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init: m5 forecasting FE benchmark #136

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MarcoGorelli
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@MarcoGorelli MarcoGorelli commented Sep 4, 2024 •
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@MarcoGorelli MarcoGorelli mentioned this pull request Sep 4, 2024
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ritchie46
ritchie46 reviewed Sep 14, 2024
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m5-forecasting-feature-engineering/polars_queries.py Outdated


def q2_polars(df):
return df.with_columns(
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Can we use the select + explode mapping here?

ritchie46
ritchie46 reviewed Sep 14, 2024
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m5-forecasting-feature-engineering/README.md Outdated
Participants typically used pandas (Polars was only just getting started at the time), so here we benchmark how long it have
taken to do the same feature engineering with Polars (and, coming soon, DuckDB).

We believe this to be a useful task to benchmark, because:
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I think we can remove L9-L12.

I think this can serve as a basis for more time-series related benchmarks on this datasets. I don't think we have to strictly limit to what was used in the kaggle competition.

@MarcoGorelli MarcoGorelli marked this pull request as draft September 14, 2024 11:15
@MarcoGorelli MarcoGorelli marked this pull request as ready for review November 10, 2024 14:34
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Just got back to this - running locally, I'm seeing very good results for Polars:

*** polars lazy ***
q1 took: 20.075744923997263
q2 took: 24.77264992900018
q3 took: 28.980969234995428
(polars-benchmark) marcogorelli@DESKTOP-U8OKFP3:~/polars-benchmark/m5-forecasting-feature-engineering$ python duckdb_queries.py 
*** duckdb ***
q1 took: 176.87156045399752
q2 took: 101.69693301500229
q3 took: 115.6844151769983

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