Comments (3)
Could you, please, provide an illustration of how the python client can be used for regression task like the Iris data classification example?
The type of mining function - regression, classification, clustering etc - is an PMML-internal implementation detail, which is not reflected in Openscoring server or client APIs. In other words, you should be able to deploy and score any PMML document using a standardized workflow/code, without bothering what's actually inside it.
Also, please, what does the first parameter in the
os.evaluateCsvFile
represent - is it the model or target name? Yours was Iris
It's the model's identifier.
You assign the identifier using the deployFile
command. Whatever ID you chose, you need to keep using the same ID (case sensitive) throughout the remainder of your Python script. If you are pulling models from an external source where they already have identifiers, then your best best is to keep (re)using the same identifiers with Openscoring. For example, you can replace "Iris" with "Iris_Species_20190111-v1".
from openscoring-python.
it seems openscoring only support classification models.
Openscoring is a thin REST wrapper around the JPMML-Evaluator library: https://github.com/jpmml/jpmml-evaluator
See the list of supported and unsupported models under the features section:
https://github.com/jpmml/jpmml-evaluator#features
The RegressionModel
element, in both its regression and classification variants, is one of the simplest model types, and is definitely 100% supported.
And I get the following information on the SERVER terminal
See the log messages right before and after that INFO message. There should be a description of the associated EvaluationRequest object (were all the inputs correctly received?), and if some Java exception was thrown (in case of unsupported PMML markup, it should be either an org.jpmml.evaluator.UnsupportedElementException
or o.j.e.UnsupportedAttributeException
), its full stack trace.
My input file just contain class
I would recommend you to first run your PMML + CSV combo using the org.jpmml.evaluator.EvaluationExample
command-line application as described here:
https://github.com/jpmml/jpmml-evaluator#example-applications
from openscoring-python.
Thanks for your reply. I will test it out using the JPMML albeit my simple project is in python. In fact, I see that the server successfully reads the argument but the regression output is None.
Could you, please, provide an illustration of how the python client can be used for regression task like the Iris data classification example? Also, please, what does the first parameter in the os.evaluateCsvFile
represent - is it the model or target name? Yours was Iris .
from openscoring-python.
Related Issues (12)
- Add Pandas' DataFrame support to CSV evaluation function
- Connection refused HOT 6
- the same question 0.5.0 xgbValue is not defined HOT 1
- The web server at http://localhost:8080/openscoring did not identify itself as Openscoring/2.0 service HOT 5
- ConnectionError: HTTPConnectionPool(host='localhost', port=8080) HOT 9
- No JSON Object could be decoded HOT 3
- Batch csv evaluation returns NULL id's HOT 5
- The `Openscoring.deploy` method throws exception "No Json Object could be decoded" HOT 2
- Package requirements not updated
- How to evaluate model with many records at once? HOT 6
- How to get prediction probabilities? HOT 1
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