Error while using it with CatBoost

[Diyacmenezes202]

`TypeError Traceback (most recent call last)
File ~\anaconda3\Lib\site-packages\boruta\boruta_py.py:384, in BorutaPy._get_imp(self, X, y)
383 try:
--> 384 self.estimator.fit(X, y)
385 except Exception as e:

File ~\anaconda3\Lib\site-packages\catboost\core.py:5220, in CatBoostClassifier.fit(self, X, y, cat_features, text_features, embedding_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)
5218 CatBoostClassifier._check_is_compatible_loss(params['loss_function'])
-> 5220 self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,
5221 eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period,
5222 silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)
5223 return self

File ~\anaconda3\Lib\site-packages\catboost\core.py:2385, in CatBoost._fit(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)
2383 raise CatBoostError("y may be None only when X is an instance of catboost.Pool or string")
-> 2385 train_params = self._prepare_train_params(
2386 X=X, y=y, cat_features=cat_features, text_features=text_features, embedding_features=embedding_features,
2387 pairs=pairs, sample_weight=sample_weight, group_id=group_id, group_weight=group_weight,
2388 subgroup_id=subgroup_id, pairs_weight=pairs_weight, baseline=baseline, use_best_model=use_best_model,
2389 eval_set=eval_set, verbose=verbose, logging_level=logging_level, plot=plot, plot_file=plot_file,
2390 column_description=column_description, verbose_eval=verbose_eval, metric_period=metric_period,
2391 silent=silent, early_stopping_rounds=early_stopping_rounds, save_snapshot=save_snapshot,
2392 snapshot_file=snapshot_file, snapshot_interval=snapshot_interval, init_model=init_model,
2393 callbacks=callbacks
2394 )
2395 params = train_params["params"]

File ~\anaconda3\Lib\site-packages\catboost\core.py:2311, in CatBoost._prepare_train_params(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks)
2310 params = _params_type_cast(params)
-> 2311 _check_train_params(params)
2313 if params.get('eval_fraction', 0.0) != 0.0:

File _catboost.pyx:6393, in _catboost._check_train_params()

File _catboost.pyx:6414, in _catboost._check_train_params()

File _catboost.pyx:1830, in _catboost._PreprocessParams.init()

File ~\anaconda3\Lib\json_init_.py:238, in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
233 cls = JSONEncoder
234 return cls(
235 skipkeys=skipkeys, ensure_ascii=ensure_ascii,
236 check_circular=check_circular, allow_nan=allow_nan, indent=indent,
237 separators=separators, default=default, sort_keys=sort_keys,
--> 238 **kw).encode(obj)

File ~\anaconda3\Lib\json\encoder.py:200, in JSONEncoder.encode(self, o)
197 # This doesn't pass the iterator directly to ''.join() because the
198 # exceptions aren't as detailed. The list call should be roughly
199 # equivalent to the PySequence_Fast that ''.join() would do.
--> 200 chunks = self.iterencode(o, _one_shot=True)
201 if not isinstance(chunks, (list, tuple)):

File ~\anaconda3\Lib\json\encoder.py:258, in JSONEncoder.iterencode(self, o, _one_shot)
254 _iterencode = _make_iterencode(
255 markers, self.default, _encoder, self.indent, floatstr,
256 self.key_separator, self.item_separator, self.sort_keys,
257 self.skipkeys, _one_shot)
--> 258 return _iterencode(o, 0)

File _catboost.pyx:158, in _catboost._NumpyAwareEncoder.default()

File ~\anaconda3\Lib\json\encoder.py:180, in JSONEncoder.default(self, o)
162 """Implement this method in a subclass such that it returns
163 a serializable object for o, or calls the base implementation
164 (to raise a TypeError).
(...)
178
179 """
--> 180 raise TypeError(f'Object of type {o.class.name} '
181 f'is not JSON serializable')

TypeError: Object of type RandomState is not JSON serializable

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
Cell In[12], line 46
43 boruta = BorutaPy(catboost, n_estimators='auto',random_state=random_state, verbose=2)
45 # Fit Boruta
---> 46 boruta.fit(X_scaled, y)
48 # Get selected features
49 selected_features = X.columns[boruta.support_].tolist()

File ~\anaconda3\Lib\site-packages\boruta\boruta_py.py:201, in BorutaPy.fit(self, X, y)
188 def fit(self, X, y):
189 """
190 Fits the Boruta feature selection with the provided estimator.
191
(...)
198 The target values.
199 """
--> 201 return self._fit(X, y)

File ~\anaconda3\Lib\site-packages\boruta\boruta_py.py:285, in BorutaPy._fit(self, X, y)
282 self.estimator.set_params(random_state=self.random_state)
284 # add shadow attributes, shuffle them and train estimator, get imps
--> 285 cur_imp = self._add_shadows_get_imps(X, y, dec_reg)
287 # get the threshold of shadow importances we will use for rejection
288 imp_sha_max = np.percentile(cur_imp[1], self.perc)

File ~\anaconda3\Lib\site-packages\boruta\boruta_py.py:412, in BorutaPy._add_shadows_get_imps(self, X, y, dec_reg)
410 x_sha = np.apply_along_axis(self._get_shuffle, 0, x_sha)
411 # get importance of the merged matrix
--> 412 imp = self._get_imp(np.hstack((x_cur, x_sha)), y)
413 # separate importances of real and shadow features
414 imp_sha = imp[x_cur_w:]

File ~\anaconda3\Lib\site-packages\boruta\boruta_py.py:386, in BorutaPy.get_imp(self, X, y)
384 self.estimator.fit(X, y)
385 except Exception as e:
--> 386 raise ValueError('Please check your X and y variable. The provided'
387 'estimator cannot be fitted to your data.\n' + str(e))
388 try:
389 imp = self.estimator.feature_importances

ValueError: Please check your X and y variable. The providedestimator cannot be fitted to your data.
Object of type RandomState is not JSON serializable`

I'm getting this error and I've tried serializing the random_state variable to json, but it still persists with the same error.