diff options
Diffstat (limited to 'lazar.rb')
| -rw-r--r-- | lazar.rb | 150 |
1 files changed, 96 insertions, 54 deletions
@@ -3,7 +3,6 @@ # Get RDF/XML representation of the lazar algorithm # @return [application/rdf+xml] OWL-DL representation of the lazar algorithm get '/lazar/?' do - response['Content-Type'] = 'application/rdf+xml' algorithm = OpenTox::Algorithm::Generic.new(url_for('/lazar',:full)) algorithm.metadata = { DC.title => 'lazar', @@ -17,7 +16,17 @@ get '/lazar/?' do { DC.description => "Further parameters for the feaature generation service", OT.paramScope => "optional" } ] } - algorithm.to_rdfxml + 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 @@ -28,29 +37,42 @@ end # @return [text/uri-list] Task URI post '/lazar/?' do + LOGGER.debug "building lazar model with params: "+params.inspect params[:subjectid] = @subjectid - halt 404, "No dataset_uri parameter." unless params[:dataset_uri] + raise OpenTox::NotFoundError.new "No dataset_uri parameter." unless params[:dataset_uri] dataset_uri = params[:dataset_uri] - halt 404, "Dataset #{dataset_uri} not found." unless training_activities = OpenTox::Dataset.new(dataset_uri) - training_activities.load_all(@subjectid) - - prediction_feature = OpenTox::Feature.find(params[:prediction_feature],@subjectid) - unless params[:prediction_feature] # try to read prediction_feature from dataset - halt 404, "#{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 + task = OpenTox::Task.create("Create lazar model",url_for('/lazar',:full)) do |task| - feature_generation_uri = @@feature_generation_default unless feature_generation_uri = params[:feature_generation_uri] + raise OpenTox::NotFoundError.new "Dataset #{dataset_uri} not found." unless training_activities = OpenTox::Dataset.new(dataset_uri) + training_activities.load_all(@subjectid) - halt 404, "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) + 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 + + feature_generation_uri = @@feature_generation_default unless feature_generation_uri = params[:feature_generation_uri] - task = OpenTox::Task.create("Create lazar model",url_for('/lazar',:full)) do |task| + 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) lazar = OpenTox::Model::Lazar.new - lazar.min_sim = params[:min_sim] if params[:min_sim] + lazar.min_sim = params[:min_sim].to_f if params[:min_sim] + lazar.nr_hits = true if params[:nr_hits] == "true" + + 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 + } + elsif prediction_feature.feature_type == "regression" + lazar.prediction_algorithm = "Neighbors.local_svm_regression" + end + task.progress 10 if params[:feature_dataset_uri] feature_dataset_uri = params[:feature_dataset_uri] @@ -66,31 +88,36 @@ post '/lazar/?' do if feature_generation_uri.match(/fminer/) lazar.feature_calculation_algorithm = "Substructure.match" else - halt 404, "External feature generation services not yet supported" + 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/) params[:feature_type] = "paths" end - feature_dataset_uri = OpenTox::Algorithm::Generic.new(feature_generation_uri).run(params).to_s + 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 training_features.load_all(@subjectid) - halt 404, "Dataset #{feature_dataset_uri} not found." if training_features.nil? + raise OpenTox::NotFoundError.new "Dataset #{feature_dataset_uri} not found." if training_features.nil? # sorted features for index lookups lazar.features = training_features.features.sort if prediction_feature.feature_type == "regression" and lazar.feature_calculation_algorithm != "Substructure.match" training_features.data_entries.each do |compound,entry| - lazar.fingerprints[compound] = [] unless lazar.fingerprints[compound] + 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 + #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] @@ -102,7 +129,8 @@ post '/lazar/?' do 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 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}" @@ -111,6 +139,7 @@ post '/lazar/?' do # 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}" end @@ -118,45 +147,58 @@ post '/lazar/?' do end end end - - @training_classes = training_activities.feature_classes(prediction_feature.uri, @subjectid) if prediction_feature.feature_type == "classification" - lazar.prediction_algorithm = "Neighbors.local_svm_regression" 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| - if prediction_feature.feature_type == "classification" - case value.to_s - when "true" - lazar.activities[compound] << true - when "false" - lazar.activities[compound] << false - when /#{@training_classes.last}/ - lazar.activities[compound] << true - when /#{@training_classes.first}/ - lazar.activities[compound] << false - else - LOGGER.warn "Unknown class \"#{value.to_s}\"." - end - elsif prediction_feature.feature_type == "regression" - halt 404, "0 values not allowed in training dataset. log10 is calculated internally." if value.to_f == 0 - lazar.activities[compound] << value.to_f + 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 + 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 + 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 lazar.metadata[DC.title] = "lazar model for #{URI.decode(File.basename(prediction_feature.uri))}" - # TODO: fix dependentVariable lazar.metadata[OT.dependentVariables] = prediction_feature.uri lazar.metadata[OT.trainingDataset] = dataset_uri lazar.metadata[OT.featureDataset] = feature_dataset_uri - - if prediction_feature.feature_type == "classification" - lazar.metadata[RDF.type] = [OTA.ClassificationLazySingleTarget] - elsif prediction_feature.feature_type == "regression" - lazar.metadata[RDF.type] = [OTA.RegressionLazySingleTarget] + 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] = [ @@ -170,7 +212,7 @@ post '/lazar/?' do model_uri end response['Content-Type'] = 'text/uri-list' - halt 503,task.uri+"\n" if task.status == "Cancelled" + raise OpenTox::ServiceUnavailableError.newtask.uri+"\n" if task.status == "Cancelled" halt 202,task.uri end |