theftdlc (version 0.2.1)

compare_features: Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets

Description

Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets

Usage

compare_features(
  data,
  metric = c("accuracy", "precision", "recall", "f1"),
  by_set = TRUE,
  hypothesis = c("null", "pairwise"),
  p_adj = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"),
  n_workers = 1
)

Value

data.frame containing the results

Arguments

data

list object containing the classification outputs produce by tsfeature_classifier

metric

character denoting the classification performance metric to use in statistical testing. Can be one of "accuracy", "precision", "recall", "f1". Defaults to "accuracy"

by_set

Boolean specifying whether you want to compare feature sets (if TRUE) or individual features (if FALSE). Defaults to TRUE but this is contingent on whether you computed by set or not in tsfeature_classifier

hypothesis

character denoting whether p-values should be calculated for each feature set or feature (depending on by_set argument) individually relative to the null if use_null = TRUE in tsfeature_classifier through "null", or whether pairwise comparisons between each set or feature should be conducted on main model fits only through "pairwise". Defaults to "null"

p_adj

character denoting the adjustment made to p-values for multiple comparisons. Should be a valid argument to stats::p.adjust. Defaults to "none" for no adjustment. "holm" is recommended as a starting point for adjustments

n_workers

integer denoting the number of parallel processes to use. Defaults to 1 for serial processing

Author

Trent Henderson

References

Henderson, T., Bryant, A. G., and Fulcher, B. D. Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification. 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2023).

Examples

library(theft)

features <- theft::calculate_features(theft::simData,
  feature_set = NULL,
  features = list("mean" = mean, "sd" = sd))

classifiers <- classify(features,
                        by_set = FALSE,
                        n_resamples = 3)

compare_features(classifiers,
                 by_set = FALSE,
                 hypothesis = "pairwise")