Change plotting_backend deprecation warning for implicit and explicit matplotlib use

[LeonieFreisinger]

referring Issue: #998

Key Changes:

• outsourced warning message in plot_utils.py
• implement warning message to be shown for matplotlib on every plot function that the user can call.

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.
• My code follows the style guidelines described under CONTRIBUTING.md.
• I have added pytests to check whether my feature / fix works
• All gh actions pass or the failure is not related to my code.

[codecov-commenter]

Codecov Report

Merging #1006 (9592225) into main (5290746) will increase coverage by 0.03%.
The diff coverage is 88.88%.

@@            Coverage Diff             @@
##             main    #1006      +/-   ##
==========================================
+ Coverage   90.17%   90.21%   +0.03%     
==========================================
  Files          20       20              
  Lines        4734     4740       +6     
==========================================
+ Hits         4269     4276       +7     
+ Misses        465      464       -1     

Impacted Files	Coverage Δ
neuralprophet/forecaster.py	88.45% <83.33%> (+0.15%)	⬆️
neuralprophet/plot_utils.py	89.91% <100.00%> (+0.13%)	⬆️

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[LeonieFreisinger]

@noxan changed the warning to be displayed for any matplotlib plotting. Would be great if you can review it. P.S. I allocated the warning message in the plot_utils.py, since it is used multiple times. For having an overview of warning messages, I think it would be generally nice to have them all in one place, e.g. in a utils.py. However, it might as well be a little overkill if some cases have a warning message that is only used once. What do you think?

[noxan]

Awesome, so little code - love how efficiently you tackled the issue 👏

We might need to resolve the merge conflict, if you find time would be awesome, otherwise I'll check tomorrow.

[github-actions]

af98e9d

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	106.89	102.16	-4.43%	✅
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	12.65	12.59	-0.47%	✅
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	4.97	4.8	-3.42%	✅

Model training plots

Model Training

PeytonManning

YosemiteTemps

AirPassengers