depynd.information
¶
Module contents¶
-
depynd.information.
mutual_information
(X, Y, mi_estimator='auto', is_discrete='auto', force_non_negative=False, **kwargs)¶ Estimate mutual information between
X
andY
.Parameters: - X (array-like, shape (n_samples, n_features_x) or (n_samples)) – Observations of a variable.
- Y (array-like, shape (n_samples, n_features_y) or (n_samples)) – Observations of the other variable.
- mi_estimator ({'knn', 'dr', 'plugin', 'auto'}, default 'auto') – MI estimator. If ‘auto’, MI estimator will be selected depending on whether all features are purely discrete or not. If purely discrete, ‘plugin’ estimator will be used. Otherwise, ‘knn’ estimator will be selected.
- is_discrete ({'auto', bool}, default 'auto') – If
bool
, then it determines whether to consider all features purely discrete or purely continuous. If ‘auto’, a column which contains duplicate elements will be considered discrete. - force_non_negative (bool, default False) – If
True
, the result will be taken max with zero. - kwargs (dict) – Optional parameters for MI estimation.
Returns: mi – Estimated mutual information between
X
andY
.Return type:
-
depynd.information.
conditional_mutual_information
(X, Y, Z, mi_estimator='auto', is_discrete='auto', force_non_negative=False, **kwargs)¶ Estimate conditional mutual information between
X
andY
givenZ
.Parameters: - X (array-like, shape (n_samples, n_features_x) or (n_samples)) – Observations of a conditioned variable.
- Y (array-like, shape (n_samples, n_features_y) or (n_samples)) – Observations of the other conditioned variable.
- Z (array-like, shape (n_samples, n_features_z) or (n_samples)) – Observations of the conditioning variable.
- mi_estimator ({'knn', 'dr', 'plugin', 'auto'}, default 'auto') – MI estimator. If ‘auto’, MI estimator will be selected depending on whether all features are purely discrete or not. If purely discrete, ‘plugin’ estimator will be used. Otherwise, ‘knn’ estimator will be selected.
- is_discrete ({'auto', bool}, default 'auto') – If
bool
, then it determines whether to consider all features purely discrete or purely continuous. If ‘auto’, a column which contains duplicate elements will be considered discrete. - force_non_negative (bool, default False) – If
True
, the result will be taken max with zero. - kwargs (dict, default None) – Optional parameters for MI estimation.
Returns: cmi – Estimated conditional mutual information between
X
andY
, givenZ
.Return type: