| Title: | Robust Aggregative Feature Selection |
| Version: | 0.2.5 |
| Date: | 2025-01-25 |
| URL: | https://www.mdfs.it/ |
| Description: | A cross-validated minimal-optimal feature selection algorithm. It utilises popularity counting, hierarchical clustering with feature dissimilarity measures, and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features. |
| Depends: | R (≥ 4.2.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | fastcluster, MDFS (≥ 1.5.3), splitTools |
| NeedsCompilation: | no |
| Packaged: | 2025-01-25 12:56:59 UTC; radek |
| Author: | Radosław Piliszek [aut, cre], Witold Remigiusz Rudnicki [ths, aut] |
| Maintainer: | Radosław Piliszek <radek@piliszek.it> |
| Repository: | CRAN |
| Date/Publication: | 2025-01-29 17:30:16 UTC |
All built-in feature dissimilarity functions
Description
To be used in run_rafs.
Usage
builtin_dist_funs
Format
An object of class list of length 5.
Details
See also default_dist_funs.
Compute preliminary feature selection results for RAFS
Description
This is a secondary function, useful when experimenting with different
feature selection filters and rankings. Its output is used in run_rafs_with_fs_results
and it is called for the user in run_rafs.
Usage
compute_fs_results(data, decision, k, seeds, fs_fun = default_fs_fun)
Arguments
data |
input data where columns are variables and rows are observations (all numeric) |
decision |
decision variable as a binary sequence of length equal to number of observations |
k |
number of folds for internal cross validation |
seeds |
a vector of seeds used for fold generation for internal cross validation |
fs_fun |
function to compute feature selection p-values, it must have the same signature as |
Value
A list with feature selection results, e.g. from default_fs_fun.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
fs_results <- compute_fs_results(madelon$data, madelon$decision, 2, c(12345))
run_rafs_with_fs_results(madelon$data, madelon$decision, fs_results)
Feature dissimilarity based on Pearson's Correlation (cor)
Description
To be used as one of the dist_funs in run_rafs.
Usage
cor_dist(relevant_train_data, train_decision = NULL, seed = NULL)
Arguments
relevant_train_data |
input data where columns are variables and rows are observations (all numeric); assumed to contain only relevant data |
train_decision |
decision variable as a binary sequence of length equal to number of observations |
seed |
a numerical seed |
Value
A matrix of distances (dissimilarities).
Create seeded folds
Description
A utility function used in RAFS but useful also for external cross-validation.
Usage
create_seeded_folds(decision, k, seed)
Arguments
decision |
decision variable as a binary sequence of length equal to number of observations |
k |
number of folds for cross validation |
seed |
a numerical seed |
Value
A vector of folds. Each fold being a vector of selected indices.
Default feature dissimilarity functions
Description
As used in run_rafs.
Usage
default_dist_funs
Format
An object of class list of length 3.
Details
The default functions compute:
Pearson's correlation (cor: cor_dist),
Variation of Information (vi: vi_dist) and
Symmetric Target Information Gain (stig: stig_dist).
These functions follow a similar protocol to default_fs_fun.
They expect the same input except for the assumption that the data passed in is relevant.
Each of them outputs a matrix of distances (dissimilarities) between features.
See also builtin_dist_funs.
Default (example) feature selection function for RAFS
Description
See run_rafs for how it is used. Only the train portion of the
dataset is to be fed into this function.
Usage
default_fs_fun(train_data, train_decision, seed)
Arguments
train_data |
input data where columns are variables and rows are observations (all numeric) |
train_decision |
decision variable as a binary sequence of length equal to number of observations |
seed |
a numerical seed |
Details
The function MUST use this train_data and MAY ignore
the train_decision.
If the function depends on randomness, it MUST use the seed parameter to seed the PRNG.
The function needs to return a list with at least two elements:
rel_vars and rel_vars_rank, which are vectors and contain,
respectively, the indices of variables considered relevant and the rank
for each relevant variable. The function MAY return a list with more elements.
Other examples of sensible functions are included in the tests of this package.
Value
A list with at least two fields:
rel_vars and rel_vars_rank, which are vectors and contain,
respectively, the indices of variables considered relevant and the rank
for each relevant variable.
Default hclust methods
Description
As used in run_rafs to call hclust.
Usage
default_hclust_methods
Format
An object of class character of length 4.
Get all representatives from their popcnts
Description
This helper function works on results of get_rafs_reps_popcnts
to obtain all representatives at the chosen number of clusters.
Usage
get_rafs_all_reps_from_popcnts(reps_popcnts, n_clusters)
Arguments
reps_popcnts |
representatives' popcnts for the chosen variant as obtained from |
n_clusters |
the desired number of clusters |
Value
A vector of all representatives.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
rafs_results <- run_rafs(madelon$data, madelon$decision, 2, c(12345))
rafs_reps_popcnts <- get_rafs_reps_popcnts(rafs_results, 5)
get_rafs_all_reps_from_popcnts(rafs_reps_popcnts$stig_single, 5)
Get co-occurrence matrix from RAFS results
Description
This function obtains a matrix describing a graph of co-occurrence
at each count of clusters (from n_clusters_range) computed
over all runs of RAFS.
Usage
get_rafs_occurrence_matrix(
rafs_results,
interesting_reps,
n_clusters_range = 2:15
)
Arguments
rafs_results |
RAFS results as obtained from |
interesting_reps |
the interesting representatives to build matrices for (in principle, these need not be representatives but it is more common) |
n_clusters_range |
range of clusters number to obtain matrices for |
Details
If a single result over a cluster number range is desired, the selected matrices can be summed.
Value
A nested list with matrices.
The first level is per the RAFS variant (combination of feature dissimilarity function and hclust method).
The second level is per the number of clusters.
The third (and last) level is the co-occurrence matrix.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
rafs_results <- run_rafs(madelon$data, madelon$decision, 2, c(12345))
rafs_reps_popcnts <- get_rafs_reps_popcnts(rafs_results, 5)
rafs_top_reps <- get_rafs_top_reps_from_popcnts(rafs_reps_popcnts$stig_single, 5)
get_rafs_occurrence_matrix(rafs_results, rafs_top_reps, 5)
Get representatives' tuples' co-representation matrix from RAFS results
Description
This function obtains a matrix of representatives's describing a graph of co-representation
at each count of clusters (from n_clusters_range) computed
over all runs of RAFS.
Usage
get_rafs_rep_tuples_matrix(
rafs_results,
interesting_reps,
n_clusters_range = 2:15
)
Arguments
rafs_results |
RAFS results as obtained from |
interesting_reps |
the interesting representatives to build matrices for |
n_clusters_range |
range of clusters number to obtain matrices for |
Details
If a single result over a cluster number range is desired, the selected matrices can be summed.
Value
A nested list with matrices.
The first level is per the RAFS variant (combination of feature dissimilarity function and hclust method).
The second level is per the number of clusters.
The third (and last) level is the co-representation matrix.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
rafs_results <- run_rafs(madelon$data, madelon$decision, 2, c(12345))
rafs_reps_popcnts <- get_rafs_reps_popcnts(rafs_results, 5)
rafs_top_reps <- get_rafs_top_reps_from_popcnts(rafs_reps_popcnts$stig_single, 5)
get_rafs_rep_tuples_matrix(rafs_results, rafs_top_reps, 5)
Get representatives' tuples' popularity counts (popcnts) from RAFS results
Description
This function obtains popularity counts (popcnts) of representatives' tuples
present at each count of clusters (from n_clusters_range) computed
over all runs of RAFS.
Usage
get_rafs_rep_tuples_popcnts(rafs_results, n_clusters_range = 2:15)
Arguments
rafs_results |
RAFS results as obtained from |
n_clusters_range |
range of clusters number to obtain popcnts for |
Value
A nested list with popcnts.
The first level is per the RAFS variant (combination of feature dissimilarity function and hclust method).
The second level is per the number of clusters.
The third (and last) level is popcnts per representatives' tuple.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
rafs_results <- run_rafs(madelon$data, madelon$decision, 2, c(12345))
get_rafs_rep_tuples_popcnts(rafs_results, 2:5)
Get representatives' popularity counts (popcnts) from RAFS results
Description
This function obtains popularity counts (popcnts) of representatives
present at each count of clusters (from n_clusters_range) computed
over all runs of RAFS.
Usage
get_rafs_reps_popcnts(rafs_results, n_clusters_range = 2:15)
Arguments
rafs_results |
RAFS results as obtained from |
n_clusters_range |
range of clusters number to obtain popcnts for |
Details
These results might be fed into further helper functions:
get_rafs_top_reps_from_popcnts and get_rafs_all_reps_from_popcnts.
Value
A nested list with popcnts.
The first level is per the RAFS variant (combination of feature dissimilarity function and hclust method).
The second level is per the number of clusters.
The third (and last) level is popcnts per representative.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
rafs_results <- run_rafs(madelon$data, madelon$decision, 2, c(12345))
get_rafs_reps_popcnts(rafs_results, 2:5)
Get top (i.e., most common) representatives's tuples from their popcnts
Description
This helper function works on results of get_rafs_rep_tuples_popcnts
to obtain the desired number of top (most common) representatives' tuples at the chosen number of clusters.
Usage
get_rafs_top_rep_tuples_from_popcnts(
rep_tuples_popcnts,
n_clusters,
n_tuples = 1
)
Arguments
rep_tuples_popcnts |
tuples' popcnts for the chosen variant as obtained from |
n_clusters |
the desired number of clusters |
n_tuples |
the desired number of top tuples |
Value
A list of top tuples (each tuple being a vector of representatives).
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
rafs_results <- run_rafs(madelon$data, madelon$decision, 2, c(12345))
rafs_rep_tuples_popcnts <- get_rafs_rep_tuples_popcnts(rafs_results, 5)
get_rafs_top_rep_tuples_from_popcnts(rafs_rep_tuples_popcnts$stig_single, 5)
Get top (i.e., most common) representatives from their popcnts
Description
This helper function works on results of get_rafs_reps_popcnts
to obtain the desired number of top (most common) representatives at the chosen number of clusters.
Usage
get_rafs_top_reps_from_popcnts(reps_popcnts, n_clusters, n_reps = n_clusters)
Arguments
reps_popcnts |
popcnts for the chosen variant as obtained from |
n_clusters |
the desired number of clusters |
n_reps |
the desired number of top representatives |
Value
A vector of top representatives.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
rafs_results <- run_rafs(madelon$data, madelon$decision, 2, c(12345))
rafs_reps_popcnts <- get_rafs_reps_popcnts(rafs_results, 5)
get_rafs_top_reps_from_popcnts(rafs_reps_popcnts$stig_single, 5)
Get top popularity counts (popcnts) from FS results
Description
This function obtains popularity counts (popcnts) of top variables computed over all runs of FS.
Usage
get_rafs_tops_popcnts(fs_results, n_top_range = 2:15)
Arguments
fs_results |
RAFS FS results as obtained from |
n_top_range |
range of top number to obtain popcnts for |
Details
These results might be fed into further helper functions:
get_rafs_top_reps_from_popcnts and get_rafs_all_reps_from_popcnts.
Value
A nested list with popcnts.
The first level is per the number of top variables.
The second (and last) level is popcnts per top variable.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
fs_results <- compute_fs_results(madelon$data, madelon$decision, 2, c(12345))
get_rafs_tops_popcnts(fs_results, 2:5)
Generate CV run identifiers
Description
A utility function used in RAFS to generate cross validation run identifiers, thus useful also for external cross-validation.
Usage
get_run_id(seed, k, i)
Arguments
seed |
a numerical seed |
k |
number of folds for cross validation |
i |
current fold number (1 to |
Value
A string with the run identifier.
Robust Aggregative Feature Selection (RAFS)
Description
This is the main function of the RAFS library to run for analysis.
Usage
run_rafs(
data,
decision,
k = 5,
seeds = sample.int(32767, 10),
fs_fun = default_fs_fun,
dist_funs = default_dist_funs,
hclust_methods = default_hclust_methods
)
Arguments
data |
input data where columns are variables and rows are observations (all numeric) |
decision |
decision variable as a binary sequence of length equal to number of observations |
k |
number of folds for internal cross validation |
seeds |
a vector of seeds used for fold generation for internal cross validation |
fs_fun |
function to compute feature selection p-values, it must have the same signature as |
dist_funs |
a list of feature dissimilarity functions computed over the relevant portion of the training dataset (see the example |
hclust_methods |
a vector of |
Details
Depending on your pipeline, you may want to also check out run_rafs_with_fs_results and compute_fs_results
which this function simply wraps over.
The results from this function can be fed into one of the helper functions
to analyse them further: get_rafs_reps_popcnts,
get_rafs_rep_tuples_popcnts,
get_rafs_rep_tuples_matrix and
get_rafs_occurrence_matrix.
Value
A nested list with hclust results.
The first level is per the cross validation run.
The second level is per the feature dissimilarity function.
The third (and last) level is per the hclust method.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
run_rafs(madelon$data, madelon$decision, 2, c(12345))
Robust Aggregative Feature Selection (RAFS) from feature selection results
Description
This is a secondary function, useful when experimenting with different
feature selection filters and rankings. The output is exactly the same as
from run_rafs.
Usage
run_rafs_with_fs_results(
data,
decision,
fs_results,
dist_funs = default_dist_funs,
hclust_methods = default_hclust_methods
)
Arguments
data |
input data where columns are variables and rows are observations (all numeric) |
decision |
decision variable as a binary sequence of length equal to number of observations |
fs_results |
output from |
dist_funs |
a list of feature dissimilarity functions computed over the relevant portion of the training dataset (see the example |
hclust_methods |
a vector of |
Value
A nested list with hclust results.
The first level is per the cross validation run.
The second level is per the feature dissimilarity function.
The third (and last) level is per the hclust method.
Examples
library(MDFS)
mdfs_omp_set_num_threads(1) # only to pass CRAN checks
data(madelon)
fs_results <- compute_fs_results(madelon$data, madelon$decision, 2, c(12345))
run_rafs_with_fs_results(madelon$data, madelon$decision, fs_results)
Symmetric Target Information Gain (STIG) computed directly
Description
To be used as one of the dist_funs in run_rafs.
Usage
stig_dist(relevant_train_data, train_decision, seed)
Arguments
relevant_train_data |
input data where columns are variables and rows are observations (all numeric); assumed to contain only relevant data |
train_decision |
decision variable as a binary sequence of length equal to number of observations |
seed |
a numerical seed |
Details
This function computes the STIG metric directly from the data, maximising it over 30 discretisations.
Value
A matrix of distances (dissimilarities).
Symmetric Target Information Gain (STIG) computed from single Information Gains (IGs)
Description
To be used as one of the dist_funs in run_rafs.
Usage
stig_from_ig_dist(relevant_train_data, train_decision, seed)
Arguments
relevant_train_data |
input data where columns are variables and rows are observations (all numeric); assumed to contain only relevant data |
train_decision |
decision variable as a binary sequence of length equal to number of observations |
seed |
a numerical seed |
Details
This function computes the STIG metric from single Information Gains (IGs) maximised over 30 discretisations and then summed pair-wise.
This function is similar to stig_dist but the results differ slightly. We recommend the direct computation in general.
Value
A matrix of distances (dissimilarities).
Symmetric Target Information Gain (STIG) computed directly but with pre-computed 1D conditional entropy (aka stable)
Description
To be used as one of the dist_funs in run_rafs.
Usage
stig_stable_dist(relevant_train_data, train_decision, seed)
Arguments
relevant_train_data |
input data where columns are variables and rows are observations (all numeric); assumed to contain only relevant data |
train_decision |
decision variable as a binary sequence of length equal to number of observations |
seed |
a numerical seed |
Details
This function computes the STIG metric directly from the data, maximising it over 30 discretisations, but reusing the common 1D conditional entropy.
Value
A matrix of distances (dissimilarities).
Variation of Information (VI)
Description
To be used as one of the dist_funs in run_rafs.
Usage
vi_dist(relevant_train_data, train_decision = NULL, seed)
Arguments
relevant_train_data |
input data where columns are variables and rows are observations (all numeric); assumed to contain only relevant data |
train_decision |
decision variable as a binary sequence of length equal to number of observations |
seed |
a numerical seed |
Details
This function computes the Variation of Information (VI) averaged over 30 discretisations.
Value
A matrix of distances (dissimilarities).