Générer PMML pour pipeline de classification de texte en python

Question

J'essaie de générer PMML (en utilisant jpmml-sklearn) pour le pipeline de classification de texte. La dernière ligne du code - sklearn2pmml (Textpipeline, "TextMiningClassifier.pmml", with_repr = True) - se bloque. Il semblerait que sklearn2pmml() ne soit pas capable d'utiliser Textpipeline en tant qu'entrée. Le code fonctionne bien pour les autres pipelines (exemples ici: https://github.com/jpmml/sklearn2pmml) mais pas pour le pipeline de classification de texte ci-dessus. Donc ma question est: comment puis-je générer PMML pour le problème de classification de texte?

erreur que je reçois:

Jun 15, 2017 12:48:00 PM org.jpmml.sklearn.Main run 
INFO: Parsing PKL.. 
Jun 15, 2017 12:48:01 PM org.jpmml.sklearn.Main run 
INFO: Parsed PKL in 489 ms. 
Jun 15, 2017 12:48:01 PM org.jpmml.sklearn.Main run 
INFO: Converting.. 
Jun 15, 2017 12:48:01 PM sklearn2pmml.PMMLPipeline encodePMML 
WARNING: The 'target_field' attribute is not set. Assuming y as the name of the target field 
Jun 15, 2017 12:48:01 PM sklearn2pmml.PMMLPipeline initFeatures 
WARNING: The 'active_fields' attribute is not set. Assuming [x1] as the names of active fields 
Jun 15, 2017 12:48:01 PM org.jpmml.sklearn.Main run 
SEVERE: Failed to convert 
java.lang.IllegalArgumentException: The tokenizer object (null) is not Splitter 
at sklearn.feature_extraction.text.CountVectorizer.getTokenizer(CountVectorizer.java:263) 
at sklearn.feature_extraction.text.CountVectorizer.encodeDefineFunction(CountVectorizer.java:164) 
at sklearn.feature_extraction.text.CountVectorizer.encodeFeatures(CountVectorizer.java:124) 
at sklearn.pipeline.Pipeline.encodeFeatures(Pipeline.java:93) 
at sklearn2pmml.PMMLPipeline.encodePMML(PMMLPipeline.java:122) 
at org.jpmml.sklearn.Main.run(Main.java:144) 
at org.jpmml.sklearn.Main.main(Main.java:93) 

Exception in thread "main" java.lang.IllegalArgumentException: The tokenizer object (null) is not Splitter 
at sklearn.feature_extraction.text.CountVectorizer.getTokenizer(CountVectorizer.java:263) 
at sklearn.feature_extraction.text.CountVectorizer.encodeDefineFunction(CountVectorizer.java:164) 
at sklearn.feature_extraction.text.CountVectorizer.encodeFeatures(CountVectorizer.java:124) 
at sklearn.pipeline.Pipeline.encodeFeatures(Pipeline.java:93) 
at sklearn2pmml.PMMLPipeline.encodePMML(PMMLPipeline.java:122) 
at org.jpmml.sklearn.Main.run(Main.java:144) 
at org.jpmml.sklearn.Main.main(Main.java:93) 
Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
    File "C:\Data\Anaconda2\lib\site-packages\sklearn2pmml\__init__.py", line 142, in sklearn2pmml 
raise RuntimeError("The JPMML-SkLearn conversion application has failed. The Java process should have printed more information about the failure into its standard output and/or error streams") 
RuntimeError: The JPMML-SkLearn conversion application has failed. The Java process should have printed more information about the failure into its standard output and/or error streams 

Answer 1

Vous devez utiliser du texte PMML compatible fonction tokens. L'implémentation par défaut est la classe sklearn2pmml.feature_extraction.text.Splitter:

from sklearn.feature_extraction.text import TfidfVectorizer 
from sklearn2pmml.feature_extraction.text import Splitter 
vectorizer = TfidfVectorizer(analyzer = "word", token_pattern = None, tokenizer = Splitter()) 

Plus de détails et références sont disponibles dans la liste de diffusion JPMML: https://groups.google.com/forum/#!topic/jpmml/wi-0rxzUn1o