Comments (5)
pudr
commented on August 20, 2024
PMML model exported from SAS:
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scorebot
commented on August 20, 2024
@pudr Thanks for your info, there are two issues in the error:
- The input data should contain the target value because the model defines the residuals for each target value, for example:
<OutputField name="R_classIris_setosa" displayName="Residual: class=Iris-setosa" optype="continuous" dataType="double" targetField="class" feature="residual" value="IRIS-SETOSA"/>
<OutputField name="R_classIris_versicolor" displayName="Residual: class=Iris-versicolor" optype="continuous" dataType="double" targetField="class" feature="residual" value="IRIS-VERSICOLOR"/>
<OutputField name="R_classIris_virginica" displayName="Residual: class=Iris-virginica" optype="continuous" dataType="double" targetField="class" feature="residual" value="IRIS-VIRGINICA"/>
- There is a bug of the PMML4S library on computing the residuals, I'll let you know once it's fixed.
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pudr
commented on August 20, 2024
@scorebot Thanks for your prompt reply!
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scorebot
commented on August 20, 2024
@pudr I have fixed the issue of PMML4S. Please, reinstall the latest version from Github by the following command:
pip install --upgrade git+https://github.com/autodeployai/pypmml.git
If you want the residuals, you need to input the target class value, for example:
result = model.predict({'sepal_length': 5.1, 'sepal_width': 3.5, 'petal_length': 1.4, 'petal_width': 0.2, 'class': 'IRIS-SETOSA'})
Otherwise, you'll get none values for residuals.
Please, let me know if you still have a problem.
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pudr
commented on August 20, 2024
@scorebot works like a charm. thanks a lot!
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