:py:mod:`hypso.experimental.chlorophyll.estimate_chlorophyll_ml`
================================================================

.. py:module:: hypso.experimental.chlorophyll.estimate_chlorophyll_ml


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   hypso.experimental.chlorophyll.estimate_chlorophyll_ml.get_best_features
   hypso.experimental.chlorophyll.estimate_chlorophyll_ml.start_chl_estimation



.. py:function:: get_best_features(X_train_rrs, y_train_rrs, X_test_rrs, y_test_rrs, dataset_name = 'hypso')

   Compute the optimal features based on the findings of the Master thesis "Improving Ocean Chlorophyll Estimation in
   Satellite Hyperspectral Images Using Ensemble Machine Learning" by Flores-Romero Alvaro

   :param X_train_rrs: Surface reflectance values for training
   :param y_train_rrs: Surface chlorophyll values for training
   :param X_test_rrs: Surface reflectance values for testing
   :param y_test_rrs: Surface chlorophyll values for testing
   :param dataset_name: Dataset name to estimate values. Defaults to "hypso"

   :return: Returns the features for training and testing


.. py:function:: start_chl_estimation(sat_obj, model_path=None)

   Estimates the chlorophyll using a trained ML Scikit-Learn Model

   :param sat_obj: Hypso satellite object
   :param model_path: Absolute path for the pre-trained Scikit-Learn model

   :return: No return.