1 files changed, 0 insertions, 99 deletions
diff --git a/lib/experiment.rb b/lib/experiment.rb
deleted file mode 100644
index 0dfdf86..0000000
--- a/lib/experiment.rb
+++ /dev/null
@@ -1,99 +0,0 @@
-module OpenTox
-
-  class Experiment
-    field :dataset_ids, type: Array
-    field :model_settings, type: Array, default: []
-    field :results, type: Hash, default: {}
-
-    def run 
-      dataset_ids.each do |dataset_id|
-        dataset = Dataset.find(dataset_id)
-        results[dataset_id.to_s] = []
-        model_settings.each do |setting|
-          setting = setting.dup
-          model_algorithm = setting.delete :model_algorithm #if setting[:model_algorithm]
-          model = Object.const_get(model_algorithm).create dataset, setting
-          $logger.debug model
-          model.save
-          repeated_crossvalidation = RepeatedCrossValidation.create model
-          results[dataset_id.to_s] << {:model_id => model.id, :repeated_crossvalidation_id => repeated_crossvalidation.id}
-        end
-      end
-      save
-    end
-
-    def report
-      # statistical significances http://www.r-bloggers.com/anova-and-tukeys-test-on-r/
-      report = {}
-      report[:name] = name
-      report[:experiment_id] = self.id.to_s
-      report[:results] = {}
-      parameters = []
-      dataset_ids.each do |dataset_id|
-        dataset_name = Dataset.find(dataset_id).name
-        report[:results][dataset_name] = {}
-        report[:results][dataset_name][:anova] = {}
-        report[:results][dataset_name][:data] = []
-        # TODO results[dataset_id.to_s] does not exist
-        results[dataset_id.to_s].each do |result|
-          model = Model::Lazar.find(result[:model_id])
-          repeated_cv = RepeatedCrossValidation.find(result[:repeated_crossvalidation_id])
-          crossvalidations = repeated_cv.crossvalidations
-          if crossvalidations.first.is_a? ClassificationCrossValidation
-            parameters = [:accuracy,:true_rate,:predictivity]
-          elsif crossvalidations.first.is_a? RegressionCrossValidation
-            parameters = [:rmse,:mae,:r_squared]
-          end
-          summary = {}
-          [:neighbor_algorithm, :neighbor_algorithm_parameters, :prediction_algorithm].each do |key|
-            summary[key] = model[key]
-          end
-          summary[:nr_instances] = crossvalidations.first.nr_instances
-          summary[:nr_unpredicted] = crossvalidations.collect{|cv| cv.nr_unpredicted}
-          summary[:time] = crossvalidations.collect{|cv| cv.time}
-          parameters.each do |param|
-            summary[param] = crossvalidations.collect{|cv| cv.send(param)}
-          end
-          report[:results][dataset_name][:data] << summary
-        end
-      end
-      report[:results].each do |dataset,results|
-        ([:time,:nr_unpredicted]+parameters).each do |param|
-          experiments = []
-          outcome = []
-          results[:data].each_with_index do |result,i|
-            result[param].each do |p|
-              experiments << i
-              p = nil if p.kind_of? Float and p.infinite? # TODO fix @ division by 0
-              outcome << p
-            end
-          end
-          begin
-          R.assign "experiment_nr",experiments.collect{|i| "Experiment #{i}"}
-          R.eval "experiment_nr = factor(experiment_nr)"
-          R.assign "outcome", outcome
-          R.eval "data = data.frame(experiment_nr,outcome)"
-          # one-way ANOVA
-          R.eval "fit = aov(outcome ~ experiment_nr, data=data,na.action='na.omit')"
-          # http://stackoverflow.com/questions/3366506/extract-p-value-from-aov
-          p_value = R.eval("summary(fit)[[1]][['Pr(>F)']][[1]]").to_ruby
-          # aequivalent
-          # sum = R.eval("summary(fit)")
-          #p_value = sum.to_ruby.first.last.first
-          rescue 
-            p_value = nil
-          end
-          report[:results][dataset][:anova][param] = p_value
-=begin
-=end
-        end
-      end
-      report
-    end
-
-    def summary
-      report[:results].collect{|dataset,data| {dataset => data[:anova].select{|param,p_val| p_val < 0.1}}}
-    end
-  end
-
-end