inst/ui.R
In RamiKrispin/MLstudio: Platform for Machine Learning Applications
#------------------------------ UI Function -------------------------------------
ui <- dashboardPage(
dashboardHeader(title = "ML Studio"),
#------------------------------ Side Bar Function -------------------------------------
dashboardSidebar(
sidebarMenu(id = "tabs",
menuItem("Data", tabName = "data", icon = icon("table"), startExpanded = TRUE,
menuSubItem("Load", tabName = "data1"),
menuSubItem("Prep", tabName = "data2")
),
menuItem("Visualization", icon = icon("bar-chart-o"), tabName = "vis"),
menuItem("Models", icon = icon("cog"), tabName = "models",
menuSubItem("Regression & Classification", tabName = "models1")
)
)
),
#------------------------------ Dashboard Body -------------------------------------
dashboardBody(
#------------------------------ Tags Style -------------------------------------
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }",
"
.selectize-dropdown-content {
max-height: 300px;
overflow-y: auto;
background: ghostwhite;
}
"
,
"
select ~ .selectize-control .selectize-input {
max-height: 100px;
overflow-y: auto;
}
"
),
#------------------------------ Tabs Start -------------------------------------
tabItems(
#------------------------------ Tabs Data Start-------------------------------------
tabItem(tabName = "data1",
#------------------------------ Tabs Data - fluid page start -------------------------------------
fluidPage(
#------------------------------ Tabs Data - fluid row 1 -------------------------------------
fluidRow(
infoBoxOutput("installed_datasets"),
infoBoxOutput("in_memory_df"),
infoBoxOutput("load_datasets")
),
#------------------------------ Tabs Data - fluid row 2 -------------------------------------
fluidRow(
box(
width = 4, height = 100,
selectInput('data_source', 'Select Data Source',
list(
"R Data Frame" = "data_frame",
"R Time Series" = "time_series",
"Installed Package Dataset" = "inst_pack",
"Import CSV File" = "import"
))
),
box(width = 4, height = 100,
conditionalPanel(condition = "input.data_source.includes('data_frame') || input.data_source.includes('inst_pack') || input.data_source.includes('time_series')",
uiOutput("df_list")
),
conditionalPanel(condition = "input.data_source == 'import'",
shinyWidgets::dropdownButton(
fileInput('file1', 'Choose CSV File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
shinyWidgets::awesomeCheckbox(inputId = "csv_header",
label = "Header",
value = TRUE),
radioButtons('sep', 'Separator',
c(Comma=',',
Semicolon=';',
Tab='\t'),
','),
radioButtons('quote', 'Quote',
c(None='',
'Double Quote'='"',
'Single Quote'="'"),
'"'),
circle = TRUE, status = "danger",
icon = icon("file-text", lib = "font-awesome"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Click to set csv file parameters !")
)
)
),
box(width = 4, height = 100,
conditionalPanel(condition = "(output.load_flag == '2' && input.data_source == 'inst_pack') || (output.load_flag == '2' && input.data_source == 'import' ) || (output.load_flag == '1' && input.data_source == 'data_frame' ) || ( output.load_flag == '1' && input.data_source == 'inst_pack') || (output.load_flag == '1' && input.data_source == 'time_series')",
actionButton("load", "Load")
),
conditionalPanel(condition = "output.loaded_table_flag == '1'",
actionButton("remove", "Remove")
)
)
),
fluidRow(
box(width = 7, title = "Preview Table",
div(style = 'overflow-x: scroll',
DT::dataTableOutput('view_table'))
),
box(width = 5, title = "Loaded Datasets",
div(style = 'overflow-x: scroll',
DT::dataTableOutput('list_loaded_df'))
)
)
#------------------------------ Tabs Data - fluid row 2 -------------------------------------
)
#------------------------------ Tabs Data - fluid page end -------------------------------------
),
#------------------------------ Tabs Data2 Start-------------------------------------
tabItem(tabName = "data2",
fluidPage(
fluidRow(
conditionalPanel(condition = "output.loaded_table_flag == '1'",
infoBoxOutput("data_name"),
infoBoxOutput("num_var"),
infoBoxOutput("num_obs")
)
),
fluidRow(
conditionalPanel(condition = "output.loaded_table_flag == '1'",
box(width = 4, title = "Select Dataset",
uiOutput("loaded_ds_list"),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == false ",
selectInput('data_option', 'Select Option',
list(
"Variables Attributes" = "var_attr",
"Data Summary" = "data_summary"
))
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == false && input.data_option == 'var_attr'",
radioButtons("class_selection", label = "Variables Modification",
choices = list(Numeric = "numeric", Factor = "factor",
Character = "character",
Date = "date"),
selected = "numeric"),
conditionalPanel(condition = "input.class_selection == 'date' && output.loaded_table_flag == '1' && output.class_df_flag == false && input.data_option == 'var_attr'",
selectInput('date_format', "Select the Date Format",
list(
YMD = "ymd",
YDM = "ydm",
MYD = "myd",
MDY = "mdy",
DMY = "dmy",
DYM = "dym"
)),
#titlePanel(h5("Date Preview")),
tags$h5("Date Preview"),
verbatimTextOutput("date_prev")
),
actionButton("var_modify", "Modify")
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == true ",
tableOutput("ts_table")
)
)),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == false && input.data_option == 'var_attr'",
box(width = 4, title = "List of Variables",
DT::dataTableOutput("data_tab2_var")
),
box(width = 4, title = "Variable Summary",
shinycssloaders::withSpinner(plotly::plotlyOutput("data_tab2_summary_plot",height = 200)),
tableOutput("data_tab2_var_summary")
)
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && input.data_option == 'data_summary'",
box(width = 4, title = "Create a Summary",
uiOutput("group_by"),
conditionalPanel(condition = "output.loaded_table_flag == '1' && input.data_option == 'data_summary' && output.group_by_flag == '1'",
uiOutput("summarise_var")
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && input.data_option == 'data_summary' && output.group_by_flag == '1'",
uiOutput("dplyr_fun"),
uiOutput("summary_name"),
actionButton("run_summary", "Run"),
conditionalPanel(condition = "output.dplyr_table_flag == '1'",
actionButton("load_summary_table", "Load")
)
)
),
conditionalPanel(condition = "output.dplyr_table_flag == '1' && input.data_option == 'data_summary' && output.group_by_flag == '1'",
box(width = 4, title = "Summary",
div(style = 'overflow-x: scroll',
DT::dataTableOutput('dplyr_table')
)
)
)
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == true ",
box(width = 8, title = "Time Series Plot",
shinyWidgets::dropdownButton(
tags$h3("List of Input"),
shinyWidgets::materialSwitch(inputId = "ts_plot_log", label = "Log Transformation",
status = "primary", right = FALSE),
shinyWidgets::awesomeRadio(inputId = "ts_prep_mode",
label = "Radio buttons",
choices = c("lines","lines+markers", "markers")
, selected = "lines"),
circle = TRUE, status = "danger", icon = icon("gear"), width = "200px",
tooltip = shinyWidgets::tooltipOptions(title = "Plot Setting")
),
shinycssloaders::withSpinner(plotly::plotlyOutput("data_tab2_ts"))
)
)
),
fluidRow(
conditionalPanel(condition = "output.loaded_table_flag == '1'",
div(style = 'overflow-x: scroll',
DT::dataTableOutput("data_tab2_table"))
)
)
)
),
#------------------------------ Tabs Data End-------------------------------------
#------------------------------ Tabs Visualization Start-------------------------------------
tabItem(tabName = "vis",
conditionalPanel(condition = "output.loaded_table_flag == '1'",
fluidPage(
fluidRow(
box(width = 2,
uiOutput("loaded_ds_list_vis"),
uiOutput("vis_plot_type")
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag_vis == false && input.plot_type != 'cor' ",
box(width = 2,
conditionalPanel(condition = "input.plot_type != 'density' && input.plot_type != 'box' && input.plot_type != 'hist'",
uiOutput("vis_x"),
uiOutput("vis_y")
),
conditionalPanel(condition = "input.plot_type == 'density' || input.plot_type == 'hist' || input.plot_type == 'box'",
uiOutput("vis_one_var")
)
),
conditionalPanel(condition = "input.plot_type == 'scatter' || input.plot_type == 'box'|| input.plot_type == 'hist' || input.plot_type == 'density'",
box(width = 2,
uiOutput("vis_factor"))
)
)
),
fluidRow(
box(width = 12, title = "plot",
shinycssloaders::withSpinner(plotly::plotlyOutput("main_plot"))
)
)
)
)
),
#------------------------------ Tabs Visualization End-------------------------------------
#------------------------------ Tabs Classification Start-------------------------------------
tabItem(tabName = "models1",
fluidRow(conditionalPanel(condition = "input.model_package.includes('H2O')",
infoBoxOutput("h2o_status_box"),
infoBoxOutput("h2o_cpu"),
infoBoxOutput("h2o_cluster_mem")
)
),
fluidRow(
box(width = 2, title = "Model Inputs",
conditionalPanel(condition = "input.model_package.includes('H2O')",
uiOutput("models1_df_list"),
conditionalPanel(condition = "output.model_tab_input == '1' || output.model_tab_input == '2'",
uiOutput("models1_var_list"),
uiOutput("models1_independent_list")
)
),
shinyWidgets::awesomeCheckboxGroup(inputId = "model_package",
label = "Set Packages",
choices = c("H2O"), selected = NULL,
inline = TRUE)
),
conditionalPanel(condition = "output.dep_var_class == '1' && output.h2o_flag == '1' && output.model_tab_ind == '1'",
tabBox(
title = "Model Setting & Output", width = 10,
id = "class_setting", height = "500px",
tabPanel("Model Setting",
fluidRow(
box(width = 4, title = "Model Setting",
selectInput("binomial_models", "Select Classification Model",
choices = c("Deep Learning (H2O)" = "h2o_dl",
"GLM (H2O)" = "h2o_glm",
"GBM (H2O)" = "h2o_gbm",
"Random Forest (H2O)" = "h2o_rf")
),
shinyWidgets::materialSwitch(inputId = "h2o_validation",
label = "Add Validation Partition",
status = "primary", right = FALSE),
conditionalPanel(condition = "input.h2o_validation == true",
sliderInput("h2o_split_v", "Set the Training/Testing/Validation Partitions:",
min = 0.05, max = 1,
value = c(0.6,0.8))),
conditionalPanel(condition = "input.h2o_validation == false",
sliderInput("h2o_split", "Set the Training/Testing Partitions:",
min = 0.05, max = 1,
value = 0.7)),
shinyWidgets::materialSwitch(inputId = "nfolds_flag",
label = "N-fold Cross-Validation",
status = "primary", right = FALSE),
conditionalPanel(condition = "input.nfolds_flag == true",
sliderInput("nfolds", "Set the Number of folds:",
min = 3, max = 10, step = 1,
value = 5)),
actionButton("h2o_run_class", "Run Model")
),
box(width = 4, title = "Model Tuning",
conditionalPanel( condition = "input.binomial_models == 'h2o_rf'",
shinyWidgets::dropdownButton(
tags$h4("More Tunning Parameters"),
selectInput("rf_histogram_type", "Optimal Split Histogram Type",
choices = c("AUTO" = "AUTO",
"UniformAdaptive" = "UniformAdaptive",
"Random" = "Random",
"QuantilesGlobal" = "QuantilesGlobal",
"RoundRobin" = "RoundRobin"
),
selected = "AUTO"
),
sliderInput("h2o_rf_col_sample_rate_change_per_level", "Column Sample Rate Change Per Level",
min = 0, max = 2,
value = 1, step = 0.01
),
sliderInput("h2o_rf_col_sample_rate_per_tree", "Column Sample Rate Per Tree",
min = 0, max = 1,
value = 1, step = 0.01
),
sliderInput("h2o_rf_sample_rate", "Row Sampling Rate",
min = 0, max = 1,
value = 0.632, step = 0.01
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "More Tunning ")
),
sliderInput("h2o_rf_ntree", "Number of Trees",
min = 25, max = 1000,
value = 50, step = 25),
sliderInput("h2o_rf_max_depth", "Maximum Tree Depth",
min = 1, max = 30,
value = 20
)
),
conditionalPanel( condition = "input.binomial_models == 'h2o_gbm'",
shinyWidgets::dropdownButton(
tags$h4("More Tunning Parameters"),
selectInput("gbm_histogram_type", "Optimal Split Histogram Type",
choices = c("AUTO" = "AUTO",
"UniformAdaptive" = "UniformAdaptive",
"Random" = "Random",
"QuantilesGlobal" = "QuantilesGlobal",
"RoundRobin" = "RoundRobin"
),
selected = "AUTO"
),
sliderInput("h2o_gbm_learn_rate", "Learning Rate",
min = 0, max = 1,
value = 0.1
),
sliderInput("h2o_gbm_learn_rate_annealing", "Learning Rate",
min = 0, max = 1,
value = 1
),
sliderInput("h2o_gbm_min_rows", "Min. Rows",
min = 5, max = 20,
value = 1
),
sliderInput("h2o_gbm_min_split_improvement", "Min. Split Improvement",
min = 1e-10, max = 1e-3,
value = 1e-10
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "More Tunning ")
),
sliderInput("h2o_gbm_ntree", "Number of Trees",
min = 25, max = 1000,
value = 50, step = 25),
sliderInput("h2o_gbm_max_depth", "Maximum Tree Depth",
min = 1, max = 30,
value = 5
)
),
fluidRow(
conditionalPanel( condition = "input.binomial_models == 'h2o_dl'",
fluidRow(
column(width = 2,
shinyWidgets::dropdownButton(
tags$h4("Layer Setting"),
sliderInput("h2o_dl_num_hidden", "Number of Hidden Layers",
min = 1, max = 4,
value = 2, step = 1
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 1",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 2",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer2", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 3",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer2", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer3", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 4",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer2", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer3", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer4", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Layer Setting ")
)),
column(width = 2, offset = 2,
shinyWidgets::dropdownButton(
tags$h4("Layer Setting"),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Early Stop")
))),
sliderInput("h2o_dl_epochs", "Number of Epochs",
min = 1, max = 10000,
value = 10, step = 1),
sliderInput("h2o_dl_l1", "L1 Regularization",
min = 0, max = 1,
value = 0, step = 1e-5
),
sliderInput("h2o_dl_l2", "L2 Regularization",
min = 0, max = 1,
value = 0, step = 1e-5
)
)
),
conditionalPanel( condition = "input.binomial_models == 'h2o_glm'",
shinyWidgets::dropdownButton(
tags$h4("More Tunning Parameters"),
selectInput("glm_solver", "Solver",
choices = c("AUTO" = "AUTO",
"IRLSM" = "IRLSM",
"L_BFGS" = "L_BFGS",
"COORDINATE_DESCENT" = "COORDINATE_DESCENT",
"COORDINATE_DESCENT_NAIVE" = "COORDINATE_DESCENT_NAIVE"
),
selected = "AUTO"
),
sliderInput("h2o_glm_max_iterations", "Solver Max Iterations",
min = 10, max = 1000,
value = 50, step = 10),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "More Tunning ")
),
sliderInput("h2o_glm_alpha", "Alpha Parameter",
min = 0, max = 1,
value = 0.5, step = 0.01),
shinyWidgets::materialSwitch(inputId = "h2o_glm_lambda_search",
label = "Lambda Search",
status = "primary", right = FALSE,
value = TRUE),
conditionalPanel(condition = "input.h2o_glm_lambda_search == true",
sliderInput("h2o_glm_lambda_min_ratio", "Lambda Min. Ratio",
min = 0.0001, max = 0.001,
value = 0.0001, step = 0.0001),
sliderInput("h2o_glm_nlambdas", "Number of Lambdas",
min = 10, max = 200,
value = 100, step = 1)
)
)
)
)
),
tabPanel("Model Output",
conditionalPanel(condition = "input.binomial_models == 'h2o_glm' && output.h2o_glm_flag == '1'",
textOutput("h2o_glm_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_glm_cm_table"))
)
),
conditionalPanel(condition = "input.binomial_models == 'h2o_rf' && output.h2o_rf_flag == '1'",
textOutput("h2o_rf_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_rf_cm_table")),
box(width = 8, title = "Plots",
shinyWidgets::dropdownButton(
selectInput(inputId = "h2o_rf_plots_select", label = "Select Plot",
choices = c("Variable Importance" = "var_imp",
"RMSE" = "rmse",
"Classification Error" = "class_error",
"Logloss" = "logloss"
),
selected = "Variable Importance"
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Additional Plots")
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'var_imp'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_var_imp_plot"))
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'rmse'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_rmse_plot"))
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'class_error'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_error_plot"))
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'logloss'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_logloss_plot"))
)
)
)
),
conditionalPanel(condition = "input.binomial_models == 'h2o_gbm' && output.h2o_gbm_flag == '1'",
textOutput("h2o_gbm_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_gbm_cm_table")),
box(width = 8, title = "Plots",
shinyWidgets::dropdownButton(
selectInput(inputId = "h2o_gbm_plots_select", label = "Select Plot",
choices = c("Variable Importance" = "var_imp",
"RMSE" = "rmse",
"Classification Error" = "class_error",
"Logloss" = "logloss"
),
selected = "Variable Importance"
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Additional Plots")
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'var_imp'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_var_imp_plot"))
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'rmse'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_rmse_plot"))
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'class_error'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_error_plot"))
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'logloss'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_logloss_plot"))
)
)
)
),
conditionalPanel(condition = "input.binomial_models == 'h2o_dl' && output.h2o_dl_flag == '1'",
textOutput("h2o_dl_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_dl_cm_table")),
box(width = 8, title = "Plots",
shinyWidgets::dropdownButton(
selectInput(inputId = "h2o_dl_plots_select", label = "Select Plot",
choices = c("Variable Importance" = "var_imp",
"RMSE" = "rmse",
"Classification Error" = "class_error",
"Logloss" = "logloss"
),
selected = "Variable Importance"
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Additional Plots")
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'var_imp'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_var_imp_plot"))
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'rmse'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_rmse_plot"))
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'class_error'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_error_plot"))
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'logloss'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_logloss_plot"))
)
)
)
)
)
)
)
)
)
#------------------------------ Tabs Classification End-------------------------------------
)
)
)
RamiKrispin/MLstudio documentation built on May 28, 2019, 2:26 p.m.
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#------------------------------ UI Function -------------------------------------
ui <- dashboardPage(
dashboardHeader(title = "ML Studio"),
#------------------------------ Side Bar Function -------------------------------------
dashboardSidebar(
sidebarMenu(id = "tabs",
menuItem("Data", tabName = "data", icon = icon("table"), startExpanded = TRUE,
menuSubItem("Load", tabName = "data1"),
menuSubItem("Prep", tabName = "data2")
),
menuItem("Visualization", icon = icon("bar-chart-o"), tabName = "vis"),
menuItem("Models", icon = icon("cog"), tabName = "models",
menuSubItem("Regression & Classification", tabName = "models1")
)
)
),
#------------------------------ Dashboard Body -------------------------------------
dashboardBody(
#------------------------------ Tags Style -------------------------------------
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }",
"
.selectize-dropdown-content {
max-height: 300px;
overflow-y: auto;
background: ghostwhite;
}
"
,
"
select ~ .selectize-control .selectize-input {
max-height: 100px;
overflow-y: auto;
}
"
),
#------------------------------ Tabs Start -------------------------------------
tabItems(
#------------------------------ Tabs Data Start-------------------------------------
tabItem(tabName = "data1",
#------------------------------ Tabs Data - fluid page start -------------------------------------
fluidPage(
#------------------------------ Tabs Data - fluid row 1 -------------------------------------
fluidRow(
infoBoxOutput("installed_datasets"),
infoBoxOutput("in_memory_df"),
infoBoxOutput("load_datasets")
),
#------------------------------ Tabs Data - fluid row 2 -------------------------------------
fluidRow(
box(
width = 4, height = 100,
selectInput('data_source', 'Select Data Source',
list(
"R Data Frame" = "data_frame",
"R Time Series" = "time_series",
"Installed Package Dataset" = "inst_pack",
"Import CSV File" = "import"
))
),
box(width = 4, height = 100,
conditionalPanel(condition = "input.data_source.includes('data_frame') || input.data_source.includes('inst_pack') || input.data_source.includes('time_series')",
uiOutput("df_list")
),
conditionalPanel(condition = "input.data_source == 'import'",
shinyWidgets::dropdownButton(
fileInput('file1', 'Choose CSV File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
shinyWidgets::awesomeCheckbox(inputId = "csv_header",
label = "Header",
value = TRUE),
radioButtons('sep', 'Separator',
c(Comma=',',
Semicolon=';',
Tab='\t'),
','),
radioButtons('quote', 'Quote',
c(None='',
'Double Quote'='"',
'Single Quote'="'"),
'"'),
circle = TRUE, status = "danger",
icon = icon("file-text", lib = "font-awesome"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Click to set csv file parameters !")
)
)
),
box(width = 4, height = 100,
conditionalPanel(condition = "(output.load_flag == '2' && input.data_source == 'inst_pack') || (output.load_flag == '2' && input.data_source == 'import' ) || (output.load_flag == '1' && input.data_source == 'data_frame' ) || ( output.load_flag == '1' && input.data_source == 'inst_pack') || (output.load_flag == '1' && input.data_source == 'time_series')",
actionButton("load", "Load")
),
conditionalPanel(condition = "output.loaded_table_flag == '1'",
actionButton("remove", "Remove")
)
)
),
fluidRow(
box(width = 7, title = "Preview Table",
div(style = 'overflow-x: scroll',
DT::dataTableOutput('view_table'))
),
box(width = 5, title = "Loaded Datasets",
div(style = 'overflow-x: scroll',
DT::dataTableOutput('list_loaded_df'))
)
)
#------------------------------ Tabs Data - fluid row 2 -------------------------------------
)
#------------------------------ Tabs Data - fluid page end -------------------------------------
),
#------------------------------ Tabs Data2 Start-------------------------------------
tabItem(tabName = "data2",
fluidPage(
fluidRow(
conditionalPanel(condition = "output.loaded_table_flag == '1'",
infoBoxOutput("data_name"),
infoBoxOutput("num_var"),
infoBoxOutput("num_obs")
)
),
fluidRow(
conditionalPanel(condition = "output.loaded_table_flag == '1'",
box(width = 4, title = "Select Dataset",
uiOutput("loaded_ds_list"),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == false ",
selectInput('data_option', 'Select Option',
list(
"Variables Attributes" = "var_attr",
"Data Summary" = "data_summary"
))
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == false && input.data_option == 'var_attr'",
radioButtons("class_selection", label = "Variables Modification",
choices = list(Numeric = "numeric", Factor = "factor",
Character = "character",
Date = "date"),
selected = "numeric"),
conditionalPanel(condition = "input.class_selection == 'date' && output.loaded_table_flag == '1' && output.class_df_flag == false && input.data_option == 'var_attr'",
selectInput('date_format', "Select the Date Format",
list(
YMD = "ymd",
YDM = "ydm",
MYD = "myd",
MDY = "mdy",
DMY = "dmy",
DYM = "dym"
)),
#titlePanel(h5("Date Preview")),
tags$h5("Date Preview"),
verbatimTextOutput("date_prev")
),
actionButton("var_modify", "Modify")
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == true ",
tableOutput("ts_table")
)
)),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == false && input.data_option == 'var_attr'",
box(width = 4, title = "List of Variables",
DT::dataTableOutput("data_tab2_var")
),
box(width = 4, title = "Variable Summary",
shinycssloaders::withSpinner(plotly::plotlyOutput("data_tab2_summary_plot",height = 200)),
tableOutput("data_tab2_var_summary")
)
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && input.data_option == 'data_summary'",
box(width = 4, title = "Create a Summary",
uiOutput("group_by"),
conditionalPanel(condition = "output.loaded_table_flag == '1' && input.data_option == 'data_summary' && output.group_by_flag == '1'",
uiOutput("summarise_var")
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && input.data_option == 'data_summary' && output.group_by_flag == '1'",
uiOutput("dplyr_fun"),
uiOutput("summary_name"),
actionButton("run_summary", "Run"),
conditionalPanel(condition = "output.dplyr_table_flag == '1'",
actionButton("load_summary_table", "Load")
)
)
),
conditionalPanel(condition = "output.dplyr_table_flag == '1' && input.data_option == 'data_summary' && output.group_by_flag == '1'",
box(width = 4, title = "Summary",
div(style = 'overflow-x: scroll',
DT::dataTableOutput('dplyr_table')
)
)
)
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag == true ",
box(width = 8, title = "Time Series Plot",
shinyWidgets::dropdownButton(
tags$h3("List of Input"),
shinyWidgets::materialSwitch(inputId = "ts_plot_log", label = "Log Transformation",
status = "primary", right = FALSE),
shinyWidgets::awesomeRadio(inputId = "ts_prep_mode",
label = "Radio buttons",
choices = c("lines","lines+markers", "markers")
, selected = "lines"),
circle = TRUE, status = "danger", icon = icon("gear"), width = "200px",
tooltip = shinyWidgets::tooltipOptions(title = "Plot Setting")
),
shinycssloaders::withSpinner(plotly::plotlyOutput("data_tab2_ts"))
)
)
),
fluidRow(
conditionalPanel(condition = "output.loaded_table_flag == '1'",
div(style = 'overflow-x: scroll',
DT::dataTableOutput("data_tab2_table"))
)
)
)
),
#------------------------------ Tabs Data End-------------------------------------
#------------------------------ Tabs Visualization Start-------------------------------------
tabItem(tabName = "vis",
conditionalPanel(condition = "output.loaded_table_flag == '1'",
fluidPage(
fluidRow(
box(width = 2,
uiOutput("loaded_ds_list_vis"),
uiOutput("vis_plot_type")
),
conditionalPanel(condition = "output.loaded_table_flag == '1' && output.class_df_flag_vis == false && input.plot_type != 'cor' ",
box(width = 2,
conditionalPanel(condition = "input.plot_type != 'density' && input.plot_type != 'box' && input.plot_type != 'hist'",
uiOutput("vis_x"),
uiOutput("vis_y")
),
conditionalPanel(condition = "input.plot_type == 'density' || input.plot_type == 'hist' || input.plot_type == 'box'",
uiOutput("vis_one_var")
)
),
conditionalPanel(condition = "input.plot_type == 'scatter' || input.plot_type == 'box'|| input.plot_type == 'hist' || input.plot_type == 'density'",
box(width = 2,
uiOutput("vis_factor"))
)
)
),
fluidRow(
box(width = 12, title = "plot",
shinycssloaders::withSpinner(plotly::plotlyOutput("main_plot"))
)
)
)
)
),
#------------------------------ Tabs Visualization End-------------------------------------
#------------------------------ Tabs Classification Start-------------------------------------
tabItem(tabName = "models1",
fluidRow(conditionalPanel(condition = "input.model_package.includes('H2O')",
infoBoxOutput("h2o_status_box"),
infoBoxOutput("h2o_cpu"),
infoBoxOutput("h2o_cluster_mem")
)
),
fluidRow(
box(width = 2, title = "Model Inputs",
conditionalPanel(condition = "input.model_package.includes('H2O')",
uiOutput("models1_df_list"),
conditionalPanel(condition = "output.model_tab_input == '1' || output.model_tab_input == '2'",
uiOutput("models1_var_list"),
uiOutput("models1_independent_list")
)
),
shinyWidgets::awesomeCheckboxGroup(inputId = "model_package",
label = "Set Packages",
choices = c("H2O"), selected = NULL,
inline = TRUE)
),
conditionalPanel(condition = "output.dep_var_class == '1' && output.h2o_flag == '1' && output.model_tab_ind == '1'",
tabBox(
title = "Model Setting & Output", width = 10,
id = "class_setting", height = "500px",
tabPanel("Model Setting",
fluidRow(
box(width = 4, title = "Model Setting",
selectInput("binomial_models", "Select Classification Model",
choices = c("Deep Learning (H2O)" = "h2o_dl",
"GLM (H2O)" = "h2o_glm",
"GBM (H2O)" = "h2o_gbm",
"Random Forest (H2O)" = "h2o_rf")
),
shinyWidgets::materialSwitch(inputId = "h2o_validation",
label = "Add Validation Partition",
status = "primary", right = FALSE),
conditionalPanel(condition = "input.h2o_validation == true",
sliderInput("h2o_split_v", "Set the Training/Testing/Validation Partitions:",
min = 0.05, max = 1,
value = c(0.6,0.8))),
conditionalPanel(condition = "input.h2o_validation == false",
sliderInput("h2o_split", "Set the Training/Testing Partitions:",
min = 0.05, max = 1,
value = 0.7)),
shinyWidgets::materialSwitch(inputId = "nfolds_flag",
label = "N-fold Cross-Validation",
status = "primary", right = FALSE),
conditionalPanel(condition = "input.nfolds_flag == true",
sliderInput("nfolds", "Set the Number of folds:",
min = 3, max = 10, step = 1,
value = 5)),
actionButton("h2o_run_class", "Run Model")
),
box(width = 4, title = "Model Tuning",
conditionalPanel( condition = "input.binomial_models == 'h2o_rf'",
shinyWidgets::dropdownButton(
tags$h4("More Tunning Parameters"),
selectInput("rf_histogram_type", "Optimal Split Histogram Type",
choices = c("AUTO" = "AUTO",
"UniformAdaptive" = "UniformAdaptive",
"Random" = "Random",
"QuantilesGlobal" = "QuantilesGlobal",
"RoundRobin" = "RoundRobin"
),
selected = "AUTO"
),
sliderInput("h2o_rf_col_sample_rate_change_per_level", "Column Sample Rate Change Per Level",
min = 0, max = 2,
value = 1, step = 0.01
),
sliderInput("h2o_rf_col_sample_rate_per_tree", "Column Sample Rate Per Tree",
min = 0, max = 1,
value = 1, step = 0.01
),
sliderInput("h2o_rf_sample_rate", "Row Sampling Rate",
min = 0, max = 1,
value = 0.632, step = 0.01
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "More Tunning ")
),
sliderInput("h2o_rf_ntree", "Number of Trees",
min = 25, max = 1000,
value = 50, step = 25),
sliderInput("h2o_rf_max_depth", "Maximum Tree Depth",
min = 1, max = 30,
value = 20
)
),
conditionalPanel( condition = "input.binomial_models == 'h2o_gbm'",
shinyWidgets::dropdownButton(
tags$h4("More Tunning Parameters"),
selectInput("gbm_histogram_type", "Optimal Split Histogram Type",
choices = c("AUTO" = "AUTO",
"UniformAdaptive" = "UniformAdaptive",
"Random" = "Random",
"QuantilesGlobal" = "QuantilesGlobal",
"RoundRobin" = "RoundRobin"
),
selected = "AUTO"
),
sliderInput("h2o_gbm_learn_rate", "Learning Rate",
min = 0, max = 1,
value = 0.1
),
sliderInput("h2o_gbm_learn_rate_annealing", "Learning Rate",
min = 0, max = 1,
value = 1
),
sliderInput("h2o_gbm_min_rows", "Min. Rows",
min = 5, max = 20,
value = 1
),
sliderInput("h2o_gbm_min_split_improvement", "Min. Split Improvement",
min = 1e-10, max = 1e-3,
value = 1e-10
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "More Tunning ")
),
sliderInput("h2o_gbm_ntree", "Number of Trees",
min = 25, max = 1000,
value = 50, step = 25),
sliderInput("h2o_gbm_max_depth", "Maximum Tree Depth",
min = 1, max = 30,
value = 5
)
),
fluidRow(
conditionalPanel( condition = "input.binomial_models == 'h2o_dl'",
fluidRow(
column(width = 2,
shinyWidgets::dropdownButton(
tags$h4("Layer Setting"),
sliderInput("h2o_dl_num_hidden", "Number of Hidden Layers",
min = 1, max = 4,
value = 2, step = 1
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 1",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 2",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer2", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 3",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer2", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer3", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
conditionalPanel(condition = "input.h2o_dl_num_hidden == 4",
sliderInput("h2o_dl_layer1", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer2", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer3", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
),
sliderInput("h2o_dl_layer4", "Number of Hidden Layers",
min = 1, max = 1000,
value = 200, step = 1
)
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Layer Setting ")
)),
column(width = 2, offset = 2,
shinyWidgets::dropdownButton(
tags$h4("Layer Setting"),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Early Stop")
))),
sliderInput("h2o_dl_epochs", "Number of Epochs",
min = 1, max = 10000,
value = 10, step = 1),
sliderInput("h2o_dl_l1", "L1 Regularization",
min = 0, max = 1,
value = 0, step = 1e-5
),
sliderInput("h2o_dl_l2", "L2 Regularization",
min = 0, max = 1,
value = 0, step = 1e-5
)
)
),
conditionalPanel( condition = "input.binomial_models == 'h2o_glm'",
shinyWidgets::dropdownButton(
tags$h4("More Tunning Parameters"),
selectInput("glm_solver", "Solver",
choices = c("AUTO" = "AUTO",
"IRLSM" = "IRLSM",
"L_BFGS" = "L_BFGS",
"COORDINATE_DESCENT" = "COORDINATE_DESCENT",
"COORDINATE_DESCENT_NAIVE" = "COORDINATE_DESCENT_NAIVE"
),
selected = "AUTO"
),
sliderInput("h2o_glm_max_iterations", "Solver Max Iterations",
min = 10, max = 1000,
value = 50, step = 10),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "More Tunning ")
),
sliderInput("h2o_glm_alpha", "Alpha Parameter",
min = 0, max = 1,
value = 0.5, step = 0.01),
shinyWidgets::materialSwitch(inputId = "h2o_glm_lambda_search",
label = "Lambda Search",
status = "primary", right = FALSE,
value = TRUE),
conditionalPanel(condition = "input.h2o_glm_lambda_search == true",
sliderInput("h2o_glm_lambda_min_ratio", "Lambda Min. Ratio",
min = 0.0001, max = 0.001,
value = 0.0001, step = 0.0001),
sliderInput("h2o_glm_nlambdas", "Number of Lambdas",
min = 10, max = 200,
value = 100, step = 1)
)
)
)
)
),
tabPanel("Model Output",
conditionalPanel(condition = "input.binomial_models == 'h2o_glm' && output.h2o_glm_flag == '1'",
textOutput("h2o_glm_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_glm_cm_table"))
)
),
conditionalPanel(condition = "input.binomial_models == 'h2o_rf' && output.h2o_rf_flag == '1'",
textOutput("h2o_rf_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_rf_cm_table")),
box(width = 8, title = "Plots",
shinyWidgets::dropdownButton(
selectInput(inputId = "h2o_rf_plots_select", label = "Select Plot",
choices = c("Variable Importance" = "var_imp",
"RMSE" = "rmse",
"Classification Error" = "class_error",
"Logloss" = "logloss"
),
selected = "Variable Importance"
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Additional Plots")
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'var_imp'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_var_imp_plot"))
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'rmse'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_rmse_plot"))
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'class_error'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_error_plot"))
),
conditionalPanel(condition = "input.h2o_rf_plots_select == 'logloss'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_rf_class_logloss_plot"))
)
)
)
),
conditionalPanel(condition = "input.binomial_models == 'h2o_gbm' && output.h2o_gbm_flag == '1'",
textOutput("h2o_gbm_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_gbm_cm_table")),
box(width = 8, title = "Plots",
shinyWidgets::dropdownButton(
selectInput(inputId = "h2o_gbm_plots_select", label = "Select Plot",
choices = c("Variable Importance" = "var_imp",
"RMSE" = "rmse",
"Classification Error" = "class_error",
"Logloss" = "logloss"
),
selected = "Variable Importance"
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Additional Plots")
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'var_imp'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_var_imp_plot"))
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'rmse'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_rmse_plot"))
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'class_error'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_error_plot"))
),
conditionalPanel(condition = "input.h2o_gbm_plots_select == 'logloss'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_gbm_class_logloss_plot"))
)
)
)
),
conditionalPanel(condition = "input.binomial_models == 'h2o_dl' && output.h2o_dl_flag == '1'",
textOutput("h2o_dl_model_text"),
fluidRow(
box(width = 4, title = "Confusion Matrix",
tableOutput("h2o_dl_cm_table")),
box(width = 8, title = "Plots",
shinyWidgets::dropdownButton(
selectInput(inputId = "h2o_dl_plots_select", label = "Select Plot",
choices = c("Variable Importance" = "var_imp",
"RMSE" = "rmse",
"Classification Error" = "class_error",
"Logloss" = "logloss"
),
selected = "Variable Importance"
),
circle = TRUE, status = "danger", icon = icon("gear"), width = "300px",
tooltip = shinyWidgets::tooltipOptions(title = "Additional Plots")
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'var_imp'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_var_imp_plot"))
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'rmse'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_rmse_plot"))
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'class_error'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_error_plot"))
),
conditionalPanel(condition = "input.h2o_dl_plots_select == 'logloss'",
shinycssloaders::withSpinner(plotly::plotlyOutput("h2o_dl_class_logloss_plot"))
)
)
)
)
)
)
)
)
)
#------------------------------ Tabs Classification End-------------------------------------
)
)
)
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