nano caret regression fixed

1 files changed, 29 insertions, 35 deletions

diff --git a/lib/model.rb b/lib/model.rb
index a272580..290309a 100644
--- a/lib/model.rb
+++ b/lib/model.rb
@@ -23,10 +23,12 @@ module OpenTox
         # explicit prediction algorithm
         if algorithms[:prediction] and algorithms[:prediction][:method]
           case algorithms[:prediction][:method]
-          when /Classifiction/
+          when /Classification/i
             model = LazarClassification.new
-          when /Regression/
+          when /Regression/i
             model = LazarRegression.new 
+          else
+            bad_request_error "Prediction method '#{algorithms[:prediction][:method]}' not implemented."
           end
 
         # guess model type
@@ -36,6 +38,10 @@ module OpenTox
           model = LazarClassification.new
         end
 
+        model.prediction_feature_id = prediction_feature.id
+        model.training_dataset_id = training_dataset.id
+        model.name = "#{training_dataset.name} #{prediction_feature.name}" 
+
         # set defaults
         substance_classes = training_dataset.substances.collect{|s| s.class.to_s}.uniq
         bad_request_error "Cannot create models for mixed substance classes '#{substance_classes.join ', '}'." unless substance_classes.size == 1
@@ -60,7 +66,7 @@ module OpenTox
             }
           elsif model.class == LazarRegression
             model.algorithms[:prediction] = {
-              :method => "Algorithm::Regression.caret",
+              :method => "Algorithm::Caret.regression",
               :parameters => "pls",
             }
           end
@@ -77,7 +83,7 @@ module OpenTox
               :min => 0.5
             },
             :prediction => {
-              :method => "Algorithm::Regression.caret",
+              :method => "Algorithm::Caret.regression",
               :parameters => "rf",
             },
             :feature_selection => {
@@ -100,10 +106,6 @@ module OpenTox
           end
         end
 
-        model.prediction_feature_id = prediction_feature.id
-        model.training_dataset_id = training_dataset.id
-        model.name = "#{training_dataset.name} #{prediction_feature.name}" 
-
         if model.algorithms[:feature_selection] and model.algorithms[:feature_selection][:method]
           model.relevant_features = Algorithm.run model.algorithms[:feature_selection][:method], dataset: training_dataset, prediction_feature: prediction_feature, types: model.algorithms[:descriptors][:types]
         end
@@ -151,8 +153,12 @@ module OpenTox
           else
             bad_request_error "Descriptor method '#{algorithms[:descriptors][:method]}' not available."
           end
-          params = algorithms[:prediction].merge({:descriptors => descriptors, :neighbors => neighbors})
-          params.delete :method
+          params = {
+            :method => algorithms[:prediction][:parameters],
+            :descriptors => descriptors,
+            :neighbors => neighbors,
+            :relevant_features => relevant_features
+          }
           result = Algorithm.run algorithms[:prediction][:method], params
           prediction.merge! result
           prediction[:neighbors] = neighbors
@@ -218,11 +224,9 @@ module OpenTox
     end
 
     class LazarClassification < Lazar
-
     end
 
     class LazarRegression < Lazar
-
     end
 
     class Prediction
@@ -240,7 +244,7 @@ module OpenTox
       field :leave_one_out_validation_id, type: BSON::ObjectId
 
       def predict object
-        Lazar.find(model_id).predict object
+        model.predict object
       end
 
       def training_dataset
@@ -251,6 +255,10 @@ module OpenTox
         Lazar.find model_id
       end
 
+      def prediction_feature
+        model.prediction_feature
+      end
+
       def repeated_crossvalidation
         Validation::RepeatedCrossValidation.find repeated_crossvalidation_id
       end
@@ -276,15 +284,8 @@ module OpenTox
         bad_request_error "No metadata file #{metadata_file}" unless File.exist? metadata_file
         prediction_model = self.new JSON.parse(File.read(metadata_file))
         training_dataset = Dataset.from_csv_file file
-        prediction_feature = training_dataset.features.first
-        model = nil
-        if prediction_feature.nominal?
-          model = LazarClassification.create prediction_feature, training_dataset
-        elsif prediction_feature.numeric?
-          model = LazarRegression.create prediction_feature, training_dataset
-        end
+        model = Lazar.create training_dataset: training_dataset
         prediction_model[:model_id] = model.id
-        prediction_model[:prediction_feature_id] = prediction_feature.id
         prediction_model[:repeated_crossvalidation_id] = Validation::RepeatedCrossValidation.create(model).id
         #prediction_model[:leave_one_out_validation_id] = Validation::LeaveOneOut.create(model).id
         prediction_model.save
@@ -297,26 +298,19 @@ module OpenTox
 
       def self.from_json_dump dir, category
         Import::Enanomapper.import dir
-
+        training_dataset = Dataset.where(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first
+        unless training_dataset
+          Import::Enanomapper.import File.join(File.dirname(__FILE__),"data","enm")
+          training_dataset = Dataset.where(name: "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles").first
+        end
         prediction_model = self.new(
           :endpoint => "log2(Net cell association)",
           :source => "https://data.enanomapper.net/",
           :species => "A549 human lung epithelial carcinoma cells",
           :unit => "log2(ug/Mg)"
         )
-        params = {
-          :feature_selection_algorithm => :correlation_filter,
-          :feature_selection_algorithm_parameters => {:category => category},
-          :neighbor_algorithm => "physchem_neighbors",
-          :neighbor_algorithm_parameters => {:min_sim => 0.5},
-          :prediction_algorithm => "OpenTox::Algorithm::Regression.physchem_regression",
-          :prediction_algorithm_parameters => {:method => 'rf'}, # random forests
-          } 
-        training_dataset = Dataset.find_or_create_by(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles")
-        prediction_feature = Feature.find_or_create_by(name: "log2(Net cell association)", category: "TOX")
-        #prediction_feature = Feature.find("579621b84de73e267b414e55")
-        prediction_model[:prediction_feature_id] = prediction_feature.id
-        model = Model::LazarRegression.create(prediction_feature, training_dataset, params)
+        prediction_feature = Feature.where(name: "log2(Net cell association)", category: "TOX").first
+        model = Model::LazarRegression.create(prediction_feature: prediction_feature, training_dataset: training_dataset)
         prediction_model[:model_id] = model.id
         repeated_cv = Validation::RepeatedCrossValidation.create model
         prediction_model[:repeated_crossvalidation_id] = Validation::RepeatedCrossValidation.create(model).id