Custom Callbacks

sklearn-genetic-opt comes with some pre-defined callbacks, but you can make one of your own by defining a callable with certain methods.

The callback must be a class with inheritance from the class BaseCallback that implements the __call__ and on_step methods, the result of them must be a bool, True means that the optimization must stop, False, means it can continue.

In this example, we are going to define a dummy callback that stops the process if there have been more that N fitness values bellow a threshold value.

The callback must have three parameters: record, logbook and estimator. Those are a dictionary, a deap’s Logbook object respectively, and the current GASearchCV with the current iteration metrics, all the past iterations metrics and all the properties saved in the estimator. You can choice which to use, but all of them must be parameters on the on_step and __call__ methods.

So to check inside the logbook, we could define a function like this:

N=4
metric='fitness'
threshold=0.8

def on_step(record, logbook, threshold, estimator=None):
    # Not enough data points
    if len(logbook) <= N:
        return False
    # Get the last N metrics
    stats = logbook.select(metric)[(-N - 1):]

    n_met_condition = [x for x in stats if x < threshold]

    if len(n_met_condition) > N:
        return True

    return False

As sklearn-genetic-opt expects all this logic in a single object, we must define a class that will have all this parameters, so we can rewrite it like this:

from sklearn_genetic.callbacks.base import BaseCallback

class DummyThreshold(BaseCallback):
    def __init__(self, threshold, N, metric='fitness'):
        self.threshold = threshold
        self.N = N
        self.metric = metric

    def on_step(self, record, logbook, estimator=None):
        # Not enough data points
        if len(logbook) <= self.N:
            return False
        # Get the last N metrics
        stats = logbook.select(self.metric)[(-self.N - 1):]

        n_met_condition = [x for x in stats if x < self.threshold]

        if len(n_met_condition) > self.N:
            return True

        return False

    def __call__(self, record, logbook, estimator=None):
        return self.on_step(record, logbook, estimator)

So that is it, now you can initialize the DummyThreshold and pass it to a in the fit method of a GASearchCV instance:

callback = DummyThreshold(threshold=0.85, N=4, metric='fitness')
evolved_estimator.fit(X, y, callbacks=callback)

Here there is an output example of this callback: