watex.utils.predict_NGA_labels#

watex.utils.predict_NGA_labels(X, /, n_clusters, random_state=0, keep_label_0=False, return_cluster_centers=False, **kws)[source]#

Predict the Naive Group of Aquifer (NGA) labels.

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.

  • n_clusters (int, default=8) – The number of clusters to form as well as the number of centroids to generate.

  • random_state (int, RandomState instance or None, default=42) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic.

  • keep_label_0 (bool, default=False) – The prediction already include the label 0. However, including 0 in the predicted label refers to ‘k=0’ i.e. no permeability coefficient equals to 0, which is not True in principle, because all rocks have a permeability coefficient ‘k’. Here we considered ‘k=0’ as an undefined permeability coefficient. Therefore, ‘0’ , can be exclude since, it can also considered as a missing ‘k’-value. If predicted ‘0’ is in the target it should mean a missing ‘k’-value rather than being a concrete label. Therefore, to avoid any confusion, ‘0’ is altered to ‘1’ so the value +1 is used to move forward all class labels thereby excluding the ‘0’ label. To force include 0 in the label, set keep_label_0 to True.

  • return_cluster_centers (bool, default=False,) – export the array of clusters centers if True.

  • kws (dict,) – Additional keyword arguments passed to sklearn.clusters.KMeans.

Returns:

  • NGA (array_like of shape (n_samples, n_features)) – Predicted NGA labels.

  • ( NGA , cluster_centers) (Tuple of array-like,) – MGA and clusters centers if return_cluster_centers` is set to ``True.