How do I increment a variable after each training iteration using the CatBoost Classifier in Python?

As the title says, I'm trying to increment a variable after each training iteration using the Catboost Classifier, to update a progress bar in a gui, and I can't seem to find anything about it on the internet.

So far I've tried to make a class containing an after_iteration function and passing that or an instance of it as a parameter the fit method:

class Test_Callback():
    def after_iteration(self, info):
        global progress
        progress += 1
test = Test_Callback()
model.fit(train_data, eval_set=test_data, callbacks=[test])
#Alternatively pass the class directly
model.fit(train_data, eval_set=test_data, callbacks=[Test_Callback()])

This does increment the variable, but also ends the training after a single iteration.
I've also tried passing an after_iteration() - function to the callbacks parameter directly, but it just crashes the script.

I can't test it but I found example callbacks in catboot's repo MetricsCheckerCallback and EarlyStopCallback and it seems it uses returned value (True/False) to continue or stop iterartions.

Because you don't use return so it automatically returns None (which is treated as False) and this can stop iterations.

You may need to add return True

class Test_Callback():   
    def after_iteration(self, info):
        global progress
        progress += 1
        return True

---

BTW:

I would put progess inside class

class Test_Callback():   
    def __init__(self):
        self.progress = 0

    def after_iteration(self, info):
        self.progress += 1

        return True

but it would need to create class before fit() to have access self.progress after running fit()

test = Test_Callback()
model.fit(train_data, eval_set=test_data, callbacks=[test])
print(test.progress)

---

Example EarlyStopCallback also suggests that you can use info.iteration for it
(but it didn't test it)

class Test_Callback():   
    def __init__(self):
        self.progress = 0

    def after_iteration(self, info):
        self.progress = info.iteration

        return True

---

Example callbacks from Catboost repo - so nobody have to visit links:

class MetricsCheckerCallback:
    def after_iteration(self, info):
        for dataset_name in ['learn', 'validation_0', 'validation_1']:
            assert dataset_name in info.metrics
            for metric_name in metric_names:
                assert metric_name in info.metrics[dataset_name]
                assert len(info.metrics[dataset_name][metric_name]) == info.iteration
        return True

model.fit(train_data, train_labels,
          callbacks=[MetricsCheckerCallback()],
          eval_set=[validation_0, validation_1])

and

class EarlyStopCallback:
    def __init__(self, stop_iteration):
        self._stop_iteration = stop_iteration

    def after_iteration(self, info):
        return info.iteration != self._stop_iteration

model.fit(train_data, train_labels, callbacks=[
    EarlyStopCallback(7),
    EarlyStopCallback(5),
    EarlyStopCallback(6)
])