Improving performance of dynamic characterization #57
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The current implementation of the dynamic characterization does not scale very well. The main issue is the repeated creation of pd.Series at several points when characterizing of each row of the inventory df.
Instead of handling pd.Series and DataFrames all the time, I switched it to namedtuple. In an example case, this reduced computing time from ~30s to <1s.
I've also noticed that the test for dynamic characterization seem to be incomplete, but some preparation has been done. I can try do add this, so I'll keep this PR as a draft for now.