本文整理汇总了Java中org.jpmml.converter.Schema.getFeatures方法的典型用法代码示例。如果您正苦于以下问题:Java Schema.getFeatures方法的具体用法?Java Schema.getFeatures怎么用?Java Schema.getFeatures使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.jpmml.converter.Schema
的用法示例。
在下文中一共展示了Schema.getFeatures方法的14个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: encodeMiningModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
List<MiningModel> miningModels = new ArrayList<>();
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
for(int i = 0, rows = categoricalLabel.size(), columns = (trees.size() / rows); i < rows; i++){
MiningModel miningModel = createMiningModel(FortranMatrixUtil.getRow(trees, rows, columns, i), numIteration, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));
miningModels.add(miningModel);
}
return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
示例2: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
GBTClassificationModel model = getTransformer();
String lossType = model.getLossType();
switch(lossType){
case "logistic":
break;
default:
throw new IllegalArgumentException("Loss function " + lossType + " is not supported");
}
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
List<TreeModel> treeModels = TreeModelUtil.encodeDecisionTreeEnsemble(model, segmentSchema);
MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(segmentSchema.getLabel()))
.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.WEIGHTED_SUM, treeModels, Doubles.asList(model.treeWeights())))
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbtValue"), OpType.CONTINUOUS, DataType.DOUBLE));
return MiningModelUtil.createBinaryLogisticClassification(miningModel, 2d, 0d, RegressionModel.NormalizationMethod.LOGIT, false, schema);
}
示例3: encodeMiningModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<RegTree> regTrees, float base_score, Integer ntreeLimit, Schema schema){
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.FLOAT), schema.getFeatures());
List<MiningModel> miningModels = new ArrayList<>();
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
for(int i = 0, columns = categoricalLabel.size(), rows = (regTrees.size() / columns); i < columns; i++){
MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(regTrees, rows, columns, i), base_score, ntreeLimit, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.FLOAT));
miningModels.add(miningModel);
}
return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
示例4: encodeMiningModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
MiningModel miningModel = createMiningModel(trees, numIteration, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue"), OpType.CONTINUOUS, DataType.DOUBLE, new SigmoidTransformation(-1d * BinomialLogisticRegression.this.sigmoid_)));
return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.NONE, true, schema);
}
示例5: encodeMiningModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<RegTree> regTrees, float base_score, Integer ntreeLimit, Schema schema){
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.FLOAT), schema.getFeatures());
MiningModel miningModel = createMiningModel(regTrees, base_score, ntreeLimit, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("xgbValue"), OpType.CONTINUOUS, DataType.FLOAT));
return MiningModelUtil.createBinaryLogisticClassification(miningModel, 1d, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
示例6: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public GeneralRegressionModel encodeModel(Schema schema){
RGenericVector mvr = getObject();
RDoubleVector coefficients = (RDoubleVector)mvr.getValue("coefficients");
RDoubleVector xMeans = (RDoubleVector)mvr.getValue("Xmeans");
RDoubleVector yMeans = (RDoubleVector)mvr.getValue("Ymeans");
RNumberVector<?> ncomp = (RNumberVector<?>)mvr.getValue("ncomp");
RStringVector rowNames = coefficients.dimnames(0);
RStringVector columnNames = coefficients.dimnames(1);
RStringVector compNames = coefficients.dimnames(2);
int rows = rowNames.size();
int columns = columnNames.size();
int components = compNames.size();
List<Feature> features = schema.getFeatures();
List<Double> featureCoefficients = FortranMatrixUtil.getColumn(coefficients.getValues(), rows, (columns * components), 0 + (ValueUtil.asInt(ncomp.asScalar()) - 1));
Double intercept = yMeans.getValue(0);
for(int j = 0; j < rowNames.size(); j++){
intercept -= (featureCoefficients.get(j) * xMeans.getValue(j));
}
GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), null, null, null)
.setLinkFunction(GeneralRegressionModel.LinkFunction.IDENTITY);
GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, intercept, featureCoefficients, null);
return generalRegressionModel;
}
示例7: encodeBinaryClassification
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
private MiningModel encodeBinaryClassification(List<TreeModel> treeModels, Double initF, double coefficient, Schema schema){
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
MiningModel miningModel = createMiningModel(treeModels, initF, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));
return MiningModelUtil.createBinaryLogisticClassification(miningModel, -coefficient, 0d, RegressionModel.NormalizationMethod.LOGIT, true, schema);
}
示例8: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public RegressionModel encodeModel(Schema schema){
int[] shape = getCoefShape();
int numberOfClasses = shape[0];
int numberOfFeatures = shape[1];
boolean hasProbabilityDistribution = hasProbabilityDistribution();
List<? extends Number> coef = getCoef();
List<? extends Number> intercepts = getIntercept();
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
List<Feature> features = schema.getFeatures();
if(numberOfClasses == 1){
ClassifierUtil.checkSize(2, categoricalLabel);
return RegressionModelUtil.createBinaryLogisticClassification(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, 0)), ValueUtil.asDouble(intercepts.get(0)), RegressionModel.NormalizationMethod.LOGIT, hasProbabilityDistribution, schema);
} else
if(numberOfClasses >= 3){
ClassifierUtil.checkSize(numberOfClasses, categoricalLabel);
List<RegressionTable> regressionTables = new ArrayList<>();
for(int i = 0, rows = categoricalLabel.size(); i < rows; i++){
RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(features, ValueUtil.asDoubles(CMatrixUtil.getRow(coef, numberOfClasses, numberOfFeatures, i)), ValueUtil.asDouble(intercepts.get(i)))
.setTargetCategory(categoricalLabel.getValue(i));
regressionTables.add(regressionTable);
}
RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables)
.setNormalizationMethod(RegressionModel.NormalizationMethod.LOGIT)
.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel) : null);
return regressionModel;
} else
{
throw new IllegalArgumentException();
}
}
示例9: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public Model encodeModel(Schema schema){
RGenericVector glm = getObject();
RDoubleVector coefficients = (RDoubleVector)glm.getValue("coefficients");
RGenericVector family = (RGenericVector)glm.getValue("family");
Double intercept = coefficients.getValue(getInterceptName(), true);
RStringVector familyFamily = (RStringVector)family.getValue("family");
RStringVector familyLink = (RStringVector)family.getValue("link");
Label label = schema.getLabel();
List<Feature> features = schema.getFeatures();
if(coefficients.size() != (features.size() + (intercept != null ? 1 : 0))){
throw new IllegalArgumentException();
}
List<Double> featureCoefficients = getFeatureCoefficients(features, coefficients);
MiningFunction miningFunction = getMiningFunction(familyFamily.asScalar());
String targetCategory = null;
switch(miningFunction){
case CLASSIFICATION:
{
CategoricalLabel categoricalLabel = (CategoricalLabel)label;
if(categoricalLabel.size() != 2){
throw new IllegalArgumentException();
}
targetCategory = categoricalLabel.getValue(1);
}
break;
default:
break;
}
GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, miningFunction, ModelUtil.createMiningSchema(label), null, null, null)
.setDistribution(parseFamily(familyFamily.asScalar()))
.setLinkFunction(parseLinkFunction(familyLink.asScalar()))
.setLinkParameter(parseLinkParameter(familyLink.asScalar()));
GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, intercept, featureCoefficients, targetCategory);
switch(miningFunction){
case CLASSIFICATION:
generalRegressionModel.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, (CategoricalLabel)label));
break;
default:
break;
}
return generalRegressionModel;
}
示例10: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public Model encodeModel(Schema schema){
RGenericVector lm = getObject();
RDoubleVector coefficients = (RDoubleVector)lm.getValue("coefficients");
Double intercept = coefficients.getValue(getInterceptName(), true);
List<Feature> features = schema.getFeatures();
if(coefficients.size() != (features.size() + (intercept != null ? 1 : 0))){
throw new IllegalArgumentException();
}
List<Double> featureCoefficients = getFeatureCoefficients(features, coefficients);
return RegressionModelUtil.createRegression(features, featureCoefficients, intercept, null, schema);
}
示例11: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public GeneralRegressionModel encodeModel(Schema schema){
RGenericVector earth = getObject();
RDoubleVector coefficients = (RDoubleVector)earth.getValue("coefficients");
Double intercept = coefficients.getValue(0);
List<Feature> features = schema.getFeatures();
if(coefficients.size() != (features.size() + 1)){
throw new IllegalArgumentException();
}
List<Double> featureCoefficients = (coefficients.getValues()).subList(1, features.size() + 1);
GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), null, null, null)
.setLinkFunction(GeneralRegressionModel.LinkFunction.IDENTITY);
GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, intercept, featureCoefficients, null);
return generalRegressionModel;
}
示例12: encodeMultinomialClassification
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
private MiningModel encodeMultinomialClassification(List<TreeModel> treeModels, Double initF, Schema schema){
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
List<Model> miningModels = new ArrayList<>();
for(int i = 0, columns = categoricalLabel.size(), rows = (treeModels.size() / columns); i < columns; i++){
MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(treeModels, rows, columns, i), initF, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("gbmValue(" + categoricalLabel.getValue(i) + ")"), OpType.CONTINUOUS, DataType.DOUBLE));
miningModels.add(miningModel);
}
return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
示例13: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public Model encodeModel(Schema schema){
RGenericVector lrm = getObject();
RDoubleVector coefficients = (RDoubleVector)lrm.getValue("coefficients");
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
if(categoricalLabel.size() != 2){
throw new IllegalArgumentException();
}
String targetCategory = categoricalLabel.getValue(1);
Double intercept = coefficients.getValue(getInterceptName(), true);
List<Feature> features = schema.getFeatures();
if(coefficients.size() != (features.size() + (intercept != null ? 1 : 0))){
throw new IllegalArgumentException();
}
List<Double> featureCoefficients = getFeatureCoefficients(features, coefficients);
GeneralRegressionModel generalRegressionModel = new GeneralRegressionModel(GeneralRegressionModel.ModelType.GENERALIZED_LINEAR, MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), null, null, null)
.setLinkFunction(GeneralRegressionModel.LinkFunction.LOGIT)
.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
GeneralRegressionModelUtil.encodeRegressionTable(generalRegressionModel, features, intercept, featureCoefficients, targetCategory);
return generalRegressionModel;
}
示例14: encodeModel
import org.jpmml.converter.Schema; //导入方法依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
RGenericVector gbm = getObject();
RDoubleVector initF = (RDoubleVector)gbm.getValue("initF");
RGenericVector trees = (RGenericVector)gbm.getValue("trees");
RGenericVector c_splits = (RGenericVector)gbm.getValue("c.splits");
RGenericVector distribution = (RGenericVector)gbm.getValue("distribution");
RStringVector distributionName = (RStringVector)distribution.getValue("name");
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.DOUBLE), schema.getFeatures());
List<TreeModel> treeModels = new ArrayList<>();
for(int i = 0; i < trees.size(); i++){
RGenericVector tree = (RGenericVector)trees.getValue(i);
TreeModel treeModel = encodeTreeModel(MiningFunction.REGRESSION, tree, c_splits, segmentSchema);
treeModels.add(treeModel);
}
MiningModel miningModel = encodeMiningModel(distributionName, treeModels, initF.asScalar(), schema);
return miningModel;
}