CrossFoldLearner (Mahout Core 0.8-SNAPSHOT API)

java.lang.Object
- org.apache.mahout.classifier.AbstractVectorClassifier
- - org.apache.mahout.classifier.sgd.CrossFoldLearner

All Implemented Interfaces:

Closeable, AutoCloseable, org.apache.hadoop.io.Writable, OnlineLearner
```
public class CrossFoldLearner
extends AbstractVectorClassifier
implements OnlineLearner, org.apache.hadoop.io.Writable
```
Does cross-fold validation of log-likelihood and AUC on several online logistic regression models. Each record is passed to all but one of the models for training and to the remaining model for evaluation. In order to maintain proper segregation between the different folds across training data iterations, data should either be passed to this learner in the same order each time the training data is traversed or a tracking key such as the file offset of the training record should be passed with each training example.

Constructor Summary

Constructors
Constructor and Description
`CrossFoldLearner()`
`CrossFoldLearner(int folds, int numCategories, int numFeatures, PriorFunction prior)`

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`addModel(OnlineLogisticRegression model)`
`CrossFoldLearner`	`alpha(double alpha)`
`double`	`auc()`
`org.apache.mahout.math.Vector`	`classify(org.apache.mahout.math.Vector instance)` Classify a vector returning a vector of numCategories-1 scores.
`org.apache.mahout.math.Vector`	`classifyNoLink(org.apache.mahout.math.Vector instance)` Classify a vector, but don't apply the inverse link function.
`double`	`classifyScalar(org.apache.mahout.math.Vector instance)` Classifies a vector in the special case of a binary classifier where `AbstractVectorClassifier.classify(Vector)` would return a vector with only one element.
`void`	`close()` Prepares the classifier for classification and deallocates any temporary data structures.
`CrossFoldLearner`	`copy()`
`CrossFoldLearner`	`decayExponent(double x)`
`OnlineAuc`	`getAucEvaluator()`
`double`	`getLogLikelihood()`
`List<OnlineLogisticRegression>`	`getModels()`
`int`	`getNumFeatures()`
`double[]`	`getParameters()`
`PriorFunction`	`getPrior()`
`int`	`getRecord()`
`CrossFoldLearner`	`lambda(double v)`
`CrossFoldLearner`	`learningRate(double x)`
`double`	`logLikelihood()`
`int`	`numCategories()` Returns the number of categories for the target variable.
`double`	`percentCorrect()`
`void`	`readFields(DataInput in)`
`void`	`resetLineCounter()`
`void`	`setAucEvaluator(OnlineAuc auc)`
`void`	`setLogLikelihood(double logLikelihood)`
`void`	`setNumFeatures(int numFeatures)`
`void`	`setParameters(double[] parameters)`
`void`	`setPrior(PriorFunction prior)`
`void`	`setRecord(int record)`
`void`	`setWindowSize(int windowSize)`
`CrossFoldLearner`	`stepOffset(int x)`
`void`	`train(int actual, org.apache.mahout.math.Vector instance)` Updates the model using a particular target variable value and a feature vector.
`void`	`train(long trackingKey, int actual, org.apache.mahout.math.Vector instance)` Updates the model using a particular target variable value and a feature vector.
`void`	`train(long trackingKey, String groupKey, int actual, org.apache.mahout.math.Vector instance)` Updates the model using a particular target variable value and a feature vector.
`boolean`	`validModel()`
`void`	`write(DataOutput out)`

Methods inherited from class org.apache.mahout.classifier.AbstractVectorClassifier
classify, classifyFull, classifyFull, classifyFull, classifyScalar, logLikelihood

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

- Constructor Detail
  - CrossFoldLearner
```
public CrossFoldLearner()
```
  - CrossFoldLearner
```
public CrossFoldLearner(int folds,
                int numCategories,
                int numFeatures,
                PriorFunction prior)
```
- Method Detail
  - lambda
```
public CrossFoldLearner lambda(double v)
```
  - learningRate
```
public CrossFoldLearner learningRate(double x)
```
  - stepOffset
```
public CrossFoldLearner stepOffset(int x)
```
  - decayExponent
```
public CrossFoldLearner decayExponent(double x)
```
  - alpha
```
public CrossFoldLearner alpha(double alpha)
```
  - train
```
public void train(int actual,
         org.apache.mahout.math.Vector instance)
```
    Description copied from interface: OnlineLearner
    
    Updates the model using a particular target variable value and a feature vector.
    There may an assumption that if multiple passes through the training data are necessary, then the training examples will be presented in the same order. This is because the order of training examples may be used to assign records to different data splits for evaluation by cross-validation. Without the order invariance, records might be assigned to training and test splits and error estimates could be seriously affected.
    If re-ordering is necessary, then using the alternative API which allows a tracking key to be added to the training example can be used.
    
    Specified by:
    
    train in interface OnlineLearner
    
    Parameters:
    actual - The value of the target variable. This value should be in the half-open interval [0..n) where n is the number of target categories.
    instance - The feature vector for this example.
  - train
```
public void train(long trackingKey,
         int actual,
         org.apache.mahout.math.Vector instance)
```
    Description copied from interface: OnlineLearner
    
    Updates the model using a particular target variable value and a feature vector.
    There may an assumption that if multiple passes through the training data are necessary that the tracking key for a record will be the same for each pass and that there will be a relatively large number of distinct tracking keys and that the low-order bits of the tracking keys will not correlate with any of the input variables. This tracking key is used to assign training examples to different test/training splits.
    Examples of useful tracking keys include id-numbers for the training records derived from a database id for the base table from the which the record is derived, or the offset of the original data record in a data file.
    
    Specified by:
    
    train in interface OnlineLearner
    
    Parameters:
    trackingKey - The tracking key for this training example.
    actual - The value of the target variable. This value should be in the half-open interval [0..n) where n is the number of target categories.
    instance - The feature vector for this example.
  - train
```
public void train(long trackingKey,
         String groupKey,
         int actual,
         org.apache.mahout.math.Vector instance)
```
    Description copied from interface: OnlineLearner
    
    Updates the model using a particular target variable value and a feature vector.
    There may an assumption that if multiple passes through the training data are necessary that the tracking key for a record will be the same for each pass and that there will be a relatively large number of distinct tracking keys and that the low-order bits of the tracking keys will not correlate with any of the input variables. This tracking key is used to assign training examples to different test/training splits.
    Examples of useful tracking keys include id-numbers for the training records derived from a database id for the base table from the which the record is derived, or the offset of the original data record in a data file.
    
    Specified by:
    
    train in interface OnlineLearner
    
    Parameters:
    trackingKey - The tracking key for this training example.
    groupKey - An optional value that allows examples to be grouped in the computation of the update to the model.
    actual - The value of the target variable. This value should be in the half-open interval [0..n) where n is the number of target categories.
    instance - The feature vector for this example.
  - close
```
public void close()
```
    Description copied from interface: OnlineLearner
    
    Prepares the classifier for classification and deallocates any temporary data structures. An online classifier should be able to accept more training after being closed, but closing the classifier may make classification more efficient.
    
    Specified by:
    
    close in interface Closeable
    
    Specified by:
    
    close in interface AutoCloseable
    
    Specified by:
    
    close in interface OnlineLearner
  - resetLineCounter
```
public void resetLineCounter()
```
  - validModel
```
public boolean validModel()
```
  - classify
```
public org.apache.mahout.math.Vector classify(org.apache.mahout.math.Vector instance)
```
    Description copied from class: AbstractVectorClassifier
    
    Classify a vector returning a vector of numCategories-1 scores. It is assumed that the score for the missing category is one minus the sum of the scores that are returned. Note that the missing score is the 0-th score.
    
    Specified by:
    
    classify in class AbstractVectorClassifier
    
    Parameters:
    instance - A feature vector to be classified.
    
    Returns:
    A vector of probabilities in 1 of n-1 encoding.
  - classifyNoLink
```
public org.apache.mahout.math.Vector classifyNoLink(org.apache.mahout.math.Vector instance)
```
    Description copied from class: AbstractVectorClassifier
    
    Classify a vector, but don't apply the inverse link function. For logistic regression and other generalized linear models, this is just the linear part of the classification.
    
    Overrides:
    
    classifyNoLink in class AbstractVectorClassifier
    
    Parameters:
    instance - A feature vector to be classified.
    
    Returns:
    A vector of scores. If transformed by the link function, these will become probabilities.
  - classifyScalar
```
public double classifyScalar(org.apache.mahout.math.Vector instance)
```
    Description copied from class: AbstractVectorClassifier
    
    Classifies a vector in the special case of a binary classifier where AbstractVectorClassifier.classify(Vector) would return a vector with only one element. As such, using this method can void the allocation of a vector.
    
    Specified by:
    
    classifyScalar in class AbstractVectorClassifier
    
    Parameters:
    instance - The feature vector to be classified.
    
    Returns:
    The score for category 1.
    See Also:
    AbstractVectorClassifier.classify(Vector)
  - numCategories
```
public int numCategories()
```
    Description copied from class: AbstractVectorClassifier
    
    Returns the number of categories for the target variable. A vector classifier will encode it's output using a zero-based 1 of numCategories encoding.
    
    Specified by:
    
    numCategories in class AbstractVectorClassifier
    
    Returns:
    The number of categories.
  - auc
```
public double auc()
```
  - logLikelihood
```
public double logLikelihood()
```
  - percentCorrect
```
public double percentCorrect()
```
  - copy
```
public CrossFoldLearner copy()
```
  - getRecord
```
public int getRecord()
```
  - setRecord
```
public void setRecord(int record)
```
  - getAucEvaluator
```
public OnlineAuc getAucEvaluator()
```
  - setAucEvaluator
```
public void setAucEvaluator(OnlineAuc auc)
```
  - getLogLikelihood
```
public double getLogLikelihood()
```
  - setLogLikelihood
```
public void setLogLikelihood(double logLikelihood)
```
  - getModels
```
public List<OnlineLogisticRegression> getModels()
```
  - addModel
```
public void addModel(OnlineLogisticRegression model)
```
  - getParameters
```
public double[] getParameters()
```
  - setParameters
```
public void setParameters(double[] parameters)
```
  - getNumFeatures
```
public int getNumFeatures()
```
  - setNumFeatures
```
public void setNumFeatures(int numFeatures)
```
  - setWindowSize
```
public void setWindowSize(int windowSize)
```
  - getPrior
```
public PriorFunction getPrior()
```
  - setPrior
```
public void setPrior(PriorFunction prior)
```
  - write
```
public void write(DataOutput out)
           throws IOException
```
    Specified by:
    
    write in interface org.apache.hadoop.io.Writable
    
    Throws:
    
    IOException
  - readFields
```
public void readFields(DataInput in)
                throws IOException
```
    Specified by:
    
    readFields in interface org.apache.hadoop.io.Writable
    
    Throws:
    
    IOException

Class CrossFoldLearner

Constructor Summary

Method Summary

Methods inherited from class org.apache.mahout.classifier.AbstractVectorClassifier

Methods inherited from class java.lang.Object

Constructor Detail

CrossFoldLearner

CrossFoldLearner

Method Detail

lambda

learningRate

stepOffset

decayExponent

alpha

train

train

train

close

resetLineCounter

validModel

classify

classifyNoLink

classifyScalar

numCategories

auc

logLikelihood

percentCorrect

copy

getRecord

setRecord

getAucEvaluator

setAucEvaluator

getLogLikelihood

setLogLikelihood

getModels

addModel

getParameters

setParameters

getNumFeatures

setNumFeatures

setWindowSize

getPrior

setPrior

write

readFields