LIMEBase (Tribuo 4.1.1 API)

java.lang.Object
- org.tribuo.classification.explanations.lime.LIMEBase

All Implemented Interfaces:

TabularExplainer<Regressor>

Direct Known Subclasses:

LIMEColumnar, LIMEText
```
public class LIMEBase
extends Object
implements TabularExplainer<Regressor>
```
LIMEBase merges the lime_base.py and lime_tabular.py implementations, and deals with simple matrices of numerical or categorical data. If you want a mixture of text, numerical and categorical data try LIMEColumnar. For plain text data use LIMEText.
See:
```
 Ribeiro MT, Singh S, Guestrin C.
 "Why should I trust you?: Explaining the predictions of any classifier"
 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2016.
 
```

Field Summary

Fields
Modifier and Type	Field and Description
`static double`	`DISTANCE_DELTA`
`protected static RegressionEvaluator`	`evaluator`
`protected SparseTrainer<Regressor>`	`explanationTrainer`
`protected Model<Label>`	`innerModel`
`protected double`	`kernelWidth`
`protected int`	`numSamples`
`protected long`	`numTrainingExamples`
`protected static OutputFactory<Regressor>`	`regressionFactory`
`protected SplittableRandom`	`rng`
`static double`	`WIDTH_CONSTANT`

Constructor Summary

Constructors
Constructor and Description
`LIMEBase(SplittableRandom rng, Model<Label> innerModel, SparseTrainer<Regressor> explanationTrainer, int numSamples)` Constructs a LIME explainer for a model which uses tabular data (i.e., no special treatment for text features).

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`LIMEExplanation`	`explain(Example<Label> example)` Explain why the supplied `Example` is classified a certain way.
`protected com.oracle.labs.mlrg.olcut.util.Pair<LIMEExplanation,List<Example<Regressor>>>`	`explainWithSamples(Example<Label> example)`
`static double`	`kernelDist(double input, double width)` Calculates an RBF kernel of a specific width.
`static double`	`measureDistance(ImmutableFeatureMap fMap, long numTrainingExamples, SparseVector input, SparseVector sample)` Measures the distance between an input point and a sampled point.
`static Example<Label>`	`samplePoint(Random rng, ImmutableFeatureMap fMap, long numTrainingExamples, SparseVector input)` Samples a single example from the supplied feature map and input vector.
`protected SparseModel<Regressor>`	`trainExplainer(Example<Regressor> target, List<Example<Regressor>> samples)` Trains the explanation model using the supplied sampled data and the input example.
`static Regressor`	`transformOutput(Prediction<Label> prediction)` Transforms a `Prediction` for a multiclass problem into a `Regressor` output which represents the probability for each class.

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

- Field Detail
  - WIDTH_CONSTANT
```
public static final double WIDTH_CONSTANT
```
    See Also:
    
    Constant Field Values
  - DISTANCE_DELTA
```
public static final double DISTANCE_DELTA
```
    See Also:
    
    Constant Field Values
  - regressionFactory
```
protected static final OutputFactory<Regressor> regressionFactory
```
  - evaluator
```
protected static final RegressionEvaluator evaluator
```
  - rng
```
protected final SplittableRandom rng
```
  - innerModel
```
protected final Model<Label> innerModel
```
  - explanationTrainer
```
protected final SparseTrainer<Regressor> explanationTrainer
```
  - numSamples
```
protected final int numSamples
```
  - numTrainingExamples
```
protected final long numTrainingExamples
```
  - kernelWidth
```
protected final double kernelWidth
```
- Constructor Detail
  - LIMEBase
```
public LIMEBase(SplittableRandom rng,
                Model<Label> innerModel,
                SparseTrainer<Regressor> explanationTrainer,
                int numSamples)
```
    Constructs a LIME explainer for a model which uses tabular data (i.e., no special treatment for text features).
    
    Parameters:
    
    rng - The rng to use for sampling.
    
    innerModel - The model to explain.
    
    explanationTrainer - The sparse trainer used to explain predictions.
    
    numSamples - The number of samples to generate for an explanation.
- Method Detail
  - explain
```
public LIMEExplanation explain(Example<Label> example)
```
    Description copied from interface: TabularExplainer
    
    Explain why the supplied Example is classified a certain way.
    
    Specified by:
    
    explain in interface TabularExplainer<Regressor>
    
    Parameters:
    
    example - The Example to explain.
    
    Returns:
    
    An Explanation for this example.
  - explainWithSamples
```
protected com.oracle.labs.mlrg.olcut.util.Pair<LIMEExplanation,List<Example<Regressor>>> explainWithSamples(Example<Label> example)
```
  - samplePoint
```
public static Example<Label> samplePoint(Random rng,
                                         ImmutableFeatureMap fMap,
                                         long numTrainingExamples,
                                         SparseVector input)
```
    Samples a single example from the supplied feature map and input vector.
    
    Parameters:
    
    rng - The rng to use.
    
    fMap - The feature map describing the domain of the features.
    
    numTrainingExamples - The number of training examples the fMap has seen.
    
    input - The input sparse vector to use.
    
    Returns:
    
    An Example sampled from the supplied feature map and input vector.
  - trainExplainer
```
protected SparseModel<Regressor> trainExplainer(Example<Regressor> target,
                                                List<Example<Regressor>> samples)
```
    Trains the explanation model using the supplied sampled data and the input example.
    The labels are usually the predicted probabilities from the real model.
    
    Parameters:
    
    target - The input example to explain.
    
    samples - The sampled data around the input.
    
    Returns:
    
    An explanation model.
  - kernelDist
```
public static double kernelDist(double input,
                                double width)
```
    Calculates an RBF kernel of a specific width.
    
    Parameters:
    
    input - The input value.
    
    width - The width of the kernel.
    
    Returns:
    
    sqrt ( exp ( - input*input / width))
  - measureDistance
```
public static double measureDistance(ImmutableFeatureMap fMap,
                                     long numTrainingExamples,
                                     SparseVector input,
                                     SparseVector sample)
```
    Measures the distance between an input point and a sampled point.
    This distance function takes into account categorical and real values. It uses the hamming distance for categoricals and the euclidean distance for real values.
    
    Parameters:
    
    fMap - The feature map used to determine if a feature is categorical or real.
    
    numTrainingExamples - The number of training examples the fMap has seen.
    
    input - The input point.
    
    sample - The sampled point.
    
    Returns:
    
    The distance between the two points.
  - transformOutput
```
public static Regressor transformOutput(Prediction<Label> prediction)
```
    Transforms a Prediction for a multiclass problem into a Regressor output which represents the probability for each class.
    Used as the target for LIME Models.
    
    Parameters:
    
    prediction - A multiclass prediction object. Must contain probabilities.
    
    Returns:
    
    The n dimensional probability output.

Class LIMEBase

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

WIDTH_CONSTANT

DISTANCE_DELTA

regressionFactory

evaluator

rng

innerModel

explanationTrainer

numSamples

numTrainingExamples

kernelWidth

Constructor Detail

LIMEBase

Method Detail

explain

explainWithSamples

samplePoint

trainExplainer

kernelDist

measureDistance

transformOutput