🐛 fix bug with bound_method + ✨ new integrations #62
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| @@ -367,7 +367,7 @@ def calculate( | |||
| # determing the bounds of the series dict items and slice on them | |||
| start, end = _determine_bounds(bound_method, list(series_dict.values())) | |||
| series_dict = { | |||
| n: s[s.index.dtype.type(start) : s.index.dtype.type(end)] | |||
| n: s.loc[s.index.dtype.type(start) : s.index.dtype.type(end)] # TODO: check memory efficiency of ths | |||
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Check memory efficiency of this
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Runtime is the same as previous implementation ✔️
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Memory profiling indicates no memory peak ✔️
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1 tests will fail (as we need to agree what indexing is preferred). |
jvdd
changed the title
Codecov Report
@@ Coverage Diff @@
## main #62 +/- ##
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Coverage 97.73% 97.73%
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Files 23 23
Lines 1103 1106 +3
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+ Hits 1078 1081 +3
Misses 25 25
Continue to review full report at Codecov.
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@jvdd, might also be interesting too look into:
Thanks to @jellevhb |
| @@ -21,6 +21,7 @@ tsflex is a domain independent package for time series processing & feature extr | |||
| | Climate modelling | [Ozone level detection](https://archive.ics.uci.edu/ml/datasets/Ozone%20Level%20Detection) | [example_ozone_level_detection.ipynb](https://github.com/predict-idlab/tsflex/blob/main/examples/example_ozone_level_detection.ipynb) | | |||
| | Household data | [Electric power consumption](https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption) | [example_power_consumption_estimation.ipynb](example_power_consumption_estimation.ipynb) | | |||
| | Clinical data | [Sleep-EDF Database Expanded](https://physionet.org/content/sleep-edfx/1.0.0/) | [example_sleep_staging.ipynb](example_sleep_staging.ipynb) | | |||
| | kaggle competition | [Tabular Playground Series - Apr 2022](https://www.kaggle.com/competitions/tabular-playground-series-apr-2022)| https://www.kaggle.com/code/jeroenvdd/tpsapr22-best-non-dl-model-tsflex-powershap | | |||
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TODO: maybe state here that the data was already segmented -> so here you can find an example on how to use tsflex on already segmented data?
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Hmm not very proud about how we did it (considering the long table as one large series and having a stride that is equal to your sample size)
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okay, will create an issue or extend an existing one with this topic
This PR handles
(1) 🐛 a bug in the
bound_method+ sequence based strided rolling.locinduces memory peakwindow_idx="begin"default instead of"end"end-index of the segments.(2) ✨ extends integration with other feature extraction packages
catch22integration wrapperscikit-learn transformerwrapper(3): ⚡ faster irregular data check
(4): 🔥 add kaggle TPSAPR2022 notebook to ML examples