diff options
Diffstat (limited to 'lazar.rb')
| -rw-r--r-- | lazar.rb | 147 |
1 files changed, 92 insertions, 55 deletions
@@ -38,26 +38,26 @@ end # @return [text/uri-list] Task URI post '/lazar/?' do - LOGGER.debug "building lazar model with params: "+params.inspect params[:subjectid] = @subjectid raise OpenTox::NotFoundError.new "No dataset_uri parameter." unless params[:dataset_uri] dataset_uri = params[:dataset_uri] - task = OpenTox::Task.create("Create lazar model",url_for('/lazar',:full)) do |task| + task = OpenTox::Task.create("Create lazar model",url_for('/lazar',:full)) do |task| - # # # Dataset present, prediction feature present? - raise OpenTox::NotFoundError.new "Dataset #{dataset_uri} not found." unless training_activities = OpenTox::Dataset.new(dataset_uri) - training_activities.load_all(@subjectid) + # # # BASIC SETTINGS + + raise OpenTox::NotFoundError.new "Dataset #{dataset_uri} not found." unless training_dataset = OpenTox::Dataset.new(dataset_uri) + training_dataset.load_all(@subjectid) # Prediction Feature prediction_feature = OpenTox::Feature.find(params[:prediction_feature],@subjectid) unless params[:prediction_feature] # try to read prediction_feature from dataset - raise OpenTox::NotFoundError.new "#{training_activities.features.size} features in dataset #{dataset_uri}. Please provide a prediction_feature parameter." unless training_activities.features.size == 1 - prediction_feature = OpenTox::Feature.find(training_activities.features.keys.first,@subjectid) + raise OpenTox::NotFoundError.new "#{training_dataset.features.size} features in dataset #{dataset_uri}. Please provide a prediction_feature parameter." unless training_dataset.features.size == 1 + prediction_feature = OpenTox::Feature.find(training_dataset.features.keys.first,@subjectid) params[:prediction_feature] = prediction_feature.uri # pass to feature mining service end - raise OpenTox::NotFoundError.new "No feature #{prediction_feature.uri} in dataset #{params[:dataset_uri]}. (features: "+ training_activities.features.inspect+")" unless training_activities.features and training_activities.features.include?(prediction_feature.uri) + raise OpenTox::NotFoundError.new "No feature #{prediction_feature.uri} in dataset #{params[:dataset_uri]}. (features: "+ training_dataset.features.inspect+")" unless training_dataset.features and training_dataset.features.include?(prediction_feature.uri) # Feature Generation URI feature_generation_uri = @@feature_generation_default unless ( (feature_generation_uri = params[:feature_generation_uri]) || (params[:feature_dataset_uri]) ) @@ -65,19 +65,10 @@ post '/lazar/?' do # Create instance lazar = OpenTox::Model::Lazar.new - - - - # # # ENDPOINT RELATED - - # Default Values # Classification: Weighted Majority, Substructure.match if prediction_feature.feature_type == "classification" - @training_classes = training_activities.accept_values(prediction_feature.uri).sort - @training_classes.each_with_index { |c,i| - lazar.value_map[i+1] = c # don't use '0': we must take the weighted mean later. - params[:value_map] = lazar.value_map - } + lazar.value_map = training_dataset.value_map(params[:prediction_feature]) + # Regression: SVM, Substructure.match_hits elsif prediction_feature.feature_type == "regression" lazar.feature_calculation_algorithm = "Substructure.match_hits" @@ -85,8 +76,6 @@ post '/lazar/?' do end - - # # # USER VALUES # Min Sim @@ -108,34 +97,66 @@ post '/lazar/?' do propositionalized = (lazar.prediction_algorithm=="Neighbors.weighted_majority_vote" ? false : true) # PC type - pc_type = params[:pc_type] unless params[:pc_type].nil? + pc_type = params[:pc_type] if params[:pc_type] + lib = params[:lib] if params[:lib] # Min train performance min_train_performance = params[:min_train_performance].to_f if params[:min_train_performance] min_train_performance = 0.1 unless params[:min_train_performance] - - - - task.progress 10 - - - # # # Features - # Read Features + # Read Features, currently only OT.NumericFeatures if params[:feature_dataset_uri] lazar.feature_calculation_algorithm = "Substructure.lookup" feature_dataset_uri = params[:feature_dataset_uri] training_features = OpenTox::Dataset.new(feature_dataset_uri) - if training_features.feature_type(@subjectid) == "regression" + training_feature_types = training_features.feature_types(@subjectid) + + if training_feature_types.collect { |id, info| info.include? OT.NumericFeature }.include?(false) # <- extend this + raise OpenTox::NotFoundError.new "Found a non-numeric feature in feature dataset" + + elsif training_dataset.compounds.size < training_feature_types.size + raise OpenTox::BadRequestError.new "Number of training compounds (#{training_dataset.compounds.size}) smaller than number of non-missing features (#{training_feature_types.size})" + + else lazar.similarity_algorithm = "Similarity.cosine" - min_sim = 0.4 unless params[:min_sim] - raise OpenTox::NotFoundError.new "No pc_type parameter." unless params[:pc_type] + min_sim = 0.7 unless params[:min_sim] + min_sim = (min_sim * 2.0 -1.0 ) # transform to cosine range [-1,1] + + training_features_tl = training_features.features.collect{|f,info| + unless info[DC.description] + [nil, nil] + else + info[DC.description].gsub(/.*\[/,"").chop.split(", ") + end + + } + training_features_pc_types = training_features_tl.collect{|info| info[0]}.flatten.uniq.compact + training_features_lib = training_features_tl.collect{|info| info[1]}.flatten.uniq.compact + unless (params[:pc_type] and params[:lib]) + + if (!params[:pc_type] && training_features_pc_types.size>0) + pc_type=training_features_pc_types.join(',') + LOGGER.info "pc_type '#{pc_type}' auto-detected from feature dataset" + end + + if (!params[:lib] && training_features_lib.size>0) + lib=training_features_lib.join(',') + LOGGER.info "lib '#{lib}' auto-detected from feature dataset" + end + + unless (pc_type and lib) + raise OpenTox::NotFoundError.new "No pc_type parameter given, and autodetection from feature dataset failed" unless pc_type + raise OpenTox::NotFoundError.new "No lib parameter given, and autodetection from feature dataset failed" unless lib + end + + end + end # Create Features @@ -144,22 +165,36 @@ post '/lazar/?' do params[:subjectid] = @subjectid prediction_feature = OpenTox::Feature.find params[:prediction_feature], @subjectid if prediction_feature.feature_type == "regression" && feature_generation_uri.match(/fminer/) - params[:feature_type] = "paths" + params[:feature_type] = "paths" unless params[:feature_type] end feature_dataset_uri = OpenTox::Algorithm::Generic.new(feature_generation_uri).run(params, OpenTox::SubTask.new(task,10,70)).to_s training_features = OpenTox::Dataset.new(feature_dataset_uri) end - # # # Write fingerprints + training_features.load_all(@subjectid) raise OpenTox::NotFoundError.new "Dataset #{feature_dataset_uri} not found." if training_features.nil? - training_features.data_entries.each do |compound,entry| - if training_activities.data_entries.has_key? compound - - lazar.fingerprints[compound] = {} unless lazar.fingerprints[compound] + del_master_compounds = [] + + # Creating InChi/URI Hash from trainig_feature for comparison with training_dataset to avoid missmatches caused by different URI authorities + feature_compounds = {} + training_features.compounds.each {|f_c_uri| + f_compound = OpenTox::Compound.new(f_c_uri) + feature_compounds[f_compound.to_inchi] = f_c_uri + } + + training_dataset.compounds.each do |t_c_uri| + + t_compound = OpenTox::Compound.new(t_c_uri) + entry = training_features.data_entries[feature_compounds[t_compound.to_inchi]] + + if entry.nil? # Training compound not found in feature dataset + del_master_compounds << t_c_uri # Delete if training compound not found in feature dataset + else + lazar.fingerprints[t_c_uri] = {} unless lazar.fingerprints[t_c_uri] entry.keys.each do |feature| # CASE 1: Substructure @@ -168,9 +203,9 @@ post '/lazar/?' do smarts = training_features.features[feature][OT.smarts] #lazar.fingerprints[compound] << smarts if lazar.feature_calculation_algorithm == "Substructure.match_hits" - lazar.fingerprints[compound][smarts] = entry[feature].flatten.first * training_features.features[feature][OT.pValue] + lazar.fingerprints[t_c_uri][smarts] = entry[feature].flatten.first * training_features.features[feature][OT.pValue] else - lazar.fingerprints[compound][smarts] = 1 * training_features.features[feature][OT.pValue] + lazar.fingerprints[t_c_uri][smarts] = 1 * training_features.features[feature][OT.pValue] end unless lazar.features.include? smarts lazar.features << smarts @@ -181,25 +216,28 @@ post '/lazar/?' do # CASE 2: Others elsif entry[feature].flatten.size == 1 - lazar.fingerprints[compound][feature] = entry[feature].flatten.first + lazar.fingerprints[t_c_uri][feature] = entry[feature].flatten.first lazar.features << feature unless lazar.features.include? feature else - LOGGER.warn "More than one entry (#{entry[feature].inspect}) for compound #{compound}, feature #{feature}" + LOGGER.warn "More than one entry (#{entry[feature].inspect}) for compound #{t_c_uri}, feature #{feature}" end end - end end - task.progress 80 - - - + task.progress 80 + + # Show compounds without feature information + if del_master_compounds.size>0 + del_master_compounds.each{|compound| LOGGER.info "Compound: '#{compound.to_s}' not found in feature dataset and will be removed from compound list."} + end + # # # Compounds + lazar.compounds=training_dataset.compounds.collect - del_master_compounds # Add only compounds with fingerprints # # # Activities - if prediction_feature.feature_type == "regression" - training_activities.data_entries.each do |compound,entry| + lazar.compounds.each do |compound| + entry = training_dataset.data_entries[compound] lazar.activities[compound] = [] unless lazar.activities[compound] unless entry[prediction_feature.uri].empty? entry[prediction_feature.uri].each do |value| @@ -208,7 +246,8 @@ post '/lazar/?' do end end elsif prediction_feature.feature_type == "classification" - training_activities.data_entries.each do |compound,entry| + lazar.compounds.each do |compound| + entry = training_dataset.data_entries[compound] lazar.activities[compound] = [] unless lazar.activities[compound] unless entry[prediction_feature.uri].empty? entry[prediction_feature.uri].each do |value| @@ -220,10 +259,7 @@ post '/lazar/?' do task.progress 90 - - - # Metadata - + # # # Metadata lazar.metadata[DC.title] = "lazar model for #{URI.decode(File.basename(prediction_feature.uri))}" lazar.metadata[OT.dependentVariables] = prediction_feature.uri lazar.metadata[OT.trainingDataset] = dataset_uri @@ -241,6 +277,7 @@ post '/lazar/?' do {DC.title => "feature_generation_uri", OT.paramValue => feature_generation_uri}, {DC.title => "propositionalized", OT.paramValue => propositionalized}, {DC.title => "pc_type", OT.paramValue => pc_type}, + {DC.title => "lib", OT.paramValue => lib}, {DC.title => "nr_hits", OT.paramValue => nr_hits}, {DC.title => "min_sim", OT.paramValue => min_sim}, {DC.title => "min_train_performance", OT.paramValue => min_train_performance}, |