"normalizing error (works backwards in workflow??)"

New Altair Community Member
Updated by Jocelyn
Hello,
I have a workflow which starts with a
1. Excel file reader
2. then selects attributes
3. then send the original data to a CSV writer
4. and the selected attributes to a normalizer for further processing.
If I now run the workflow and have a look to the written csv file, all "real" columns are normalized despite the normalizer is applied after sending the data to the CSV writer. This is really strange.
So - what can I do to store the original data?
I have a workflow which starts with a
1. Excel file reader
2. then selects attributes
3. then send the original data to a CSV writer
4. and the selected attributes to a normalizer for further processing.
If I now run the workflow and have a look to the written csv file, all "real" columns are normalized despite the normalizer is applied after sending the data to the CSV writer. This is really strange.
So - what can I do to store the original data?
Sort by:
1 - 3 of
31
Thank You, this might work, but isn't a solution to my original problem.
Nevertheless, I'm glad (not really) that it is a bug ant not my own incompetence
I've got data with different (more than one) id-columns that I want to pass through the workflow.
If I don't care, RapidMiner selects one column to be the only id. The selected column unfortunately
isn't unique. Therefore I removed all non-necessary (non-unique) id columns prior to the actual workflow
but want to add these again at the end of the workflow, before I write all to the csv-file. To be able to
understand the result of the workflow I also wanted to write the non-normalized columns - which didn't
work. That's why I need to write the csv at the end of the workflow.
Meanwhile I found that I can join all data with the ori-output of the normalizer. This
seems to be a better workaround.
By the way: I wonder why there is no de-normalizer node to improve the readability of
the output.
Nevertheless, I'm glad (not really) that it is a bug ant not my own incompetence

I've got data with different (more than one) id-columns that I want to pass through the workflow.
If I don't care, RapidMiner selects one column to be the only id. The selected column unfortunately
isn't unique. Therefore I removed all non-necessary (non-unique) id columns prior to the actual workflow
but want to add these again at the end of the workflow, before I write all to the csv-file. To be able to
understand the result of the workflow I also wanted to write the non-normalized columns - which didn't
work. That's why I need to write the csv at the end of the workflow.
Meanwhile I found that I can join all data with the ori-output of the normalizer. This
seems to be a better workaround.
By the way: I wonder why there is no de-normalizer node to improve the readability of
the output.
the normalization "branch" of your process is done before your write csv operator starts working. I suggest the following quick fix: That way, your csv gets created before anything else, and then your data is modified.
Regards,
Marco