GenModel (h2o-genmodel version 3.1.0.3167 API)

java.lang.Object
- hex.genmodel.GenModel

All Implemented Interfaces:

IGenModel, java.io.Serializable, IGeneratedModel
```
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

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 bitoff, int num)`
`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 void`	`GBM_rescale(double[] preds)`
`int`	`getColIdx(java.lang.String name)` Returns index of column with give name or -1 if column is not found.
`java.lang.String[][]`	`getDomainValues()` Returns domain values for all columns including response column.
`java.lang.String[]`	`getDomainValues(int i)` Returns domain values for i-th column.
`java.lang.String[]`	`getDomainValues(java.lang.String name)` Gets domain of given column.
`java.lang.String`	`getHeader()` ???
`java.lang.String[]`	`getNames()` The names of columns used in the model.
`int`	`getNumClasses(int colIdx)` Get number of classes in in given column.
`int`	`getNumCols()` Returns number of columns used as input for training (i.e., exclude response column).
`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 `IGeneratedModel.predict(double[], float[])` function.
`int`	`getResponseIdx()` Returns an index of the response column inside getDomains().
`java.lang.String`	`getResponseName()` The name of the response column.
`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)`
`boolean`	`isAutoEncoder()`
`boolean`	`isClassifier()`
`boolean`	`isSupervised()` Returns true for supervised models.
`static int`	`KMeans_closest(double[][] centers, double[] point, java.lang.String[][] domains, double[] means, double[] mults)`
`static double`	`KMeans_distance(double[] center, double[] point, java.lang.String[][] domains, double[] means, double[] mults)`
`static double`	`KMeans_distance(double[] center, float[] point, java.lang.String[][] domains, double[] means, double[] mults, double[] colSum, double[] colSumSq)`
`static double`	`log_rescale(double[] preds)`
`double[]`	`map(java.util.Map<java.lang.String,java.lang.Double> row, double[] data)` Takes a HashMap mapping column names to doubles.
`int`	`mapEnum(int colIdx, java.lang.String enumValue)` Maps given column's enum to integer used by this model.
`int`	`nclasses()` Returns number of output classes for classifiers or 1 for regression models.
`int`	`nfeatures()` Returns number of input features.
`float[]`	`predict(double[] data, float[] preds)` Predict the given row and return prediction.
`float[]`	`predict(double[] data, float[] preds, int maxIters)` Predict the given row and return prediction using given number of iterations (e.g., number of trees from forest).
`abstract double[]`	`score0(double[] data, double[] preds)` Subclasses implement the scoring logic.
`double[]`	`score0(java.util.Map<java.lang.String,java.lang.Double> row)`
`double[]`	`score0(java.util.Map<java.lang.String,java.lang.Double> row, double[] preds)`
`double[]`	`score0(java.util.Map<java.lang.String,java.lang.Double> row, double[] data, double[] preds)`
`static double[]`	`SharedTree_clean(double[] data)`

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

Methods inherited from interface hex.genmodel.IGenModel
getModelCategory

Methods inherited from interface water.genmodel.IGeneratedModel
getUUID

- 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.
- Constructor Detail
  - GenModel
```
public GenModel(java.lang.String[] names,
        java.lang.String[][] domains)
```
- Method Detail
  - isSupervised
```
public boolean isSupervised()
```
    Description copied from interface: IGenModel
    
    Returns true for supervised models.
    
    Specified by:
    
    isSupervised in interface IGenModel
    
    Returns:
    true if this class represents supervised model.
  - nfeatures
```
public int nfeatures()
```
    Description copied from interface: IGenModel
    
    Returns number of input features.
    
    Specified by:
    
    nfeatures in interface IGenModel
    
    Returns:
    number of input features used for training.
  - nclasses
```
public int nclasses()
```
    Description copied from interface: IGenModel
    
    Returns number of output classes for classifiers or 1 for regression models.
    
    Specified by:
    
    nclasses in interface IGenModel
    
    Returns:
    returns number of output classes or 1 for regression models.
  - getNumCols
```
public int getNumCols()
```
    Description copied from interface: IGeneratedModel
    
    Returns number of columns used as input for training (i.e., exclude response column).
    
    Specified by:
    
    getNumCols in interface IGeneratedModel
  - getResponseIdx
```
public int getResponseIdx()
```
    Description copied from interface: IGeneratedModel
    
    Returns an index of the response column inside getDomains().
    
    Specified by:
    
    getResponseIdx in interface IGeneratedModel
  - getResponseName
```
public java.lang.String getResponseName()
```
    Description copied from interface: IGeneratedModel
    
    The name of the response column.
    
    Specified by:
    
    getResponseName in interface IGeneratedModel
  - getNumResponseClasses
```
public int getNumResponseClasses()
```
    Description copied from interface: IGeneratedModel
    
    Return a number of classes in response column.
    
    Specified by:
    
    getNumResponseClasses in interface IGeneratedModel
    
    Returns:
    number of response classes
  - getNames
```
public java.lang.String[] getNames()
```
    Description copied from interface: IGeneratedModel
    
    The names of columns used in the model. It contains names of input columns and a name of response column.
    
    Specified by:
    
    getNames in interface IGeneratedModel
  - getColIdx
```
public int getColIdx(java.lang.String name)
```
    Description copied from interface: IGeneratedModel
    
    Returns index of column with give name or -1 if column is not found.
    
    Specified by:
    
    getColIdx in interface IGeneratedModel
  - getNumClasses
```
public int getNumClasses(int colIdx)
```
    Description copied from interface: IGeneratedModel
    
    Get number of classes in in 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
  - getDomainValues
```
public java.lang.String[] getDomainValues(java.lang.String name)
```
    Description copied from interface: IGeneratedModel
    
    Gets domain of 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)
```
    Description copied from interface: IGeneratedModel
    
    Returns domain values for i-th column.
    
    Specified by:
    
    getDomainValues in interface IGeneratedModel
    
    Parameters:
    i - index of column
    
    Returns:
    domain for given enum column or null if columns contains numeric value
  - mapEnum
```
public int mapEnum(int colIdx,
          java.lang.String enumValue)
```
    Description copied from interface: IGeneratedModel
    
    Maps given column's enum to integer used by this model. Returns -1 if mapping is not found.
    
    Specified by:
    
    mapEnum in interface IGeneratedModel
  - getDomainValues
```
public java.lang.String[][] getDomainValues()
```
    Description copied from interface: IGeneratedModel
    
    Returns domain values for all columns including response column.
    
    Specified by:
    
    getDomainValues in interface IGeneratedModel
  - isClassifier
```
public boolean isClassifier()
```
    Specified by:
    
    isClassifier in interface IGeneratedModel
    
    Returns:
    true if this model represents a classifier, else it is used for regression.
  - isAutoEncoder
```
public boolean isAutoEncoder()
```
    Specified by:
    
    isAutoEncoder in interface IGeneratedModel
    
    Returns:
    true if this model represents an AutoEncoder.
  - getPredsSize
```
public int getPredsSize()
```
    Description copied from interface: IGeneratedModel
    
    Returns the expected size of preds array which is passed to IGeneratedModel.predict(double[], float[]) function.
    
    Specified by:
    
    getPredsSize in interface IGeneratedModel
    
    Returns:
    expected size of preds array
  - getHeader
```
public java.lang.String getHeader()
```
    ???
  - map
```
public double[] map(java.util.Map<java.lang.String,java.lang.Double> row,
           double[] data)
```
    Takes a HashMap mapping column names to doubles.
    Looks up the column names needed by the model, and places the doubles into the data array in the order needed by the model. Missing columns use NaN.
  - predict
```
public float[] predict(double[] data,
              float[] preds)
```
    Description copied from interface: IGeneratedModel
    
    Predict the given row and return prediction.
    
    Specified by:
    
    predict in interface IGeneratedModel
    
    Parameters:
    data - row holding the data. Ordering should follow ordering of columns returned by getNames()
    preds - allocated array to hold a prediction
    
    Returns:
    returned preds parameter filled by prediction
  - predict
```
public float[] predict(double[] data,
              float[] preds,
              int maxIters)
```
    Description copied from interface: IGeneratedModel
    
    Predict the given row and return prediction using given number of iterations (e.g., number of trees from forest).
    
    Specified by:
    
    predict in interface IGeneratedModel
    
    Parameters:
    data - row holding the data. Ordering should follow ordering of columns returned by getNames()
    preds - allocated array to hold a prediction
    maxIters - maximum number of iterations to use during predicting process
    
    Returns:
    returned preds parameter filled by prediction
  - score0
```
public abstract double[] score0(double[] data,
              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(java.util.Map<java.lang.String,java.lang.Double> row,
              double[] data,
              double[] preds)
```
  - score0
```
public double[] score0(java.util.Map<java.lang.String,java.lang.Double> row,
              double[] preds)
```
  - score0
```
public double[] score0(java.util.Map<java.lang.String,java.lang.Double> row)
```
  - 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, the prior probabilities are used, and if they are the same, resort to a pseudo-random way (using the test data).
    
    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 bitoff,
                     int num)
```
  - KMeans_closest
```
public static int KMeans_closest(double[][] centers,
                 double[] point,
                 java.lang.String[][] domains,
                 double[] means,
                 double[] mults)
```
  - KMeans_distance
```
public static double KMeans_distance(double[] center,
                     float[] point,
                     java.lang.String[][] domains,
                     double[] means,
                     double[] mults,
                     double[] colSum,
                     double[] colSumSq)
```
  - KMeans_distance
```
public static double KMeans_distance(double[] center,
                     double[] point,
                     java.lang.String[][] domains,
                     double[] means,
                     double[] mults)
```
  - SharedTree_clean
```
public static double[] SharedTree_clean(double[] data)
```
  - 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)
```

Class GenModel

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface hex.genmodel.IGenModel

Methods inherited from interface water.genmodel.IGeneratedModel

Field Detail

_names

_domains

Constructor Detail

GenModel

Method Detail

isSupervised

nfeatures

nclasses

getNumCols

getResponseIdx

getResponseName

getNumResponseClasses

getNames

getColIdx

getNumClasses

getDomainValues

getDomainValues

mapEnum

getDomainValues

isClassifier

isAutoEncoder

getPredsSize

getHeader

map

predict

predict

score0

score0

score0

score0

correctProbabilities

getPrediction

bitSetContains

KMeans_closest

KMeans_distance

KMeans_distance

SharedTree_clean

log_rescale

GBM_rescale

GLM_identityInv

GLM_logitInv

GLM_logInv

GLM_inverseInv

GLM_tweedieInv