simulation_pipeline.ml 19.9 KB
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open Base
open Printf
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open Bistro
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open File_formats
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let calc_fixed_seed ~(str:string) (seed:int) : int =
  let str_hash = Hashtbl.hash str in
  Hashtbl.hash (str_hash + seed)

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type tree =
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  | NHX of nhx file
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  | Pair_tree of {
      npairs : int ;
      branch_length1 : float ;
      branch_length2 : float ;
    }
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module type S = sig
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  type query
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  include Detection_pipeline.Query with type t := query
  include Detection_pipeline.S with type query := query
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  val alignment_plot : query -> svg file
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end

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let tree_workflow = function
  | NHX w -> w
  | Pair_tree { branch_length1 ;
                branch_length2 ;
                npairs } ->
    Simulator.pair_tree ~branch_length1 ~branch_length2 ~npairs

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module Make(Q : Detection_pipeline.Query) = struct
  include Detection_pipeline.Make(Q)

  let alignment_plot d =
    Convergence_detection.plot_convergent_sites
      ~tree:(Q.tree ~branch_length_unit:`Amino_acid d)
      ~alignment:(amino_acid_alignment d)
      ~detection_results:(multinomial_asymptotic_lrt d)
      ()
end

module Mutsel_query = struct
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  type t = {
    tree : tree ;
    branch_scale : float ;
    profiles : string ;
    n_h0 : int ;
    n_ha : int ;
    ne_s : float * float ;
    gBGC : float * float ;
    seed : int ;
  }

  let make ?(branch_scale = 1.) ?(ne_s = 1., 1.) ?(gBGC = 0., 0.) ?(seed = 0) ~tree ~profiles ~n_h0 ~n_ha () = {
    tree ;
    profiles ;
    n_h0 ;
    n_ha ;
    ne_s ;
    gBGC ;
    branch_scale ;
    seed : int ;
  }

  let simulation { n_h0 ; n_ha ; profiles ; ne_s ; gBGC ; branch_scale ; seed ; tree ; _ } =
    let tree = tree_workflow tree in
    let fitness_profiles = Workflow.input profiles in
    Simulator.simulation ~branch_scale ~n_ha ~n_h0 ~ne_s ~gBGC ~tree ~seed ~fitness_profiles ()

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  let nucleotide_alignment p =
    simulation p
    |> Simulator.alignment_of_simulation
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  let tree ~branch_length_unit:_ { tree ; _ } = tree_workflow tree
end
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module Mutsel = struct
  type query = Mutsel_query.t
  let query = Mutsel_query.make
  let simulation = Mutsel_query.simulation
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  include Make(Mutsel_query)
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  type benchmark = {
    method_labels : string list ;
    method_outputs : float option array list ;
    average_precision : (float * (float * float)) list ;
    site_model : [`Convergent | `Non_convergent] array ;
    ancestral_counts : int Phylogenetics.Amino_acid.table array ;
    convergent_counts : int Phylogenetics.Amino_acid.table array ;
  }
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  let%pworkflow benchmark_statistics simulation ~labels ~results =
    let open Phylogenetics in
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    let open Codepitk in
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    let open OCamlR_base in
    let open Codepitk.Simulator.Site_independent_mutsel in
    let module Codon = Codon.Universal_genetic_code.NS in
    let load_results fn col =
      let open Codepitk in
      let df = Dataframe.from_file fn  |> Rresult.R.failwith_error_msg in
      match Dataframe.get_col df col with
      | Some (Floats xs) -> Array.map xs ~f:Option.some
      | Some (Float_opts xs) -> xs
      | Some (Ints xs) -> Array.map xs ~f:(fun i -> Some (Float.of_int i))
      | Some (Int_opts xs) -> Array.map xs ~f:(Option.map ~f:Float.of_int)
      | _ -> failwith "expected a numeric column at pos 1"
    in
    let sim : simulation = [%eval simulation] in
    let result_paths = [%eval Bistro.Workflow.path_list (List.map ~f:fst results)] in
    let results = List.map2_exn result_paths (List.map ~f:snd results) ~f:load_results in
    let labels = [%param labels] in
    let n_h0 = Array.length sim.h0_params in
    let n_ha = Array.length sim.ha_params in
    let nsites = n_h0 + n_ha in
    let columns = List.map2_exn labels results ~f:(fun l r ->
        l, `Numeric (Numeric.of_array_opt r)
      ) in
    let amino_acid_vector_of_codon_vector xs =
      Amino_acid.Vector.init (fun aa ->
          List.fold Codon.all ~init:0. ~f:(fun acc c ->
              if Amino_acid.equal aa (Codon.aa_of_codon c) then
                acc +. xs.Codon.%(c)
              else acc
            )
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        )
    in
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    let collect_profiles sel =
      Array.append sim.h0_params sim.ha_params
      |> Array.map ~f:(fun p ->
          sel p
          |> Mutsel.stationary_distribution
          |> amino_acid_vector_of_codon_vector
          |> Amino_acid.Vector.to_array
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        )
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      |> Numeric.Matrix.of_arrays
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    in
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    let ancestral_profiles = collect_profiles fst in
    let convergent_profiles = collect_profiles snd in
    let counts seqs i =
      let t =
        Amino_acid.Table.init (fun aa ->
            let aa = Amino_acid.to_char aa in
            List.count seqs ~f:(fun s ->
                let codon_str = String.sub (s : Dna.t :> string) ~pos:(i * 3) ~len:3 in
                let codon = match Codon.of_string codon_str with
                  | Some c -> c
                  | None -> assert false
                in
                Char.equal (Amino_acid.to_char (Codon.aa_of_codon codon)) aa)
          )
      in
      (t :> int array)
    in
    let collect_counts cond =
      let species = Convergence_tree.leaves sim.tree in
      let seqs =
        List.map2_exn sim.sequences species ~f:(fun s (_, cond_s) ->
            if Poly.equal cond cond_s then Some s else None
          )
        |> List.filter_opt
      in
      Array.init nsites ~f:(counts seqs)
      |> Integer.Matrix.of_arrays
    in
    let ancestral_counts = collect_counts `Ancestral in
    let convergent_counts = collect_counts `Convergent in
    let make_classification_data x y =
      Prc.Classification_data (
        List.init (Array.length x) ~f:(fun i ->
            match x.(i), y.(i) with
            | Some x_i, Some y_i -> Some (x_i, y_i)
            | None, _ | _, None -> None
          )
        |> List.filter_opt
      )
    in
    let estimates, lower_bounds, upper_bounds =
      let oracle = Array.init nsites ~f:(fun i -> if i < n_h0 then Some false else Some true) in
      List.map results ~f:(fun scores ->
          let Prc.Classification_data xs as data = make_classification_data scores oracle in
          let n = List.count xs ~f:snd in
          let theta_hat = Prc.auc_trapezoidal_lt data in
          let lb, ub = Prc.logit_confidence_interval ~alpha:0.05 ~theta_hat ~n in
          theta_hat, lb, ub
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        )
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      |> List.unzip3
    in
    let open OCamlR_base in
    let auc_estimates = Dataframe.create [
        "method", `Character (Character.of_list labels) ;
        "estimate", `Numeric (Numeric.of_list estimates) ;
        "lower_bound", `Numeric (Numeric.of_list lower_bounds) ;
        "upper_bound", `Numeric (Numeric.of_list upper_bounds) ;
      ]
    in
    let oracle =
      Array.(
        append
          (map sim.h0_profiles ~f:(Fn.const false))
          (map sim.ha_profiles ~f:(Fn.const true))
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      )
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      |> Logical.of_array
    in
    let results = Dataframe.create columns in
    let data = List_.create [
        Some "results", Dataframe.to_sexp results ;
        Some "oracle", Logical.to_sexp oracle ;
        Some "ancestral_profiles", Numeric.Matrix.to_sexp ancestral_profiles ;
        Some "convergent_profiles", Numeric.Matrix.to_sexp convergent_profiles ;
        Some "ancestral_counts", Integer.Matrix.to_sexp ancestral_counts ;
        Some "convergent_counts", Integer.Matrix.to_sexp convergent_counts ;
        Some "auc_estimates", Dataframe.to_sexp auc_estimates ;
      ]
    in
    saveRDS ~file:[%dest] (List_.to_sexp data)

  (* param exploration for SMBE paper *)
  (* type branch_scale_t = float *)
  let branch_scale_range = [ 1.; 3.; 6.; 9. ]

  type gBGC_t = Global of float | Convergent of float * float
  let gBGC_range =
    let range = [ 0.; 2.; 4.; 8.; 16.; 32.; 64.; ] in
    List.concat [
      (* List.map ~f:(fun x -> Global x) range ; *)
      List.map ~f:(fun x -> Convergent (0., x)) range ;
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    ]
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  type param_t = float * gBGC_t

  let explore_params ~(f: param_t -> _) =
    List.map branch_scale_range ~f:(fun (bf:float) ->
        List.map gBGC_range ~f:(fun (gbgc:gBGC_t) -> ((bf, gbgc), f (bf, gbgc)))
      ) |> List.concat

  let simu_of_param ?n_h0:(n_h0=50) (p: param_t) =
    let bf, gbgc = p in
    Mutsel_query.make
      ~tree:(NHX (Workflow.input "example/trees_analyses/C4AmaranthaceaePolyroot.nhx"))
      ~profiles:"example/aa_fitness/263SelectedProfiles.tsv"
      ~branch_scale:bf
      ~gBGC:(match gbgc with
          | Convergent (a,c) -> (a, c)
          | Global g -> (g, g))
      ~ne_s:(4., 4.)
      ~n_ha:0
      ~n_h0
      ()

  let filter_results ~(f: _ -> bool) (results: (param_t * _) list) =
    List.filter results ~f:(fun (_, x) -> f x)

  type record_t = {
    gc_means_ancestral: ([`first | `second | `third] * float) list ;
    gc_means_convergent: ([`first | `second | `third] * float) list
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  }
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  let%workflow record_of_simu s =
    let tree = [%path tree ~branch_length_unit:`Nucleotide s] in
    let nucleotide_alignment = [%path nucleotide_alignment s] in
    let gc_mean_from_simu ~pos =
      Alistats.nucleotide_fasta_gc_ac ~pos tree nucleotide_alignment
    in let (m1_a, m1_c), (m2_a, m2_c), (m3_a, m3_c) =
         gc_mean_from_simu ~pos:`first,
         gc_mean_from_simu ~pos:`second,
         gc_mean_from_simu ~pos:`third
    in {
      gc_means_ancestral = [(`first, m1_a.gc_mean) ; (`second, m2_a.gc_mean) ; (`third, m3_a.gc_mean)] ;
      gc_means_convergent = [(`first, m1_c.gc_mean) ; (`second, m2_c.gc_mean) ; (`third, m3_c.gc_mean)]
    }

  let expected_gc = [
    (`first,  (0.3326, 0.5157, 0.5589, 0.6080, 0.8621)) ;
    (`second, (0.2102, 0.3784, 0.4160, 0.4626, 0.7499)) ;
    (`third,  (0.2242, 0.4852, 0.6274, 0.7358, 0.9575))
  ]

  let quartile (min_, fq_, mean_, tq_, max_) x =
    match Float.( x < min_, x < fq_, x < mean_, x < tq_, x < max_) with
    | true, _, _, _, _     -> `below_min
    | false, true, _, _, _ -> `first
    | _, false, true, _, _ -> `second
    | _, _, false, true, _ -> `third
    | _, _, _, false, true -> `fourth
    | _, _, _, _, false    -> `over_max

  let adjacent q1 q2 =
    match q1, q2 with
    | `first,  `first | `second, `second
    | `third,  `third | `fourth, `fourth
    | `first, `second | `second, `first
    | `second, `third | `third, `second
    | `third, `fourth | `fourth, `third -> true
    | _ -> false

  let quartile_of_record (r: record_t) =
    List.map r.gc_means_convergent ~f:(fun (q, x) ->
        let q_list = List.Assoc.find_exn expected_gc ~equal:(fun x y -> Caml.(x = y)) q in
        quartile q_list x
      )

  let realistic_result (r: record_t) =
    match quartile_of_record r with
    | [q1 ; q2 ; q3] -> adjacent q1 q2 && adjacent q2 q3 && adjacent q1 q3
    | _ -> failwith "oh no"
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  (* let v = g.gc_stat.gc_variance_among_sequences in
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     Float.(v >= 8.388e-05 && v <= 5.262e-02) *)

  (* let%workflow benchmark_statistics q methods =
   *   let open Phylogenetics in
   *   let open Codepitk in
   *   let open OCamlR_base in
   *   let open Codepitk.Simulator.Site_independent_mutsel in
   *   let module Codon = Codon.Universal_genetic_code.NS in
   *   let results_paths = [%eval Workflow.path_list (List.map methods ~f:(fun f -> f q))] in
   *   let sim : simulation = [%eval simulation q] in
   *   let load_results fn =
   *     let open Codepitk in
   *     let df = Dataframe.from_file fn  |> Rresult.R.failwith_error_msg in
   *     List.init (Dataframe.ncols df - 1) ~f:(fun i ->
   *         match Dataframe.get_col df (i + 1) with
   *         | Some (Floats xs) -> Array.map xs ~f:Option.some
   *         | Some (Float_opts xs) -> xs
   *         | Some (Ints xs) -> Array.map xs ~f:(fun i -> Some (Float.of_int i))
   *         | Some (Int_opts xs) -> Array.map xs ~f:(Option.map ~f:Float.of_int)
   *         | Some _ -> failwith "expected a numeric column at pos 1"
   *         | None -> assert false
   *       )
   *   in
   *   let results = List.map2_exn result_paths (List.map ~f:snd results) ~f:load_results in
   *   let labels = [%param labels] in
   *   let n_h0 = Array.length sim.h0_params in
   *   let n_ha = Array.length sim.ha_params in
   *   let nsites = n_h0 + n_ha in
   *   let columns = List.map2_exn labels results ~f:(fun l r ->
   *       l, `Numeric (Numeric.of_array_opt r)
   *     ) in
   *   let amino_acid_vector_of_codon_vector xs =
   *     Amino_acid.Vector.init (fun aa ->
   *         List.fold Codon.all ~init:0. ~f:(fun acc c ->
   *             if Amino_acid.equal aa (Codon.aa_of_codon c) then
   *               acc +. xs.Codon.%(c)
   *             else acc
   *           )
   *       )
   *   in
   *   let collect_profiles sel =
   *     Array.append sim.h0_params sim.ha_params
   *     |> Array.map ~f:(fun p ->
   *         sel p
   *         |> Mutsel.stationary_distribution
   *         |> amino_acid_vector_of_codon_vector
   *         |> Amino_acid.Vector.to_array
   *       )
   *     |> Numeric.Matrix.of_arrays
   *   in
   *   let ancestral_profiles = collect_profiles fst in
   *   let convergent_profiles = collect_profiles snd in
   *   let counts seqs i =
   *     let t =
   *       Amino_acid.Table.init (fun aa ->
   *           let aa = Amino_acid.to_char aa in
   *           List.count seqs ~f:(fun s ->
   *               let codon_str = String.sub (s : Dna.t :> string) ~pos:(i * 3) ~len:3 in
   *               let codon = match Codon.of_string codon_str with
   *                 | Some c -> c
   *                 | None -> assert false
   *               in
   *               Char.equal (Amino_acid.to_char (Codon.aa_of_codon codon)) aa)
   *         )
   *     in
   *     (t :> int array)
   *   in
   *   let collect_counts cond =
   *     let species = Convergence_tree.leaves sim.tree in
   *     let seqs =
   *       List.map2_exn sim.sequences species ~f:(fun s (_, cond_s) ->
   *           if Poly.equal cond cond_s then Some s else None
   *         )
   *       |> List.filter_opt
   *     in
   *     Array.init nsites ~f:(counts seqs)
   *     |> Integer.Matrix.of_arrays
   *   in
   *   let ancestral_counts = collect_counts `Ancestral in
   *   let convergent_counts = collect_counts `Convergent in
   *   let make_classification_data x y =
   *     Prc.Classification_data (
   *       List.init (Array.length x) ~f:(fun i ->
   *           match x.(i), y.(i) with
   *           | Some x_i, Some y_i -> Some (x_i, y_i)
   *           | None, _ | _, None -> None
   *         )
   *       |> List.filter_opt
   *     )
   *   in
   *   let estimates, lower_bounds, upper_bounds =
   *     let oracle = Array.init nsites ~f:(fun i -> if i < n_h0 then Some false else Some true) in
   *     List.map results ~f:(fun scores ->
   *         let Prc.Classification_data xs as data = make_classification_data scores oracle in
   *         let n = List.count xs ~f:snd in
   *         let theta_hat = Prc.auc_trapezoidal_lt data in
   *         let lb, ub = Prc.logit_confidence_interval ~alpha:0.05 ~theta_hat ~n in
   *         theta_hat, lb, ub
   *       )
   *     |> List.unzip3
   *   in
   *   let open OCamlR_base in
   *   let auc_estimates = Dataframe.create [
   *       "method", `Character (Character.of_list labels) ;
   *       "estimate", `Numeric (Numeric.of_list estimates) ;
   *       "lower_bound", `Numeric (Numeric.of_list lower_bounds) ;
   *       "upper_bound", `Numeric (Numeric.of_list upper_bounds) ;
   *     ]
   *   in
   *   let oracle =
   *     Array.(
   *       append
   *         (map sim.h0_profiles ~f:(Fn.const false))
   *         (map sim.ha_profiles ~f:(Fn.const true))
   *     )
   *     |> Logical.of_array
   *   in
   *   let results = Dataframe.create columns in
   *   let data = List_.create [
   *       Some "results", Dataframe.to_sexp results ;
   *       Some "oracle", Logical.to_sexp oracle ;
   *       Some "ancestral_profiles", Numeric.Matrix.to_sexp ancestral_profiles ;
   *       Some "convergent_profiles", Numeric.Matrix.to_sexp convergent_profiles ;
   *       Some "ancestral_counts", Integer.Matrix.to_sexp ancestral_counts ;
   *       Some "convergent_counts", Integer.Matrix.to_sexp convergent_counts ;
   *       Some "auc_estimates", Dataframe.to_sexp auc_estimates ;
   *     ]
   *   in
   *   saveRDS ~file:[%dest] (List_.to_sexp data) *)
end

module Bppseqgen = struct
  type t =
    | Bppseqgen of {
        hypothesis : Convergence_hypothesis.t ;
        tree : tree ;
        profiles : string ;
        nb_sites : int ;
        seed : int ;
      }
    | Bppseqgen_mixed of {
        tree : tree ;
        profiles : string ;
        seed : int ;
        n_h0 : int ;
        n_ha : int ;
        ne_s : float ;
      }

  let bppseqgen_mixed ?(ne_s = 1.) ?(seed = 0) ~tree ~profiles ~n_h0 ~n_ha () =
    Bppseqgen_mixed {
      tree ;
      profiles ;
      seed ;
      n_ha ;
      n_h0 ;
      ne_s ;
    }

  let bppseqgen ~hyp ~tree ~profiles ~nb_sites ~seed =
    Bppseqgen {
      hypothesis = hyp ;
      tree ;
      profiles ;
      nb_sites ;
      seed ;
    }

  let tree ~branch_length_unit:_ = function
    | Bppseqgen { tree ; _ }
    | Bppseqgen_mixed { tree ; _ } ->
      tree_workflow tree

  let seed = function
    | Bppseqgen_mixed s -> s.seed
    | Bppseqgen s -> s.seed

  let profile ~nb_sites ~profiles ~seed =
    Profile.profile_l_of_splitted_profile
      ~nb_cat:All
      ~nb_sites
      profiles
      ~seed:(calc_fixed_seed ~str:profiles seed)

  let bppseqgen_simulation sim ~hypothesis ~nb_sites ~profiles =
    let model_prefix = Convergence_hypothesis.string_of_model hypothesis in
    let descr = sprintf ".%s" model_prefix in
    let profile = profile ~nb_sites ~profiles ~seed:(seed sim) in
    let profile_f = profile.profile_f in
    let profile_c = profile.profile_c in
    Bppsuite.Bppseqgen.multi_profiles
      ~descr
      ~input_tree:(tree ~branch_length_unit:`Nucleotide sim)
      ~hypothesis ~profile_f ~profile_c ~seed:(seed sim)

  let rec nucleotide_alignment = function
    | Bppseqgen { hypothesis ; nb_sites ; profiles ; _ } as sim ->
      bppseqgen_simulation sim ~hypothesis ~nb_sites ~profiles
      |> Bppsuite.Bppseqgen.alignment
    | Bppseqgen_mixed { profiles ; seed ; n_h0 ; n_ha ; ne_s ; tree } ->
      let h0 = nucleotide_alignment (Bppseqgen { hypothesis = H0 (Fixed ne_s) ; profiles ; seed ; nb_sites = n_h0 ; tree }) in
      let ha = nucleotide_alignment (Bppseqgen { hypothesis = HaPC (Fixed ne_s) ; profiles ; seed ; nb_sites = n_ha ; tree }) in
      Utils.fasta_cappend h0 ha

  include Detection_pipeline.Make(struct
      type nonrec t = t
      let tree = tree
      let nucleotide_alignment = nucleotide_alignment
    end)

  let alignment_plot d =
    Convergence_detection.plot_convergent_sites
      ~tree:(tree ~branch_length_unit:`Amino_acid d)
      ~alignment:(amino_acid_alignment d)
      ~detection_results:(multinomial_asymptotic_lrt d)
      ()

  let oracle d =
    let n_h0, n_ha =
      match d with
      | Bppseqgen { nb_sites ; hypothesis ; _ } -> (
          match hypothesis with
          | H0 _ -> nb_sites, 0
          | HaPC _ | HaPCOC _ -> 0, nb_sites
        )
      | Bppseqgen_mixed { n_h0 ; n_ha ; _ } ->
        n_h0, n_ha
    in
    Convergence_detection.oracle ~n_h0 ~n_ha

  let multinomial_benchmark d =
    Utils.recall_precision_curve
      ~oracle:(oracle d)
      ~labels:["LRT";"LRTsim";"sparse";"sparse_sim"]
      ~results:[
        multinomial_asymptotic_lrt d, 1 ;
        multinomial_simulation_lrt d, 1 ;
        multinomial_asymptotic_sparse d, 1 ;
        multinomial_simulation_sparse d, 1 ;
      ]


  let result_table ?(mode = `fast) d =
    Convergence_detection.merge_result_tables
      ~multinomial:(multinomial_asymptotic_lrt d)
      ~tdg09:(tdg09 d)
      ~identical:(identical d)
      ~topological:(topological d)
      ~pcoc:(
        match mode with
        | `fast -> pcoc ~gamma:false ~ncat:10 d
        | `full -> pcoc d
      )
      ?diffsel:(
        match mode with
        | `fast -> None
        | `full -> Some (diffsel d)
      )
      ~oracle:(oracle d)
      ()

  let benchmark ?mode d =
    result_table ?mode d
    |> Convergence_detection.recall_precision_curve

  let benchmark2 d =
    Utils.recall_precision_curve
      ~oracle:(oracle d)
      ~labels:["identical";"topological";"multinomial";"pcoc";"tdg09"]
      ~results:[
        identical d, 1 ;
        topological d, 1 ;
        multinomial_asymptotic_lrt d, 1 ;
        pcoc d, 3 ;
        tdg09 d, 1 ;
      ]
end