Module: OpenTox::Algorithm::FeatureSelection

Includes:

OpenTox::Algorithm

Defined in:

lib/algorithm.rb

Instance Attribute Summary

Attributes included from OpenTox

#metadata, #uri

Class Method Summary

• .rfe(params) ⇒ String

  Recursive Feature Elimination using caret.

Methods included from OpenTox::Algorithm

effect, gauss, get_pc_descriptors, isnull_or_singular?, load_ds_csv, min_frequency, numeric?, pc_descriptors, #run, sum_size, #to_rdfxml, zero_variance?

Methods included from OpenTox

#add_metadata, all, #delete, #initialize, #load_metadata, sign_in, text_to_html, #to_rdfxml

Class Method Details

.rfe(params) ⇒ String

Recursive Feature Elimination using caret

Parameters:

• required (Hash) —

  keys: ds_csv_file, prediction_feature, fds_csv_file (dataset CSV file, prediction feature column name, and feature dataset CSV file), optional: del_missing (delete rows with missing values).

Returns:

• (String) —

  feature dataset CSV file composed of selected features.

# File 'lib/algorithm.rb', line 451

def self.rfe(params)
  @r=RinRuby.new(false,false)
  @r.ds_csv_file = params[:ds_csv_file].to_s
  @r.prediction_feature = params[:prediction_feature].to_s
  @r.fds_csv_file = params[:fds_csv_file].to_s
  @r.del_missing = params[:del_missing] == true ? 1 : 0
  r_result_file = params[:fds_csv_file].sub("rfe_", "rfe_R_")
  @r.f_fds_r = r_result_file.to_s
  
  # need packs 'randomForest', 'RANN'
  @r.eval "    set.seed(1)\n    suppressPackageStartupMessages(library('caret'))\n    suppressPackageStartupMessages(library('randomForest'))\n    suppressPackageStartupMessages(library('RANN'))\n    suppressPackageStartupMessages(library('doMC'))\n    registerDoMC()\n    \n    acts = read.csv(ds_csv_file, check.names=F)\n    feats = read.csv(fds_csv_file, check.names=F)\n    ds = merge(acts, feats, by=\"SMILES\") # duplicates features for duplicate SMILES :-)\n    \n    features = ds[,(dim(acts)[2]+1):(dim(ds)[2])]\n    y = ds[,which(names(ds) == prediction_feature)] \n    \n    # assumes a data matrix 'features' and a vector 'y' of target values\n    row.names(features)=NULL\n    \n    pp = NULL\n    if (del_missing) {\n      # needed if rows should be removed\n      na_ids = apply(features,1,function(x)any(is.na(x)))\n      features = features[!na_ids,]\n      y = y[!na_ids]\n      pp = preProcess(features, method=c(\"scale\", \"center\"))\n    } else {\n      # Use imputation if NA's random (only then!)\n      pp = preProcess(features, method=c(\"scale\", \"center\", \"knnImpute\"))\n    }\n    features = predict(pp, features)\n    \n    # determine subsets\n    subsets = dim(features)[2]*c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7)\n    subsets = c(2,3,4,5,7,10,subsets)\n    subsets = unique(sort(round(subsets))) \n    subsets = subsets[subsets<=dim(features)[2]]\n    subsets = subsets[subsets>1] \n    \n    # Recursive feature elimination\n    rfProfile = rfe( x=features, y=y, rfeControl=rfeControl(functions=rfFuncs, number=50), sizes=subsets)\n    \n    # read existing dataset and select most useful features\n    csv=feats[,c(\"SMILES\", rfProfile$optVariables)]\n    write.csv(x=csv,file=f_fds_r, row.names=F, quote=F, na='')\n  EOR\n  r_result_file\nend\n"