Logs the result into a run (MLFlow)

Description

This operator is the central operator to store several results into a run which was created before. The operator allows you to store: The model, parameters of the model, performance as well as processes associated to the run in the run.

The user has to provide a connection object to the mlflow server on the con port as well as a RunIOObject to the run port. A RunIOObject can be created with the Create Run operator, which creates a new run or with the Get Run operator which can retrieve an previously created run.

The user can provide a performance vector, a model and the parameters of the model as a ParameterSet object to the operator. The parameters and performances are stored in the run object as parameters and metrics. The model is serialized into its json representation and put into the artifact store on top level.

If log_repository is set to true, the operator is also storing the processes creating the results. If manually_define_process folder is not set to true it will log all the processes which are on the same level as the current process and will go recursively go down. You can change the root folder for this behaviour using the process_folder setting which will appear if you set manually_define_process_folder to true.

If log_ioobjects is set to true also ioobjects will be stored. This is sometimes necessary, because you need to include preprocessing models like normalization, but can cause big memory consumption on the mlflow server.