Export scikit-learn model files to JSON for sharing or deploying predictive models with peace of mind.

Other methods for exporting scikit-learn models require Pickle or Joblib (based on Pickle). Serializing model files with Pickle provides a simple attack vector for malicious users-- they give an attacker the ability to execute arbitrary code wherever the file is deserialized. For an example see: https://www.smartfile.com/blog/python-pickle-security-problems-and-solutions/.

sklearn-json is a safe and transparent solution for exporting scikit-learn model files.

Export model files to 100% JSON which cannot execute code on deserialization.

Model files are serialized in JSON (i.e., not binary), so you have the ability to see exactly what's inside.

sklearn-json makes exporting model files to JSON simple.

pip install sklearn-json

import sklearn_json as skljson
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=0).fit(X, y)

skljson.to_json(model, file_name)
deserialized_model = skljson.from_json(file_name)

deserialized_model.predict(X)

The list of supported models is rapidly growing. If you have a request for a model or feature, please reach out to [email protected].

sklearn-json requires scikit-learn >= 0.21.3.

• Classification

  • sklearn.linear_model.LogisticRegression
  • sklearn.linear_model.Perceptron
  • sklearn.discriminant_analysis.LinearDiscriminantAnalysis
  • sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis
  • sklearn.svm.SVC
  • sklearn.naive_bayes.GaussianNB
  • sklearn.naive_bayes.MultinomialNB
  • sklearn.naive_bayes.ComplementNB
  • sklearn.naive_bayes.BernoulliNB
  • sklearn.tree.DecisionTreeClassifier
  • sklearn.ensemble.RandomForestClassifier
  • sklearn.ensemble.GradientBoostingClassifier
  • sklearn.neural_network.MLPClassifier

• Regression

  • sklearn.linear_model.LinearRegression
  • sklearn.linear_model.Ridge
  • sklearn.linear_model.Lasso
  • sklearn.svm.SVR
  • sklearn.tree.DecisionTreeRegressor
  • sklearn.ensemble.RandomForestRegressor
  • sklearn.ensemble.GradientBoostingRegressor
  • sklearn.neural_network.MLPRegressor