serial batch prediction with task

1 files changed, 276 insertions, 196 deletions

diff --git a/application.rb b/application.rb
index 380b48a..4f73ad6 100644
--- a/application.rb
+++ b/application.rb
@@ -1,6 +1,5 @@
 include OpenTox
 
-
 configure :production do
   $logger = Logger.new(STDOUT)
   enable :reloader
@@ -39,8 +38,151 @@ helpers do
     end
     doc.to_html.html_safe
   end
-end
 
+  def to_csv(m,predictions,compounds)
+    model = (m != "Cramer" ? Model::Validation.find(m.to_s) : "Cramer")
+    csv = ""
+    if model == "Cramer"
+      compounds = compounds.collect{|c| c.smiles}
+      
+      prediction = [Toxtree.predict(compounds, "Cramer rules"), Toxtree.predict(compounds, "Cramer rules with extensions")]
+      output = {}
+      output["model_name"] = "Oral toxicity (Cramer rules)"
+      output["model_type"] = false
+      output["model_unit"] = false
+      ["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
+        output["#{key}"] = false
+      end
+      output["toxtree"] = true
+      output["cramer_rules"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules"]}}.flatten.compact
+      output["cramer_rules_extensions"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules, with extensions"]}}.flatten.compact
+
+      # header
+      csv = "ID,Endpoint,Unique SMILES,Cramer rules,Cramer rules with extensions\n"
+      
+      compounds.each_with_index do |smiles, idx|
+        csv << "#{idx+1},#{output["model_name"]},#{smiles},"\
+          "#{output["cramer_rules"][idx] != "nil" ? output["cramer_rules"][idx] : "none" },"\
+          "#{output["cramer_rules_extensions"][idx] != "nil" ? output["cramer_rules_extensions"][idx] : "none"}\n"
+      end
+
+    else
+      output = {}
+      predictions.each_with_index do |prediction,idx|
+        compound = compounds[idx]
+        line = ""
+        output["model_name"] = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
+        output["model_type"] = model.model.class.to_s.match("Classification") ? type = "Classification" : type = "Regression"
+        output["model_unit"] = (type == "Regression") ? "(#{model.unit})" : ""
+        output["converted_model_unit"] = (type == "Regression") ? "#{model.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
+        ["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
+          output["#{key}"] = false
+        end
+
+        if prediction[:value]
+          inApp = (prediction[:warnings].join(" ") =~ /Cannot/ ? "no" : (prediction[:warnings].join(" ") =~ /may|Insufficient/ ? "maybe" : "yes"))
+          if prediction[:info] =~ /\b(identical)\b/i
+            prediction[:info] = "This compound was part of the training dataset. All information "\
+              "from this compound was removed from the training data before the "\
+              "prediction, to obtain unbiased results."
+          end
+          note = "\"#{prediction[:warnings].uniq.join(" ")}\""
+
+          output["prediction_value"] = (type == "Regression") ? "#{prediction[:value].delog10.signif(3)}" : "#{prediction[:value]}"
+          output["converted_value"] = "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)}" if type == "Regression"
+
+          output["db_hit"] = prediction[:info] if prediction[:info]
+          
+          if prediction[:measurements].is_a?(Array)
+            output["measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} (#{model.unit})"} : prediction[:measurements].collect{|value| "#{value}"}
+            output["converted_measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{compound.mmol_to_mg(value.delog10).signif(3)} #{model.unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"} : false
+          else
+            output["measurements"] = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} (#{model.unit})}" : "#{prediction[:measurements]}"
+            output["converted_measurements"] = (type == "Regression") ? "#{compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(model.unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : false
+
+          end #db_hit
+
+          if type == "Regression"
+
+            if !prediction[:prediction_interval].nil?
+              interval = prediction[:prediction_interval]
+              output['interval'] = "#{interval[1].delog10.signif(3)} - #{interval[0].delog10.signif(3)}"
+              output['converted_interval'] = "#{compound.mmol_to_mg(interval[1].delog10).signif(3)} - #{compound.mmol_to_mg(interval[0].delog10).signif(3)}"
+            end #prediction interval
+
+            line += "#{idx+1},#{output['model_name']},#{compound.smiles},"\
+              "\"#{prediction[:info] ? prediction[:info] : "no"}\",\"#{prediction[:measurements].join("; ") if prediction[:info]}\","\
+              "#{output['prediction_value'] != false ? output['prediction_value'] : ""},"\
+              "#{output['converted_value'] != false ? output['converted_value'] : ""},"\
+              "#{output['interval'].split(" - ").first.strip unless output['interval'] == false},"\
+              "#{output['interval'].split(" - ").last.strip unless output['interval'] == false},"\
+              "#{output['converted_interval'].split(" - ").first.strip unless output['converted_interval'] == false},"\
+              "#{output['converted_interval'].split(" - ").last.strip unless output['converted_interval'] == false},"\
+              "#{inApp},#{note.nil? ? "" : note.chomp}\n"
+          else # Classification
+
+            # consensus mutagenicity
+            sa_prediction = KaziusAlerts.predict(compound.smiles)
+            lazar_mutagenicity = prediction
+            confidence = 0
+            lazar_mutagenicity_val = (lazar_mutagenicity[:value] == "non-mutagenic" ? false : true)
+            if sa_prediction[:prediction] == false && lazar_mutagenicity_val == false
+              confidence = 0.85
+            elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == true
+              confidence = 0.85 * ( 1 - sa_prediction[:error_product] )
+            elsif sa_prediction[:prediction] == false && lazar_mutagenicity_val == true
+              confidence = 0.11
+            elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == false
+              confidence = ( 1 - sa_prediction[:error_product] ) - 0.57
+            end
+            output['sa_prediction'] = sa_prediction
+            output['sa_matches'] = sa_prediction[:matches].collect{|a| a.first}.join("; ") unless sa_prediction[:matches].blank?
+            output['confidence'] = confidence.signif(3)
+            output['model_name'] = "Lazar #{model.endpoint.gsub('_', ' ').downcase} (#{model.species}):"
+            output['probability'] = prediction[:probabilities] ? prediction[:probabilities].collect{|k,v| "#{k}: #{v.signif(3)}"} : false
+
+            line += "#{idx+1},Consensus mutagenicity,#{compound.smiles},"\
+              "\"#{prediction[:info] ? prediction[:info] : "no"}\",\"#{prediction[:measurements].join("; ") if prediction[:info]}\","\
+              "#{sa_prediction[:prediction] == false ? "non-mutagenic" : "mutagenic"},"\
+              "#{output['confidence']},#{output['sa_matches'] != false ? "\"#{output['sa_matches']}\"" : "none"},"\
+              "#{output['prediction_value']},"\
+              "#{output['probability'][0] != false ? output['probability'][0].split(":").last : ""},"\
+              "#{output['probability'][1] != false ? output['probability'][1].split(":").last : ""},"\
+              "#{inApp},#{note.nil? ? "" : note}\n"
+
+          end
+          
+          output["warnings"] = prediction[:warnings] if prediction[:warnings]
+
+        else #no prediction value
+          inApp = "no"
+          if prediction[:info] =~ /\b(identical)\b/i
+            prediction[:info] = "This compound was part of the training dataset. All information "\
+              "from this compound was removed from the training data before the "\
+              "prediction, to obtain unbiased results."
+          end
+          note = "\"#{prediction[:warnings].join(" ")}\""
+
+          output["warnings"] = prediction[:warnings]
+          output["info"] = prediction[:info] if prediction[:info]
+
+          if type == "Regression"
+            line += "#{idx+1},#{output['model_name']},#{compound.smiles},#{prediction[:info] ? prediction[:info] : "no"},"\
+              "#{prediction[:measurements] if prediction[:info]},,,,,,,"+ [inApp,note].join(",")+"\n"
+          else
+            line += "#{idx+1},Consensus mutagenicity,#{compound.smiles},#{prediction[:info] ? prediction[:info] : "no"},"\
+              "#{prediction[:measurements] if prediction[:info]},,,,,,,"+ [inApp,note].join(",")+"\n"
+          end
+
+        end
+        csv += line
+      end
+      csv
+    end
+  end
+
+end
+              
 get '/?' do
   redirect to('/predict') 
 end
@@ -53,6 +195,42 @@ get '/predict/?' do
   @models.count <= 0 ? (haml :info) : (haml :predict)
 end
 
+get '/task/?' do
+  if params[:turi]
+    task = Task.find(params[:turi].to_s)
+    return JSON.pretty_generate(:percent => task.percent)
+  elsif params[:predictions]
+    pageSize = params[:pageSize].to_i - 1
+    pageNumber= params[:pageNumber].to_i - 1
+    compound = Compound.find @@compounds_ids[pageNumber]
+    image = compound.svg
+    smiles = compound.smiles
+    task = Task.find(params[:predictions].to_s)
+    unless task.predictions[params[:model]].nil?
+      array = []
+      html = "<table class=\"table table-bordered single-batch\"><tr>"
+      html += "<td>#{image}</br>#{smiles}</br></td>"
+      string = "<td><table class=\"table\">"
+      prediction = task.predictions[params[:model]][pageNumber.to_i]
+      sort = []
+      if prediction[:info]
+        sort << {"info" => prediction[:info]}
+        sort << {"measurements" => prediction[:measurements].join("</br>")}
+        prediction.delete("info")
+        prediction.delete("measurements")
+      end
+      task.predictions[params[:model]][pageNumber.to_i].each do |k,v|
+       string += "<tr><td>#{(k=="value" ? "prediction" : k).capitalize}:</td> "\
+          "<td>#{v.blank? ? "none" : (v.kind_of?(Array) ? v.join("\</br>") : v)}</td></tr>"
+      end
+      string += "</table></td>"
+      html += "#{string}</tr></table>"
+      array << [html]
+    end
+    return JSON.pretty_generate(:predictions => array)
+  end
+end
+
 get '/predict/modeldetails/:model' do
   model = Model::Validation.find params[:model]
   crossvalidations = Validation::RepeatedCrossValidation.find(model.repeated_crossvalidation_id).crossvalidations
@@ -71,175 +249,13 @@ get '/predict/dataset/:name' do
   csv
 end
 
-get '/predict/:tmppath/:model/:filename?' do
+get '/predict/csv/:task/:model/:filename/?' do
   response['Content-Type'] = "text/csv"
-  path = File.join("tmp", params[:tmppath])
-  `sort -gk1 #{path} -o #{path}`
-
-  send_file path, :filename => "#{Time.now.strftime("%Y-%m-%d")}_lazar_batch_prediction_#{params[:model]}_#{params[:filename]}", :type => "text/csv", :disposition => "attachment"
-end
-
-get '/batch/:model/' do
-
-  if params[:model] == "Cramer"
-    dataset = Dataset.find params[:dataset]
-    compounds = dataset.compounds.collect{|c| c.smiles}
-    
-    prediction = [Toxtree.predict(compounds, "Cramer rules"), Toxtree.predict(compounds, "Cramer rules with extensions")]
-    output = {}
-    output["model_name"] = "Oral toxicity (Cramer rules)"
-    output["model_type"] = false
-    output["model_unit"] = false
-    ["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
-      output["#{key}"] = false
-    end
-    output["toxtree"] = true
-    output["cramer_rules"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules"]}}.flatten.compact
-    output["cramer_rules_extensions"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules, with extensions"]}}.flatten.compact
-
-    # td paths to insert results in GUI
-    compound_ids = dataset.compounds.collect{|c| c.id}
-    output["tds"] = compound_ids.each_with_index.map{|cid,idx| "prediction_#{cid}_Cramer_#{idx}"}
-    
-    # write to file
-    # header
-    csv = "ID,Endpoint,Unique SMILES,Cramer rules,Cramer rules with extensions\n"
-    
-    compounds.each_with_index do |smiles, idx|
-      csv << "#{idx+1},#{output["model_name"]},#{smiles},"\
-        "#{output["cramer_rules"][idx] != "nil" ? output["cramer_rules"][idx] : "none" },"\
-        "#{output["cramer_rules_extensions"][idx] != "nil" ? output["cramer_rules_extensions"][idx] : "none"}\n"
-    end
-    File.open(File.join("tmp", params[:tmppath]),"a+"){|file| file.write(csv)}
-
-    # cleanup
-    dataset.delete
-
-    # return output
-    response['Content-Type'] = "application/json"
-    return JSON.pretty_generate output
-
-  else
-    idx = params[:idx].to_i
-    compound = Compound.find params[:compound]
-
-    model = Model::Validation.find params[:model]
-    prediction = model.predict(compound)
-    output = {}
-    output["model_name"] = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
-    output["model_type"] = model.model.class.to_s.match("Classification") ? type = "Classification" : type = "Regression"
-    output["model_unit"] = (type == "Regression") ? "(#{model.unit})" : ""
-    output["converted_model_unit"] = (type == "Regression") ? "#{model.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
-    ["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
-      output["#{key}"] = false
-    end
-
-    if prediction[:value]
-      inApp = (prediction[:warnings].join(" ") =~ /Cannot/ ? "no" : (prediction[:warnings].join(" ") =~ /may|Insufficient/ ? "maybe" : "yes"))
-      if prediction[:info] =~ /\b(identical)\b/i
-        prediction[:info] = "This compound was part of the training dataset. All information "\
-          "from this compound was removed from the training data before the "\
-          "prediction, to obtain unbiased results."
-      end
-      note = "\"#{prediction[:warnings].uniq.join(" ")}\""
-
-      output["prediction_value"] = (type == "Regression") ? "#{prediction[:value].delog10.signif(3)}" : "#{prediction[:value]}"
-      output["converted_value"] = "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)}" if type == "Regression"
-
-      output["db_hit"] = prediction[:info] if prediction[:info]
-      
-      if prediction[:measurements].is_a?(Array)
-        output["measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} (#{model.unit})"} : prediction[:measurements].collect{|value| "#{value}"}
-        output["converted_measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{compound.mmol_to_mg(value.delog10).signif(3)} #{model.unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"} : false
-      else
-        output["measurements"] = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} (#{model.unit})}" : "#{prediction[:measurements]}"
-        output["converted_measurements"] = (type == "Regression") ? "#{compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(model.unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : false
-
-      end #db_hit
-
-      if type == "Regression"
-
-        if !prediction[:prediction_interval].nil?
-          interval = prediction[:prediction_interval]
-          output['interval'] = "#{interval[1].delog10.signif(3)} - #{interval[0].delog10.signif(3)}"
-          output['converted_interval'] = "#{compound.mmol_to_mg(interval[1].delog10).signif(3)} - #{compound.mmol_to_mg(interval[0].delog10).signif(3)}"
-        end #prediction interval
-
-        csv = "#{idx+1},#{output['model_name']},#{compound.smiles},"\
-          "\"#{prediction[:info] ? prediction[:info] : "no"}\",\"#{prediction[:measurements].join("; ") if prediction[:info]}\","\
-          "#{output['prediction_value'] != false ? output['prediction_value'] : ""},"\
-          "#{output['converted_value'] != false ? output['converted_value'] : ""},"\
-          "#{output['interval'].split(" - ").first.strip unless output['interval'] == false},"\
-          "#{output['interval'].split(" - ").last.strip unless output['interval'] == false},"\
-          "#{output['converted_interval'].split(" - ").first.strip unless output['converted_interval'] == false},"\
-          "#{output['converted_interval'].split(" - ").last.strip unless output['converted_interval'] == false},"\
-          "#{inApp},#{note.nil? ? "" : note.chomp}\n"
-      else # Classification
-
-        # consensus mutagenicity
-          
-        sa_prediction = KaziusAlerts.predict(compound.smiles)
-        lazar_mutagenicity = prediction
-        confidence = 0
-        lazar_mutagenicity_val = (lazar_mutagenicity[:value] == "non-mutagenic" ? false : true)
-        if sa_prediction[:prediction] == false && lazar_mutagenicity_val == false
-          confidence = 0.85
-        elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == true
-          confidence = 0.85 * ( 1 - sa_prediction[:error_product] )
-        elsif sa_prediction[:prediction] == false && lazar_mutagenicity_val == true
-          confidence = 0.11
-        elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == false
-          confidence = ( 1 - sa_prediction[:error_product] ) - 0.57
-        end
-        output['sa_prediction'] = sa_prediction
-        output['sa_matches'] = sa_prediction[:matches].collect{|a| a.first}.join("; ") unless sa_prediction[:matches].blank?
-        output['confidence'] = confidence.signif(3)
-        output['model_name'] = "Lazar #{model.endpoint.gsub('_', ' ').downcase} (#{model.species}):"
-        output['probability'] = prediction[:probabilities] ? prediction[:probabilities].collect{|k,v| "#{k}: #{v.signif(3)}"} : false
-
-        csv = "#{idx+1},Consensus mutagenicity,#{compound.smiles},"\
-          "\"#{prediction[:info] ? prediction[:info] : "no"}\",\"#{prediction[:measurements].join("; ") if prediction[:info]}\","\
-          "#{sa_prediction[:prediction] == false ? "non-mutagenic" : "mutagenic"},"\
-          "#{output['confidence']},#{output['sa_matches'] != false ? "\"#{output['sa_matches']}\"" : "none"},"\
-          "#{output['prediction_value']},"\
-          "#{output['probability'][0] != false ? output['probability'][0].split(":").last : ""},"\
-          "#{output['probability'][1] != false ? output['probability'][1].split(":").last : ""},"\
-          "#{inApp},#{note.nil? ? "" : note}\n"
-
-      end
-      
-      output["warnings"] = prediction[:warnings] if prediction[:warnings]
-
-    else #no prediction value
-      inApp = "no"
-      if prediction[:info] =~ /\b(identical)\b/i
-        prediction[:info] = "This compound was part of the training dataset. All information "\
-          "from this compound was removed from the training data before the "\
-          "prediction, to obtain unbiased results."
-      end
-      note = "\"#{prediction[:warnings].join(" ")}\""
-
-      output["warnings"] = prediction[:warnings]
-      output["info"] = prediction[:info] if prediction[:info]
-
-      if type == "Regression"
-        csv = "#{idx+1},#{output['model_name']},#{compound.smiles},#{prediction[:info] ? prediction[:info] : "no"},"\
-          "#{prediction[:measurements] if prediction[:info]},,,,,,,"+ [inApp,note].join(",")+"\n"
-      else
-        csv = "#{idx+1},Consensus mutagenicity,#{compound.smiles},#{prediction[:info] ? prediction[:info] : "no"},"\
-          "#{prediction[:measurements] if prediction[:info]},,,,,,,"+ [inApp,note].join(",")+"\n"
-      end
-
-    end #prediction value
-
-    # write to file
-    File.open(File.join("tmp", params[:tmppath]),"a"){|file| file.write(csv)}
-
-    # return output
-    response['Content-Type'] = "application/json"
-    return JSON.pretty_generate output
-
-  end# if Cramer
+  task = Task.find params[:task].to_s
+  tempfile = Tempfile.new
+  tempfile.write(task.csv)
+  tempfile.rewind
+  send_file tempfile, :filename => "#{Time.now.strftime("%Y-%m-%d")}_lazar_batch_prediction_#{params[:model]}_#{params[:filename]}", :type => "text/csv", :disposition => "attachment"
 end
 
 post '/predict/?' do
@@ -273,33 +289,98 @@ post '/predict/?' do
     end
     
     @models = params[:selection].keys
-    @tmppaths = {}
-    @models.each do |model|
-      m = Model::Validation.find model
-      type = (m.regression? ? "Regression" : "Classification") unless model == "Cramer"
-      # add header for regression
-      if type == "Regression"
-        unit = (type == "Regression") ? "(#{m.unit})" : ""
-        converted_unit = (type == "Regression") ? "#{m.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
-        header = "ID,Endpoint,Unique SMILES,inTrainingSet,Measurements,Prediction #{unit},Prediction #{converted_unit},"\
-          "Prediction Interval Low #{unit},Prediction Interval High #{unit},"\
-          "Prediction Interval Low #{converted_unit},Prediction Interval High #{converted_unit},"\
-          "inApplicabilityDomain,Note\n"
-      end
-      # add header for classification
-      if type == "Classification"
-        av = m.prediction_feature.accept_values
-        header = "ID,Endpoint,Unique SMILES,inTrainingSet,Measurements,Consensus Prediction,Consensus Confidence,"\
-          "Structural alerts for mutagenicity,Lazar Prediction,"\
-          "Lazar predProbability #{av[0]},Lazar predProbability #{av[1]},inApplicabilityDomain,Note\n"
-      end
-      path = File.join("tmp", "#{Time.now.strftime("%Y-%m-%d")}_#{SecureRandom.urlsafe_base64(5)}")
-      File.open(path, "w"){|f| f.write(header) if header}
-      @tmppaths[model] = path.split("/").last
-    end
+    # for single predictions in batch
+    @@compounds_ids = @compounds.collect{|c| c.id.to_s}
+    @tasks = []
+    @models.each{|m| t = Task.new; t.save; @tasks << t}
+    @predictions = {}
+    task = Task.run do
+      @models.each_with_index do |model,idx|
+        t = @tasks[idx]
+        unless model == "Cramer"
+          m = Model::Validation.find model
+          type = (m.regression? ? "Regression" : "Classification")
+          # add header for regression
+          if type == "Regression"
+            unit = (type == "Regression") ? "(#{m.unit})" : ""
+            converted_unit = (type == "Regression") ? "#{m.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
+            header = "ID,Endpoint,Unique SMILES,inTrainingSet,Measurements,Prediction #{unit},Prediction #{converted_unit},"\
+              "Prediction Interval Low #{unit},Prediction Interval High #{unit},"\
+              "Prediction Interval Low #{converted_unit},Prediction Interval High #{converted_unit},"\
+              "inApplicabilityDomain,Note\n"
+          end
+          # add header for classification
+          if type == "Classification"
+            av = m.prediction_feature.accept_values
+            header = "ID,Endpoint,Unique SMILES,inTrainingSet,Measurements,Consensus Prediction,Consensus Confidence,"\
+              "Structural alerts for mutagenicity,Lazar Prediction,"\
+              "Lazar predProbability #{av[0]},Lazar predProbability #{av[1]},inApplicabilityDomain,Note\n"
+          end
+          # predict compounds
+          p = 100.0/@compounds.size
+          counter = 1
+          predictions = []
+          @compounds.each do |compound|
+            prediction = m.predict(compound)
+            if type == "Classification"# consensus mutagenicity
+              sa_prediction = KaziusAlerts.predict(compound.smiles)
+              lazar_mutagenicity = prediction
+              confidence = 0
+              lazar_mutagenicity_val = (lazar_mutagenicity[:value] == "non-mutagenic" ? false : true)
+              if sa_prediction[:prediction] == false && lazar_mutagenicity_val == false
+                confidence = 0.85
+              elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == true
+                confidence = 0.85 * ( 1 - sa_prediction[:error_product] )
+              elsif sa_prediction[:prediction] == false && lazar_mutagenicity_val == true
+                confidence = 0.11
+              elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == false
+                confidence = ( 1 - sa_prediction[:error_product] ) - 0.57
+              end
+              prediction["Consensus prediction"] = sa_prediction[:prediction] == false ? "non-mutagenic" : "mutagenic"
+              prediction["Consensus confidence"] = confidence.signif(3)
+              prediction["Structural alerts for mutagenicity"] = sa_prediction[:matches] ? sa_prediction[:matches].collect{|a| a.first}.join("; ") : "none"
+            end
+            predictions << prediction.delete_if{|k,v| k =~ /neighbors|prediction_feature_id/i}
+            t.update_percent((counter*p).ceil)
+            counter += 1
+          end
+          # write csv
+          t[:csv] = header + to_csv(model,predictions,@compounds)
+          # write predictions
+          @predictions["#{model}"] = predictions
+        else # Cramer model
+          #t[:csv] = to_csv(model,nil,@compounds)
+          compounds = @compounds.collect{|c| c.smiles}
+          prediction = [Toxtree.predict(compounds, "Cramer rules"), Toxtree.predict(compounds, "Cramer rules with extensions")]
+          output = {}
+          output["model_name"] = "Oral toxicity (Cramer rules)"
+          output["cramer_rules"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules"]}}.flatten.compact
+          output["cramer_rules_extensions"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules, with extensions"]}}.flatten.compact
+          # header
+          csv = "ID,Endpoint,Unique SMILES,Cramer rules,Cramer rules with extensions\n"
+          # content
+          compounds.each_with_index do |smiles, idx|
+            csv << "#{idx+1},#{output["model_name"]},#{smiles},"\
+              "#{output["cramer_rules"][idx] != "nil" ? output["cramer_rules"][idx] : "none" },"\
+              "#{output["cramer_rules_extensions"][idx] != "nil" ? output["cramer_rules_extensions"][idx] : "none"}\n"
+          end
+          # write csv
+          t[:csv] = csv
+          # write predictions
+          @predictions["#{model}"] = predictions
+          t.update_percent(100)
+        end
+        # save task 
+        # append predictions as last action otherwise they won't save
+        # mongoid works with shallow copy via #dup
+        t[:predictions] = @predictions
+        t.save
+      end#models
+
+    end#main task
 
     File.delete File.join("tmp", params[:fileselect][:filename])
-    return haml :batch
+    return haml :task
   end
 
   # single compound prediction
@@ -350,4 +431,3 @@ get '/style.css' do
   headers 'Content-Type' => 'text/css; charset=utf-8'
   scss :style
 end
-