10. Configuring julearn

While julearn is meant to be a user-friendly tool, this also comes with a cost. For example, in order to provide the user with information as well as to be able to detect potential errors, we have implemented several checks. These checks, however, might yield high computational costs. Therefore, we have implemented a global configuration module in julearn that allows to set flags to enable or disable certain extra functionality. This module is called julearn.config and it has a single function called set_config that given a configuration flag name and a value, it sets the flag to the given value.

Here you can find the comprehensive list of flags that can be set:

Flag	Description	Potential problem(s)
disable_x_check	Disable checking for unmatched column names in X. If set to True, any element in X that is not present in the dataframe will not result in an error.	The user might think that a certain feature is used in the model when it is not.
disable_xtypes_check	Disable checking for missing/present X_types in the X parameter of the run_cross_validation() method. If set to True, the X_types parameter will not be checked for consistency with the X parameter, including undefined columns in X, missing types in X_types or duplicated columns in X_types.	The user might think that a certain feature is considered in the model when it is not.
disable_x_verbose	Disable printing the list of expanded column names in X. If set to True, the list of column names will not be printed.	The user will not see the expanded column names in X.
disable_xtypes_verbose	Disable printing the list of expanded column names in X_types. If set to True, the list of types of X will not be printed.	The user will not see the expanded X_types column names.
enable_parallel_column_transformers	This flag enables parallel execution of column transformers by reverting to the default behaviour of scikit-learn (instead of using n_jobs=1) If set to True, the parameter will be set back to None.	Column transformers will be applied in parallel, using more resources. than expected.