Java EstimatedPerformance类代码示例

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) throws OperatorException {
	double[] values = individual.getValues();
	double[][] coefficients = getCoefficients(values);
	double[][] degrees = getDegrees(values);
	double offset = getOffset(values);

	double error = 0.0d;
	for (Example example : exampleSet) {
		double prediction = PolynomialRegressionModel.calculatePrediction(example, coefficients, degrees, offset);
		double diff = Math.abs(example.getValue(label) - prediction);
		error += diff * diff;
	}
	error = Math.sqrt(error);

	PerformanceVector performanceVector = new PerformanceVector();
	performanceVector.addCriterion(new EstimatedPerformance("Polynomial Regression Error", error, 1, true));
	return performanceVector;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
/** Delivers the fitness of the best individual as performance vector. */
@Override
public PerformanceVector getOptimizationPerformance() {
	double[] bestValuesEver = getBestValuesEver();
	double[] finalFitness = optimizationFunction.getFitness(bestValuesEver, ys, kernel);
	PerformanceVector result = new PerformanceVector();
	if (finalFitness.length == 1) {
		result.addCriterion(new EstimatedPerformance("svm_objective_function", finalFitness[0], 1, false));
	} else {
		result.addCriterion(new EstimatedPerformance("alpha_sum", finalFitness[0], 1, false));
		result.addCriterion(new EstimatedPerformance("svm_objective_function", finalFitness[1], 1, false));
		if (finalFitness.length == 3) {
			result.addCriterion(new EstimatedPerformance("alpha_label_sum", finalFitness[2], 1, false));
		}
	}
	return result;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) throws OperatorException {
	double[] values = individual.getValues();
	double[][] coefficients = getCoefficients(values);
	double[][] degrees = getDegrees(values);
	double offset = getOffset(values);

	double error = 0.0d;
	for (Example example : exampleSet) {
		double prediction = PolynomialRegressionModel.calculatePrediction(example, attributes, coefficients, degrees,
				offset);
		double diff = Math.abs(example.getValue(label) - prediction);
		error += diff * diff;
	}
	error = Math.sqrt(error);

	PerformanceVector performanceVector = new PerformanceVector();
	performanceVector.addCriterion(new EstimatedPerformance("Polynomial Regression Error", error, 1, true));
	return performanceVector;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) {
	double[] beta = individual.getValues();

	double fitness = 0.0d;
	for (Example example : exampleSet) {
		double eta = 0.0d;
		int i = 0;
		for (Attribute attribute : example.getAttributes()) {
			double value = example.getValue(attribute);
			eta += beta[i] * value;
			i++;
		}
		if (addIntercept) {
			eta += beta[beta.length - 1];
		}
		double pi = Math.exp(eta) / (1 + Math.exp(eta));

		double classValue = example.getValue(label);
		double currentFitness = classValue * Math.log(pi) + (1 - classValue) * Math.log(1 - pi);
		double weightValue = 1.0d;
		if (weight != null) {
			weightValue = example.getValue(weight);
		}
		fitness += weightValue * currentFitness;
	}

	PerformanceVector performanceVector = new PerformanceVector();
	performanceVector.addCriterion(new EstimatedPerformance("log_reg_fitness", fitness, exampleSet.size(), false));
	return performanceVector;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
/** Evaluates the individuals of the given population. */
@Override
public PerformanceVector evaluateIndividual(double[] individual) {
	double fitness = optimizationFunction.getFitness(individual, ys, kernel)[0];
	PerformanceVector result = new PerformanceVector();
	result.addCriterion(new EstimatedPerformance("SVMOptValue", fitness, 1, false));
	return result;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) {
	double[] fitness = optimizationFunction.getFitness(individual.getValues(), ys, kernel);
	PerformanceVector performanceVector = new PerformanceVector();
	performanceVector.addCriterion(new EstimatedPerformance("SVM_fitness", fitness[0], 1, false));
	performanceVector.addCriterion(new EstimatedPerformance("SVM_complexity", fitness[1], 1, false));
	return performanceVector;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
/** Delivers the fitness of the best individual as performance vector. */
@Override
public PerformanceVector getOptimizationPerformance() {
	double[] bestValuesEver = getBestValuesEver();
	double[] finalFitness = optimizationFunction.getFitness(bestValuesEver, ys, kernel);
	PerformanceVector result = new PerformanceVector();
	result.addCriterion(new EstimatedPerformance("svm_objective_function", finalFitness[0], 1, false));
	result.addCriterion(new EstimatedPerformance("no_support_vectors", -1 * finalFitness[1], 1, true));
	return result;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) {
	double[] fitness = optimizationFunction.getFitness(individual.getValues(), ys, kernel);
	PerformanceVector performanceVector = new PerformanceVector();
	if (fitness.length == 1) {
		performanceVector.addCriterion(new EstimatedPerformance("SVM_fitness", fitness[0], 1, false));
	} else {
		performanceVector.addCriterion(new EstimatedPerformance("alpha_sum", fitness[0], 1, false));
		performanceVector.addCriterion(new EstimatedPerformance("svm_objective_function", fitness[1], 1, false));
		if (fitness.length == 3) {
			performanceVector.addCriterion(new EstimatedPerformance("alpha_label_sum", fitness[2], 1, false));
		}
	}
	return performanceVector;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
/**
 * Returns the optimization performance of the best result. This method must be called after
 * training, not before.
 */
@Override
public PerformanceVector getOptimizationPerformance() {
	double finalFitness = getFitness(svmExamples.get_alphas(), svmExamples.get_ys(), kernel);
	PerformanceVector result = new PerformanceVector();
	result.addCriterion(new EstimatedPerformance("svm_objective_function", finalFitness, 1, false));
	result.addCriterion(new EstimatedPerformance("no_support_vectors", svmExamples.getNumberOfSupportVectors(), 1, true));
	return result;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
/**
 * Returns the estimated performances of this SVM. Does only work for classification tasks.
 */
@Override
public PerformanceVector getEstimatedPerformance() throws OperatorException {
	if (!pattern) {
		throw new UserError(this, 912, this, "Cannot calculate leave one out estimation of error for regression tasks!");
	}
	double[] estVector = ((SVMpattern) getSVM()).getXiAlphaEstimation(getKernel());
	PerformanceVector pv = new PerformanceVector();
	pv.addCriterion(new EstimatedPerformance("xialpha_error", estVector[0], 1, true));
	pv.addCriterion(new EstimatedPerformance("xialpha_precision", estVector[1], 1, false));
	pv.addCriterion(new EstimatedPerformance("xialpha_recall", estVector[2], 1, false));
	pv.setMainCriterionName("xialpha_error");
	return pv;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public void doWork() throws OperatorException {

	ClusterModel model = clusterModelInput.getData(ClusterModel.class);

	ExampleDistributionMeasure measure = (ExampleDistributionMeasure) MEASURE_MAP
			.getInstantiation(getParameterAsString(PARAMETER_MEASURE));

	int totalNumberOfItems = 0;
	int[] count = new int[model.getNumberOfClusters()];
	for (int i = 0; i < model.getNumberOfClusters(); i++) {

		int numItemsInCluster = model.getCluster(i).getNumberOfExamples();
		totalNumberOfItems = totalNumberOfItems + numItemsInCluster;
		count[i] = numItemsInCluster;
	}

	PerformanceVector performance = performanceInput.getDataOrNull(PerformanceVector.class);
	if (performance == null) {
		// If no performance vector is available create a new one
		performance = new PerformanceVector();
	}

	distribution = measure.evaluate(count, totalNumberOfItems);

	PerformanceCriterion criterion = new EstimatedPerformance("Example distribution", distribution, 1, false);
	performance.addCriterion(criterion);

	clusterModelOutput.deliver(model);
	performanceOutput.deliver(performance);
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) {
	double[] beta = individual.getValues();

	double fitness = 0.0d;
	Attribute[] regularAttributes = exampleSet.getAttributes().createRegularAttributeArray();
	for (Example example : exampleSet) {
		double eta = 0.0d;
		int i = 0;
		for (Attribute attribute : regularAttributes) {
			double value = example.getValue(attribute);
			eta += beta[i] * value;
			i++;
		}
		if (addIntercept) {
			eta += beta[beta.length - 1];
		}
		double pi = Math.exp(eta) / (1 + Math.exp(eta));

		double classValue = example.getValue(label);
		double currentFitness = classValue * Math.log(pi) + (1 - classValue) * Math.log(1 - pi);
		double weightValue = 1.0d;
		if (weight != null) {
			weightValue = example.getValue(weight);
		}
		fitness += weightValue * currentFitness;
	}

	PerformanceVector performanceVector = new PerformanceVector();
	performanceVector.addCriterion(new EstimatedPerformance("log_reg_fitness", fitness, exampleSet.size(), false));
	return performanceVector;
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public void doWork() throws OperatorException {

	ClusterModel model = clusterModelInput.getData(ClusterModel.class);

	ExampleDistributionMeasure measure = (ExampleDistributionMeasure) MEASURE_MAP.getInstantiation(getParameterAsString(PARAMETER_MEASURE));

	int totalNumberOfItems = 0;
	int[] count = new int[model.getNumberOfClusters()];
	for (int i = 0; i < model.getNumberOfClusters(); i++) {

		int numItemsInCluster = model.getCluster(i).getNumberOfExamples();
		totalNumberOfItems = totalNumberOfItems + numItemsInCluster;
		count[i] = numItemsInCluster;
	}

	PerformanceVector performance = performanceInput.getDataOrNull(PerformanceVector.class);
	if (performance == null) {
		// If no performance vector is available create a new one
		performance = new PerformanceVector();
	}

	distribution = measure.evaluate(count, totalNumberOfItems);

	PerformanceCriterion criterion = new EstimatedPerformance("Example distribution", distribution, 1, false);
	performance.addCriterion(criterion);

	clusterModelOutput.deliver(model);
	performanceOutput.deliver(performance);
}
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) {
       double[] beta = individual.getValues();
       
       double fitness = 0.0d;
       for (Example example : exampleSet) {
           double eta = 0.0d;
           int i = 0;
           for (Attribute attribute : example.getAttributes()) {
               double value = example.getValue(attribute);
               eta += beta[i] * value;
               i++;
           }
           if (addIntercept) {
           	eta += beta[beta.length - 1];
           }
           double pi = Math.exp(eta) / (1 + Math.exp(eta));
           
           double classValue = example.getValue(label);
           double currentFitness = classValue * Math.log(pi) + (1 - classValue) * Math.log(1 - pi);
           double weightValue = 1.0d;
           if (weight != null)
               weightValue = example.getValue(weight);
           fitness += weightValue * currentFitness;
       }
       
       PerformanceVector performanceVector = new PerformanceVector();
       performanceVector.addCriterion(new EstimatedPerformance("log_reg_fitness", fitness, exampleSet.size(), false));
       return performanceVector;
   }
 

import com.rapidminer.operator.performance.EstimatedPerformance; //导入依赖的package包/类
@Override
public PerformanceVector evaluateIndividual(Individual individual) {
       double[] fitness = optimizationFunction.getFitness(individual.getValues(), ys, kernel);
       PerformanceVector performanceVector = new PerformanceVector();
       performanceVector.addCriterion(new EstimatedPerformance("SVM_fitness", fitness[0], 1, false));
       performanceVector.addCriterion(new EstimatedPerformance("SVM_complexity", fitness[1], 1, false));
       return performanceVector;
   }