Mercurial > repos > ecology > vigiechiro_idcorrect_2ndlayer
changeset 0:6681b6ba1d7e draft
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tools/vigiechiro commit d2de8e10c11bfa3b04729e59bba58e08d23b56aa
| author | ecology |
|---|---|
| date | Wed, 13 Mar 2019 11:18:36 -0400 |
| parents | |
| children | f3fc8d243b1a |
| files | ClassifEspC2b_180222.learner IdCorrect_2ndLayer.R IdCorrect_2ndLayer_en.xml test-data/IdCor2Layer_Test1_obs.csv test-data/IdCor2Layer_Test1_out.tabular vigiechiro_macros.xml |
| diffstat | 6 files changed, 2529 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/IdCorrect_2ndLayer.R Wed Mar 13 11:18:36 2019 -0400 @@ -0,0 +1,181 @@ +#!/usr/bin/env Rscript + +suppressMessages(library(data.table)) +suppressMessages(library(randomForest)) +args <- commandArgs(trailingOnly = TRUE) + +set.seed(1) #To test reproductibility + +filename=args[3] +if (exists("ClassifEspC2b")==F){load(args[2])} + +DataPar=fread(args[1],na.strings="") #id to be corrected +DataPar$participation=substr(filename,nchar(filename)-40,nchar(filename)-17) +test1=duplicated(cbind(DataPar$'nom du fichier',DataPar$tadarida_taxon)) +test2=(DataPar$tadarida_taxon=="empty") +DataPar=subset(DataPar,(!test1)|(test2)) +DataPar$tadarida_probabilite[DataPar$tadarida_probabilite==""]="0" +DataPar$tadarida_probabilite=as.numeric(DataPar$tadarida_probabilite) + + +#table counting number of contacts per species +nbcT=as.matrix(table(DataPar$participation,DataPar$tadarida_taxon)) + +DataPar$tadarida_probabilite=as.numeric(DataPar$tadarida_probabilite) + +#generating input variables for second layer classification + +Q25=vector() +Q50=vector() +Q75=vector() +Q90=vector() +Q95=vector() +Q98=vector() +Q100=vector() +compt=0 +PropSp=nbcT[0,] +VoteO=DataPar[0,] +for (j in 1:nlevels(as.factor(DataPar$tadarida_taxon))) + { + Datasub2=subset(DataPar,DataPar$tadarida_taxon==levels(as.factor(DataPar$tadarida_taxon))[j]) + + Q25=c(Q25,rep(quantile(Datasub2$tadarida_probabilite,0.25),nrow(Datasub2))) + Q50=c(Q50,rep(quantile(Datasub2$tadarida_probabilite,0.50),nrow(Datasub2))) + Q75=c(Q75,rep(quantile(Datasub2$tadarida_probabilite,0.75),nrow(Datasub2))) + Q90=c(Q90,rep(quantile(Datasub2$tadarida_probabilite,0.90),nrow(Datasub2))) + Q95=c(Q95,rep(quantile(Datasub2$tadarida_probabilite,0.95),nrow(Datasub2))) + Q98=c(Q98,rep(quantile(Datasub2$tadarida_probabilite,0.98),nrow(Datasub2))) + Q100=c(Q100,rep(max(Datasub2$tadarida_probabilite),nrow(Datasub2))) + Ncont1=nrow(Datasub2) + VoteO=rbind(VoteO,Datasub2) + PropSp0=nbcT/Ncont1 + PropSp=rbind(PropSp,PropSp0[rep(seq_len(nrow(PropSp0)),nrow(Datasub2)),]) + compt=compt+nrow(Datasub2) + #print(paste(compt,levels(as.factor(DataPar$tadarida_taxon))[j])) + } + +VoteC2=cbind(VoteO,PropSp,Q25,Q50,Q75,Q90,Q95,Q98,Q100) + + +#editing column titles to identify var of type "proportion d'abondances" +for (i in 15:(ncol(VoteC2)-7)) +{ + colnames(VoteC2)[i]=paste0(names(VoteC2)[i],"_prop") +} + +#Add missing species +EspForm=subset(row.names(ClassifEspC2b$importance) + ,substr(row.names(ClassifEspC2b$importance) + ,nchar(row.names(ClassifEspC2b$importance))-4 + ,nchar(row.names(ClassifEspC2b$importance))) + =="_prop") +test=match(EspForm,colnames(VoteC2)) +EspM=subset(EspForm,is.na(test)) +Zeros=matrix(nrow=nrow(VoteC2),ncol=length(EspM)) +Zeros[is.na(Zeros)]=0 +colnames(Zeros)=EspM +VoteC2=cbind(VoteC2,Zeros) + +ListDV=levels(as.factor(DataPar$'nom du fichier')) +#calcule les probabilités max par espèce et par fichier +#(utile pour corriger les erreurs dues à la coexistence de taxons dans le même fichier +#ex: cris sociaux de Pipistrelles identifiées comme autre chose (Noctule, oreillard...)) +#comptue max proba per species and files +#(useful to correct errors that came from multiple taxons in the same file +#eg ; Pipistrelles socials shouting identified as something else (Noctule, oreillard..)) + +MaxI=tapply(DataPar$tadarida_probabilite + ,INDEX=list(c(DataPar$'nom du fichier'),c(DataPar$tadarida_taxon)) + ,FUN=max) +MaxI2=as.data.frame(cbind(row.names(MaxI),MaxI)) +for (i in 2:ncol(MaxI2)) +{ + MaxI2[,i]=as.numeric(as.character(MaxI2[,i])) +} +MaxI2[is.na(MaxI2)]=0 + +#édition des titres de colonne pour identifier les variables de type "indices max" +#editing col titles to identify "indices max" variables +for (i in 2:(ncol(MaxI2))) +{ + colnames(MaxI2)[i]=paste0(names(MaxI2)[i],"_maxI") +} + + +#add missing species +EspForm=subset(row.names(ClassifEspC2b$importance) + ,substr(row.names(ClassifEspC2b$importance) + ,nchar(row.names(ClassifEspC2b$importance))-4 + ,nchar(row.names(ClassifEspC2b$importance))) + =="_maxI") +test=match(EspForm,colnames(MaxI2)) +EspM=subset(EspForm,is.na(test)) +Zeros=matrix(nrow=nrow(MaxI2),ncol=length(EspM)) +Zeros[is.na(Zeros)]=0 +colnames(Zeros)=EspM +MaxI2=cbind(MaxI2,Zeros) + + + + +#indice de confiance à l'echelle de l'observation (groupe de cris identifié comme provenant d'une seule espèce par la première couche) +#Confidence indice on obs scale (shoutings groups identified as comming from a single species from the first layer) +if(exists("IdS3")){rm(IdS3)} +for (i in 1:nlevels(as.factor(DataPar$tadarida_taxon))) +{ + Idsub=subset(DataPar,DataPar$tadarida_taxon==levels(as.factor(DataPar$tadarida_taxon))[i]) + IdS2=cbind('nom du fichier'=Idsub$'nom du fichier',tadarida_taxon=Idsub$tadarida_taxon,prob=Idsub$tadarida_probabilite) + colnames(IdS2)[3]=paste(levels(as.factor(DataPar$tadarida_taxon))[i]) + if(exists("IdS3")){IdS3=merge(IdS3,IdS2,all=T)}else{IdS3=IdS2} +} + +for (i in 3:ncol(IdS3)) +{ + IdS3[,i]=as.numeric(as.character(IdS3[,i])) +} + +#édition des titres de colonne pour identifier les variables de type "indices de l'observation" +#editing col titles to identify "indices de l'observation" variables +for (i in 3:(ncol(IdS3))) +{ + colnames(IdS3)[i]=paste0(names(IdS3)[i],"_ValI") +} + +IdS3[is.na(IdS3)]=0 + +#add missing species +EspForm=subset(row.names(ClassifEspC2b$importance) + ,substr(row.names(ClassifEspC2b$importance) + ,nchar(row.names(ClassifEspC2b$importance))-4 + ,nchar(row.names(ClassifEspC2b$importance))) + =="_ValI") +test=match(EspForm,colnames(IdS3)) +EspM=subset(EspForm,is.na(test)) +Zeros=matrix(nrow=nrow(IdS3),ncol=length(EspM)) +Zeros[is.na(Zeros)]=0 +colnames(Zeros)=EspM +IdS3=cbind(IdS3,Zeros) + +#on merge les prop d'espèces, les quantiles et les indices par fichiers et par observations +#merge species probabilities, quantiles and indice per files and per obs +VoteC3=merge(VoteC2,MaxI2,by.x="nom du fichier",by.y="V1") +VoteC4=merge(VoteC3,IdS3,by=c("nom du fichier","tadarida_taxon")) +VoteC4$temps_fin=as.numeric(as.character(VoteC4$temps_fin)) +VoteC4$temps_debut=as.numeric(as.character(VoteC4$temps_debut)) +VoteC4$frequence=as.numeric(as.character(VoteC4$frequence_mediane)) +VoteC4$durseq=VoteC4$temps_fin-VoteC4$temps_debut + +ProbEsp_C2b=predict(ClassifEspC2b,VoteC4,type="prob",norm.votes=TRUE) +ProbEsp_C2bs=predict(ClassifEspC2b,VoteC4,type="response",norm.votes=TRUE) + +colnum=match("participation",colnames(VoteC4)) +DataCorrC2=cbind(VoteC4[,1:colnum],ProbEsp_C2b,ProbEsp_C2bs) +DataCorrC2=DataCorrC2[order(DataCorrC2$tadarida_probabilite,decreasing=T),] +DataCorrC2=DataCorrC2[order(DataCorrC2$'nom du fichier'),] + +DataCorrC2$ProbEsp_C2bs=as.character(DataCorrC2$ProbEsp_C2bs) +DataCorrC2$ProbEsp_C2bs[is.na(DataCorrC2$ProbEsp_C2bs)]="empty" + +fout_name="output.tabular" + +write.table(DataCorrC2,file=fout_name,row.names=FALSE,sep="\t",quote=FALSE,na="NA")
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/IdCorrect_2ndLayer_en.xml Wed Mar 13 11:18:36 2019 -0400 @@ -0,0 +1,68 @@ +<tool id="vigiechiro_idcorrect_2ndlayer" name="Tadarida data cleaner" version="@VERSION@"> + <description>clean data from animal detection on acoustic recordings</description> + <macros> + <import>vigiechiro_macros.xml</import> + </macros> + <requirements> + <requirement type="package" version="1.10.4">r-data.table</requirement> + <requirement type="package" version="4.6_12">r-randomforest</requirement> + </requirements> + <command detect_errors="exit_code"><![CDATA[ + Rscript + '$__tool_directory__/IdCorrect_2ndLayer.R' '$participation_file' + + #if $learner.condi_learner == "custom" + '$learner.user_learner' + #else + '$__tool_directory__/ClassifEspC2b_180222.learner' + #end if + '${participation_file.name}' + '$output' +]]> + </command> + <inputs> + <param name="participation_file" type="data" format="csv" label="Participation file" help="Summary table of TadaridaC"/> + <param name="learner" type="data" label="Choose your leaner" format="rdata"/> + <conditional name="learner"> + <param name="condi_learner" type="select" label="Select a classifier" help="Default is ClassifEspC2b_180222"> + <option value="c18">ClassifEspC2b_180222</option> + <option value="custom">Custom Classifier</option> + </param> + <when value="c18"> + </when> + <when value="custom"> + <param name="user_learner" type="data" label="Choose your classifier" format="rdata"/> + </when> + </conditional> + </inputs> + <outputs> + <data name="output" from_work_dir="output.tabular" format="tabular"/> + </outputs> + <tests> + <test> + <param name="participation_file" value="IdCor2Layer_Test1_obs.csv"/> + <param name="condi_learner" value="default_learner"/> + <output name="output" file="IdCor2Layer_Test1_out.tabular"/> + </test> + </tests> + <help><![CDATA[ + +.. class:: infomark + +========================== +Tadarida data cleaner +========================== + +**What it does** + +This tool intends to correct ids from 1st layer of Tadarida software, and improves data output according to context (= the whole output of a sampling session). + +**Arguments** + +It should be called with 2 consecutive arguments : + +- a summary table of TadaridaC output from vigiechiro.herokuapp.com web portal. +- the 2nd layer classifier built on validated id in Vigie-Chiro database (eg : "ClassifEspC2b_180222.learner") + ]]></help> + <expand macro="vigiechiro_bibref" /> +</tool>
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/IdCor2Layer_Test1_out.tabular Wed Mar 13 11:18:36 2019 -0400 @@ -0,0 +1,1120 @@ +nom du fichier tadarida_taxon temps_debut temps_fin frequence_mediane tadarida_probabilite tadarida_taxon_autre observateur_taxon observateur_probabilite validateur_taxon validateur_probabilite participation amph Antius Antped Antsor Aposp Barbar Barfis Barsan Barser Bicbic chirp Cicatr Cicorn Concon Confus Corcho criq Cympud Cyrscu Decalb Epheph Eptnil Eptser Eupsp gril Hypsav Insp11 Insp12 Inssp5 Inssp6 Inssp7 Inssp8 Inssp9 Isopyr Lamsp. Leppun Lyrple mamm Metsau Micagr Minsch Mussp Myoalc Myobec Myobra Myocap Myodas Myodau Myoema MyoGT Myomys Myonat noise Nyclas Nyclei Nycnoc Ortsp. 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