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Cleaning up models #1265

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Merged
merged 5 commits into from
May 22, 2021
Merged

Cleaning up models #1265

merged 5 commits into from
May 22, 2021

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ngupta23
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Describe the changes you've made

  • Added seasonal naive model
  • Cleaned up models by creating property methods instead of defining everything in init

Type of change

  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Code style update (formatting, local variables)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • This change requires a documentation update

How Has This Been Tested?

Unit tests are passing locally

Checklist:

  • My code follows the style guidelines of this project.
  • I have performed a self-review of my own code.
  • I have commented my code, particularly in hard-to-understand areas.
  • I have made corresponding changes to the documentation.
  • My changes generate no new warnings.
  • I have added tests that prove my fix is effective or that my feature works.
  • New and existing unit tests pass locally with my changes.
  • Any dependent changes have been merged and published in downstream modules.

@ngupta23 ngupta23 marked this pull request as ready for review May 22, 2021 03:34
@ngupta23 ngupta23 requested review from Yard1 and TremaMiguel May 22, 2021 03:34
Yard1
Yard1 reviewed May 22, 2021
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pycaret/containers/models/time_series.py
return tune_grid

@property
def _set_tune_distributions(self) -> Dict[str, List[Any]]:
tune_distributions = {
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Question - why are LGBM tune distributions different than the ones in the regression container? I think it would be good to use the same ones - for other models as well. Also, regressor__subsample, regressor__colsample_bytree and regressor__min_split_gain fail because UniformDistribution is inclusive by default and 0 is not a valid value for them.

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LGBM is mostly the same - right? Which parameters are different?

In some cases, I have used a smaller subset for the fixed (exhaustive) grid since there are too many combinations in regression case and using the same slows down the execution.

Also, why does the UniformDistribution not give an error for regression case? It is also using the value inclusive of 0.

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Ok, the commented parameters threw me off. Aside from those the ranges are indeed the same. The parameters that cause the 0-error aren't present at all in regression, though.

@Yard1 Yard1 mentioned this pull request May 22, 2021
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@ngupta23 ngupta23 merged commit 6e04d5f into time_series May 22, 2021
@ngupta23 ngupta23 deleted the ts_clean_models branch May 22, 2021 23:22
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@Yard1 Yard1 Yard1 approved these changes

@TremaMiguel TremaMiguel Awaiting requested review from TremaMiguel

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@ngupta23 @Yard1