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Diffstat (limited to 'lib/bbrc.rb')
| -rw-r--r-- | lib/bbrc.rb | 159 |
1 files changed, 159 insertions, 0 deletions
diff --git a/lib/bbrc.rb b/lib/bbrc.rb new file mode 100644 index 0000000..6a2eed7 --- /dev/null +++ b/lib/bbrc.rb @@ -0,0 +1,159 @@ +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 + 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| + @bbrc.AddCompound(compound.smiles,i+1) + act = value2act[training_dataset.data_entries[i].first] + @bbrc.AddActivity(act,i+1) + 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! + +=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 |