#[ 'rubygems', 'datamapper' ].each do |lib|
#  require lib
#end
require "lib/merge.rb"

module Validation

  VAL_PROPS_GENERAL = [ :validation_uri, :validation_type, :model_uri, :algorithm_uri, :training_dataset_uri, :prediction_feature,
                :test_dataset_uri, :test_target_dataset_uri, :prediction_dataset_uri, :date ] 
  VAL_PROPS_SUM = [ :num_instances, :num_without_class, :num_unpredicted ]
  VAL_PROPS_AVG = [:real_runtime, :percent_without_class, :percent_unpredicted ]
  VAL_PROPS = VAL_PROPS_GENERAL + VAL_PROPS_SUM + VAL_PROPS_AVG
  
  # :crossvalidation_info
  VAL_CV_PROPS = [ :crossvalidation_id, :crossvalidation_uri, :crossvalidation_fold ]
  
  # :classification_statistics
  VAL_CLASS_PROPS_SINGLE_SUM = [ :num_correct, :num_incorrect, :confusion_matrix  ]
  VAL_CLASS_PROPS_SINGLE_AVG = [ :percent_correct, :percent_incorrect, 
    :weighted_area_under_roc, :accuracy, :weighted_accuracy ] 
  VAL_CLASS_PROPS_SINGLE = VAL_CLASS_PROPS_SINGLE_SUM + VAL_CLASS_PROPS_SINGLE_AVG
  
  # :class_value_statistics
  VAL_CLASS_PROPS_PER_CLASS_SUM = [ :num_false_positives, :num_false_negatives, 
                                :num_true_positives, :num_true_negatives ]
  VAL_CLASS_PROPS_PER_CLASS_AVG = [ :area_under_roc, :false_negative_rate, :false_positive_rate,
                                :f_measure, :precision, 
                                :true_negative_rate, :true_positive_rate ] #:recall,
  VAL_CLASS_PROPS_PER_CLASS = VAL_CLASS_PROPS_PER_CLASS_SUM + VAL_CLASS_PROPS_PER_CLASS_AVG
  VAL_CLASS_PROPS_PER_CLASS_COMPLEMENT_EXISTS = [ :num_false_positives, :num_false_negatives, 
                                :num_true_positives, :num_true_negatives, :false_negative_rate, :false_positive_rate,
                                :true_negative_rate, :true_positive_rate ] #:precision, :recall, 
                                
  VAL_CLASS_PROPS = VAL_CLASS_PROPS_SINGLE + VAL_CLASS_PROPS_PER_CLASS

  # :regression_statistics
  VAL_REGR_PROPS = [ :root_mean_squared_error, :mean_absolute_error, :r_square, :weighted_r_square,
    :target_variance_actual, :target_variance_predicted, :sum_squared_error, :sample_correlation_coefficient,
    :weighted_mean_absolute_error, :weighted_root_mean_squared_error ]
  
  CROSS_VAL_PROPS = [:dataset_uri, :num_folds, :stratified, :random_seed]
  CROSS_VAL_PROPS_REDUNDANT = [:crossvalidation_uri, :algorithm_uri, :date] + CROSS_VAL_PROPS 
  
  ALL_PROPS = VAL_PROPS + VAL_CV_PROPS + VAL_CLASS_PROPS + VAL_REGR_PROPS + CROSS_VAL_PROPS

  VAL_MERGE_GENERAL = VAL_PROPS_GENERAL + VAL_CV_PROPS + [:classification_statistics, :regression_statistics] + CROSS_VAL_PROPS
  VAL_MERGE_SUM = VAL_PROPS_SUM + VAL_CLASS_PROPS_SINGLE_SUM + VAL_CLASS_PROPS_PER_CLASS_SUM
  VAL_MERGE_AVG = VAL_PROPS_AVG + VAL_CLASS_PROPS_SINGLE_AVG + VAL_CLASS_PROPS_PER_CLASS_AVG + VAL_REGR_PROPS
  

  class Validation < Ohm::Model 
  
    attribute :validation_type
    attribute :model_uri
    attribute :algorithm_uri
    attribute :training_dataset_uri
    attribute :test_target_dataset_uri
    attribute :test_dataset_uri
    attribute :prediction_dataset_uri
    attribute :prediction_feature
    attribute :date
    attribute :num_instances
    attribute :num_without_class
    attribute :num_unpredicted
    attribute :crossvalidation_id
    attribute :crossvalidation_fold
    attribute :real_runtime
    attribute :percent_without_class
    attribute :percent_unpredicted
    attribute :classification_statistics_yaml
    attribute :regression_statistics_yaml
    attribute :finished    
    
    index :model_uri
    index :validation_type
    index :crossvalidation_id
    
    attr_accessor :subjectid
    
    def self.create(params={})
      params[:date] = Time.new
      super params
    end
    
    def classification_statistics
      YAML.load(self.classification_statistics_yaml) if self.classification_statistics_yaml
    end
    
    def classification_statistics=(cs)
      self.classification_statistics_yaml = cs.to_yaml
    end
    
    def regression_statistics
      YAML.load(self.regression_statistics_yaml) if self.regression_statistics_yaml
    end
    
    def regression_statistics=(rs)
      self.regression_statistics_yaml = rs.to_yaml
    end

    def save
      super
      OpenTox::Authorization.check_policy(validation_uri, subjectid)
    end
    
    public
    def validation_uri
      raise "no id" if self.id==nil
      $url_provider.url_for("/"+self.id.to_s, :full)
    end
    
    def crossvalidation_uri
      $url_provider.url_for("/crossvalidation/"+self.crossvalidation_id.to_s, :full) if self.crossvalidation_id
    end
    
    def self.classification_property?( property )
      VAL_CLASS_PROPS.include?( property )
    end
    
    def self.depends_on_class_value?( property )
      VAL_CLASS_PROPS_PER_CLASS.include?( property )
    end
    
    def self.complement_exists?( property )
      VAL_CLASS_PROPS_PER_CLASS_COMPLEMENT_EXISTS.include?( property )
    end
    
  end
  
  class Crossvalidation < Ohm::Model
  
    attribute :algorithm_uri
    attribute :dataset_uri
    attribute :date
    attribute :num_folds
    attribute :random_seed
    attribute :finished
    attribute :stratified
    attribute :loo
    
    attr_accessor :subjectid
    
    index :algorithm_uri
    index :dataset_uri
    index :num_folds
    index :random_seed
    index :stratified
    index :finished
    index :loo

    def self.create(params={})
      params[:date] = Time.new
      super params
    end
    
    def save
      super
      OpenTox::Authorization.check_policy(crossvalidation_uri, subjectid)
    end
    
    public
    def crossvalidation_uri
      raise "no id" if self.id==nil
      $url_provider.url_for("/crossvalidation/"+self.id.to_s, :full) if self.id
    end
    
    # convenience method to list all crossvalidations that are unique 
    # in terms of dataset_uri,num_folds,stratified,random_seed
    # further conditions can be specified in __conditions__
    def self.find_all_uniq(conditions={}, subjectid=nil )
      #cvs = Lib::Crossvalidation.find(:all, :conditions => conditions)
      cvs = Crossvalidation.find( conditions )
      uniq = []
      cvs.each do |cv|
        next if AA_SERVER and !OpenTox::Authorization.authorized?(cv.crossvalidation_uri,"GET",subjectid)
        match = false
        uniq.each do |cv2|
          if cv.dataset_uri == cv2.dataset_uri and cv.num_folds == cv2.num_folds and 
            cv.stratified == cv2.stratified and cv.random_seed == cv2.random_seed
            match = true
            break
          end
        end
        uniq << cv unless match
      end
      uniq
    end
  end
end