GenModel (h2o-genmodel version 3.12.0.1 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).

Constructor Summary

Constructors
Constructor and Description

GenModel(java.lang.String[] names, java.lang.String[][] domains)

Constructors
Constructor and Description
`GenModel(java.lang.String[] names, java.lang.String[][] domains)`

Method Summary

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 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)`
`static void`	`GBM_rescale(double[] preds)`
`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.
`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.
`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_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)`
`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 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`	`nclasses()` Returns number of output classes for classifiers, 1 for regression models, and 0 for unsupervised models.
`int`	`nfeatures()` Returns number of input features.
`abstract double[]`	`score0(double[] row, double[] preds)` Subclasses implement the scoring logic.
`double[]`	`score0(double[] row, double offset, double[] preds)`
`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.
  - _offsetColumn
```
public java.lang.String _offsetColumn
```
    Name of the column with offsets (used for certain types of models).
- Constructor Detail
  - GenModel
```
public GenModel(java.lang.String[] names,
        java.lang.String[][] domains)
```
- Method Detail
  - 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.
  - 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
  - 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
  - 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
  - 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
  - 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
  - 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)
  - 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_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_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)
```
  - 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

Class GenModel

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

_names

_domains

_offsetColumn

Constructor Detail

GenModel

Method Detail

isSupervised

nfeatures

nclasses

getModelCategory

getModelCategories

getUUID

getNumCols

getNames

getResponseName

getResponseIdx

getNumClasses

getNumResponseClasses

isClassifier

isAutoEncoder

getDomainValues

getDomainValues

getDomainValues

getColIdx

mapEnum

getPredsSize

getPredsSize

createAuxKey

score0

score0

calibrateClassProbabilities

correctProbabilities

getPrediction

bitSetContains

bitSetIsInRange

Kmeans_preprocessData

Kmeans_preprocessData

KMeans_closest

KMeans_simplex

KMeans_distance

KMeans_distance

log_rescale

GBM_rescale

GLM_identityInv

GLM_logitInv

GLM_logInv

GLM_inverseInv

GLM_tweedieInv

getHeader

setInput

img2pixels