Description

Add the ability to handle multi-dimensional thresholds (lambdas)

• 'predict_function` can now also be a general function (X *params) -> 0/1
• predict_params is now an argument (even when only one-dimensional lambda) and docstring should be clearer
• best_predict_param is a tuple for multi-dimensional parameters
• added an automatic flag is_multi_dimensional_param (based on predict_params dimension)
• _get_predictions_per_param handles general predict_functions, but will process all parameter values sequentially (i don't know how to do it easily in parallel).
• get_predictions_per_param will check the prediction values in the calibration step (using a new argument is_calibration_step, because I don't want to check at test time as it is not necessary and it might happen that when predicting a single probability, which can happen at test time, a value of 0 or 1 and would raise a warning)
• predictions are checked so that for one-dimensional parameters, predictions should not be 0 or 1 all the time, and for multi-dimensional parameters, they should be 0 or 1 all the time.
• I don't think it's necessary to add a custom error message when the dimension of the parameters do not match the inputs of the general predict function, as the default message seems explicit (cf test_error_multi_dim_params_dim_mismatch)

To manage the two types of predict functions in __init__ there are a few options:

1. Manual flag in the arguments of BinaryClassificationController
2. auto detection based on the function signature (does it takes one or more arguments) but it can cause issues when the predict_proba can also take several arguments (e.g. XGBoost)
3. add another argument e.g., predict_function_general and the user has to provide at least this or the original predict_function.
4. Automatic check of predict_params.shape[1] > 1: the user has to provide a custom array of predict_params to do multi params

I will go with option 4.