smile.feature

Class GAFE

java.lang.Object

smile.feature.GAFE

public class GAFE
extends java.lang.Object

Genetic algorithm based feature selection. This method finds many (random) subsets of variables of expected classification power using a Genetic Algorithm. The "fitness" of each subset of variables is determined by its ability to classify the samples according to a given classification method. When many such subsets of variables are obtained, the one with best performance may be used as selected features. Alternatively, the frequencies with which variables are selected may be analyzed further. The most frequently selected variables may be presumed to be the most relevant to sample distinction and are finally used for prediction. Although GA avoids brute-force search, it is still much slower than univariate feature selection.

References

1. Leping Li and Clarice R. Weinberg. Gene Selection and Sample Classification Using a Genetic Algorithm/k-Nearest Neighbor Method.

Constructor Summary

Constructors

Constructor and Description
GAFE() Constructor.
GAFE(Selection selection, int elitism, Crossover crossover, double crossoverRate, double mutationRate) Constructor.

Method Summary

Modifier and Type	Method and Description
BitString[]	apply(int size, int generation, int length, FitnessMeasure<BitString> fitness) Genetic algorithm based feature selection for classification.
static FitnessMeasure<BitString>	fitness(double[][] x, double[] y, double[][] testx, double[] testy, RegressionMeasure measure, java.util.function.BiFunction<double[][],double[],Regression<double[]>> trainer) Returns a regression fitness measure.
static FitnessMeasure<BitString>	fitness(double[][] x, int[] y, double[][] testx, int[] testy, ClassificationMeasure measure, java.util.function.BiFunction<double[][],int[],Classifier<double[]>> trainer) Returns a classification fitness measure.
static FitnessMeasure<BitString>	fitness(java.lang.String y, smile.data.DataFrame train, smile.data.DataFrame test, ClassificationMeasure measure, java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameClassifier> trainer) Returns a classification fitness measure.
static FitnessMeasure<BitString>	fitness(java.lang.String y, smile.data.DataFrame train, smile.data.DataFrame test, RegressionMeasure measure, java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameRegression> trainer) Returns a regression fitness measure.

Methods inherited from class java.lang.Object

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

Constructor Detail

GAFE

public GAFE()

Constructor.

GAFE

public GAFE(Selection selection,
            int elitism,
            Crossover crossover,
            double crossoverRate,
            double mutationRate)

Constructor.

Parameters:

selection - the selection strategy.

crossover - the strategy of crossover operation.

crossoverRate - the crossover rate.

mutationRate - the mutation rate.

Method Detail

apply

public BitString[] apply(int size,
                         int generation,
                         int length,
                         FitnessMeasure<BitString> fitness)

Genetic algorithm based feature selection for classification.

Parameters:

size - the population size of Genetic Algorithm.

generation - the maximum number of iterations.

length - the length of bit string, i.e. the number of features.

Returns:

bit strings of last generation.

fitness

public static FitnessMeasure<BitString> fitness(double[][] x,
                                                int[] y,
                                                double[][] testx,
                                                int[] testy,
                                                ClassificationMeasure measure,
                                                java.util.function.BiFunction<double[][],int[],Classifier<double[]>> trainer)

Returns a classification fitness measure.

Parameters:

x - training samples.

y - training labels.

testx - testing samples.

testy - testing labels.

measure - classification measure.

trainer - the lambda to train a model.

fitness

public static FitnessMeasure<BitString> fitness(double[][] x,
                                                double[] y,
                                                double[][] testx,
                                                double[] testy,
                                                RegressionMeasure measure,
                                                java.util.function.BiFunction<double[][],double[],Regression<double[]>> trainer)

Returns a regression fitness measure.

Parameters:

x - training samples.

y - training response.

testx - testing samples.

testy - testing response.

measure - classification measure.

trainer - the lambda to train a model.

fitness

public static FitnessMeasure<BitString> fitness(java.lang.String y,
                                                smile.data.DataFrame train,
                                                smile.data.DataFrame test,
                                                ClassificationMeasure measure,
                                                java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameClassifier> trainer)

Returns a classification fitness measure.

Parameters:

y - the column name of class labels.

train - training data.

test - testing data.

measure - classification measure.

trainer - the lambda to train a model.

fitness

public static FitnessMeasure<BitString> fitness(java.lang.String y,
                                                smile.data.DataFrame train,
                                                smile.data.DataFrame test,
                                                RegressionMeasure measure,
                                                java.util.function.BiFunction<smile.data.formula.Formula,smile.data.DataFrame,DataFrameRegression> trainer)

Returns a regression fitness measure.

Parameters:

y - the column name of response variable.

train - training data.

test - testing data.

measure - classification measure.

trainer - the lambda to train a model.