mim¶
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bcselector.information_theory.j_criterion_approximations.
mim
(data, target_variable, candidate_variable_index, **kwargs)[source]¶ This estimator computes the Mutual Information Maximisation criterion.
- Parameters
data (np.array matrix) – Matrix of data set. Columns are variables, rows are observations.
target_variable (int or float) – Target variable. Can not be in data!
candidate_variable_index (int) – Index of candidate variable in data matrix.
- Returns
j_criterion_value – J_criterion approximated by the Mutual Information Maximisation.
- Return type
float
mifs¶
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bcselector.information_theory.j_criterion_approximations.
mifs
(data, target_variable, prev_variables_index, candidate_variable_index, **kwargs)[source]¶ This estimator computes the Mutual Information Feature Selection criterion.
- Parameters
data (np.array matrix) – Matrix of data set. Columns are variables, rows are observations.
target_variable (int or float) – Target variable. Can not be in data!
prev_variables_index (list of ints, set of ints) – Indexes of previously selected variables.
candidate_variable_index (int) – Index of candidate variable in data matrix.
beta (float) – Impact of redundancy segment in MIFS approximation. Higher the beta is, higher the impact.
- Returns
j_criterion_value – J_criterion approximated by the Mutual Information Feature Selection.
- Return type
float
mrmr¶
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bcselector.information_theory.j_criterion_approximations.
mrmr
(data, target_variable, prev_variables_index, candidate_variable_index, **kwargs)[source]¶ This estimator computes the Max-Relevance Min-Redundancy criterion.
- Parameters
data (np.array matrix) – Matrix of data set. Columns are variables, rows are observations.
target_variable (int or float) – Target variable. Can not be in data!
prev_variables_index (list of ints) – Indexes of previously selected variables.
candidate_variable_index (int) – Index of candidate variable in data matrix.
- Returns
j_criterion_value – J_criterion approximated by the Max-Relevance Min-Redundancy.
- Return type
float
jmi¶
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bcselector.information_theory.j_criterion_approximations.
jmi
(data, target_variable, prev_variables_index, candidate_variable_index, **kwargs)[source]¶ This estimator computes the Joint Mutual Information criterion.
- Parameters
data (np.array matrix) – Matrix of data set. Columns are variables, rows are observations.
target_variable (int or float) – Target variable. Can not be in data!
prev_variables_index (list of ints) – Indexes of previously selected variables.
candidate_variable_index (int) – Index of candidate variable in data matrix.
- Returns
j_criterion_value – J_criterion approximated by the Joint Mutual Information.
- Return type
float
cife¶
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bcselector.information_theory.j_criterion_approximations.
cife
(data, target_variable, prev_variables_index, candidate_variable_index, **kwargs)[source]¶ This estimator computes the Conditional Infomax Feature Extraction criterion.
- Parameters
data (np.array matrix) – Matrix of data set. Columns are variables, rows are observations.
target_variable (int or float) – Target variable. Can not be in data!
prev_variables_index (list of ints) – Indexes of previously selected variables.
candidate_variable_index (int) – Index of candidate variable in data matrix.
beta (float) – Impact of redundancy segment in MIFS approximation. Higher the beta is, higher the impact.
- Returns
j_criterion_value – J_criterion approximated by the Conditional Infomax Feature Extraction.
- Return type
float