merge with opentox/development

5 files changed, 102 insertions, 62 deletions

diff --git a/fminer.rb b/fminer.rb
index 0b18c01..ff192ca 100644
--- a/fminer.rb
+++ b/fminer.rb
@@ -23,7 +23,7 @@ get "/fminer/bbrc/?" do
     DC.title => 'fminer backbone refinement class representatives',
     DC.creator => "andreas@maunz.de, helma@in-silico.ch",
     DC.contributor => "vorgrimmlerdavid@gmx.de",
-    OT.isA => OTA.PatternMiningSupervised,
+    RDF.type => [OTA.PatternMiningSupervised],
     OT.parameters => [
     { DC.description => "Dataset URI", OT.paramScope => "mandatory", DC.title => "dataset_uri" },
     { DC.description => "Feature URI for dependent variable", OT.paramScope => "mandatory", DC.title => "prediction_feature" },
@@ -44,7 +44,7 @@ get "/fminer/last/?" do
     DC.title => 'fminer latent structure class representatives',
     DC.creator => "andreas@maunz.de, helma@in-silico.ch",
     DC.contributor => "vorgrimmlerdavid@gmx.de",
-    OT.isA => OTA.PatternMiningSupervised,
+    RDF.type => [OTA.PatternMiningSupervised],
     OT.parameters => [
     { DC.description => "Dataset URI", OT.paramScope => "mandatory", DC.title => "dataset_uri" },
     { DC.description => "Feature URI for dependent variable", OT.paramScope => "mandatory", DC.title => "prediction_feature" },
@@ -70,8 +70,7 @@ post '/fminer/bbrc/?' do
 
   halt 404, "Please submit a dataset_uri." unless params[:dataset_uri] and  !params[:dataset_uri].nil?
   halt 404, "Please submit a prediction_feature." unless params[:prediction_feature] and  !params[:prediction_feature].nil?
-  prediction_feature = params[:prediction_feature]
-
+  prediction_feature = OpenTox::Feature.find params[:prediction_feature]
   training_dataset = OpenTox::Dataset.find "#{params[:dataset_uri]}", @subjectid
   halt 404, "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless training_dataset.features and training_dataset.features.include?(params[:prediction_feature])
 
@@ -88,6 +87,11 @@ post '/fminer/bbrc/?' do
     @@bbrc.SetBackbone(eval params[:backbone]) if params[:backbone] and ( params[:backbone] == "true" or params[:backbone] == "false" ) # convert string to boolean
     @@bbrc.SetChisqSig(params[:min_chisq_significance]) if params[:min_chisq_significance]
     @@bbrc.SetConsoleOut(false)
+    if prediction_feature.feature_type == "regression"
+      @@bbrc.SetRegression(true)
+    else
+      @training_classes = training_dataset.feature_classes(prediction_feature.uri)
+    end
 
     feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
     feature_dataset.add_metadata({
@@ -119,21 +123,30 @@ post '/fminer/bbrc/?' do
         next
       end
       entry.each do |feature,values|
-        if feature == prediction_feature
+        if feature == prediction_feature.uri
           values.each do |value|
             if value.nil? 
               LOGGER.warn "No #{feature} activiity for #{compound.to_s}."
             else
-              case value.to_s
-              when "true"
-                nr_active += 1
-                activity = 1
-              when "false"
-                nr_inactive += 1
-                activity = 0
-              else
+              if prediction_feature.feature_type == "classification"
+                case value.to_s
+                when "true"
+                  nr_active += 1
+                  activity = 1
+                when "false"
+                  nr_inactive += 1
+                  activity = 0
+                when /#{@training_classes.last}/
+                  nr_active += 1
+                  activity = 1
+                when /#{@training_classes.first}/
+                  nr_inactive += 1
+                  activity = 0
+                else
+                  LOGGER.warn "Unknown class \"#{value.to_s}\"."
+                end
+              elsif prediction_feature.feature_type == "regression"
                 activity = value.to_f
-                @@bbrc.SetRegression(true)
               end
               begin
                 @@bbrc.AddCompound(smiles,id)
@@ -192,7 +205,7 @@ post '/fminer/bbrc/?' do
           features << smarts
           metadata = {
             OT.hasSource => url_for('/fminer/bbrc', :full),
-            OT.isA => OT.Substructure,
+            RDF.type => [OT.Substructure],
             OT.smarts => smarts,
             OT.pValue => p_value.to_f,
             OT.effect => effect,
@@ -229,7 +242,7 @@ post '/fminer/last/?' do
 
   halt 404, "Please submit a dataset_uri." unless params[:dataset_uri] and  !params[:dataset_uri].nil?
   halt 404, "Please submit a prediction_feature." unless params[:prediction_feature] and  !params[:prediction_feature].nil?
-  prediction_feature = params[:prediction_feature]
+  prediction_feature = OpenTox::Feature.find params[:prediction_feature]
   training_dataset = OpenTox::Dataset.new "#{params[:dataset_uri]}", @subjectid
   training_dataset.load_all(@subjectid)
   halt 404, "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless training_dataset.features and training_dataset.features.include?(params[:prediction_feature])
@@ -246,8 +259,13 @@ post '/fminer/last/?' do
     @@last.SetType(1) if params[:feature_type] == "paths"
     @@last.SetMaxHops(params[:hops]) if params[:hops]
     @@last.SetConsoleOut(false)
+    if prediction_feature.feature_type == "regression"
+      @@bbrc.SetRegression(true)
+    else
+      @training_classes = training_dataset.feature_classes(prediction_feature.uri)
+    end
 
-    feature_dataset = OpenTox::Dataset.new
+    feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
     feature_dataset.add_metadata({
       DC.title => "LAST representatives for " + training_dataset.metadata[DC.title].to_s,
       DC.creator => url_for('/fminer/last',:full),
@@ -278,28 +296,36 @@ post '/fminer/last/?' do
         next
       end
       entry.each do |feature,values|
-        if feature == prediction_feature
+        if feature == prediction_feature.uri
           values.each do |value|
             if value.nil? 
               LOGGER.warn "No #{feature} activiity for #{compound.to_s}."
             else
-              case value.to_s
-              when "true"
-                nr_active += 1
-                activity = 1
-              when "false"
-                nr_inactive += 1
-                activity = 0
-              else
+              if prediction_feature.feature_type == "classification"
+                case value.to_s
+                when "true"
+                  nr_active += 1
+                  activity = 1
+                when "false"
+                  nr_inactive += 1
+                  activity = 0
+                when /#{@training_classes.last}/
+                  nr_active += 1
+                  activity = 1
+                when /#{@training_classes.first}/
+                  nr_inactive += 1
+                  activity = 0
+                else
+                  LOGGER.warn "Unknown class \"#{value.to_s}."
+                end
+              elsif prediction_feature.feature_type == "regression"
                 activity = value.to_f
-                @@last.SetRegression(true)
               end
               begin
-                @@last.AddCompound(smiles,id)
-                @@last.AddActivity(activity, id)
+                @@bbrc.AddCompound(smiles,id)
+                @@bbrc.AddActivity(activity, id)
                 all_activities[id]=activity # DV: insert global information
                 compounds[id] = compound
-                smi[id] = smiles # AM LAST: changed this to store SMILES.
                 id += 1
               rescue
                 LOGGER.warn "Could not add " + smiles + "\t" + value.to_s + " to fminer"
@@ -340,7 +366,7 @@ post '/fminer/last/?' do
       unless features.include? smarts
         features << smarts
         metadata = {
-          OT.isA => OT.Substructure,
+          RDF.type => [OT.Substructure],
           OT.hasSource => feature_dataset.uri,
           OT.smarts => smarts,
           OT.pValue => p_value.to_f,
diff --git a/last-utils b/last-utils
-Subproject daafa32e330b27111df6dc7193a6ed72fae2be4
+Subproject 75bea7645601fd296aa68c6678ee9b0a49a7b91
diff --git a/lazar.rb b/lazar.rb
index 637b623..a45886c 100644
--- a/lazar.rb
+++ b/lazar.rb
@@ -35,17 +35,17 @@ post '/lazar/?' do
   halt 404, "Dataset #{dataset_uri} not found." unless training_activities = OpenTox::Dataset.new(dataset_uri)
   training_activities.load_all(@subjectid)
 
-  prediction_feature = params[:prediction_feature]
-  unless prediction_feature # try to read prediction_feature from dataset
+  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 = training_activities.features.keys.first
-    params[:prediction_feature] = prediction_feature
+    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]
 
-	halt 404, "No feature #{prediction_feature} in dataset #{params[:dataset_uri]}. (features: "+
-    training_activities.features.inspect+")" unless training_activities.features and training_activities.features.include?(prediction_feature)
+	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)
 
   task = OpenTox::Task.create("Create lazar model",url_for('/lazar',:full)) do |task|
 
@@ -77,18 +77,21 @@ post '/lazar/?' do
 		halt 404, "Dataset #{feature_dataset_uri} not found." if training_features.nil?
 
     # sorted features for index lookups
-    lazar.features = training_features.features.sort if training_features.feature_type(@subjectid) == "regression"
+
+    lazar.features = training_features.features.sort if prediction_feature.feature_type(@subjectid) == "regression" and lazar.feature_calculation_algorithm != "Substructure.match"
 
     training_features.data_entries.each do |compound,entry|
       lazar.fingerprints[compound] = [] unless lazar.fingerprints[compound]
       entry.keys.each do |feature|
-        if feature_generation_uri.match(/fminer/)
-          smarts = training_features.features[feature][OT.smarts]
-          lazar.fingerprints[compound] << smarts
-          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]
+        if lazar.feature_calculation_algorithm == "Substructure.match"
+          if training_features.features[feature]
+            smarts = training_features.features[feature][OT.smarts]
+            lazar.fingerprints[compound] << smarts
+            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)
@@ -112,38 +115,49 @@ post '/lazar/?' do
       end
     end
       
+    @training_classes = training_activities.feature_classes(prediction_feature.uri) 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[params[:prediction_feature]].empty?
-        entry[params[:prediction_feature]].each do |value|
-          case value.to_s
-          when "true"
-            lazar.activities[compound] << true
-          when "false"
-            lazar.activities[compound] << false
-          else 
+      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
-            lazar.prediction_algorithm = "Neighbors.local_svm_regression"
           end
         end
       end
     end
 
-    lazar.metadata[DC.title] = "lazar model for #{URI.decode(File.basename(prediction_feature))}"
+    lazar.metadata[DC.title] = "lazar model for #{URI.decode(File.basename(prediction_feature.uri))}"
     # TODO: fix dependentVariable
-    lazar.metadata[OT.dependentVariables] = params[:prediction_feature]
+    lazar.metadata[OT.dependentVariables] = prediction_feature.uri
     lazar.metadata[OT.trainingDataset] = dataset_uri
 		lazar.metadata[OT.featureDataset] = feature_dataset_uri
-    if training_activities.feature_type(@subjectid).to_s == "classification"
-      lazar.metadata[OT.isA] = OTA.ClassificationLazySingleTarget
-    elsif training_activities.feature_type(@subjectid).to_s == "regression"
-      lazar.metadata[OT.isA] = OTA.RegressionLazySingleTarget
+
+    if prediction_feature.feature_type(@subjectid) == "classification"
+      lazar.metadata[RDF.type] = [OTA.ClassificationLazySingleTarget]
+    elsif prediction_feature.feature_type(@subjectid) == "regression"
+      lazar.metadata[RDF.type] = [OTA.RegressionLazySingleTarget]
     end
 
     lazar.metadata[OT.parameters] = [
       {DC.title => "dataset_uri", OT.paramValue => dataset_uri},
-      {DC.title => "prediction_feature", OT.paramValue => prediction_feature},
+      {DC.title => "prediction_feature", OT.paramValue => prediction_feature.uri},
       {DC.title => "feature_generation_uri", OT.paramValue => feature_generation_uri}
     ]
 		
diff --git a/libfminer b/libfminer
-Subproject 01b8e50e8e6fb3ce29fc8bf0a65a8c6f6af94b3
+Subproject d51f5e784ce0f5b7ef1c47c52ea55d1c874ec2e
diff --git a/openbabel.rb b/openbabel.rb
index 3a873c0..1644455 100644
--- a/openbabel.rb
+++ b/openbabel.rb
@@ -44,7 +44,7 @@ get '/openbabel/:property' do
       DC.title => params[:property],
       DC.creator => "helma@in-silico.ch",
       DC.description => description,
-      OT.isA => OTA.DescriptorCalculation,
+      RDF.type => [OTA.DescriptorCalculation],
     }
     response['Content-Type'] = 'application/rdf+xml'
     algorithm.to_rdfxml