standard_self_training.R

library(dplyr)
library(checkmate,asserthat)


  
  
  standard_self_training <- function(labeled_data,
                                     unlabeled_data,
                                     test_data,
                                     target,
                                     glm_formula) {
    
    # some input checking
    assert_data_frame(labeled_data)
    assert_data_frame(unlabeled_data)
    assert_data_frame(test_data)
    assert_formula(glm_formula)
    assert_character(target)
    
    n_imp = nrow(unlabeled_data)
    results = matrix(nrow = n_imp, ncol = 3)
    params_results = list()
    
    which_flip = seq(n_imp)
    for (i in seq(n_imp)) {
      
      
      logistic_model <- glm(formula = formula, 
                            data = labeled_data, 
                            family = "binomial")
      #choose instance whose prediction has most CERTAINTY (as opposed to confidence)
      winner <- predict(logistic_model, newdata= unlabeled_data, se.fit = T)$se.fit %>% which.min()
      # predict it
      predicted_target <- predict(logistic_model, newdata= unlabeled_data[winner,], type = "response")
      
      new_labeled_obs <- unlabeled_data[winner,]
      new_labeled_obs[c(target)] <- ifelse(predicted_target > 0.5, 1,0)  
      
      
      # update labeled data
      labeled_data<- rbind(labeled_data, new_labeled_obs)
      # evaluate test error (on-the-fly inductive learning results)
      logistic_model <- glm(formula = formula, data = labeled_data, family = "binomial") # refit model with added label
      scores = predict(logistic_model, newdata = test_data, type = "response") 
      prediction_test <- ifelse(scores > 0.5, 1, 0)
      test_acc <- sum(prediction_test == test_data[c(target)])/nrow(test_data)
      
      
      # store results
      results[i,] <- c(unlabeled_data[winner,]$nr, new_labeled_obs[c(target)], test_acc) %>% unlist()
      unlabeled_data <- unlabeled_data[-winner,]
      # save parameter vector
      params = logistic_model$coefficients
      params_results[[i]] = params
    }
    
    # return transductive results (labels) and final model
    list(results, logistic_model,params_results)
    
  }