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module OpenTox
class Validation
field :prediction_dataset_id, type: BSON::ObjectId
field :crossvalidation_id, type: BSON::ObjectId
field :test_dataset_id, type: BSON::ObjectId
field :nr_instances, type: Integer
field :nr_unpredicted, type: Integer
field :predictions, type: Array
def prediction_dataset
Dataset.find prediction_dataset_id
end
def test_dataset
Dataset.find test_dataset_id
end
def self.create model, training_set, test_set, crossvalidation=nil
validation_model = model.class.create training_set#, features
test_set_without_activities = Dataset.new(:compound_ids => test_set.compound_ids) # just to be sure that activities cannot be used
prediction_dataset = validation_model.predict test_set_without_activities
predictions = []
nr_unpredicted = 0
activities = test_set.data_entries.collect{|de| de.first}
prediction_dataset.data_entries.each_with_index do |de,i|
if de[0] and de[1] and de[1].numeric?
activity = activities[i]
prediction = de.first
confidence = de[1]
predictions << [prediction_dataset.compound_ids[i], activity, prediction,confidence]
else
nr_unpredicted += 1
end
end
validation = self.new(
:prediction_dataset_id => prediction_dataset.id,
:test_dataset_id => test_set.id,
:nr_instances => test_set.compound_ids.size,
:nr_unpredicted => nr_unpredicted,
:predictions => predictions.sort{|a,b| b[3] <=> a[3]} # sort according to confidence
)
validation.crossvalidation_id = crossvalidation.id if crossvalidation
validation.save
validation
end
end
class ClassificationValidation < Validation
end
class RegressionValidation < Validation
end
end
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