Add a custom algorithm

In order to integrate a custom algorithm in PHOTONAI, all you need to do is provide a class adhering to the popular scikit-learn object API. In the following we will demonstrate an example to integrate a custom transformer to the Hyperpipe.

First, implement your data processing logic like this. 

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# we use BaseEstimator as to prepare the transformer for hyperparameter optimization
# we inherit the get_params and set_params methods
from sklearn.base import BaseEstimator


class CustomTransformer(BaseEstimator):

    def __init__(self, param1=0, param2=None):
        # it is important that you name your params the same in the constructor
        #  stub as well as in your class variables!
        self.param1 = param1
        self.param2 = param2

    def fit(self, data, targets=None, **kwargs):
        """
        Adjust the underlying model or method to the data.

        Returns
        -------
        IMPORTANT: must return self!
        """
        return self

    def transform(self, data, targets=None, **kwargs):
        """
        Apply the method's logic to the data.
        """
        return data

Afterwards, register your element with the photon registry like this. Custom elements must only be registered once.

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from photonai.base import PhotonRegistry

custom_element_root_folder = "./"
registry = PhotonRegistry(custom_elements_folder=custom_element_root_folder)

registry.register(photon_name='MyCustomTransformer',
                  class_str='custom_transformer.CustomTransformer',
                  element_type='Transformer')

# show information about the element
registry.info("MyCustomTransformer")
Afterwards, you can use your custom element in the pipeline like this. Importantly, the custom_elements_folder must be activated for each use as the folder's content, and therefore the custom class implementation might otherwise not be accessible by the python script. 

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from photonai.base import PhotonRegistry, Hyperpipe, PipelineElement

custom_element_root_folder = "./"
registry = PhotonRegistry(custom_elements_folder=custom_element_root_folder)

# This add the custom algorithm folder to the python path in order to import and instantiate the algorithm 
registry.activate()

# then use it 
my_pipe = Hyperpipe("...")
my_pipe += PipelineElement('MyCustomTransformer', hyperparameters={'param1': [1, 2, 3]})