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visualize_original_results.R
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visualize_original_results.R
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library(data.table)
library(ggplot2)
library(RColorBrewer)
es <- TRUE
for (measure in c('Accuracy',
'Precision',
'Recall',
'Specificity',
'F1',
'MCC',
'AUC',
'AUPR')) {
print(measure)
#### result prefixes
custom_res <- '../algorithms/Custom/results/'
deepFE_res <- '../algorithms/DeepFE-PPI/result/custom/'
deepPPI_res <- '../algorithms/DeepPPI/keras/results_custom/'
seqppi_res <-
'../algorithms/seq_ppi/binary/model/lasagna/results/'
sprint_res <- '../algorithms/SPRINT/results/original/'
dscript_res <-
'../algorithms/D-SCRIPT-main/results_dscript/original/'
tt_res <-
'../algorithms/D-SCRIPT-main/results_topsyturvy/original/'
# read in data
all_results <-
data.table(1)[, `:=` (c("Model", "Dataset", measure), NA)][, V1 := NULL][.0]
# custom
custom_results <-
lapply(paste0(
custom_res,
list.files(custom_res, pattern = '^original_(du|guo|huang|pan|richoux|dscript).*.csv')
), fread)
file_names <-
tstrsplit(
list.files(custom_res, pattern = '^original_(du|guo|huang|pan|richoux|dscript).*.csv'),
'.csv',
keep = 1
)[[1]]
file_names[grepl('richoux', file_names, fixed = TRUE)] <-
gsub('richoux_*', 'richoux-', file_names[grepl('richoux', file_names, fixed =
TRUE)])
names(custom_results) <- file_names
custom_results <- rbindlist(custom_results, idcol = 'filename')
custom_results[, c('dataset', 'encoding', 'method') := tstrsplit(filename, '_', keep =
c(2, 3, 4))]
custom_results[is.na(method), method := 'degree']
custom_results[, Model := paste(method, encoding, sep = '_')]
if (measure == 'Recall') {
custom_results <- custom_results[V1 == 'Sensitivity']
} else{
if (measure == 'Accuracy') {
wide_df <-
dcast(custom_results[dataset == 'dscript', ],
filename + dataset + encoding + method + Model ~ V1,
value.var = 'V2')
wide_df[, Balanced_Accuracy := 0.5 * (Sensitivity + Specificity)]
wide_df <- wide_df[order(filename)]
custom_results <- custom_results[order(filename)]
custom_results[dataset == 'dscript' &
V1 == 'Accuracy', V2 := wide_df$Balanced_Accuracy]
}
custom_results <- custom_results[V1 == measure]
}
colnames(custom_results) <-
c('filename',
'Measure',
measure,
'Dataset',
'Encoding',
'Method',
'Model')
all_results <-
rbind(all_results, custom_results[, c('Model', 'Dataset', measure), with =
FALSE])
# deepFE
if (es) {
deepFE_results <-
lapply(paste0(
deepFE_res,
list.files(deepFE_res, pattern = '^original_scores_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript)_es.csv', recursive = TRUE)
), fread)
file_names <-
tstrsplit(
list.files(deepFE_res, pattern = '^original_scores_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript)_es.csv', recursive = TRUE),
'/',
keep = 1
)[[1]]
} else{
deepFE_results <-
lapply(paste0(
deepFE_res,
list.files(deepFE_res, pattern = '^original_scores_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript).csv', recursive = TRUE)
), fread)
file_names <-
tstrsplit(
list.files(deepFE_res, pattern = '^original_scores_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript).csv', recursive = TRUE),
'/',
keep = 1
)[[1]]
}
file_names[grepl('richoux', file_names, fixed = TRUE)] <-
gsub('richoux_*', 'richoux-', file_names[grepl('richoux', file_names, fixed =
TRUE)])
names(deepFE_results) <- file_names
deepFE_results <- rbindlist(deepFE_results, idcol = 'Dataset')
if (measure == 'Accuracy') {
balanced_accuracy <-
0.5 * (deepFE_results[Dataset == 'dscript' &
V1 == 'Recall', Score] + deepFE_results[Dataset == 'dscript' &
V1 == 'Specificity', Score])
deepFE_results[Dataset == 'dscript' &
V1 == 'Accuracy', Score := balanced_accuracy]
}
deepFE_results <- deepFE_results[V1 == measure]
colnames(deepFE_results) <- c('Dataset', 'Measure', measure)
deepFE_results$Model <- 'DeepFE'
all_results <-
rbind(all_results, deepFE_results[, c('Model', 'Dataset', measure), with =
FALSE])
# deepPPI
if (es) {
deepPPI_results <-
lapply(paste0(
deepPPI_res,
list.files(deepPPI_res, pattern = 'original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript)_es.csv')
), fread)
file_names <-
tstrsplit(
list.files(deepPPI_res, pattern = 'original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript)_es.csv'),
'.csv',
keep = 1
)[[1]]
} else{
deepPPI_results <-
lapply(paste0(
deepPPI_res,
list.files(deepPPI_res, pattern = 'original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript).csv')
), fread)
file_names <-
tstrsplit(
list.files(deepPPI_res, pattern = 'original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript).csv'),
'.csv',
keep = 1
)[[1]]
}
file_names[grepl('richoux', file_names, fixed = TRUE)] <-
gsub('richoux_*', 'richoux-', file_names[grepl('richoux', file_names, fixed =
TRUE)])
names(deepPPI_results) <- file_names
deepPPI_results <- rbindlist(deepPPI_results, idcol = 'filename')
deepPPI_results <-
deepPPI_results[, c('Model', 'Dataset') := tstrsplit(filename, '_', keep = c(1, 3))]
if (measure == 'Accuracy') {
balanced_accuracy_FC <-
0.5 * (deepPPI_results[Model == 'FC' &
Dataset == 'dscript' &
variable == 'Recall', value] + deepPPI_results[Model == 'FC' &
Dataset == 'dscript' &
variable == 'Specificity', value])
balanced_accuracy_LSTM <-
0.5 * (deepPPI_results[Model == 'LSTM' &
Dataset == 'dscript' &
variable == 'Recall', value] + deepPPI_results[Model == 'LSTM' &
Dataset == 'dscript' &
variable == 'Specificity', value])
deepPPI_results[Model == 'FC' &
Dataset == 'dscript' &
variable == 'Accuracy', value := balanced_accuracy_FC]
deepPPI_results[Model == 'LSTM' &
Dataset == 'dscript' &
variable == 'Accuracy', value := balanced_accuracy_LSTM]
}
deepPPI_results <- deepPPI_results[variable == measure]
deepPPI_results[, Model := paste('deepPPI', Model, sep = '_')]
colnames(deepPPI_results) <-
c('filename', 'variable', measure, 'Model', 'Dataset')
all_results <-
rbind(all_results, deepPPI_results[, c('Model', 'Dataset', measure), with =
FALSE])
# PIPR
if (es) {
pipr_results <-
lapply(paste0(
seqppi_res,
list.files(seqppi_res, pattern = '^original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript)_es.csv')
), fread)
file_names <-
tstrsplit(
list.files(seqppi_res, pattern = '^original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript)_es.csv'),
'.csv',
keep = 1
)[[1]]
} else{
pipr_results <-
lapply(paste0(
seqppi_res,
list.files(seqppi_res, pattern = '^original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript).csv')
), fread)
file_names <-
tstrsplit(
list.files(seqppi_res, pattern = '^original_(du|guo|huang|pan|richoux_regular|richoux_strict|dscript).csv'),
'.csv',
keep = 1
)[[1]]
}
file_names[grepl('richoux', file_names, fixed = TRUE)] <-
gsub('richoux_*', 'richoux-', file_names[grepl('richoux', file_names, fixed =
TRUE)])
names(pipr_results) <- file_names
pipr_results <- rbindlist(pipr_results, idcol = 'Filename')
pipr_results <-
pipr_results[, c('Test', 'Dataset') := tstrsplit(Filename, '_', keep =
c(1, 2))]
if (measure == 'Accuracy') {
balanced_accuracy <-
0.5 * (pipr_results[Dataset == 'dscript' &
V1 == 'Recall', Score] + pipr_results[Dataset == 'dscript' &
V1 == 'Specificity', Score])
pipr_results[Dataset == 'dscript' &
V1 == 'Accuracy', Score := balanced_accuracy]
}
pipr_results <- pipr_results[V1 == measure]
pipr_results$Model <- 'PIPR'
colnames(pipr_results) <-
c('Filename', 'Measure', measure, 'Test', 'Dataset', 'Model')
all_results <-
rbind(all_results, pipr_results[, c('Model', 'Dataset', measure), with =
FALSE])
# SPRINT
sprint_results <- fread(paste0(sprint_res, 'all_results.tsv'))
sprint_results$Model <- 'SPRINT'
if(measure != 'AUC' & measure != 'AUPR'){
# take the AUPR
colnames(sprint_results) <- c('Dataset', 'AUC', measure, 'Model')
}
sprint_results$Dataset[grepl('richoux', sprint_results$Dataset, fixed =
TRUE)] <-
gsub('richoux_*', 'richoux-', sprint_results$Dataset[grepl('richoux', sprint_results$Dataset, fixed =
TRUE)])
sprint_results <- sprint_results[Dataset != 'gold_standard']
all_results <-
rbind(all_results, sprint_results[, c('Model', 'Dataset', measure), with =
FALSE])
# D-Script
if (es) {
dscript_results <- fread(paste0(dscript_res, 'all_results_es.tsv'))
} else{
dscript_results <- fread(paste0(dscript_res, 'all_results.tsv'))
}
if (measure == 'Accuracy') {
balanced_accuracy <-
0.5 * (dscript_results[Dataset == 'dscript' &
Metric == 'Recall', Value] + dscript_results[Dataset == 'dscript' &
Metric == 'Specificity', Value])
dscript_results[Dataset == 'dscript' &
Metric == 'Accuracy', Value := balanced_accuracy]
}
dscript_results <-
dscript_results[Metric == measure & Dataset != 'gold']
colnames(dscript_results) <-
c('Model', 'Dataset', 'Metric', measure, 'Split')
dscript_results$Model <- 'D-SCRIPT'
dscript_results[Dataset == 'richoux_regular', Dataset := 'richoux-regular']
dscript_results[Dataset == 'richoux_strict', Dataset := 'richoux-strict']
all_results <-
rbind(all_results, dscript_results[, c('Model', 'Dataset', measure), with =
FALSE])
# Topsy_Turvy
if (es) {
tt_results <- fread(paste0(tt_res, 'all_results_es.tsv'))
} else{
tt_results <- fread(paste0(tt_res, 'all_results.tsv'))
}
if (measure == 'Accuracy') {
balanced_accuracy <-
0.5 * (tt_results[Dataset == 'dscript' &
Metric == 'Recall', Value] + tt_results[Dataset == 'dscript' &
Metric == 'Specificity', Value])
tt_results[Dataset == 'dscript' &
Metric == 'Accuracy', Value := balanced_accuracy]
}
tt_results <- tt_results[Metric == measure & Dataset != 'gold']
colnames(tt_results) <-
c('Model', 'Dataset', 'Metric', measure, 'Split')
tt_results$Model <- 'Topsy_Turvy'
tt_results[Dataset == 'richoux_regular', Dataset := 'richoux-regular']
tt_results[Dataset == 'richoux_strict', Dataset := 'richoux-strict']
all_results <-
rbind(all_results, tt_results[, c('Model', 'Dataset', measure), with =
FALSE])
# visualization
all_results <- all_results[, Dataset := factor(
Dataset,
levels = c(
"huang",
"guo",
"du",
"pan",
"richoux-regular",
"richoux-strict",
"dscript"
)
)]
all_results <- all_results[, Model := factor(
Model,
levels = c(
"RF_PCA",
"SVM_PCA",
"RF_MDS",
"SVM_MDS",
"RF_node2vec",
"SVM_node2vec",
"degree_cons",
"degree_hf",
"SPRINT",
"deepPPI_FC",
"deepPPI_LSTM",
"DeepFE",
"PIPR",
"D-SCRIPT",
"Topsy_Turvy"
)
)]
if (es) {
fwrite(all_results,
file = paste0('results/original_', measure, '_es.csv'))
} else{
fwrite(all_results, file = paste0('results/original_', measure, '.csv'))
}
}