How to add new models

One of the idea of the package is to offer ways to quickly test ideas without much burden. It is thus relatively easy to add new models/transformers to the framework.

Those models/transformers can be included in the search of the auto-ml component to be tested on different databases and with other transformers/models.

A model needs to be added at two different places in order to be fully integrated within the framework.

Let’s see what’s need to be done to include an hypothetic new models:

class ReallyCoolNewTransformer(BaseEstimator, TransformerMixin):
    """ This is a great new  transformer """
    def __init__(self, super_choice):
        self.super_choice = super_choice

    def fit(self,X, y = None):

    def transform(self,X):

Add model to Simple register

This will allow the function ‘sklearn_model_from_param’ to be able to use your new model. The class simply needs to be added to the DICO_NAME_KLASS object:

from aikit.model_definition import DICO_NAME_KLASS

Now that this is done, you can call the transformer by its name:

from aikit.model_definition import sklearn_model_from_param
model = sklearn_model_from_param(("ReallyCoolNewTransformer",{}))

model is an instance of ReallyCoolNewTransformer

Add model to Auto-Ml framework

This is a little more complicated, a few more informations need to be entered:

  • type of model
  • type of variable it uses
  • hyper-parameters

To do that you need to use the @register decorator:

from aikit.ml_machine.ml_machine_registration import register, _AbstractModelRepresentationDefault, StepCategories
import aikit.ml_machine.hyper_parameters as hp

class DimensionReduction_ReallyCoolNewTransformer(_AbstractModelRepresentationDefault):
    klass = ReallyCoolNewTransformer

    category = StepCategories.DimensionReduction
    type_of_variable = None
    type_of_model = None # Used for all problem

    custom_hyper = {"super_parameters":hp.HyperChoice(("superchoice_a","superchoice_b"))}

See Model Register for complete description of register. See Hyperparameters for complete description of register.

Remark: The registers behaves like singletons so you can modify them in any part of the code. You just need the code to be executed somewhere for it to work.

If a model is stable and tested enough the new entry can be added to the python files :

  • ‘’ : for the simple register
  • ml_machine/ : for the full auto-ml register

(See Contribution for detailed about how to contribute to the evolution of the library)

Remark : you don’t need to use the wrapper for your model to be incorporated in the framework. However, it is best to do so. That way you can focus on the logic and let the wrapper make your model more generic.