Counting the number of measures in ComplexityMeasures.jl

In this page we will count all the possible complexity measures than one can compute with the current version of ComplexityMeasures.jl!

using ComplexityMeasures
using InteractiveUtils: subtypes
import Pkg; Pkg.status("ComplexityMeasures")

Status `~/work/ComplexityMeasures.jl/ComplexityMeasures.jl/docs/Project.toml`
  [ab4b797d] ComplexityMeasures v3.7.3 `~/work/ComplexityMeasures.jl/ComplexityMeasures.jl`

First let's define a function that counts concrete subtypes that we will be re-using to count measures in ComplexityMeasures.jl.

concrete_subtypes(type::Type) = concrete_subtypes!(Any[], type)
function concrete_subtypes!(out, type::Type)
    if !isabstracttype(type)
        push!(out, type)
    else
        foreach(T -> concrete_subtypes!(out, T), subtypes(type))
    end
    out
end

concrete_subtypes! (generic function with 1 method)

Count Based Outcome Spaces

Each OutcomeSpace is a possible way of discretizing the input data. For the purpose of counting measures, we treat the an outcome space with different input parameters as the same outcome space overall.

Some outcome spaces are count-based. We estimate these separately, because they may be estimated with various different probabilities estimators. For our counting here it doesn't matter whether the outcome space supports spatiotemporal or trajectory (uni/multi variate) date. We only care if it is counting based or not.

OUTCOME_SPACES_COUNT = concrete_subtypes(ComplexityMeasures.CountBasedOutcomeSpace)

12-element Vector{Any}:
 BubbleSortSwaps
 AmplitudeAwareOrdinalPatterns
 OrdinalPatterns
 WeightedOrdinalPatterns
 SpatialBubbleSortSwaps
 SpatialDispersion
 SpatialOrdinalPatterns
 CosineSimilarityBinning
 Dispersion
 SequentialPairDistances
 UniqueElements
 ValueBinning

We do a small correction here because two outcome spaces aren't count-based but for internal convenience they satisfy the subtyping relationship

correction_ospaces = (AmplitudeAwareOrdinalPatterns, WeightedOrdinalPatterns)
foreach(
    T -> deleteat!(OUTCOME_SPACES_COUNT, findfirst(isequal(T), OUTCOME_SPACES_COUNT)),
    correction_ospaces
)

OUTCOME_SPACES_COUNT

10-element Vector{Any}:
 BubbleSortSwaps
 OrdinalPatterns
 SpatialBubbleSortSwaps
 SpatialDispersion
 SpatialOrdinalPatterns
 CosineSimilarityBinning
 Dispersion
 SequentialPairDistances
 UniqueElements
 ValueBinning

Probabilities can be estimated from count-based outcome spaces in different ways. We count the same ProbabilitiesEstimators with different input parameters as the same estimator. Each probabilities estimator can be combined with any outcome space.

PROBESTS_COUNT = concrete_subtypes(ProbabilitiesEstimator)

4-element Vector{Any}:
 AddConstant
 BayesianRegularization
 RelativeAmount
 Shrinkage

and we count the combinations

n_outcome_spaces_count = length(OUTCOME_SPACES_COUNT)
n_probests_count = length(PROBESTS_COUNT)
n_probs_count = n_outcome_spaces_count * n_probests_count

Non-count-based outcome spaces

We also provide some outcome spaces that are not count-based, but can still be used to estimate discrete probabilities by using some sort of "relative amount" estimation.

OUTCOME_SPACES_NOCOUNT = setdiff(
    concrete_subtypes(ComplexityMeasures.OutcomeSpace),
    concrete_subtypes(ComplexityMeasures.CountBasedOutcomeSpace),
)

4-element Vector{Any}:
 NaiveKernel
 PowerSpectrum
 TransferOperator
 WaveletOverlap

to which we add back the outcome spaces correction

push!(OUTCOME_SPACES_NOCOUNT, correction_ospaces...)
OUTCOME_SPACES_NOCOUNT

6-element Vector{Any}:
 NaiveKernel
 PowerSpectrum
 TransferOperator
 WaveletOverlap
 AmplitudeAwareOrdinalPatterns
 WeightedOrdinalPatterns

Only RelativeAmount probabilities estimator works with non-count-based outcome spaces

n_probs_noncount = length(OUTCOME_SPACES_NOCOUNT) * 1

Grand total of extracting PMFs from data

Therefore the total ways to estimate discrete probabilities from data in ComplexityMeasures.jl is just

n_probs_discrete = n_probs_noncount + n_probs_count

Discrete Information measures

Currently, the InformationMeasures implemented are different types of entropies and the lesser-known extropies. Each of these measures, in their discrete form, are functions of probability mass functions (PMFs). Therefore, we can combine each of these measure with probabilities estimated using any count-based outcome space and any probabilities estimator.

Let's collect all of these discrete measures

INFO_MEASURES_DISCRETE = concrete_subtypes(InformationMeasure)

12-element Vector{Any}:
 Curado
 Identification
 Kaniadakis
 Renyi
 Shannon
 StretchedExponential
 Tsallis
 ElectronicEntropy
 FluctuationComplexity
 RenyiExtropy
 ShannonExtropy
 TsallisExtropy

Each information measure can be estimated using any of the generic estimators:

INFO_MEASURE_ESTIMATOR_GENERIC = concrete_subtypes(ComplexityMeasures.DiscreteInfoEstimatorGeneric)

2-element Vector{Any}:
 Jackknife
 PlugIn

so we count by multiplying

n_discrete_infoest_generic = length(INFO_MEASURES_DISCRETE)*length(INFO_MEASURE_ESTIMATOR_GENERIC)

In addition to the generic estimators, we also provide additional estimators specific to Shannon entropy.

INFO_MEASURE_ESTIMATOR_SHANNON = concrete_subtypes(ComplexityMeasures.DiscreteInfoEstimatorShannon)

5-element Vector{Any}:
 ChaoShen
 GeneralizedSchuermann
 HorvitzThompson
 MillerMadow
 Schuermann

For these there is no variability of the information measure so

n_discrete_estimators_shannon = length(INFO_MEASURE_ESTIMATOR_SHANNON)

This gives us the total possible ways of estimating information measures given a PMF:

n_discrete_info_est = n_discrete_estimators_shannon + n_discrete_infoest_generic

Grand total of discrete information measures

This total is obtained as the direct multiplication of all ways to obtain a PMF and all ways to compute an information measure from PMF

n_discrete_info = n_discrete_info_est * n_probs_discrete

That's quite a lot and we are only half-way done!

Differential information measures

The differential information measures and their estimators are all grouped into one level of abstraction as long as the user is concerned, so counting things here is very simple!

DIFF_INFO_EST = concrete_subtypes(DifferentialInfoEstimator)

12-element Vector{Any}:
 AlizadehArghami
 Gao
 Goria
 KozachenkoLeonenko
 Kraskov
 Lord
 Zhu
 ZhuSingh
 Correa
 Ebrahimi
 LeonenkoProzantoSavani
 Vasicek

All of these estimate one quantity (the differential Shannon entropy), with the exception of one particular estimator (LeonenkoProzantoSavani) that can estimate also Tsallis and Renyi entropies. Therefore, the number of differential measures one can estimate within ComplexityMeasures.jl is:

n_diff_info = length(DIFF_INFO_EST) + 2

Complexity measures

We also provide a number of estimators that are not probability based, which we call just complexity estimators for this discussion. We count each of these as a separate measure.

COMPLEXITY_ESTIMATORS = concrete_subtypes(ComplexityEstimator)

7-element Vector{Any}:
 ApproximateEntropy
 BubbleEntropy
 LempelZiv76
 MissingDispersionPatterns
 ReverseDispersion
 SampleEntropy
 StatisticalComplexity

However, from these we need to treat StatisticalComplexity separately, so we have

n_complexity_measures_basic = length(COMPLEXITY_ESTIMATORS) - 1

In ComplexityMeasures.jl StatisticalComplexity can be combined with any discrete information measure, any information estimator, and any count-based outcome space. Additionally, StatisticalComplexity in ComplexityMeasures.jl can be combined with any metric from the Distances.jl package.

For StatisticalComplexity, counting all possible combinations of outcome spaces, probabilities estimators, information measure definitions, information measure estimators, along with distance measures, as unique measures would over-inflate the measure count. For practicality, we here count different version of StatisticalComplexity by considering the number of statistical complexity measures resulting from counting unique outcome spaces and information measures, since these are the largest contributors to changes in the computed numerical value of the measure. Therefore we have:

n_complexity_measures_statistical_complexity = length(INFO_MEASURES_DISCRETE) * length(concrete_subtypes(OutcomeSpace))

which gives us the following total number of complexity estimators

n_complexity_measures_total = n_complexity_measures_basic + n_complexity_measures_statistical_complexity

Probabilities functions

Besides calculating complexity measures, ComplexityMeasures.jl gives the user the unique possibility of accessing the probability mass function directly. As we show in the associated article, this allows rather straightforwardly defining new, or expanding existing, complexity measures. For example, the MissingDispersionPatterns is just a wrapper of the missing_outcomes function.

Therefore, we believe it is fair to count a couple of probabilities functions by themselves as additional complexity measures. In particular, we count here the functions probabilities, allproabilities as candidates for it, as all other functions of the library are simple processing of these two. Given that each function can work with any type of outcome space and probability estimation technique, we obtain

n_extra_prob_measures = 2 * n_probs_discrete

Grand total of measures

Right, so the grand total of all measures that can be estimated with ComplexityMeasures.jl are:

n_grand_total =
  n_discrete_info +
  n_diff_info +
  n_complexity_measures_total +
  n_extra_prob_measures