substance and nanoparticle model creation and predictions

1 files changed, 61 insertions, 2 deletions

diff --git a/lib/substance.rb b/lib/substance.rb
index 6768ce7..d271327 100644
--- a/lib/substance.rb
+++ b/lib/substance.rb
@@ -1,9 +1,68 @@
 module OpenTox
 
   class Substance
-    field :physchem_descriptors, type: Hash, default: {}
+    field :properties, type: Hash, default: {}
     field :dataset_ids, type: Array, default: []
   end
 
-end
+  def neighbors dataset_id:,prediction_feature_id:,descriptors:,similarity:,relevant_features:nil
+    # TODO enable empty dataset_id -> use complete db
+    case descriptors[:method]
+    when "fingerprint"
+      fingerprint_neighbors dataset_id:dataset_id, prediction_feature_id:prediction_feature_id, descriptors:descriptors, similarity:similarity
+    when "properties"
+      properties_neighbors dataset_id:dataset_id, prediction_feature_id:prediction_feature_id, descriptors:descriptors, similarity:similarity, relevant_features: relevant_features
+    else
+      bad_request_error "Descriptor method '#{descriptors[:method]}' not implemented."
+    end
+  end
+
+  def fingerprint_neighbors dataset_id:,prediction_feature_id:,descriptors:,similarity:
+    neighbors = []
+    dataset = Dataset.find(dataset_id)
+    dataset.substances.each do |substance|
+      values = dataset.values(substance,prediction_feature_id)
+      if values
+        query_descriptors = self.send(descriptors[:method].to_sym, descriptors[:type])
+        candidate_descriptors = substance.send(descriptors[:method].to_sym, descriptors[:type])
+        sim = Algorithm.run similarity[:method], [query_descriptors, candidate_descriptors]
+        neighbors << {"_id" => substance.id, "measurements" => values, "descriptors" => candidate_descriptors, "similarity" => sim} if sim >= similarity[:min]
+      end
+    end
+    neighbors.sort{|a,b| b["similarity"] <=> a["similarity"]}
+  end
 
+  def properties_neighbors dataset_id:,prediction_feature_id:,descriptors:,similarity:,relevant_features:
+    neighbors = []
+    dataset = Dataset.find(dataset_id)
+    weights = relevant_features.collect{|k,v| v["r"]**2}
+    means = relevant_features.collect{|k,v| v["mean"]}
+    standard_deviations = relevant_features.collect{|k,v| v["sd"]}
+    query_descriptors = relevant_features.keys.collect{|i| properties[i].is_a?(Array) ? properties[i].median : nil  }
+    dataset.substances.each do |substance|
+      values = dataset.values(substance,prediction_feature_id)
+      # exclude nanoparticles with different core
+      # TODO validate exclusion
+      next if substance.is_a? Nanoparticle and substance.core != self.core
+      if values
+        candidate_descriptors = relevant_features.keys.collect{|i| substance.properties[i].is_a?(Array) ? substance.properties[i].median : nil  }
+        q = []
+        c = []
+        w = []
+        (0..relevant_features.size-1).each do |i|
+          # add only complete pairs
+          if query_descriptors[i] and candidate_descriptors[i]
+            w << weights[i]
+            # scale values
+            q << (query_descriptors[i] - means[i])/standard_deviations[i]
+            c << (candidate_descriptors[i] - means[i])/standard_deviations[i]
+          end
+        end
+        sim = Algorithm.run similarity[:method], [q, c, w]
+        neighbors << {"_id" => substance.id, "measurements" => values, "descriptors" => candidate_descriptors, "similarity" => sim} if sim >= similarity[:min]
+      end
+    end
+    neighbors.sort{|a,b| b["similarity"] <=> a["similarity"]}
+  end
+
+end