[models] Add LightGBM classification pipeline

[jsakv]

Motivation

So far we have only implemented two training pipelines (XGBoost and Random Forest), but I think we should explore different algorithms and modeling strategies!

🚀 Feature

✨ LightGBM pipeline ✨

Design Proposal

In order to comply with the pyro_risks project the LightGBM scoring pipeline should:

• Comply with the scikit-learn API (model step → LGBMClassifier)
• Defined as an imbalanced-learn Pipeline
• Defined with the hyperparameters set in the config/models.py

keeping up with updates mentioned in the issue #46 would require to:

• [ ] Implement a resampling steps.
• [ ] Select optimal hyperparameters
• [ ] Account for production constraints. As discussed in the issue #31 , the dataset features are only available on day D+5, so we would need to shift the target by 5 days.
• [ ] (Optional) Add new preprocessing steps

Additional context

• The dataset used for modeling is available with MergedEraFwiViirs() the documentation of the different features is available here https://pyronear.org/pyro-risks/
• It's better to split the dataset using the TEST_SIZE and RANDOM_STATE values in the config module:
• This article demonstrates how to set up a pipeline with imbalanced-learn SMOTE for Imbalanced Classification with Python
• For avoiding skewing the training datasets (less accurate imputation of missing values) is better to define pipelines steps in the following order: preprocessing >> upsampling >> modeling

• Hyperparameter tuning can be long, use the script bellow for caching pipeline steps during the search and speeding up hyperparameter tuning

  from joblib import Memory
  from imblearn.pipeline import Pipeline
  
  location = './cachedir'
  memory = Memory(location, verbose=0)
  
  pipe = Pipeline([('step', Step)],
                  memory=memory)

[Acruve15]

@dataJSA is this still needed? If it is, I can work on it :)