hypso.experimental.chlorophyll.estimate_chlorophyll_ml

Module Contents

Functions

get_best_features(X_train_rrs, y_train_rrs, ...[, ...])

Compute the optimal features based on the findings of the Master thesis "Improving Ocean Chlorophyll Estimation in

start_chl_estimation(sat_obj[, model_path])

Estimates the chlorophyll using a trained ML Scikit-Learn Model
hypso.experimental.chlorophyll.estimate_chlorophyll_ml.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

Parameters:

  • X_train_rrs (hypso.experimental.chlorophyll.indices.np.ndarray) – Surface reflectance values for training

  • y_train_rrs (hypso.experimental.chlorophyll.indices.np.ndarray) – Surface chlorophyll values for training

  • X_test_rrs (hypso.experimental.chlorophyll.indices.np.ndarray) – Surface reflectance values for testing

  • y_test_rrs (hypso.experimental.chlorophyll.indices.np.ndarray) – Surface chlorophyll values for testing

  • dataset_name (str) – Dataset name to estimate values. Defaults to “hypso”

Returns:

Returns the features for training and testing

Return type:

Tuple[pandas.DataFrame, pandas.DataFrame]

hypso.experimental.chlorophyll.estimate_chlorophyll_ml.start_chl_estimation(sat_obj, model_path=None)

Estimates the chlorophyll using a trained ML Scikit-Learn Model

Parameters:

  • sat_obj – Hypso satellite object

  • model_path – Absolute path for the pre-trained Scikit-Learn model

Returns:

No return.

Return type:

None