1 files changed, 99 insertions, 0 deletions
diff --git a/lib/experiment.rb b/lib/experiment.rb
new file mode 100644
index 0000000..0dfdf86
--- /dev/null
+++ b/lib/experiment.rb
@@ -0,0 +1,99 @@
+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