Import sklearn2pmml generated .pmml back into ScikitLearn or Python

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Apologies if this may have been answered somewhere but I've been looking for about an hour and can't find a good answer.

I have a simple Logistic Regression model trained in Scikit-Learn that I'm exporting to a .pmml file.

  from sklearn2pmml import PMMLPipeline, sklearn2pmml
  my_pipeline = PMMLPipeline(
  ( classifier", LogisticRegression() )
      ) blah)
  sklearn2pmml(my_pipeline, "filename.pmml")


So what I'm wondering is if/how I can import this file back into Python (2.7 preferably) or Scikit-Learn to use as I would in Java/Scala. Something along the lines of

"import (filename.pmml) as pm pm.predict(data)

Thanks for any help!

Scikit-learn does not offer support for importing PMML files, so what you're trying to achieve cannot be done I'm afraid.

The concept of using libraries such as sklearn2pmml is really to extend the functionality that sklearn does not have when it comes to supporting the model export to a PMML format.

Typically, those who use sklearn2pmml are really looking to re-use the PMML models in other platforms (e.g. IBM's SPSS, Apache Spark ML, Weka or any other consumer as listed in the Data Mining Group's website).

If you're looking to save a model created with scikit-learn and re-use it afterwards with scikit-learn as well then you should explore its native persistence model mechanism named Pickle, which uses a binary data format.

You can read more about how to save/load models in Pickle format (together with its known issues) here.

jpmml/sklearn2pmml: Python library for converting Scikit , import pandas iris_df = pandas.read_csv("Iris.csv") from sklearn.tree import DecisionTreeClassifier from sklearn2pmml.pipeline import PMMLPipeline pipeline� Both GridSearchCV and RandomizedSearchCV meta-estimator classes split the original dataset into training and validation subsets. As a result, the fit method of the tuneable estimator is exposed to less data records than the fit methods of all the other estimators in the pipeline.

I created a simple solution to generate sklearn kmeans models from pmml files which i exported from knime analytics platform. You can check it out pmml2sklearn

Extending Scikit-Learn with business rules (BR) model type , between the two, where machine generated candidates are filtered, After some tinkering they were all pushed aside in favour of Python from sklearn2pmml import sklearn2pmml from sklearn2pmml.pipeline import PMMLPipeline back to a Numpy array, thereby enforcing positional cell references. While correct, the above PMML markup is not particularly elegant. LightGBM uses a type system, where continuous and categorical features are represented using double and integer values, respectively. This is different from Scikit-Learn GBT algorithms, which do not use the notion of an operational type, and represent everything using float values.

You could use PyPMML to make predictions on a new dataset using 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.

Converting logistic regression models to PMML documents , Converting the model to a PMML document using JPMML family conversion tools and libraries. from sklearn.linear_model import LogisticRegression from sklearn2pmml string representation to low-level bit vector representation, and back, Created columns stay in place until replaced or removed. SkLearn2PMML. Python library for converting Scikit-Learn pipelines to PMML.. Features. This library is a thin wrapper around the JPMML-SkLearn command-line application. For a list of supported Scikit-Learn Estimator and Transformer types, please refer to the documentation of the JPMML-SkLearn project.

Putting Scikit-Learn models into production with PMML, A model could be created in Python/R, saved as an XML file, and then The sklearn2pmml package takes that pipeline and create a PMML file� The model can now be exported to PMML through the sklearn2pmml package developed by with a single line of code: from sklearn2pmml import sklearn2pmml sklearn2pmml(pipeline, "churn_sklearn.pmml", with_repr = True) This will create a PMML file that you can now import into Pega.

sklearn2pmml: How to choose the PMML version when outputting , PMML 4.2(.1) models can be back-ported to earlier versions by identifying and and describe your use case (example Python code?) in more detail: I am using the Java JPMML evaluator API to load the PMML and test my model. Sklearn2pmml is generating a correct DefineFunction element for the Please note that stateless Scikit-Learn selector objects need to be wrapped into an sklearn2pmml.SelectprProxy object. Third, creating an Estimator object: from sklearn . tree import DecisionTreeClassifier classifier = DecisionTreeClassifier ( min_samples_leaf = 5 )

This blog post shows how the sklearn2pmml package extends Scikit-Learn with PMML-compatible date and datetime features. Temporal data. A datetime is a data structure that represents an instant (point in time) according to some calendar and time zone. The calendar component takes care of mapping larger periods of time such as years, months and days.

  • Were you going to export it, change it, and then you want to reload it back into python? Or you just want to reopen the original at some point?
  • Hi Tony. No changes, just reload it back into Python and then perform a simple prediction. So if somebody built a simple regression, and emailed me a .pmml file, I could open that .pmml file in my own Jupyter notebook or Python REPL and hand it some data and then make a prediction. You can do it in something like Spark, but I haven't seen it done in Python (yet).
  • I'm not familiar with pmml, but have you tried pickle, or another example of sklearn+pickle
  • Thanks Tony. We were just trying to do it in PMML for a proof of concept.
  • Thank you very much. I'm aware of Pickle and we have been using PMML for Apache Spark, and was curious if this could be achieved in Python. Thanks again!