StochasticSTLinearL1 (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.linear.StochasticSTLinearL1

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, Regressor, SimpleWeightVectorModel, SingleWeightVectorModel

Direct Known Subclasses:

LinearL1SCD, SMIDAS
```
public abstract class StochasticSTLinearL1
extends Object
implements Classifier, Regressor, Parameterized, SingleWeightVectorModel
```
This base class provides shared functionality and variables used by two different training algorithms for L₁ regularized linear models. Both squared and log loss are supported, making the results equivalent to LASSO regression and Logistic regression respectively.

These algorithms requires all feature values to be in the range [-1, 1]. The implementation can do implicit rescaling, but rescaling may destroy sparsity. If the data set is sparse and all values are zero or positive use the default [0,1] rescaling to perform efficient rescaling that will not destroy sparsity.

Both algorithms are from:
Shalev-Shwartz, S.,&Tewari, A. (2009). Stochastic Methods for L₁-regularized Loss Minimization. 26th International Conference on Machine Learning (Vol. 12, pp. 929–936).

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class StochasticSTLinearL1.Loss

Nested Classes
Modifier and Type	Class and Description
`static class`	`StochasticSTLinearL1.Loss`

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`bias` The bias term to add
`static int`	`DEFAULT_EPOCHS`
`static StochasticSTLinearL1.Loss`	`DEFAULT_LOSS`
`static double`	`DEFAULT_REG`
`protected int`	`epochs` The number of training iterations
`protected double`	`lambda` The regularization penalty
`protected StochasticSTLinearL1.Loss`	`loss` The loss function to use
`protected double`	`maxScaled` The scaled maximum
`protected double`	`minScaled` The scaled minimum
`protected double[]`	`obvMax` The maximum observed value for each feature
`protected double[]`	`obvMin` The minimum observed value for each feature
`protected boolean`	`reScale` Whether or not to perform feature rescaling
`protected Vec`	`w` The final weight vector

Constructor Summary

Constructors
Constructor and Description

StochasticSTLinearL1()

Constructors
Constructor and Description
`StochasticSTLinearL1()`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`abstract StochasticSTLinearL1`	`clone()`
`double`	`getBias()` Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
`double`	`getBias(int index)` Returns the bias term used with the weight vector for the given class index.
`double`	`getEpochs()` Returns the number of iterations of updating that will be done
`double`	`getLambda()` Returns the amount of regularization to used in training
`StochasticSTLinearL1.Loss`	`getLoss()` returns the loss function in use
`double`	`getMaxScaled()` Returns the maximum feature value after scaling
`double`	`getMinScaled()` Returns the minimum feature value after scaling
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`Vec`	`getRawWeight()` Returns the only weight vector used for the model
`Vec`	`getRawWeight(int index)` Returns the raw weight vector associated with the given class index.
`Vec`	`getW()` Returns a copy of the weight vector used to compute results via a dot product.
`Vec`	`getWRaw()` Returns the weight vector used to compute results via a dot product.
`boolean`	`isReScale()` Returns if scaling is in use
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`void`	`setEpochs(int epochs)` Sets the number of iterations of training that will be performed.
`void`	`setLambda(double lambda)` Sets the regularization constant used for learning.
`void`	`setLoss(StochasticSTLinearL1.Loss loss)` Sets the loss function to use.
`void`	`setMaxScaled(double maxFeature)` Sets the maximum value of any feature after scaling is applied.
`void`	`setMinScaled(double minFeature)` Sets the minimum value of any feature after scaling is applied.
`void`	`setReScale(boolean reScale)` Sets whether or not scaling should be applied on th feature values of the training vectors.
`protected double`	`wDot(Vec x)` Computes `w`.`Vec.dot(jsat.linear.Vec)x` and does so by rescaling `x` as needed automatically and efficiently, even if `x` is sparse.

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

Methods inherited from interface jsat.classifiers.Classifier
classify, supportsWeightedData, trainC, trainC

Methods inherited from interface jsat.regression.Regressor
regress, supportsWeightedData, train, train

- Field Detail
  - epochs
```
protected int epochs
```
    The number of training iterations
  - lambda
```
protected double lambda
```
    The regularization penalty
  - loss
```
protected StochasticSTLinearL1.Loss loss
```
    The loss function to use
  - w
```
protected Vec w
```
    The final weight vector
  - bias
```
protected double bias
```
    The bias term to add
  - obvMin
```
protected double[] obvMin
```
    The minimum observed value for each feature
  - obvMax
```
protected double[] obvMax
```
    The maximum observed value for each feature
  - reScale
```
protected boolean reScale
```
    Whether or not to perform feature rescaling
  - minScaled
```
protected double minScaled
```
    The scaled minimum
  - maxScaled
```
protected double maxScaled
```
    The scaled maximum
  - DEFAULT_EPOCHS
```
public static final int DEFAULT_EPOCHS
```
    See Also:
    
    Constant Field Values
  - DEFAULT_REG
```
public static final double DEFAULT_REG
```
    See Also:
    
    Constant Field Values
  - DEFAULT_LOSS
```
public static final StochasticSTLinearL1.Loss DEFAULT_LOSS
```
- Constructor Detail
  - StochasticSTLinearL1
```
public StochasticSTLinearL1()
```
- Method Detail
  - clone
```
public abstract StochasticSTLinearL1 clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of iterations of training that will be performed.
    
    Parameters:
    
    epochs - the number of iterations
  - getEpochs
```
public double getEpochs()
```
    Returns the number of iterations of updating that will be done
    
    Returns:
    
    the number of iterations
  - setMaxScaled
```
public void setMaxScaled(double maxFeature)
```
    Sets the maximum value of any feature after scaling is applied. This value can be no greater than 1.
    
    Parameters:
    
    maxFeature - the maximum feature value after scaling
  - getMaxScaled
```
public double getMaxScaled()
```
    Returns the maximum feature value after scaling
    
    Returns:
    
    the maximum feature value after scaling
  - setMinScaled
```
public void setMinScaled(double minFeature)
```
    Sets the minimum value of any feature after scaling is applied. This value can be no smaller than -1
    
    Parameters:
    
    minFeature - the minimum feature value after scaling
  - getMinScaled
```
public double getMinScaled()
```
    Returns the minimum feature value after scaling
    
    Returns:
    
    the minimum feature value after scaling
  - setLambda
```
public void setLambda(double lambda)
```
    Sets the regularization constant used for learning. The regularization must be positive, and the learning rate is proportional to the regularization value. This means regularizations very near zero will take a long time to converge.
    
    Parameters:
    
    lambda - the regularization to apply
  - getLambda
```
public double getLambda()
```
    Returns the amount of regularization to used in training
    
    Returns:
    
    the regularization parameter.
  - setLoss
```
public void setLoss(StochasticSTLinearL1.Loss loss)
```
    Sets the loss function to use. This should not be altered after training unless the leaner is going to be trained again.
    
    Parameters:
    
    loss - the loss function to use
  - getLoss
```
public StochasticSTLinearL1.Loss getLoss()
```
    returns the loss function in use
    
    Returns:
    
    the loss function in use
  - setReScale
```
public void setReScale(boolean reScale)
```
    Sets whether or not scaling should be applied on th feature values of the training vectors. Scaling should be used intelligently, scaling can destroy sparsity in the data set. If scaling is not applied, and a value is not in the range [-1, 1], a FailedToFitException could occur.
    Rescaling does not alter the data points passed in.
    
    Parameters:
    
    reScale - whether or not to rescale feature values
  - isReScale
```
public boolean isReScale()
```
    Returns if scaling is in use
    
    Returns:
    
    true if feature values are rescaled during training.
  - wDot
```
protected double wDot(Vec x)
```
    Computes w.Vec.dot(jsat.linear.Vec)x and does so by rescaling x as needed automatically and efficiently, even if x is sparse.
    
    Parameters:
    
    x - the value to compute the dot product with
    
    Returns:
    
    the dot produce of w and x with the bias term
  - getWRaw
```
public Vec getWRaw()
```
    Returns the weight vector used to compute results via a dot product.
    Do not modify this value, or you will alter the results returned.
    
    Returns:
    
    the learned weight vector for prediction
  - getW
```
public Vec getW()
```
    Returns a copy of the weight vector used to compute results via a dot product.
    
    Returns:
    
    a copy of the learned weight vector for prediction
  - getRawWeight
```
public Vec getRawWeight()
```
    Description copied from interface: SingleWeightVectorModel
    
    Returns the only weight vector used for the model
    
    Specified by:
    
    getRawWeight in interface SingleWeightVectorModel
    
    Returns:
    
    the only weight vector used for the model
  - getBias
```
public double getBias()
```
    Description copied from interface: SingleWeightVectorModel
    
    Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
    
    Specified by:
    
    getBias in interface SingleWeightVectorModel
    
    Returns:
    
    the bias term for the model
  - getRawWeight
```
public Vec getRawWeight(int index)
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the raw weight vector associated with the given class index. If the given class is an implicit zero vector, a ConstantVector object may be returned.
    Do not alter the returned weight vector, as it will change the model's values.
    
    If a regression problem, only index = 0 should be used
    
    Specified by:
    
    getRawWeight in interface SimpleWeightVectorModel
    
    Parameters:
    
    index - the class index to get the weight vector for
    
    Returns:
    
    the weight vector used for the specified class
  - getBias
```
public double getBias(int index)
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the bias term used with the weight vector for the given class index. If the model does not support or was not trained with bias weights, 0 will be returned.
    
    If a regression problem, only index = 0 should be used
    
    Specified by:
    
    getBias in interface SimpleWeightVectorModel
    
    Parameters:
    
    index - the class index to get the weight vector for
    
    Returns:
    
    the bias term for the specified class
  - numWeightsVecs
```
public int numWeightsVecs()
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the number of weight vectors that can be returned. For binary classification problems the value may be 1 if only a single weight vector's sign is used to determine the class. For multi-class problems, the weight vector count includes the implicit zero vector (if one is being used).
    
    Specified by:
    
    numWeightsVecs in interface SimpleWeightVectorModel
    
    Returns:
    
    the number of weight vectors for which SimpleWeightVectorModel.getRawWeight(int) can be called.
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class StochasticSTLinearL1

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface jsat.classifiers.Classifier

Methods inherited from interface jsat.regression.Regressor

Field Detail

epochs

lambda

loss

w

bias

obvMin

obvMax

reScale

minScaled

maxScaled

DEFAULT_EPOCHS

DEFAULT_REG

DEFAULT_LOSS

Constructor Detail

StochasticSTLinearL1

Method Detail

clone

setEpochs

getEpochs

setMaxScaled

getMaxScaled

setMinScaled

getMinScaled

setLambda

getLambda

setLoss

getLoss

setReScale

isReScale

wDot

getWRaw

getW

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

getParameters

getParameter