SequentialFeatureSelection¶
-
class
museotoolbox.ai.
SequentialFeatureSelection
(classifier, param_grid, path_to_save_models=False, n_comp=1, verbose=False)[source]¶ Sequential Feature Selection
- Parameters
classifier (class.) – Classifier from scikit-learn.
param_grid (np.ndarray.) – param_grid for hyperparameters of the classifier.
path_to_save_models (False or str, optional (default=False)) – If False, will store best model per combination in memory. If str, must be path to save each model and accuracy per feature.
n_comp (int, optional (default=1)) – The number of component per feature. If 4, each feature has 4 columns.
verbose (bool or int, optional (default=False)) – The higher it is the more sequential will show progression.
Methods
__init__
(classifier, param_grid[, …])Initialize self.
customize_array
(xFunction, **kwargs)fit
(X, y[, group, cv, scoring, standardize, …])- param X
shape of np.ndarray is (n_size,n_bands).
get_best_model
([clone])predict
(X, idx)Predict in raster using the best features.
predict_best_combination
(in_image, out_image)Predict in raster using the best features.
predict_images
(in_image, out_image_prefix[, …])Predict each best found features with SFFS.fit(X,y).
transform
(X[, idx, customizeX])- param idx
The idx to return X array