[refactor] Extract holiday getter helper to single function

[noxan]

🔬 Background

• Repetitive code to get holidays

🔮 Key changes

• Merged into a single function
• Added some typing annotations

[github-actions]

0c9d751

Model Benchmark

Benchmark	Metric	main	current	diff
AirPassengers	MAE_val	15.2698	15.2698	0.0%	✅
AirPassengers	RMSE_val	19.4209	19.4209	0.0%	✅
AirPassengers	Loss_val	0.00195	0.00195	0.0%	✅
AirPassengers	MAE	9.82902	9.82902	0.0%	✅
AirPassengers	RMSE	11.7005	11.7005	0.0%	✅
AirPassengers	Loss	0.00056	0.00056	0.0%	✅
AirPassengers	time	4.23059	4.38	3.53%	⚠️
PeytonManning	MAE_val	0.64636	0.64636	0.0%	✅
PeytonManning	RMSE_val	0.79276	0.79276	0.0%	✅
PeytonManning	Loss_val	0.01494	0.01494	0.0%	✅
PeytonManning	MAE	0.42701	0.42701	0.0%	✅
PeytonManning	RMSE	0.57032	0.57032	0.0%	✅
PeytonManning	Loss	0.00635	0.00635	0.0%	✅
PeytonManning	time	11.6376	12.15	4.4%	⚠️
YosemiteTemps	MAE_val	1.72949	1.72949	0.0%	✅
YosemiteTemps	RMSE_val	2.27386	2.27386	0.0%	✅
YosemiteTemps	Loss_val	0.00096	0.00096	0.0%	✅
YosemiteTemps	MAE	1.45189	1.45189	0.0%	✅
YosemiteTemps	RMSE	2.16631	2.16631	0.0%	✅
YosemiteTemps	Loss	0.00066	0.00066	0.0%	✅
YosemiteTemps	time	91.3679	99.55	8.96%	❌

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers

[codecov-commenter]

Codecov Report

Merging #1072 (2947c91) into main (39a6a86) will increase coverage by 0.03%.
The diff coverage is 89.47%.

❗ Current head 2947c91 differs from pull request most recent head 20d1bdb. Consider uploading reports for the commit 20d1bdb to get more accurate results

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@@            Coverage Diff             @@
##             main    #1072      +/-   ##
==========================================
+ Coverage   90.34%   90.37%   +0.03%     
==========================================
  Files          21       22       +1     
  Lines        4857     4854       -3     
==========================================
- Hits         4388     4387       -1     
+ Misses        469      467       -2     

Impacted Files	Coverage Δ
neuralprophet/hdays_utils.py	83.33% <83.33%> (ø)
neuralprophet/time_dataset.py	95.27% <100.00%> (+0.57%)	⬆️
neuralprophet/utils.py	81.48% <100.00%> (+0.21%)	⬆️

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[karl-richter]

@noxan what is the status of this PR? It is ready for review?

[karl-richter]

LGTM

[github-actions]

Model Benchmark

Benchmark	Metric	main	current	diff
PeytonManning	MAE_val	0.64636	0.64636	0.0%	✅
PeytonManning	RMSE_val	0.79276	0.79276	0.0%	✅
PeytonManning	Loss_val	0.01494	0.01494	0.0%	✅
PeytonManning	MAE	0.42701	0.42701	0.0%	✅
PeytonManning	RMSE	0.57032	0.57032	0.0%	✅
PeytonManning	Loss	0.00635	0.00635	0.0%	✅
PeytonManning	time	14.8782	11.42	-23.24%	🎉
YosemiteTemps	MAE_val	1.72948	1.72949	0.0%	✅
YosemiteTemps	RMSE_val	2.27386	2.27386	0.0%	✅
YosemiteTemps	Loss_val	0.00096	0.00096	0.0%	✅
YosemiteTemps	MAE	1.45189	1.45189	0.0%	✅
YosemiteTemps	RMSE	2.16631	2.16631	0.0%	✅
YosemiteTemps	Loss	0.00066	0.00066	0.0%	✅
YosemiteTemps	time	114.287	91.49	-19.95%	🎉
AirPassengers	MAE_val	15.2698	15.2698	0.0%	✅
AirPassengers	RMSE_val	19.4209	19.4209	0.0%	✅
AirPassengers	Loss_val	0.00195	0.00195	0.0%	✅
AirPassengers	MAE	9.82902	9.82902	0.0%	✅
AirPassengers	RMSE	11.7005	11.7005	0.0%	✅
AirPassengers	Loss	0.00056	0.00056	0.0%	✅
AirPassengers	time	5.14935	4.08	-20.77%	🎉

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers