Package smile.classification

Class OneVersusOne<T>

java.lang.Object

smile.classification.AbstractClassifier<T>

smile.classification.OneVersusOne<T>

Type Parameters:

T - the data type of model input objects.

All Implemented Interfaces:

Serializable, ToDoubleFunction<T>, ToIntFunction<T>, Classifier<T>

public class OneVersusOne<T>
extends AbstractClassifier<T>

One-vs-one strategy for reducing the problem of multiclass classification to multiple binary classification problems. This approach trains K (K − 1) / 2 binary classifiers for a K-way multiclass problem; each receives the samples of a pair of classes from the original training set, and must learn to distinguish these two classes. At prediction time, a voting scheme is applied: all K (K − 1) / 2 classifiers are applied to an unseen sample and the class that got the highest number of positive predictions gets predicted by the combined classifier. Like One-vs-rest, one-vs-one suffers from ambiguities in that some regions of its input space may receive the same number of votes.

See Also:

• Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface smile.classification.Classifier

Classifier.Trainer<T,M extends Classifier<T>>

Field Summary

Fields inherited from class smile.classification.AbstractClassifier

classes

Constructor Summary

Constructors

Constructor	Description
OneVersusOne(Classifier<T>[][] classifiers, PlattScaling[][] platt)	Constructor.
OneVersusOne(Classifier<T>[][] classifiers, PlattScaling[][] platt, IntSet labels)	Constructor.

Method Summary

Modifier and Type	Method	Description
static DataFrameClassifier	fit(Formula formula, DataFrame data, BiFunction<Formula,DataFrame,DataFrameClassifier> trainer)	Fits a multi-class model with binary data frame classifiers.
static <T> OneVersusOne<T>	fit(T[] x, int[] y, int pos, int neg, BiFunction<T[],int[],Classifier<T>> trainer)	Fits a multi-class model with binary classifiers.
static <T> OneVersusOne<T>	fit(T[] x, int[] y, BiFunction<T[],int[],Classifier<T>> trainer)	Fits a multi-class model with binary classifiers.
int	predict(T x)	Prediction is based on voting.
int	predict(T x, double[] posteriori)	Prediction is based posteriori probability estimation.
boolean	soft()	Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.

Methods inherited from class smile.classification.AbstractClassifier

classes, numClasses

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, online, predict, predict, predict, predict, predict, predict, score, update, update, update

Constructor Details

OneVersusOne

public OneVersusOne(Classifier<T>[][] classifiers,
 PlattScaling[][] platt)

Constructor.

Parameters:

classifiers - the binary classifier for each one-vs-one case. Only the lower half is needed.

platt - Platt scaling models.

OneVersusOne

public OneVersusOne(Classifier<T>[][] classifiers,
 PlattScaling[][] platt,
 IntSet labels)

Constructor.

Parameters:

classifiers - the binary classifier for each one-vs-one case. Only the lower half is needed.

platt - Platt scaling models.

labels - the class label encoder.

Method Details

fit

public static <T> OneVersusOne<T> fit(T[] x,
 int[] y,
 BiFunction<T[],int[],Classifier<T>> trainer)

Fits a multi-class model with binary classifiers. Use +1 and -1 as positive and negative class labels.

Type Parameters:

T - the data type.

Parameters:

x - the training samples.

y - the training labels.

trainer - the lambda to train binary classifiers.

Returns:

the model.

fit

public static <T> OneVersusOne<T> fit(T[] x,
 int[] y,
 int pos,
 int neg,
 BiFunction<T[],int[],Classifier<T>> trainer)

Fits a multi-class model with binary classifiers.

Type Parameters:

T - the data type.

Parameters:

x - the training samples.

y - the training labels.

pos - the class label for one case.

neg - the class label for rest cases.

trainer - the lambda to train binary classifiers.

Returns:

the model.

fit

public static DataFrameClassifier fit(Formula formula,
 DataFrame data,
 BiFunction<Formula,DataFrame,DataFrameClassifier> trainer)

Fits a multi-class model with binary data frame classifiers.

Parameters:

formula - a symbolic description of the model to be fitted.

data - the data frame of the explanatory and response variables.

trainer - the lambda to train binary classifiers.

Returns:

the model.

predict

public int predict(T x)

Prediction is based on voting.

Parameters:

x - the instance to be classified.

Returns:

the predicted class label.

soft

public boolean soft()

Description copied from interface: Classifier

Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.

Returns:

true if soft classifier.

predict

public int predict(T x,
 double[] posteriori)

Prediction is based posteriori probability estimation. The result may be different from predict(T x).

Parameters:

x - an instance to be classified.

posteriori - a posteriori probabilities on output.

Returns:

the predicted class label