public abstract class AbstractLinearSGDModel<T extends Output<T>> extends AbstractSGDModel<T>
AbstractSGDModel.PredAndActive
addBias, modelParameters
ALL_OUTPUTS, BIAS_FEATURE, featureIDMap, generatesProbabilities, name, outputIDInfo, provenance, provenanceOutput
Modifier | Constructor and Description |
---|---|
protected |
AbstractLinearSGDModel(String name,
ModelProvenance provenance,
ImmutableFeatureMap featureIDMap,
ImmutableOutputInfo<T> outputIDInfo,
LinearParameters parameters,
boolean generatesProbabilities)
Constructs a linear model trained via SGD.
|
Modifier and Type | Method and Description |
---|---|
protected abstract String |
getDimensionName(int index)
Gets the name of the indexed output dimension.
|
Optional<Excuse<T>> |
getExcuse(Example<T> example)
Generates an excuse for an example.
|
Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> |
getTopFeatures(int n)
Gets the top
n features associated with this model. |
DenseMatrix |
getWeightsCopy()
Returns a copy of the weights.
|
getModelParameters, predictSingle
copy, copy, generatesProbabilities, getExcuses, getFeatureIDMap, getName, getOutputIDInfo, getProvenance, innerPredict, predict, predict, predict, setName, toString, validate
protected AbstractLinearSGDModel(String name, ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, LinearParameters parameters, boolean generatesProbabilities)
name
- The model name.provenance
- The model provenance.featureIDMap
- The feature domain.outputIDInfo
- The output domain.parameters
- The model parameters.generatesProbabilities
- Does this model generate probabilities?public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)
Model
n
features associated with this model.
If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.
If the model cannot describe it's top features then it returns Collections.emptyMap()
.
getTopFeatures
in class Model<T extends Output<T>>
n
- the number of features to return. If this value is less than 0,
all features should be returned for each class, unless the model cannot score it's features.public Optional<Excuse<T>> getExcuse(Example<T> example)
Model
This attempts to explain a classification result. Generating an excuse may be quite an expensive operation.
This excuse either contains per class information or an entry with key Model.ALL_OUTPUTS.
The optional is empty if the model does not provide excuses.
protected abstract String getDimensionName(int index)
index
- The output dimension index.public DenseMatrix getWeightsCopy()
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