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Commit a46650d4 authored by Carine Rey's avatar Carine Rey
Browse files

use recall/precision to choose the threshold + new hypothesis (bigNeinSmallNe and SmallNeInBigNe)

parent 3d0b2086
......@@ -10,6 +10,8 @@ type t =
| H0_NeVerySmall
| H0_BigNeInSmallNe
| H0_SmallNeInBigNe
| HaPC_BigNeInSmallNe
| HaPC_SmallNeInBigNe
| HaPCOC_NeSmall
| HaPC_NeSmall
| H0_NeBig
......@@ -17,18 +19,20 @@ type t =
| HaPC_NeBig
let string_of_model m = match m with
| H0 -> "H0"
| HaPC -> "HaPC"
| HaPCOC -> "HaPCOC"
| H0_NeSmall -> "H0_NeSmall"
| H0_NeVerySmall -> "H0_NeVerySmall"
| H0 -> "H0"
| HaPC -> "HaPC"
| HaPCOC -> "HaPCOC"
| H0_NeSmall -> "H0_NeSmall"
| H0_NeVerySmall -> "H0_NeVerySmall"
| H0_BigNeInSmallNe -> "H0_BigNeInSmallNe"
| H0_SmallNeInBigNe -> "H0_SmallNeInBigNe"
| HaPCOC_NeSmall -> "HaPCOC_NeSmall"
| HaPC_NeSmall -> "HaPC_NeSmall"
| H0_NeBig -> "H0_NeBig"
| HaPCOC_NeBig -> "HaPCOC_NeBig"
| HaPC_NeBig -> "HaPC_NeBig"
| HaPC_BigNeInSmallNe -> "HaPC_BigNeInSmallNe"
| HaPC_SmallNeInBigNe -> "HaPC_SmallNeInBigNe"
| HaPCOC_NeSmall -> "HaPCOC_NeSmall"
| HaPC_NeSmall -> "HaPC_NeSmall"
| H0_NeBig -> "H0_NeBig"
| HaPCOC_NeBig -> "HaPCOC_NeBig"
| HaPC_NeBig -> "HaPC_NeBig"
let assign k v =
......@@ -92,6 +96,8 @@ let bpp_config_F nodes hyp = [
| H0_NeVerySmall -> bpp_config_H0_F_Ne
| H0_BigNeInSmallNe -> bpp_config_H0_F_Ne
| H0_SmallNeInBigNe -> bpp_config_H0_F_Ne
| HaPC_BigNeInSmallNe -> bpp_config_HaPC_F_Ne
| HaPC_SmallNeInBigNe -> bpp_config_HaPC_F_Ne
| HaPC_NeSmall -> bpp_config_HaPC_F_Ne
| HaPCOC_NeSmall -> bpp_config_HaPCOC_F_Ne
| H0_NeBig -> bpp_config_H0_F_Ne
......
......@@ -67,11 +67,15 @@ let derive_from_model ~model ~input_tree ~tree_dataset ~tree_prefix ~profile ~pr
| HaPCOC_NeBig -> 10.
| H0_BigNeInSmallNe -> 10.
| H0_SmallNeInBigNe -> 0.1
| HaPC_BigNeInSmallNe -> 10.
| HaPC_SmallNeInBigNe -> 0.1
| _ -> 1.
in
let ne_a = match model with
| H0_BigNeInSmallNe -> 0.1
| H0_SmallNeInBigNe -> 10.
| HaPC_BigNeInSmallNe -> 0.1
| HaPC_SmallNeInBigNe -> 10.
| _ -> 1.
in
let profile_f = profile.profile_f in
......@@ -114,9 +118,11 @@ let derive_from_tree ~tree_dir ~tree ~profile ~preview ~use_concat ~ns ~no_Ne ~n
]
);
(if ne_test then
[H0_NeVerySmall;
[
H0_BigNeInSmallNe;
H0_SmallNeInBigNe;
HaPC_BigNeInSmallNe;
HaPC_SmallNeInBigNe;
]
else
[]
......
......@@ -46,19 +46,38 @@ let is_hyp ~hyp (dataset_results :dataset_res) =
let model_prefix = dataset_results.model_prefix in
model_prefix = hyp
let make_t_choices ~h0_merged_results ~ha_merged_results : post_analyses_dir directory workflow =
let build_cmd_t_choices (opt_name : string) mr_option =
match mr_option with
| Some x -> [opt opt_name dep x]
| None -> []
let make_t_choices ~h0_mr ~h0_NeBig_mr ~h0_NeSmall_mr ~haPCOC_mr ~haPC_mr ~haPC_NeBig_mr ~haPC_NeSmall_mr ~h0_NeBigInSmall_mr
~h0_NeSmallInBig_mr ~haPC_NeBigInSmall_mr ~haPC_NeSmallInBig_mr () : post_analyses_dir directory workflow =
let env = r_env in
let out = dest // "out" in
let cmd_mr = List.map [
("--H0" , h0_mr );
("--H0NeBig" , h0_NeBig_mr );
("--H0NeSmall" , h0_NeSmall_mr );
("--H0NeBigInSmall" , h0_NeBigInSmall_mr );
("--H0NeSmallInBig" , h0_NeSmallInBig_mr );
("--HaPCOC" , haPCOC_mr );
("--HaPC" , haPC_mr );
("--HaPCNeBig" , haPC_NeBig_mr );
("--HaPCNeSmall" , haPC_NeSmall_mr);
("--HaPCNeBigInSmall" , haPC_NeBigInSmall_mr );
("--HaPCNeSmallInBig" , haPC_NeSmallInBig_mr);
] ~f:(fun (opt_name, mr_option) -> build_cmd_t_choices opt_name mr_option)
|> List.concat in
workflow ~descr:"post_analyses.t_choices" [
docker env (
and_list [
mkdir_p dest ;
cmd "Rscript" [
file_dump (string Scripts.calc_t_per_meth) ;
opt "--H0" dep h0_merged_results;
opt "--Ha" dep ha_merged_results;
opt "--out " ident out;
];
cmd "Rscript" ([
[file_dump (string Scripts.calc_t_per_meth) ;
opt "--out " ident out; ] ;
cmd_mr ;
] |> List.concat) ;
])
]
......@@ -99,15 +118,65 @@ let group_simu_infos ~simu_infos_l : simu_infos directory workflow =
)
]
let get_merged_results_opt hx = match hx with
| Some w -> Some w.merged_results
| None -> None
type res_all_hyp = {
h0_res : dataset_res option ;
h0_NeBig_res : dataset_res option ;
h0_NeSmall_res : dataset_res option ;
ha_PC_res : dataset_res option ;
ha_PCOC_res : dataset_res option ;
ha_PC_NeBig_res : dataset_res option ;
ha_PC_NeSmall_res : dataset_res option ;
h0_NeBigInSmall_res : dataset_res option ;
h0_NeSmallInBig_res : dataset_res option ;
ha_PC_NeBigInSmall_res : dataset_res option ;
ha_PC_NeSmallInBig_res : dataset_res option ;
}
let make_t_choices_per_couple {h0_res; h0_NeBig_res; h0_NeSmall_res; ha_PC_res; ha_PCOC_res; ha_PC_NeBig_res ; ha_PC_NeSmall_res;
h0_NeBigInSmall_res; h0_NeSmallInBig_res; ha_PC_NeBigInSmall_res; ha_PC_NeSmallInBig_res} =
let h0_mr = get_merged_results_opt h0_res in
let h0_NeBig_mr = get_merged_results_opt h0_NeBig_res in
let h0_NeSmall_mr = get_merged_results_opt h0_NeSmall_res in
let h0_NeBigInSmall_mr = get_merged_results_opt h0_NeBigInSmall_res in
let h0_NeSmallInBig_mr = get_merged_results_opt h0_NeSmallInBig_res in
let haPCOC_mr = get_merged_results_opt ha_PCOC_res in
let haPC_mr = get_merged_results_opt ha_PC_res in
let haPC_NeBig_mr = get_merged_results_opt ha_PC_NeBig_res in
let haPC_NeSmall_mr = get_merged_results_opt ha_PC_NeSmall_res in
let haPC_NeBigInSmall_mr = get_merged_results_opt ha_PC_NeBigInSmall_res in
let haPC_NeSmallInBig_mr = get_merged_results_opt ha_PC_NeSmallInBig_res in
make_t_choices ~h0_mr ~h0_NeBig_mr ~h0_NeSmall_mr ~haPCOC_mr ~haPC_mr ~haPC_NeBig_mr ~haPC_NeSmall_mr ~h0_NeBigInSmall_mr
~h0_NeSmallInBig_mr ~haPC_NeBigInSmall_mr ~haPC_NeSmallInBig_mr ()
let get_t_choices ~(dataset_results_l: dataset_res list) : t_choices option =
let h0_res = List.find dataset_results_l (is_hyp ~hyp: "H0") in
let ha_res = List.find dataset_results_l (is_hyp ~hyp: "HaPCOC") in
match (h0_res, ha_res) with
| (Some h0, Some ha) ->
let h0_merged_results = h0.merged_results in
let ha_merged_results = ha.merged_results in
let t_choices_dir = make_t_choices ~h0_merged_results ~ha_merged_results in
let t_choices_max = t_choices_dir / selector ["out.max_per_meth.tsv"] in
let h0_res = List.find dataset_results_l (is_hyp ~hyp: "H0" ) in
let h0_NeBig_res = List.find dataset_results_l (is_hyp ~hyp: "H0_NeBig" ) in
let h0_NeSmall_res = List.find dataset_results_l (is_hyp ~hyp: "H0_NeSmall" ) in
let h0_NeBigInSmall_res = List.find dataset_results_l (is_hyp ~hyp: "H0_BigNeInSmallNe") in
let h0_NeSmallInBig_res = List.find dataset_results_l (is_hyp ~hyp: "H0_SmallNeInBigNe") in
let ha_PCOC_res = List.find dataset_results_l (is_hyp ~hyp: "HaPCOC" ) in
let ha_PC_res = List.find dataset_results_l (is_hyp ~hyp: "HaPC" ) in
let ha_PC_NeBig_res = List.find dataset_results_l (is_hyp ~hyp: "HaPC_NeBig" ) in
let ha_PC_NeSmall_res = List.find dataset_results_l (is_hyp ~hyp: "HaPC_NeSmall" ) in
let ha_PC_NeBigInSmall_res = List.find dataset_results_l (is_hyp ~hyp: "HaPC_BigNeInSmallNe") in
let ha_PC_NeSmallInBig_res = List.find dataset_results_l (is_hyp ~hyp: "HaPC_SmallNeInBigNe") in
match (h0_res, ha_PCOC_res) with
| (Some h0, Some _) ->
let t_choices_dir = make_t_choices_per_couple {h0_res; h0_NeBig_res; h0_NeSmall_res; ha_PC_res; ha_PCOC_res; ha_PC_NeBig_res ; ha_PC_NeSmall_res;
h0_NeBigInSmall_res; h0_NeSmallInBig_res; ha_PC_NeBigInSmall_res; ha_PC_NeSmallInBig_res} in
let t_choices_max = t_choices_dir / selector ["out.recall09_per_meth.tsv"] in
let t_choices_complete = t_choices_dir / selector ["out.complete.tsv"] in
let t_choices_plot = t_choices_dir / selector ["out.pdf"] in
let tree_prefix = h0.tree_prefix in
......
......@@ -9,11 +9,19 @@ library("ggplot2")
library("cowplot")
option_list = list(
make_option(c("--H0"), type="character", default=NULL,
help="merged_results H0", metavar="character"),
make_option(c("--Ha"), type="character", default=NULL,
help="merged_results Ha", metavar="character"),
make_option(c("-o","--out"), type="character", default="out",
make_option(c("--H0") , type="character", default=NULL, help="merged_results H0", metavar="character"),
make_option(c("--H0NeBig") , type="character", default=NULL, help="merged_results H0NeBig", metavar="character"),
make_option(c("--H0NeSmall") , type="character", default=NULL, help="merged_results H0NeSmall", metavar="character"),
make_option(c("--H0NeBigInSmall") , type="character", default=NULL, help="merged_results H0NeBigInSmall", metavar="character"),
make_option(c("--H0NeSmallInBig") , type="character", default=NULL, help="merged_results H0NeSmallInBig", metavar="character"),
make_option(c("--HaPCOC") , type="character", default=NULL, help="merged_results HaPCOC", metavar="character"),
make_option(c("--HaPC") , type="character", default=NULL, help="merged_results HaPC", metavar="character"),
make_option(c("--HaPCNeBig") , type="character", default=NULL, help="merged_results HaPCNeBig", metavar="character"),
make_option(c("--HaPCNeSmall") , type="character", default=NULL, help="merged_results HaPCNeSmall", metavar="character"),
make_option(c("--HaPCNeBigInSmall") , type="character", default=NULL, help="merged_results HaPCNeBigInSmall", metavar="character"),
make_option(c("--HaPCNeSmallInBig") , type="character", default=NULL, help="merged_results HaPCNeSmallInBig", metavar="character"),
make_option(c("-o","--out"), type="character", default="out",
help="output prefix [default= %default]", metavar="character")
);
......@@ -24,9 +32,9 @@ if (is.null(opt$H0)){
print_help(opt_parser)
stop("At least one argument must be supplied (H0 input file)", call.=FALSE)
}
if (is.null(opt$Ha)){
if (is.null(opt$HaPCOC)){
print_help(opt_parser)
stop("At least one argument must be supplied (Ha input file)", call.=FALSE)
stop("At least one argument must be supplied (HaPCOC input file)", call.=FALSE)
}
## fun...
......@@ -34,7 +42,7 @@ if (is.null(opt$Ha)){
calc_TN_FP = function(vals, t){
TN = 0
FP = 0
vals[is.na(vals)] = 0
vals = na.omit(vals)
if (length(vals) > 0) {
TN = sum(vals <= t)
FP = sum(vals > t)
......@@ -64,44 +72,97 @@ calc_TN_FP_TP_FN = function(t, df_H0_melt, df_Ha_melt){
## program...
df_H0 = read.table(opt$H0, header=TRUE, sep = '\t')
df_Ha = read.table(opt$Ha, header=TRUE, sep = '\t')
read_hyp = function(opt_name) {
if (! is.null(opt_name)){
df = read.table(opt_name, header=TRUE, sep = '\t',na.strings = "NA")
df = df[, ! colnames(df) %in% c("Indel_prop", "Indel_prop.ConvLeaves.")]
df_melt = melt(df, id.vars = c("Sites"))
return(df_melt)
} else {
return(NULL)
}
}
df_H0_melt = read_hyp(opt$H0)
df_H0NeBig_melt = read_hyp(opt$H0NeBig)
df_H0NeSmall_melt = read_hyp(opt$H0NeSmall)
df_H0NeBigInSmall_melt = read_hyp(opt$H0NeBigInSmall)
df_H0NeSmallInBig_melt = read_hyp(opt$H0NeSmallInBig)
df_HaPCOC_melt = read_hyp(opt$HaPCOC)
df_HaPC_melt = read_hyp(opt$HaPC)
df_HaPCNeBig_melt = read_hyp(opt$HaPCNeBig)
df_HaPCNeSmall_melt = read_hyp(opt$HaPCNeSmall)
df_HaPCNeBigInSmall_melt = read_hyp(opt$HaPCNeBigInSmall)
df_HaPCNeSmallInBig_melt = read_hyp(opt$HaPCNeSmallInBig)
df_H0 = df_H0[, ! colnames(df_H0) %in% c("Indel_prop", "Indel_prop.ConvLeaves.")]
df_Ha = df_Ha[, ! colnames(df_Ha) %in% c("Indel_prop", "Indel_prop.ConvLeaves.")]
df_H0_melt = melt(df_H0, id.vars = c("Sites"))
df_Ha_melt = melt(df_Ha, id.vars = c("Sites"))
build_df_couple = function (df_h0,df_ha,name) {
if ((! is.null(df_h0)) & (! is.null(df_ha))) {
df = do.call(rbind.data.frame,lapply(seq(0,0.999,0.001), calc_TN_FP_TP_FN, df_H0_melt = df_h0, df_Ha_melt = df_ha))
df$couple = name
} else {
df = NULL
}
return(df)
}
df = do.call(rbind.data.frame,lapply(seq(0,0.999,0.01), calc_TN_FP_TP_FN, df_H0_melt = df_H0_melt, df_Ha_melt = df_Ha_melt))
df_H0HaPC = build_df_couple(df_H0_melt, df_HaPC_melt, "H0/HaPC")
df_H0HaPC_NeBig = build_df_couple(df_H0NeBig_melt, df_HaPCNeBig_melt, "H0/HaPC NeBig")
df_H0HaPC_NeSmall = build_df_couple(df_H0NeSmall_melt, df_HaPCNeSmall_melt, "H0/HaPC NeSmall")
df_H0HaPC_NeBigInSmall = build_df_couple(df_H0NeBigInSmall_melt, df_HaPCNeBigInSmall_melt, "H0/HaPC NeBigInSmall")
df_H0HaPC_NeSmallInBig = build_df_couple(df_H0NeSmallInBig_melt, df_HaPCNeSmallInBig_melt, "H0/HaPC NeSmallInBig")
df_H0HaPCOC = build_df_couple(df_H0_melt, df_HaPCOC_melt, "H0/HaPCOC")
df = rbind.data.frame(df_H0HaPC, df_H0HaPCOC,df_H0HaPC_NeBig,df_H0HaPC_NeSmall,
df_H0HaPC_NeBigInSmall,df_H0HaPC_NeSmallInBig)
print(head(df))
print(tail(df))
## Sensitivity (= recall)
df$sens = df$TP / (df$TP+df$FN)
df$sens[is.na(df$sens)] = 0
## Specificity
df$spe = df$TN / (df$FP+df$TN)
df$spe[is.na(df$spe)] = 0
## MCC
n_sites = sum(df[1,c("TP","FN")])
p = 140 / n_sites
n = (6000 -140)/ n_sites
df$FP_2 = df$FP * n
df$TP_2 = df$TP* p
df$FN_2 = df$FN* p
df$TN_2 = df$TN* n
df$TP_2 = df$TP * p
df$FN_2 = df$FN * p
df$TN_2 = df$TN * n
df$mcc = (df$TP_2 * df$TN_2 - df$FP_2 * df$FN_2) / sqrt((df$TP_2+df$FP_2)*(df$TP_2+df$FN_2)*(df$TN_2+df$FP_2)*(df$TN_2+df$FN_2))
df$mcc[is.na(df$mcc)] = 0
df_out = df[,c("Row.names", "t", "sens", "spe", "mcc")]
colnames(df_out) = c("methode", "threshold", "sensitivity", "specificity", "MCC")
## Precision
df$precision = (df$sens * 0.02) / (df$sens * 0.02 + (1-df$spe) * 0.98)
print(head(df))
print(tail(df))
df_out = df[,c("Row.names", "t", "sens", "spe", "mcc","precision","couple")]
colnames(df_out) = c("methode", "threshold", "sensitivity", "specificity", "MCC", "precision","couple")
df_out = df_out[order(df_out$methode),]
df_out_melt = melt(df_out, id.vars = c("methode","threshold"))
print(summary(df_out))
df_out_melt = melt(df_out, id.vars = c("couple", "methode","threshold"))
df_max_mcc_per_method = do.call(rbind, lapply(split(df_out,df_out$methode),
function(x) {return(x[which.max(x$MCC),c("methode", "threshold", "MCC","sensitivity","specificity")])}))
print("prep plot max mcc")
df_max_mcc_per_method = do.call(rbind, lapply(split(df_out,paste0(df_out$methode,df_out$couple)),
function(x) {return(x[which.max(x$MCC),c("couple","methode", "threshold", "MCC","sensitivity","specificity","precision")])}))
print(df_max_mcc_per_method)
......@@ -109,22 +170,93 @@ print(df_max_mcc_per_method)
df_max_mcc_per_method_2 = df_max_mcc_per_method
df_max_mcc_per_method_2$variable="MCC"
alpha = 0.7
print("prep plot recall_precision_per_meth")
df_recall_sup09_per_meth = do.call(rbind, lapply(split(df_out,paste0(df_out$methode,df_out$couple)),
function(x) {
print(x)
x = x[x$precision > 0.9,]
if (nrow(x) > 0) {
print(x)
return(x[which.max(x$sensitivity),c("couple","methode", "threshold", "MCC","sensitivity","specificity","precision")])
} else {
return()
}
}))
print(df_recall_sup09_per_meth)
print("plot recall_precision_per_meth")
alpha = 0.5
plot = ggplot(df_out, aes(x=sensitivity, y=precision, col = couple))
plot = plot + theme_bw()
plot = plot + labs(x="Sensitivity ( = Recall)", y="Precision")
plot = plot + ylim(c(0,1)) + xlim(c(0,1))
plot = plot + guides(fill=FALSE) + guides(col=FALSE)
plot = plot + scale_color_brewer(palette="Set1")
plot = plot + geom_point(size=0.5)
plot = plot + geom_line()
plot = plot + geom_hline( aes(yintercept = 0.9), col="black" , alpha=alpha, show.legend = NA,linetype="dotted")
plot = plot + facet_grid(couple ~ methode)
plot_recall_precision = plot
save_plot(paste0(opt$out,".recall_precision_per_meth.pdf"),
plot_recall_precision,
ncol = 0.4* length(unique(df_out_melt$methode)),
nrow = 0.4,
base_aspect_ratio = 1,
limitsize = FALSE
)
print("plot per indicator")
x_labs = "Threshold"
y_labs = ""
plot = ggplot(df_out_melt, aes(x=threshold, y=value)) + theme_bw() + guides(fill=FALSE) + labs(x=x_labs, y=y_labs) #+ ylim(y_lim)
plot = plot + geom_point()
plot = plot + geom_hline( data = df_max_mcc_per_method_2, aes(yintercept = MCC), col="red", alpha=alpha, show.legend = NA)
plot = plot + geom_vline( data = df_max_mcc_per_method, aes(xintercept = threshold), col="red", alpha=alpha, show.legend = NA,linetype="dotted")
plot = ggplot(df_out_melt, aes(x=threshold, y=value, col=couple))
plot = plot + theme_bw()
plot = plot + theme(legend.position="top")
plot = plot + guides(fill=FALSE) + theme(legend.position="top")
plot = plot + labs(x=x_labs, y=y_labs) #+ ylim(y_lim)
plot = plot + geom_point(size=0.5)
plot = plot + scale_color_brewer(palette="Set1")
#plot = plot + geom_hline( data = df_max_mcc_per_method_2, aes(yintercept = MCC), alpha=alpha, show.legend = NA)
plot = plot + geom_vline( data = df_recall_sup09_per_meth, aes(xintercept = threshold, col=couple), alpha=alpha, show.legend = NA,linetype="dotted")
plot = plot + facet_grid(variable ~ methode, scales="free")
save_plot(paste0(opt$out,".pdf"),
plot,
plot_max_MCC = plot
save_plot(paste0(opt$out,".max_MCC_per_meth.pdf"),
plot_max_MCC,
ncol = 0.4 * length(unique(df_out_melt$methode)),
nrow = 1.7,
base_aspect_ratio = 1.5,
limitsize = FALSE
)
plot = plot_grid(plot_recall_precision,plot_max_MCC,
labels = c("A", "B"),
rel_heights = c(length(unique(df_out$couple))*0.8,3),
nrow=2)
save_plot(paste0(opt$out,".pdf"),
plot,
ncol = 0.4* length(unique(df_out_melt$methode)),
nrow = length(unique(df_out$couple)) * 0.5 + 1,
base_aspect_ratio = 1,
limitsize = FALSE
)
write.table(df_out, file=paste0(opt$out,".complete.tsv"), row.names=FALSE, quote=F, sep = "\t")
write.table(df_max_mcc_per_method, file=paste0(opt$out,".max_per_meth.tsv"), row.names=FALSE, quote=F, sep = "\t")
write.table(df_max_mcc_per_method, file=paste0(opt$out,".max_MCC_per_meth.tsv"), row.names=FALSE, quote=F, sep = "\t")
write.table(df_recall_sup09_per_meth, file=paste0(opt$out,".recall09_per_meth.tsv"), row.names=FALSE, quote=F, sep = "\t")
......@@ -166,4 +166,4 @@ if len(set(df_list_len)) != 1:
df_final = reduce(lambda x, y: pd.merge(x, y, on = 'Sites', how='outer'), df_list)
df_final.to_csv(OutName, sep='\t', index=False)
df_final.to_csv(OutName, sep='\t', index=False, na_rep='NA')
......@@ -29,6 +29,8 @@ let nodes dataset (model : Convergence_hypothesis.t) =
|H0_NeBig -> [ "tree.H0_a.node_ids" ]
|H0_BigNeInSmallNe-> [ "tree.H0_a.node_ids" ]
|H0_SmallNeInBigNe-> [ "tree.H0_a.node_ids" ]
|HaPC_BigNeInSmallNe-> [ "tree.Ha.node_ids" ]
|HaPC_SmallNeInBigNe-> [ "tree.Ha.node_ids" ]
)
let tree dataset mode =
......
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