# The Theory Behind gips

# What the `gips` is based on

The package is based on the article [1]. There the math behind the package is precisely demonstrated, and all the theorems are proven.

In this vignette, we present a gentle introduction. We want to point out all the most important results from this work from the user’s point of view. We will also show examples of those results in the `gips` package.

    library(gips)

As mentioned in the abstract, the outline of the paper is to “derive the distribution of the maximum likelihood estimate of the covariance parameter \(\Sigma\) (…)” and then to “perform Bayesian model selection in the class of complete Gaussian models invariant by the action of a subgroup of the symmetric group (…)”. Those ideas are implemented in the `gips` package.

## Alternative reference

The theory derived in [1] is for general group invariance, while in this package, we only consider invariance under cyclic groups. This allows for massive simplifications. This simplified version is comprehensibly set out in [2].

# Basic definitions

Let \(V=\{1,\ldots,p\}\) be a finite index set, and for every \(i\in \{1, \dots, n\}\), \(Z^{(i)}=(Z_1^{(i)},\ldots, Z_p^{(i)})^\top\) be a multivariate random variable following a centered Gaussian model \(\mathrm{N}_p(0,\Sigma)\), and let \(Z^{(1)},\ldots, Z^{(n)}\) be an i.i.d. (independent and identically distributed) sample from this distribution. Name the whole sample \(Z = (Z^{(1)},\ldots, Z^{(n)})\).

Let \(\mathfrak{S}_p\) denote the symmetric group on \(V\), that is, the set of all permutations on \(\{1,\ldots,p\}\) with function composition as the group operation. Let \(\Gamma\) be an arbitrary subgroup of \(\mathfrak{S}_p\). The model \(\mathrm{N}_p(0,\Sigma)\) is said to be invariant under the action of \(\Gamma\) if for all \(g\in \Gamma\), \(g\cdot\Sigma\cdot g^\top=\Sigma\) (here, we identify a permutation \(g\) with its permutation matrix).

For a subgroup \(\Gamma \subset \mathfrak{S}_p\), we define the colored space, i.e., the space of symmetric matrices invariant under \(\Gamma\), \[\mathcal{Z}_{\Gamma} := \{S \in \mathrm{Sym}(p;\mathbb{R})\colon S_{i,j} = S_{\sigma(i),\sigma(j)} \text{ for all }\sigma \in \Gamma\mbox{ for all }i,j\in V\},\] and the colored cone of positive definite matrices valued in \(\mathcal{Z}_{\Gamma}\), \[\mathcal{P}_{\Gamma} := \mathcal{Z}_{\Gamma} \cap \mathrm{Sym}^+(p;\mathbb{R}).\]

# Block Decomposition - [1], Theorem 1

The main theoretical result in this theory (Theorem 1 in [1]) states that given a permutation subgroup \(\Gamma\) there exists an orthogonal matrix \(U_\Gamma\) such that all the symmetric matrices \(S\in\mathcal{Z}_\Gamma\) can be transformed into block-diagonal form.

The exact form of blocks depends on so-called structure constants \((k_i,d_i,r_i)_{i=1}^L\). It is worth noting that \(k = d\) for cyclic groups \(\Gamma = \langle\sigma\rangle\). Because gips searches only within cyclic subgroups, this simplifies the theory.

### Examples

    p <- 6
    S <- matrix(c(
      1.1, 0.9, 0.8, 0.7, 0.8, 0.9,
      0.9, 1.1, 0.9, 0.8, 0.7, 0.8,
      0.8, 0.9, 1.1, 0.9, 0.8, 0.7,
      0.7, 0.8, 0.9, 1.1, 0.9, 0.8,
      0.8, 0.7, 0.8, 0.9, 1.1, 0.9,
      0.9, 0.8, 0.7, 0.8, 0.9, 1.1
    ), nrow = p)

`S` is a symmetric matrix invariant under the group \(\Gamma = \left<(1,2,3,4,5,6)\right>\).

    g_perm <- gips_perm("(1,2,3,4,5,6)", p)
    U_Gamma <- prepare_orthogonal_matrix(g_perm)

    block_decomposition <- t(U_Gamma) %*% S %*% U_Gamma
    round(block_decomposition, 5)
    #>      [,1] [,2] [,3] [,4] [,5] [,6]
    #> [1,]  5.2  0.0  0.0  0.0  0.0  0.0
    #> [2,]  0.0  0.5  0.0  0.0  0.0  0.0
    #> [3,]  0.0  0.0  0.5  0.0  0.0  0.0
    #> [4,]  0.0  0.0  0.0  0.1  0.0  0.0
    #> [5,]  0.0  0.0  0.0  0.0  0.1  0.0
    #> [6,]  0.0  0.0  0.0  0.0  0.0  0.2

The transformed matrix is in the block-diagonal form of [1], Theorem 1. Blank entries are off-block entries and equal to 0. Notice that, for example, position [2,3] is not blank even though its value is 0. This is because it is a part of the block-diagonal form but happens to have a value of 0.

The result was rounded to the 5th place after the decimal to hide the inaccuracies of floating point arithmetic.

Let’s see the other example:

    p <- 6
    S <- matrix(c(
      1.2, 0.9, 0.9, 0.4, 0.2, 0.1,
      0.9, 1.2, 0.9, 0.1, 0.4, 0.2,
      0.9, 0.9, 1.2, 0.2, 0.1, 0.4,
      0.4, 0.1, 0.2, 1.2, 0.9, 0.9,
      0.2, 0.4, 0.1, 0.9, 1.2, 0.9,
      0.1, 0.2, 0.4, 0.9, 0.9, 1.2
    ), nrow = p)

Now, `S` is a symmetric matrix invariant under the group \(\Gamma = \left<(1,2,3)(4,5,6)\right>\).

    g_perm <- gips_perm("(1,2,3)(4,5,6)", p)
    U_Gamma <- prepare_orthogonal_matrix(g_perm)

    block_decomposition <- t(U_Gamma) %*% S %*% U_Gamma
    round(block_decomposition, 5)
    #>      [,1] [,2]   [,3]    [,4]    [,5]   [,6]
    #> [1,]  3.0  0.7 0.0000  0.0000  0.0000 0.0000
    #> [2,]  0.7  3.0 0.0000  0.0000  0.0000 0.0000
    #> [3,]  0.0  0.0 0.3000  0.0000  0.2500 0.0866
    #> [4,]  0.0  0.0 0.0000  0.3000 -0.0866 0.2500
    #> [5,]  0.0  0.0 0.2500 -0.0866  0.3000 0.0000
    #> [6,]  0.0  0.0 0.0866  0.2500  0.0000 0.3000

Again, this result is in accordance with [1], Theorem 1. Notice the zeros in `block_decomposition`: \[\forall_{i\in\{1,2\},j\in\{3,4,5,6\}}\text{block_decomposition}[i,j] = 0\]

# Project Matrix - [1, Eq. (6)]

One can also take any symmetric square matrix `S` and find the orthogonal projection on \(\mathcal{Z}_{\Gamma}\), the space of matrices invariant under the given permutation:

\[\pi_\Gamma(S) := \frac{1}{|\Gamma|}\sum_{\sigma\in\Gamma}\sigma\cdot S\cdot\sigma^\top\]

The projected matrix is the element of the cone \(\pi_\Gamma(S)\in\mathcal{Z}_{\Gamma}\), which means: \[\forall_{i,j\in \{1,\ \dots,\ p\}} \pi_\Gamma(S)[i,j] = \pi_\Gamma(S)[\sigma(i),\sigma(j)] \text{ for all }\sigma\in\Gamma\]

So it has some identical elements.

### Trivial case

Note that for \(\Gamma = \{\text{id}\} = \{(1)(2)\dots(p)\}\) we have \(\pi_{\{\text{id}\}}(S) = S\).

So, no additional assumptions are made; thus, the standard covariance estimator is the best we can do.

### Notation

We will abbreviate the notation: when the \(\Gamma = \left< c \right>\) is a cyclic group of a permutation \(c\), we will write \(\pi_{c}(S) := \pi_{\Gamma}(S) = \pi_{\left< c \right>}(S)\).

### Example

Let `S` be any symmetric square matrix:

    round(S, 2)
    #>        [,1]   [,2]   [,3]   [,4]   [,5]   [,6]
    #> [1,] 137.51 -16.21  10.03   0.16 -24.35 -17.42
    #> [2,] -16.21  34.08 -10.62  15.93  12.23  -2.74
    #> [3,]  10.03 -10.62  35.47   3.10  -3.81  -9.60
    #> [4,]   0.16  15.93   3.10  26.74   7.71 -13.51
    #> [5,] -24.35  12.23  -3.81   7.71  26.00  -7.24
    #> [6,] -17.42  -2.74  -9.60 -13.51  -7.24  16.77

One can project this matrix, for example, on \(\Gamma = \left< \text{perm} \right> = \left<(1,2)(3,4,5,6)\right>\):

    S_projected <- project_matrix(S, perm = "(1,2)(3,4,5,6)")
    round(S_projected, 2)
    #>        [,1]   [,2]  [,3]  [,4]  [,5]  [,6]
    #> [1,]  85.80 -16.21 -0.28 -3.91 -0.28 -3.91
    #> [2,] -16.21  85.80 -3.91 -0.28 -3.91 -0.28
    #> [3,]  -0.28  -3.91 26.25 -1.51 -8.66 -1.51
    #> [4,]  -3.91  -0.28 -1.51 26.25 -1.51 -8.66
    #> [5,]  -0.28  -3.91 -8.66 -1.51 26.25 -1.51
    #> [6,]  -3.91  -0.28 -1.51 -8.66 -1.51 26.25

Notice in the `S_projected` matrix there are identical elements according to the equation from the beginning of this section. For example, `S_projected[1,1] = S_projected[2,2]`.

## \(C_\sigma\) and `n0`

It is a well-known fact that without additional assumptions, the Maximum Likelihood Estimator (MLE) of the covariance matrix in the Gaussian model exists if and only if \(n \ge p\). However, if the additional assumption is added as the covariance matrix is invariant under permutation \(\sigma\), then the sample size \(n\) required for the MLE to exist is lower than \(p\). It is equal to the number of cycles, denoted hereafter by \(C_\sigma\).

For example, when \(p=6\) and the permutation \(\sigma = (1,2,3,4,5,6)\) is discovered by the `find_MAP()` function, it consists of a single cycle, so \(C_\sigma = 1\). Therefore a single observation would be enough to estimate a covariance matrix with `project_matrix()`. If the permutation \(\sigma = (1,2)(3,4,5,6)\) is discovered, then \(C_\sigma = 2\), and so 2 observations would be enough.

To get this \(C_\sigma\) number in `gips`, one can call `summary()` on the appropriate `gips` object:

    g1 <- gips(S, n, perm = "(1,2,3,4,5,6)", was_mean_estimated = FALSE)
    summary(g1)$n0
    #> [1] 1
    g2 <- gips(S, n, perm = "(1,2)(3,4,5,6)", was_mean_estimated = FALSE)
    summary(g2)$n0
    #> [1] 2

This is called `n0` and not \(C_\sigma\) because it is increased by 1 when the mean was estimated:

    S <- cov(Z)
    g1 <- gips(S, n, perm = "(1,2,3,4,5,6)", was_mean_estimated = TRUE)
    summary(g1)$n0
    #> [1] 2
    g2 <- gips(S, n, perm = "(1,2)(3,4,5,6)", was_mean_estimated = TRUE)
    summary(g2)$n0
    #> [1] 3

# Bayesian model selection

Given data matrix Z, we would like to discover any hidden structure of dependencies between features. Luckily, the paper demonstrates a way how to find it.

### General workflow

1. Choose the prior distribution on \(\Gamma\) and \(\Sigma\).
2. Calculate the posteriori distribution (up to a normalizing constant) by the formula [1], (30).
3. Use the Metropolis-Hastings algorithm to find the permutation with the biggest value of the posterior probability \(\mathbb{P}(\Gamma|Z)\).

### Details on the prior distribution

The considered prior distribution of \(\Gamma\) and \(K=\Sigma^{-1}\):

1. \(\Gamma\) is uniformly distributed on the set of all cyclic subgroups of \(\mathfrak{S}_p\).
2. \(K\) given \(\Gamma\) follows the Diaconis-Ylvisaker conjugate prior distribution with parameters \(\delta\) (real number, \(\delta > 1\)) and \(D\) (symmetric, positive definite square matrix of the same size as `S`), see [1], Sec. 3.4.

Footnote: Actually, for the special case of \(\Gamma = \{\text{id}\}\), the parameter \(\delta > 0\) is theoretically correct. In `gips`, we want this to be defined for all cyclic groups \(\Gamma\), so we restrict \(\delta > 1\). Refer to the [1].

### `gips` technical details

In `gips`, \(\delta\) is named `delta`, and \(D\) is named `D_matrix`. By default, they are set to \(3\) and `diag(d, p)`, respectively, where `d = mean(diag(S))`. However, it is worth running the procedure for several parameters `D_matrix` of form \(d\cdot diag(p)\) for positive constant \(d\). Small \(d\) (compared to the data) favors small structures. Large \(d\) will “forget” the data.

One can calculate the logarithm of formula (30) with the function `log_posteriori_of_gips()`.

### Interpretation

When all assumptions are met, the formula (30) puts a number on each permutation’s cyclic group. The bigger its value, the more likely the data was drawn from that model.

When one finds the permutation group \(c_{\text{max}}\) that maximizes (30), \[c_{\text{map}} = \operatorname{arg\,max}_{c\in\mathfrak{S}_p} \mathbb{P}\left(\Gamma=c|Z^{(1)},\ldots,Z^{(n)}\right)\]

one can reasonably assume the data \(Z\) was drawn from the model \[\mathrm{N}_p(0,\pi_{c_{\text{map}}}(S))\]

where \(S = \frac{1}{n} \sum_{i=1}^n Z^{(i)}\cdot {Z^{(i)}}^\top\)

In such a case, we call \(c_{\text{map}}\) the Maximum A Posteriori (MAP).

### Finding the MAP Estimator

The space of all permutations is enormous for bigger \(p\) (in our experiments, \(p\ge 10\) is too big). In such a big space, estimating the MAP is more practical than calculating it exactly.

Metropolis-Hastings algorithm suggested by the authors of [1] is a natural way to do it. To see the discussion on it and other options available in `gips`, see `vignette("Optimizers", package="gips")` or its pkgdown page.

### Example

    # Prepare model, multivariate normal distribution
    p <- 6
    number_of_observations <- 4
    mu <- numeric(p)
    sigma_matrix <- matrix(
      data = c(
        1.05, 0.8, 0.6, 0.4, 0.6, 0.8,
        0.8, 1.05, 0.8, 0.6, 0.4, 0.6,
        0.6, 0.8, 1.05, 0.8, 0.6, 0.4,
        0.4, 0.6, 0.8, 1.05, 0.8, 0.6,
        0.6, 0.4, 0.6, 0.8, 1.05, 0.8,
        0.8, 0.6, 0.4, 0.6, 0.8, 1.05
      ),
      nrow = p, byrow = TRUE
    ) # sigma_matrix is a matrix invariant under permutation (1,2,3,4,5,6)

    # Generate example data from a model:
    Z <- withr::with_seed(2022,
      code = MASS::mvrnorm(number_of_observations,
        mu = mu, Sigma = sigma_matrix
      )
    )
    # End of prepare model

Let’s say we have this data, `Z`. It has dimension \(p=6\) and only \(4\) observations. Let’s assume `Z` was drawn from the normal distribution with the mean \((0,0,0,0,0,0)\). We want to estimate the covariance matrix:

    dim(Z)
    #> [1] 4 6
    number_of_observations <- nrow(Z) # 4
    p <- ncol(Z) # 6

    # Calculate the covariance matrix from the data (assume the mean is 0):
    S <- (t(Z) %*% Z) / number_of_observations

    # Make the gips object out of data:
    g <- gips(S, number_of_observations, was_mean_estimated = FALSE)

    g_map <- find_MAP(g, optimizer = "brute_force")
    #> ================================================================================
    print(g_map)
    #> The permutation (1,2,3,4,5,6):
    #>  - was found after 362 posteriori calculations;
    #>  - is 133.158 times more likely than the () permutation.

    S_projected <- project_matrix(S, g_map)

We see the posterior probability [1,(30)] has the biggest value for the permutation \((1,2,3,4,5,6)\). It is 133.16 times bigger than for the trivial \(\text{id} = (1)(2)\ldots(p)\) permutation. Under the assumptions (centered Gaussian), we therefore interpret the model \(\mathrm{N}_p(0,\text{S_projected})\) as more reasonable than \(\mathrm{N}_p(0,\text{S})\) for the data `Z`.

# Information Criterion - AIC and BIC

One may be interested in Akaike’s An Information Criterion (AIC) or Schwarz’s Bayesian Information Criterion (BIC) of the found model. Those are defined based on log-Likelihood:

\[\log L\left(\Sigma; Z^{(1)},\ldots,Z^{(n)}\right) = \sum_{i=1}^n \left(- \frac{p}{2}\log (2\pi) - \frac{1}{2}\log\left( \det\left( \Sigma\right)\right) - \frac12 {Z^{(i)}}^\top \Sigma^{-1} Z^{(i)}\right)= \]

\[- \frac{np}{2}\log (2\pi) - \frac{n}{2}\log\left( \det\left( \Sigma\right)\right) - \frac{n}2\mathrm{tr}(\Sigma^{-1} S),\] where \(S = \frac{1}{n} \sum_{i=1}^n Z^{(i)}\cdot {Z^{(i)}}^\top\).

The MLE of \(\Sigma\) in a model invariant under \(\Gamma=c\) is \(\hat{\Sigma} = \pi_{c}(S)\). Further, for every \(c\) we have \(\mathrm{tr}(\pi_{c}(S)^{-1} \cdot S) = p\), so:

\[\log L\left(\pi_{c}(S); Z^{(1)},\ldots,Z^{(n)}\right) = - \frac{np}{2}\log (2\pi) - \frac{n}{2}\log\left( \det\left( \pi_{c}(S)\right)\right) - \frac{np}2\]

which can be calculated by `logLik.gips()`.

Then AIC and BIC are defined by:

\[AIC = 2 \cdot (\dim M) -2 \log L(\pi_{c}(S))\] \[BIC = (\log n) \cdot (\dim M) -2 \log L(\pi_{c}(S))\]

A smaller value of the criteria for a given model indicates a better fit.

Those can be calculated by `AIC.gips()` and `BIC.gips()`.

### Estimated mean

When the mean was estimated, we have \(S = \frac{1}{n-1} \sum_{i=1}^n (Z^{(i)} - \bar{Z})\cdot ({Z^{(i)} - \bar{Z})}^\top\), where \(\bar{Z} = \frac{1}{n} \sum_{i=1}^n Z^{(i)}\). Then in the \(\log L\) we use \(n-1\) in stead of \(n\). Definitions of AIC and BIC stay the same.

### Example

Consider an example similar to one in the Bayesian model selection section:

    # Prepare model, multivariate normal distribution
    p <- 6
    number_of_observations <- 7
    mu <- numeric(p)
    sigma_matrix <- matrix(
      data = c(
        1.05, 0.8, 0.6, 0.4, 0.6, 0.8,
        0.8, 1.05, 0.8, 0.6, 0.4, 0.6,
        0.6, 0.8, 1.05, 0.8, 0.6, 0.4,
        0.4, 0.6, 0.8, 1.05, 0.8, 0.6,
        0.6, 0.4, 0.6, 0.8, 1.05, 0.8,
        0.8, 0.6, 0.4, 0.6, 0.8, 1.05
      ),
      nrow = p, byrow = TRUE
    ) # sigma_matrix is a matrix invariant under permutation (1,2,3,4,5,6)

    # Generate example data from a model:
    Z <- withr::with_seed(2022,
      code = MASS::mvrnorm(number_of_observations,
        mu = mu, Sigma = sigma_matrix
      )
    )
    # End of prepare model

Let’s say we have this data, `Z`. It has dimension \(p=6\) and \(7\) observations. Let’s assume `Z` was drawn from the normal distribution with the mean \((0,0,0,0,0,0)\). We want to estimate the covariance matrix:

    dim(Z)
    #> [1] 7 6
    number_of_observations <- nrow(Z) # 7
    p <- ncol(Z) # 6

    S <- (t(Z) %*% Z) / number_of_observations

    g <- gips(S, number_of_observations, was_mean_estimated = FALSE)
    g_map <- find_MAP(g, optimizer = "brute_force")
    #> ================================================================================

    AIC(g)
    #> [1] 64.19906
    AIC(g_map) # this is smaller, so this is better
    #> [1] 62.99751

    BIC(g)
    #> [1] 63.06318
    BIC(g_map) # this is smaller, so this is better
    #> [1] 62.78115

We will consider a `g_map` better model both in terms of the AIC and the BIC.

# References

[1] Piotr Graczyk, Hideyuki Ishi, Bartosz Kołodziejek, Hélène Massam. “Model selection in the space of Gaussian models invariant by symmetry.” The Annals of Statistics, 50(3) 1747-1774 June 2022. arXiv link; DOI: 10.1214/22-AOS2174

[2] “Learning permutation symmetries with gips in R” by `gips` developers Adam Chojecki, Paweł Morgen, and Bartosz Kołodziejek, Journal of Statistical Software.