Python Scikit-learn To Json

I have a model built with Python scikit-learn. I understand that the models can be saved in Pickle or Joblib formats. Are there any existing methods out there to save the jobs in J

Solution 1:

You'll have to cook up your own serialization/deserialization recipe. Fortunately, logistic regression can basically be captured by the coefficients and the intercept. However, the LogisticRegression object keeps some other metadata around which we might as well capture. I threw together the following functions that does the dirty-work. Keep in mind, this is still rough:

import numpy as np
import json
from sklearn.linear_model import LogisticRegression

deflogistic_regression_to_json(lrmodel, file=None):
    if file isnotNone:
        serialize = lambda x: json.dump(x, file)
    else:
        serialize = json.dumps
    data = {}
    data['init_params'] = lrmodel.get_params()
    data['model_params'] = mp = {}
    for p in ('coef_', 'intercept_','classes_', 'n_iter_'):
        mp[p] = getattr(lrmodel, p).tolist()
    return serialize(data)

deflogistic_regression_from_json(jstring):
    data = json.loads(jstring)
    model = LogisticRegression(**data['init_params'])
    for name, p in data['model_params'].items():
        setattr(model, name, np.array(p))
    return model

Note, with just 'coef_', 'intercept_','classes_' you could do the predictions yourself, since logistic regression is a straight-forward linear model, it's simply matrix-multiplication.