Unified API : `model.params` attribute

[norihitoishida]

Hello! Firstly, I wanted to thank you for this wonderful library.

Unified API to get model's params might make darts more convenient. For example, to get params of Exponential Smoothing model, We have to code like this:

model = ExponentialSmoothing()
model.fit(train)

print(model.model.model.params) # {'smoothing_level': 0.5789473661331209, 'smoothing_slope': ...

So, I want to implement model.get_params() method like sklearn. model.params attribute with property like statsmodels and fbprophet.

I would like to get your opinion before send PR. Thanks!

• [ ] Models
  • [ ] abstract params() function in the ForecastingModel superclass
  • [ ] ARIMA
  • [ ] AutoARIMA
  • [ ] Baseline Models
    • [ ] NaiveDrift
    • [ ] NaiveMean
    • [ ] NaiveSeasonal
  • [x] ExponentialSmoothing
  • [ ] FFT
  • [x] Prophet
  • [ ] StandardRegressionModel
  • [ ] TCNModel
  • [ ] Theta
  • [ ] TorchForecastingModel (RNNModel)
• [ ] Preprocessing
  • [ ] Scaler wrapper

[LeoTafti]

Hello @norihitoishida, thanks for your suggestion and offering to help! I think that would be a nice addition to our library.

Make sure to check out our guidelines first and to include a couple unit tests. Thanks!

[norihitoishida]

Thanks @LeoTafti !

[hrzn]

+1 for that, it's a good idea

[norihitoishida]

Related to #195 , Unittest fails in my development environment. So I will prioritize resolving #195 . This shouldn't be a problem on the develop branch.

[norihitoishida]

I found it more intuitive to use the model.params attribute instead of the model.get_params() method (following the fbprophet and statsmodels).

[norihitoishida]

Sorry, due to my personal health issues, I have found that it will take some time to complete this task. Let me close this once.

[hrzn]

Take care @norihitoishida

[norihitoishida]

Thank you for your kindness : )