Spaces:
Sleeping
Sleeping
| # Import necessary libraries | |
| library(shiny) | |
| library(caret) | |
| library(readr) | |
| library(catboost) | |
| library(ggplot2) | |
| library(gridExtra) | |
| source("calculate_shap.R") | |
| source("plot_shap.R") | |
| # Load the pre-trained model | |
| model <- readRDS("goat_behavior_model_caret.rds") | |
| # Define UI for application | |
| ui <- fluidPage( | |
| # App title ---- | |
| titlePanel("Detecting Goat Behaviors"), | |
| # Sidebar layout with input and output definitions ---- | |
| sidebarLayout( | |
| # Sidebar panel for inputs ---- | |
| sidebarPanel( | |
| # Input: Select a file ---- | |
| fileInput("file1", "Choose TSV File", | |
| accept = c( | |
| "text/tsv", | |
| "text/tab-separated-values,text/plain", | |
| ".tsv") | |
| ) | |
| ), | |
| # Main panel for displaying outputs ---- | |
| mainPanel( | |
| # Output: Tabset with data, confusion matrix, and download button | |
| tabsetPanel( | |
| id = "dataset", | |
| tabPanel("About", | |
| HTML(" | |
| <h5> The following model was part of the the research article: </h5> | |
| <p><p> <h5> You can test the app using an example dataset available <a href='https://github.com/harpomaxx/goat-behavior-model/blob/881ed7251a58a55b05d5eb3a3bc40225ba6694cb/data/split/dataset_a.tsv' > here </a></h5> | |
| <h4>Developing an Interpretable Machine Learning Model for the Detection of Mimosa Grazing in Goats</h4> | |
| <em>In the last years, several machine learning approaches for detecting animal behaviors have been proposed. | |
| However, despite their successful application, their complexity and lack of explainability have difficulty in their | |
| application to real-world scenarios. The article presents a machine-learning model for differentiating between grazing mimosa and other activities | |
| (resting, walking, and grazing ) in goats using sensor data. Boruta, an algorithm for selecting the most relevant features, and SHAP, | |
| a technique for interpreting the decision of a machine learning model are two fundamental components of the methodology used for creating the model. | |
| The resulting model, a gradient boost algorithm with 15 selected features proved to be extremely accurate in detecting Grazing activities. | |
| The study demonstrates the fundamental role of model explainability in identifying model weaknesses and errors, thereby creating a path for future | |
| improvements. In addition, the simplicity of the resulting model not only reduces computational complexity and processing time but also enhances | |
| interpretability and facilitates the deployment of real-life scenarios.</em> | |
| <p> | |
| <p>This application allows users to test the pre-trained machine learning model that predicts goat behavior based on input sensor data. | |
| The input data should be a tab-separated value (.tsv) file containing specific sensor data related to the goat's activity. | |
| <p>The application then generates predictions, provides a confusion matrix result, and offers the option to download the predictions. In addition you can explore the decisions of the model via SHAP analysis. | |
| <p>The key features expected in the dataset are: | |
| <table> | |
| <thead> | |
| <tr> | |
| <th>No</th> | |
| <th>Feature</th> | |
| <th>Definition</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr> | |
| <td>1</td> | |
| <td>Steps</td> | |
| <td>Number of steps</td> | |
| </tr> | |
| <tr> | |
| <td>2</td> | |
| <td>HeadDown</td> | |
| <td>% time with head down</td> | |
| </tr> | |
| <tr> | |
| <td>3</td> | |
| <td>Standing</td> | |
| <td>% time Standing</td> | |
| </tr> | |
| <tr> | |
| <td>4</td> | |
| <td>Active</td> | |
| <td>% time Active</td> | |
| </tr> | |
| <tr> | |
| <td>5</td> | |
| <td>MeanXY</td> | |
| <td>Arithmetic mean between X and Y positions</td> | |
| </tr> | |
| <tr> | |
| <td>6</td> | |
| <td>Distance</td> | |
| <td>Distance in meters</td> | |
| </tr> | |
| <tr> | |
| <td>7</td> | |
| <td>prev_steps1</td> | |
| <td>Number of steps one step backward</td> | |
| </tr> | |
| <tr> | |
| <td>8</td> | |
| <td>X_Act</td> | |
| <td>X position actuator</td> | |
| </tr> | |
| <tr> | |
| <td>9</td> | |
| <td>prev_Active1</td> | |
| <td>% time Active one step backward</td> | |
| </tr> | |
| <tr> | |
| <td>10</td> | |
| <td>prev_Standing1</td> | |
| <td>% time Standing one step backward</td> | |
| </tr> | |
| <tr> | |
| <td>11</td> | |
| <td>DFA123</td> | |
| <td>Accumulative Euclidean distance from actual position to three positions forward</td> | |
| </tr> | |
| <tr> | |
| <td>12</td> | |
| <td>prev_headdown1</td> | |
| <td>% time with head down one step backward</td> | |
| </tr> | |
| <tr> | |
| <td>13</td> | |
| <td>Lying</td> | |
| <td>% time Lying</td> | |
| </tr> | |
| <tr> | |
| <td>14</td> | |
| <td>Y_Act</td> | |
| <td>Y position actuator</td> | |
| </tr> | |
| <tr> | |
| <td>15</td> | |
| <td>DBA123</td> | |
| <td>Accumulative Euclidean distance from actual position to three positions backward</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p><p> <h5> Experiments, datasets and source code and more <a href='https://github.com/harpomaxx/goat-behavior/'> here<a/> </h5>" | |
| ) | |
| ), | |
| tabPanel("Results", | |
| tableOutput("contents"), | |
| verbatimTextOutput("confusionMatText"), | |
| plotOutput("confusionMatPlot"), | |
| downloadButton("downloadData", "Download Predictions")), | |
| tabPanel("SHAP Summary", | |
| plotOutput("SHAPSummary")), | |
| tabPanel("SHAP Summary per class", | |
| plotOutput("SHAPSummaryperclass")), | |
| tabPanel("SHAP Dependency", | |
| plotOutput("SHAPDependency")) | |
| ) | |
| ) | |
| ) | |
| ) | |
| # Define server logic | |
| server <- function(input, output) { | |
| # For the predictions dataset | |
| # Path to the default file | |
| default_file_path <- "./dataset_b.tsv" | |
| predictions <- reactive({ | |
| # Use default file if no file is uploaded | |
| file_path <- if (is.null(input$file1)) { | |
| default_file_path | |
| } else { | |
| input$file1$datapath | |
| } | |
| dataset <- readr::read_delim(file_path, delim='\t') | |
| predict(model, dataset) | |
| }) | |
| # For the table | |
| output$contents <- renderTable({ | |
| # input$file1 will be NULL initially. After the user selects | |
| # a file, it will be a data frame with 'name', 'size', 'type', and 'datapath' variables. | |
| inFile <- input$file1 | |
| if (is.null(inFile)) | |
| return(NULL) | |
| # Read the file from the input$file1 path and return it | |
| dataset <- readr::read_delim(inFile$datapath,delim='\t') | |
| head(dataset, n = 5) | |
| }) | |
| # Download function for predictions | |
| output$downloadData <- downloadHandler( | |
| filename = function() { | |
| paste("predictions-", Sys.Date(), ".csv", sep="") | |
| }, | |
| content = function(file) { | |
| write.csv(data.frame(Index = 1:length(predictions()), Prediction = predictions()), file, row.names = FALSE) | |
| } | |
| ) | |
| # Confusion Matrix | |
| output$confusionMatText <- renderPrint({ | |
| if (is.null(input$file1)) | |
| return(NULL) | |
| inFile <- input$file1 | |
| dataset <- readr::read_delim(inFile$datapath,delim='\t',progress = FALSE) | |
| predictions <- predict(model, dataset) | |
| cm<-caret::confusionMatrix(reference=as.factor(dataset$Activity),predictions,mode="everything") | |
| cm$overall | |
| }) | |
| output$confusionMatPlot <- renderPlot({ | |
| if (is.null(input$file1)) | |
| return(NULL) | |
| inFile <- input$file1 | |
| dataset <- readr::read_delim(inFile$datapath,delim='\t') | |
| predictions <- predict(model, dataset) | |
| cm<-caret::confusionMatrix(reference=as.factor(dataset$Activity),predictions,mode="everything") | |
| # Extract table data from confusion matrix | |
| confusionMatrixTable <- as.table(cm$table) | |
| # Plot the confusion matrix | |
| ggplot(as.data.frame(confusionMatrixTable), aes(x=Reference, y=Prediction)) + | |
| geom_tile(aes(fill = log(Freq)), colour = "white") + | |
| geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) + | |
| scale_fill_gradient(low = "white", high = "steelblue") + | |
| theme_minimal() + | |
| theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) | |
| }) | |
| output$SHAPSummary <- renderPlot({ | |
| if (is.null(input$file1)) | |
| return(NULL) | |
| inFile <- input$file1 | |
| dataset <- readr::read_delim(inFile$datapath,delim='\t') | |
| predictions <- predict(model, dataset) | |
| selected_variables <- | |
| readr::read_delim( | |
| "selected_features.tsv", | |
| col_types = cols(), | |
| delim = '\t' | |
| ) | |
| new_dataset <- | |
| dataset %>% select(selected_variables$variable, Anim, Activity) | |
| new_dataset <- cbind(new_dataset, predictions) | |
| shap_values <- calculate_shap(new_dataset, model, nsim = 30) | |
| pall<-shap_summary_plot(shap_values %>% as.data.frame()) | |
| pall+xlim(0,0.35) | |
| }) | |
| output$SHAPSummaryperclass <- renderPlot({ | |
| if (is.null(input$file1)) | |
| return(NULL) | |
| inFile <- input$file1 | |
| dataset <- readr::read_delim(inFile$datapath,delim='\t') | |
| predictions <- predict(model, dataset) | |
| selected_variables <- | |
| readr::read_delim( | |
| "selected_features.tsv", | |
| col_types = cols(), | |
| delim = '\t' | |
| ) | |
| new_dataset <- | |
| dataset %>% select(selected_variables$variable, Anim, Activity) | |
| new_dataset <- cbind(new_dataset, predictions) | |
| shap_values <- calculate_shap(new_dataset, model, nsim = 30) | |
| pW<-shap_summary_plot_perclass(shap_values, class= "W",color="#C77CFF")+xlab("Activity W")+xlim(0,0.25) | |
| pGM<-shap_summary_plot_perclass(shap_values, class= "GM",color="#7CAE00")+xlab("Activity GM")+xlim(0,0.25) | |
| pG<-shap_summary_plot_perclass(shap_values, class= "G",color="#F8766D")+xlab("Activity G")+xlim(0,0.25) | |
| pR<-shap_summary_plot_perclass(shap_values, class= "R",color="#00BFC4")+xlab("Activity R")+xlim(0,0.25) | |
| grid.arrange(pW,pR,pG,pGM) | |
| }) | |
| output$SHAPDependency <- renderPlot({ | |
| if (is.null(input$file1)) | |
| return(NULL) | |
| inFile <- input$file1 | |
| dataset <- readr::read_delim(inFile$datapath,delim='\t') | |
| predictions <- predict(model, dataset) | |
| selected_variables <- | |
| readr::read_delim( | |
| "selected_features.tsv", | |
| col_types = cols(), | |
| delim = '\t' | |
| ) | |
| new_dataset <- | |
| dataset %>% select(selected_variables$variable, Anim, Activity) | |
| new_dataset <- cbind(new_dataset, predictions) | |
| shap_values <- calculate_shap(new_dataset, model, nsim = 30) | |
| li<-list() | |
| li[[1]]<-dependency_plot("Steps",dataset = new_dataset,shap=shap_values) | |
| #li[[2]]<-dependency_plot("prev_steps1",dataset = new_dataset,shap=shap_values) | |
| li[[2]]<-dependency_plot("%HeadDown",dataset = new_dataset,shap=shap_values) | |
| #li[[4]]<-dependency_plot("prev_headdown1",dataset = new_dataset,shap=shap_values) | |
| li[[3]]<-dependency_plot("Active",dataset = new_dataset,shap=shap_values) | |
| #li[[6]]<-dependency_plot("prev_Active1",dataset = new_dataset,shap=shap_values) | |
| li[[4]]<-dependency_plot("Standing",dataset = new_dataset,shap=shap_values) | |
| #li[[8]]<-dependency_plot("prev_Standing1",dataset = new_dataset,shap=shap_values) | |
| #li[[9]]<-dependency_plot("X_Act",dataset = new_dataset, shap=shap_values) | |
| #li[[10]]<-dependency_plot("Y_Act",dataset = new_dataset, shap=shap_values) | |
| #li[[11]]<-dependency_plot("DBA123",dataset = new_dataset, shap=shap_values) | |
| #li[[12]]<-dependency_plot("DFA123",dataset = new_dataset, shap=shap_values) | |
| do.call(grid.arrange, c(li, ncol = 1)) | |
| }) | |
| } | |
| # Create a Shiny app object | |
| shinyApp(ui = ui, server = server) | |