Add `monitor()` method for monitoring model performance in production

[pplonski]

The AutoML API should be extended with monitor() method:

• the monitor() should track the model performance on new data
• it should check prediction distribution on new data and compare with the distribution from training (out of folds predictions)
• it should detect outliers in new data
• it should detect data drifts in new data

I propose to have the following arguments in monitor():

• X (new test data)
• y (new test data targets)
• y_predicted (predictions from the AutoML)

The monitor() should return a report about incidents in new data. For example, warnings list with explanations what was the problem.

[pplonski]

There will be a new method need_retrain(). It will take new data as input. It will invoke two new methods:

• is_drift() to check changes in new data
• performance_decrease() to check the performance on new data

Example pseudo-code:

def need_retrain(self, X, y):
    return self.is_drift(X, y) or self.performance_decrease(X, y)

Maybe there should be also some summary Markdown file created with reasons why the model needs to be retrained.

[pplonski]

closed by mistake

[pplonski]

OK, at the beginning I want to make this feature super sensitive to any input data changes. But in the end, I finished with a simple approach of just performance monitoring. I hope it will be enough. If there will be a change in the data then the performance of AutoML prediction will decrease.

There is a new method added:

def need_retrain(self, X, y, sample_weight=None, decrease=0.1):
        """Decides about model retraining based on new data.

        Arguments:
            X (numpy.ndarray or pandas.DataFrame):
                New data.

            y (numpy.ndarray or pandas.Series):
                True labels for X.

            sample_weight (numpy.ndarray or pandas.Series):
                Sample weights.

            decrease (float): The ratio of change in the performance used as a threshold for retraining decision.
                By default, it is set to `0.1` which means that if the performance of AutoML will decrease by 10% 
                on new data then there is a need to retrain. This value should be set depending on your project needs.
                Sometimes, 10% is enough, but for some projects, it can be even lower than 1%.

            Returns:
                boolean: Decides if there is a need to retrain the AutoML.
        """

It works as follows:

• a user calls the need_retrain() with new X and y data
• the metric score is computed on new data
• the performance of the best model is restored from its params.json file
• if there is a decrease we check if it is larger than the decrease parameter.

The change is computed as follows:

change = np.abs((old_score - new_score) / old_score)