Update app.R

app.R

@@ -3,90 +3,8 @@ library(shinyjs)
 library(bslib)
 library(dplyr)
 library(ggplot2)
-library(tm)
-library(SnowballC)
-library(plotly)
-library(dplyr)
-library(tidyr)
-library(igraph)
-library(ggraph)
-library(reshape2)
-library(SnowballC)
-library(RColorBrewer)
-library(syuzhet)
-library(cluster)
-library(Rtsne)
-library(umap)
-library(MASS)
-library(koRpus)
-library(openxlsx)
-library(tools)
-library(shinyWidgets)
 library(readxl)
-library(scales)
-library(caret)
-library(BBmisc)
-library(glmnet)
-library(pROC)
-library(ROCR)
-library(car)
-library(ResourceSelection)
-library(tree)
-library(ggplotify)
-library(lmtest)
-library(gridExtra)
-library(patchwork)
-library(caret)
-library(randomForest)
-library(gbm)
-library(earth)
-library(broom)
-library(rlang)
-library(ggdendro)
-library(pastecs)
-library(forecast)
-library(scales)
-library(caret)
-library(BBmisc)
-library(glmnet)
-library(pROC)
-library(ROCR)
-library(car)
-library(ResourceSelection)
-library(tree)
-library(ggplotify)
-library(lmtest)
-library(gridExtra)
-library(patchwork)
-library(caret)
-library(randomForest)
-library(gbm)
-library(earth)
-library(broom)
-library(rlang)
-library(ggdendro)
-library(pastecs)
-library(dbscan)
-library(fpc)
-library(factoextra)
-library(scales)
-library(openxlsx)
-library(arules)
-library(arulesViz)
-library(viridis)
-library(kohonen)
-library(purrr)
-library(rvest)
-library(Rtsne)
-library(shinydashboard)
-library(DT)
-library(DataExplorer)
-library(lubridate)
-library(readr)
 library(htmlwidgets)
-library(GGally)
-library(keras)
-library(tensorflow)
 library(neuralnet)
 library(rsample)
 options(width = 150)
@@ -356,77 +274,85 @@ server <- function(input, output, session) {
   })
   
   ##Multiple Perceptron Model
+ ##Multiple Perceptron Model
   # Reactive expression for data input
-# Reactive expression for data input
-dataMLP <- reactive({
-  req(input$mlp_fileInput)
-  inFile <- input$mlp_fileInput
-  if (grepl("\\.csv$", inFile$name)) {
-    read.csv(inFile$datapath, stringsAsFactors = FALSE)
-  } else if (grepl("\\.(xlsx|xls)$", inFile$name)) {
-    readxl::read_xlsx(inFile$datapath)
-  } else {
-    stop("Unsupported file type")
-  }
-})
-
-output$mlp_preprocessUI <- renderUI({
-  req(dataMLP())
-  varNames <- names(dataMLP())
-  tagList(
-    selectInput("mlp_targetVariable", "Select Target Variable", choices = varNames),
-    selectInput("mlp_variables", "Select Predictor Variables", choices = varNames, multiple = TRUE),
-    selectInput("mlp_covariate", "Select Covariate Variable", choices = varNames),
-    tags$p(HTML("Please select Covariate Variable from Predictor Variables"))
-  )
-})
-
-observeEvent(input$mlp_trainButton, {
-  req(dataMLP(), input$mlp_targetVariable, input$mlp_variables)
-  data <- dataMLP()
-  data <- na.omit(data)
-  
-  # Define the formula for the neural network
-  formula <- as.formula(paste(input$mlp_targetVariable, "~", paste(input$mlp_variables, collapse = "+")))
-  
-  # Train the neural network model
-  nn <- neuralnet(formula, data, hidden = rep(input$mlp_neurons, input$mlp_hiddenLayers), linear.output = FALSE, threshold = 0.01, stepmax = input$mlp_epochs)
+  dataMLP <- reactive({
+    req(input$mlp_fileInput)
+    inFile <- input$mlp_fileInput
+    if (grepl("\\.csv$", inFile$name)) {
+      read.csv(inFile$datapath, stringsAsFactors = FALSE)
+    } else if (grepl("\\.(xlsx|xls)$", inFile$name)) {
+      readxl::read_xlsx(inFile$datapath)
+    } else {
+      stop("Unsupported file type")
+    }
+  })
   
-  # Plot the neural network
-  output$mlp_trainingPlot <- renderPlot({
-    plot(nn, rep = "best")
+  output$mlp_preprocessUI <- renderUI({
+    req(dataMLP())
+    varNames <- names(dataMLP())
+    tagList(
+      selectInput("mlp_targetVariable", "Select Target Variable", choices = varNames),
+      selectInput("mlp_variables", "Select Predictor Variables", choices = varNames, multiple = TRUE),
+      selectInput("mlp_covariate", "Select Covariate Variable", choices = varNames),
+      tags$p(HTML("Please select Covariate Variable from Predictor Variables"))
+    )
   })
   
-  # Print the result matrix of the neural network
-  output$mlp_evaluation <- renderPrint({
-    print(nn$result.matrix)
-    cat("\nNeural Network Model Performance Summary:\n")
+  observeEvent(input$mlp_trainButton, {
+    req(dataMLP(), input$mlp_targetVariable, input$mlp_variables)
+    data <- dataMLP()
+    data <- na.omit(data)
     
-    if (nn$result.matrix["error", ] > 200) {
-      cat("The model error of", nn$result.matrix["error", ], "is above the expected threshold. This may indicate that the model does not fit the data well. Consider collecting more data, feature engineering, or adjusting the model's complexity.\n")
-    } else {
-      cat("The model error of", nn$result.matrix["error", ], "is within the acceptable range, suggesting the model has learned the patterns from the data effectively.\n")
-    }
+    # Define the formula for the neural network
+    formula <- as.formula(paste(input$mlp_targetVariable, "~", paste(input$mlp_variables, collapse = "+")))
     
-    cat("The model took", nn$result.matrix["steps", ], "steps to converge, which indicates ", ifelse(nn$result.matrix["steps", ] < 3000, "an efficient training process.", "that the maximum set steps were reached without sufficient convergence."), "\n")
+    # Train the neural network model
+    nn <- neuralnet(formula, data, hidden = c(input$mlp_neurons), linear.output = FALSE, threshold = 0.01, stepmax = input$mlp_epochs)
     
-    cat("The model's weights have been optimized through training. Each weight reflects the importance of the corresponding input feature for predicting the output. For instance, the weight for 'Price' to the first hidden neuron is", nn$result.matrix["Price.to.1layhid1", ], ".\n")
+    # Plot the neural network
+    output$mlp_trainingPlot <- renderPlot({
+      plot(nn,rep = "best")
+    })
     
-    cat("The threshold for stopping the training was set to", nn$result.matrix["reached.threshold", ], ", and the model reached an error close to this threshold, which is a good sign of model convergence.\n")
     
-    cat("This trained model can now be used to make predictions on new data. It's important to validate the model's performance on a separate test set to ensure its predictive accuracy.\n")
-  })
-  
-  output$mlp_gwplot <- renderPlot({
-    req(input$mlp_covariate)
-    selected_var <- input$mlp_covariate
-    if (selected_var %in% colnames(nn$covariate)) {
-      gwplot(nn, selected.covariate = selected_var, min = -2.5, max = 5)
-    } else {
-      cat("Please select a valid predictor variable to view its weight distribution.")
-    }
+    # Print the result matrix of the neural network
+    output$mlp_evaluation <- renderPrint({
+      print(nn$result.matrix)
+      # Neural Network Model Performance Summary
+      cat("\nNeural Network Model Performance Summary:\n")
+      
+      # If the error is not within a reasonable range, you could give more context:
+      if (nn$result.matrix["error", ] > 200) {
+        cat("The model error of", nn$result.matrix["error", ], "is above the expected threshold. This may indicate that the model does not fit the data well. Consider collecting more data, feature engineering, or adjusting the model's complexity.\n")
+      } else {
+        cat("The model error of", nn$result.matrix["error", ], "is within the acceptable range, suggesting the model has learned the patterns from the data effectively.\n")
+      }
+      
+      # Comment on the number of steps
+      cat("The model took", nn$result.matrix["steps", ], "steps to converge, which indicates ", ifelse(nn$result.matrix["steps", ] < 3000, "an efficient training process.", "that the maximum set steps were reached without sufficient convergence."), "\n")
+      
+      # Comment on the weights
+      cat("The model's weights have been optimized through training. Each weight reflects the importance of the corresponding input feature for predicting the output. For instance, the weight for 'Price' to the first hidden neuron is", nn$result.matrix["Price.to.1layhid1", ], ".\n")
+      
+      # Mention the threshold
+      cat("The threshold for stopping the training was set to", nn$result.matrix["reached.threshold", ], ", and the model reached an error close to this threshold, which is a good sign of model convergence.\n")
+      
+      # Add a note on the usage of the model
+      cat("This trained model can now be used to make predictions on new data. It's important to validate the model's performance on a separate test set to ensure its predictive accuracy.\n")
+    })
+    
+    output$mlp_gwplot <- renderPlot({
+      req(input$mlp_covariate) # Make sure input$mlp_variables is available
+      selected_var <- input$mlp_covariate # This should be a vector of selected variable names
+      if (length(selected_var) == 1) { # gwplot may only accept a single variable
+        gwplot(nn, selected.covariate = selected_var, min = -2.5, max = 5)
+      } else {
+        cat("Please select a single predictor variable to view its weight distribution.")
+      }
+    })
+    
   })
-})
 
   
 }