper(GenModel model) Create a wrapper for a generated model.

Method Summary

Modifier and Type	Method and Description
protected double[]	fillRawData(RowData data, double[] rawData)
java.lang.String	getHeader() Some autoencoder thing, I'm not sure what this does.
ModelCategory	getModelCategory() Get the category (type) of model.
java.lang.String[]	getResponseDomainValues() Get the array of levels for the response column.
java.lang.String[]	leafNodeAssignment(RowData data)
SharedTreeMojoModel.LeafNodeAssignments	leafNodeAssignmentExtended(RowData data)
protected double[]	preamble(ModelCategory c, RowData data)
protected double[]	preamble(ModelCategory c, RowData data, double offset)
AbstractPrediction	predict(RowData data)
protected double[]	predict(RowData data, double offset, double[] preds)
AbstractPrediction	predict(RowData data, ModelCategory mc) Make a prediction on a new data point.
AutoEncoderModelPrediction	predictAutoEncoder(RowData data) Make a prediction on a new data point using an AutoEncoder model.
BinomialModelPrediction	predictBinomial(RowData data) Make a prediction on a new data point using a Binomial model.
BinomialModelPrediction	predictBinomial(RowData data, double offset) Make a prediction on a new data point using a Binomial model.
ClusteringModelPrediction	predictClustering(RowData data) Make a prediction on a new data point using a Clustering model.
DimReductionModelPrediction	predictDimReduction(RowData data) Make a prediction on a new data point using a Dimension Reduction model (PCA, GLRM)
MultinomialModelPrediction	predictMultinomial(RowData data) Make a prediction on a new data point using a Multinomial model.
MultinomialModelPrediction	predictMultinomial(RowData data, double offset) Make a prediction on a new data point using a Multinomial model.
OrdinalModelPrediction	predictOrdinal(RowData data) Make a prediction on a new data point using a Ordinal model.
OrdinalModelPrediction	predictOrdinal(RowData data, double offset) Make a prediction on a new data point using a Ordinal model.
RegressionModelPrediction	predictRegression(RowData data) Make a prediction on a new data point using a Regression model.
RegressionModelPrediction	predictRegression(RowData data, double offset) Make a prediction on a new data point using a Regression model.
Word2VecPrediction	predictWord2Vec(RowData data) Lookup word embeddings for a given word (or set of words).
SortedClassProbability[]	sortByDescendingClassProbability(BinomialModelPrediction p) A helper function to return an array of binomial class probabilities for a prediction in sorted order.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

m

public final GenModel m

domainMap

public final java.util.HashMap<java.lang.Integer,java.util.HashMap<java.lang.String,java.lang.Integer>> domainMap

Constructor Detail

EasyPredictModelWrapper

public EasyPredictModelWrapper(EasyPredictModelWrapper.Config config)

Create a wrapper for a generated model.

Parameters:

config - The wrapper configuration

EasyPredictModelWrapper

public EasyPredictModelWrapper(GenModel model)

Create a wrapper for a generated model.

Parameters:

model - The generated model

Method Detail

predict

public AbstractPrediction predict(RowData data,
                                  ModelCategory mc)
                           throws PredictException

Make a prediction on a new data point. The type of prediction returned depends on the model type. The caller needs to know what type of prediction to expect. This call is convenient for generically automating model deployment. For specific applications (where the kind of model is known and doesn't change), it is recommended to call specific prediction calls like predictBinomial() directly.

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predict

public AbstractPrediction predict(RowData data)
                           throws PredictException

Throws:

PredictException

predictAutoEncoder

public AutoEncoderModelPrediction predictAutoEncoder(RowData data)
                                              throws PredictException

Make a prediction on a new data point using an AutoEncoder model.

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predictDimReduction

public DimReductionModelPrediction predictDimReduction(RowData data)
                                                throws PredictException

Make a prediction on a new data point using a Dimension Reduction model (PCA, GLRM)

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predictWord2Vec

public Word2VecPrediction predictWord2Vec(RowData data)
                                   throws PredictException

Lookup word embeddings for a given word (or set of words).

Parameters:

data - RawData structure, every key with a String value will be translated to an embedding

Returns:

The prediction

Throws:

PredictException - if model is not a WordEmbedding model

predictBinomial

public BinomialModelPrediction predictBinomial(RowData data)
                                        throws PredictException

Make a prediction on a new data point using a Binomial model.

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predictBinomial

public BinomialModelPrediction predictBinomial(RowData data,
                                               double offset)
                                        throws PredictException

Make a prediction on a new data point using a Binomial model.

Parameters:

data - A new data point.

offset - An offset for the prediction.

Returns:

The prediction.

Throws:

PredictException

leafNodeAssignment

public java.lang.String[] leafNodeAssignment(RowData data)
                                      throws PredictException

Throws:

PredictException

leafNodeAssignmentExtended

public SharedTreeMojoModel.LeafNodeAssignments leafNodeAssignmentExtended(RowData data)
                                                                   throws PredictException

Throws:

PredictException

predictMultinomial

public MultinomialModelPrediction predictMultinomial(RowData data)
                                              throws PredictException

Make a prediction on a new data point using a Multinomial model.

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predictMultinomial

public MultinomialModelPrediction predictMultinomial(RowData data,
                                                     double offset)
                                              throws PredictException

Make a prediction on a new data point using a Multinomial model.

Parameters:

data - A new data point.

offset - Prediction offset

Returns:

The prediction.

Throws:

PredictException

predictOrdinal

public OrdinalModelPrediction predictOrdinal(RowData data)
                                      throws PredictException

Make a prediction on a new data point using a Ordinal model.

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predictOrdinal

public OrdinalModelPrediction predictOrdinal(RowData data,
                                             double offset)
                                      throws PredictException

Make a prediction on a new data point using a Ordinal model.

Parameters:

data - A new data point.

offset - Prediction offset

Returns:

The prediction.

Throws:

PredictException

sortByDescendingClassProbability

public SortedClassProbability[] sortByDescendingClassProbability(BinomialModelPrediction p)

A helper function to return an array of binomial class probabilities for a prediction in sorted order. The returned array has the most probable class in position 0.

Parameters:

p - The prediction.

Returns:

An array with sorted class probabilities.

predictClustering

public ClusteringModelPrediction predictClustering(RowData data)
                                            throws PredictException

Make a prediction on a new data point using a Clustering model.

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predictRegression

public RegressionModelPrediction predictRegression(RowData data)
                                            throws PredictException

Make a prediction on a new data point using a Regression model.

Parameters:

data - A new data point.

Returns:

The prediction.

Throws:

PredictException

predictRegression

public RegressionModelPrediction predictRegression(RowData data,
                                                   double offset)
                                            throws PredictException

Make a prediction on a new data point using a Regression model.

Parameters:

data - A new data point.

offset - Prediction offset

Returns:

The prediction.

Throws:

PredictException

getModelCategory

public ModelCategory getModelCategory()

Get the category (type) of model.

Returns:

The category.

getResponseDomainValues

public java.lang.String[] getResponseDomainValues()

Get the array of levels for the response column. "Domain" just means list of level names for a categorical (aka factor, enum) column. If the response column is numerical and not categorical, this will return null.

Returns:

The array.

getHeader

public java.lang.String getHeader()

Some autoencoder thing, I'm not sure what this does.

Returns:

CSV header for autoencoder.

preamble

protected double[] preamble(ModelCategory c,
                            RowData data)
                     throws PredictException

Throws:

PredictException

preamble

protected double[] preamble(ModelCategory c,
                            RowData data,
                            double offset)
                     throws PredictException

Throws:

PredictException

fillRawData

protected double[] fillRawData(RowData data,
                               double[] rawData)
                        throws PredictException

Throws:

PredictException

predict

protected double[] predict(RowData data,
                           double offset,
                           double[] preds)
                    throws PredictException

Throws:

PredictException