holisticai.bias.metrics.confusion_tensor#

holisticai.bias.metrics.confusion_tensor(p_attr, y_pred, y_true, groups=None, classes=None, as_tensor=False)[source]#

Confusion Tensor

This function computes the confusion tensor. The k, i, jth entry is the number of instances of group k with predicted class i and true class j.

Parameters

p_attrarray-like: Protected attribute vector (categorical)
y_predarray-like: Prediction vector (categorical)
y_truearray-like: Target vector (categorical)
groupslist, optional: Unique groups from p_attr in order
classeslist, optional: The unique output classes in order
as_tensorbool, optional: Whether we return a tensor or DataFrame, default False

Returns

numpy ndarray: Confusion Tensor : shape (num_groups, num_classes, num_classes)

Examples

>>> import numpy as np
>>> from holisticai.bias.metrics import confusion_tensor
>>> p_attr = np.array(["A", "A", "A", "A", "B", "B", "B", "B", "C", "C"])
>>> y_pred = np.array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0])
>>> y_true = np.array([0, 1, 1, 0, 1, 0, 2, 1, 2, 1])
>>> confusion_tensor(p_attr, y_pred, y_true, as_tensor=True).shape
(3, 3, 3)
>>> confusion_tensor(p_attr, y_pred, y_true, as_tensor=True)
array([[[2., 0., 0.],
        [0., 1., 0.],
        [0., 1., 0.]],

[[0., 0., 1.],
[0., 2., 0.], [1., 0., 0.]],

[[0., 1., 0.],
[0., 0., 0.], [0., 0., 1.]]])

>>> confusion_tensor(p_attr, y_pred, y_true, as_tensor=False)
    A               B               C
    0       1       2       0       1       2       0       1       2
0   2.0     0.0     0.0     0.0     0.0     1.0     0.0     1.0     0.0
1   0.0     1.0     0.0     0.0     2.0     0.0     0.0     0.0     0.0
2   0.0     1.0     0.0     1.0     0.0     0.0     0.0     0.0     1.0