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Diffstat (limited to 'lib/bbrc.rb')
| -rw-r--r-- | lib/bbrc.rb | 165 |
1 files changed, 0 insertions, 165 deletions
diff --git a/lib/bbrc.rb b/lib/bbrc.rb deleted file mode 100644 index c83b9b3..0000000 --- a/lib/bbrc.rb +++ /dev/null @@ -1,165 +0,0 @@ -module OpenTox - module Algorithm - class Fminer - TABLE_OF_ELEMENTS = [ -"H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr", "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th", "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt", "Ds", "Rg", "Cn", "Uut", "Fl", "Uup", "Lv", "Uus", "Uuo"] - - # - # Run bbrc algorithm on dataset - # - # @param [OpenTox::Dataset] training dataset - # @param [optional] parameters BBRC parameters, accepted parameters are - # - min_frequency Minimum frequency (default 5) - # - feature_type Feature type, can be 'paths' or 'trees' (default "trees") - # - backbone BBRC classes, pass 'false' to switch off mining for BBRC representatives. (default "true") - # - min_chisq_significance Significance threshold (between 0 and 1) - # - nr_hits Set to "true" to get hit count instead of presence - # - get_target Set to "true" to obtain target variable as feature - # @return [OpenTox::Dataset] Fminer Dataset - def self.bbrc training_dataset, params={} - - time = Time.now - bad_request_error "More than one prediction feature found in training_dataset #{training_dataset.id}" unless training_dataset.features.size == 1 - - prediction_feature = training_dataset.features.first - if params[:min_frequency] - minfreq = params[:min_frequency] - else - per_mil = 5 # value from latest version - per_mil = 8 # as suggested below - i = training_dataset.feature_ids.index prediction_feature.id - nr_labeled_cmpds = training_dataset.data_entries.select{|de| !de[i].nil?}.size - minfreq = per_mil * nr_labeled_cmpds.to_f / 1000.0 # AM sugg. 8-10 per mil for BBRC, 50 per mil for LAST - minfreq = 2 unless minfreq > 2 - minfreq = minfreq.round - end - - @bbrc ||= Bbrc::Bbrc.new - @bbrc.Reset - if prediction_feature.numeric - @bbrc.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations! - else - bad_request_error "No accept values for "\ - "dataset '#{training_dataset.id}' and "\ - "feature '#{prediction_feature.id}'" unless prediction_feature.accept_values - value2act = Hash[[*prediction_feature.accept_values.map.with_index]] - end - @bbrc.SetMinfreq(minfreq) - @bbrc.SetType(1) if params[:feature_type] == "paths" - @bbrc.SetBackbone(false) if params[:backbone] == "false" - @bbrc.SetChisqSig(params[:min_chisq_significance].to_f) if params[:min_chisq_significance] - @bbrc.SetConsoleOut(false) - - params[:nr_hits] ? nr_hits = params[:nr_hits] : nr_hits = false - feature_dataset = FminerDataset.new( - :training_dataset_id => training_dataset.id, - :training_algorithm => "#{self.to_s}.bbrc", - :training_feature_id => prediction_feature.id , - :training_parameters => { - :min_frequency => minfreq, - :nr_hits => nr_hits, - :backbone => (params[:backbone] == false ? false : true) - } - - ) - feature_dataset.compounds = training_dataset.compounds - - # add data - training_dataset.compounds.each_with_index do |compound,i| - act = value2act[training_dataset.data_entries[i].first] - if act # TODO check if this works - @bbrc.AddCompound(compound.smiles,i+1) - @bbrc.AddActivity(act,i+1) - end - end - #g_median=@fminer.all_activities.values.to_scale.median - - #task.progress 10 - #step_width = 80 / @bbrc.GetNoRootNodes().to_f - - $logger.debug "BBRC setup: #{Time.now-time}" - time = Time.now - ftime = 0 - itime = 0 - rtime = 0 - - # run @bbrc - (0 .. @bbrc.GetNoRootNodes()-1).each do |j| - results = @bbrc.MineRoot(j) - results.each do |result| - rt = Time.now - f = YAML.load(result)[0] - smarts = f.shift - # convert fminer SMARTS representation into a more human readable format - smarts.gsub!(%r{\[#(\d+)&(\w)\]}) do - element = TABLE_OF_ELEMENTS[$1.to_i-1] - $2 == "a" ? element.downcase : element - end - p_value = f.shift - f.flatten! - compound_idxs = f.collect{|e| e.first.first-1} - # majority class - effect = compound_idxs.collect{|i| training_dataset.data_entries[i].first}.mode - -=begin - if (!@bbrc.GetRegression) - id_arrs = f[2..-1].flatten - max = OpenTox::Algorithm::Fminer.effect(f[2..-1].reverse, @fminer.db_class_sizes) # f needs reversal for bbrc - effect = max+1 - else #regression part - id_arrs = f[2] - # DV: effect calculation - f_arr=Array.new - f[2].each do |id| - id=id.keys[0] # extract id from hit count hash - f_arr.push(@fminer.all_activities[id]) - end - f_median=f_arr.to_scale.median - if g_median >= f_median - effect = 'activating' - else - effect = 'deactivating' - end - end -=end - rtime += Time.now - rt - - ft = Time.now - feature = OpenTox::FminerSmarts.find_or_create_by({ - "smarts" => smarts, - "p_value" => p_value.to_f.abs.round(5), - "effect" => effect, - "dataset_id" => feature_dataset.id - }) - feature_dataset.feature_ids << feature.id - ftime += Time.now - ft - - it = Time.now - f.each do |id_count_hash| - id_count_hash.each do |id,count| - nr_hits ? count = count.to_i : count = 1 - feature_dataset.data_entries[id-1] ||= [] - feature_dataset.data_entries[id-1][feature_dataset.feature_ids.size-1] = count - end - end - itime += Time.now - it - - end - end - - $logger.debug "Fminer: #{Time.now-time} (read: #{rtime}, iterate: #{itime}, find/create Features: #{ftime})" - time = Time.now - - feature_dataset.fill_nil_with 0 - - $logger.debug "Prepare save: #{Time.now-time}" - time = Time.now - feature_dataset.save_all - - $logger.debug "Save: #{Time.now-time}" - feature_dataset - - end - end - end -end |