---
title: "ARLClustering - Testing NetScience dataset"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{ARLClustering - Testing NetScience dataset}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(arlclustering)
#library(igraph)
```

## Dataset description
The LesMiserables network dataset is provided as a gml file, containing 1589 nodes and 2742 edges.


## Loading network dataset
- Graph Label    : NetScience Network
- Total Nodes    : 1589
- Total Edges    : 2742
- Average Degree : 3.451227
```{r}
# Start the timer
t1 <- system.time({
  dataset_path <- system.file("extdata", "netscience.gml", package = "arlclustering")
  if (dataset_path == "") {
    stop("netscience.gml file not found")
  }
  
  g <- arlc_get_network_dataset(dataset_path, "NetScience")
  g$graphLabel
  g$totalNodes
  g$totalEdges
  g$averageDegree
})

# Display the total processing time
message("Graph loading Processing Time: ", t1["elapsed"], " seconds\n")
```

## Generate Transactions

Next, we generate transactions from the graph, with a total rows of 1140.

```{r}
# Start the timer
t2 <- system.time({
  transactions <- arlc_gen_transactions(g$graph)
  transactions
})

# Display the total processing time
message("Transaction dataset Processing Time: ", t2["elapsed"], " seconds\n")
```

## Get Apriori Thresholds

We obtain the apriori thresholds for the generated transactions. The following are the thresholds for the apriori execution:
- The Minimum Support    : 0.011
- The Minimum Confidence : 0.5
- The Lift               : 66.16071
- The Gross Rules length : 875908
- The selection Ratio    : 768

```{r}
# Start the timer
t3 <- system.time({
  params <- arlc_get_apriori_thresholds(transactions,
                                        supportRange = seq(0.011, 0.012, by = 0.001),
                                        Conf = 0.5)
  params$minSupp
  params$minConf
  params$bestLift
  params$lenRules
  params$ratio
})

# Display the total processing time
message("Graph loading Processing Time: ", t3["elapsed"], " seconds\n")
```

## Generate Gross Rules

We use the obtained parameters to generate gross rules, where we obtain 875908 rules.

```{r}
# Start the timer
t4 <- system.time({
  minLenRules <- 1
  maxLenRules <- params$lenRules
  if (!is.finite(maxLenRules) || maxLenRules > 5*length(transactions)) {
    maxLenRules <- 5*length(transactions)
  }
  
  grossRules <- arlc_gen_gross_rules(transactions,
                                     minSupp = params$minSupp,
                                     minConf = params$minConf,
                                     minLenRules = minLenRules+1,
                                     maxLenRules = maxLenRules)
  grossRules$TotalRulesWithLengthFilter
})
# Display the total number of clusters and the total processing time
message("Gross rules generation Time: ", t4["elapsed"], " seconds\n")
```

## Filter Significant and Non-Redundant Rules

We filter out redundant rules from the generated gross rules. Next, we filter out non-significant rules from the non-redundant rules, and we obtain the 388 rule items.

```{r}
t5 <- system.time({
  NonRedRules <- arlc_get_NonR_rules(grossRules$GrossRules)
  NonRSigRules <- arlc_get_significant_rules(transactions,
                                             NonRedRules$FiltredRules)
  NonRSigRules$TotFiltredRules
})
# Display the total number of clusters and the total processing time
message("\nClearing rules Processing Time: ", t5["elapsed"], " seconds\n")
```

## Clean and genarate final Rules

We clean the final set of rules to prepare for clustering. Then, we generate clusters based on the cleaned rules. The total identified clusters is 4 clusters.

```{r}
t6 <- system.time({
  cleanedRules <- arlc_clean_final_rules(NonRSigRules$FiltredRules)
  clusters <- arlc_generate_clusters(cleanedRules)
  clusters$TotClusters
})
# Display the total number of clusters and the total processing time
message("Cleaning final rules Processing Time: ", t6["elapsed"], " seconds\n")

message("The total comsumed time is:",t1["elapsed"]+ t2["elapsed"]+t3["elapsed"]+t4["elapsed"]+t5["elapsed"]+t6["elapsed"], "seconds\n")
```

## Plot Clusters

Finally, we visualize the identified clusters.

```{r}
arlc_clusters_plot(g$graph,
                   g$graphLabel,
                   clusters$Clusters)

```


