code snippet for Tuning Hyperparameters (part I): SuccessiveHalving

SHBaseEstimator_illustration

class SHBaseEstimator(ABC):
    def __init__(self,model):
        self.model = model
        self.env = None

    def fit(self,X,y):
        self.model.fit(X,y)

    def predict(self,X):
        return self.model.predict(X)

    @abstractmethod
    def save(self,name=None):
        return NotImplementedError

    @abstractmethod
    def load(self,model_name):
        return NotImplementedError

    @abstractmethod
    def remove(self,model_name):
        return NotImplementedError

    def get_params(self):
        return self.model.get_params()

    def set_params(self,*args,**kwargs):
        self.model.set_params(*args,**kwargs)

    def n_iteration(self,ressource_name):
        return self.model.get_params()[ressource_name]

    @abstractmethod
    def update(self,Xtrain,ytrain,Xval,yval,scoring,n_iterations):
'''
Further train the model, after a reload! This is definition can vary,
depending on which library you are wrapping around!
'''
        return NotImplementedError