Barebones JSON serialization for simple (non-nested) sklearn models

skleanr2json.py

from pandas.io import json
import numpy as np


def model_to_dict(model):
    attrs = model.__dict__
    init_attrs = {key: val for key, val in attrs.items() if not key.endswith("_")}
    fitted_attrs = {key: val for key, val in attrs.items() if key.endswith("_")}
    name = model.__class__.__name__
    module = model.__module__
    return {'modelType': name,
            'loadFrom': module,
            'initParameters': init_attrs,
            'fittedParameters': fitted_attrs}


def model_to_json(model):
    return json.dumps(model_to_dict(model))


def model_from_dict(model_desc):
    mod = __import__(model_desc['loadFrom'], 
                     fromlist=[model_desc['modelType']])
    Clf = getattr(mod, model_desc['modelType'])
    clf = Clf(**model_desc['initParameters'])
    for key, val in model_desc['fittedParameters'].items():
        if isinstance(val, list):
            setattr(clf, key, np.array(val))
        else:
            setattr(clf, key, val)
    return clf


def model_from_json(json_str):
    return model_from_dict(json.loads(json_str))

This is some very hackish code I wrote to test the feasibility of providing JSON serialization for fitted sklearn models.
It doesn't always work, and it uses some questionable code practices like the manual call to __import__; needless
to say this should be kept away from any production environments!