Computation times#
18:39.503 total execution time for 19 files from all galleries:
Example |
Time |
Mem (MB) |
|---|---|---|
Quick overview of DataOps ( |
03:08.406 |
612.0 |
AggJoiner on a credit fraud dataset ( |
03:03.792 |
613.3 |
Various string encoders: a sentiment analysis example ( |
02:44.607 |
1307.6 |
Multiples tables: building machine learning pipelines with DataOps ( |
02:43.078 |
859.7 |
Tuning DataOps with Optuna ( |
01:28.763 |
820.3 |
Spatial join for flight data: Joining across multiple columns ( |
00:44.886 |
2985.5 |
Hyperparameter tuning with DataOps ( |
00:42.164 |
609.1 |
Encoding: from a dataframe to a numerical matrix for machine learning ( |
00:41.675 |
610.7 |
Interpolation join: infer missing rows when joining two tables ( |
00:41.264 |
2367.7 |
Fuzzy joining dirty tables with the Joiner ( |
00:35.584 |
621.9 |
Using PyTorch (via skorch) in DataOps ( |
00:24.729 |
621.7 |
SquashingScaler: Robust numerical preprocessing for neural networks ( |
00:21.306 |
611.5 |
Subsampling for faster development ( |
00:18.973 |
637.4 |
Getting Started with skrub ( |
00:17.920 |
608.8 |
Hands-On with Column Selection and Transformers ( |
00:10.198 |
608.7 |
Handling datetime features with the DatetimeEncoder ( |
00:10.011 |
616.6 |
Use case: developing locally and deploying to production ( |
00:09.919 |
737.2 |
Sessions in time-based data: Predicting user purchases with the SessionEncoder ( |
00:07.788 |
609.7 |
Deduplicating misspelled categories ( |
00:04.439 |
609.0 |