ITMO_FS API¶
This is the full API documentation of the ITMO_FS toolbox.
ITMO_FS.filters: Filter methods¶
ITMO_FS.filters.univariate: Univariate filter methods¶
filters.univariate.VDM([weighted, q]) |
Creates Value Difference Metric builder. |
filters.univariate.UnivariateFilter(measure) |
Basic interface for using univariate measures for feature selection. |
Measures for univariate filters¶
filters.univariate.fit_criterion_measure(x, y) |
Calculate the FitCriterion score for features. |
filters.univariate.f_ratio_measure(x, y) |
Calculate Fisher score for features. |
filters.univariate.gini_index(x, y) |
Calculate Gini index for features. |
filters.univariate.su_measure(x, y) |
SU is a correlation measure between the features and the class calculated via formula SU(X,Y) = 2 * I(X|Y) / (H(X) + H(Y)). |
filters.univariate.spearman_corr(x, y) |
Calculate Spearman’s correlation for each feature. |
filters.univariate.pearson_corr(x, y) |
Calculate Pearson’s correlation for each feature. |
filters.univariate.fechner_corr(x, y) |
Calculate Sample sign correlation (Fechner correlation) for each feature. |
filters.univariate.kendall_corr(x, y) |
Calculate Sample sign correlation (Kendall correlation) for each feature. |
filters.univariate.reliefF_measure(x, y[, …]) |
Calculate ReliefF measure for each feature. |
filters.univariate.chi2_measure(x, y) |
Calculate the Chi-squared measure for each feature. |
filters.univariate.information_gain(x, y) |
Calculate mutual information for each feature by formula I(X,Y) = H(Y) - H(Y|X). |
Cutting rules for univariate filters¶
ITMO_FS.filters.multivariate: Multivariate filter methods¶
filters.multivariate.DISRWithMassive(n_features) |
Create DISR (Double Input Symmetric Relevance) feature selection filter based on kASSI criterin for feature selection which aims at maximizing the mutual information avoiding, meanwhile, large multivariate density estimation. |
filters.multivariate.FCBFDiscreteFilter([delta]) |
Create FCBF (Fast Correlation Based filter) feature selection filter based on mutual information criteria for data with discrete features. |
filters.multivariate.MultivariateFilter(…) |
Provides basic functionality for multivariate filters. |
filters.multivariate.STIR(n_features[, …]) |
Feature selection using STIR algorithm. |
filters.multivariate.TraceRatioFisher(n_features) |
Creates TraceRatio(similarity based) feature selection filter performed in supervised way, i.e. |
filters.multivariate.MIMAGA(mim_size, pop_size) |
Measures for multivariate filters¶
filters.multivariate.MIM(selected_features, …) |
Mutual Information Maximization feature scoring criterion. |
filters.multivariate.MRMR(selected_features, …) |
Minimum-Redundancy Maximum-Relevance feature scoring criterion. |
filters.multivariate.JMI(selected_features, …) |
Joint Mutual Information feature scoring criterion. |
filters.multivariate.CIFE(selected_features, …) |
Conditional Infomax Feature Extraction feature scoring criterion. |
filters.multivariate.MIFS(selected_features, …) |
Mutual Information Feature Selection feature scoring criterion. |
filters.multivariate.CMIM(selected_features, …) |
Conditional Mutual Info Maximisation feature scoring criterion. |
filters.multivariate.ICAP(selected_features, …) |
Interaction Capping feature scoring criterion. |
filters.multivariate.DCSF(selected_features, …) |
Dynamic change of selected feature with the class scoring criterion. |
filters.multivariate.CFR(selected_features, …) |
The criterion of CFR maximizes the correlation and minimizes the redundancy. |
filters.multivariate.MRI(selected_features, …) |
Max-Relevance and Max-Independence feature scoring criteria. |
filters.multivariate.IWFS(selected_features, …) |
Interaction Weight base feature scoring criteria. |
filters.multivariate.generalizedCriteria(…) |
This feature scoring criteria is a linear combination of all relevance, redundancy, conditional dependency Given set of already selected features and set of remaining features on dataset X with labels y selects next feature. |
ITMO_FS.filters.unsupervised: Unsupervised filter methods¶
filters.unsupervised.TraceRatioLaplacian(…) |
TraceRatio(similarity based) feature selection filter performed in unsupervised way, i.e laplacian version |
ITMO_FS.filters.sparse: Sparse filter methods¶
filters.sparse.MCFS |
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filters.sparse.NDFS |
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filters.sparse.RFS |
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filters.sparse.SPEC |
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filters.sparse.UDFS |
ITMO_FS.ensembles: Ensemble methods¶
ITMO_FS.ensembles.measure_based: Measure based ensemble methods¶
ensembles.measure_based.WeightBased(filters) |
Weight-based filter ensemble. |
ITMO_FS.ensembles.model_based: Model based ensemble methods¶
ensembles.model_based.BestSum(models, …[, …]) |
Best weighted sum ensemble. |
ITMO_FS.ensembles.ranking_based: Ranking based ensemble methods¶
ensembles.ranking_based.Mixed(filters, …) |
Perform feature selection based on several filters, selecting features this way: Get ranks from every filter from input. |
ITMO_FS.embedded: Embedded methods¶
embedded.MOS(model, weight_func[, loss, …]) |
Perform Minimizing Overlapping Selection under SMOTE (MOSS) or under No-Sampling (MOSNS) algorithm. |
ITMO_FS.hybrid: Hybrid methods¶
hybrid.FilterWrapperHybrid(filter_, wrapper) |
Perform the filter + wrapper hybrid algorithm by first running the filter algorithm on the full dataset, leaving the selected features and running the wrapper algorithm on the cut dataset. |
hybrid.Melif(estimator, measure, …[, …]) |
MeLiF algorithm. |
ITMO_FS.wrappers: Wrapper methods¶
ITMO_FS.wrappers.deterministic: Deterministic wrapper methods¶
wrappers.deterministic.AddDelWrapper(…[, …]) |
Add-Del feature wrapper. |
wrappers.deterministic.BackwardSelection(…) |
Backward Selection removes one feature at a time until the number of features to be removed is reached. |
wrappers.deterministic.RecursiveElimination(…) |
Recursive feature elimination algorithm. |
wrappers.deterministic.SequentialForwardSelection(…) |
Sequentially add features that maximize the classifying function when combined with the features already used. |
Deterministic wrapper function¶
wrappers.deterministic.qpfs_wrapper |
ITMO_FS.wrappers.randomized: Randomized wrapper methods¶
wrappers.randomized.HillClimbingWrapper(…) |
Hill Climbing algorithm. |
wrappers.randomized.SimulatedAnnealing(…) |
Simulated Annealing algorithm. |
wrappers.randomized.TPhMGWO(estimator, measure) |
Grey Wolf optimization with Two-Phase Mutation. |