Feature/Check input for NaNs when available_mask = 1 #894
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
|
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
|
|
VascoSch92
reviewed
Feb 18, 2024
VascoSch92
reviewed
Feb 19, 2024
|
@jmoralez Appreciate your review on this. Thanks. |
jmoralez
reviewed
Feb 20, 2024
jmoralez
reviewed
Feb 20, 2024
|
@quest-bot loot #890 |
|
👋 Hey @jmoralez, you can't loot on a PR you didn't create. If you think this is a mistake, contact us at help@quine.sh |
|
@quest-bot loot #890 |
of code logic review: nbs_dev
Review: .py file
|
@jmoralez |
jmoralez
approved these changes
Feb 22, 2024
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Outdated suggestions cannot be applied.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.
This addresses #890
Main changes
_check_nan()function to validate the input whereby for row records withavailable_mask=1, we shall raise ValueError if there is NaN value.Rationale
check_nan()is called as part of_prepare_fit(). One main reason we perform the validation check before the execution ofTimeSeriesDataset.from_df()is that within thefrom_df(), it shall useutilsforecast.process_df->to_numpy()which shall replace NaN values of categorical variable type as -1.0. Hence, we cannot perform validation based on the dataset returned byTimeSeriesDataset.from_df()to flag the NaN values in categorical features.available_mask=1is automatically assigned as part ofTimeSeriesDataset.from_df(). incheck_nan(), we follow the same practice and assignavailable_mask=1if users do not provideavailable_maskas one of their input columns.