Knime has generated for me a PMML model. At this time I want to apply this model to a python process. What is the right way to do this?
More in depth: I develop a django student attendance system . The application is already so mature that I have time to implement the 'I'm feeling lucky' button to automatically fill an attendance form. Here is where PMML comes in. Knime has generated a PMML model that predicts student attendance. Also, thanks to django for being so productive that I time for this great work ;)
Finally I have wrote my own code. Be free to contribute or fork it:
使用Python对PMML模型进行评分的Augustus代码位于https://code.google.com/p/augustus/
You could use PyPMML to apply PMML in Python, for example:
from pypmml import Model
model = Model.fromFile('the/pmml/file/path')
result = model.predict(data)
The data could be dict, json, Series or DataFrame of Pandas.
If you use PMML in PySpark, you could use PyPMML-Spark , for example:
from pypmml_spark import ScoreModel
model = ScoreModel.fromFile('the/pmml/file/path')
score_df = model.transform(df)
The df is a DataFrame of PySpark.
For more info about other PMML libraries, be free to see: https://github.com/autodeployai
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