diff options
Diffstat (limited to 'lazar.rb')
| -rw-r--r-- | lazar.rb | 202 |
1 files changed, 117 insertions, 85 deletions
@@ -8,12 +8,13 @@ get '/lazar/?' do 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 genration service. Default: #{@@feature_generation_default}", OT.paramScope => "optional", DC.title => "feature_generation_uri" }, + { 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 feaature generation service", OT.paramScope => "optional" } + { DC.description => "Further parameters for the feature generation service", OT.paramScope => "optional" } ] } case request.env['HTTP_ACCEPT'] @@ -44,61 +45,102 @@ post '/lazar/?' do 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_default unless feature_generation_uri = params[:feature_generation_uri] - - 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 - lazar.min_sim = params[:min_sim].to_f if params[:min_sim] + + + + # # # 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.nr_hits = true + lazar.feature_calculation_algorithm = "Substructure.match_hits" lazar.prediction_algorithm = "Neighbors.local_svm_regression" end - if params[:nr_hits] == "false" # if nr_hits is set optional to true/false it will return as String (but should be True/FalseClass) - lazar.nr_hits = false - #params[:nr_hits] = false - elsif params[:nr_hits] == "true" - lazar.nr_hits = true + + + + # # # 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.nr_hits + 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 - if params[:feature_dataset_uri] + + + + + # # # 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) - case training_features.feature_type(@subjectid) - when "classification" - lazar.similarity_algorithm = "Similarity.tanimoto" - when "regression" - lazar.similarity_algorithm = "Similarity.euclid" + 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 - else # create features + + # Create Features + else params[:feature_generation_uri] = feature_generation_uri - if feature_generation_uri.match(/fminer/) - lazar.feature_calculation_algorithm = "Substructure.match" - else - raise OpenTox::NotFoundError.new "External feature generation services not yet supported" - end params[:subjectid] = @subjectid prediction_feature = OpenTox::Feature.find params[:prediction_feature], @subjectid if prediction_feature.feature_type == "regression" && feature_generation_uri.match(/fminer/) @@ -108,82 +150,60 @@ post '/lazar/?' do 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? - # sorted features for index lookups + training_features.data_entries.each do |compound,entry| + if training_activities.data_entries.has_key? compound - lazar.features = training_features.features.sort if prediction_feature.feature_type == "regression" and lazar.feature_calculation_algorithm != "Substructure.match" + lazar.fingerprints[compound] = {} unless lazar.fingerprints[compound] + entry.keys.each do |feature| - training_features.data_entries.each do |compound,entry| - lazar.fingerprints[compound] = {} unless lazar.fingerprints[compound] - entry.keys.each do |feature| - if lazar.feature_calculation_algorithm == "Substructure.match" - if training_features.features[feature] - smarts = training_features.features[feature][OT.smarts] - #lazar.fingerprints[compound] << smarts - if params[:nr_hits] - lazar.fingerprints[compound][smarts] = entry[feature].flatten.first - else - lazar.fingerprints[compound][smarts] = 1 - 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 - else - case training_features.feature_type(@subjectid) - when "classification" - # fingerprints are sets - if entry[feature].flatten.size == 1 - #lazar.fingerprints[compound] << feature if entry[feature].flatten.first.to_s.match(TRUE_REGEXP) - lazar.fingerprints[compound][feature] = entry[feature].flatten.first if entry[feature].flatten.first.to_s.match(TRUE_REGEXP) - lazar.features << feature unless lazar.features.include? feature - else - LOGGER.warn "More than one entry (#{entry[feature].inspect}) for compound #{compound}, feature #{feature}" - end - when "regression" - # fingerprints are arrays - if entry[feature].flatten.size == 1 - lazar.fingerprints[compound][lazar.features.index(feature)] = entry[feature].flatten.first - #lazar.fingerprints[compound][feature] = entry[feature].flatten.first - else - LOGGER.warn "More than one entry (#{entry[feature].inspect}) for compound #{compound}, feature #{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 - # AM: allow settings override by user - lazar.prediction_algorithm = "Neighbors.#{params[:prediction_algorithm]}" unless params[:prediction_algorithm].nil? - if prediction_feature.feature_type == "regression" - lazar.transform["class"] = "Log10" if lazar.transform["class"] == "NOP" - end - lazar.transform["class"] = params[:activity_transform] unless params[:activity_transform].nil? - lazar.prop_kernel = true if (params[:local_svm_kernel] == "propositionalized" || params[:prediction_algorithm] == "local_mlr_prop") - lazar.conf_stdev = false - lazar.conf_stdev = true if params[:conf_stdev] == "true" - # AM: Feed Data using Transformations + + + + # # # Activities + if prediction_feature.feature_type == "regression" - transformed_acts = [] - training_activities.data_entries.each do |compound,entry| - transformed_acts.concat entry[prediction_feature.uri] unless entry[prediction_feature.uri].empty? - end - transformer = eval "OpenTox::Algorithm::Transform::#{lazar.transform["class"]}.new(transformed_acts)" - transformed_acts = transformer.values - lazar.transform["offset"] = transformer.offset - t_count=0 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] << transformed_acts[t_count].to_s - t_count+=1 + lazar.activities[compound] << value end end end @@ -199,6 +219,11 @@ post '/lazar/?' do 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 @@ -213,12 +238,19 @@ post '/lazar/?' do 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 => "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" |