watex.methods.hydro.AqGroup.findGroups

AqGroup.findGroups(method='naive', default_arr=None, **g_kws)

Find the existing group between the permeability coefficient k and the group of aquifer.

It computes the occurence between the true labels and the group of aquifer as a function of occurence and repesentativity.

Parameters:

• 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.

• method (str ['naive', 'strict'], default='naive') –

  The kind of strategy to compute the representativity of a label in the predicted array ‘y_pred’. It can also be ‘strict’. Indeed:

  • naive computes the importance of the label by the number of its

    occurence for this specific label in the array ‘y_true’. It does not take into account of the occurence of other existing labels. This is usefull for unbalanced class labels in y_true.

  • strict computes the importance of the label by the number of

    occurence in the whole valid y_true i.e. under the total of occurence of all the labels that exist in the whole ‘arra_aq’. This can give a suitable anaylse results if the data is not unbalanced for each labels in y_pred.

Returns:

g – Use attribute .groups to find the group values.

Return type:

_Group: _Group class object