Adding a simple yet powerful and universal approach of unlearning that is compatible with all kinds of model

[ShiroganeMiyuki-0]

``class ModelUpdater:
def init(self, model):
self.model = model

def update_model(self, dataset_path, data_to_delete):
    # Load dataset
    dataset = pd.read_csv(dataset_path)

    # Delete requested data
    dataset = dataset.drop(data_to_delete, axis=0)

    # Split dataset into features and target variable
    X = dataset.drop("target_column", axis=1)  # Assuming "target_column" is the name of the target column
    y = dataset["target_column"]

    # Split dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Retrain the model
    self.model.fit(X_train, y_train)

    # Evaluate the model
    accuracy = self.model.score(X_test, y_test)
    print("Model retrained. Accuracy:", accuracy)

``

The code takes the model that's been trained.
The code unlearns the data by deleting the data and retraining it.

Why this?
It achieves perfect unlearning that doesn't compromise security (membership inference attacks).
The model used to train is also the model used to untrain, this way it is compatible with all kinds of model be it:
Model agnostic.
Model intrinsic.
Data intrinsic.

Since most of the implementation is done in learning part it makes this approach more powerful by simply inheriting most of it.

[mo-shahab]

this code is just low level design of the whole process. it can be used as the reference and nothing more than that.
how will you define the data_to_delete ?
are you assuming the model is pretrained or not ?
you talked about the compatibility with all models, but this design assumes that the dataset will be a csv file which is labelled, which would mean that it would practically cater only to the models that use the csv file, and whose data in the file is easily alterable, what about the models that use binaries as training data ? what if the dataset is unlabeled and is not structured ?

The code takes the model that's been trained.
i am not sure about it though.

this lld also needs work arounds, consider various edge cases, the case about the datasets being of different types and also its not clear how the model is being passed, it might seem ok when looked at first, but there are underlying conditions to do so, work on those cases too,

we can make use of this to generally unlearn the models, but you have to do more work on this, first lets discuss with everyone if at all its ok if we go with this, and i dont know if you have realised yet, that it is a framework, sure this can also be a part given that we provide all that is required to make it work. and let me tell you again, what you are giving is just an lld as i can see, its so brief, it seems simple but it is not complete and not even close and we should not assume the results of this being powerful. dont assume anything in softwares

and mostly dont assume how the framework is going to be used by the user, work more on it. but also keep the track of what is currently going on with the project.

[ShiroganeMiyuki-0]

To answer this question I have already mentioned that most of the implementation will be inherited from the learning part so the choice of file or model is also inherited from that according to how we deal with data. Unlearning process using a different model from the learning process is not a usual scenario or rather can you think of one?

[mo-shahab]

To answer this question I have already mentioned that most of the implementation will be inherited from the learning part so the choice of file or model is also inherited from that according to how we deal with data.

its too vague to understand anything, there are lot assumptions you are making which is why it may sound right but this implementation is just an lld, the part or choice of file wont just be inherited, there are underlying factors that you are not considering. it might seem like that the choice of file of model wont matter, but it does.

To answer this question

you have only answered one question i asked more questions. and give an example on how you will implement it, you are overseeing a lot of things, read the comment and try to give an insight to those.

and iirc you had mention in the description of the thread of not being sure about something, and the thread is edited too, so i must have asked something related to the original description, this will create unnecessary confusion, if you are making major changes in your description give it as a PS or a edit: footer

dont assume, work on this more, its not correct as it is now, and avoid rhetorical questions in the thread

[ShiroganeMiyuki-0]

It's not vague think high level. Framework is a combination of many lld some are frequently used than others which becomes the main feature of the framework. You can infer the answers to the question from this. I have not edited anything after your reply don't just assume.