Introduction to theftdlc

Data visualisation and low-dimensional projections

The package also comes with additional statistical and graphical functionality:

Feature by time-series matrix as a heatmap
Low dimensional projections of the feature space and plotting as a scatterplot
Pairwise feature correlation matrix as a heatmap

Feature matrices

The function calling type = "matrix" in plot() on a feature_calculations object takes it and produces a ggplot object heatmap showing the feature vectors across the x axis and each time series down the y axis. Prior to plotting, the function hierarchically clusters the data across both rows and columns to visually highlight the empirical structure. Note that you have several options for the hierarchical clustering linkage algorithm to use:

"average" (default)
"ward.D"
"ward.D2"
"single"
"complete"
"mcquitty"
"median"
"centroid"

See the hclust documentation for more information.

Note that the legend for this plot (and other matrix visualisations in theftdlc) have been discretised for visual clarity as continuous legends can be difficult to interpret meaningful value differences easily.

plot(feature_matrix, type = "matrix", norm_method = "RobustSigmoid")

You can control the normalisation type with the norm_method argument, whether to rescale to the unit interval after normalisation with the unit_int argument. norm_method and all normalisation of feature vectors in theftdlc is handled by the normaliseR package. You can also control the hierarchical clustering method with the clust_method argument (the example above used defaults so manual specification was not needed).

Individual feature distributions

Plotting the entire feature matrix is useful, but sometimes we wish to understand the distributions of individual features. This is particularly useful if there are different groups in your data (such as in a time-series classification context). We can again use the plot() generic here to draw violin plots through setting type = "violin". Note that for violin plots, we also need to tell the function which features we wish to plot (i.e., a vector of characters specifying feature names from the names column in your feature_calculations object). For simplicity, we will just plot two random features from catch22 here:

plot(feature_matrix, type = "violin",
     feature_names = c("CO_f1ecac", "PD_PeriodicityWang_th0_01"))

Note that when using these defined plot() generics, you can pass any additional arguments to certain geoms to control the plot look through the ... argument in the plot() function. Below is a guide to where these arguments go depending on the plot type:

type = "quality"—... goes to ggplot2::geom_bar
type = "matrix"—... goes to ggplot2::geom_raster
type = "cor"—... goes to ggplot2::geom_raster
type = "violin"—... goes to ggplot2::geom_point

For example, we may wish to control the point size and transparency in the above plot (not rendered here for space):

plot(feature_matrix, type = "violin",
     feature_names = c("CO_f1ecac", "PD_PeriodicityWang_th0_01"),
     size = 0.7, alpha = 0.9)

Low dimensional projections

Low-dimensional projections are a useful tool for visualising the structure of high-dimensional datasets in low-dimensional spaces. In machine learning for time-series data, we are often interested in representing a time-series dataset in a two-dimensional projection of the high-dimensional feature space. This projection which can reveal structure in the dataset, including how different labeled classes are organized.

The theftdlc function project takes the feature_calculations object and performs one of the following dimension reduction techniques on it to reduce its dimensionality to a bivariate state which can then be easily plotted:

Principal components analysis (PCA)—"PCA"
\(t\)-Stochastic Neighbor Embedding (\(t\)-SNE)—"tSNE"
Classical multidimensional scaling (MDS)—"ClassicalMDS"
Kruskal’s non-metric multidimensional scaling—"KruskalMDS"
Sammon’s non-linear mapping non-metric multidimensional scaling—"SammonMDS"
Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP)—"UMAP"

The result is stored in a custom object class called feature_projection. project takes the following arguments:

data—feature_calculations object containing the raw feature matrix produced by theft::calculate_features
norm_method—character denoting the rescaling/normalising method to apply. Can be one of "zScore", "Sigmoid", "RobustSigmoid", "MinMax", or "MaxAbs". Defaults to "zScore"
unit_int—Boolean whether to rescale into unit interval \([0,1]\) after applying normalisation method. Defaults to FALSE
low_dim_method—character specifying the low dimensional embedding method to use. Can be one of "PCA" or "tSNE", "ClassicalMDS", "KruskalMDS", "SammonMDS", or "UMAP". Defaults to "PCA"
na_removal—character defining the way to deal with NAs produced during feature calculation. Can be one of "feature" or "sample". "feature" removes all features that produced any NAs in any sample, keeping the number of samples the same. "sample" omits all samples that produced at least one NA. Defaults to "feature"
seed—integer to fix R’s random number generator to ensure reproducibility. Defaults to 123
... arguments to be passed to the respective function specified by low_dim_method

project returns an object of class feature_projection which is essentially a named list comprised of four elements:

"Data"—the feature_calculations object supplied to project
"ModelData"—the wide matrix of filtered data supplied to the model fit
"ProjectedData"—a tidy data.frame of the two-dimensional embedding
"ModelFit"—the raw model object from the dimensionality reduction algorithm

low_dim <- project(feature_matrix,
                   norm_method = "RobustSigmoid",
                   unit_int = TRUE,
                   low_dim_method = "PCA",
                   seed = 123)

We can similarly call plot() on this object to produce a two-dimensional scatterplot of the results:

plot(low_dim)

As another example, a t-SNE version can be specified in a similar fashion, with any function parameters for the method supplied to the ... argument to project. Shaded covariance ellipses can also be disabled when plotting feature_projection objects by setting show_covariance = FALSE. Here is an example where we modify the perplexity of the t-SNE algorithm:

low_dim2 <- project(feature_matrix,
                    norm_method = "RobustSigmoid",
                    unit_int = TRUE,
                    low_dim_method = "tSNE",
                    perplexity = 10,
                    seed = 123)

plot(low_dim2, show_covariance = FALSE)

Pairwise correlations

You can plot correlations between feature vectors using plot(type = "cor") on a feature_calculations object:

plot(feature_matrix, type = "cor")

Similarly, you can control the normalisation type with the norm_method argument and the hierarchical clustering method with the clust_method argument (the example above used defaults so manual specification was not needed).

Time-series classification

Feature-by-feature

Since feature-based time-series analysis has shown particular promise for classification problems, theftdlc includes functionality for exploring group separation. The function classify enables you to fit a range of classification models to enable statistical comparisons using the resampling methodology presented in this paper for a detailed review¹. This function is meant to serve as a fast answer that can be used to guide analysis and not a replacement for the development of a careful statistical pipeline. classify has the following arguments:

data—feature_calculations object containing the raw feature matrix produced by theft::calculate_features with an included group column as per theft::calculate_features
classifier—function specifying the classifier to fit. Should be a function with 2 arguments: formula and data. Please note that classify z-scores data prior to modelling using the train set’s information so disabling default scaling if your function uses it is recommended. Defaults to NULL which means the following linear SVM is fit: classifier = function(formula, data){mod <- e1071::svm(formula, data = data, kernel = "linear", scale = FALSE, probability = TRUE)}
train_size—Numeric value denoting the proportion of samples to use in the training set. Defaults to 0.75
n_resamples—Integer denoting the number of resamples to calculate. Defaults to 30
by_set—Boolean specifying whether to compute classifiers for each feature set. Defaults to TRUE (see below section “Multi-feature” for more on this). If FALSE, the function will instead find the best individually-performing features
use_null—Boolean whether to fit null models where class labels are shuffled in order to generate a null distribution that can be compared to performance on correct class labels. Defaults to FALSE. This is known as permutation testing
seed—Integer to fix R’s random number generator to ensure reproducibility. Defaults to 123

Since we are interested in individual features in this section, we will calculate both main and null results for each feature using just 5 resamples for efficiency (in practice, we would use more!) with the default linear SVM:

feature_classifiers <- classify(feature_matrix,
                                by_set = FALSE,
                                n_resamples = 5,
                                use_null = TRUE)

To show you how simple it is to specify a different classifier, we can instead maybe use a radial basis function SVM (though you are absolutely not limited to just e1071 models! You can use anything that can be used with R’s predict generic as classify internally constructs confusion matrices from model predictions):

myclassifier <- function(formula, data){
  mod <- e1071::svm(formula, data = data, kernel = "radial", scale = FALSE,
                    probability = TRUE)
}

feature_classifiers_radial <- classify(feature_matrix,
                                       classifier = myclassifier,
                                       by_set = FALSE,
                                       n_resamples = 5,
                                       use_null = TRUE)

While have raw classification results is useful, we often also would like to statistical evaluate some facet of it. theftdlc includes the function compare_features for doing this. compare_features contains the following arguments:

data—List object containing the classification outputs produce by classify
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 classify
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 classify 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 if adjustments are sought

We can use compare_features to evaluate how well each individual feature performs relative to its empirical null distribution (noting that we are using the defaults for the other arguments for code cleanliness):

feature_vs_null <- compare_features(feature_classifiers,
                                    by_set = FALSE,
                                    hypothesis = "null")

head(feature_vs_null)
#>                                                 hypothesis
#> 1 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff != own null
#> 2                       catch22_CO_FirstMin_ac != own null
#> 3             catch22_CO_HistogramAMI_even_2_5 != own null
#> 4                            catch22_CO_f1ecac != own null
#> 5                        catch22_CO_trev_1_num != own null
#> 6                  catch22_DN_HistogramMode_10 != own null
#>                                          names
#> 1 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff
#> 2                       catch22_CO_FirstMin_ac
#> 3             catch22_CO_HistogramAMI_even_2_5
#> 4                            catch22_CO_f1ecac
#> 5                        catch22_CO_trev_1_num
#> 6                  catch22_DN_HistogramMode_10
#>                         original_names feature_set   metric feature_mean
#> 1 CO_Embed2_Dist_tau_d_expfit_meandiff     catch22 accuracy   0.40444444
#> 2                       CO_FirstMin_ac     catch22 accuracy   0.31111111
#> 3             CO_HistogramAMI_even_2_5     catch22 accuracy   0.29777778
#> 4                            CO_f1ecac     catch22 accuracy   0.29777778
#> 5                        CO_trev_1_num     catch22 accuracy   0.11111111
#> 6                  DN_HistogramMode_10     catch22 accuracy   0.08444444
#>    null_mean t_statistic     p.value
#> 1 0.12000000    4.894202 0.004038794
#> 2 0.09777778    2.317462 0.040681479
#> 3 0.10666667    2.007068 0.057591771
#> 4 0.09777778    2.371708 0.038339070
#> 5 0.07111111    3.674235 0.010655821
#> 6 0.06666667    0.560112 0.302643313

Or to conduct pairwise comparisons between individual features:

pairwise_features <- compare_features(feature_classifiers,
                                      by_set = FALSE,
                                      hypothesis = "pairwise",
                                      p_adj = "holm")

head(pairwise_features)
#>                                                                         hypothesis
#> 1           catch22_CO_Embed2_Dist_tau_d_expfit_meandiff != catch22_CO_FirstMin_ac
#> 2 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff != catch22_CO_HistogramAMI_even_2_5
#> 3                catch22_CO_Embed2_Dist_tau_d_expfit_meandiff != catch22_CO_f1ecac
#> 4            catch22_CO_Embed2_Dist_tau_d_expfit_meandiff != catch22_CO_trev_1_num
#> 5      catch22_CO_Embed2_Dist_tau_d_expfit_meandiff != catch22_DN_HistogramMode_10
#> 6       catch22_CO_Embed2_Dist_tau_d_expfit_meandiff != catch22_DN_HistogramMode_5
#>                                        names_a                          names_b
#> 1 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff           catch22_CO_FirstMin_ac
#> 2 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff catch22_CO_HistogramAMI_even_2_5
#> 3 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff                catch22_CO_f1ecac
#> 4 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff            catch22_CO_trev_1_num
#> 5 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff      catch22_DN_HistogramMode_10
#> 6 catch22_CO_Embed2_Dist_tau_d_expfit_meandiff       catch22_DN_HistogramMode_5
#>     metric names_a_mean names_b_mean t_statistic      p.value p_value_adj
#> 1 accuracy    0.4044444   0.31111111    3.500000 0.0124480817  1.00000000
#> 2 accuracy    0.4044444   0.29777778    2.449490 0.0352419985  1.00000000
#> 3 accuracy    0.4044444   0.29777778    5.237229 0.0031761284  0.46053861
#> 4 accuracy    0.4044444   0.11111111    9.241849 0.0003810008  0.07467616
#> 5 accuracy    0.4044444   0.08444444    6.743418 0.0012602882  0.21676958
#> 6 accuracy    0.4044444   0.06222222   15.717559 0.0000478576  0.01076796

We can then use ggplot2 to summarise and visualise our results. Here is a pairwise correlation plot between the top 10 features in catch22 for this toy problem. We are just simply filtering the original full feature data and making use of the plot generic defined for objects of class feature_calculations:

top_10 <- feature_vs_null %>%
  dplyr::slice_min(p.value, n = 10) %>%
  dplyr::select(c(feature_set, original_names, p.value))

feature_matrix_filt <- feature_matrix %>%
  dplyr::filter(feature_set %in% top_10$feature_set & names %in% top_10$original_names)

feature_matrix_filt <- structure(feature_matrix_filt, class = c("feature_calculations", "data.frame"))
plot(feature_matrix_filt, type = "cor")

We can also easily draw a violin plot of the top 10 features to visualise the distributions by group:

plot(feature_matrix_filt,
     type = "violin",
     feature_names = top_10$original_names)

Finally, theftdlc also contains a function interval for summarising the results of classify. interval takes the following arguments:

data—list object containing the classification outputs produce by classify
metric—character denoting the classification performance metric to calculate intervals for. Can be one of "accuracy", "precision", "recall", "f1". Defaults to "accuracy"
by_set—Boolean specifying whether to compute intervals for each feature set. Defaults to TRUE. If FALSE, the function will instead calculate intervals for each feature
type—character denoting whether to calculate a \(\pm\) SD interval with "sd", confidence interval based off the \(t\)-distribution with "qt", or based on a quantile with "quantile". Defaults to "sd"
interval—numeric scalar denoting the width of the interval to calculate. Defaults to 1 if type = "sd" to produce a \(\pm 1\) SD interval. Defaults to 0.95 if type = "qt" or type = "quantile" for a \(95\%\) interval
model_type—character denoting whether to calculate intervals for main models with "main" or null models with "null" if the use_null argument when using classify was use_null = TRUE. Defaults to "main"

We can evidently use interval to produce a variety of different summaries for us. For example, we might wish to compute the \(\pm1\) SD interval for each feature’s main model classification accuracy values (note that the defaults for the function do this for us, so we only need to set by_set = FALSE manually):

interval(feature_classifiers, by_set = FALSE)
#>                                                  names      .mean     .lower
#> 1         catch22_CO_Embed2_Dist_tau_d_expfit_meandiff 0.40444444 0.38585200
#> 2                               catch22_CO_FirstMin_ac 0.31111111 0.28888889
#> 3                     catch22_CO_HistogramAMI_even_2_5 0.29777778 0.26407611
#> 4                                    catch22_CO_f1ecac 0.29777778 0.27790162
#> 5                                catch22_CO_trev_1_num 0.11111111 0.09539763
#> 6                          catch22_DN_HistogramMode_10 0.08444444 0.05547021
#> 7                           catch22_DN_HistogramMode_5 0.06222222 0.05228414
#> 8                catch22_DN_OutlierInclude_n_001_mdrmd 0.05777778 0.04560617
#> 9                catch22_DN_OutlierInclude_p_001_mdrmd 0.06222222 0.02246990
#> 10              catch22_FC_LocalSimple_mean1_tauresrat 0.28888889 0.25746192
#> 11                 catch22_FC_LocalSimple_mean3_stderr 0.50222222 0.47688499
#> 12     catch22_IN_AutoMutualInfoStats_40_gaussian_fmmi 0.29333333 0.27474089
#> 13                        catch22_MD_hrv_classic_pnn40 0.25333333 0.18931173
#> 14                   catch22_PD_PeriodicityWang_th0_01 0.15111111 0.13251867
#> 15            catch22_SB_BinaryStats_diff_longstretch0 0.33333333 0.28121760
#> 16            catch22_SB_BinaryStats_mean_longstretch1 0.35111111 0.30836581
#> 17                   catch22_SB_MotifThree_quantile_hh 0.46222222 0.43324799
#> 18          catch22_SB_TransitionMatrix_3ac_sumdiagcov 0.32000000 0.27391902
#> 19      catch22_SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 0.08444444 0.06010122
#> 20 catch22_SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 0.25333333 0.22799610
#> 21            catch22_SP_Summaries_welch_rect_area_5_1 0.60000000 0.56486358
#> 22            catch22_SP_Summaries_welch_rect_centroid 0.53333333 0.50611678
#>        .upper
#> 1  0.42303689
#> 2  0.33333333
#> 3  0.33147945
#> 4  0.31765394
#> 5  0.12682460
#> 6  0.11341868
#> 7  0.07216030
#> 8  0.06994939
#> 9  0.10197454
#> 10 0.32031586
#> 11 0.52755945
#> 12 0.31192578
#> 13 0.31735493
#> 14 0.16970356
#> 15 0.38544906
#> 16 0.39385641
#> 17 0.49119646
#> 18 0.36608098
#> 19 0.10878767
#> 20 0.27867057
#> 21 0.63513642
#> 22 0.56054989

Multi-feature

Since theft contains entire sets of features, we can also use classify to compare them at the set level through the by_set argument. Let’s try both catch22 and a custom set of just mean and standard deviation:

feature_matrix2 <- calculate_features(data = simData, 
                                      group_var = "process", 
                                      feature_set = "catch22",
                                      features = list("mean" = mean, "sd" = sd),
                                      seed = 123)

set_classifiers <- classify(feature_matrix2,
                            by_set = TRUE,
                            n_resamples = 5,
                            use_null = TRUE)

head(set_classifiers)
#> $TrainTestSizes
#> train_size  test_size 
#>        135         45 
#> 
#> $ClassificationResults
#>    model_type resample   accuracy mean_precision mean_recall mean_f1_score
#> 1        Main        1 0.86666667     0.90235690  0.90235690     0.9000000
#> 2        Main        2 0.84444444     0.88047138  0.88194444     0.8806479
#> 3        Main        3 0.80000000     0.84785354  0.82702020     0.8317460
#> 4        Main        4 0.82222222     0.86195286  0.86446886     0.8611111
#> 5        Main        5 0.86666667     0.89562290  0.90109890     0.8958333
#> 6        Null        1 0.15555556     0.16734007  0.15575397     0.2266282
#> 7        Null        2 0.13333333     0.18518519  0.10370370     0.3771930
#> 8        Null        3 0.06666667     0.10404040  0.07936508     0.1444444
#> 9        Null        4 0.24444444     0.26153199  0.27103175     0.2406925
#> 10       Null        5 0.24444444     0.22853535  0.23164683     0.2714130
#> 11       Main        1 0.71111111     0.79629630  0.83285714     0.8658046
#> 12       Main        2 0.73333333     0.81481481  0.85666667     0.8941914
#> 13       Main        3 0.68888889     0.77546296  0.78285714     0.8238998
#> 14       Main        4 0.71111111     0.79629630  0.83285714     0.8658046
#> 15       Main        5 0.68888889     0.77777778  0.81500000     0.8379841
#> 16       Null        1 0.00000000     0.00000000  0.00000000           NaN
#> 17       Null        2 0.28888889     0.35416667  0.35294118     0.4358312
#> 18       Null        3 0.04444444     0.04166667  0.20000000     0.4000000
#> 19       Null        4 0.11111111     0.20833333  0.26923077     0.2714286
#> 20       Null        5 0.11111111     0.16666667  0.06410256     0.2272727
#> 21       Main        1 0.68888889     0.72727273  0.68398268     0.6865741
#> 22       Main        2 0.62222222     0.59764310  0.60178710     0.7068449
#> 23       Main        3 0.75555556     0.80744949  0.75476190     0.7578947
#> 24       Main        4 0.77777778     0.79124579  0.77167508     0.7766106
#> 25       Main        5 0.68888889     0.76430976  0.70138889     0.7086835
#> 26       Null        1 0.17777778     0.19730640  0.19444444     0.3869031
#> 27       Null        2 0.08888889     0.10774411  0.08215488     0.1638889
#> 28       Null        3 0.08888889     0.08552189  0.12500000     0.1939394
#> 29       Null        4 0.26666667     0.24532828  0.27420635     0.3099160
#> 30       Null        5 0.17777778     0.15172559  0.14587496     0.2203209
#>     feature_set
#> 1  All features
#> 2  All features
#> 3  All features
#> 4  All features
#> 5  All features
#> 6  All features
#> 7  All features
#> 8  All features
#> 9  All features
#> 10 All features
#> 11         User
#> 12         User
#> 13         User
#> 14         User
#> 15         User
#> 16         User
#> 17         User
#> 18         User
#> 19         User
#> 20         User
#> 21      catch22
#> 22      catch22
#> 23      catch22
#> 24      catch22
#> 25      catch22
#> 26      catch22
#> 27      catch22
#> 28      catch22
#> 29      catch22
#> 30      catch22

Note that classify constructs a set of "All features" (i.e., all features across all computed sets) automatically when \(>2\) unique feature sets are detected in the feature data. Similar to the individual feature case, we can also use interval combined with ggplot2 to summarise our findings. Here is a comparison of mean accuracy \(\pm 1SD\) between feature sets:

interval_calcs <- interval(set_classifiers)

interval_calcs %>%
  ggplot2::ggplot(ggplot2::aes(x = reorder(feature_set, -.mean), y = .mean,
                               colour = feature_set)) +
  ggplot2::geom_errorbar(ggplot2::aes(ymin = .lower, ymax = .upper)) +
  ggplot2::geom_point(size = 5) +
  ggplot2::labs(x = "Feature set",
                y = "Classification accuracy") +
  ggplot2::scale_colour_brewer(palette = "Dark2") +
  ggplot2::theme_bw() +
  ggplot2::theme(legend.position = "none",
                 panel.grid.minor = ggplot2::element_blank())

Cluster analysis

theftdlc also supports quick and simple cluster analysis using either \(k\)-means, hierarchical clustering, or Gaussian mixture models through the cluster function. cluster takes a few similar key arguments to other theftdlc functions (though defaults are set for all, and so only data is required for cluster to work):

data—feature_calculations object containing the raw feature matrix produced by theft::calculate_features
norm_method—character denoting the rescaling/normalising method to apply. Can be one of "zScore", "Sigmoid", "RobustSigmoid", "MinMax", or "MaxAbs". Defaults to "zScore"
unit_int—Boolean whether to rescale into unit interval \([0,1]\) after applying normalisation method. Defaults to FALSE
clust_method—character specifying the clustering algorithm to use. Can be one of "kmeans", "hclust", or "mclust". Defaults to "kmeans"
k—integer denoting the number of clusters to extract. Defaults to 2
features—character vector denoting the names of time-series features to use in the clustering algorithm. Defaults to NULL for no feature filtering and usage of the entire feature matrix
na_removal—character defining the way to deal with NAs produced during feature calculation. Can be one of "feature" or "sample". "feature" removes all features that produced any NAs in any sample, keeping the number of samples the same. "sample" omits all samples that produced at least one NA. Defaults to "feature"
seed—integer to fix R’s random number generator to ensure reproducibility. Defaults to 123
...—additional arguments to be passed to stats::kmeans, stats::hclust, or mclust::Mclust depending on clust_method

cluster returns an object of class feature_clusters which is essentially a named list comprised of two elements:

"Data"—the feature_calculations object supplied to cluster with the cluster label appended
"ModelFit"—the raw model object from the clustering algorithm

We can easily fit a \(k\)-means model with k = 6 (since theft::simData contains data for six different temporal processes):

feature_clusters <- cluster(feature_matrix, k = 6)

From here, it’s easy to do any further analysis or data visualisation:

feature_clusters$Data %>%
    dplyr::filter(names %in% c("CO_HistogramAMI_even_2_5", 
                               "DN_OutlierInclude_p_001_mdrmd")) %>%
    tidyr::pivot_wider(id_cols = c("id", "group", "cluster"), 
                       names_from = "names", values_from = "values") %>%
    ggplot2::ggplot(ggplot2::aes(x = CO_HistogramAMI_even_2_5, 
                                 DN_OutlierInclude_p_001_mdrmd, 
                                 colour = as.factor(cluster))) +
    ggplot2::stat_ellipse(ggplot2::aes(fill = as.factor(cluster)), geom = "polygon", alpha = 0.2) +
    ggplot2::geom_point() +
    ggplot2::labs(colour = "Cluster") +
    ggplot2::guides(fill = "none") +
    ggplot2::scale_fill_brewer(palette = "Dark2") +
    ggplot2::scale_colour_brewer(palette = "Dark2") +
    ggplot2::theme_bw() +
    ggplot2::theme(legend.position = "bottom",
                   panel.grid.minor = ggplot2::element_blank())

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

Introduction to theftdlc

Trent Henderson

2024-10-04

Purpose

Core calculation functions

Data quality checks