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Continuing COoPLBM application

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Louis Lacoste 2023-08-16 15:50:17 +02:00
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152
Rcodes/real_data/CoOPLBM_completion_analyze.Rmd
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@ -3,13 +3,25 @@ output:
md_document:
citation_package: biblatex
---
\subsection{Completing raw data using CoOPLBM~\parencite{anakokDisentanglingStructureEcological2022}}
```{r, setup, include=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = FALSE, dpi = 300)
```
```{r libraries, echo = FALSE, include=FALSE}
require("colSBM")
require(aricode)
require("aricode")
require("ggplot2")
```
```{r useful_functions, echo = FALSE}
if (getwd() == "/home/polarolouis/Nextcloud/Documents/APT/CEI/Stage Recherche Mathématiques/Depuis PC Portable/Stage MIA 2023/rapport-MIA-2023") {
path_to_add <- "Rcodes/real_data/"
} else {
path_to_add <- ""
}
extract_unlist <- function(data) {
readRDS(data) |>
extract_best_bipartite_partition() |>
@ -71,10 +83,10 @@ extract_full_reorder <- function(model_collections_list, target) {
```{r data_importation, echo = FALSE}
# Uncompleted
uncompleted_model_list <- list(
"iid" = extract_unlist("data/dore_uncompleted_collection_clustering_nb_run_1_iid_70_networks_08-06-23-16:31:17.Rds"),
"pi" = extract_unlist("data/dore_uncompleted_collection_clustering_nb_run_1_pi_70_networks_08-06-23-16:52:16.Rds"),
"rho" = extract_unlist("data/dore_uncompleted_collection_clustering_nb_run_1_rho_70_networks_08-06-23-16:49:58.Rds"),
"pirho" = extract_unlist("data/dore_uncompleted_collection_clustering_nb_run_1_pirho_70_networks_08-06-23-16:41:33.Rds")
"iid" = extract_unlist(paste0(path_to_add, "data/dore_uncompleted_collection_clustering_nb_run_1_iid_70_networks_08-06-23-16:31:17.Rds")),
"pi" = extract_unlist(paste0(path_to_add, "data/dore_uncompleted_collection_clustering_nb_run_1_pi_70_networks_08-06-23-16:52:16.Rds")),
"rho" = extract_unlist(paste0(path_to_add, "data/dore_uncompleted_collection_clustering_nb_run_1_rho_70_networks_08-06-23-16:49:58.Rds")),
"pirho" = extract_unlist(paste0(path_to_add, "data/dore_uncompleted_collection_clustering_nb_run_1_pirho_70_networks_08-06-23-16:41:33.Rds"))
)
# Below we will need to have the netid in the same order so we choose to use the
@ -86,28 +98,28 @@ uncompleted_clusterings <- extract_full_reorder(uncompleted_model_list, netid_or
# 0.2 threshold
point_2_model_list <- list(
"iid" = extract_unlist("data/dore_point_2_completed_collection_clustering_nb_run_1_iid_70_networks_07-06-23-18:40:10.Rds"),
"pi" = extract_unlist("data/dore_point_2_completed_collection_clustering_nb_run_1_pi_70_networks_07-06-23-19:22:19.Rds"),
"rho" = extract_unlist("data/dore_point_2_completed_collection_clustering_nb_run_1_rho_70_networks_07-06-23-20:03:53.Rds"),
"pirho" = extract_unlist("data/dore_point_2_completed_collection_clustering_nb_run_1_pirho_70_networks_07-06-23-21:09:12.Rds")
"iid" = extract_unlist(paste0(path_to_add, "data/dore_point_2_completed_collection_clustering_nb_run_1_iid_70_networks_07-06-23-18:40:10.Rds")),
"pi" = extract_unlist(paste0(path_to_add, "data/dore_point_2_completed_collection_clustering_nb_run_1_pi_70_networks_07-06-23-19:22:19.Rds")),
"rho" = extract_unlist(paste0(path_to_add, "data/dore_point_2_completed_collection_clustering_nb_run_1_rho_70_networks_07-06-23-20:03:53.Rds")),
"pirho" = extract_unlist(paste0(path_to_add, "data/dore_point_2_completed_collection_clustering_nb_run_1_pirho_70_networks_07-06-23-21:09:12.Rds"))
)
point_2_clusterings <- extract_full_reorder(point_2_model_list, netid_order)
# 0.5 threshold
point_5_model_list <- list(
"iid" = extract_unlist("data/dore_point_5_completed_collection_clustering_nb_run_1_iid_70_networks_07-06-23-19:19:53.Rds"),
"pi" = extract_unlist("data/dore_point_5_completed_collection_clustering_nb_run_1_pi_70_networks_07-06-23-21:31:20.Rds"),
"rho" = extract_unlist("data/dore_point_5_completed_collection_clustering_nb_run_1_rho_70_networks_07-06-23-21:03:50.Rds"),
"pirho" = extract_unlist("data/dore_point_5_completed_collection_clustering_nb_run_1_pirho_70_networks_07-06-23-21:13:10.Rds")
"iid" = extract_unlist(paste0(path_to_add, "data/dore_point_5_completed_collection_clustering_nb_run_1_iid_70_networks_07-06-23-19:19:53.Rds")),
"pi" = extract_unlist(paste0(path_to_add, "data/dore_point_5_completed_collection_clustering_nb_run_1_pi_70_networks_07-06-23-21:31:20.Rds")),
"rho" = extract_unlist(paste0(path_to_add, "data/dore_point_5_completed_collection_clustering_nb_run_1_rho_70_networks_07-06-23-21:03:50.Rds")),
"pirho" = extract_unlist(paste0(path_to_add, "data/dore_point_5_completed_collection_clustering_nb_run_1_pirho_70_networks_07-06-23-21:13:10.Rds"))
)
point_5_clusterings <- extract_full_reorder(point_5_model_list, netid_order)
# Uniform re-sampled
random_model_list <- list(
"iid" = extract_unlist("data/dore_random_completed_collection_clustering_nb_run_1_iid_70_networks_07-06-23-21:44:14.Rds"),
"pi" = extract_unlist("data/dore_random_completed_collection_clustering_nb_run_1_pi_70_networks_07-06-23-22:52:47.Rds"),
"rho" = extract_unlist("data/dore_random_completed_collection_clustering_nb_run_1_rho_70_networks_08-06-23-18:16:04.Rds"),
"pirho" = extract_unlist("data/dore_random_completed_collection_clustering_nb_run_1_pirho_70_networks_07-06-23-23:07:08.Rds")
"iid" = extract_unlist(paste0(path_to_add, "data/dore_random_completed_collection_clustering_nb_run_1_iid_70_networks_07-06-23-21:44:14.Rds")),
"pi" = extract_unlist(paste0(path_to_add, "data/dore_random_completed_collection_clustering_nb_run_1_pi_70_networks_07-06-23-22:52:47.Rds")),
"rho" = extract_unlist(paste0(path_to_add, "data/dore_random_completed_collection_clustering_nb_run_1_rho_70_networks_08-06-23-18:16:04.Rds")),
"pirho" = extract_unlist(paste0(path_to_add, "data/dore_random_completed_collection_clustering_nb_run_1_pirho_70_networks_07-06-23-23:07:08.Rds"))
)
random_clusterings <- extract_full_reorder(random_model_list, netid_order)
```
@ -116,8 +128,11 @@ random_clusterings <- extract_full_reorder(random_model_list, netid_order)
After performing a netclustering on the raw data, we will see if the detect
structure resulting in the clustering comes from the sampling effort. To test
this we will use the CoOPLBM model by
\cite{anakokDisentanglingStructureEcological2022} to complete the data.
this we will use the CoOPLBM model by~\cite{anakokDisentanglingStructureEcological2022} to complete the data.
\emph{Note:}~\cite{anakokDisentanglingStructureEcological2022} provided data
for the networks for which the method was applicable, this explains that
there are fewer networks in the collections.
The CoOPLBM model assumes that the observed incidence matrix $R$ is an
element-wise product of an $M$ matrix following an LBM and an $N$ matrix which
@ -149,17 +164,20 @@ $$X_{ij} = \begin{cases}
ARI_netclustering_models <- function(
clustering_compare,
uncompleted_clustering = uncompleted_clustering,
models = c("iid", "pi", "rho", "pirho")) {
sapply(models, function(model) {
ARI(
uncompleted_clusterings[
which(uncompleted_clusterings$model == model),
]$collection_id,
clustering_compare[
which(clustering_compare$model == model),
]$collection_id
)
})
models = c("iid", "pi", "rho", "pirho"),
models_names = c("$iid\\text{-}colSBM$", "$\\pi\\text{-}colSBM$", "$\\rho\\text{-}colSBM$", "$\\pi\\rho\\text{-}colSBM$")) {
out <- sapply(models, function(model) {
ARI(
uncompleted_clusterings[
which(uncompleted_clusterings$model == model),
]$collection_id,
clustering_compare[
which(clustering_compare$model == model),
]$collection_id
)
})
names(out) <- models_names
out
}
```
@ -168,26 +186,82 @@ Here, the completion threshold is set to $0.5$.
First we will compute an ARI on the collection id given by the raw data and the
completed matrix.
```{r 0.5_ARI, echo = FALSE, results="asis"}
```{r 0.5_ARI, echo = FALSE}
knitr::kable(ARI_netclustering_models(point_5_clusterings),
col.names = c("ARI with uncompleted data")
col.names = c("ARI with uncompleted data"),
escape = FALSE,
booktabs = TRUE,
digits = 2,
position = "h!",
caption = "\\label{tab:ari-table-0-5-completed} Table of ARI between 0.5 completed data and uncompleted data"
)
```
In the above table, one can see the network clustering obtained after applying
CoOPLBM has not much in common with the clustering of the uncompleted data.
In the table \ref{tab:ari-table-0-5-completed}, one can see the network clustering obtained after applying
CoOPLBM has not much in common with the clustering of the uncompleted data. Thus
we can think that the completion changed significantly the interactions in
the collections.
##### Number of sub-collections and details of each sub-collection
```{r 0.5_partition_numbers, echo = FALSE}
```
##### Supplementary information
```{r supinfo, echo = FALSE}
supinfo <- readxl::read_xlsx(paste0(path_to_add, "data/supinfo.xlsx"), sheet = 2)
interaction_data <- read.table(file = paste0(path_to_add, "data/interaction-data.txt"), sep = "\t", header = TRUE)
seq_ids_network_aggreg <- unique(interaction_data$id_network_aggreg)
incidence_matrices <- readRDS(file = paste0(path_to_add, "data/dore-matrices.Rds"))
names_aggreg_networks <- names(incidence_matrices)
get_vector_clustering_net <- function(unlisted_model) {
vectorClusteringNet <- numeric(nrow(supinfo))
for (k in 1:length(unlisted_model)) {
idclust <- match(unlisted_model[[k]]$net_id, names_aggreg_networks)
supinfoclust <- match(seq_ids_network_aggreg[idclust], supinfo$Idweb)
vectorClusteringNet[supinfoclust] <- k
}
vectorClusteringNet
# [! vectorClusteringNet %in% 0] # Filtering the network not present in uncompleted data
}
```
```{r boxplot-function, echo = FALSE}
supinfo_boxplot <- function(supinfo_vector, parameter, pretty_name) {
return(ggplot(supinfo) +
aes(
x = factor(supinfo_vector), y = parameter,
fill = as.factor(supinfo_vector), group = as.factor(supinfo_vector)
) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
labs(
x = "Collection number", y = pretty_name,
fill = "Collection number"
))
}
```
```{r, supinfo_point_5}
supinfo_point_5_iid <- get_vector_clustering_net(point_5_model_list[["iid"]])
```
### 0.2 completed threshold
The $0.2$ threshold adds a lot of interactions compared to raw matrix.
```{r 0.2_ARI, echo = FALSE, results="asis"}
knitr::kable(ARI_netclustering_models(point_2_clusterings),
col.names = c("ARI with uncompleted data")
col.names = c("ARI with uncompleted data"),
escape = FALSE,
booktabs = TRUE,
digits = 2,
position = "h!",
caption = "\\label{tab:ari-table-0-2-completed} Table of ARI between 0.2 completed data and uncompleted data"
)
```
@ -202,6 +276,12 @@ $$\mathbb{P}(X_{i,j} = 1) = M_{i,j} $$
```{r random_ARI, echo = FALSE, results="asis"}
knitr::kable(ARI_netclustering_models(random_clusterings),
col.names = c("ARI with uncompleted data")
col.names = c("ARI with uncompleted data"),
escape = FALSE,
booktabs = TRUE,
digits = 2,
position = "h!",
caption = "\\label{tab:ari-table-random-completed} Table of ARI between
randomly completed data and uncompleted data"
)
```

89
Rcodes/real_data/CoOPLBM_completion_analyze.tex
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@ -1,11 +1,18 @@
\subsection{Completing raw data using CoOPLBM~\parencite{anakokDisentanglingStructureEcological2022}}
\hypertarget{context-of-this-analysis}{%
\subsubsection{Context of this
analysis}\label{context-of-this-analysis}}
After performing a netclustering on the raw data, we will see if the
detect structure resulting in the clustering comes from the sampling
effort. To test this we will use the CoOPLBM model by
\cite{anakokDisentanglingStructureEcological2022} to complete the data.
effort. To test this we will use the CoOPLBM model
by\textasciitilde{}\cite{anakokDisentanglingStructureEcological2022} to
complete the data.
\emph{Note:}\textasciitilde{}\cite{anakokDisentanglingStructureEcological2022}
provided data for the networks for which the method was applicable, this
explains that there are fewer networks in the collections.
The CoOPLBM model assumes that the observed incidence matrix \(R\) is an
element-wise product of an \(M\) matrix following an LBM and an \(N\)
@ -49,42 +56,55 @@ Here, the completion threshold is set to \(0.5\).
First we will compute an ARI on the collection id given by the raw data
and the completed matrix.
\begin{longtable}[]{@{}lr@{}}
\toprule
& ARI with uncompleted data\tabularnewline
\midrule
\endhead
iid & 0.1142823\tabularnewline
pi & 0.0263660\tabularnewline
rho & 0.0933340\tabularnewline
pirho & 0.2158747\tabularnewline
\bottomrule
\end{longtable}
\begin{table}[h!]
In the above table, one can see the network clustering obtained after
applying CoOPLBM has not much in common with the clustering of the
uncompleted data.
\caption{\label{tab:0.5_ARI}\label{tab:ari-table-0-5-completed} Table of ARI between 0.5 completed data and uncompleted data}
\centering
\begin{tabular}[t]{lr}
\toprule
& ARI with uncompleted data\\
\midrule
$iid\text{-}colSBM$ & 0.11\\
$\pi\text{-}colSBM$ & 0.03\\
$\rho\text{-}colSBM$ & 0.09\\
$\pi\rho\text{-}colSBM$ & 0.22\\
\bottomrule
\end{tabular}
\end{table}
In the table \ref{tab:ari-table-0-5-completed}, one can see the network
clustering obtained after applying CoOPLBM has not much in common with
the clustering of the uncompleted data. Thus we can think that the
completion changed significantly the interactions in the collections.
\hypertarget{number-of-sub-collections-and-details-of-each-sub-collection}{%
\subparagraph{Number of sub-collections and details of each
sub-collection}\label{number-of-sub-collections-and-details-of-each-sub-collection}}
\hypertarget{supplementary-information}{%
\subparagraph{Supplementary
information}\label{supplementary-information}}
\hypertarget{completed-threshold-1}{%
\subsubsection{0.2 completed threshold}\label{completed-threshold-1}}
The \(0.2\) threshold adds a lot of interactions compared to raw matrix.
\begin{longtable}[]{@{}lr@{}}
\begin{table}[h!]
\caption{\label{tab:0.2_ARI}\label{tab:ari-table-0-2-completed} Table of ARI between 0.2 completed data and uncompleted data}
\centering
\begin{tabular}[t]{lr}
\toprule
& ARI with uncompleted data\tabularnewline
& ARI with uncompleted data\\
\midrule
\endhead
iid & 0.0429465\tabularnewline
pi & 0.0330057\tabularnewline
rho & 0.0187305\tabularnewline
pirho & 0.0357728\tabularnewline
$iid\text{-}colSBM$ & 0.04\\
$\pi\text{-}colSBM$ & 0.03\\
$\rho\text{-}colSBM$ & 0.02\\
$\pi\rho\text{-}colSBM$ & 0.04\\
\bottomrule
\end{longtable}
\end{tabular}
\end{table}
Same as for \(0.5\), after applying CoOPLBM the obtained clustering
doesn't match the uncompleted data.
@ -97,14 +117,19 @@ The \(M\) matrix is used to sample a new \(X\) matrix which elements are
the realisation of Bernoulli distributions of probability \(M_{i,j}\).
\[\mathbb{P}(X_{i,j} = 1) = M_{i,j} \]
\begin{longtable}[]{@{}lr@{}}
\begin{table}[h!]
\caption{\label{tab:random_ARI}\label{tab:ari-table-random-completed} Table of ARI between
randomly completed data and uncompleted data}
\centering
\begin{tabular}[t]{lr}
\toprule
& ARI with uncompleted data\tabularnewline
& ARI with uncompleted data\\
\midrule
\endhead
iid & 0.0148172\tabularnewline
pi & 0.0265793\tabularnewline
rho & 0.0051536\tabularnewline
pirho & 0.0152299\tabularnewline
$iid\text{-}colSBM$ & 0.01\\
$\pi\text{-}colSBM$ & 0.03\\
$\rho\text{-}colSBM$ & 0.01\\
$\pi\rho\text{-}colSBM$ & 0.02\\
\bottomrule
\end{longtable}
\end{tabular}
\end{table}

1
Rcodes/real_data/application_dore.Rmd
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@ -103,7 +103,6 @@ for (k in 1:length(iid_unlist)) {
Using supplementary information we obtain the following boxplots.
A
```{r boxplot-function, echo = FALSE}
supinfo_boxplot <- function(parameter, pretty_name) {

2
Rcodes/real_data/application_dore.tex
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@ -40,8 +40,6 @@ information}\label{comparison-with-additional-information}}
Using supplementary information we obtain the following boxplots.
A
\begin{figure}
\centering
\includegraphics{./img/de77b630fb66744d3a3ed68e45be765532d1eb0f.png}

BIN
rapport.pdf
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1
rapport.tex
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@ -1098,7 +1098,6 @@ analysis can be found on the report repository at
\chapter{Applications}
\include{Rcodes/real_data/application_dore}
\subsection{Completing raw data using CoOPLBM \parencite{anakokDisentanglingStructureEcological2022}}
\include{Rcodes/real_data/CoOPLBM_completion_analyze}
\printbibliography