Merge branch 'pc_new_1' into development

6 files changed, 228 insertions, 133 deletions

diff --git a/README.md b/README.md
index 8383cb6..344f747 100644
--- a/README.md
+++ b/README.md
@@ -9,44 +9,58 @@ OpenTox Algorithm
 REST operations
 ---------------
 
-    Get a list of all algorithms  GET   /               -                           URIs of algorithms        200
-    Get a representation of the   GET   /fminer/        -                           fminer representation     200,404
+    Get a list of all algorithms  GET   /                       -  URIs of algorithms                    200
+    Get a representation of the   GET   /fminer/                -  fminer representation                 200,404
      fminer algorithms
-    Get a representation of the   GET   /fminer/bbrc    -                           bbrc representation       200,404
+    Get a representation of the   GET   /fminer/bbrc            -  bbrc representation                   200,404
     bbrc algorithm
-    Get a representation of the   GET   /fminer/last    -                           last representation       200,404
+    Get a representation of the   GET   /fminer/last            -  last representation                   200,404
      last algorithm
-    Get a representation of the   GET   /lazar          -                           lazar representation      200,404
+    Get a representation of the   GET   /lazar                  -  lazar representation                  200,404
      lazar algorithm
-    Create bbrc features          POST  /fminer/bbrc    dataset_uri,                URI for feature dataset   200,400,404,500
-                                                        feature_uri,
-                                                        [min_frequency=5 per-mil],
-                                                        [feature_type=trees],
-                                                        [backbone=true],
-                                                        [min_chisq_significance=0.95],
-                                                        [nr_hits=false]
-    Create last features          POST  /fminer/last    dataset_uri,                URI for feature dataset   200,400,404,500
-                                                        feature_uri,
-                                                        [min_frequency=8 %],
-                                                        [feature_type=trees],
-                                                        [nr_hits=false]
-    Create lazar model            POST  /lazar          dataset_uri,                URI for lazar model       200,400,404,500
-                                                        prediction_feature,
-                                                        feature_generation_uri
-                                                        prediction_algorithm
-                                                        [local_svm_kernel=weighted_tanimoto]
-                                                        [min_sim=0.3]
-                                                        [nr_hits=false]
-                                                        [conf_stdev=false]
+    Get a representation of the   GET   /feature_selection      -  feature selection representation      200,404
+     feature selection algorithms
+    Get a representation of the   GET   /feature_selection/rfe  -  rfe representation                    200,404
+     rfe algorithm
+
+
+    Create bbrc features          POST  /fminer/bbrc            dataset_uri,                URI for feature dataset   200,400,404,500
+                                                                feature_uri,
+                                                                [min_frequency=5 per-mil],
+                                                                [feature_type=trees],
+                                                                [backbone=true],
+                                                                [min_chisq_significance=0.95],
+                                                                [nr_hits=false]
+    Create last features          POST  /fminer/last            dataset_uri,                URI for feature dataset   200,400,404,500
+                                                                feature_uri,
+                                                                [min_frequency=8 %],
+                                                                [feature_type=trees],
+                                                                [nr_hits=false]
+    Create lazar model            POST  /lazar                  dataset_uri,                URI for lazar model       200,400,404,500
+                                                                [prediction_feature],
+                                                                [feature_generation_uri],
+                                                                [prediction_algorithm],
+                                                                [feature_dataset_uri],
+                                                                [pc_type=null],
+                                                                [nr_hits=false (class. using wt. maj. vote), true (else)],
+                                                                [min_sim=0.3 (nominal), 0.4 (numeric features)]
+                                                                [min_train_performance=0.1]
+
+    Create selected features      POST /feature_selection/rfe   dataset_uri,                URI for dataset 200,400,404,500
+                                                                prediction_feature,
+                                                                feature_dataset_uri,
+                                                                [del_missing=false]
+
 
 Synopsis
 --------
 
-- prediction\_algorithm: One of "weighted\_majority\_vote" (default for classification),  "local\_svm\_classification", "local\_svm\_regression (default for regression)", "local\_mlr\_prop". "weighted\_majority\_vote"  is not applicable for regression. "local\_mlr\_prop" is not applicable for classification.
-- local\_svm\_kernel: One of "weighted\_tanimoto", "propositionalized". local\_svm\_kernel is not appplicable when prediction\_algorithm="weighted\_majority\_vote".
-- min_sim: The minimum similarity threshold for neighbors. Numeric value in [0,1]. 
-- nr_hits: Whether for instantiated models (local\_svm\_kernel = "propositionalized" for prediction_algorithm="local\_svm\_classification" or "local\_svm\_regression", or for prediction_algorithm="local\_mlr\_prop") nominal features should be instantiated with their occurrence counts in the instances. For non-instantiated models (local\_svm\_kernel = "weighted\_tanimoto" for prediction_algorithm="local\_svm\_classification" or "local\_svm\_regression", or for prediction_algorithm="weighted\_majority\_vote") the neighbor-to-neighbor and neighbor-to-query similarity also integrates these counts, when the parameter is set. One of "true", "false". 
-- conf_stdev: Whether confidence integrates distribution of neighbor activity values. When "true", the exp(-1.0*(standard deviation of neighbor activities)) is multiplied on the similarity. One of "true", "false".
+- prediction\_algorithm: One of "weighted\_majority\_vote" (default for classification),  "local\_svm\_classification", "local\_svm\_regression" (default for regression). "weighted\_majority\_vote"  is not applicable for regression.
+- pc_type: Mandatory for feature dataset, one of [geometrical, topological, electronic, constitutional, hybrid, cpsa].
+- nr_hits: Whether nominal features should be instantiated with their occurrence counts in the instances. One of "true", "false". 
+- min_sim: The minimum similarity threshold for neighbors. Numeric value in [0,1].
+- min_train_performance. The minimum training performance for "local\_svm\_classification" (Accuracy) and "local\_svm\_regression" (R-squared). Numeric value in [0,1].
+- del_missing: one of true, false
 
 See http://www.maunz.de/wordpress/opentox/2011/lazar-models-and-how-to-trigger-them for a graphical overview.
 
@@ -108,4 +122,9 @@ Creates a standard Lazar model.
 [API documentation](http://rdoc.info/github/opentox/algorithm)
 --------------------------------------------------------------
 
+* * *
+
+### Create a feature dataset of selected features
+    curl -X POST -d dataset_uri={dataset_uri} -d prediction_feature_uri={prediction_feature_uri} -d feature_dataset_uri={feature_dataset_uri} -d del_missing=true http://webservices.in-silico.ch/test/algorithm/feature_selection/rfe
+
 Copyright (c) 2009-2011 Christoph Helma, Martin Guetlein, Micha Rautenberg, Andreas Maunz, David Vorgrimmler, Denis Gebele. See LICENSE for details.
diff --git a/application.rb b/application.rb
index b62f6f5..f5b331f 100644
--- a/application.rb
+++ b/application.rb
@@ -11,6 +11,7 @@ require 'opentox-ruby'
 require 'openbabel.rb'
 require 'fminer.rb'
 require 'lazar.rb'
+require 'feature_selection.rb'
 
 set :lock, true
 
@@ -22,7 +23,7 @@ end
 #
 # @return [text/uri-list] algorithm URIs
 get '/?' do
-	list = [ url_for('/lazar', :full), url_for('/fminer/bbrc', :full), url_for('/fminer/last', :full) ].join("\n") + "\n"
+	list = [ url_for('/lazar', :full), url_for('/fminer/bbrc', :full), url_for('/fminer/last', :full), url_for('/feature_selection/rfe', :full) ].join("\n") + "\n"
   case request.env['HTTP_ACCEPT']
   when /text\/html/
     content_type "text/html"
diff --git a/feature_selection.rb b/feature_selection.rb
new file mode 100644
index 0000000..d375a0e
--- /dev/null
+++ b/feature_selection.rb
@@ -0,0 +1,85 @@
+# Get list of feature_selection algorithms
+#
+# @return [text/uri-list] URIs of feature_selection algorithms
+get '/feature_selection/?' do
+  list = [ url_for('/feature_selection/rfe', :full) ].join("\n") + "\n"
+  case request.env['HTTP_ACCEPT']
+  when /text\/html/
+    content_type "text/html"
+    OpenTox.text_to_html list
+  else
+    content_type 'text/uri-list'
+    list
+  end
+end
+
+# Get RDF/XML representation of feature_selection rfe algorithm
+# @return [application/rdf+xml] OWL-DL representation of feature_selection rfe algorithm
+get "/feature_selection/rfe/?" do
+  algorithm = OpenTox::Algorithm::Generic.new(url_for('/feature_selection/rfe',:full))
+  algorithm.metadata = {
+    DC.title => 'recursive feature elimination',
+    DC.creator => "andreas@maunz.de, helma@in-silico.ch",
+    DC.contributor => "vorgrimmlerdavid@gmx.de",
+    BO.instanceOf => "http://opentox.org/ontology/ist-algorithms.owl#feature_selection_rfe",
+    RDF.type => [OT.Algorithm,OTA.PatternMiningSupervised],
+    OT.parameters => [
+      { DC.description => "Dataset URI", OT.paramScope => "mandatory", DC.title => "dataset_uri" },
+      { DC.description => "Prediction Feature URI", OT.paramScope => "mandatory", DC.title => "prediction_feature_uri" },
+      { DC.description => "Feature Dataset URI", OT.paramScope => "mandatory", DC.title => "feature_dataset_uri" },
+      { DC.description => "Delete Instances with missing values", OT.paramScope => "optional", DC.title => "del_missing" }
+  ]
+  }
+  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
+
+# Run rfe algorithm on dataset
+#
+# @param [String] dataset_uri URI of the training dataset
+# @param [String] feature_dataset_uri URI of the feature dataset
+# @return [text/uri-list] Task URI
+post '/feature_selection/rfe/?' do 
+
+  raise OpenTox::NotFoundError.new "Please submit a dataset_uri." unless params[:dataset_uri]
+  raise OpenTox::NotFoundError.new "Please submit a prediction_feature_uri." unless params[:prediction_feature_uri]
+  raise OpenTox::NotFoundError.new "Please submit a feature_dataset_uri." unless params[:feature_dataset_uri]
+
+  ds_csv=OpenTox::RestClientWrapper.get( params[:dataset_uri], {:accept => "text/csv"} )
+  tf_ds=Tempfile.open(['rfe_', '.csv'])
+  tf_ds.puts(ds_csv)
+  tf_ds.flush()
+
+  prediction_feature = params[:prediction_feature_uri].split('/').last # get col name
+  
+  fds_csv=OpenTox::RestClientWrapper.get( params[:feature_dataset_uri], {:accept => "text/csv"})
+  tf_fds=Tempfile.open(['rfe_', '.csv'])
+  tf_fds.puts(fds_csv)
+  tf_fds.flush()
+
+  del_missing = params[:del_missing] == "true" ? true : false
+
+  task = OpenTox::Task.create("Recursive Feature Elimination", url_for('/feature_selection',:full)) do |task|
+    r_result_file = OpenTox::Algorithm::FeatureSelection.rfe( { :ds_csv_file => tf_ds.path, :prediction_feature => prediction_feature, :fds_csv_file => tf_fds.path, :del_missing => del_missing } )
+    r_result_uri = OpenTox::Dataset.create_from_csv_file(r_result_file).uri
+    begin
+      tf_ds.close!; tf_fds.close! 
+      File.unlink(r_result_file)
+    rescue
+    end
+    r_result_uri
+  end
+  response['Content-Type'] = 'text/uri-list'
+  raise OpenTox::ServiceUnavailableError.newtask.uri+"\n" if task.status == "Cancelled"
+  halt 202,task.uri.to_s+"\n"
+end
+
diff --git a/last-utils b/last-utils
-Subproject 8c02f7e71450cac6d8c5d7d34ecb620046b4ea4
+Subproject cf0238477127e54509b6ab8b5c38f50dd6ffce0
diff --git a/lazar.rb b/lazar.rb
index 9aac0d8..81929c6 100644
--- a/lazar.rb
+++ b/lazar.rb
@@ -12,9 +12,9 @@ get '/lazar/?' do
     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']
@@ -45,45 +45,74 @@ 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]
     
 
 
 
-    # AM: Manage endpoint related variables.
+    # # # 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
-    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]
 
 
 
@@ -96,29 +125,22 @@ post '/lazar/?' do
 
 
 
-    # 
-    # AM: features
-    # 
-    # 
-    # 
+    # # # Features
 
-    # READ OR CREATE
+    # 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/) 
@@ -130,57 +152,42 @@ post '/lazar/?' do
 
 
 
-    # WRITE IN MODEL
+    # # # 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
-
-    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]
-      entry.keys.each do |feature|
-
-        # CASE 1: Substructure
-        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]
+      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
-          end
 
-        # CASE 2: Others
-        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}"
-            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
@@ -188,28 +195,8 @@ post '/lazar/?' do
 
 
 
-
-    # 
-    # AM: SETTINGS
-    # 
-    # 
-    # 
-
-    # AM: allow settings override by user
-    lazar.prediction_algorithm = "Neighbors.#{params[:prediction_algorithm]}" unless params[:prediction_algorithm].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
-    # 
-    # 
-    # 
+    
+    # # # Activities
   
     if prediction_feature.feature_type == "regression"
       training_activities.data_entries.each do |compound,entry| 
@@ -235,11 +222,7 @@ post '/lazar/?' do
 
 
 
-    # 
-    # AM: Metadata
-    # 
-    # 
-    # 
+    # Metadata
 
     lazar.metadata[DC.title] = "lazar model for #{URI.decode(File.basename(prediction_feature.uri))}"
     lazar.metadata[OT.dependentVariables] = prediction_feature.uri
@@ -255,12 +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"
diff --git a/libfminer b/libfminer
-Subproject 17932e809c35c93374ed3d5fd19a313325c35b4
+Subproject f9e560dc0a7a5d5af439814ab5fa9ce027a025b