dataeval.core.mutual_info_classwise¶

dataeval.core.mutual_info_classwise(class_labels, factor_data, discrete_features=None, num_neighbors=5)¶

Compute mutual information (MI) between factors, transformed to lie in [0, 1].

Factors include class label, metadata, and label/image properties.

Parameters:¶

class_labels : Array1D[int]¶: Target class labels as integer indices. Can be a 1D list, or array-like object.
factor_data : Array2D[int]¶: Factor values after binning or digitization. Can be a 1D list, or array-like object.
discrete_features : Array1D[bool] | None = None¶: Boolean array or iterable defining whether or not the feature set is discretized. Can be a 1D list, or array-like object.
num_neighbors : int = 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:¶

NDArray[np.float64]

Notes

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. We return a transformation of MI onto the interval [0, 1].

Example

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

>>> rng = np.random.default_rng(175)
>>> class_labels = rng.choice([0, 1, 2], size=100)
>>> factor_data = np.column_stack(
...     [
...         rng.choice([25, 35, 45, 55], size=100),  # age
...         rng.choice([50000, 65000, 80000], size=100),  # income
...         rng.choice([0, 1], size=100),  # gender
...     ]
... )
>>> mutual_info_classwise(class_labels=class_labels, factor_data=factor_data)
array([[1.000e+00, 2.077e-02, 2.296e-03, 7.317e-04],
       [1.000e+00, 4.893e-02, 2.451e-02, 4.362e-03],
       [1.000e+00, 1.868e-02, 3.820e-02, 1.006e-03]])