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Merged
merged 6 commits into from
Jan 8, 2023
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1.3.0 #16

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8debb13
Updated adventure works for 1.3.0
liuzicheng1987 Jul 3, 2022
a68ad76
Adapted various notebooks to 1.3.0
liuzicheng1987 Jul 4, 2022
44ee8b5
Adapted yet more notebooks to 1.3.0
liuzicheng1987 Jul 4, 2022
8b09015
Updated the air pollution notebook to 1.3.0
liuzicheng1987 Jul 16, 2022
fccaf16
Updated four more notebooks to 1.3.0
liuzicheng1987 Jul 16, 2022
23e95b5
Removed the propositionalization notebooks
liuzicheng1987 Jul 17, 2022
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27 changes: 0 additions & 27 deletions README.md
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Expand Up @@ -81,28 +81,6 @@ If you are mainly interested in how getML performs compared to other approaches,
| [SFScores: Predicting health check scores][sfscoresnb] | featuretools | R-squared (getML 29.1%, featuretools 26.5%) |
| [Stats: Predicting users' reputation][statsnb] | featuretools | R-squared (getML 98.1%, featuretools 96.6%) |

### Propositionalization

In particular, we have benchmarked getML's _FastProp_ (short for fast propositionalization) against other implementations of the propositionalization algorithm.

<p align="center" style="text-align: center;">
<img src="propositionalization/comparisons/nrpf_performance.png" />
</p>

As we can see, _FastProp_ is true to its name: It achieves similar or slightly better performance than _featuretools_ or _tsfresh_, but generates features between 11x to 65x faster than these implementations.

If you want to reproduce these results, please refer to the following notebooks:

| | Results | Remarks |
| ------------------------------------ | ------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Air pollution][airpollutionnb_prop] | ~65x faster than featuretools, ~33x faster than tsfresh | The predictive accuracy can be significantly improved by using RelMT instead of propositionalization approaches, please refer to [this notebook][airpollutionnb]. |
| [Dodgers][dodgersnb_prop] | ~42x faster than featuretools, ~75x faster than tsfresh | The predictive accuracy can be significantly improved by using the mapping preprocessor and/or more advanced feature learning algorithms, please refer to [this notebook][dodgersnb]. |
| [Interstate94][interstate94nb_prop] | ~55x faster than featuretools | |
| [Occupancy][occupancynb_prop] | ~87x faster than featuretools, ~41x faster than tsfresh | |
| [Robot][robotnb_prop] | ~162x faster than featuretools, ~77x faster than tsfresh | |

These results are very hardware-dependent and may be different on your machine. However, we have no doubt that you will find that getML's _FastProp_ is significantly faster than _featuretools_ and _tsfresh_ while consuming considerably less memory.

### Relational Dataset Repository

Some benchmarks are also featured on the [Relational Dataset Repository](https://relational.fit.cvut.cz/):
Expand Down Expand Up @@ -139,10 +117,5 @@ Some benchmarks are also featured on the [Relational Dataset Repository](https:/
[sfscoresnb]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/sfscores.ipynb
[statsnb]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/stats.ipynb

[airpollutionnb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/air_pollution_prop.ipynb
[dodgersnb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/dodgers_prop.ipynb
[interstate94nb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/interstate94_prop.ipynb
[occupancynb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/occupancy_prop.ipynb
[robotnb_prop]: https://nbviewer.getml.com/github/getml/getml-demo/blob/master/propositionalization/robot_prop.ipynb


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