unit8co/darts

Unified API : `model.params` attribute

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#192 opened on Sep 28, 2020

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good first issueimprovement

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Description

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
    • ExponentialSmoothing
    • FFT
    • Prophet
    • StandardRegressionModel
    • TCNModel
    • Theta
    • TorchForecastingModel (RNNModel)
  • Preprocessing
    • Scaler wrapper

Contributor guide

Research direction
Examine the ForecastingModel abstract class and implement a params property that returns model parameters. Start with implementing for models that don't yet expose params, such as ARIMA, AutoARIMA, and baseline models, following the pattern already established in ExponentialSmoothing and Prophet. Look at the `params` method in those models for guidance.
Tech stack
python
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
80