If we want to use new metadata like it's explained in tutorials, we may need to modify the dummifier object.
If we have metadata with a type list, we need to modify the code of the Dummifier to be able to use our new metadata, like it's done in the list of attachment type.
For example, this is the actual code of the fit() method from the Dummifier:
In our case, I wanted to add the name of receivers in metadata used for the training of our model.
To do it, I needed to create the case, in the Dummifier object, where we have a column receivers in df_emails.
It was not very clean, so I made modifications on the Dummifier object to make it more flexible.
In the PR that I will link, we have a new parameter for the Dummifier object, which is a list of the columns with a type list.
Then, we apply the same functions on these columns.
Hi !
If we want to use new metadata like it's explained in tutorials, we may need to modify the dummifier object.
If we have metadata with a type list, we need to modify the code of the Dummifier to be able to use our new metadata, like it's done in the list of attachment type. For example, this is the actual code of the fit() method from the Dummifier:
In our case, I wanted to add the name of receivers in metadata used for the training of our model. To do it, I needed to create the case, in the Dummifier object, where we have a column receivers in df_emails. It was not very clean, so I made modifications on the Dummifier object to make it more flexible.
In the PR that I will link, we have a new parameter for the Dummifier object, which is a list of the columns with a type list. Then, we apply the same functions on these columns.
I hope I was clear.
Best regards,
Maxime
Python version : 3.9.7
Melusine version : 2.3.4
Operating System : Windows