# import inspect
#
# from sklearn.base import BaseEstimator, ClassifierMixin
# from sklearn import ensemble
#
# CREATED_BY = "<NAME>"
# MODEL_NAME = "Random Forest - activity"
#
#
# class RandomForest(BaseEstimator):
#     def __init__(self, n_estimators=250, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1,
#                  min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0,
#                  min_impurity_split=None, bootstrap=True, oob_score=True, n_jobs=-1, random_state=None, verbose=0,
#                  warm_start=False, class_weight=None):
#         input_args = locals()
#         del input_args["self"]
#         self.estimator = ensemble.RandomForestClassifier(**input_args)
#         args, _, _, values = inspect.getargvalues(inspect.currentframe())
#         values.pop("self")
#
#         for arg, val in values.items():
#             setattr(self, arg, val)
#
#     def fit(self, X, y):
#         return self.estimator.fit(X, y)
#
#     def predict_proba(self, X):
#         return self.estimator.predict_proba(X)
#
#     def predict(self, X):
#         return self.estimator.predict(X)
#
#     def score(self, X, y=None):
#         return (sum(self.predict(X)))

# import inspect
#
# from sklearn.base import BaseEstimator, ClassifierMixin
# from sklearn import ensemble
#
# CREATED_BY = "<NAME>"
# MODEL_NAME = "Random Forest - activity"
#
#
# class RandomForest(BaseEstimator):
#     def __init__(self, n_estimators=250, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1,
#                  min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0,
#                  min_impurity_split=None, bootstrap=True, oob_score=True, n_jobs=-1, random_state=None, verbose=0,
#                  warm_start=False, class_weight=None):
#         input_args = locals()
#         del input_args["self"]
#         self.estimator = ensemble.RandomForestClassifier(**input_args)
#         args, _, _, values = inspect.getargvalues(inspect.currentframe())
#         values.pop("self")
#
#         for arg, val in values.items():
#             setattr(self, arg, val)
#
#     def fit(self, X, y):
#         return self.estimator.fit(X, y)
#
#     def predict_proba(self, X):
#         return self.estimator.predict_proba(X)
#
#     def predict(self, X):
#         return self.estimator.predict(X)
#
#     def score(self, X, y=None):
#         return (sum(self.predict(X)))

# import inspect # # from sklearn.base import BaseEstimator, ClassifierMixin # from sklearn import ensemble # # CREATED_BY = "<NAME>" # MODEL_NAME = "Random Forest - activity" # # # class RandomForest(BaseEstimator): #     def __init__(self, n_estimators=250, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, #                  min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, #                  min_impurity_split=None, bootstrap=True, oob_score=True, n_jobs=-1, random_state=None, verbose=0, #                  warm_start=False, class_weight=None): #         input_args = locals() #         del input_args["self"] #         self.estimator = ensemble.RandomForestClassifier(**input_args) #         args, _, _, values = inspect.getargvalues(inspect.currentframe()) #         values.pop("self") # #         for arg, val in values.items(): #             setattr(self, arg, val) # #     def fit(self, X, y): #         return self.estimator.fit(X, y) # #     def predict_proba(self, X): #         return self.estimator.predict_proba(X) # #     def predict(self, X): #         return self.estimator.predict(X) # #     def score(self, X, y=None): #         return (sum(self.predict(X)))