## @knitr metrics

eta <- new_metric("eta", "eta",
                  metric = function(model, out) out$eta)

sigma2 <- new_metric("sigma2", "sigma2",
                     metric = function(model, out) out$sigma2)

modelerror <- new_metric("me", "model error",
                         metric = function(model, out) out$model_error)

prederror <- new_metric("prederror", "Prediction error",
                        metric = function(model, out) out$prediction_error)

errorvariance <- new_metric("errorvar", "Error variance",
                            metric = function(model, out) out$error_variance)

estimationerror <- new_metric("estimationerror", "Estimation error",
                              metric = function(model, out) {
                                l2norm(out$beta_refit - out$beta_truth)^2
                              })

tpr <- new_metric("tpr", "True Positive Rate",
                  metric = function(model, out) {
                    length(intersect(out$nonzero_names, out$causal))/length(out$causal)
                  })

tprFPR5 <- new_metric("tprFPR5", "True Positive Rate at FPR of 5%",
                  metric = function(model, out) {
                    out$TPR_at_5_percent_FPR
                  })

"%ni%" <- Negate("%in%")

fpr <- new_metric("fpr", "False Positive Rate",
                  metric = function(model, out){
                    FP <- length(setdiff(out$nonzero_names, out$causal)) # false positives
                    TN <- length(out$not_causal) # True negatives
                    FPR <- FP / (FP + TN)
                    FPR
                  })

nactive <- new_metric("nactive", "Number of Active Variables",
                      metric = function(model, out) {
                        length(out$nonzero_names)
                      })

nactiveFPR5 <- new_metric("nactiveFPR5", "Number of Active Variablesat FPR of 5%",
                      metric = function(model, out) {
                        length(out$ACTIVES_at_5_percent_FPR)
                      })


correct_sparsity <- new_metric("correct_sparsity", "Correct Sparsity",
                               metric = function(model, out){
                                 causal <- out$causal
                                 not_causal <- out$not_causal
                                 active <- out$nonzero_names
                                 p <- out$p

                                 correct_nonzeros <- sum(active %in% causal)
                                 correct_zeros <- length(setdiff(not_causal, active))
                                 #correct sparsity
                                 (1 / p) * (correct_nonzeros + correct_zeros)
                               })


mse <- new_metric("mse", "Test Set MSE",
                  metric = function(model, out) {
                    as.numeric(crossprod(out$yhat - out$yvalidate) / (length(out$yvalidate)))
                  })


selected <- new_metric("selected", "Selected Variables",
                       metric = function(model, out) {
                         out$nonzero_names
                       })