Hi Marocco,
What you need is something like this.
@Import('inputStream:1.0.0')
define stream inputTest (field_0 double, field_1 double, field_2 double,
field_3 double, field_4 double, field_5 double, field_6 double, field_7
double, field_8 double, field_9 double, field_10 double, field_11 double,
field_12 double, field_13 double, field_14 double, field_15 double,
field_16 double, field_17 double, field_18 double, field_19 double,
field_20 double, field_21 double, field_22 double, field_23 double,
field_24 double, field_25 double, field_26 double, field_27 double,
field_28 double, field_29 double, field_30 double, field_31 double,
field_32 double);
@Import('outputStream:1.0.0')
define stream outputTest (field_0 double, field_1 double, field_2 double,
field_3 double, field_4 double, field_5 double, field_6 double, field_7
double, field_8 double, field_9 double, field_10 double, field_11 double,
field_12 double, field_13 double, field_14 double, field_15 double,
field_16 double, field_17 double, field_18 double, field_19 double,
field_20 double, field_21 double, field_22 double, field_23 double,
field_24 double, field_25 double, field_26 double, field_27 double,
field_28 double, field_29 double, field_30 double, field_31 double,
field_32 double, *target string*);
from inputTest#pmml:predict('myfile.pmml')
select *
insert into outputTest;
you have to create two streams (inputStream, outputStream) and create a
logger publisher for outputStream. This way when an even is received to the
outputStream, the event will be logged in the server console with the
prediction value named as "target".
On Tue, Apr 19, 2016 at 5:42 PM, <[email protected]> wrote:
> Thank you for the explanation..
> But I'm not able to write the correct shiddi query..
> I have this file pmml:
>
> <?xml version="1.0" encoding="UTF-8" standalone="yes"?>
> <PMML xmlns="http://www.dmg.org/PMML-4_2";>
> <Header description="linear SVM">
> <Application name="Apache Spark MLlib"/>
> <Timestamp>2016-04-12T07:06:37</Timestamp>
> </Header>
> <DataDictionary numberOfFields="34">
> <DataField name="field_0" optype="continuous" dataType="double"/>
> <DataField name="field_1" optype="continuous" dataType="double"/>
> <DataField name="field_2" optype="continuous" dataType="double"/>
> <DataField name="field_3" optype="continuous" dataType="double"/>
> <DataField name="field_4" optype="continuous" dataType="double"/>
> <DataField name="field_5" optype="continuous" dataType="double"/>
> <DataField name="field_6" optype="continuous" dataType="double"/>
> <DataField name="field_7" optype="continuous" dataType="double"/>
> <DataField name="field_8" optype="continuous" dataType="double"/>
> <DataField name="field_9" optype="continuous" dataType="double"/>
> <DataField name="field_10" optype="continuous" dataType="double"/>
> <DataField name="field_11" optype="continuous" dataType="double"/>
> <DataField name="field_12" optype="continuous" dataType="double"/>
> <DataField name="field_13" optype="continuous" dataType="double"/>
> <DataField name="field_14" optype="continuous" dataType="double"/>
> <DataField name="field_15" optype="continuous" dataType="double"/>
> <DataField name="field_16" optype="continuous" dataType="double"/>
> <DataField name="field_17" optype="continuous" dataType="double"/>
> <DataField name="field_18" optype="continuous" dataType="double"/>
> <DataField name="field_19" optype="continuous" dataType="double"/>
> <DataField name="field_20" optype="continuous" dataType="double"/>
> <DataField name="field_21" optype="continuous" dataType="double"/>
> <DataField name="field_22" optype="continuous" dataType="double"/>
> <DataField name="field_23" optype="continuous" dataType="double"/>
> <DataField name="field_24" optype="continuous" dataType="double"/>
> <DataField name="field_25" optype="continuous" dataType="double"/>
> <DataField name="field_26" optype="continuous" dataType="double"/>
> <DataField name="field_27" optype="continuous" dataType="double"/>
> <DataField name="field_28" optype="continuous" dataType="double"/>
> <DataField name="field_29" optype="continuous" dataType="double"/>
> <DataField name="field_30" optype="continuous" dataType="double"/>
> <DataField name="field_31" optype="continuous" dataType="double"/>
> <DataField name="field_32" optype="continuous" dataType="double"/>
> <DataField name="target" optype="categorical" dataType="string"/>
> </DataDictionary>
> <RegressionModel modelName="linear SVM" functionName="classification"
> normalizationMethod="none">
> <MiningSchema>
> <MiningField name="field_0" usageType="active"/>
> <MiningField name="field_1" usageType="active"/>
> <MiningField name="field_2" usageType="active"/>
> <MiningField name="field_3" usageType="active"/>
> <MiningField name="field_4" usageType="active"/>
> <MiningField name="field_5" usageType="active"/>
> <MiningField name="field_6" usageType="active"/>
> <MiningField name="field_7" usageType="active"/>
> <MiningField name="field_8" usageType="active"/>
> <MiningField name="field_9" usageType="active"/>
> <MiningField name="field_10" usageType="active"/>
> <MiningField name="field_11" usageType="active"/>
> <MiningField name="field_12" usageType="active"/>
> <MiningField name="field_13" usageType="active"/>
> <MiningField name="field_14" usageType="active"/>
> <MiningField name="field_15" usageType="active"/>
> <MiningField name="field_16" usageType="active"/>
> <MiningField name="field_17" usageType="active"/>
> <MiningField name="field_18" usageType="active"/>
> <MiningField name="field_19" usageType="active"/>
> <MiningField name="field_20" usageType="active"/>
> <MiningField name="field_21" usageType="active"/>
> <MiningField name="field_22" usageType="active"/>
> <MiningField name="field_23" usageType="active"/>
> <MiningField name="field_24" usageType="active"/>
> <MiningField name="field_25" usageType="active"/>
> <MiningField name="field_26" usageType="active"/>
> <MiningField name="field_27" usageType="active"/>
> <MiningField name="field_28" usageType="active"/>
> <MiningField name="field_29" usageType="active"/>
> <MiningField name="field_30" usageType="active"/>
> <MiningField name="field_31" usageType="active"/>
> <MiningField name="field_32" usageType="active"/>
> <MiningField name="target" usageType="target"/>
> </MiningSchema>
> <RegressionTable intercept="0.0" targetCategory="1">
> <NumericPredictor name="field_0"
> coefficient="-33.663867609108465"/>
> <NumericPredictor name="field_1"
> coefficient="-26.928869029283113"/>
> <NumericPredictor name="field_2"
> coefficient="-15.7589879271062"/>
> <NumericPredictor name="field_3"
> coefficient="4.801827945644439"/>
> <NumericPredictor name="field_4"
> coefficient="4.37913587227952"/>
> <NumericPredictor name="field_5"
> coefficient="5.017699542764569"/>
> <NumericPredictor name="field_6"
> coefficient="6.881223937543283"/>
> <NumericPredictor name="field_7"
> coefficient="7.436600728282555"/>
> <NumericPredictor name="field_8"
> coefficient="2.779684237173268"/>
> <NumericPredictor name="field_9"
> coefficient="-11.1563341379461"/>
> <NumericPredictor name="field_10"
> coefficient="-11.603667328060018"/>
> <NumericPredictor name="field_11"
> coefficient="-1.927205925226167"/>
> <NumericPredictor name="field_12"
> coefficient="-0.13042330215893916"/>
> <NumericPredictor name="field_13"
> coefficient="4.606266797295681"/>
> <NumericPredictor name="field_14"
> coefficient="4.198456448492548"/>
> <NumericPredictor name="field_15"
> coefficient="1.3866370557496586"/>
> <NumericPredictor name="field_16"
> coefficient="-1.6863769626309484"/>
> <NumericPredictor name="field_17"
> coefficient="-1.8279467318593428"/>
> <NumericPredictor name="field_18"
> coefficient="0.1893987855143311"/>
> <NumericPredictor name="field_19"
> coefficient="12.645268212559404"/>
> <NumericPredictor name="field_20"
> coefficient="14.115013455939593"/>
> <NumericPredictor name="field_21"
> coefficient="13.544000807391123"/>
> <NumericPredictor name="field_22"
> coefficient="6.0112812628907015"/>
> <NumericPredictor name="field_23"
> coefficient="4.03765113349721"/>
> <NumericPredictor name="field_24"
> coefficient="4.145995153508418"/>
> <NumericPredictor name="field_25"
> coefficient="2.08758768566234"/>
> <NumericPredictor name="field_26"
> coefficient="1.1271455002504913"/>
> <NumericPredictor name="field_27"
> coefficient="-0.28071996147406497"/>
> <NumericPredictor name="field_28"
> coefficient="7.761062281510407"/>
> <NumericPredictor name="field_29"
> coefficient="0.25888510113432384"/>
> <NumericPredictor name="field_30"
> coefficient="-8.756880183958602"/>
> <NumericPredictor name="field_31"
> coefficient="-4.332351913516022"/>
> <NumericPredictor name="field_32"
> coefficient="2.0548737734231493"/>
> </RegressionTable>
> <RegressionTable intercept="0.0" targetCategory="0"/>
> </RegressionModel>
> </PMML>
>
> so I have to write something like:
>
> @Import('fonometro:1.0.0')
> define stream inputTest (field_0 double, field_1 double, field_2 double,
> field_3 double, field_4 double, field_5 double, field_6 double, field_7
> double, field_8 double, field_9 double, field_10 double, field_11 double,
> field_12 double, field_13 double, field_14 double, field_15 double,
> field_16 double, field_17 double, field_18 double, field_19 double,
> field_20 double, field_21 double, field_22 double, field_23 double,
> field_24 double, field_25 double, field_26 double, field_27 double,
> field_28 double, field_29 double, field_30 double, field_31 double,
> field_32 double);
>
> from inputTest#pmml:predict('samples/myfile.pmml')
> select *
> insert into ????
>
> or
>
> @Import('fonometro:1.0.0')
> define stream inputTest (field_0 double, field_1 double, field_2 double,
> field_3 double, field_4 double, field_5 double, field_6 double, field_7
> double, field_8 double, field_9 double, field_10 double, field_11 double,
> field_12 double, field_13 double, field_14 double, field_15 double,
> field_16 double, field_17 double, field_18 double, field_19 double,
> field_20 double, field_21 double, field_22 double, field_23 double,
> field_24 double, field_25 double, field_26 double, field_27 double,
> field_28 double, field_29 double, field_30 double, field_31 double,
> field_32 double);
>
> from inputTest#pmml:predict('myfile.pmml',field_0, field_1, field_2,
> field_3, field_4, field_5, field_6, field_7, field_8, field_9, field_10,
> field_11, field_12, field_13, field_14, field_15, field_16, field_17,
> field_18, field_19, field_20, field_21, field_22, field_23, field_24,
> field_25, field_26, field_27, field_28, field_29, field_30, field_31,
> field_32)
> select *
> insert into ????
>
> everything I put instead of ???? raise an error saying that I'm redefining
> the output stream..
>
> Can you help me? I need the "target" field?
> Thank you,
>
> Silvia
>
> Quoting Fazlan Nazeem <[email protected]>:
>
> Hi Marocco,
>>
>> The reason behind the decision tree PMML model having an output tag and
>> SVM
>> not having an output tag solely depends on what library you have used to
>> generate the PMML file. The PMML specification[1] does not specify that
>> every PMML model should include an output tag. Please refer to the line 7
>> of the XSD file attached herewith which specifies that output tag
>> "minOccurs="0", which implies that a PMML file may contain a minimum of 0
>> output fields. For example you may try out WSO2 Machine Learner's[2] PMML
>> generation functionality which does not include output tags for most of
>> its
>> PMML models.
>>
>> Therefore two specific libraries for PMML can generate two slightly
>> different PMML files for the same model depending on how they have
>> implemented the PMML generation functionality. The PMML readers should be
>> able to interpret these different cases and predict the output for a new
>> input stream.
>>
>> With CEP 4.0.0, you may experience an issue when there is no output tag
>> present in the PMML file, the PMML extension might not work. We have
>> identified this issue[3] and fixed it with the 4.1.0 release.
>>
>>
>> [1] http://dmg.org/pmml/v4-1/GeneralStructure.html
>> [2] http://wso2.com/products/machine-learner/
>> [3] https://wso2.org/jira/browse/CEP-1438
>>
>> On Fri, Apr 15, 2016 at 8:12 PM, <[email protected]> wrote:
>>
>>
>>> Hi!
>>>
>>> I installed the cep version 4, becouse I need to test the pmml
>>> functionalities. I have already executed this example:
>>>
>>> https://docs.wso2.com/display/CEP410/Sample+0301+-+Network+Intruder+Detection+with+PMML+Extension+Predictions
>>> which is based on a decision-tree and it works. I need to understand for
>>> a
>>> SVM (support vector machine) PMML, which is the output stream I have to
>>> create on CEP. The difference between the two pmml is that decision-tree
>>> specifies Output tag, so the outputstream is equal to the inputstrams
>>> plus
>>> the output tag, in svm instead there is no output tag.
>>>
>>> Thank you for help.
>>> Silvia
>>>
>>> _______________________________________________
>>> Dev mailing list
>>> [email protected]
>>> http://wso2.org/cgi-bin/mailman/listinfo/dev
>>>
>>>
>>
>>
>> --
>> Thanks & Regards,
>>
>> Fazlan Nazeem
>>
>> *Software Engineer*
>>
>> *WSO2 Inc*
>> Mobile : +94772338839
>> <%2B94%20%280%29%20773%20451194>
>> [email protected]
>>
>
>
>
>
--
Thanks & Regards,
Fazlan Nazeem
*Software Engineer*
*WSO2 Inc*
Mobile : +94772338839
<%2B94%20%280%29%20773%20451194>
[email protected]
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