本文整理汇总了Java中org.dmg.pmml.regression.RegressionModel类的典型用法代码示例。如果您正苦于以下问题：Java RegressionModel类的具体用法？Java RegressionModel怎么用？Java RegressionModel使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
RegressionModel类属于org.dmg.pmml.regression包，在下文中一共展示了RegressionModel类的20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，您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: encodeMiningModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的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);
}
开发者ID:jpmml，项目名称:jpmml-lightgbm，代码行数:18，代码来源:MultinomialLogisticRegression.java
示例2: encodeModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的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);
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:23，代码来源:GBTClassificationModelConverter.java
示例3: encodeMiningModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的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);
}
开发者ID:jpmml，项目名称:jpmml-xgboost，代码行数:18，代码来源:MultinomialLogisticRegression.java
示例4: encodeMiningModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
public static MiningModel encodeMiningModel(List<List<RegressionTree>> regTrees, float base_score, Schema schema){
Schema segmentSchema = new Schema(new ContinuousLabel(null, DataType.FLOAT), schema.getFeatures());
List<MiningModel> miningModels = new ArrayList<>();
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
int numClasses = regTrees.size();
for (int l=0;l<numClasses;l++){
MiningModel miningModel = createMiningModel(regTrees.get(l), base_score, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("class_(" + categoricalLabel.getValue(l) + ")"), OpType.CONTINUOUS, DataType.FLOAT));
miningModels.add(miningModel);
}
return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
}
开发者ID:cheng-li，项目名称:pyramid，代码行数:18，代码来源:PMMLConverter.java
示例5: marshal
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
@Test
public void marshal() throws Exception {
RegressionModel regressionModel = new RegressionModel()
.addRegressionTables(new RegressionTable());
PMML pmml = new PMML(Version.PMML_4_3.getVersion(), new Header(), new DataDictionary())
.addModels(regressionModel);
JAXBContext context = JAXBContextFactory.createContext(new Class[]{org.dmg.pmml.ObjectFactory.class, org.dmg.pmml.regression.ObjectFactory.class}, null);
Marshaller marshaller = context.createMarshaller();
ByteArrayOutputStream os = new ByteArrayOutputStream();
marshaller.marshal(pmml, os);
String string = os.toString("UTF-8");
assertTrue(string.contains("<PMML xmlns=\"http://www.dmg.org/PMML-4_3\" version=\"4.3\">"));
assertTrue(string.contains("<RegressionModel>"));
assertTrue(string.contains("</RegressionModel>"));
assertTrue(string.contains("</PMML>"));
}
开发者ID:jpmml，项目名称:jpmml-model，代码行数:24，代码来源:MarshallerTest.java
示例6: normalizeRegressionResult
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
static
public <V extends Number> Value<V> normalizeRegressionResult(Value<V> value, RegressionModel.NormalizationMethod normalizationMethod){
switch(normalizationMethod){
case NONE:
return value;
case SOFTMAX:
case LOGIT:
return value.inverseLogit();
case EXP:
return value.exp();
case PROBIT:
return value.inverseProbit();
case CLOGLOG:
return value.inverseCloglog();
case LOGLOG:
return value.inverseLoglog();
case CAUCHIT:
return value.inverseCauchit();
default:
throw new IllegalArgumentException();
}
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:24，代码来源:RegressionModelUtil.java
示例7: normalizeBinaryLogisticClassificationResult
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
static
public <V extends Number> Value<V> normalizeBinaryLogisticClassificationResult(Value<V> value, RegressionModel.NormalizationMethod normalizationMethod){
switch(normalizationMethod){
case NONE:
return value.restrict(0d, 1d);
case LOGIT:
return value.inverseLogit();
case PROBIT:
return value.inverseProbit();
case CLOGLOG:
return value.inverseCloglog();
case LOGLOG:
return value.inverseLoglog();
case CAUCHIT:
return value.inverseCauchit();
default:
throw new IllegalArgumentException();
}
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:21，代码来源:RegressionModelUtil.java
示例8: encodeMiningModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的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);
}
开发者ID:jpmml，项目名称:jpmml-lightgbm，代码行数:10，代码来源:BinomialLogisticRegression.java
示例9: encodeMiningModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
@Override
public MiningModel encodeMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
Schema segmentSchema = schema.toAnonymousSchema();
MiningModel miningModel = super.encodeMiningModel(trees, numIteration, segmentSchema)
.setOutput(ModelUtil.createPredictedOutput(FieldName.create("lgbmValue"), OpType.CONTINUOUS, DataType.DOUBLE));
return MiningModelUtil.createRegression(miningModel, RegressionModel.NormalizationMethod.EXP, schema);
}
开发者ID:jpmml，项目名称:jpmml-lightgbm，代码行数:10，代码来源:PoissonRegression.java
示例10: encodeModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
@Override
public RegressionModel encodeModel(TensorFlowEncoder encoder){
DataField dataField = encoder.createDataField(FieldName.create("_target"), OpType.CONTINUOUS, DataType.FLOAT);
Label label = new ContinuousLabel(dataField);
RegressionModel regressionModel = encodeRegressionModel(encoder)
.setMiningFunction(MiningFunction.REGRESSION)
.setMiningSchema(ModelUtil.createMiningSchema(label));
return regressionModel;
}
开发者ID:jpmml，项目名称:jpmml-tensorflow，代码行数:13，代码来源:LinearRegressor.java
示例11: encodeMiningModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的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);
}
开发者ID:jpmml，项目名称:jpmml-xgboost，代码行数:10，代码来源:BinomialLogisticRegression.java
示例12: createRegression
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
static
public MiningModel createRegression(Model model, RegressionModel.NormalizationMethod normalizationMethod, Schema schema){
Feature feature = MiningModelUtil.MODEL_PREDICTION.apply(model);
RegressionModel regressionModel = RegressionModelUtil.createRegression(model.getMathContext(), Collections.singletonList(feature), Collections.singletonList(1d), null, normalizationMethod, schema);
return createModelChain(Arrays.asList(model, regressionModel), schema);
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:9，代码来源:MiningModelUtil.java
示例13: createBinaryLogisticClassification
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
static
public MiningModel createBinaryLogisticClassification(Model model, double coefficient, double intercept, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
Feature feature = MiningModelUtil.MODEL_PREDICTION.apply(model);
RegressionModel regressionModel = RegressionModelUtil.createBinaryLogisticClassification(model.getMathContext(), Collections.singletonList(feature), Collections.singletonList(coefficient), intercept, normalizationMethod, hasProbabilityDistribution, schema);
return createModelChain(Arrays.asList(model, regressionModel), schema);
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:9，代码来源:MiningModelUtil.java
示例14: createRegression
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
static
public RegressionModel createRegression(MathContext mathContext, List<? extends Feature> features, List<Double> coefficients, Double intercept, RegressionModel.NormalizationMethod normalizationMethod, Schema schema){
ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();
if(normalizationMethod != null){
switch(normalizationMethod){
case NONE:
case SOFTMAX:
case LOGIT:
case PROBIT:
case CLOGLOG:
case EXP:
case LOGLOG:
case CAUCHIT:
break;
default:
throw new IllegalArgumentException();
}
}
RegressionModel regressionModel = new RegressionModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel), null)
.setNormalizationMethod(normalizationMethod)
.setMathContext(ModelUtil.simplifyMathContext(mathContext))
.addRegressionTables(createRegressionTable(features, coefficients, intercept));
return regressionModel;
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:29，代码来源:RegressionModelUtil.java
示例15: createBinaryLogisticClassification
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
static
public RegressionModel createBinaryLogisticClassification(MathContext mathContext, List<? extends Feature> features, List<Double> coefficients, Double intercept, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema){
CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();
if(categoricalLabel.size() != 2){
throw new IllegalArgumentException();
} // End if
if(normalizationMethod != null){
switch(normalizationMethod){
case NONE:
case LOGIT:
case PROBIT:
case CLOGLOG:
case LOGLOG:
case CAUCHIT:
break;
default:
throw new IllegalArgumentException();
}
}
RegressionTable activeRegressionTable = RegressionModelUtil.createRegressionTable(features, coefficients, intercept)
.setTargetCategory(categoricalLabel.getValue(1));
RegressionTable passiveRegressionTable = RegressionModelUtil.createRegressionTable(Collections.<Feature>emptyList(), Collections.<Double>emptyList(), null)
.setTargetCategory(categoricalLabel.getValue(0));
RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), null)
.setNormalizationMethod(normalizationMethod)
.setMathContext(ModelUtil.simplifyMathContext(mathContext))
.addRegressionTables(activeRegressionTable, passiveRegressionTable)
.setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);
return regressionModel;
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:38，代码来源:RegressionModelUtil.java
示例16: encodeModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
@Override
public RegressionModel encodeModel(Schema schema){
List<? extends Number> coef = getCoef();
List<? extends Number> intercept = getIntercept();
return RegressionModelUtil.createRegression(schema.getFeatures(), ValueUtil.asDoubles(coef), ValueUtil.asDouble(Iterables.getOnlyElement(intercept)), null, schema);
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:8，代码来源:BaseLinearRegressor.java
示例17: encodeModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
@Override
public RegressionModel encodeModel(Schema schema){
String multiClass = getMultiClass();
if(!("ovr").equals(multiClass)){
throw new IllegalArgumentException(multiClass);
}
return super.encodeModel(schema);
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:11，代码来源:LogisticRegression.java
示例18: encodeModel
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
@Override
public RegressionModel encodeModel(Schema schema){
List<? extends Number> constant = getConstant();
Number intercept = Iterables.getOnlyElement(constant);
return RegressionModelUtil.createRegression(Collections.<Feature>emptyList(), Collections.<Double>emptyList(), intercept.doubleValue(), null, schema);
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:9，代码来源:DummyRegressor.java
示例19: encodeBinaryClassification
import org.dmg.pmml.regression.RegressionModel; //导入依赖的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);
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:9，代码来源:GBMConverter.java
示例20: copyState
import org.dmg.pmml.regression.RegressionModel; //导入依赖的package包/类
@Test
public void copyState(){
PMML pmml = new PMML(Version.PMML_4_3.getVersion(), new Header(), new DataDictionary());
// Initialize the live list instance
pmml.getModels();
CustomPMML customPmml = new CustomPMML();
ReflectionUtil.copyState(pmml, customPmml);
assertSame(pmml.getVersion(), customPmml.getVersion());
assertSame(pmml.getHeader(), customPmml.getHeader());
assertSame(pmml.getDataDictionary(), customPmml.getDataDictionary());
assertFalse(pmml.hasModels());
assertFalse(customPmml.hasModels());
pmml.addModels(new RegressionModel());
assertTrue(pmml.hasModels());
assertTrue(customPmml.hasModels());
assertSame(pmml.getModels(), customPmml.getModels());
try {
ReflectionUtil.copyState(customPmml, pmml);
fail();
} catch(IllegalArgumentException iae){
// Ignored
}
}
开发者ID:jpmml，项目名称:jpmml-model，代码行数:34，代码来源:ReflectionUtilTest.java
注：本文中的org.dmg.pmml.regression.RegressionModel类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
客服电话
APP下载
官方微信
请发表评论