begin/rescue blocks removed

url_for substituted with to

11 files changed, 521 insertions, 605 deletions

diff --git a/.gitignore b/.gitignore
index e3debba..d1c494e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@ tmp/*
 log/*
 public/*.owl
 *.*~
+Gemfile.lock
diff --git a/application.rb b/application.rb
index 09b90f6..1b9c776 100644
--- a/application.rb
+++ b/application.rb
@@ -2,8 +2,6 @@
 # Loads sub-repositories, library code, and webapps.
 # Author: Andreas Maunz
 
-require 'sinatra/url_for'
-
 # Require sub-Repositories
 require File.join(File.expand_path(File.dirname(__FILE__)), 'libfminer/libbbrc/bbrc') # include before openbabel
 require File.join(File.expand_path(File.dirname(__FILE__)), 'libfminer/liblast/last') # 
@@ -19,14 +17,14 @@ Dir['./webapp/*.rb'].each { |f| require f } # Webapps
 module OpenTox
   class Application < Service
     get '/?' do
-      list = [ url_for('/lazar', :full), 
-               url_for('/fminer/bbrc', :full), 
-               url_for('/fminer/bbrc/sample', :full), 
-               url_for('/fminer/last', :full), 
-               url_for('/fminer/bbrc/match', :full), 
-               url_for('/fminer/last/match', :full), 
-               url_for('/fs/rfe', :full), 
-               url_for('/pc', :full) ].join("\n") + "\n"
+      list = [ to('/lazar', :full), 
+               to('/fminer/bbrc', :full), 
+               to('/fminer/bbrc/sample', :full), 
+               to('/fminer/last', :full), 
+               to('/fminer/bbrc/match', :full), 
+               to('/fminer/last/match', :full), 
+               to('/fs/rfe', :full), 
+               to('/pc', :full) ].join("\n") + "\n"
       format_output (list)
     end
   end
diff --git a/lib/algorithm/fminer.rb b/lib/algorithm/fminer.rb
index 3a69858..93737b2 100644
--- a/lib/algorithm/fminer.rb
+++ b/lib/algorithm/fminer.rb
@@ -121,17 +121,12 @@ module OpenTox
             elsif @prediction_feature.feature_type == "regression"
               activity= compound_activities.to_f 
             end
-            begin
-              fminer_instance.AddCompound(compound.smiles,id) if fminer_instance
-              fminer_instance.AddActivity(activity, id) if fminer_instance 
-              @all_activities[id]=activity # DV: insert global information
-              @compounds[id] = compound
-              @smi[id] = compound.smiles
-              id += 1
-            rescue Exception => e
-              LOGGER.warn "Could not add " + smiles + "\t" + values[i].to_s + " to fminer"
-              LOGGER.warn e.backtrace
-            end
+            fminer_instance.AddCompound(compound.smiles,id) if fminer_instance
+            fminer_instance.AddActivity(activity, id) if fminer_instance 
+            @all_activities[id]=activity # DV: insert global information
+            @compounds[id] = compound
+            @smi[id] = compound.smiles
+            id += 1
           end
         end
       end
diff --git a/lib/algorithm/neighbors.rb b/lib/algorithm/neighbors.rb
index cdfc5b7..1a82dc8 100644
--- a/lib/algorithm/neighbors.rb
+++ b/lib/algorithm/neighbors.rb
@@ -69,29 +69,24 @@ module OpenTox
       # @return [Numeric] A prediction value.
       def self.local_svm_regression(params)
 
-        begin
-          confidence = 0.0
-          prediction = nil
-
-          $logger.debug "Local SVM."
-          if params[:acts].size>0
-            if params[:props]
-              n_prop = params[:props][0].collect.to_a
-              q_prop = params[:props][1].collect.to_a
-              props = [ n_prop, q_prop ]
-            end
-            acts = params[:acts].collect.to_a
-            prediction = local_svm_prop( props, acts, params[:min_train_performance]) # params[:props].nil? signals non-prop setting
-            prediction = nil if (!prediction.nil? && prediction.infinite?)
-            #$logger.debug "Prediction: '" + prediction.to_s + "' ('#{prediction.class}')."
-            confidence = get_confidence({:sims => params[:sims][1], :acts => params[:acts]})
-            confidence = 0.0 if prediction.nil?
+        confidence = 0.0
+        prediction = nil
+
+        $logger.debug "Local SVM."
+        if params[:acts].size>0
+          if params[:props]
+            n_prop = params[:props][0].collect.to_a
+            q_prop = params[:props][1].collect.to_a
+            props = [ n_prop, q_prop ]
           end
-          {:prediction => prediction, :confidence => confidence}
-        rescue Exception => e
-          $logger.debug "#{e.class}: #{e.message}"
-          $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
+          acts = params[:acts].collect.to_a
+          prediction = local_svm_prop( props, acts, params[:min_train_performance]) # params[:props].nil? signals non-prop setting
+          prediction = nil if (!prediction.nil? && prediction.infinite?)
+          #$logger.debug "Prediction: '" + prediction.to_s + "' ('#{prediction.class}')."
+          confidence = get_confidence({:sims => params[:sims][1], :acts => params[:acts]})
+          confidence = 0.0 if prediction.nil?
         end
+        {:prediction => prediction, :confidence => confidence}
 
       end
 
@@ -101,30 +96,25 @@ module OpenTox
       # @return [Numeric] A prediction value.
       def self.local_svm_classification(params)
 
-        begin
-          confidence = 0.0
-          prediction = nil
+        confidence = 0.0
+        prediction = nil
 
-          $logger.debug "Local SVM."
-          if params[:acts].size>0
-            if params[:props]
-              n_prop = params[:props][0].collect.to_a
-              q_prop = params[:props][1].collect.to_a
-              props = [ n_prop, q_prop ]
-            end
-            acts = params[:acts].collect.to_a
-            acts = acts.collect{|v| "Val" + v.to_s} # Convert to string for R to recognize classification
-            prediction = local_svm_prop( props, acts, params[:min_train_performance]) # params[:props].nil? signals non-prop setting
-            prediction = prediction.sub(/Val/,"") if prediction # Convert back
-            confidence = 0.0 if prediction.nil?
-            #$logger.debug "Prediction: '" + prediction.to_s + "' ('#{prediction.class}')."
-            confidence = get_confidence({:sims => params[:sims][1], :acts => params[:acts]})
+        $logger.debug "Local SVM."
+        if params[:acts].size>0
+          if params[:props]
+            n_prop = params[:props][0].collect.to_a
+            q_prop = params[:props][1].collect.to_a
+            props = [ n_prop, q_prop ]
           end
-          {:prediction => prediction, :confidence => confidence}
-        rescue Exception => e
-          $logger.debug "#{e.class}: #{e.message}"
-          $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
+          acts = params[:acts].collect.to_a
+          acts = acts.collect{|v| "Val" + v.to_s} # Convert to string for R to recognize classification
+          prediction = local_svm_prop( props, acts, params[:min_train_performance]) # params[:props].nil? signals non-prop setting
+          prediction = prediction.sub(/Val/,"") if prediction # Convert back
+          confidence = 0.0 if prediction.nil?
+          #$logger.debug "Prediction: '" + prediction.to_s + "' ('#{prediction.class}')."
+          confidence = get_confidence({:sims => params[:sims][1], :acts => params[:acts]})
         end
+        {:prediction => prediction, :confidence => confidence}
 
       end
 
@@ -216,9 +206,9 @@ module OpenTox
             prediction = nil if prediction =~ /NA/
             prediction = nil unless train_success
             $logger.debug "Performance: '#{sprintf("%.2f", @r.perf)}'"
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
+          #rescue Exception => e
+            #$logger.debug "#{e.class}: #{e.message}"
+            #$logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
           ensure
             @r.quit # free R
           end
diff --git a/lib/algorithm/transform.rb b/lib/algorithm/transform.rb
index afc76f9..ec25526 100644
--- a/lib/algorithm/transform.rb
+++ b/lib/algorithm/transform.rb
@@ -18,42 +18,27 @@ module OpenTox
 
         # @param [GSL::Vector] values Values to transform using AutoScaling.
         def initialize values
-          begin
-            raise "Cannot transform, values empty." if values.size==0
-            vs = values.clone
-            @mean = vs.to_scale.mean
-            @stdev = vs.to_scale.standard_deviation_population
-            @stdev = 0.0 if @stdev.nan? 
-            @vs = transform vs
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          bad_request_error "Cannot transform, values empty." if values.size==0
+          vs = values.clone
+          @mean = vs.to_scale.mean
+          @stdev = vs.to_scale.standard_deviation_population
+          @stdev = 0.0 if @stdev.nan? 
+          @vs = transform vs
         end
 
         # @param [GSL::Vector] values Values to transform.
         # @return [GSL::Vector] transformed values.
         def transform values
-          begin
-            raise "Cannot transform, values empty." if values.size==0
-            autoscale values.clone
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          bad_request_error "Cannot transform, values empty." if values.size==0
+          autoscale values.clone
         end
 
         # @param [GSL::Vector] values Values to restore.
         # @return [GSL::Vector] transformed values.
         def restore values
-          begin
-            raise "Cannot transform, values empty." if values.size==0
-            rv_ss = values.clone.to_scale * @stdev unless @stdev == 0.0
-            (rv_ss + @mean).to_gsl
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          bad_request_error "Cannot transform, values empty." if values.size==0
+          rv_ss = values.clone.to_scale * @stdev unless @stdev == 0.0
+          (rv_ss + @mean).to_gsl
         end
 
         # @param [GSL::Vector] values to transform.
@@ -77,68 +62,63 @@ module OpenTox
         # @return [GSL::Matrix] Data transformed matrix.
         def initialize data_matrix, compression=0.05, maxcols=(1.0/0.0)
 
-          begin
-            @data_matrix = data_matrix.clone
-            @compression = compression.to_f
-            @mean = Array.new
-            @autoscaler = Array.new
-            @cols = Array.new
-            @maxcols = maxcols
-
-            # Objective Feature Selection
-            raise "Error! PCA needs at least two dimensions." if data_matrix.size2 < 2
-            @data_matrix_selected = nil
-            (0..@data_matrix.size2-1).each { |i|
-              if !@data_matrix.col(i).to_a.zero_variance?
-                if @data_matrix_selected.nil?
-                  @data_matrix_selected = GSL::Matrix.alloc(@data_matrix.size1, 1) 
-                  @data_matrix_selected.col(0)[0..@data_matrix.size1-1] = @data_matrix.col(i)
-                else
-                  @data_matrix_selected = @data_matrix_selected.horzcat(GSL::Matrix.alloc(@data_matrix.col(i).to_a,@data_matrix.size1, 1))
-                end
-                @cols << i
-              end             
-            }
-            raise "Error! PCA needs at least two dimensions." if (@data_matrix_selected.nil? || @data_matrix_selected.size2 < 2)
-
-            # PCA uses internal centering on 0
-            @data_matrix_scaled = GSL::Matrix.alloc(@data_matrix_selected.size1, @cols.size)
-            (0..@cols.size-1).each { |i|
-              as = OpenTox::Algorithm::Transform::AutoScale.new(@data_matrix_selected.col(i))
-              @data_matrix_scaled.col(i)[0..@data_matrix.size1-1] = as.vs * as.stdev # re-adjust by stdev
-              @mean << as.mean
-              @autoscaler << as
-            }
-
-            # PCA
-            data_matrix_hash = Hash.new
-            (0..@cols.size-1).each { |i|
-              column_view = @data_matrix_scaled.col(i)
-              data_matrix_hash[i] = column_view.to_scale
-            }
-            dataset_hash = data_matrix_hash.to_dataset # see http://goo.gl/7XcW9
-            cor_matrix=Statsample::Bivariate.correlation_matrix(dataset_hash)
-            pca=Statsample::Factor::PCA.new(cor_matrix)
-
-            # Select best eigenvectors
-            pca.eigenvalues.each { |ev| raise "PCA failed!" unless !ev.nan? }
-            @eigenvalue_sums = Array.new
-            (0..@cols.size-1).each { |i|
-              @eigenvalue_sums << pca.eigenvalues[0..i].inject{ |sum, ev| sum + ev }
-            }
-            eigenvectors_selected = Array.new
-            pca.eigenvectors.each_with_index { |ev, i|
-              if (@eigenvalue_sums[i] <= ((1.0-@compression)*@cols.size)) || (eigenvectors_selected.size == 0)
-                eigenvectors_selected << ev.to_a unless @maxcols <= eigenvectors_selected.size
+          @data_matrix = data_matrix.clone
+          @compression = compression.to_f
+          @mean = Array.new
+          @autoscaler = Array.new
+          @cols = Array.new
+          @maxcols = maxcols
+
+          # Objective Feature Selection
+          bad_request_error "Error! PCA needs at least two dimensions." if data_matrix.size2 < 2
+          @data_matrix_selected = nil
+          (0..@data_matrix.size2-1).each { |i|
+            if !@data_matrix.col(i).to_a.zero_variance?
+              if @data_matrix_selected.nil?
+                @data_matrix_selected = GSL::Matrix.alloc(@data_matrix.size1, 1) 
+                @data_matrix_selected.col(0)[0..@data_matrix.size1-1] = @data_matrix.col(i)
+              else
+                @data_matrix_selected = @data_matrix_selected.horzcat(GSL::Matrix.alloc(@data_matrix.col(i).to_a,@data_matrix.size1, 1))
               end
-            }
-            @eigenvector_matrix = GSL::Matrix.alloc(eigenvectors_selected.flatten, eigenvectors_selected.size, @cols.size).transpose
-            @data_transformed_matrix = (@eigenvector_matrix.transpose * @data_matrix_scaled.transpose).transpose
+              @cols << i
+            end             
+          }
+          bad_request_error "Error! PCA needs at least two dimensions." if (@data_matrix_selected.nil? || @data_matrix_selected.size2 < 2)
+
+          # PCA uses internal centering on 0
+          @data_matrix_scaled = GSL::Matrix.alloc(@data_matrix_selected.size1, @cols.size)
+          (0..@cols.size-1).each { |i|
+            as = OpenTox::Algorithm::Transform::AutoScale.new(@data_matrix_selected.col(i))
+            @data_matrix_scaled.col(i)[0..@data_matrix.size1-1] = as.vs * as.stdev # re-adjust by stdev
+            @mean << as.mean
+            @autoscaler << as
+          }
+
+          # PCA
+          data_matrix_hash = Hash.new
+          (0..@cols.size-1).each { |i|
+            column_view = @data_matrix_scaled.col(i)
+            data_matrix_hash[i] = column_view.to_scale
+          }
+          dataset_hash = data_matrix_hash.to_dataset # see http://goo.gl/7XcW9
+          cor_matrix=Statsample::Bivariate.correlation_matrix(dataset_hash)
+          pca=Statsample::Factor::PCA.new(cor_matrix)
+
+          # Select best eigenvectors
+          pca.eigenvalues.each { |ev| bad_request_error "PCA failed!" unless !ev.nan? }
+          @eigenvalue_sums = Array.new
+          (0..@cols.size-1).each { |i|
+            @eigenvalue_sums << pca.eigenvalues[0..i].inject{ |sum, ev| sum + ev }
+          }
+          eigenvectors_selected = Array.new
+          pca.eigenvectors.each_with_index { |ev, i|
+            if (@eigenvalue_sums[i] <= ((1.0-@compression)*@cols.size)) || (eigenvectors_selected.size == 0)
+              eigenvectors_selected << ev.to_a unless @maxcols <= eigenvectors_selected.size
+            end
+          }
+          @eigenvector_matrix = GSL::Matrix.alloc(eigenvectors_selected.flatten, eigenvectors_selected.size, @cols.size).transpose
+          @data_transformed_matrix = (@eigenvector_matrix.transpose * @data_matrix_scaled.transpose).transpose
 
-          rescue Exception => e
-              $logger.debug "#{e.class}: #{e.message}"
-              $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
         end
 
         # Transforms data to feature space found by PCA.
@@ -146,35 +126,25 @@ module OpenTox
         # @param [GSL::Matrix] values Data matrix.
         # @return [GSL::Matrix] Transformed data matrix.
         def transform values
-          begin
-            vs = values.clone
-            raise "Error! Too few columns for transformation." if vs.size2 < @cols.max
-            data_matrix_scaled = GSL::Matrix.alloc(vs.size1, @cols.size)
-            @cols.each_with_index { |i,j|
-              data_matrix_scaled.col(j)[0..data_matrix_scaled.size1-1] = @autoscaler[j].transform(vs.col(i).to_a) * @autoscaler[j].stdev
-            }
-            (@eigenvector_matrix.transpose * data_matrix_scaled.transpose).transpose
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          vs = values.clone
+          bad_request_error "Error! Too few columns for transformation." if vs.size2 < @cols.max
+          data_matrix_scaled = GSL::Matrix.alloc(vs.size1, @cols.size)
+          @cols.each_with_index { |i,j|
+            data_matrix_scaled.col(j)[0..data_matrix_scaled.size1-1] = @autoscaler[j].transform(vs.col(i).to_a) * @autoscaler[j].stdev
+          }
+          (@eigenvector_matrix.transpose * data_matrix_scaled.transpose).transpose
         end
 
         # Restores data in the original feature space (possibly with compression loss).
         #
         # @return [GSL::Matrix] Data matrix.
         def restore
-          begin 
-            data_matrix_restored = (@eigenvector_matrix * @data_transformed_matrix.transpose).transpose # reverse pca
-            # reverse scaling
-            (0..@cols.size-1).each { |i|
-              data_matrix_restored.col(i)[0..data_matrix_restored.size1-1] += @mean[i]
-            }
-            data_matrix_restored
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          data_matrix_restored = (@eigenvector_matrix * @data_transformed_matrix.transpose).transpose # reverse pca
+          # reverse scaling
+          (0..@cols.size-1).each { |i|
+            data_matrix_restored.col(i)[0..data_matrix_restored.size1-1] += @mean[i]
+          }
+          data_matrix_restored
         end
 
       end
@@ -191,45 +161,40 @@ module OpenTox
         # @return [GSL::Matrix] Data transformed matrix
 
         def initialize data_matrix, compression=0.05
-          begin
-            @data_matrix = data_matrix.clone
-            @compression = compression
-
-            # Compute the SV Decomposition X=USV
-            # vt is *not* the transpose of V here, but V itself (see http://goo.gl/mm2xz)!
-            u, vt, s = data_matrix.SV_decomp 
-            
-            # Determine cutoff index
-            s2 = s.mul(s) ; s2_sum = s2.sum
-            s2_run = 0
-            k = s2.size - 1
-            s2.to_a.reverse.each { |v| 
-              s2_run += v
-              frac = s2_run / s2_sum
-              break if frac > compression
-              k -= 1
-            }
-            k += 1 if k == 0 # avoid uni-dimensional (always cos sim of 1)
-            
-            # Take the k-rank approximation of the Matrix
-            #   - Take first k columns of u
-            #   - Take first k columns of vt
-            #   - Take the first k eigenvalues
-            @uk = u.submatrix(nil, (0..k)) # used to transform column format data
-            @vk = vt.submatrix(nil, (0..k)) # used to transform row format data
-            s = GSL::Matrix.diagonal(s)
-            @eigk = s.submatrix((0..k), (0..k))
-            @eigk_inv = @eigk.inv
-
-            # Transform data
-            @data_transformed_matrix = @uk # = u for all SVs
-            # NOTE: @data_transformed_matrix is also equal to
-            # @data_matrix * @vk * @eigk_inv
-
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          @data_matrix = data_matrix.clone
+          @compression = compression
+
+          # Compute the SV Decomposition X=USV
+          # vt is *not* the transpose of V here, but V itself (see http://goo.gl/mm2xz)!
+          u, vt, s = data_matrix.SV_decomp 
+          
+          # Determine cutoff index
+          s2 = s.mul(s) ; s2_sum = s2.sum
+          s2_run = 0
+          k = s2.size - 1
+          s2.to_a.reverse.each { |v| 
+            s2_run += v
+            frac = s2_run / s2_sum
+            break if frac > compression
+            k -= 1
+          }
+          k += 1 if k == 0 # avoid uni-dimensional (always cos sim of 1)
+          
+          # Take the k-rank approximation of the Matrix
+          #   - Take first k columns of u
+          #   - Take first k columns of vt
+          #   - Take the first k eigenvalues
+          @uk = u.submatrix(nil, (0..k)) # used to transform column format data
+          @vk = vt.submatrix(nil, (0..k)) # used to transform row format data
+          s = GSL::Matrix.diagonal(s)
+          @eigk = s.submatrix((0..k), (0..k))
+          @eigk_inv = @eigk.inv
+
+          # Transform data
+          @data_transformed_matrix = @uk # = u for all SVs
+          # NOTE: @data_transformed_matrix is also equal to
+          # @data_matrix * @vk * @eigk_inv
+
         end
 
 
@@ -238,12 +203,7 @@ module OpenTox
         # @param [GSL::Matrix] values Data matrix (1 x m).
         # @return [GSL::Matrix] Transformed data matrix.
         def transform_instance values
-          begin
-            values * @vk * @eigk_inv
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          values * @vk * @eigk_inv
         end
         alias :transform :transform_instance # make this the default (see PCA interface)
 
@@ -252,12 +212,7 @@ module OpenTox
         # @param [GSL::Matrix] values Data matrix (1 x n).
         # @return [GSL::Matrix] Transformed data matrix.
         def transform_feature values
-          begin
-            values * @uk * @eigk_inv
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          values * @uk * @eigk_inv
         end
 
 
@@ -265,12 +220,7 @@ module OpenTox
         #
         # @return [GSL::Matrix] Data matrix.
         def restore
-          begin 
-            @data_transformed_matrix * @eigk * @vk.transpose  # reverse svd
-          rescue Exception => e
-            $logger.debug "#{e.class}: #{e.message}"
-            $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-          end
+          @data_transformed_matrix * @eigk * @vk.transpose  # reverse svd
         end
 
 
diff --git a/lib/model.rb b/lib/model.rb
index 76c5963..cb3e561 100644
--- a/lib/model.rb
+++ b/lib/model.rb
@@ -20,6 +20,7 @@ module OpenTox
 
     # Internal use only
     def prepare_prediction_model(params)
+      puts params.inspect
       params.each {|k,v|
         self.class.class_eval { attr_accessor k.to_sym }
         instance_variable_set(eval(":@"+k), v)
diff --git a/libfminer b/libfminer
-Subproject 4577dcacb352af3fdca925714dc570de9e02582
+Subproject 9c7ca17efe84fa722ac7a9ba40fee92f51d0427
diff --git a/webapp/fminer.rb b/webapp/fminer.rb
index e928476..1e0f355 100644
--- a/webapp/fminer.rb
+++ b/webapp/fminer.rb
@@ -22,11 +22,11 @@ module OpenTox
     # Get list of fminer algorithms
     # @return [text/uri-list] URIs
     get '/fminer/?' do
-      list = [ url_for('/fminer/bbrc', :full), 
-               url_for('/fminer/bbrc/sample', :full), 
-               url_for('/fminer/last', :full), 
-               url_for('/fminer/bbrc/match', :full), 
-               url_for('/fminer/last/match', :full) 
+      list = [ to('/fminer/bbrc', :full), 
+               to('/fminer/bbrc/sample', :full), 
+               to('/fminer/last', :full), 
+               to('/fminer/bbrc/match', :full), 
+               to('/fminer/last/match', :full) 
              ].join("\n") + "\n"
       format_output(list)
     end
@@ -34,7 +34,7 @@ module OpenTox
     # Get representation of BBRC algorithm
     # @return [String] Representation
     get "/fminer/bbrc/?" do
-      algorithm = OpenTox::Algorithm.new(url_for('/fminer/bbrc',:full))
+      algorithm = OpenTox::Algorithm.new(to('/fminer/bbrc',:full))
       algorithm.metadata = {
         DC.title => 'Backbone Refinement Class Representatives',
         DC.creator => "andreas@maunz.de",
@@ -56,7 +56,7 @@ module OpenTox
     # Get representation of BBRC-sample algorithm
     # @return [String] Representation
     get "/fminer/bbrc/sample/?" do
-      algorithm = OpenTox::Algorithm.new(url_for('/fminer/bbrc/sample',:full))
+      algorithm = OpenTox::Algorithm.new(to('/fminer/bbrc/sample',:full))
       algorithm.metadata = {
         DC.title => 'Backbone Refinement Class Representatives, obtained from samples of a dataset',
         DC.creator => "andreas@maunz.de",
@@ -78,7 +78,7 @@ module OpenTox
     # Get representation of fminer LAST-PM algorithm
     # @return [String] Representation
     get "/fminer/last/?" do
-      algorithm = OpenTox::Algorithm.new(url_for('/fminer/last',:full))
+      algorithm = OpenTox::Algorithm.new(to('/fminer/last',:full))
       algorithm.metadata = {
         DC.title => 'Latent Structure Pattern Mining descriptors',
         DC.creator => "andreas@maunz.de",
@@ -99,7 +99,7 @@ module OpenTox
     # Get representation of matching algorithm
     # @return [String] Representation
     get "/fminer/:method/match?" do
-      algorithm = OpenTox::Algorithm.new(url_for("/fminer/#{params[:method]}/match",:full))
+      algorithm = OpenTox::Algorithm.new(to("/fminer/#{params[:method]}/match",:full))
       algorithm.metadata = {
         DC.title => 'fminer feature matching',
         DC.creator => "mguetlein@gmail.com, andreas@maunz.de",
@@ -129,156 +129,150 @@ module OpenTox
     post '/fminer/bbrc/?' do
     
 
-      @@fminer=OpenTox::Algorithm::Fminer.new(url_for('/fminer/bbrc',:full))
+      @@fminer=OpenTox::Algorithm::Fminer.new(to('/fminer/bbrc',:full))
       @@fminer.check_params(params,5,@subjectid)
     
       task = OpenTox::Task.create( 
                                   $task[:uri], 
                                   @subjectid, 
                                   { RDF::DC.description => "Mining BBRC features", 
-                                    RDF::DC.creator => url_for('/fminer/bbrc',:full) 
+                                    RDF::DC.creator => to('/fminer/bbrc',:full) 
                                   } 
                                  ) do |task|
 
-        begin
 
-          @@bbrc.Reset
-          if @@fminer.prediction_feature.feature_type == "regression"
-            @@bbrc.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations!
-          else
-            bad_request_error "No accept values for "\
-                              "dataset '#{@@fminer.training_dataset.uri}' and "\
-                              "feature '#{@@fminer.prediction_feature.uri}'" unless 
-                               @@fminer.prediction_feature.accept_values
-            value_map=@@fminer.training_dataset.value_map(@@fminer.prediction_feature)
-          end
-          @@bbrc.SetMinfreq(@@fminer.minfreq)
-          @@bbrc.SetType(1) if params[:feature_type] == "paths"
-          @@bbrc.SetBackbone(false) if params[:backbone] == "false"
-          @@bbrc.SetChisqSig(params[:min_chisq_significance].to_f) if params[:min_chisq_significance]
-          @@bbrc.SetConsoleOut(false)
+        @@bbrc.Reset
+        if @@fminer.prediction_feature.feature_type == "regression"
+          @@bbrc.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations!
+        else
+          bad_request_error "No accept values for "\
+                            "dataset '#{@@fminer.training_dataset.uri}' and "\
+                            "feature '#{@@fminer.prediction_feature.uri}'" unless 
+                             @@fminer.prediction_feature.accept_values
+          value_map=@@fminer.training_dataset.value_map(@@fminer.prediction_feature)
+        end
+        @@bbrc.SetMinfreq(@@fminer.minfreq)
+        @@bbrc.SetType(1) if params[:feature_type] == "paths"
+        @@bbrc.SetBackbone(false) if params[:backbone] == "false"
+        @@bbrc.SetChisqSig(params[:min_chisq_significance].to_f) if params[:min_chisq_significance]
+        @@bbrc.SetConsoleOut(false)
 
-    
-          feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
-          feature_dataset.metadata = {
-            DC.title => "BBRC representatives",
-            DC.creator => url_for('/fminer/bbrc',:full),
-            OT.hasSource => url_for('/fminer/bbrc', :full),
-          }
-          feature_dataset.parameters = [
-              { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
-              { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] },
-              { DC.title => "min_frequency", OT.paramValue => @@fminer.minfreq },
-              { DC.title => "nr_hits", OT.paramValue => (params[:nr_hits] == "true" ? "true" : "false") },
-              { DC.title => "backbone", OT.paramValue => (params[:backbone] == "false" ? "false" : "true") }
-          ]
-    
-          @@fminer.compounds = []
-          @@fminer.db_class_sizes = Array.new # AM: effect
-          @@fminer.all_activities = Hash.new # DV: for effect calculation in regression part
-          @@fminer.smi = [] # AM LAST: needed for matching the patterns back
-    
-          # Add data to fminer
-          @@fminer.add_fminer_data(@@bbrc, value_map)
-          g_median=@@fminer.all_activities.values.to_scale.median
+  
+        feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
+        feature_dataset.metadata = {
+          DC.title => "BBRC representatives",
+          DC.creator => to('/fminer/bbrc',:full),
+          OT.hasSource => to('/fminer/bbrc', :full),
+        }
+        feature_dataset.parameters = [
+            { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
+            { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] },
+            { DC.title => "min_frequency", OT.paramValue => @@fminer.minfreq },
+            { DC.title => "nr_hits", OT.paramValue => (params[:nr_hits] == "true" ? "true" : "false") },
+            { DC.title => "backbone", OT.paramValue => (params[:backbone] == "false" ? "false" : "true") }
+        ]
+  
+        @@fminer.compounds = []
+        @@fminer.db_class_sizes = Array.new # AM: effect
+        @@fminer.all_activities = Hash.new # DV: for effect calculation in regression part
+        @@fminer.smi = [] # AM LAST: needed for matching the patterns back
+  
+        # Add data to fminer
+        @@fminer.add_fminer_data(@@bbrc, value_map)
+        g_median=@@fminer.all_activities.values.to_scale.median
 
-          #task.progress 10
-          step_width = 80 / @@bbrc.GetNoRootNodes().to_f
-          features_smarts = Set.new
-          features = Array.new
-    
-          # run @@bbrc
-          
-          # prepare to receive results as hash { c => [ [f,v], ... ] }
-          fminer_results = {}
+        #task.progress 10
+        step_width = 80 / @@bbrc.GetNoRootNodes().to_f
+        features_smarts = Set.new
+        features = Array.new
+  
+        # run @@bbrc
+        
+        # prepare to receive results as hash { c => [ [f,v], ... ] }
+        fminer_results = {}
 
-          (0 .. @@bbrc.GetNoRootNodes()-1).each do |j|
-            results = @@bbrc.MineRoot(j)
-            #task.progress 10+step_width*(j+1)
-            results.each do |result|
-              f = YAML.load(result)[0]
-              smarts = f[0]
-              p_value = f[1]
-    
-              if (!@@bbrc.GetRegression)
-                id_arrs = f[2..-1].flatten
-                max = OpenTox::Algorithm::Fminer.effect(f[2..-1].reverse, @@fminer.db_class_sizes) # f needs reversal for bbrc
-                effect = max+1
-              else #regression part
-                id_arrs = f[2]
-                # DV: effect calculation
-                f_arr=Array.new
-                f[2].each do |id|
-                  id=id.keys[0] # extract id from hit count hash
-                  f_arr.push(@@fminer.all_activities[id])
-                end
-                f_median=f_arr.to_scale.median
-                if g_median >= f_median
-                  effect = 'activating'
-                else
-                  effect = 'deactivating'
-                end
+        (0 .. @@bbrc.GetNoRootNodes()-1).each do |j|
+          results = @@bbrc.MineRoot(j)
+          #task.progress 10+step_width*(j+1)
+          results.each do |result|
+            f = YAML.load(result)[0]
+            smarts = f[0]
+            p_value = f[1]
+  
+            if (!@@bbrc.GetRegression)
+              id_arrs = f[2..-1].flatten
+              max = OpenTox::Algorithm::Fminer.effect(f[2..-1].reverse, @@fminer.db_class_sizes) # f needs reversal for bbrc
+              effect = max+1
+            else #regression part
+              id_arrs = f[2]
+              # DV: effect calculation
+              f_arr=Array.new
+              f[2].each do |id|
+                id=id.keys[0] # extract id from hit count hash
+                f_arr.push(@@fminer.all_activities[id])
               end
-    
-              #feature_uri = File.join feature_dataset.uri,"feature","bbrc", features.size.to_s
-              unless features_smarts.include? smarts
-                features_smarts << smarts
-                metadata = {
-                  OT.hasSource => url_for('/fminer/bbrc', :full),
-                  RDF.type => [OT.Feature, OT.Substructure, OT.NumericFeature],
-                  OT.smarts => smarts.dup,
-                  OT.pValue => p_value.to_f.abs.round(5),
-                  OT.effect => effect
-                }
-                feature = OpenTox::Feature.find_by_title(smarts.dup,metadata)
-                features << feature
+              f_median=f_arr.to_scale.median
+              if g_median >= f_median
+                effect = 'activating'
+              else
+                effect = 'deactivating'
               end
-
-              id_arrs.each { |id_count_hash|
-                id=id_count_hash.keys[0].to_i
-                count=id_count_hash.values[0].to_i
-                fminer_results[@@fminer.compounds[id]] || fminer_results[@@fminer.compounds[id]] = {}
-                if params[:nr_hits] == "true"
-                  fminer_results[@@fminer.compounds[id]][feature.uri] = count
-                else
-                  fminer_results[@@fminer.compounds[id]][feature.uri] = 1
-                end
+            end
+  
+            #feature_uri = File.join feature_dataset.uri,"feature","bbrc", features.size.to_s
+            unless features_smarts.include? smarts
+              features_smarts << smarts
+              metadata = {
+                OT.hasSource => to('/fminer/bbrc', :full),
+                RDF.type => [OT.Feature, OT.Substructure, OT.NumericFeature],
+                OT.smarts => smarts.dup,
+                OT.pValue => p_value.to_f.abs.round(5),
+                OT.effect => effect
               }
-    
-            end # end of
-          end   # feature parsing
+              feature = OpenTox::Feature.find_by_title(smarts.dup,metadata)
+              features << feature
+            end
 
-          fminer_compounds = @@fminer.training_dataset.compounds.collect.to_a
-          @@fminer.training_dataset.build_feature_positions
-          prediction_feature_idx = @@fminer.training_dataset.feature_positions[@@fminer.prediction_feature.uri]
-          prediction_feature_all_acts = fminer_compounds.each_with_index.collect { |c,idx| 
-            @@fminer.training_dataset.data_entries[idx][prediction_feature_idx] 
-          }
-          fminer_noact_compounds = fminer_compounds - @@fminer.compounds
+            id_arrs.each { |id_count_hash|
+              id=id_count_hash.keys[0].to_i
+              count=id_count_hash.values[0].to_i
+              fminer_results[@@fminer.compounds[id]] || fminer_results[@@fminer.compounds[id]] = {}
+              if params[:nr_hits] == "true"
+                fminer_results[@@fminer.compounds[id]][feature.uri] = count
+              else
+                fminer_results[@@fminer.compounds[id]][feature.uri] = 1
+              end
+            }
+  
+          end # end of
+        end   # feature parsing
+
+        fminer_compounds = @@fminer.training_dataset.compounds.collect.to_a
+        @@fminer.training_dataset.build_feature_positions
+        prediction_feature_idx = @@fminer.training_dataset.feature_positions[@@fminer.prediction_feature.uri]
+        prediction_feature_all_acts = fminer_compounds.each_with_index.collect { |c,idx| 
+          @@fminer.training_dataset.data_entries[idx][prediction_feature_idx] 
+        }
+        fminer_noact_compounds = fminer_compounds - @@fminer.compounds
 
-          feature_dataset.features = features
+        feature_dataset.features = features
+        if (params[:get_target] == "true")
+          feature_dataset.features = [ @@fminer.prediction_feature ] + feature_dataset.features
+        end
+        fminer_compounds.each_with_index { |c,idx|
+          row = [ c ]
           if (params[:get_target] == "true")
-            feature_dataset.features = [ @@fminer.prediction_feature ] + feature_dataset.features
+            row = row + [ prediction_feature_all_acts[idx] ]
           end
-          fminer_compounds.each_with_index { |c,idx|
-            row = [ c ]
-            if (params[:get_target] == "true")
-              row = row + [ prediction_feature_all_acts[idx] ]
-            end
-            features.each { |f|
-              row << (fminer_results[c] ? fminer_results[c][f.uri] : nil)
-            }
-            row.collect! { |v| v ? v : 0 } unless fminer_noact_compounds.include? c
-            feature_dataset << row
+          features.each { |f|
+            row << (fminer_results[c] ? fminer_results[c][f.uri] : nil)
           }
-          feature_dataset.put @subjectid
-          $logger.debug feature_dataset.uri
-          feature_dataset.uri
-
-        rescue => e
-          $logger.debug "#{e.class}: #{e.message}"
-          $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-        end
+          row.collect! { |v| v ? v : 0 } unless fminer_noact_compounds.include? c
+          feature_dataset << row
+        }
+        feature_dataset.put @subjectid
+        $logger.debug feature_dataset.uri
+        feature_dataset.uri
 
       end
       response['Content-Type'] = 'text/uri-list'
@@ -301,117 +295,109 @@ module OpenTox
     # @return [text/uri-list] Task URI
     post '/fminer/last/?' do
     
-      @@fminer=OpenTox::Algorithm::Fminer.new(url_for('/fminer/last',:full))
+      @@fminer=OpenTox::Algorithm::Fminer.new(to('/fminer/last',:full))
       @@fminer.check_params(params,80,@subjectid)
     
       task = OpenTox::Task.create( 
                                     $task[:uri], 
                                     @subjectid, 
                                     { RDF::DC.description => "Mining LAST features", 
-                                      RDF::DC.creator => url_for('/fminer/last',:full) 
+                                      RDF::DC.creator => to('/fminer/last',:full) 
                                     } 
                                    ) do |task|
 
-        begin
-
-          @@last.Reset
-          if @@fminer.prediction_feature.feature_type == "regression"
-            @@last.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations!
-          else
-            bad_request_error "No accept values for "\
-                            "dataset '#{fminer.training_dataset.uri}' and "\
-                            "feature '#{fminer.prediction_feature.uri}'" unless 
-                             @@fminer.prediction_feature.accept_values
-            value_map=@@fminer.training_dataset.value_map(@@fminer.prediction_feature)
-          end
-          @@last.SetMinfreq(@@fminer.minfreq)
-          @@last.SetType(1) if params[:feature_type] == "paths"
-          @@last.SetConsoleOut(false)
-    
-    
-          feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
-          feature_dataset.metadata = {
-            DC.title => "LAST representatives for " + @@fminer.training_dataset.metadata[DC.title].to_s,
-            DC.creator => url_for('/fminer/last',:full),
-            OT.hasSource => url_for('/fminer/last', :full)
-          }
-          feature_dataset.parameters = [
-              { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
-              { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] },
-              { DC.title => "min_frequency", OT.paramValue => @@fminer.minfreq },
-              { DC.title => "nr_hits", OT.paramValue => (params[:nr_hits] == "true" ? "true" : "false") }
-          ]
-          
-          @@fminer.compounds = []
-          @@fminer.db_class_sizes = Array.new # AM: effect
-          @@fminer.all_activities = Hash.new # DV: for effect calculation (class and regr)
-          @@fminer.smi = [] # needed for matching the patterns back
-    
-          # Add data to fminer
-          @@fminer.add_fminer_data(@@last, value_map)
-          #task.progress 10
-          step_width = 80 / @@bbrc.GetNoRootNodes().to_f
-          # run @@last
-          xml = ""
-          (0 .. @@last.GetNoRootNodes()-1).each do |j|
-            results = @@last.MineRoot(j)
-            #task.progress 10+step_width*(j+1)
-            results.each do |result|
-              xml << result
-            end
-          end
-    
-          lu = LU.new                             # uses last-utils here
-          dom=lu.read(xml)                        # parse GraphML
-          smarts=lu.smarts_rb(dom,'nls')          # converts patterns to LAST-SMARTS using msa variant (see last-pm.maunz.de)
-          params[:nr_hits] == "true" ? hit_count=true : hit_count=false
-          matches, counts = lu.match_rb(@@fminer.smi,smarts,hit_count,true)       # creates instantiations
-
-          features = []
-          # prepare to receive results as hash { c => [ [f,v], ... ] }
-          fminer_results = {}
-          matches.each do |smarts, ids|
-            metadata, parameters = @@fminer.calc_metadata(smarts, ids, counts[smarts], @@last, nil, value_map, params)
-            feature = OpenTox::Feature.find_by_title(smarts.dup,metadata)
-            features << feature
-            ids.each_with_index { |id,idx| 
-              fminer_results[@@fminer.compounds[id]] || fminer_results[@@fminer.compounds[id]] = {}
-              fminer_results[@@fminer.compounds[id]][feature.uri] = counts[smarts][idx]
-            }
+        @@last.Reset
+        if @@fminer.prediction_feature.feature_type == "regression"
+          @@last.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations!
+        else
+          bad_request_error "No accept values for "\
+                          "dataset '#{fminer.training_dataset.uri}' and "\
+                          "feature '#{fminer.prediction_feature.uri}'" unless 
+                           @@fminer.prediction_feature.accept_values
+          value_map=@@fminer.training_dataset.value_map(@@fminer.prediction_feature)
+        end
+        @@last.SetMinfreq(@@fminer.minfreq)
+        @@last.SetType(1) if params[:feature_type] == "paths"
+        @@last.SetConsoleOut(false)
+  
+  
+        feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
+        feature_dataset.metadata = {
+          DC.title => "LAST representatives for " + @@fminer.training_dataset.metadata[DC.title].to_s,
+          DC.creator => to('/fminer/last',:full),
+          OT.hasSource => to('/fminer/last', :full)
+        }
+        feature_dataset.parameters = [
+            { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
+            { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] },
+            { DC.title => "min_frequency", OT.paramValue => @@fminer.minfreq },
+            { DC.title => "nr_hits", OT.paramValue => (params[:nr_hits] == "true" ? "true" : "false") }
+        ]
+        
+        @@fminer.compounds = []
+        @@fminer.db_class_sizes = Array.new # AM: effect
+        @@fminer.all_activities = Hash.new # DV: for effect calculation (class and regr)
+        @@fminer.smi = [] # needed for matching the patterns back
+  
+        # Add data to fminer
+        @@fminer.add_fminer_data(@@last, value_map)
+        #task.progress 10
+        step_width = 80 / @@bbrc.GetNoRootNodes().to_f
+        # run @@last
+        xml = ""
+        (0 .. @@last.GetNoRootNodes()-1).each do |j|
+          results = @@last.MineRoot(j)
+          #task.progress 10+step_width*(j+1)
+          results.each do |result|
+            xml << result
           end
+        end
+  
+        lu = LU.new                             # uses last-utils here
+        dom=lu.read(xml)                        # parse GraphML
+        smarts=lu.smarts_rb(dom,'nls')          # converts patterns to LAST-SMARTS using msa variant (see last-pm.maunz.de)
+        params[:nr_hits] == "true" ? hit_count=true : hit_count=false
+        matches, counts = lu.match_rb(@@fminer.smi,smarts,hit_count,true)       # creates instantiations
 
-          fminer_compounds = @@fminer.training_dataset.compounds.collect.to_a
-          @@fminer.training_dataset.build_feature_positions
-          prediction_feature_idx = @@fminer.training_dataset.feature_positions[@@fminer.prediction_feature.uri]
-          prediction_feature_all_acts = fminer_compounds.each_with_index.collect { |c,idx| 
-            @@fminer.training_dataset.data_entries[idx][prediction_feature_idx] 
+        features = []
+        # prepare to receive results as hash { c => [ [f,v], ... ] }
+        fminer_results = {}
+        matches.each do |smarts, ids|
+          metadata, parameters = @@fminer.calc_metadata(smarts, ids, counts[smarts], @@last, nil, value_map, params)
+          feature = OpenTox::Feature.find_by_title(smarts.dup,metadata)
+          features << feature
+          ids.each_with_index { |id,idx| 
+            fminer_results[@@fminer.compounds[id]] || fminer_results[@@fminer.compounds[id]] = {}
+            fminer_results[@@fminer.compounds[id]][feature.uri] = counts[smarts][idx]
           }
-          fminer_noact_compounds = fminer_compounds - @@fminer.compounds
+        end
 
-          feature_dataset.features = features
+        fminer_compounds = @@fminer.training_dataset.compounds.collect.to_a
+        @@fminer.training_dataset.build_feature_positions
+        prediction_feature_idx = @@fminer.training_dataset.feature_positions[@@fminer.prediction_feature.uri]
+        prediction_feature_all_acts = fminer_compounds.each_with_index.collect { |c,idx| 
+          @@fminer.training_dataset.data_entries[idx][prediction_feature_idx] 
+        }
+        fminer_noact_compounds = fminer_compounds - @@fminer.compounds
+
+        feature_dataset.features = features
+        if (params[:get_target] == "true")
+          feature_dataset.features = [ @@fminer.prediction_feature ] + feature_dataset.features
+        end
+        fminer_compounds.each_with_index { |c,idx|
+          row = [ c ]
           if (params[:get_target] == "true")
-            feature_dataset.features = [ @@fminer.prediction_feature ] + feature_dataset.features
+            row = row + [ prediction_feature_all_acts[idx] ]
           end
-          fminer_compounds.each_with_index { |c,idx|
-            row = [ c ]
-            if (params[:get_target] == "true")
-              row = row + [ prediction_feature_all_acts[idx] ]
-            end
-            features.each { |f|
-              row << (fminer_results[c] ? fminer_results[c][f.uri] : nil)
-            }
-            row.collect! { |v| v ? v : 0 } unless fminer_noact_compounds.include? c
-            feature_dataset << row
+          features.each { |f|
+            row << (fminer_results[c] ? fminer_results[c][f.uri] : nil)
           }
-          feature_dataset.put @subjectid
-          $logger.debug feature_dataset.uri
-          feature_dataset.uri
-
-        rescue => e
-          $logger.debug "#{e.class}: #{e.message}"
-          $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-        end
-
+          row.collect! { |v| v ? v : 0 } unless fminer_noact_compounds.include? c
+          feature_dataset << row
+        }
+        feature_dataset.put @subjectid
+        $logger.debug feature_dataset.uri
+        feature_dataset.uri
 
       end
       response['Content-Type'] = 'text/uri-list'
diff --git a/webapp/fs.rb b/webapp/fs.rb
index 670885f..6286629 100644
--- a/webapp/fs.rb
+++ b/webapp/fs.rb
@@ -11,14 +11,14 @@ module OpenTox
   # Get list of feature selection algorithms
   # @return [text/uri-list] URIs
   get '/fs/?' do
-    list = [ url_for('/fs/rfe', :full) ].join("\n") + "\n"
+    list = [ to('/fs/rfe', :full) ].join("\n") + "\n"
     format_output(list)
   end
   
   # Get representation of Recursive Feature Elimination algorithm
   # @return [String] Representation
   get "/fs/rfe/?" do
-    algorithm = OpenTox::Algorithm.new(url_for('/fs/rfe',:full))
+    algorithm = OpenTox::Algorithm.new(to('/fs/rfe',:full))
     algorithm.metadata = {
       DC.title => 'Recursive Feature Elimination',
       DC.creator => "andreas@maunz.de",
diff --git a/webapp/lazar.rb b/webapp/lazar.rb
index 7fb7ea3..c139b4d 100644
--- a/webapp/lazar.rb
+++ b/webapp/lazar.rb
@@ -29,7 +29,7 @@ module OpenTox
     # Get representation of lazar algorithm
     # @return [String] Representation
     get '/lazar/?' do
-      algorithm = OpenTox::Algorithm.new(url_for('/lazar',:full))
+      algorithm = OpenTox::Algorithm.new(to('/lazar',:full))
       algorithm.metadata = {
         DC.title => 'lazar',
         DC.creator => 'helma@in-silico.ch, andreas@maunz.de',
@@ -59,33 +59,28 @@ module OpenTox
                                   $task[:uri],
                                   @subjectid,
                                   { RDF::DC.description => "Create lazar model",
-                                    RDF::DC.creator => url_for('/lazar',:full)
+                                    RDF::DC.creator => to('/lazar',:full)
                                   }
                                 ) do |task|
-        begin 
-          lazar = OpenTox::Model.new(nil, @subjectid)
-          lazar.parameters = lazar.check_params($lazar_params, params)
-          lazar.metadata = { 
-            DC.title => "lazar model", 
-            OT.dependentVariables => lazar.find_parameter_value("prediction_feature_uri"),
-            OT.trainingDataset => lazar.find_parameter_value("training_dataset_uri"),
-            OT.featureDataset => lazar.find_parameter_value("feature_dataset_uri"),
-            RDF.type => ( OpenTox::Feature.find(lazar.find_parameter_value("prediction_feature_uri")).feature_type == "classification" ? 
-              [OT.Model, OTA.ClassificationLazySingleTarget] :
-              [OT.Model, OTA.RegressionLazySingleTarget] 
-            )
-          }
-          # task.progress 10
-          lazar.put @subjectid
-          $logger.debug lazar.uri
-          lazar.uri
-        rescue => e
-          $logger.debug "#{e.class}: #{e.message}"
-          $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-        end
+
+        lazar = OpenTox::Model.new(nil, @subjectid)
+        lazar.parameters = lazar.check_params($lazar_params, params)
+        lazar.metadata = { 
+          DC.title => "lazar model", 
+          OT.dependentVariables => lazar.find_parameter_value("prediction_feature_uri"),
+          OT.trainingDataset => lazar.find_parameter_value("training_dataset_uri"),
+          OT.featureDataset => lazar.find_parameter_value("feature_dataset_uri"),
+          RDF.type => ( OpenTox::Feature.find(lazar.find_parameter_value("prediction_feature_uri")).feature_type == "classification" ? 
+            [OT.Model, OTA.ClassificationLazySingleTarget] :
+            [OT.Model, OTA.RegressionLazySingleTarget] 
+          )
+        }
+        # task.progress 10
+        lazar.put @subjectid
+        lazar.uri
       end
       response['Content-Type'] = 'text/uri-list'
-      service_unavailable_error "Service unavailable" if task.cancelled?
+      #service_unavailable_error "Service unavailable" if task.cancelled?
       halt 202,task.uri.to_s+"\n"
     end
 
@@ -117,139 +112,142 @@ module OpenTox
         @subjectid,
         { 
           RDF::DC.description => "Create lazar model",
-          RDF::DC.creator => url_for('/lazar/predict',:full)
+          RDF::DC.creator => to('/lazar/predict',:full)
         }
       ) do |task|
 
-        begin 
 
-          if params[:dataset_uri]
-            compounds = OpenTox::Dataset.find(params[:dataset_uri]).compounds
-          else
-            compounds = [ OpenTox::Compound.new(params[:compound_uri]) ]
+        if params[:dataset_uri]
+          compounds = OpenTox::Dataset.find(params[:dataset_uri]).compounds
+        else
+          compounds = [ OpenTox::Compound.new(params[:compound_uri]) ]
+        end
+
+        compounds.each { |query_compound|
+          params[:compound_uri] = query_compound.uri # AM: store compound in params hash
+          unless @prediction_dataset # AM: only once for dataset predictions
+            @prediction_dataset = OpenTox::Dataset.new(nil, @subjectid) 
+
+            @model_params_hash = $lazar_params.inject({}){ |h,p|
+              h[p] = params[p].to_s unless params[p].nil?
+              h
+            }
+            @model = OpenTox::Model.new(@model_params_hash)
+
+            @prediction_dataset.metadata = {
+              DC.title => "Lazar prediction",
+              DC.creator => @uri.to_s,
+              OT.hasSource => @uri.to_s,
+              OT.dependentVariables => @model_params_hash["prediction_feature_uri"],
+              OT.predictedVariables => @model_params_hash["prediction_feature_uri"]
+            }
+
+            puts "Loading t dataset"
+            @training_dataset = OpenTox::Dataset.find(params[:training_dataset_uri], @subjectid)
+            @prediction_feature = OpenTox::Feature.find(params[:prediction_feature_uri],@subjectid)
+            #@training_dataset = OpenTox::Dataset.find(params[:training_dataset], @subjectid)
+            #@prediction_feature = OpenTox::Feature.find(params[:prediction_feature],@subjectid)
+            @confidence_feature = OpenTox::Feature.find_by_title("confidence", {RDF.type => [RDF::OT.NumericFeature]})
+            @similarity_feature = OpenTox::Feature.find_by_title("similarity", {RDF.type => [RDF::OT.NumericFeature]})
+            @prediction_dataset.features = [ @prediction_feature, @confidence_feature, @similarity_feature ]
           end
+          
+          database_activity = @training_dataset.database_activity(params)
+          if database_activity
 
-          compounds.each { |query_compound|
-            params[:compound_uri] = query_compound.uri # AM: store compound in params hash
-            unless @prediction_dataset # AM: only once for dataset predictions
-              @prediction_dataset = OpenTox::Dataset.new(nil, @subjectid) 
+            prediction_value = database_activity.to_f
+            confidence_value = 1.0
 
-              @model_params_hash = $lazar_params.inject({}){ |h,p|
-                h[p] = params[p].to_s unless params[p].nil?
-                h
-              }
-              @model = OpenTox::Model.new(@model_params_hash)
-
-              @prediction_dataset.metadata = {
-                DC.title => "Lazar prediction",
-                DC.creator => @uri.to_s,
-                OT.hasSource => @uri.to_s,
-                OT.dependentVariables => @model_params_hash["prediction_feature_uri"],
-                OT.predictedVariables => @model_params_hash["prediction_feature_uri"]
-              }
+          else
+            puts "Creating prediction"
+            @model = OpenTox::Model.new(@model_params_hash)
 
-              $logger.debug "Loading t dataset"
-              @training_dataset = OpenTox::Dataset.find(params[:training_dataset_uri], @subjectid)
-              @prediction_feature = OpenTox::Feature.find(params[:prediction_feature_uri],@subjectid)
-              @confidence_feature = OpenTox::Feature.find_by_title("confidence", {RDF.type => [RDF::OT.NumericFeature]})
-              @similarity_feature = OpenTox::Feature.find_by_title("similarity", {RDF.type => [RDF::OT.NumericFeature]})
-              @prediction_dataset.features = [ @prediction_feature, @confidence_feature, @similarity_feature ]
+            unless @feature_dataset
+              puts "Loading f dataset"
+              @feature_dataset = OpenTox::Dataset.find(params[:feature_dataset_uri], @subjectid)
+              #@feature_dataset = OpenTox::Dataset.find(params[:feature_dataset], @subjectid)
             end
-            
-            database_activity = @training_dataset.database_activity(params)
-            if database_activity
-
-              prediction_value = database_activity.to_f
-              confidence_value = 1.0
 
+            case @feature_dataset.find_parameter_value("nr_hits")
+              when "true" then @model.feature_calculation_algorithm = "match_hits"
+              when "false" then @model.feature_calculation_algorithm = "match"
+            end
+              puts @model.feature_calculation_algorithm 
+            pc_type = @feature_dataset.find_parameter_value("pc_type")
+            @model.pc_type = pc_type unless pc_type.nil?
+            lib = @feature_dataset.find_parameter_value("lib")
+            @model.lib = lib unless lib.nil?
+
+            print "cosine transformation ..."
+            # AM: transform to cosine space
+            @model.min_sim = (@model.min_sim.to_f*2.0-1.0).to_s if @model.similarity_algorithm =~ /cosine/
+              puts "finished"
+
+            #puts @model.pc_type
+            puts @feature_dataset.features.size 
+            if @feature_dataset.features.size > 0
+              compound_params = { 
+                :compound => query_compound, 
+                :feature_dataset => @feature_dataset,
+                :pc_type => @model.pc_type,
+                :lib => @model.lib
+              }
+              # use send, not eval, for calling the method (good backtrace)
+              $logger.debug "Calculating q fps"
+              compound_fingerprints = OpenTox::Algorithm::FeatureValues.send( @model.feature_calculation_algorithm, compound_params, @subjectid )
             else
-              @model = OpenTox::Model.new(@model_params_hash)
-
-              unless @feature_dataset
-                $logger.debug "Loading f dataset"
-                @feature_dataset = OpenTox::Dataset.find(params[:feature_dataset_uri], @subjectid)
-              end
-
-              case @feature_dataset.find_parameter_value("nr_hits")
-                when "true" then @model.feature_calculation_algorithm = "match_hits"
-                when "false" then @model.feature_calculation_algorithm = "match"
-              end
-              pc_type = @feature_dataset.find_parameter_value("pc_type")
-              @model.pc_type = pc_type unless pc_type.nil?
-              lib = @feature_dataset.find_parameter_value("lib")
-              @model.lib = lib unless lib.nil?
-
-              # AM: transform to cosine space
-              @model.min_sim = (@model.min_sim.to_f*2.0-1.0).to_s if @model.similarity_algorithm =~ /cosine/
-
-              if @feature_dataset.features.size > 0
-                compound_params = { 
-                  :compound => query_compound, 
-                  :feature_dataset => @feature_dataset,
-                  :pc_type => @model.pc_type,
-                  :lib => @model.lib
-                }
-                # use send, not eval, for calling the method (good backtrace)
-                $logger.debug "Calculating q fps"
-                compound_fingerprints = OpenTox::Algorithm::FeatureValues.send( @model.feature_calculation_algorithm, compound_params, @subjectid )
-              else
-                bad_request_error "No features found"
-              end
-
-              @model.add_data(@training_dataset, @feature_dataset, @prediction_feature, compound_fingerprints, @subjectid)
-              mtf = OpenTox::Algorithm::Transform::ModelTransformer.new(@model)
-              mtf.transform
-              $logger.debug "Predicting q"
-              prediction = OpenTox::Algorithm::Neighbors.send(@model.prediction_algorithm,  { :props => mtf.props,
-                                                            :acts => mtf.acts,
-                                                            :sims => mtf.sims,
-                                                            :value_map => @training_dataset.value_map(@prediction_feature),
-                                                            :min_train_performance => @model.min_train_performance
-                                                          } )
-              prediction_value = prediction[:prediction].to_f
-              confidence_value = prediction[:confidence].to_f
-
-              # AM: transform to original space
-              confidence_value = ((confidence_value+1.0)/2.0).abs if @model.similarity_algorithm =~ /cosine/
-              prediction_value = @training_dataset.value_map(@prediction_feature)[prediction[:prediction].to_i] if @prediction_feature.feature_type == "classification"
-
-              $logger.debug "Prediction: '#{prediction_value}'"
-              $logger.debug "Confidence: '#{confidence_value}'"
+              bad_request_error "No features found"
             end
 
+            @model.add_data(@training_dataset, @feature_dataset, @prediction_feature, compound_fingerprints, @subjectid)
+            mtf = OpenTox::Algorithm::Transform::ModelTransformer.new(@model)
+            mtf.transform
+            $logger.debug "Predicting q"
+            prediction = OpenTox::Algorithm::Neighbors.send(@model.prediction_algorithm,  { :props => mtf.props,
+                                                          :acts => mtf.acts,
+                                                          :sims => mtf.sims,
+                                                          :value_map => @training_dataset.value_map(@prediction_feature),
+                                                          :min_train_performance => @model.min_train_performance
+                                                        } )
+            prediction_value = prediction[:prediction].to_f
+            confidence_value = prediction[:confidence].to_f
+
+            # AM: transform to original space
+            confidence_value = ((confidence_value+1.0)/2.0).abs if @model.similarity_algorithm =~ /cosine/
+            prediction_value = @training_dataset.value_map(@prediction_feature)[prediction[:prediction].to_i] if @prediction_feature.feature_type == "classification"
+
+            $logger.debug "Prediction: '#{prediction_value}'"
+            $logger.debug "Confidence: '#{confidence_value}'"
+          end
+
+          @prediction_dataset << [ 
+            query_compound, 
+            prediction_value, 
+            confidence_value, 
+            nil
+          ]
+          @model.neighbors.each { |neighbor|
             @prediction_dataset << [ 
-              query_compound, 
-              prediction_value, 
-              confidence_value, 
-              nil
+              OpenTox::Compound.new(neighbor[:compound]), 
+              @training_dataset.value_map(@prediction_feature)[neighbor[:activity]], 
+              nil, 
+              neighbor[:similarity] 
             ]
-            @model.neighbors.each { |neighbor|
-              @prediction_dataset << [ 
-                OpenTox::Compound.new(neighbor[:compound]), 
-                @training_dataset.value_map(@prediction_feature)[neighbor[:activity]], 
-                nil, 
-                neighbor[:similarity] 
-              ]
-            }
-
           }
 
-         @prediction_dataset.parameters = $lazar_params.collect { |p|
-           {DC.title => p, OT.paramValue => @model.instance_variable_get("@#{p}")} unless  @model.instance_variable_get("@#{p}").nil?
-         }
+        }
 
-          @prediction_dataset.put
-          $logger.debug @prediction_dataset.uri
-          @prediction_dataset.uri
+       @prediction_dataset.parameters = $lazar_params.collect { |p|
+         {DC.title => p, OT.paramValue => @model.instance_variable_get("@#{p}")} unless  @model.instance_variable_get("@#{p}").nil?
+       }
 
-        rescue => e
-          $logger.debug "#{e.class}: #{e.message}"
-          $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
-        end
+        @prediction_dataset.put
+        $logger.debug @prediction_dataset.uri
+        @prediction_dataset.uri
 
       end
       response['Content-Type'] = 'text/uri-list'
-      service_unavailable_error "Service unavailable" if task.cancelled?
+      #service_unavailable_error "Service unavailable" if task.cancelled?
       halt 202,task.uri.to_s+"\n"
     end
 
diff --git a/webapp/sinatra.rb b/webapp/sinatra.rb
index d40633f..cf0e7c5 100644
--- a/webapp/sinatra.rb
+++ b/webapp/sinatra.rb
@@ -8,9 +8,6 @@
 module OpenTox
   class Application < Service
 
-    # Get url_for support
-    helpers Sinatra::UrlForHelper
-
     # Put any code here that should be executed immediately before 
     # request is processed
     before {