[typing] Resolve type annotation issues in configure file

[noxan]

Resolve type annotation issues in configure file and ignore epochs None type warning due to almost impossible to fix with dataclasses - see [source]

[codecov-commenter]

Codecov Report

Merging #987 (7884858) into main (f9a1fb6) will increase coverage by 0.10%.
The diff coverage is 87.50%.

@@            Coverage Diff             @@
##             main     #987      +/-   ##
==========================================
+ Coverage   89.72%   89.82%   +0.10%     
==========================================
  Files          18       18              
  Lines        4611     4631      +20     
==========================================
+ Hits         4137     4160      +23     
+ Misses        474      471       -3     

Impacted Files	Coverage Δ
neuralprophet/configure.py	90.15% <85.71%> (+0.82%)	⬆️
neuralprophet/forecaster.py	88.01% <100.00%> (ø)
neuralprophet/hdays.py	99.72% <0.00%> (+<0.01%)	⬆️
neuralprophet/df_utils.py	94.72% <0.00%> (+<0.01%)	⬆️
neuralprophet/time_net.py	90.01% <0.00%> (+0.07%)	⬆️
neuralprophet/logger.py	100.00% <0.00%> (+4.34%)	⬆️
neuralprophet/np_types.py	100.00% <0.00%> (+8.33%)	⬆️

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[github-actions]

d347096

Model Benchmark

Benchmark	Metric	main	current	diff
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	RegLoss_val	0	0	0.0%	✅
YosemiteTemps	epoch	84	84	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	RegLoss	0	0	0.0%	✅
YosemiteTemps	time	101.45	93.44	-7.9%	✅
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	RegLoss_val	0	0	0.0%	✅
PeytonManning	epoch	37	37	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	RegLoss	0	0	0.0%	✅
PeytonManning	time	12.4	11.72	-5.48%	✅
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	RegLoss_val	0	0	0.0%	✅
AirPassengers	epoch	89	89	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	RegLoss	0	0	0.0%	✅
AirPassengers	time	4.85	4.51	-7.01%	✅

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers

[karl-richter]

Can you check the comments in configure.py? Otherwise lgtm

[karl-richter]

lgtm