ValueError: invalid literal for int() with base 10: 'post1' on Example from Readme

[MarcusSchilling]

Hello,

I tried to run Porter to transform my RandomForestClassifier to JavaScript. I ran into an unexpected "error invalid literal for int() with base 10: 'post1'". Then I thought, let's try the demo from the README of sklearn-porter. The result remained the same. Can anyone tell me if this is a mistake by sklearn-porter or where my real problem might lie?

Error Message

ValueError Traceback (most recent call last)

in 10 11 # Export: ---> 12 porter = Porter(clf, language='java') 13 output = porter.export(embed_data=True) 14 print(output) /opt/conda/lib/python3.7/site-packages/sklearn_porter/Porter.py in __init__(self, estimator, language, method, **kwargs) 59 from sklearn import __version__ as sklearn_ver 60 sklearn_ver = str(sklearn_ver).split('.') ---> 61 sklearn_ver = [int(v) for v in sklearn_ver] 62 major, minor = sklearn_ver[0], sklearn_ver[1] 63 patch = sklearn_ver[2] if len(sklearn_ver) >= 3 else 0 /opt/conda/lib/python3.7/site-packages/sklearn_porter/Porter.py in (.0) 59 from sklearn import __version__ as sklearn_ver 60 sklearn_ver = str(sklearn_ver).split('.') ---> 61 sklearn_ver = [int(v) for v in sklearn_ver] 62 major, minor = sklearn_ver[0], sklearn_ver[1] 63 patch = sklearn_ver[2] if len(sklearn_ver) >= 3 else 0 ValueError: invalid literal for int() with base 10: 'post1' # Demo Code from sklearn.datasets import load_iris from sklearn.tree import tree from sklearn_porter import Porter # Load data and train the classifier: samples = load_iris() X, y = samples.data, samples.target clf = tree.DecisionTreeClassifier() clf.fit(X, y) # Export: porter = Porter(clf, language='java') output = porter.export(embed_data=True) print(output)

[lucasavila00]

It's a bug in sklearn_porter. Use another version of scikit_learn for now, 0.22.2.post1 is not compatible. Use 0.22.2 or 0.22. They all work fine.

[nok]

See duplicate https://github.com/nok/sklearn-porter/issues/67.