__init__ method is not called on creation

[geiseri]

On restoring from JSON the objects are created but properties that are defined in init are not created if they are missing in the JSON. I am not sure if this is intended though. Is there a way to define the behavior of missing properties in the object?

[davvid]

If you have a unit case we can add to the test suite it might help illuminate the issue.

Generally, if you need to special-case the handling of a specific class then it can either implement the __getstate__(self) -> data and __setstate__(self, data) protocol, or you can register a custom handler.

[GuillemGSubies]

I have the same problem. I think I found what happens. If I specify every parameter that the init method has, everything works correctly, but if I don't, jsonpickle instances them to None

These are the default scikit-learn parameters:

jsonpickle.decode('''{
    "py/object": "sklearn.ensemble.AdaBoostRegressor",
    "py/state": {
        "base_estimator": null,
        "n_estimators": 50,
        "learning_rate": 1.0,
        "loss": "linear",
        "random_state": null
}}''')

AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear', n_estimators=50, random_state=None)

However if I do this:

jsonpickle.decode('''{"py/object": "sklearn.ensemble.AdaBoostRegressor"}''')

I get all Nones:

AdaBoostRegressor(base_estimator=None, learning_rate=None, loss=None, n_estimators=None, random_state=None)

I understand the behaviour of this, jsonpickle does not initialize the object, it unpickles it as if it were already initialized and saved.

However I think my problem is a kinda common use case and it would be really cool if we had the chance to tell jsonpickle.decode that we want it to call the init method instead of trying to load an object.

EDIT: Actually you cannot use the loaded model that way:

See the model with all its params:

jsonpickle.encode(AdaBoostRegressor())

'{"py/object": "sklearn.ensemble.weight_boosting.AdaBoostRegressor", "py/state": {"_sklearn_version": "0.21.2", "base_estimator": null, "estimator_params": {"py/tuple": []}, "learning_rate": 1.0, "loss": "linear", "n_estimators": 50, "random_state": null}}'

However, this is not the same:

jsonpickle.encode(jsonpickle.decode('''{"py/object": "sklearn.ensemble.AdaBoostRegressor"}'''))

'{"py/object": "sklearn.ensemble.weight_boosting.AdaBoostRegressor", "py/state": {"_sklearn_version": "0.21.2"}}'

Actually I cannot fit with that last one:

X, y = load_boston(return_X_y=True)
jsonpickle.decode('''{"py/object": "sklearn.ensemble.AdaBoostRegressor"}''').fit(X, y)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-20-1a87b9f2bcc2> in <module>
      1 from sklearn.datasets import load_iris, load_boston
      2 X, y = load_boston(return_X_y=True)
----> 3 jsonpickle.decode('''{"py/object": "sklearn.ensemble.AdaBoostRegressor"}''').fit(X, y)

~/.conda/envs/xxxx/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py in fit(self, X, y, sample_weight)
    989         """
    990         # Check loss
--> 991         if self.loss not in ('linear', 'square', 'exponential'):
    992             raise ValueError(
    993                 "loss must be 'linear', 'square', or 'exponential'")

AttributeError: 'AdaBoostRegressor' object has no attribute 'loss'

Even if i tried to write all the parameters by myself I could not use it because it has other private class attributes:

jsonpickle.decode('''{
    "py/object": "sklearn.ensemble.AdaBoostRegressor",
    "py/state": {
        "base_estimator": null,
        "n_estimators": 50,
        "learning_rate": 1.0,
        "loss": "linear",
        "random_state": null
}}''').fit(X,y)

AttributeError                            Traceback (most recent call last)
<ipython-input-22-12cce3852ba8> in <module>
      7         "loss": "linear",
      8         "random_state": null
----> 9 }}''').fit(X,y)

~/.conda/envs/xxxx/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py in fit(self, X, y, sample_weight)
    994 
    995         # Fit
--> 996         return super().fit(X, y, sample_weight)
    997 
    998     def _validate_estimator(self):

~/.conda/envs/xxxx/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py in fit(self, X, y, sample_weight)
    148                 X, y,
    149                 sample_weight,
--> 150                 random_state)
    151 
    152             # Early termination

~/.conda/envs/xxxx/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py in _boost(self, iboost, X, y, sample_weight, random_state)
   1039             If None then boosting has terminated early.
   1040         """
-> 1041         estimator = self._make_estimator(random_state=random_state)
   1042 
   1043         # Weighted sampling of the training set with replacement

~/.conda/envs/xxxx/lib/python3.6/site-packages/sklearn/ensemble/base.py in _make_estimator(self, append, random_state)
    126         estimator = clone(self.base_estimator_)
    127         estimator.set_params(**{p: getattr(self, p)
--> 128                                 for p in self.estimator_params})
    129 
    130         if random_state is not None:

AttributeError: 'AdaBoostRegressor' object has no attribute 'estimator_params