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|
#[ '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
|
