如何从使用 sklearn MLPClassifier 训练的神经网络中使用 Syncfusion PMML 进行预测？

Question

我使用 sklearn.neural_network.MLPClassifier (0.20.3) 在 Python 中训练了一个模型，并使用 sklearn2pmml (0.48.0) 将其保存为 PMML 格式。使用 org.jpmml:pmml-evaluator:1.4.14.

在 Java 中加载时，保存的 PMML 模型按预期工作

我现在想加载 PMML 模型并使用 Syncfusion 包在 C# 中进行预测：

      <ItemGroup>
        <PackageReference Include="Syncfusion.PMML.AspNet" Version="17.4.0.44" />
      </ItemGroup>

using System;
using Syncfusion.PMML;

namespace myprogram
{
    class Program
    {
        static void Main(string[] args)
        {

            var predictors = new           
                {                
                predictor_1 = 0.05,                
                predictor_2 = 203.0,               
                predictor_3 = 400.0,
                predictor_4 = 22.0,
                predictor_5 = 9.01         
                };

            string PmmlFilePath = “/project/model.pmml";  

            //Create instance for PMML Document            
            PMMLDocument pmmlDocument = new PMMLDocument(PmmlFilePath);            

            //Create instance for Mining model            
            NeuralNetworkModelEvaluator neuralNetworkModel = new NeuralNetworkModelEvaluator(pmmlDocument);            

            //Gets the predicted result            
            PredictedResult predictedResult = neuralNetworkModel.GetResult(predictors, null);
        }
    }
}

但是上面代码的最后一行引发了以下异常：

Unhandled exception. System.NullReferenceException: Object reference not set to an instance of an object.
   at Syncfusion.PMML.NeuralNetworkModelEvaluator.ComputeResult(Dictionary`2 fieldValuePair, NeuralNetworkModel neuralNetworkModel)
   at Syncfusion.PMML.NeuralNetworkModelEvaluator.GetResult(Object obj, IModelOptions modelOptions)
   at myprogram.Program.Main(String[] args) in /project/Program.cs:line 66

model.pmml

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<PMML xmlns="http://www.dmg.org/PMML-4_3" xmlns:data="http://jpmml.org/jpmml-model/InlineTable" version="4.3">
    <Header>
        <Application name="JPMML-SkLearn" version="1.5.20"/>
        <Timestamp>2020-20-15T03:42:46Z</Timestamp>
    </Header>
    <DataDictionary>
        <DataField name="target_state" optype="categorical" dataType="string">
            <Value value="RED"/>
            <Value value="GREEN"/>
        </DataField>
        <DataField name="predictor_1" optype="continuous" dataType="double"/>
        <DataField name="predictor_2" optype="continuous" dataType="double"/>
        <DataField name="predictor_3" optype="continuous" dataType="double"/>
        <DataField name="predictor_4" optype="continuous" dataType="double"/>
        <DataField name="predictor_5" optype="continuous" dataType="double"/>
    </DataDictionary>
    <TransformationDictionary/>
    <MiningModel functionName="classification">
        <MiningSchema>
            <MiningField name="target_state" usageType="target"/>
            <MiningField name="predictor_1"/>
            <MiningField name="predictor_2"/>
            <MiningField name="predictor_3"/>
            <MiningField name="predictor_4"/>
            <MiningField name="predictor_5"/>
        </MiningSchema>
        <Segmentation multipleModelMethod="modelChain" x-missingPredictionTreatment="returnMissing">
            <Segment id="1">
                <True/>
                <RegressionModel functionName="regression">
                    <MiningSchema>
                        <MiningField name="predictor_2"/>
                        <MiningField name="predictor_5"/>
                        <MiningField name="predictor_1"/>
                        <MiningField name="predictor_3"/>
                        <MiningField name="predictor_4"/>
                    </MiningSchema>
                    <Output>
                        <OutputField name="decisionFunction" optype="continuous" dataType="double" isFinalResult="false"/>
                    </Output>
                    <LocalTransformations>
                        <DerivedField name="robust_scaler(predictor_1)" optype="continuous" dataType="double">
                            <Apply function="/">
                                <Apply function="-">
                                    <FieldRef field="predictor_1"/>
                                    <Constant dataType="double">38.0</Constant>
                                </Apply>
                                <Constant dataType="double">36.0</Constant>
                            </Apply>
                        </DerivedField>
                        <DerivedField name="robust_scaler(predictor_3)" optype="continuous" dataType="double">
                            <Apply function="/">
                                <Apply function="-">
                                    <FieldRef field="predictor_3"/>
                                    <Constant dataType="double">29.5</Constant>
                                </Apply>
                                <Constant dataType="double">15.5</Constant>
                            </Apply>
                        </DerivedField>
                        <DerivedField name="robust_scaler(predictor_4)" optype="continuous" dataType="double">
                            <Apply function="/">
                                <Apply function="-">
                                    <FieldRef field="predictor_4"/>
                                    <Constant dataType="double">-2.0</Constant>
                                </Apply>
                                <Constant dataType="double">11.0</Constant>
                            </Apply>
                        </DerivedField>
                    </LocalTransformations>
                    <RegressionTable intercept="0.4485538242235567">
                        <NumericPredictor name="robust_scaler(predictor_1)" coefficient="0.09187667567720746"/>
                        <NumericPredictor name="predictor_2" coefficient="1.002293414783222337"/>
                        <NumericPredictor name="robust_scaler(predictor_3)" coefficient="-0.1790001566845147"/>
                        <NumericPredictor name="robust_scaler(predictor_4)" coefficient="-0.20065445270398309"/>
                        <NumericPredictor name="predictor_5" coefficient="-0.08789985419968031"/>
                    </RegressionTable>
                </RegressionModel>
            </Segment>
            <Segment id="2">
                <True/>
                <RegressionModel functionName="classification" normalizationMethod="softmax">
                    <MiningSchema>
                        <MiningField name="target_state" usageType="target"/>
                        <MiningField name="decisionFunction"/>
                    </MiningSchema>
                    <Output>
                        <OutputField name="probability(RED)" optype="continuous" dataType="double" feature="probability" value="RED"/>
                        <OutputField name="probability(GREEN)" optype="continuous" dataType="double" feature="probability" value="GREEN"/>
                    </Output>
                    <RegressionTable intercept="0.0" targetCategory="RED">
                        <NumericPredictor name="decisionFunction" coefficient="-1.0"/>
                    </RegressionTable>
                    <RegressionTable intercept="0.0" targetCategory="GREEN">
                        <NumericPredictor name="decisionFunction" coefficient="1.0"/>
                    </RegressionTable>
                </RegressionModel>
            </Segment>
        </Segmentation>
        <ModelVerification recordCount="1">
            <VerificationFields>
                <VerificationField field="predictor_1" column="data:predictor_1"/>
                <VerificationField field="predictor_2" column="data:predictor_2"/>
                <VerificationField field="predictor_3" column="data:predictor_3"/>
                <VerificationField field="predictor_4" column="data:predictor_4"/>
                <VerificationField field="predictor_5" column="data:predictor_5"/>
                <VerificationField field="probability(RED)" column="data:probability_RED" precision="1.0E-13" zeroThreshold="1.0E-13"/>
                <VerificationField field="probability(GREEN)" column="data:probability_GREEN" precision="1.0E-13" zeroThreshold="1.0E-13"/>
            </VerificationFields>
            <InlineTable>
                <row>
                    <data:predictor_1>595.0</data:predictor_1>
                    <data:predictor_2>0.0</data:predictor_2>
                    <data:predictor_3>201.0</data:predictor_3>
                    <data:predictor_4>-2.0</data:predictor_4>
                    <data:predictor_5>0.1</data:predictor_5>
                    <data:probability_RED>0.2555804919272633</data:probability_RED>
                    <data:probability_GREEN>0.9974195080727367</data:probability_GREEN>
                </row>
            </InlineTable>
        </ModelVerification>
    </MiningModel>
</PMML>

谁能帮我找出问题所在？

Answer 1

我们已使用 NeuralNetworkModelEvaluator 检查示例 PMML 文件，但无法重现该问题。您能否分享您的 PMML 文件以检查我们这边并尽快为您提供解决方案。

此外，我们建议您尝试以下代码，

        string pmmlFilePath = “/project/model.pmml”;  

        //Create instance for PMML Document
        PMMLEvaluator PMMLEvaluator = new PMMLEvaluatorFactory().GetPMMLEvaluatorInstance(pmmlFilePath);

        //Gets the predicted result            
        PredictedResult predictedResult = PMMLEvaluator.GetResult(anonymousType, null);

注意：Syncfusion PMML 库通过匹配 dmg.org and you can check Syncfusion help 支持模型和用户指南文档中定义的架构来工作。

如有任何进一步的疑问，请从我们的支持网站创建一个新事件（在您的帐户下）以快速提供解决方案。请在下面找到支持网站 link。 https://www.syncfusion.com/support/directtrac/incidents/newincident

注意：我为 Syncfusion 工作。

如何从使用 sklearn MLPClassifier 训练的神经网络中使用 Syncfusion PMML 进行预测？

How to make predictions with Syncfusion PMML from a Neural Network trained with sklearn MLPClassifier?

c#

machine-learning

syncfusion

neural-network

pmml