initial k-nn weighted average implementation

1 files changed, 143 insertions, 0 deletions

diff --git a/lib/lazar_regression.rb b/lib/lazar_regression.rb
new file mode 100644
index 0000000..b64bc8d
--- /dev/null
+++ b/lib/lazar_regression.rb
@@ -0,0 +1,143 @@
+module OpenTox
+
+  module Model
+
+    class LazarRegression < Lazar
+
+      # Create a lazar model from a training_dataset and a feature_dataset
+      # @param [OpenTox::Dataset] training_dataset
+      # @param [OpenTox::Dataset] feature_dataset
+      # @return [OpenTox::Model::Lazar] Regression or classification model
+      def self.create training_dataset#, feature_dataset
+
+        #bad_request_error "No features found in feature dataset #{feature_dataset.id}." if feature_dataset.features.empty?
+        bad_request_error "More than one prediction feature found in training_dataset #{training_dataset.id}" unless training_dataset.features.size == 1
+        #bad_request_error "Training dataset compounds do not match feature dataset compounds. Please ensure that they are in the same order." unless training_dataset.compounds == feature_dataset.compounds
+
+        prediction_feature = training_dataset.features.first
+        prediction_feature.nominal ?  lazar = OpenTox::Model::LazarClassification.new : lazar = OpenTox::Model::LazarRegression.new
+        #lazar.feature_dataset_id = feature_dataset.id
+        lazar.training_dataset_id = training_dataset.id
+        lazar.prediction_feature_id = prediction_feature.id
+        lazar.title = prediction_feature.title 
+
+        # log transform activities (create new dataset)
+        # scale, normalize features, might not be necessary
+        # http://stats.stackexchange.com/questions/19216/variables-are-often-adjusted-e-g-standardised-before-making-a-model-when-is
+        # http://stats.stackexchange.com/questions/7112/when-and-how-to-use-standardized-explanatory-variables-in-linear-regression
+        # zero-order correlation and the semi-partial correlation
+        # seems to be necessary for svm
+        #   http://stats.stackexchange.com/questions/77876/why-would-scaling-features-decrease-svm-performance?lq=1
+        #   http://stackoverflow.com/questions/15436367/svm-scaling-input-values
+        # use lasso or elastic net??
+        # select relevant features
+        #   remove features with a single value
+        #   remove correlated features
+        #   remove features not correlated with endpoint
+
+        lazar.save
+        lazar
+      end
+
+      def predict object
+
+        t = Time.now
+        at = Time.now
+
+        training_dataset = OpenTox::Dataset.find(training_dataset_id)
+
+        compounds = []
+        case object.class.to_s
+        when "OpenTox::Compound"
+          compounds = [object] 
+        when "Array"
+          compounds = object
+        when "OpenTox::Dataset"
+          compounds = object.compounds
+        else 
+          bad_request_error "Please provide a OpenTox::Compound an Array of OpenTox::Compounds or an OpenTox::Dataset as parameter."
+        end
+
+        $logger.debug "Setup: #{Time.now-t}"
+        t = Time.now
+
+        $logger.debug "Query fingerprint calculation: #{Time.now-t}"
+        t = Time.now
+
+        predictions = []
+        prediction_feature = OpenTox::Feature.find prediction_feature_id
+        tt = 0
+        pt = 0
+        nt = 0
+        st = 0
+        nit = 0
+        predictions = []
+        compounds.each_with_index do |compound,c|
+          t = Time.new
+          neighbors = compound.neighbors 
+          weighted_sum = 0
+          sim_sum = 0
+          neighbors.each do |row|
+            n,sim = row
+            i = training_dataset.compound_ids.index n.id
+            if i
+              act = training_dataset.data_entries[i].first
+              if act
+                weighted_sum += sim*Math.log10(act)
+                sim_sum += sim
+              end
+            end
+          end
+          weighted_average = 10**(weighted_sum/sim_sum)
+          p weighted_average
+        end 
+        $logger.debug "Transform time: #{tt}"
+        $logger.debug "Neighbor search time: #{nt} (Similarity calculation: #{st}, Neighbor insert: #{nit})"
+        $logger.debug "Prediction time: #{pt}"
+        $logger.debug "Total prediction time: #{Time.now-at}"
+
+        # serialize result
+        case object.class.to_s
+        when "OpenTox::Compound"
+          return predictions.first
+        when "Array"
+          return predictions
+        when "OpenTox::Dataset"
+          # prepare prediction dataset
+          prediction_dataset = LazarPrediction.new(
+            :title => "Lazar prediction for #{prediction_feature.title}",
+            :creator =>  __FILE__,
+            :prediction_feature_id => prediction_feature.id
+
+          )
+          confidence_feature = OpenTox::NumericFeature.find_or_create_by( "title" => "Prediction confidence" )
+          warning_feature = OpenTox::NominalFeature.find_or_create_by("title" => "Warnings")
+          prediction_dataset.features = [ prediction_feature, confidence_feature, warning_feature ]
+          prediction_dataset.compounds = compounds
+          prediction_dataset.data_entries = predictions.collect{|p| [p[:value], p[:confidence],p[:warning]]}
+          prediction_dataset.save_all
+          return prediction_dataset
+        end
+
+      end
+
+      def training_dataset
+        Dataset.find training_dataset_id
+      end
+
+      def prediction_feature
+        Feature.find prediction_feature_id
+      end
+      
+      def training_activities
+        i = @training_dataset.feature_ids.index prediction_feature_id
+        @training_dataset.data_entries.collect{|de| de[i]}
+      end
+
+    end
+
+
+  end
+
+end
+