diff options
Diffstat (limited to 'lazar.rb')
| -rw-r--r-- | lazar.rb | 259 |
1 files changed, 0 insertions, 259 deletions
diff --git a/lazar.rb b/lazar.rb deleted file mode 100644 index 1fe28f8..0000000 --- a/lazar.rb +++ /dev/null @@ -1,259 +0,0 @@ -@@feature_generation_default = File.join(CONFIG[:services]["opentox-algorithm"],"fminer","bbrc") - -# Get RDF/XML representation of the lazar algorithm -# @return [application/rdf+xml] OWL-DL representation of the lazar algorithm -get '/lazar/?' do - algorithm = OpenTox::Algorithm::Generic.new(url_for('/lazar',:full)) - algorithm.metadata = { - DC.title => 'lazar', - DC.creator => "helma@in-silico.ch, andreas@maunz.de", - DC.contributor => "vorgrimmlerdavid@gmx.de", -# BO.instanceOf => "http://opentox.org/ontology/ist-algorithms.owl#lazar", - OT.parameters => [ - { DC.description => "Dataset URI with the dependent variable", OT.paramScope => "mandatory", DC.title => "dataset_uri" }, - { DC.description => "Feature URI for dependent variable. Optional for datasets with only a single feature.", OT.paramScope => "optional", DC.title => "prediction_feature" }, - { DC.description => "URI of feature generation service. Default: #{@@feature_generation_default}", OT.paramScope => "optional", DC.title => "feature_generation_uri" }, - { DC.description => "URI of feature dataset. If this parameter is set no feature generation algorithm will be called", OT.paramScope => "optional", DC.title => "feature_dataset_uri" }, - { DC.description => "Further parameters for the feature generation service", OT.paramScope => "optional" } - ] - } - case request.env['HTTP_ACCEPT'] - when /text\/html/ - content_type "text/html" - OpenTox.text_to_html algorithm.to_yaml - when /application\/x-yaml/ - content_type "application/x-yaml" - algorithm.to_yaml - else - response['Content-Type'] = 'application/rdf+xml' - algorithm.to_rdfxml - end -end - -# Create a lazar prediction model -# @param [String] dataset_uri Training dataset URI -# @param [optional,String] prediction_feature URI of the feature to be predicted -# @param [optional,String] feature_generation_uri URI of the feature generation algorithm -# @param [optional,String] - further parameters for the feature generation service -# @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| - - - # # # 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) - - # 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) - 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) - - # Feature Generation URI - feature_generation_uri = @@feature_generation_default unless ( (feature_generation_uri = params[:feature_generation_uri]) || (params[:feature_dataset_uri]) ) - - # 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 - } - # Regression: SVM, Substructure.match_hits - elsif prediction_feature.feature_type == "regression" - lazar.feature_calculation_algorithm = "Substructure.match_hits" - lazar.prediction_algorithm = "Neighbors.local_svm_regression" - end - - - - - # # # USER VALUES - - # Min Sim - min_sim = params[:min_sim].to_f if params[:min_sim] - min_sim = 0.3 unless params[:min_sim] - - # Algorithm - lazar.prediction_algorithm = "Neighbors.#{params[:prediction_algorithm]}" if params[:prediction_algorithm] - - # Nr Hits - nr_hits = false - if params[:nr_hits] == "true" || lazar.prediction_algorithm.include?("local_svm") - lazar.feature_calculation_algorithm = "Substructure.match_hits" - nr_hits = true - end - params[:nr_hits] = "true" if lazar.feature_calculation_algorithm == "Substructure.match_hits" #not sure if this line in needed - - # Propositionalization - propositionalized = (lazar.prediction_algorithm=="Neighbors.weighted_majority_vote" ? false : true) - - # PC type - pc_type = params[:pc_type] unless params[:pc_type].nil? - - # 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 - 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" - 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] - end - - # Create Features - else - params[:feature_generation_uri] = feature_generation_uri - 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" 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] - entry.keys.each do |feature| - - # CASE 1: Substructure - if (lazar.feature_calculation_algorithm == "Substructure.match") || (lazar.feature_calculation_algorithm == "Substructure.match_hits") - if training_features.features[feature] - 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] - else - lazar.fingerprints[compound][smarts] = 1 * training_features.features[feature][OT.pValue] - end - unless lazar.features.include? smarts - lazar.features << smarts - lazar.p_values[smarts] = training_features.features[feature][OT.pValue] - lazar.effects[smarts] = training_features.features[feature][OT.effect] - end - end - - # CASE 2: Others - elsif entry[feature].flatten.size == 1 - lazar.fingerprints[compound][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}" - end - end - - end - end - task.progress 80 - - - - - - # # # Activities - - if prediction_feature.feature_type == "regression" - training_activities.data_entries.each do |compound,entry| - lazar.activities[compound] = [] unless lazar.activities[compound] - unless entry[prediction_feature.uri].empty? - entry[prediction_feature.uri].each do |value| - lazar.activities[compound] << value - end - end - end - elsif prediction_feature.feature_type == "classification" - training_activities.data_entries.each do |compound,entry| - lazar.activities[compound] = [] unless lazar.activities[compound] - unless entry[prediction_feature.uri].empty? - entry[prediction_feature.uri].each do |value| - lazar.activities[compound] << lazar.value_map.invert[value] # insert mapped values, not originals - end - end - end - end - task.progress 90 - - - - - # 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 - lazar.metadata[OT.featureDataset] = feature_dataset_uri - case training_activities.feature_type(@subjectid) - when "classification" - lazar.metadata[RDF.type] = [OT.Model, OTA.ClassificationLazySingleTarget] - when "regression" - lazar.metadata[RDF.type] = [OT.Model, OTA.RegressionLazySingleTarget] - end - - lazar.metadata[OT.parameters] = [ - {DC.title => "dataset_uri", OT.paramValue => dataset_uri}, - {DC.title => "prediction_feature", OT.paramValue => prediction_feature.uri}, - {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 => "nr_hits", OT.paramValue => nr_hits}, - {DC.title => "min_sim", OT.paramValue => min_sim}, - {DC.title => "min_train_performance", OT.paramValue => min_train_performance}, - - ] - - model_uri = lazar.save(@subjectid) - LOGGER.info model_uri + " created #{Time.now}" - model_uri - - end - response['Content-Type'] = 'text/uri-list' - raise OpenTox::ServiceUnavailableError.newtask.uri+"\n" if task.status == "Cancelled" - halt 202,task.uri -end - |