Explore Gradient Boosting Regressor

[SarahAlidoost]

We want to explore Gradient Boosting Regressor for example, Gradient Boosting, Extreme Gradient Boosting, and Light Gradient Boosting Machine on hybrid labs data. For that, a python module needed to be called to fit, and predict. The module should:

• similar to pycaret to compare different models, see examples
• have a notebook in docs folder to show a simple example
• have tests
• have an easy interface and run fast (to be discussed)

Data:
The data folder exp699_032024_TUDelft is available on Atlas SharePoint at Documents > HybridLabs > Example_data. NOTE: The data folder exp699_032024_TUDelft is not public and cannot be shared with others. The trained model on this data cannot be also shared. For now, no need to store the trained model.

The data folder exp699_032024_TUDelft includes:

• readme.md: it contains the experiment details and ML input/output
• channel.csv: it contains the variable names and units
• exp699.mat: it contains the data and it is in matlab format.

For reading data in python, see this notebook.

Literature:

Some literature is available at on Atlas SharePoint at Documents > HybridLabs > Literature. The two most related to FOWT are:

• A hybrid methodology for wind tunnel testing of floating offshore wind turbines
• A review of modelling techniques for floating offshore wind turbines

[SarahAlidoost]

Gradient Boosting has an example about multioutput regression see https://xgboost.readthedocs.io/en/latest/python/examples/multioutput_regression.html#a-demo-for-multi-output-regression and https://xgboost.readthedocs.io/en/latest/tutorials/multioutput.html.