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cluster.R
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cluster.R
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library(cluster)
library(MASS)
library(dendextend)
VERBOSE = FALSE
options(warn=-1)
#PATH - parameters:
start.time <- Sys.time()
args = commandArgs(trailingOnly=TRUE)
algo_type = args[1]
hclust_type = "hclust"
kmedoids_type = "kmedoids"
dist_file_path = args[3]#"distances.txt"
files_dir = args[4]
out_dir = args[5]
########## parse the Jplag output ###########
data = readLines(dist_file_path)
#only keep file1-file2 and corresponding distance
data = data[grep("^Comparing", data)]
#Remove useless Comparing word at beginning of line
data = gsub("^.*? ", " ", data)
if(VERBOSE)
print("Removing spaces left at the beginning of each line")
for(i in 1:length(data)){
data[i] = substring(data[i], 2)
}
data = strsplit(data, " ")
if(VERBOSE)
print("Retrieve files names in a vector")
files_names = c()
for(i in 1:length(data)){
names_pair = unlist(strsplit(unlist(data[i]), "-"))
for(j in 1:(length(names_pair)-1)){
file_name = gsub(":", "", names_pair[j])
if(!(file_name %in% files_names)){
files_names = c(files_names, file_name)
}
}
}
##################### Build the distance matrix #####################
#Position of the name of a file in the vector files_names is used as file index
n = length(files_names)
if(VERBOSE)
print("Create distances matrix")
dist_matrix = matrix(rep(0,n*n), nrow = n, ncol = n, byrow = TRUE)
get_file_idx <- function(file_name){
which(files_names == file_name)
}
max_dist = 100 #With Jplag
for(i in 1:length(data)){
files_pair_dist = unlist(strsplit(unlist(data[i]), "-")) #file_name1 file_name2 dist
file_name_1 = files_pair_dist[1]
file_name_2 = gsub(":", "", files_pair_dist[2])
#print(c("f1 ", file_name_1, "f2 ", file_name_2))
dist = max_dist-as.numeric(files_pair_dist[3])
file_idx_1 = get_file_idx(file_name_1)
file_idx_2 = get_file_idx(file_name_2)
dist_matrix[file_idx_1, file_idx_2] = dist
dist_matrix[file_idx_2, file_idx_1] = dist
#print(c("idx1 ", file_idx_1, "idx2 ", file_idx_2))
#print(c("dist ", dist, "in matrix ", toString(dist_matrix[file_idx_1, file_idx_2])))
}
# print("DIST MATRIX:")
# for(i in 1:n){
# for(j in 1:n){
# print(dist_matrix[i,j])
# }
# }
#write.matrix(dist_matrix, file = "dist_matrix.txt", sep = " ", blocksize=n/100)
#dist_matrix = replicate(n, rnorm(n))
#print(dist_matrix[get_file_idx("attempt002_20140920_112132.cs"), get_file_idx("attempt002_20140920_110800.cs")])
if(VERBOSE)
print("Retrieve low triangle of distances matrix")
low_tri_matrix_el_vec = dist_matrix[lower.tri(dist_matrix, diag = FALSE)]
#For the clustering algorithm to interpret input as a distance matrix,
#the input object mus be of type "dissimilatiry matrix"
dissimilarity_mat<-low_tri_matrix_el_vec
class(dissimilarity_mat)='dist'
#attr(dissimilarity_mat,"Size")<-length(low_tri_matrix_el_vec)
attr(dissimilarity_mat,"Size")<-n
dissimilarity_mat<-as.dist(dissimilarity_mat)
#print(dissimilarity_mat)
################### manually made clustering ######################
c1 = c("attempt001_20140920_022105.cs",
"attempt001_20140920_032927.cs",
"attempt001_20140920_102656.cs",
"attempt001_20140920_102739.cs",
"attempt001_20140920_104554.cs",
"attempt001_20140920_112533.cs",
"attempt001_20140920_164800.cs",
"attempt001_20140920_195521.cs",
"attempt001_20140920_203756.cs",
"attempt001_20140920_210358.cs",
"attempt002_20140920_112536.cs",
"attempt002_20140920_204122.cs",
"attempt003_20140920_174456.cs")
c2 = c("attempt002_20140920_022248.cs",
"attempt003_20140920_022256.cs",
"attempt004_20140920_022426.cs",
"attempt005_20140920_022441.cs")
c3 = c("attempt002_20140920_103730.cs",
"attempt003_20140920_103748.cs")
c4 = c("attempt002_20140920_104459.cs",
"attempt004_20140920_105646.cs",
"attempt004_20140920_175138.cs",
"attempt005_20140920_105658_winning3.cs",
"attempt005_20140920_175154_winning3.cs")
c5 = c("attempt012_20140920_213449_winning2.cs",
"attempt013_20140920_213548.cs",
"attempt014_20140920_213605.cs")
c6 = c("attempt002_20140920_212357.cs",
"attempt003_20140920_212539.cs",
"attempt004_20140920_212607.cs",
"attempt005_20140920_212629.cs",
"attempt006_20140920_212741.cs",
"attempt007_20140920_212855_winning2.cs",
"attempt011_20140920_213242_winning2.cs",
"attempt015_20140920_213730.cs")
c7 = c("attempt005_20140920_103956.cs",
"attempt006_20140920_104006.cs",
"attempt007_20140920_104048.cs",
"attempt008_20140920_104121.cs",
"attempt009_20140920_104258_winning3.cs",
"attempt015_20140920_023545_winning3.cs")
c8 = c("attempt006_20140920_022537.cs",
"attempt007_20140920_022553.cs")
c9 = c("attempt008_20140920_022724.cs",
"attempt009_20140920_022742.cs",
"attempt010_20140920_022752.cs")
c10 = c("attempt011_20140920_022855.cs",
"attempt012_20140920_022927.cs")
c11 = c("attempt012_20140920_085955.cs",
"attempt013_20140920_023107.cs",
"attempt013_20140920_090132.cs",
"attempt014_20140920_023256.cs")
c12 = c("attempt002_20140920_111009_winning3.cs")
ideal_clusters <- list()
ideal_clusters[[1]] <- c1
ideal_clusters[[2]] <- c2
ideal_clusters[[3]] <- c3
ideal_clusters[[4]] <- c4
ideal_clusters[[5]] <- c5
ideal_clusters[[6]] <- c6
ideal_clusters[[7]] <- c7
ideal_clusters[[8]] <- c8
ideal_clusters[[9]] <- c9
ideal_clusters[[10]] <- c10
ideal_clusters[[11]] <- c11
ideal_clusters[[12]] <- c12
max_score <- function(){
max_score = 0
for(cluster in ideal_clusters){
max_score = max_score + length(cluster)*(length(cluster)-1)
}
max_score
}
get_user_assigned_class <- function(file_name) {
class_id = 1
for(cluster in ideal_clusters){
if(file_name %in% cluster){
return(class_id)
}
class_id = class_id+1
}
0
}
same_user_assigned_class <- function(file_name1, file_name2){
for(cluster in ideal_clusters){
if(file_name1 %in% cluster){
return(file_name2 %in% cluster)
}
}
FALSE
}
clustering_score <- function(clustering_vec){
score = 0
for(i in 1:length(clustering_vec)){
file_name1 = files_names[i]
cluster = clustering_vec[i]
for(j in 1:length(clustering_vec)){
if(i != j && clustering_vec[j] == cluster){
file_name2 = files_names[j]
if(same_user_assigned_class(file_name1, file_name2))
score = score+1
else
score = score-1
}
}
}
score/max_score()
}
out_clusters_info <- function(out_dir, cluster_nbr, clustering_vec, medoid=FALSE){
dir.create(out_dir, showWarnings = TRUE)
if(medoid){
score = clustering_score(clustering_vec)
#print(c("Clustering score: ", score))
print(score)
}
#Display members in each cluster
for(i in 1:cluster_nbr){
cluster_name = toString(i)
#print(c("Cluster ", cluster_name))
#print("############")
out_file = paste(out_dir, paste("/", paste(cluster_name, ".txt", sep=""), sep=""), sep="")
cat(cluster_name, file=out_file, append=FALSE, sep = "\n")
if(medoid){
#print("Medoid:")
medoid_idx = medoids[paste("medoids", cluster_name, sep="")]
medoid_name = files_names[medoid_idx]
cat(paste("medoid name:", medoid_name, sep=" "), file=out_file, append=TRUE, sep = "\n")
#print(medoid_name)
#print("-----------")
}
members_counter = 0
for(j in 1:length(clustering_vec)){
if(clustering_vec[j] == i){
file_name = files_names[j]
file_content = readLines(paste(files_dir, file_name, sep=""))
cat(file_name, file=out_file, append=TRUE, sep = "\n")
cat("################", file=out_file, append=TRUE, sep = "\n")
cat(file_content, file=out_file, append=TRUE, sep = "\n")
#print(files_names)
members_counter = members_counter+1
}
}
cat(paste("Number of samples in cluster: ", members_counter, sep=""), file=out_file, append=TRUE, sep = "\n")
}
}
reconstruct_err <- function(cluster_info){
cluster_info[,1] %*% cluster_info[,3]
}
#sum of the diameters
#(maximal dissimilarity between two observations of the cluster) of the clusters
sum_diameters <- function(cluster_info){
sum(cluster_info[,4])
}
#sum of the separations (minimal dissimilarity between an observation of the
#cluster and an observation of another cluster).
sum_separations <- function(cluster_info){
sum(cluster_info[,5])
}
#pam(x, k, diss = inherits(x, "dist"), metric = "euclidean",
# medoids = NULL, stand = FALSE, cluster.only = FALSE,
# do.swap = TRUE,
# keep.diss = !diss && !cluster.only && n < 100,
# keep.data = !diss && !cluster.only,
# pamonce = FALSE, trace.lev = 0)
if(algo_type == kmedoids_type){
if(VERBOSE)
print("Process clustering - k medoids")
cluster_nbr = args[2]
clusters = pam(dissimilarity_mat, cluster_nbr, diss=TRUE)
clustering_vec = unlist(clusters["clustering"]) #association of each sample to a cluster
medoids = unlist(clusters["medoids"]) #representative objects of the clusters "centers" that belong to the dataset
out_clusters_info(out_dir, cluster_nbr, clustering_vec, medoid=TRUE)
cluster_info = clusters$clusinfo
reconstruct_err = reconstruct_err(cluster_info)
#print(c("k", cluster_nbr, "reconstruct_err", reconstruct_err))
write(reconstruct_err,file="/Users/aschils/Software/plagiat_detection/clustering/results/Sector4_Level6/kmedoids/a.txt",append=TRUE)
sum_diameters = sum_diameters(cluster_info)
#print(c("k", cluster_nbr, "sum diameters", sum_diameters))
write(sum_diameters,file="/Users/aschils/Software/plagiat_detection/clustering/results/Sector4_Level6/kmedoids/b.txt",append=TRUE)
sum_separations = sum_separations(cluster_info)
#print(c("k", cluster_nbr, "sum separations", sum_separations(cluster_info)))
write(sum_separations,file="/Users/aschils/Software/plagiat_detection/clustering/results/Sector4_Level6/kmedoids/c.txt",append=TRUE)
#print(cluster_info)
}
cut_level_height <- function(hclusters, cut_level){
tree_heights = unique(unlist(hclusters["height"]))
if(cut_level <= length(tree_heights))
tree_heights[cut_level]
else
tree_heights[length(tree_heights)]
}
if(algo_type == hclust_type){
if(VERBOSE)
print("Process clustering - hclust")
hclusters = hclust(dissimilarity_mat, method = "ward.D", members = NULL)
dendogram = as.dendrogram(hclusters)
#print(unlist(hclusters["height"]))
#Plot dendogram with at leaf user assigned classfication of samples
#pdf("plots.pdf")
# jpeg("plots.jpeg", width = 8, height = 6, units = 'in', res = 1000)
# user_assigned_class = rep("",length(files_names))
# for(i in 1:length(user_assigned_class)){
# user_assigned_class[i] = paste(" ", paste(toString(get_user_assigned_class(files_names[i])), " "))
# }
#
# dendogram <- set(dendogram, "labels", user_assigned_class)
# #dendogram <- set(dendogram, "labels_cex", 0.1)
# par(cex=0.5)#mar=c(5, 8, 4, 1))
# #leaflab="textlike",
# plot(dendogram, xlab="hierarchical clustering", leaflab="perpendicular", yaxt='n', horiz=TRUE, type = "rectangle")# leaflab=leaflab)
# dev.off()
jpeg("plots.jpeg", width = 8, height = 6, units = 'in', res = 1000)
user_assigned_class = rep("",length(files_names))
for(i in 1:length(user_assigned_class)){
user_assigned_class[i] = toString(get_user_assigned_class(files_names[i]))
}
par(cex=0.8)
plot(hclusters, labels=user_assigned_class, xlab="hierarchical clustering", yaxt='n')
dev.off()
#tree_height_level = as.numeric(args[2])
k = as.numeric(args[2])
#hclust_vec = cutree(hclusters, h=tree_height)
#cut_height = cut_level_height(hclusters, tree_height_level)
#print(cut_height)
#hclust_vec = cutree(hclusters, h=cut_height)
hclust_vec = cutree(hclusters, k=k)
print(clustering_score(hclust_vec))
#k = max(hclust_vec)
#print(c("k:", k))
out_clusters_info(out_dir, k, hclust_vec, medoid=FALSE)
}
end.time <- Sys.time()
time.taken <- end.time - start.time
if(VERBOSE)
print(time.taken)
# medoid_idx = get_file_idx("attempt004_20140920_175138.cs")
# print("Dist to medoid:")
#
# print("attempt002_20140920_104459.cs")
# f_idx = get_file_idx("attempt002_20140920_104459.cs")
# print(dist_matrix[medoid_idx, f_idx])
#
# print("attempt004_20140920_105646.cs")
# f_idx = get_file_idx("attempt004_20140920_105646.cs")
# print(dist_matrix[medoid_idx, f_idx])
#
# print("attempt005_20140920_105658_winning3.cs")
# f_idx = get_file_idx("attempt005_20140920_105658_winning3.cs")
# print(dist_matrix[medoid_idx, f_idx])
#
# print("attempt005_20140920_175154_winning3.cs")
# f_idx = get_file_idx("attempt005_20140920_175154_winning3.cs")
# print(dist_matrix[medoid_idx, f_idx])