Custom Estimator
You can combine your own learning algorithm, bet it a neural net or anything else, by simply adhering to the scikit-learn interface as shown below. Then register your class with the Register module and you're done!
from sklearn.base import BaseEstimator
class CustomEstimator(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 predict(self, data):
"""
Use the learned model to make predictions.
"""
my_predictions = []
return my_predictions
