Run Arbitrary Code Every Generation

[phillip-martin]

I couldn't find this in the api or documentation, so please excuse me if this is trivial. At the end of training a model using tpot, I score the generated model based on mean absolute error and mean squared error. The following demonstrates an example.

x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=test_size, random_state=seed
)

tpot = TPOTRegressor(generations=50, population_size=20, verbosity=2)
tpot.fit(x_train, y_train)

# score at the end of training
y_predicted = tpot.predict(scaler_x.transform(x_test))
print('me: ', mean_absolute_error(y_test, y_predicted))
print('mse: ', mean_squared_error(y_test, y_predicted))

I would like to be able to run these scores every generation, ideally through a function passed to either TPOTRegressor or tpot.fit. This might look like the following.

x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=test_size, random_state=seed
)

def score_tpot(_tpot):
    y_predicted = _tpot.predict(scaler_x.transform(x_test))
    print('me: ', mean_absolute_error(y_test, y_predicted))
    print('mse: ', mean_squared_error(y_test, y_predicted))

tpot = TPOTRegressor(
    generations=50, population_size=20, verbosity=2, each_generation=score_tpot
)
tpot.fit(x_train, y_train)

Is there currently a way to do something like this that I could not find in the documentation?

Thank you for any time you put into reviewing this question.

[rhiever]

We don't have direct support for this functionality, but it could technically be feasible through the use of the warm_start parameter. Some quick code:

x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=test_size, random_state=seed
)

tpot = TPOTRegressor(generations=1, population_size=20, verbosity=2, warm_start=True)
for _ in range(50):
    tpot.fit(x_train, y_train)
    y_predicted = tpot.predict(scaler_x.transform(x_test))
    print('me: ', mean_absolute_error(y_test, y_predicted))
    print('mse: ', mean_squared_error(y_test, y_predicted))

# score at the end of training
y_predicted = tpot.predict(scaler_x.transform(x_test))
print('me: ', mean_absolute_error(y_test, y_predicted))
print('mse: ', mean_squared_error(y_test, y_predicted))