def __init__(self, model_type, codes, feature_names=None, alphas=None, **kwargs): self.model_type = model_type self.models = {} for i, code in enumerate(codes): model = SparseModel(model_type, feature_names, **kwargs) if alphas is not None: model.set_alpha(alphas[i]) self.models[code] = model
def __init__(self, model_type, codes, feature_names=None, alphas=None, **kwargs): # generate a random order over models self.order = list(codes)[:] random.shuffle(self.order) self.model_type = model_type self.models = {} # create models in the pre-determined order, adding one feature each time for the output of the previous model for i, code in enumerate(self.order): model = SparseModel(model_type, feature_names[:], **kwargs) if alphas is not None: model.set_alpha(alphas[i]) self.models[code] = model feature_names.append(code)