Nothing
fv_dsc_box_rank <- function(width = 12, collapsible = T, collapsed = F) {
box(title = HTML('<p style="font-size:120%;">Deep Statistical Comparison (DSC)
analysis - Ranking per Function</p>'),
width = width, solidHeader = T, status = "primary",
collapsible = collapsible, collapsed = collapsed,
sidebarPanel(
width = 3,
selectInput('FV_Stats.DSC.ID', 'IDs to compare', choices = NULL,
selected = NULL, multiple = T),
selectInput('FV_Stats.DSC.Funcid', 'Functions to use', choices = NULL,
selected = NULL, multiple = T),
selectInput('FV_Stats.DSC.Dim', 'Dimensions to use', choices = NULL,
selected = NULL, multiple = T),
hr(),
numericInput('FV_Stats.DSC.Alpha_rank',
label = "Threshold for statistical significance",
value = 0.05, min = 0, max = 0.5),
numericInput('FV_Stats.DSC.Epsilon_rank',
label = "Threshold for practical significance (pDSC)",
value = 0) %>%
shinyInput_label_embed(
custom_icon() %>%
bs_embed_popover(
title = "Practical Significance", content = Pdsc_info,
placement = "auto"
)
),
numericInput('FV_Stats.DSC.MCsamples_rank',
label = "Number of monte carlo samples to use in the DSC procdure",
value = 0) %>%
shinyInput_label_embed(
custom_icon() %>%
bs_embed_popover(
title = "Monte Carlo Samples", content = Pdsc_mc_info,
placement = "auto"
)
),
selectInput("FV_Stats.DSC.Test_rank", "Test Type",
choices = c("Anderson-Darling", "Kolmogorov-Smirnov"),
selected = "Anderson-Darling"),
actionButton('FV_Stats.DSC.Create_rank', 'Create Ranking'),
hr(),
selectInput('FV_Stats.DSC.Format_rank', label = 'Select the figure format',
choices = supported_fig_format, selected = supported_fig_format[[1]]),
downloadButton('FV_Stats.DSC.Download_rank', label = 'Download the figure'),
selectInput('FV_Stats.DSC.TableFormat_rank', label = 'Select the table format',
choices = supported_table_format, selected = supported_table_format[[1]]),
downloadButton('FV_Stats.DSC.Download_rank_table', label = 'Download the raw ranking data')
),
mainPanel(
width = 9,
HTML_P("The <b>DSC</b> comparison is described in the paper: 'DSCTool: A web-service-based
framework for statistical comparison of stochastic optimization algorithms.' by
T. Eftimov et al.
This is the first of 3 parts of the process: the per-function ranking procedure.
The two other processes are the omnibus test and the post-hoc processing,
which are shown in the two boxes below this one.
Note that both of these are impacted by the settings selected for this
ranking procedure!"),
HTML_P("The chosen <b>budget values</b> per (function, dimension)-pair are as follows
(double click an entry to edit it):"),
DT::dataTableOutput("FV_Stats.DSC.Targets"),
hr(),
HTML_P("The results of the ranking are shown in the following plot, using the visualization techniques
as described in the paper: 'PerformViz: A Machine Learning Approach to Visualize and
Understand the Performance of Single-objective Optimization
Algorithms' By T. Eftimov et al. Performviz allows one to clearly see,
from a single plot, which algorithms are most suited for a given problem,
the influence of each problem on the overall algorithm performance and
similarities among both algorithms and problems."),
plotOutput("FV_Stats.DSC.PerformViz", height = "800px")
)
)
}
fv_dsc_box_omni <- function(width = 12, collapsible = T, collapsed = F) {
box(title = HTML('<p style="font-size:120%;">Deep Statistical Comparison (DSC)
analysis - Omnibus Test</p>'),
width = width, solidHeader = T, status = "primary",
collapsible = collapsible, collapsed = collapsed,
sidebarPanel(
width = 3,
selectInput('FV_Stats.DSC.Omni_options', "Select which statistical test to use",
choices = NULL, selected = NULL),
numericInput('FV_Stats.DSC.Alpha_omni',
label = "Threshold for statistical significance",
value = 0.05, min = 0, max = 0.5),
actionButton('FV_Stats.DSC.Create_omni', 'Perform Omnibus Test')
),
mainPanel(
width = 9,
HTML_P('This is the result of the omnibus test on the data from the ranking procedure above.
Note that this test has to be performed before doing the post-hoc comparison!'),
hr(),
textOutput('FV_Stats.DSC.Output_omni')
)
)
}
fv_dsc_box_posthoc <- function(width = 12, collapsible = T, collapsed = F) {
box(title = HTML('<p style="font-size:120%;">Deep Statistical Comparison (DSC)
analysis - Posthoc comparison</p>'),
width = width, solidHeader = T, status = "primary",
collapsible = collapsible, collapsed = collapsed,
sidebarPanel(
width = 3,
selectInput('FV_Stats.DSC.Posthoc_test', 'Post-hoc Test', choices =
c('friedman', 'friedman-aligned-rank'),
selected = 'friedman', multiple = F),
selectInput('FV_Stats.DSC.Posthoc_method', 'Post-hoc P-value Correction Method', choices =
c('Holm', 'Hochberg', 'unadjusted P'),
selected = 'Holm', multiple = F),
numericInput('FV_Stats.DSC.Alpha_posthoc',
label = "Threshold for statistical significance",
value = 0.05, min = 0, max = 0.5),
actionButton('FV_Stats.DSC.Create_posthoc', 'Create Comparison'),
hr(),
selectInput('FV_Stats.DSC.Format', label = 'Select the figure format',
choices = supported_fig_format, selected = supported_fig_format[[1]]),
downloadButton('FV_Stats.DSC.Download', label = 'Download the figure'),
hr(),
selectInput('FV_Stats.DSC.TableFormat', label = 'Select the table format',
choices = supported_table_format, selected = supported_table_format[[1]]),
downloadButton('FV_Stats.DSC.DownloadTable', label = 'Download the table')
),
mainPanel(
width = 9,
HTML_P("The results of the post-hoc comparison are:"),
plotlyOutput.IOHanalyzer("FV_Stats.DSC.PosthocViz"),
DT::dataTableOutput('FV_Stats.DSC.PosthocTable')
)
)
}
rt_dsc_box_rank <- function(width = 12, collapsible = T, collapsed = F) {
box(title = HTML('<p style="font-size:120%;">Deep Statistical Comparison (DSC)
analysis - Ranking per Function</p>'),
width = width, solidHeader = T, status = "primary",
collapsible = collapsible, collapsed = collapsed,
sidebarPanel(
width = 3,
selectInput('RT_Stats.DSC.ID', 'Algorithms to compare', choices = NULL,
selected = NULL, multiple = T),
selectInput('RT_Stats.DSC.Funcid', 'Functions to use', choices = NULL,
selected = NULL, multiple = T),
selectInput('RT_Stats.DSC.Dim', 'Dimensions to use', choices = NULL,
selected = NULL, multiple = T),
hr(),
selectInput('RT_Stats.DSC.Value_type', "Select which type of hitting times to use",
choices = c('PAR-10', 'ERT', 'Remove-na', 'PAR-1')),
numericInput('RT_Stats.DSC.Alpha_rank',
label = "Threshold for statistical significance",
value = 0.05, min = 0, max = 0.5),
numericInput('RT_Stats.DSC.Epsilon_rank',
label = "Threshold for practical significance (pDSC)",
value = 0) %>%
shinyInput_label_embed(
custom_icon() %>%
bs_embed_popover(
title = "Practical Significance", content = Pdsc_info,
placement = "auto"
)
),
numericInput('RT_Stats.DSC.MCsamples_rank',
label = "Number of monte carlo samples to use in the DSC procdure",
value = 0) %>%
shinyInput_label_embed(
custom_icon() %>%
bs_embed_popover(
title = "Monte Carlo Samples", content = Pdsc_mc_info,
placement = "auto"
)
),
selectInput("RT_Stats.DSC.Test_rank", "Test Type",
choices = c("Anderson-Darling", "Kolmogorov-Smirnov"),
selected = "Anderson-Darling"),
actionButton('RT_Stats.DSC.Create_rank', 'Create Ranking'),
hr(),
selectInput('RT_Stats.DSC.Format_rank', label = 'Select the figure format',
choices = c("pdf"), selected = 'pdf'),
downloadButton('RT_Stats.DSC.Download_rank', label = 'Download the figure'),
selectInput('RT_Stats.DSC.TableFormat_rank', label = 'Select the table format',
choices = c('csv','tex'), selected = 'csv'),
downloadButton('RT_Stats.DSC.Download_rank_table', label = 'Download the raw ranking data')
),
mainPanel(
width = 9,
HTML_P("The <b>DSC</b> comparison is described in the paper: 'DSCTool: A web-service-based
framework for statistical comparison of stochastic optimization algorithms.' by
T. Eftimov et al.
This is the first of 3 parts of the process: the per-function ranking procedure.
The two other processes are the omnibus test and the post-hoc processing,
which are shown in the two boxes below this one.
Note that both of these are impacted by the settings selected for this
ranking procedure!"),
HTML_P("The chosen <b>budget values</b> per (function, dimension)-pair are as follows
(double click an entry to edit it):"),
DT::dataTableOutput("RT_Stats.DSC.Targets"),
hr(),
HTML_P("The results of the ranking are shown in the following plot, using the visualization techniques
as described in the paper: 'PerformViz: A Machine Learning Approach to Visualize and
Understand the Performance of Single-objective Optimization
Algorithms' By T. Eftimov et al. Performviz allows one to clearly see,
from a single plot, which algorithms are most suited for a given problem,
the influence of each problem on the overall algorithm performance and
similarities among both algorithms and problems."),
plotOutput("RT_Stats.DSC.PerformViz", height = "800px")
)
)
}
rt_dsc_box_omni <- function(width = 12, collapsible = T, collapsed = F) {
box(title = HTML('<p style="font-size:120%;">Deep Statistical Comparison (DSC)
analysis - Omnibus Test</p>'),
width = width, solidHeader = T, status = "primary",
collapsible = collapsible, collapsed = collapsed,
sidebarPanel(
width = 3,
selectInput('RT_Stats.DSC.Omni_options', "Select which statistical test to use",
choices = NULL, selected = NULL),
numericInput('RT_Stats.DSC.Alpha_omni',
label = "Threshold for statistical significance",
value = 0.05, min = 0, max = 0.5),
actionButton('RT_Stats.DSC.Create_omni', 'Perform Omnibus Test')
),
mainPanel(
width = 9,
HTML_P('This is the result of the omnibus test on the data from the ranking procedure above.
Note that this test has to be performed before doing the post-hoc comparison!'),
hr(),
textOutput('RT_Stats.DSC.Output_omni')
)
)
}
rt_dsc_box_posthoc <- function(width = 12, collapsible = T, collapsed = F) {
box(title = HTML('<p style="font-size:120%;">Deep Statistical Comparison (DSC)
analysis - Posthoc comparison</p>'),
width = width, solidHeader = T, status = "primary",
collapsible = collapsible, collapsed = collapsed,
sidebarPanel(
width = 3,
selectInput('RT_Stats.DSC.Posthoc_test', 'Post-hoc Test', choices =
c('friedman', 'friedman-aligned-rank'),
selected = 'friedman', multiple = F),
selectInput('RT_Stats.DSC.Posthoc_method', 'Post-hoc Method', choices =
c('Holm', 'Hochberg', 'unadjusted P'),
selected = 'Holm', multiple = F),
numericInput('RT_Stats.DSC.Alpha_posthoc',
label = "Threshold for statistical significance",
value = 0.05, min = 0, max = 0.5),
actionButton('RT_Stats.DSC.Create_posthoc', 'Create Comparison'),
hr(),
selectInput('RT_Stats.DSC.Format', label = 'Select the figure format',
choices = supported_fig_format, selected = supported_fig_format[[1]]),
downloadButton('RT_Stats.DSC.Download', label = 'Download the figure'),
hr(),
selectInput('RT_Stats.DSC.TableFormat', label = 'Select the table format',
choices = supported_table_format, selected = supported_table_format[[1]]),
downloadButton('RT_Stats.DSC.DownloadTable', label = 'Download the table')
),
mainPanel(
width = 9,
HTML_P("The results of the post-hoc comparison are:"),
plotlyOutput.IOHanalyzer("RT_Stats.DSC.PosthocViz"),
DT::dataTableOutput('RT_Stats.DSC.PosthocTable')
)
)
}
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