prc: added visual checks

lib/prc/prc.ml

@@ -70,7 +70,9 @@ let%expect_test "performance curve 1" =
      (0 ((tp 2) (tn 0) (fp 3) (fn 0))))"]
 
 let recall ~tp ~fn = float tp /. float (tp + fn)
-let precision ~tp ~fp = float tp /. float (tp + fp)
+let precision ~tp ~fp =
+  if tp + fp = 0 then 1.
+  else float tp /. float (tp + fp)
 
 module Precision_recall = struct
   let operating_points d =
@@ -259,3 +261,56 @@ module Plot = struct
       ~xlim:(0., 1.) ~ylim:(0., 1.) ~x:[||] ~y:[||] () ;
     List.(iter (rev curves)) ~f:plot_curve
 end
+
+module Check = struct
+  let binormal_simulation ?(sigma = 1.) ?(alpha = 0.1) ?(sample_size = 30) () =
+    let open OCamlR_base in
+    let rng = Rng.(make (default ())) in
+    let p = Binormal_model.make ~sigma_pos:sigma ~sigma_neg:sigma alpha in
+    let samples = List.init 1_000 ~f:(fun _ -> Binormal_model.simulation ~n:sample_size rng p) in
+    let compute f =
+      List.map samples ~f
+      |> Numeric.of_list
+      |> Numeric.to_sexp
+    in
+    let trapezoidal_lt = compute Precision_recall.auc_trapezoidal_lt in
+    let average_precision = compute Precision_recall.auc_average_precision in
+    let binormal = compute (fun sample -> Binormal_model.auc (Binormal_model.estimate sample)) in
+    let l = List_.create [
+        Some "binormal", binormal ;
+        Some "trapezoidal", trapezoidal_lt ;
+        Some "average_precision", average_precision ;
+      ]
+    in
+    OCamlR_graphics.list_boxplot l
+
+  let discrete_simulation ?(sample_size = 30) ?support:(d = 10) ?(alpha = 0.1) () =
+    let open OCamlR_base in
+    let rng = Rng.(make (default ())) in
+    let xs = Array.init d ~f:(fun _ -> Randist.flat rng ~a:0. ~b:1.) in
+    let simulate () =
+      Dataset (
+        List.init sample_size ~f:(fun _ ->
+            let k = Rng.uniform_int rng d in
+            let b = Float.(Randist.flat rng ~a:0. ~b:1. < xs.(k) *. alpha /. 2.) in
+            xs.(k), b
+          )
+      )
+    in
+    let samples = List.init 1_000 ~f:(fun _ -> simulate ()) in
+    let compute f =
+      List.map samples ~f
+      |> Numeric.of_list
+      |> Numeric.to_sexp
+    in
+    let trapezoidal_lt = compute Precision_recall.auc_trapezoidal_lt in
+    let average_precision = compute Precision_recall.auc_average_precision in
+    let binormal = compute (fun sample -> Binormal_model.auc (Binormal_model.estimate sample)) in
+    let l = List_.create [
+        Some "binormal", binormal ;
+        Some "trapezoidal", trapezoidal_lt ;
+        Some "average_precision", average_precision ;
+      ]
+    in
+    OCamlR_graphics.list_boxplot l
+end

lib/prc/prc.mli

@@ -110,3 +110,19 @@ module Plot : sig
     t list ->
     unit
 end
+
+module Check : sig
+  val binormal_simulation :
+    ?sigma:float ->
+    ?alpha:float ->
+    ?sample_size:int ->
+    unit ->
+    unit
+
+  val discrete_simulation :
+    ?sample_size:int ->
+    ?support:int ->
+    ?alpha:float ->
+    unit ->
+    unit
+end