Extending this package

This package was designed to be easily extendable and customizable for your own needs. All the technical aspects of this package’s extension can be found in the API reference. In this page, we only go over how to implement the algorithm itself, but you’ll still need to modify the predict.py file to make the algorithm accessible from the command line.

Adding an sklearn Algorithm

Adding a new algorithm to test against is fairly straightforward. The first step is to add a new class to the package. Let’s try adding a SVC algorithm in a python file called svc.py.

We need to make it implement the AlgorithmMeta class, which will ensure many builtin functionalities such as fit, predict, or score. The only thing we really need to do now is implement the class’s constructor.

"""svc.py"""
from mnist_classifier.algorithm_meta import AlgorithmMeta
from sklearn.svm import SVC


class SVCExtension(AlgorithmMeta):

    def __init__(self,
                 random_seed: int=None,         # for reproducibility
                 report_directory: str = None,  # for AlgorithmMeta constructor
                 test_suite_iter: int = None,   # for AlgorithmMeta constructor
                 **svc_configuration_settings): # custom SVC settings

        # initialize parent class
        super().__init__(report_directory, test_suite_iter=test_suite_iter)

        # initialize the model
        self.model = SVC(random_state=random_seed, **svc_configuration_settings)

And that’s it! If you wish to have more custom functions (such as print_results or display_results), feel free to overload any of AlgorithmMeta’s methods.

Adding a Custom Algorithm

If you want to design your own algorithm and use this framework to test it, or if you want to implement a non-sklearn algorithm, you definitely can.

We also create a class inheriting from AlgorithmMeta, but this time, we have to overload a few functions, since many functions take advantage of the fact that all we used so far was well structured sklearn classifiers which have similar methods. Namely:

  • __init__() obviously.

  • fit() to train the algorithm

  • predict() to run a prediction

  • score() which is actually inherited by ClassifierMixin

  • save() and load() depending on the library you are using.

And this should ensure smooth sailing.