GenModel (h2o-genmodel 3.46.0 API)

java.lang.Object
- hex.genmodel.GenModel

All Implemented Interfaces:

IGenModel, java.io.Serializable, IGeneratedModel

Direct Known Subclasses:

MojoModel
```
public abstract class GenModel
extends java.lang.Object
implements IGenModel, IGeneratedModel, java.io.Serializable
```
This is a helper class to support Java generated models.

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`java.lang.String[][]`	`_domains` Categorical (factor/enum) mappings, per column.
`java.lang.String[]`	`_names` Column names; last is response for supervised models
`java.lang.String`	`_offsetColumn` Name of the column with offsets (used for certain types of models).
`java.lang.String`	`_responseColumn` Name of the response column used for training (only for supervised models).
`java.lang.String`	`_treatmentColumn` Name of the column determine treatment group, currently only for UpliftDRF models

Constructor Summary

Constructors
Constructor and Description
`GenModel(java.lang.String[] names, java.lang.String[][] domains)` Deprecated. This constructor is deprecated and will be removed in a future version. use `GenModel(String[] names, String[][] domains, String responseColumn)()` instead.
`GenModel(java.lang.String[] names, java.lang.String[][] domains, java.lang.String responseColumn)`
`GenModel(java.lang.String[] names, java.lang.String[][] domains, java.lang.String responseColumn, java.lang.String treatmentColumn)`

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`static boolean`	`bitSetContains(byte[] bits, int nbits, int bitoff, double dnum)`
`static boolean`	`bitSetIsInRange(int nbits, int bitoff, double dnum)`
`boolean`	`calibrateClassProbabilities(double[] preds)` Subclasses implement calibration of class probabilities.
`static float[]`	`convertDouble2Float(double[] input)`
`static double[]`	`correctProbabilities(double[] scored, double[] priorClassDist, double[] modelClassDist)` Correct a given list of class probabilities produced as a prediction by a model back to prior class distribution
`static java.lang.String`	`createAuxKey(java.lang.String k)`
`java.lang.String[]`	`features()` Returns names of input features.
`static void`	`GBM_rescale(double[] preds)`
`CategoricalEncoding`	`getCategoricalEncoding()` Return type of encoding expected by the model implementation.
`int`	`getColIdx(java.lang.String name)` Returns index of a column with given name, or -1 if the column is not found.
`java.lang.String[][]`	`getDomainValues()` Returns domain values for all columns, including the response column.
`java.lang.String[]`	`getDomainValues(int i)` Returns domain values for the i-th column.
`java.lang.String[]`	`getDomainValues(java.lang.String name)` Gets domain of the given column.
`java.lang.String`	`getHeader()` ???
`java.util.EnumSet<ModelCategory>`	`getModelCategories()` Override this for models that may produce results in different categories.
`abstract ModelCategory`	`getModelCategory()` Returns this model category.
`java.lang.String[]`	`getNames()` The names of all columns used, including response and offset columns.
`int`	`getNumClasses(int colIdx)` Get number of classes in the given column.
`int`	`getNumCols()` Returns number of columns used as input for training (i.e., exclude response and offset columns).
`int`	`getNumResponseClasses()` Return a number of classes in response column.
`java.lang.String`	`getOffsetName()` The name of the offset column.
`java.lang.String[][]`	`getOrigDomainValues()` Returns original domain values for all columns including response column.
`java.lang.String[]`	`getOrigNames()` The original names of all columns used, including response and offset columns.
`int`	`getOrigNumCols()`
`double[]`	`getOrigProjectionArray()` Returns original Eigen encoder projections array for all columns.
`java.lang.String[][]`	`getOutputDomains()` Companion method to getOutputNames.
`java.lang.String[]`	`getOutputNames()`
`static int`	`getPrediction(double[] preds, double[] priorClassDist, double[] data, double threshold)` Utility function to get a best prediction from an array of class prediction distribution.
`static int`	`getPredictionBinomial(double[] preds, double threshold)`
`static int`	`getPredictionMultinomial(double[] preds, double[] priorClassDist, double[] data)`
`int`	`getPredsSize()` Returns the expected size of preds array which is passed to `predict(double[], double[])` function.
`int`	`getPredsSize(ModelCategory mc)`
`int`	`getResponseIdx()` Returns the index of the response column inside getDomains().
`java.lang.String`	`getResponseName()` The name of the response column.
`abstract java.lang.String`	`getUUID()` Returns model's unique identifier.
`static double`	`GLM_identityInv(double x)`
`static double`	`GLM_inverseInv(double x)`
`static double`	`GLM_logInv(double x)`
`static double`	`GLM_logitInv(double x)`
`static double`	`GLM_ologitInv(double x)`
`static double`	`GLM_tweedieInv(double x, double tweedie_link_power)`
`static void`	`img2pixels(java.awt.image.BufferedImage img, int w, int h, int channels, float[] pixels, int start, float[] mean)`
`GenModel`	`internal_threadSafeInstance()` Deprecated.
`boolean`	`isAutoEncoder()` Returns true if this model represents an AutoEncoder.
`boolean`	`isClassifier()` Returns true if this model represents a classifier, else it is used for regression.
`boolean`	`isSupervised()` Returns true for supervised models.
`static int`	`KMeans_closest(double[][] centers, double[] point, java.lang.String[][] domains)`
`static double`	`KMeans_distance(double[] center, double[] point, java.lang.String[][] domains)`
`static double`	`KMeans_distance(double[] center, float[] point, int[] modes, double[] colSum, double[] colSumSq)`
`static int`	`KMeans_distances(double[][] centers, double[] point, java.lang.String[][] domains, double[] distances)`
`static void`	`Kmeans_preprocessData(double[] data, double[] means, double[] mults, int[] modes)`
`static double`	`Kmeans_preprocessData(double d, int i, double[] means, double[] mults, int[] modes)`
`static double[]`	`KMeans_simplex(double[][] centers, double[] point, java.lang.String[][] domains)`
`static double`	`log_rescale(double[] preds)`
`int`	`mapEnum(int colIdx, java.lang.String enumValue)` Maps given column's categorical to the integer used by this model (returns -1 if mapping not found).
`int`	`nCatFeatures()`
`int`	`nclasses()` Returns number of output classes for classifiers, 1 for regression models, and 0 for unsupervised models.
`int`	`nfeatures()` Returns number of input features.
`boolean`	`requiresOffset()`
`abstract double[]`	`score0(double[] row, double[] preds)` Subclasses implement the scoring logic.
`double[]`	`score0(double[] row, double offset, double[] preds)`
`static void`	`setCats(double[] from, double[] nums, int[] cats, int _cats, int[] _catOffsets, double[] _normMul, double[] _normSub, boolean useAllFactorLevels)`
`static void`	`setCats(double[] from, int[] to, int cats, int[] catOffsets, boolean useAllFactorLevels)`
`static void`	`setInput(double[] from, double[] to, double[] nums, int[] cats, int _nums, int _cats, int[] _catOffsets, double[] _normMul, double[] _normSub, boolean useAllFactorLevels, boolean replaceMissingWithZero)`
`static void`	`setInput(double[] from, float[] to, int _nums, int _cats, int[] _catOffsets, double[] _normMul, double[] _normSub, boolean useAllFactorLevels, boolean replaceMissingWithZero)`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - _names
```
public final java.lang.String[] _names
```
    Column names; last is response for supervised models
  - _domains
```
public final java.lang.String[][] _domains
```
    Categorical (factor/enum) mappings, per column. Null for non-enum cols. Columns match the post-init cleanup columns. The last column holds the response col enums for SupervisedModels.
  - _responseColumn
```
public final java.lang.String _responseColumn
```
    Name of the response column used for training (only for supervised models).
  - _offsetColumn
```
public java.lang.String _offsetColumn
```
    Name of the column with offsets (used for certain types of models).
  - _treatmentColumn
```
public java.lang.String _treatmentColumn
```
    Name of the column determine treatment group, currently only for UpliftDRF models
- Constructor Detail
  - GenModel
```
public GenModel(java.lang.String[] names,
                java.lang.String[][] domains,
                java.lang.String responseColumn)
```
  - GenModel
```
public GenModel(java.lang.String[] names,
                java.lang.String[][] domains,
                java.lang.String responseColumn,
                java.lang.String treatmentColumn)
```
  - GenModel
```
@Deprecated
public GenModel(java.lang.String[] names,
                            java.lang.String[][] domains)
```
    Deprecated. This constructor is deprecated and will be removed in a future version. use GenModel(String[] names, String[][] domains, String responseColumn)() instead.
- Method Detail
  - requiresOffset
```
public boolean requiresOffset()
```
  - isSupervised
```
public boolean isSupervised()
```
    Returns true for supervised models.
    
    Specified by:
    
    isSupervised in interface IGenModel
    
    Returns:
    
    true if this class represents supervised model.
  - nfeatures
```
public int nfeatures()
```
    Returns number of input features.
    
    Specified by:
    
    nfeatures in interface IGenModel
    
    Returns:
    
    number of input features used for training.
  - nCatFeatures
```
public int nCatFeatures()
```
  - features
```
public java.lang.String[] features()
```
    Returns names of input features.
    
    Specified by:
    
    features in interface IGenModel
    
    Returns:
    
    names of input features used for training.
  - nclasses
```
public int nclasses()
```
    Returns number of output classes for classifiers, 1 for regression models, and 0 for unsupervised models.
    
    Specified by:
    
    nclasses in interface IGenModel
    
    Returns:
    
    returns number of output classes or 1 for regression models.
  - getModelCategory
```
public abstract ModelCategory getModelCategory()
```
    Returns this model category.
    
    Specified by:
    
    getModelCategory in interface IGenModel
    
    Returns:
    
    model category
    
    See Also:
    
    ModelCategory
  - getOutputNames
```
public java.lang.String[] getOutputNames()
```
  - getOutputDomains
```
public java.lang.String[][] getOutputDomains()
```
    Companion method to getOutputNames. For each output column specifies what is the domain of the column.
    
    Returns:
    
    array of domain values for each output column, if the type of the column is not categorical use null
  - getModelCategories
```
public java.util.EnumSet<ModelCategory> getModelCategories()
```
    Override this for models that may produce results in different categories.
    
    Specified by:
    
    getModelCategories in interface IGenModel
  - getUUID
```
public abstract java.lang.String getUUID()
```
    Description copied from interface: IGeneratedModel
    
    Returns model's unique identifier.
    
    Specified by:
    
    getUUID in interface IGeneratedModel
  - getNumCols
```
public int getNumCols()
```
    Returns number of columns used as input for training (i.e., exclude response and offset columns).
    
    Specified by:
    
    getNumCols in interface IGeneratedModel
  - getNames
```
public java.lang.String[] getNames()
```
    The names of all columns used, including response and offset columns.
    
    Specified by:
    
    getNames in interface IGeneratedModel
  - getOrigNumCols
```
public int getOrigNumCols()
```
  - getOrigNames
```
public java.lang.String[] getOrigNames()
```
    The original names of all columns used, including response and offset columns.
    
    Specified by:
    
    getOrigNames in interface IGeneratedModel
  - getResponseName
```
public java.lang.String getResponseName()
```
    The name of the response column.
    
    Specified by:
    
    getResponseName in interface IGeneratedModel
  - getResponseIdx
```
public int getResponseIdx()
```
    Returns the index of the response column inside getDomains().
    
    Specified by:
    
    getResponseIdx in interface IGeneratedModel
  - getOffsetName
```
public java.lang.String getOffsetName()
```
    Description copied from interface: IGeneratedModel
    
    The name of the offset column.
    
    Specified by:
    
    getOffsetName in interface IGeneratedModel
  - getNumClasses
```
public int getNumClasses(int colIdx)
```
    Get number of classes in the given column. Return number greater than zero if the column is categorical or -1 if the column is numeric.
    
    Specified by:
    
    getNumClasses in interface IGeneratedModel
  - getNumResponseClasses
```
public int getNumResponseClasses()
```
    Return a number of classes in response column.
    
    Specified by:
    
    getNumResponseClasses in interface IGeneratedModel
    
    Returns:
    
    number of response classes
  - getCategoricalEncoding
```
public CategoricalEncoding getCategoricalEncoding()
```
    Return type of encoding expected by the model implementation.
    
    Specified by:
    
    getCategoricalEncoding in interface IGeneratedModel
  - isClassifier
```
public boolean isClassifier()
```
    Returns true if this model represents a classifier, else it is used for regression.
    
    Specified by:
    
    isClassifier in interface IGeneratedModel
    
    Returns:
    
    true if this model represents a classifier, else it is used for regression.
  - isAutoEncoder
```
public boolean isAutoEncoder()
```
    Returns true if this model represents an AutoEncoder.
    
    Specified by:
    
    isAutoEncoder in interface IGeneratedModel
    
    Returns:
    
    true if this model represents an AutoEncoder.
  - getDomainValues
```
public java.lang.String[] getDomainValues(java.lang.String name)
```
    Gets domain of the given column.
    
    Specified by:
    
    getDomainValues in interface IGeneratedModel
    
    Parameters:
    
    name - column name
    
    Returns:
    
    return domain for given column or null if column is numeric.
  - getDomainValues
```
public java.lang.String[] getDomainValues(int i)
```
    Returns domain values for the i-th column.
    
    Specified by:
    
    getDomainValues in interface IGeneratedModel
    
    Parameters:
    
    i - index of column
    
    Returns:
    
    domain for given categorical column or null if columns contains numeric value
  - getDomainValues
```
public java.lang.String[][] getDomainValues()
```
    Returns domain values for all columns, including the response column.
    
    Specified by:
    
    getDomainValues in interface IGeneratedModel
  - getOrigDomainValues
```
public java.lang.String[][] getOrigDomainValues()
```
    Description copied from interface: IGeneratedModel
    
    Returns original domain values for all columns including response column.
    
    Specified by:
    
    getOrigDomainValues in interface IGeneratedModel
  - getOrigProjectionArray
```
public double[] getOrigProjectionArray()
```
    Returns original Eigen encoder projections array for all columns.
    
    Specified by:
    
    getOrigProjectionArray in interface IGeneratedModel
  - getColIdx
```
public int getColIdx(java.lang.String name)
```
    Returns index of a column with given name, or -1 if the column is not found.
    
    Specified by:
    
    getColIdx in interface IGeneratedModel
  - mapEnum
```
public int mapEnum(int colIdx,
                   java.lang.String enumValue)
```
    Maps given column's categorical to the integer used by this model (returns -1 if mapping not found).
    
    Specified by:
    
    mapEnum in interface IGeneratedModel
  - getPredsSize
```
public int getPredsSize()
```
    Returns the expected size of preds array which is passed to `predict(double[], double[])` function.
    
    Specified by:
    
    getPredsSize in interface IGeneratedModel
    
    Returns:
    
    expected size of preds array
  - getPredsSize
```
public int getPredsSize(ModelCategory mc)
```
  - createAuxKey
```
public static java.lang.String createAuxKey(java.lang.String k)
```
  - score0
```
public abstract double[] score0(double[] row,
                                double[] preds)
```
    Subclasses implement the scoring logic. The data is pre-loaded into a re-used temp array, in the order the model expects. The predictions are loaded into the re-used temp array, which is also returned. This call exactly matches the hex.Model.score0, but uses the light-weight GenModel class.
  - score0
```
public double[] score0(double[] row,
                       double offset,
                       double[] preds)
```
  - calibrateClassProbabilities
```
public boolean calibrateClassProbabilities(double[] preds)
```
    Subclasses implement calibration of class probabilities. The input is array of predictions returned by the scoring function (score0). Supports classification models that were trained with calibration enabled. Original probabilities in the predictions array are overwritten by their corresponding calibrated counterparts. Return false if model doesn't support calibration.
  - correctProbabilities
```
public static double[] correctProbabilities(double[] scored,
                                            double[] priorClassDist,
                                            double[] modelClassDist)
```
    Correct a given list of class probabilities produced as a prediction by a model back to prior class distribution
    The implementation is based on Eq. (27) in the paper.
    
    Parameters:
    
    scored - list of class probabilities beginning at index 1
    
    priorClassDist - original class distribution
    
    modelClassDist - class distribution used for model building (e.g., data was oversampled)
    
    Returns:
    
    corrected list of probabilities
  - getPrediction
```
public static int getPrediction(double[] preds,
                                double[] priorClassDist,
                                double[] data,
                                double threshold)
```
    Utility function to get a best prediction from an array of class prediction distribution. It returns the index of the max. probability (if that exists). In the case of ties, it samples from the tied classes with the likelihood given by the prior probabilities.
    
    Parameters:
    
    preds - an array of prediction distribution. Length of arrays is equal to a number of classes+1.
    
    priorClassDist - prior class probabilities (used to break ties)
    
    data - Test data
    
    threshold - threshold for binary classifier
    
    Returns:
    
    the best prediction (index of class, zero-based)
  - getPredictionBinomial
```
public static int getPredictionBinomial(double[] preds,
                                        double threshold)
```
  - getPredictionMultinomial
```
public static int getPredictionMultinomial(double[] preds,
                                           double[] priorClassDist,
                                           double[] data)
```
  - bitSetContains
```
public static boolean bitSetContains(byte[] bits,
                                     int nbits,
                                     int bitoff,
                                     double dnum)
```
  - bitSetIsInRange
```
public static boolean bitSetIsInRange(int nbits,
                                      int bitoff,
                                      double dnum)
```
  - Kmeans_preprocessData
```
public static void Kmeans_preprocessData(double[] data,
                                         double[] means,
                                         double[] mults,
                                         int[] modes)
```
  - Kmeans_preprocessData
```
public static double Kmeans_preprocessData(double d,
                                           int i,
                                           double[] means,
                                           double[] mults,
                                           int[] modes)
```
  - KMeans_closest
```
public static int KMeans_closest(double[][] centers,
                                 double[] point,
                                 java.lang.String[][] domains)
```
  - KMeans_distances
```
public static int KMeans_distances(double[][] centers,
                                   double[] point,
                                   java.lang.String[][] domains,
                                   double[] distances)
```
  - KMeans_simplex
```
public static double[] KMeans_simplex(double[][] centers,
                                      double[] point,
                                      java.lang.String[][] domains)
```
  - KMeans_distance
```
public static double KMeans_distance(double[] center,
                                     float[] point,
                                     int[] modes,
                                     double[] colSum,
                                     double[] colSumSq)
```
  - KMeans_distance
```
public static double KMeans_distance(double[] center,
                                     double[] point,
                                     java.lang.String[][] domains)
```
  - log_rescale
```
public static double log_rescale(double[] preds)
```
  - GBM_rescale
```
public static void GBM_rescale(double[] preds)
```
  - GLM_identityInv
```
public static double GLM_identityInv(double x)
```
  - GLM_logitInv
```
public static double GLM_logitInv(double x)
```
  - GLM_logInv
```
public static double GLM_logInv(double x)
```
  - GLM_inverseInv
```
public static double GLM_inverseInv(double x)
```
  - GLM_ologitInv
```
public static double GLM_ologitInv(double x)
```
  - GLM_tweedieInv
```
public static double GLM_tweedieInv(double x,
                                    double tweedie_link_power)
```
  - getHeader
```
public java.lang.String getHeader()
```
    ???
  - setInput
```
public static void setInput(double[] from,
                            float[] to,
                            int _nums,
                            int _cats,
                            int[] _catOffsets,
                            double[] _normMul,
                            double[] _normSub,
                            boolean useAllFactorLevels,
                            boolean replaceMissingWithZero)
```
  - setInput
```
public static void setInput(double[] from,
                            double[] to,
                            double[] nums,
                            int[] cats,
                            int _nums,
                            int _cats,
                            int[] _catOffsets,
                            double[] _normMul,
                            double[] _normSub,
                            boolean useAllFactorLevels,
                            boolean replaceMissingWithZero)
```
  - setCats
```
public static void setCats(double[] from,
                           double[] nums,
                           int[] cats,
                           int _cats,
                           int[] _catOffsets,
                           double[] _normMul,
                           double[] _normSub,
                           boolean useAllFactorLevels)
```
  - setCats
```
public static void setCats(double[] from,
                           int[] to,
                           int cats,
                           int[] catOffsets,
                           boolean useAllFactorLevels)
```
  - convertDouble2Float
```
public static float[] convertDouble2Float(double[] input)
```
  - img2pixels
```
public static void img2pixels(java.awt.image.BufferedImage img,
                              int w,
                              int h,
                              int channels,
                              float[] pixels,
                              int start,
                              float[] mean)
                       throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - internal_threadSafeInstance
```
@Deprecated
public GenModel internal_threadSafeInstance()
```
    Deprecated.
    
    For internal use only - can be removed at any time! Creates a version of the MOJO that can be used by a thread in a multi-threaded environment. This is a temporary workaround and proper fix should be put in place for the MOJOs that are not currently thread safe (GAM).

Class GenModel

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

_names

_domains

_responseColumn

_offsetColumn

_treatmentColumn

Constructor Detail

GenModel

GenModel

GenModel

Method Detail

requiresOffset

isSupervised

nfeatures

nCatFeatures

features

nclasses

getModelCategory

getOutputNames

getOutputDomains

getModelCategories

getUUID

getNumCols

getNames

getOrigNumCols

getOrigNames

getResponseName

getResponseIdx

getOffsetName

getNumClasses

getNumResponseClasses

getCategoricalEncoding

isClassifier

isAutoEncoder

getDomainValues

getDomainValues

getDomainValues

getOrigDomainValues

getOrigProjectionArray

getColIdx

mapEnum

getPredsSize

getPredsSize

createAuxKey

score0

score0

calibrateClassProbabilities

correctProbabilities

getPrediction

getPredictionBinomial

getPredictionMultinomial

bitSetContains

bitSetIsInRange

Kmeans_preprocessData

Kmeans_preprocessData

KMeans_closest

KMeans_distances

KMeans_simplex

KMeans_distance

KMeans_distance

log_rescale

GBM_rescale

GLM_identityInv

GLM_logitInv

GLM_logInv

GLM_inverseInv

GLM_ologitInv

GLM_tweedieInv

getHeader

setInput

setInput

setCats

setCats

convertDouble2Float

img2pixels