nanoparticle models fixed

5 files changed, 30 insertions, 24 deletions

diff --git a/lib/dataset.rb b/lib/dataset.rb
index 86800c6..9738c1f 100644
--- a/lib/dataset.rb
+++ b/lib/dataset.rb
@@ -62,12 +62,12 @@ module OpenTox
         training_cids = training_idxs.collect{|i| substance_ids[i]}
         chunk = [training_cids,test_cids].collect do |cids|
           dataset = self.class.create(:substance_ids => cids, :feature_ids => feature_ids, :source => self.id )
-          dataset.compounds.each do |compound|
-            compound.dataset_ids << dataset.id
-            compound.toxicities.each do |feature_id,data|
+          dataset.substances.each do |substance|
+            substance.dataset_ids << dataset.id
+            substance.toxicities.each do |feature_id,data|
               data[dataset.id.to_s] = data[self.id.to_s] # copy data entries
             end
-            compound.save
+            substance.save
           end
           dataset
         end
diff --git a/lib/import.rb b/lib/import.rb
index 11cb367..dfe5e2d 100644
--- a/lib/import.rb
+++ b/lib/import.rb
@@ -40,10 +40,10 @@ module OpenTox
             datasets[bundle_uri].substance_ids << nanoparticle.id
             nanoparticle["dataset_ids"] << datasets[bundle_uri].id
           end
+          bundle = datasets[np["bundles"].keys.first].id if np["bundles"].size == 1
           study["effects"].each do |effect|
             effect["result"]["textValue"] ?  klass = NominalFeature : klass = NumericFeature
             # TODO parse core/coating
-            # TODO parse proteomics, they come as a large textValue
             #$logger.debug File.join(np["compound"]["URI"],"study")
             effect["conditions"].delete_if { |k, v| v.nil? }
             # parse proteomics data
@@ -53,7 +53,7 @@ module OpenTox
                   :name => identifier,
                   :category => "Proteomics",
                 )
-                nanoparticle.parse_ambit_value feature, value
+                nanoparticle.parse_ambit_value feature, value, bundle
               end
             else
               feature = klass.find_or_create_by(
@@ -62,7 +62,7 @@ module OpenTox
                 :category => study["protocol"]["topcategory"],
                 :conditions => effect["conditions"]
               )
-              nanoparticle.parse_ambit_value feature, effect["result"]
+              nanoparticle.parse_ambit_value feature, effect["result"], bundle
             end
           end
           nanoparticle.save
diff --git a/lib/model.rb b/lib/model.rb
index 5b094fb..070248a 100644
--- a/lib/model.rb
+++ b/lib/model.rb
@@ -76,6 +76,7 @@ module OpenTox
         prediction = {}
         if neighbors.collect{|n| n["_id"]}.include? compound.id
 
+          me = neighbors.select{|n| n["_id"] == compound.id}.first
           database_activities = neighbors.select{|n| n["_id"] == compound.id}.first["toxicities"][prediction_feature.id.to_s][training_dataset_id.to_s].uniq
           prediction[:database_activities] = database_activities
           prediction[:warning] = "#{database_activities.size} compounds have been removed from neighbors, because they have the same structure as the query compound."
diff --git a/lib/nanoparticle.rb b/lib/nanoparticle.rb
index 9bf419d..b79981d 100644
--- a/lib/nanoparticle.rb
+++ b/lib/nanoparticle.rb
@@ -9,10 +9,14 @@ module OpenTox
     field :proteomics, type: Hash, default: {}
 
     def nanoparticle_neighbors params
-      Dataset.find(params[:training_dataset_id]).nanoparticles.collect{|np| np["tanimoto"] = 1; np}
+      dataset = Dataset.find(params[:training_dataset_id])
+      Dataset.find(params[:training_dataset_id]).nanoparticles.collect do |np|
+        np["tanimoto"] = 1
+        np unless np.toxicities.empty?
+      end.compact
     end
 
-    def add_feature feature, value
+    def add_feature feature, value, dataset_id
       case feature.category
       when "P-CHEM"
         physchem_descriptors[feature.id.to_s] ||= []
@@ -23,51 +27,52 @@ module OpenTox
         proteomics[feature.id.to_s] << value
         proteomics[feature.id.to_s].uniq!
       when "TOX"
-        toxicities[feature.id.to_s] ||= []
+        toxicities[feature.id.to_s] ||= {}
+        toxicities[feature.id.to_s][dataset_id.to_s] ||= []
         # TODO generic way of parsing TOX values
         if feature.name == "7.99 Toxicity (other) ICP-AES" and feature.unit == "mL/ug(Mg)" 
-          toxicities[feature.id.to_s] << -Math.log10(value)
+          toxicities[feature.id.to_s][dataset_id.to_s] << -Math.log10(value)
         else
-          toxicities[feature.id.to_s] << value
+          toxicities[feature.id.to_s][dataset_id.to_s] << value
         end
-        toxicities[feature.id.to_s].uniq!
+        toxicities[feature.id.to_s][dataset_id.to_s].uniq!
       else
         warn "Unknown feature type '#{feature.category}'. Value '#{value}' not inserted."
       end
     end
 
-    def parse_ambit_value feature, v
+    def parse_ambit_value feature, v, dataset_id
       v.delete "unit"
       # TODO: ppm instead of weights
       if v.keys == ["textValue"]
-        add_feature feature, v["textValue"]
+        add_feature feature, v["textValue"], dataset_id
       elsif v.keys == ["loValue"]
-        add_feature feature, v["loValue"]
+        add_feature feature, v["loValue"], dataset_id
       elsif v.keys.size == 2 and v["errorValue"]
-        add_feature feature, v["loValue"]
+        add_feature feature, v["loValue"], dataset_id
         warn "Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
       elsif v.keys.size == 2 and v["loQualifier"] == "mean"
-        add_feature feature, v["loValue"]
+        add_feature feature, v["loValue"], dataset_id
         warn "'#{feature.name}' is a mean value. Original data is not available."
       elsif v.keys.size == 2 and v["loQualifier"] #== ">="
         warn "Only min value available for '#{feature.name}', entry ignored"
       elsif v.keys.size == 2 and v["upQualifier"] #== ">="
         warn "Only max value available for '#{feature.name}', entry ignored"
       elsif v.keys.size == 3 and v["loValue"] and v["loQualifier"].nil? and v["upQualifier"].nil?
-        add_feature feature, v["loValue"]
+        add_feature feature, v["loValue"], dataset_id
         warn "loQualifier and upQualifier are empty."
       elsif v.keys.size == 3 and v["loValue"] and v["loQualifier"] == "" and v["upQualifier"] == ""
-        add_feature feature, v["loValue"]
+        add_feature feature, v["loValue"], dataset_id
         warn "loQualifier and upQualifier are empty."
       elsif v.keys.size == 4 and v["loValue"] and v["loQualifier"].nil? and v["upQualifier"].nil?
-        add_feature feature, v["loValue"]
+        add_feature feature, v["loValue"], dataset_id
         warn "loQualifier and upQualifier are empty."
       elsif v.size == 4 and v["loQualifier"] and v["upQualifier"] and v["loValue"] and v["upValue"]
-        add_feature feature, [v["loValue"],v["upValue"]].mean
+        add_feature feature, [v["loValue"],v["upValue"]].mean, dataset_id
         warn "Using mean value of range #{v["loValue"]} - #{v["upValue"]} for '#{feature.name}'. Original data is not available."
       elsif v.size == 4 and v["loQualifier"] == "mean" and v["errorValue"]
         warn "'#{feature.name}' is a mean value. Original data is not available. Ignoring errorValue '#{v["errorValue"]}' for '#{feature.name}'."
-        add_feature feature, v["loValue"]
+        add_feature feature, v["loValue"], dataset_id
       elsif v == {} # do nothing
       else
         warn "Cannot parse Ambit eNanoMapper value '#{v}' for feature '#{feature.name}'."
diff --git a/lib/regression.rb b/lib/regression.rb
index b8a7e5f..691f903 100644
--- a/lib/regression.rb
+++ b/lib/regression.rb
@@ -10,7 +10,7 @@ module OpenTox
         neighbors.each do |row|
           sim = row["tanimoto"]
           sim ||= 1 # TODO: sim f nanoparticles
-          if row["toxicities"][params[:prediction_feature_id].to_s]
+          if row["toxicities"][params[:prediction_feature_id].to_s] and row["toxicities"][params[:prediction_feature_id].to_s][params[:training_dataset_id].to_s]
             row["toxicities"][params[:prediction_feature_id].to_s][params[:training_dataset_id].to_s].each do |act|
               weighted_sum += sim*act
               sim_sum += sim