module OpenTox

  class LeaveOneOutValidation

    field :model_id, type: BSON::ObjectId
    field :dataset_id, type: BSON::ObjectId
    field :nr_instances, type: Integer
    field :nr_unpredicted, type: Integer
    field :predictions, type: Hash
    field :finished_at, type: Time 

    def self.create model
      $logger.debug "#{model.name}: LOO validation started"
      t = Time.now
      model.training_dataset.features.first.nominal? ? klass = ClassificationLeaveOneOutValidation : klass = RegressionLeaveOneOutValidation
      loo = klass.new :model_id => model.id, :dataset_id => model.training_dataset_id
      predictions = model.predict model.training_dataset.compounds
      predictions.each{|cid,p| p.delete(:neighbors)}
      nr_unpredicted = 0
      predictions.each do |cid,prediction|
        if prediction[:value]
          prediction[:measured] = Substance.find(cid).toxicities[prediction[:prediction_feature_id].to_s][dataset_id.to_s]
        else
          nr_unpredicted += 1
        end
        predictions.delete(cid) unless prediction[:value] and prediction[:measured]
      end
      loo.nr_instances = predictions.size
      loo.nr_unpredicted = nr_unpredicted
      loo.predictions = predictions
      loo.statistics
      loo.save
      $logger.debug "#{model.name}, LOO validation:  #{Time.now-t} seconds"
      loo
    end

    def model
      Model::Lazar.find model_id
    end
  end

  class ClassificationLeaveOneOutValidation < LeaveOneOutValidation

    field :accept_values, type: Array
    field :confusion_matrix, type: Array, default: []
    field :weighted_confusion_matrix, type: Array, default: []
    field :accuracy, type: Float
    field :weighted_accuracy, type: Float
    field :true_rate, type: Hash, default: {}
    field :predictivity, type: Hash, default: {}
    field :confidence_plot_id, type: BSON::ObjectId

    def statistics
      stat = ValidationStatistics.classification(predictions, Feature.find(model.prediction_feature_id).accept_values)
      update_attributes(stat)
    end

    def confidence_plot
      unless confidence_plot_id
        tmpfile = "/tmp/#{id.to_s}_confidence.svg"
        accuracies = []
        confidences = []
        correct_predictions = 0
        incorrect_predictions = 0
        predictions.each do |p|
          p[:database_activities].each do |db_act|
            if p[:value] 
              p[:value] == db_act ? correct_predictions += 1 : incorrect_predictions += 1
              accuracies << correct_predictions/(correct_predictions+incorrect_predictions).to_f
              confidences << p[:confidence]

            end
          end
        end
        R.assign "accuracy", accuracies
        R.assign "confidence", confidences
        R.eval "image = qplot(confidence,accuracy)+ylab('accumulated accuracy')+scale_x_reverse()"
        R.eval "ggsave(file='#{tmpfile}', plot=image)"
        file = Mongo::Grid::File.new(File.read(tmpfile), :filename => "#{self.id.to_s}_confidence_plot.svg")
        plot_id = $gridfs.insert_one(file)
        update(:confidence_plot_id => plot_id)
      end
      $gridfs.find_one(_id: confidence_plot_id).data
    end
  end
  

  class RegressionLeaveOneOutValidation < LeaveOneOutValidation

    field :rmse, type: Float, default: 0
    field :mae, type: Float, default: 0
    field :r_squared, type: Float
    field :correlation_plot_id, type: BSON::ObjectId
    field :confidence_plot_id, type: BSON::ObjectId

    def statistics
      stat = ValidationStatistics.regression predictions
      update_attributes(stat)
    end

    def correlation_plot
      unless correlation_plot_id
        tmpfile = "/tmp/#{id.to_s}_correlation.svg"
        predicted_values = []
        measured_values = []
        predictions.each do |pred|
          pred[:database_activities].each do |activity|
            if pred[:value]
              predicted_values << pred[:value]
              measured_values << activity
            end
          end
        end
        attributes = Model::Lazar.find(self.model_id).attributes
        attributes.delete_if{|key,_| key.match(/_id|_at/) or ["_id","creator","name"].include? key}
        attributes = attributes.values.collect{|v| v.is_a?(String) ? v.sub(/OpenTox::/,'') : v}.join("\n")
        R.assign "measurement", measured_values
        R.assign "prediction", predicted_values
        R.eval "all = c(-log(measurement),-log(prediction))"
        R.eval "range = c(min(all), max(all))"
        R.eval "image = qplot(-log(prediction),-log(measurement),main='#{self.name}',asp=1,xlim=range, ylim=range)"
        R.eval "image = image + geom_abline(intercept=0, slope=1)"
        R.eval "ggsave(file='#{tmpfile}', plot=image)"
        file = Mongo::Grid::File.new(File.read(tmpfile), :filename => "#{self.id.to_s}_correlation_plot.svg")
        plot_id = $gridfs.insert_one(file)
        update(:correlation_plot_id => plot_id)
      end
      $gridfs.find_one(_id: correlation_plot_id).data
    end
  end

end