[minor] Allow lagged regressors with only unique values for global modeling

[judussoari]

🔬 Background

Fixes #608
Fixes #1153
Picks up from #1146
Currently, NeuralProphet does not allow a lagged regressor to only contain unique values. When global modeling, however, there are reasonable cases in which some IDs may have only unique values, while others don't.

🔮 Key changes

Instead of throwing an Error immediately, we first check whether we have unique values cross all IDs. If not, we give a warning.

📋 Review Checklist

• I have performed a self-review of my own code.
• I have commented my code, added docstrings and data types to function definitions.
• I have added pytests to check whether my feature / fix works.

[github-actions]

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	5.83975	4.34	-25.68%	🎉
YosemiteTemps	MAE_val	1.71298	1.71298	0.0%	✅
YosemiteTemps	RMSE_val	2.26069	2.2607	0.0%	✅
YosemiteTemps	Loss_val	0.00095	0.00095	0.0%	✅
YosemiteTemps	MAE	1.45187	1.45187	-0.0%	✅
YosemiteTemps	RMSE	2.16721	2.16721	0.0%	✅
YosemiteTemps	Loss	0.00066	0.00066	0.0%	✅
YosemiteTemps	time	131.187	100.26	-23.57%	🎉
PeytonManning	MAE_val	0.58159	0.58159	-0.0%	✅
PeytonManning	RMSE_val	0.72216	0.72216	0.0%	✅
PeytonManning	Loss_val	0.01239	0.01239	-0.0%	✅
PeytonManning	MAE	0.41671	0.41671	-0.0%	✅
PeytonManning	RMSE	0.55961	0.55961	0.0%	✅
PeytonManning	Loss	0.00612	0.00612	0.0%	✅
PeytonManning	time	16.0709	12.31	-23.4%	🎉

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers

[codecov-commenter]

Codecov Report

Merging #1256 (4e52aff) into main (347f783) will decrease coverage by 0.01%.
The diff coverage is 88.23%.

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@@            Coverage Diff             @@
##             main    #1256      +/-   ##
==========================================
- Coverage   89.63%   89.62%   -0.01%     
==========================================
  Files          35       35              
  Lines        4938     4953      +15     
==========================================
+ Hits         4426     4439      +13     
- Misses        512      514       +2     

Impacted Files	Coverage Δ
neuralprophet/df_utils.py	94.59% <86.66%> (-0.21%)	⬇️
neuralprophet/forecaster.py	88.48% <100.00%> (ø)

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[leoniewgnr]

LGTM!

[leoniewgnr]

Would it be good to write a test because the code covarage is below the target?