Question

今天我使用Jpmml来加载我的代码中的pmml模型。但是评估＆＃39;方法需要很长时间。这是今天的工作代码：

    String modelPath = "....";
    ModelEvaluatorFactory factory = ModelEvaluatorFactory.newInstance();
    InputStream in = new   ByteArrayInputStream(modelPath.getBytes("UTF-8"));

    PMML pmmlModel = JAXBUtil.unmarshalPMML(new StreamSource(in)); 
    ModelEvaluator<?> evaluator = factory.newModelManager(pmmlModel);
    List<FieldName> activeFields = evaluator.getActiveFields();

    Map<FieldName, FieldValue> defaultFeatures = new HashMap<>();

    //after filling the 'defaultFeatures' the line below takes long time
    Map<FieldName, ?> results = evaluator.evaluate(defaultFeatures);

PMML示例：

    <?xml version="1.0" encoding="UTF-8" standalone="yes"?>
    <PMML xmlns="http://www.dmg.org/PMML-4_2" version="4.2">
    <Header>
        <Application name="JPMML-SkLearn" version="1.0-SNAPSHOT"/>
        <Timestamp>2017-01-22T14:18:05Z</Timestamp>
    </Header>
    <DataDictionary>
        <DataField name="GENDER" optype="categorical" dataType="string">
            <Value value="0"/>
            <Value value="1"/>
        </DataField>
        <DataField name="1GA_" optype="continuous" dataType="double"/>
    //67000 rows of datafield
    </DataDictionary>
    <TransformationDictionary>
        <DefineFunction name="logit" optype="continuous" dataType="double">
            <ParameterField name="value" optype="continuous" dataType="double"/>
            <Apply function="/">
                <Constant dataType="double">1</Constant>
                <Apply function="+">
                    <Constant dataType="double">1</Constant>
                    <Apply function="exp">
                        <Apply function="*">
                            <Constant dataType="double">-1</Constant>
                            <FieldRef field="value"/>
                        </Apply>
                    </Apply>
                </Apply>
            </Apply>
        </DefineFunction>
    </TransformationDictionary>
     <MiningModel functionName="classification">
        <MiningSchema>
            <MiningField name="GENDER" usageType="target"/>
            <MiningField name="1GA_"/>
      //67000 rows of MiningField
       </MiningSchema>
        <Output>
            <OutputField name="probability_0" feature="probability" value="0"/>
            <OutputField name="probability_1" feature="probability" value="1"/>
        </Output>
        <LocalTransformations>
            <DerivedField name="x1" optype="continuous" dataType="double">
                <FieldRef field="1GA_"/>
            </DerivedField>
       //100000 rows
        </LocalTransformations>
         <Segmentation multipleModelMethod="modelChain">
            <Segment id="1">
                <True/>
                <RegressionModel functionName="regression">
                    <MiningSchema>
                        <MiningField name="1GA_"/>
                  </MiningSchema>
                    <Output>
                        <OutputField name="decisionFunction_1"    feature="predictedValue"/>
                        <OutputField name="logitDecisionFunction_1" optype="continuous" dataType="double" feature="transformedValue">
                            <Apply function="logit">
<FieldRef field="decisionFunction_1"/>
                            </Apply>
                        </OutputField>
                    </Output>
                    <RegressionTable intercept="-5.303370169392045">
           <NumericPredictor name="x1" coefficient="0.18476274186559316"/>
          //100000 rows of NumericPredictor

      </RegressionTable>
                 </RegressionModel>
              </Segment>
              <Segment id="2">
                  <True/>
                <RegressionModel functionName="regression">
                    <MiningSchema>
                        <MiningField name="logitDecisionFunction_1"/>
                    </MiningSchema>
                    <Output>
                        <OutputField name="logitDecisionFunction_0"  
     feature="predictedValue"/>
                    </Output>
                    <RegressionTable intercept="1.0">
            <NumericPredictor name="logitDecisionFunction_1" 

           coefficient="-1.0"/>
                        </RegressionTable>
                    </RegressionModel>
                </Segment>
                <Segment id="3">
                    <True/>
                    <RegressionModel functionName="classification">
                        <MiningSchema>
                            <MiningField name="GENDER" usageType="target"/>
                            <MiningField name="logitDecisionFunction_1"/>
                            <MiningField name="logitDecisionFunction_0"/>
                        </MiningSchema>
                        <RegressionTable intercept="0.0" targetCategory="1">
                            <NumericPredictor name="logitDecisionFunction_1" 


     coefficient="1.0"/>
                    </RegressionTable>
                <RegressionTable intercept="0.0" targetCategory="0">
                        <NumericPredictor name="logitDecisionFunction_0"   


       coefficient="1.0"/>
                        </RegressionTable>
                    </RegressionModel>
                </Segment>
         </Segmentation>
        </MiningModel>
        </PMML>

有一种想法尝试使用MLlib而不是Jpmml。有任何想法吗？感谢

Answer 1

你是什么意思＆＃34;加载＆＃34;？它是否将PMML文档解析为内存数据结构＆＃34;或者＆＃34;执行PMML文档＆＃34;？

您的代码似乎是针对后者的。但它肯定会失败，因为JAXBUtil#unmarshalPMML(Source)方法是使用字节数组调用的，它不包含有效的PMML文档（没有XML解析器可以接受"....".getBytes("UTF-8")）。

另外，你的意思是＆＃34;需要很长时间＆＃34;？ JAXB框架的一次性初始化成本约为1秒。之后，它可以每秒解组~200到500 MB（那个兆字节）的PMML内容。你需要多少钱？

evaluate方法需要很长时间 - 使用Jpmml的PMML模型

1 个答案: