本文整理汇总了Java中org.dmg.pmml.mining.MiningModel类的典型用法代码示例。如果您正苦于以下问题：Java MiningModel类的具体用法？Java MiningModel怎么用？Java MiningModel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。
MiningModel类属于org.dmg.pmml.mining包，在下文中一共展示了MiningModel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: createNestedOutputFields
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
private List<OutputField> createNestedOutputFields(){
MiningModel miningModel = getModel();
Segmentation segmentation = miningModel.getSegmentation();
List<Segment> segments = segmentation.getSegments();
Segmentation.MultipleModelMethod multipleModelMethod = segmentation.getMultipleModelMethod();
switch(multipleModelMethod){
case SELECT_ALL:
// Ignored
break;
case SELECT_FIRST:
return createNestedOutputFields(getActiveHead(segments));
case MODEL_CHAIN:
return createNestedOutputFields(getActiveTail(segments));
default:
break;
}
return Collections.emptyList();
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:23，代码来源:MiningModelEvaluator.java
示例2: encodeMiningModel
import org.dmg.pmml.mining.MiningModel; //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3: encodeModel
import org.dmg.pmml.mining.MiningModel; //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4: getLastModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
protected org.dmg.pmml.Model getLastModel(org.dmg.pmml.Model model){
if(model instanceof MiningModel){
MiningModel miningModel = (MiningModel)model;
Segmentation segmentation = miningModel.getSegmentation();
MultipleModelMethod multipleModelMethod = segmentation.getMultipleModelMethod();
switch(multipleModelMethod){
case MODEL_CHAIN:
List<Segment> segments = segmentation.getSegments();
if(segments.size() > 0){
Segment lastSegment = segments.get(segments.size() - 1);
return lastSegment.getModel();
}
break;
default:
break;
}
}
return model;
}
开发者ID:jpmml，项目名称:jpmml-sparkml，代码行数:26，代码来源:ModelConverter.java
示例5: encodeMiningModel
import org.dmg.pmml.mining.MiningModel; //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6: createModelChain
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
static
public MiningModel createModelChain(List<? extends Model> models, Schema schema){
if(models.size() < 1){
throw new IllegalArgumentException();
}
Segmentation segmentation = createSegmentation(Segmentation.MultipleModelMethod.MODEL_CHAIN, models);
Model lastModel = Iterables.getLast(models);
MiningModel miningModel = new MiningModel(lastModel.getMiningFunction(), ModelUtil.createMiningSchema(schema.getLabel()))
.setMathContext(ModelUtil.simplifyMathContext(lastModel.getMathContext()))
.setSegmentation(segmentation);
return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-converter，代码行数:18，代码来源:MiningModelUtil.java
示例7: encodeModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
List<? extends Classifier> estimators = getEstimators();
List<List<Integer>> estimatorsFeatures = getEstimatorsFeatures();
Segmentation.MultipleModelMethod multipleModelMethod = Segmentation.MultipleModelMethod.AVERAGE;
for(Classifier estimator : estimators){
if(!estimator.hasProbabilityDistribution()){
multipleModelMethod = Segmentation.MultipleModelMethod.MAJORITY_VOTE;
break;
}
}
MiningModel miningModel = BaggingUtil.encodeBagging(estimators, estimatorsFeatures, multipleModelMethod, MiningFunction.CLASSIFICATION, schema)
.setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, (CategoricalLabel)schema.getLabel()));
return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:22，代码来源:BaggingClassifier.java
示例8: encodeModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
List<? extends Regressor> estimators = getEstimators();
List<? extends Number> estimatorWeights = getEstimatorWeights();
Schema segmentSchema = schema.toAnonymousSchema();
List<Model> models = new ArrayList<>();
for(Regressor estimator : estimators){
Model model = estimator.encodeModel(segmentSchema);
models.add(model);
}
MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()))
.setSegmentation(MiningModelUtil.createSegmentation(MultipleModelMethod.WEIGHTED_MEDIAN, models, estimatorWeights));
return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-sklearn，代码行数:21，代码来源:AdaBoostRegressor.java
示例9: encodeModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
RGenericVector ranger = getObject();
RStringVector treetype = (RStringVector)ranger.getValue("treetype");
switch(treetype.asScalar()){
case "Regression":
return encodeRegression(ranger, schema);
case "Classification":
return encodeClassification(ranger, schema);
case "Probability estimation":
return encodeProbabilityForest(ranger, schema);
default:
throw new IllegalArgumentException();
}
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:18，代码来源:RangerConverter.java
示例10: encodeRegression
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
private MiningModel encodeRegression(RGenericVector ranger, Schema schema){
RGenericVector forest = (RGenericVector)ranger.getValue("forest");
ScoreEncoder scoreEncoder = new ScoreEncoder(){
@Override
public void encode(Node node, Number splitValue, RNumberVector<?> terminalClassCount){
node.setScore(ValueUtil.formatValue(splitValue));
}
};
List<TreeModel> treeModels = encodeForest(forest, MiningFunction.REGRESSION, scoreEncoder, schema);
MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()))
.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.AVERAGE, treeModels));
return miningModel;
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:19，代码来源:RangerConverter.java
示例11: encodeModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
RGenericVector randomForest = getObject();
RStringVector type = (RStringVector)randomForest.getValue("type");
RGenericVector forest = (RGenericVector)randomForest.getValue("forest");
switch(type.asScalar()){
case "regression":
return encodeRegression(forest, schema);
case "classification":
return encodeClassification(forest, schema);
default:
throw new IllegalArgumentException();
}
}
开发者ID:jpmml，项目名称:jpmml-r，代码行数:17，代码来源:RandomForestConverter.java
示例12: encodeMiningModel
import org.dmg.pmml.mining.MiningModel; //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13: createMiningModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
static
protected MiningModel createMiningModel(List<RegressionTree> regTrees, float base_score, Schema schema){
ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();
Schema segmentSchema = schema.toAnonymousSchema();
List<TreeModel> treeModels = new ArrayList<>();
for(RegressionTree regTree : regTrees){
TreeModel treeModel = regTree.encodeTreeModel(segmentSchema);
treeModels.add(treeModel);
}
MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel))
.setMathContext(MathContext.FLOAT)
.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, treeModels))
.setTargets(ModelUtil.createRescaleTargets(null, ValueUtil.floatToDouble(base_score), continuousLabel));
return miningModel;
}
开发者ID:cheng-li，项目名称:pyramid，代码行数:22，代码来源:PMMLConverter.java
示例14: createModelChain
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
static
public MiningModel createModelChain(List<? extends Model> models, Schema schema){
if(models.size() < 1){
throw new IllegalArgumentException();
}
Segmentation segmentation = createSegmentation(Segmentation.MultipleModelMethod.MODEL_CHAIN, models);
Model lastModel = Iterables.getLast(models);
MiningModel miningModel = new MiningModel(lastModel.getMiningFunction(), ModelUtil.createMiningSchema(schema.getLabel()))
.setMathContext(ModelUtil.simplifyMathContext(lastModel.getMathContext()))
.setSegmentation(segmentation);
return miningModel;
}
开发者ID:cheng-li，项目名称:pyramid，代码行数:18，代码来源:MiningModelUtil.java
示例15: popParent
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
public PMMLObject popParent(){
PMMLObject parent = super.popParent();
if(parent instanceof MiningModel){
MiningModel miningModel = (MiningModel)parent;
processMiningModel(miningModel);
} else
if(parent instanceof Model){
Model model = (Model)parent;
processModel(model);
}
return parent;
}
开发者ID:jpmml，项目名称:jpmml-model，代码行数:19，代码来源:ModelCleaner.java
示例16: getDataField
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
protected DataField getDataField(){
MiningModel miningModel = getModel();
Segmentation segmentation = miningModel.getSegmentation();
Segmentation.MultipleModelMethod multipleModelMethod = segmentation.getMultipleModelMethod();
switch(multipleModelMethod){
case SELECT_ALL:
case SELECT_FIRST:
case MODEL_CHAIN:
return null;
default:
return super.getDataField();
}
}
开发者ID:jpmml，项目名称:jpmml-evaluator，代码行数:17，代码来源:MiningModelEvaluator.java
示例17: encodeModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
XGBoostClassificationModel model = (XGBoostClassificationModel)getTransformer();
Booster booster = model.booster();
return BoosterUtil.encodeBooster(booster, schema);
}
开发者ID:jpmml，项目名称:jpmml-sparkml-xgboost，代码行数:9，代码来源:XGBoostClassificationModelConverter.java
示例18: encodeModel
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
@Override
public MiningModel encodeModel(Schema schema){
XGBoostRegressionModel model = (XGBoostRegressionModel)getTransformer();
Booster booster = model.booster();
return BoosterUtil.encodeBooster(booster, schema);
}
开发者ID:jpmml，项目名称:jpmml-sparkml-xgboost，代码行数:9，代码来源:XGBoostRegressionModelConverter.java
示例19: encodeBooster
import org.dmg.pmml.mining.MiningModel; //导入依赖的package包/类
static
public MiningModel encodeBooster(Booster booster, Schema schema){
byte[] bytes = booster.toByteArray();
Learner learner;
try(InputStream is = new ByteArrayInputStream(bytes)){
learner = XGBoostUtil.loadLearner(is);
} catch(IOException ioe){
throw new RuntimeException(ioe);
}
Function<Feature, Feature> function = new Function<Feature, Feature>(){
@Override
public Feature apply(Feature feature){
if(feature instanceof BinaryFeature){
BinaryFeature binaryFeature = (BinaryFeature)feature;
return binaryFeature;
} else
{
ContinuousFeature continuousFeature = feature.toContinuousFeature(DataType.FLOAT);
return continuousFeature;
}
}
};
Schema xgbSchema = schema.toTransformedSchema(function);
return learner.encodeMiningModel(null, false, xgbSchema);
}
开发者ID:jpmml，项目名称:jpmml-sparkml-xgboost，代码行数:36，代码来源:BoosterUtil.java
示例20: encodeMiningModel
import org.dmg.pmml.mining.MiningModel; //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
注：本文中的org.dmg.pmml.mining.MiningModel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