dataeval.metrics.bias.balance#

dataeval.metrics.bias.balance(metadata, num_neighbors=5)#

Mutual information (MI) between factors (class label, metadata, label/image properties)

Parameters:

metadata (Metadata) – Preprocessed metadata from dataeval.utils.metadata.preprocess()
num_neighbors (int, default 5) – Number of points to consider as neighbors

Returns:

(num_factors+1) x (num_factors+1) estimate of mutual information between num_factors metadata factors and class label. Symmetry is enforced.

Return type:

BalanceOutput

Note

We use mutual_info_classif from sklearn since class label is categorical. mutual_info_classif outputs are consistent up to O(1e-4) and depend on a random seed. MI is computed differently for categorical and continuous variables.

Example

Return balance (mutual information) of factors with class_labels

>>> bal = balance(metadata)
>>> bal.balance
array([0.9999982 , 0.2494567 , 0.02994455, 0.13363788, 0.        ,
       0.        ])

Return intra/interfactor balance (mutual information)

>>> bal.factors
array([[0.99999935, 0.31360499, 0.26925848, 0.85201924, 0.36653548],
       [0.31360499, 0.99999856, 0.09725766, 0.15836905, 1.98031993],
       [0.26925848, 0.09725766, 0.99999846, 0.03713108, 0.01544656],
       [0.85201924, 0.15836905, 0.03713108, 0.47450653, 0.25509664],
       [0.36653548, 1.98031993, 0.01544656, 0.25509664, 1.06260686]])

Return classwise balance (mutual information) of factors with individual class_labels

>>> bal.classwise
array([[0.9999982 , 0.2494567 , 0.02994455, 0.13363788, 0.        ,
        0.        ],
       [0.9999982 , 0.2494567 , 0.02994455, 0.13363788, 0.        ,
        0.        ]])

dataeval.metrics.bias.balance#

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