Python scikit-learn to JSON

Question

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 JSON format? Please see the model build code below for reference:

import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
import pickle
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names =['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)
# Fit the model on 33%
model = LogisticRegression()
model.fit(X_train, Y_train)
filename = 'finalized_model.sav'
pickle.dump(model, open(filename, 'wb'))

Answer 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

def logistic_regression_to_json(lrmodel, file=None):
    if file is not None:
        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)

def logistic_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.